This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto that was originally published in April 2026.

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The LLM battlefield has shifted. To understand the new frontline, however, one must understand how the terrain has transformed under the feet of the LLM giants since ChatGPT was first released in November 2022.

For much of the post-ChatGPT era, public attention remained fixed on model capability and generative AI. As 2025 began, agents were barely part of the discourse, let alone agentic AI. The benchmark to measure who was ahead in the generative AI race came down to model power. By the summer of 2025, conventional wisdom held that OpenAI had built a commanding lead in terms of model power, app downloads, compute deals, market share, and mindshare.

Less than one year later, conventional wisdom has been turned on its head. OpenAI is scrambling to catch up to Anthropic’s structural advantages. The models are commoditized table stakes, albeit extraordinarily expensive ones. And the LLM labs are leaving the model layer behind. They are building platform companies. The fight is now for the Orchestration Layer.

The distinction between these two layers is the difference between fragile and durable. Model intelligence answers the question: Can the AI do this task? Orchestration answers the question: Can the AI do this task, here, within this organization’s systems, rules, and workflows, reliably enough that the organization will depend on it?

The evolution of this competition was, of course, influenced by many of the technical and economic factors we have already defined. But the decisive shift that changed the trajectory of this battle and clarified the new agentic dynamic came from somewhere else, somewhere very specific. It came from coding.

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The Coding Wedge

Anthropic released an early version of Claude Code in February 2025 and then made it more generally available in May 2025. OpenAI unveiled Codex in May 2025. At first glance, these looked like developer tools. In fact, they marked a turning point in the enterprise adoption of agentic AI. The evidence was visible by mid-2025. In the “State of AI in the Enterprise” report by Menlo Ventures released in July 2025, Anthropic’s share of enterprise budgets had climbed to 32% from 24% since the start of 2024, while OpenAI’s had declined from 34% to 25%. By the end of 2025, Anthropic’s US market coding share had climbed further to 54% of the US coding market compared to 21% for OpenAI.

Again, the popular perception was that OpenAI was a juggernaut that was running away with the LLM prize. But in the trenches, it was Anthropic winning the battles for budgets and developers. As I analyzed this dynamic on behalf of clients to better understand what was driving this adoption, I gave it a name: The Coding Wedge.

Even now, months later, people view the impact of Claude Code with a facile, surface-level framing that is anchored in the previous paradigm. It’s a good product. Anthropic has strong marketing. It went viral. The brand resonated. Perhaps some or all of these are true at the margins. But these fail to recognize the core structural dynamic at work.

Software engineering was the first domain where autonomous agents crossed the threshold from experimentation to reliable production use. It is uniquely suited to this transition because it concentrates three properties that rarely coexist in other enterprise workflows:

Code lives in a highly structured environment. Every task requires sequencing, dependency management, decomposition, and recombination. Writing software trains models on the primitives of orchestration.

Code offers objective ground truth. It either compiles and runs, or it fails. That deterministic feedback creates a fast verification loop unavailable in most other domains. The model can act, test, correct, and improve against clear signals.

Code has unusually high economic leverage. Every productivity gain in software engineering compounds through the products that the team builds, the workflows it enables, and the businesses those products support.

That combination made it the natural entry point for every lab seeking to move from model provider to orchestration platform. The adoption data reinforces this. Cursor, an AI-native code editor, raised funding at a $29.3 billion valuation. Claude Code surpassed $2.5 billion in ARR, with business subscriptions quadrupling since the start of 2026. And 4% of all public commits on GitHub worldwide were authored by Claude Code as of February 2026, with as much as 20% projected by the end of 2026. The coding wedge is not theoretical. Coding is the fastest-growing category in enterprise AI.

Rather than being about code completion, the coding wedge is about controlling the first enterprise domain in which agentic systems became reliably deployable. The deeper strategic point is even more important: coding agents are not merely coding tools. They are general-purpose agent Harnesses disguised as developer products.

That is what the coding wedge really is: the first opening through which orchestration enters the company. The progression was not accidental. It was strategic. Win developers through the coding wedge, then expand to the broader enterprise through the trust, behavioral patterns, and organizational adoption that coding establishes.

However, as noted in Chapter 2, Anthropic had another powerful tool in its arsenal: MCP. Released as open source in November 2024, this protocol rapidly became the industry standard for agent-to-system integration, even adopted by OpenAI and Google. This gave Anthropic a structural edge. The axis of competition had rotated from “which model is best?” to “whose ecosystem is most deeply embedded?”

That rotation could not be reversed by a single model release, however impressive. Anthropic’s coding advantage also became a powerful development lever. First, Claude Code won developers. That became a wedge to win enterprise customers. But it also became a method for learning the architectural strengths and weaknesses of agentic AI to extend it to general knowledge work.

Two Architectures, Two Bets

The new terms of this rivalry were on full display in early February 2026 as demonstrated by the volleys exchanged between the “Great Model Powers” across business systems. Every significant product that OpenAI and Anthropic have shipped in the final months of 2025 and the beginning of 2026 revealed that neither company believes the model layer alone can sustain their soaring valuations.

On February 5, OpenAI announced the latest release of Codex, its coding platform. This represented a critical moment for the company as it sought to regain ground in the critical fight for the coding wedge. OpenAI acknowledged this directly at the launch of Codex, describing coding as the foundation for a much broader class of knowledge-work agents. As part of that effort to extend its value to developers, OpenAI concomitantly launched Frontier, an enterprise platform for deploying and managing AI agents across business systems.

Meanwhile, Anthropic had leveraged Claude Code to create Cowork, and then the vertical plugins that triggered the “SaaSpocalypse.” And then^, a couple of weeks later, it closed a $30 billion funding round at a valuation of $380 billion, more than double its $183 billion valuation from just five months earlier in September 2025, and the second-largest private funding round in technology history. Finally, in late May, Anthropic announced a $65 billion Series H funding round at a $965 billion valuation.

This all points to the real story.

Consider that $965 billion valuation. At $47 billion ARR, that represents a 20.5× multiple. That is notable because after the February funding, the reported $14 billion ARR implied a 27x multiple. So, revenue has reportedly grown fast enough that the headline multiple has compressed.

Still, if Anthropic is a model company selling intelligence in a market where the intelligence is converging, then this is an extraordinarily aggressive bet on a commoditizing asset. Especially in light of the recent release of Kimi K3. But if Anthropic is a platform company, selling orchestration, workflow coordination, and accumulated enterprise context, then 20.5x may be the entry price for a generational enterprise franchise.

Claude Code, Cowork, the vertical plugins, Frontier, and Codex are not model improvements. All of them are orchestration infrastructure: systems designed to coordinate AI agents across enterprise workflows, accumulate institutional context, and create the switching costs that justify platform multiples.

And yet, the two leading AI labs have arrived at the Orchestration Layer with structurally different visions. Both have real products, launched within weeks of each other, that embody different assumptions about how the enterprise AI market will evolve. Understanding the divergence is essential for valuing the companies.

Anthropic pursued the same logic from the other direction: win developers first, then use the trust, habits, and infrastructure built inside engineering teams to expand outward into the rest of the enterprise.

Agents on Frontier receive employee-like identities with scoped permissions. They connect to data warehouses, CRM systems, and internal applications through what OpenAI calls a “semantic layer for the enterprise.” They build institutional memory from their interactions over time.

The most critical thing to know is that they do not need to be OpenAI’s agents. Frontier manages agents from Anthropic and Google. About 10 days later, on February 15, 2026, OpenAI staged a minor coup by announcing the hiring of Steinberger, the man behind OpenClaw, “to drive the next generation of personal agents,” according to CEO Sam Altman.

Frontier is a Control Plane

Agents on Frontier receive employee-like identities with scoped permissions. They connect to data warehouses, CRM systems, and internal applications through what OpenAI calls a “semantic layer for the enterprise.” They build institutional memory from their interactions over time.

The design choices reveal strategic intent. Frontier’s agent identity management mirrors human HR systems: onboarding, scoped permissions, performance monitoring. OpenAI is not being metaphorical when it describes agents as “digital coworkers”. It is making an architectural claim. If AI agents operate as enterprise employees, the platform that manages them becomes as essential as the system that manages human employees. Frontier aspires to become the Workday for AI labor.

The most critical thing to know is that they do not need to be OpenAI’s agents. Frontier manages agents from Anthropic and Google. This seems to be generous. In fact, by welcoming competitors’ agents onto its platform, OpenAI concedes agent-layer competition in exchange for control-plane dominance. If enterprises standardize on Frontier for agent governance, OpenAI captures the value of orchestration regardless of which model powers any individual agent. The agents become interchangeable components. The control plane does not. This is horizontal platform logic. AWS applied to enterprise AI. It does not require OpenAI to have the best model. It requires OpenAI to have the best coordination infrastructure.

Cowork is an Agent that Became a Platform

Rather than building a management layer on top of agents, Anthropic has built outward from the agent itself. The sequence is systematic and each step compounds on the last.

Claude Code proved the model could carry out complex, multi-step enterprise tasks in the critical domain of software engineering. It generated $2.5 billion in revenue68. MCP standardized how agents connect to external systems and was adopted by competitors, including OpenAI and Google, establishing it as an emerging industry standard. Cowork extended orchestration beyond developers to knowledge workers. The vertical plugins created purpose-built entry points into specific business functions.

The strategic logic here is vertical integration. Anthropic is betting that agent quality remains meaningfully differentiated when embedded within orchestration. In that framing, the experience of using Claude to coordinate an intricate legal review or financial analysis is sufficiently superior that enterprises will build their workflows around it. This is closer to Apple’s logic: control the end-to-end experience, build an ecosystem around your product, and make execution quality the moat.

But there is a subtle departure from the Apple analogy. MCP is open-source. Anyone can implement it. This seems to undermine lock-in.

The answer lies in a finding from enterprise AI adoption research: only 11% of enterprise builders switched AI providers, even though technical substitution was trivial. The lock-in is not technical. It is organizational. When an enterprise builds workflows around Claude’s specific orchestration patterns, the cost of switching becomes operational. In this case, those patterns include its approach to multi-step reasoning, its tool-use conventions, and its plugin interfaces.

You can swap the model in an afternoon. You cannot re-tune thousands of enterprise workflows in an afternoon. Anthropic is making a layered bet: MCP creates ecosystem breadth (every system connects), Claude’s quality creates ecosystem depth (Claude is preferred within that ecosystem), and organizational adoption creates inertia (no one switches even when they could). A market phenomenon of behavioral lock-in despite technical substitutability becomes a deliberate business strategy.

The two architectures represent different bets about the speed and completeness of model commoditization. If intelligence commoditizes fully, Frontier wins. The platform that coordinates interchangeable agents captures the greatest economic value. OpenAI wins not because GPT is superior, but because Frontier is the control plane. It wins even if Claude is the better agent, because the control plane sits above the agent layer.

If intelligence retains meaningful differentiation when embedded in orchestration, Cowork wins. Enterprises do not want “any agent, well-managed”. They want the best agent, deeply embedded. And the switching costs compound with every workflow built around it.

What $965 Billion Requires

If orchestration, not intelligence, is what drives the labs’ valuations, three conditions must hold:

Orchestration lock-in must prove durable, not merely behavioral. Enterprises are not switching AI providers today. But this was measured during a period of rapid growth when no one had reason to test the limits of their commitment. The real test arrives when Frontier offers agent-agnostic orchestration at compelling economics, or when a competitor undercuts on price. MCP is open-source. It creates connectivity but not captivity. For $965 billion to be justified, the network effects must become self-reinforcing: more workflows generate richer context, produce better orchestration, and attract more workflows. This flywheel is architecturally plausible. It is not yet empirically confirmed at scale.

The coding wedge must compound. Claude Code’s $2.5 billion ARR validates the entry strategy. But the path from coding to legal, finance, HR, and general operations is the critical progression. If the vertical plugins drive cross-functional adoption, the platform thesis holds. If coding remains the dominant revenue line while other verticals grow incrementally, Anthropic is a remarkable single-function business at enormous scale. That is a different valuation than an enterprise platform.

The labs must accumulate enterprise context faster than incumbents can defend. The systems of record hold decades of accumulated institutional context: the workflow knowledge, compliance history, and operational understanding that orchestration depends upon. The labs are starting from zero. Frontier builds “institutional memory” from agent interactions. That memory is weeks old. The institutional memory embedded in a Fortune 500 company’s Salesforce deployment spans years.

The labs have speed and pliability. The incumbents have depth and irreplaceability. The labs must build enterprise context before model commoditization erodes the intelligence advantage that gives them the right to orchestrate. This is a race against the clock. The clock does not pause for fundraising announcements.

The Enterprise Tax

There is a dimension of this transformation that the market has yet to price: the expense of becoming an enterprise software company. The market narrative portrays AI labs as asset-light technology companies that are disrupting bloated incumbents. The reality is more complex.

To win the Orchestration Layer, the labs must build what every enterprise platform company before them has built: field sales organizations, customer success infrastructure, compliance certifications, vertical domain expertise, and the organizational capacity to manage thousands of enterprise relationships simultaneously.

OpenAI has embedded Forward Deployed Engineers within customer organizations. Frontier’s enterprise customers require SOC 2 Type II, ISO 27001, and a suite of related certifications. The EU AI Act begins enforcement in August 2026, introducing compliance requirements that did not exist when these companies were founded. Enterprise sales cycles sometimes stretch twelve months or longer for major deployments. These demand specialized teams that change margin assumptions for the labs.

The funding Anthropic raised in February 2026 was not exclusively for training runs. A meaningful portion of this capital will fund the construction of an enterprise go-to-market apparatus, including a sales force, compliance infrastructure, customer success organization, and vertical expertise. These are structural costs that permanently alter the business’s margin profile. A model company has research costs and compute costs. A platform company has all of these, plus go-to-market costs that scale with customer count rather than compute capacity.

This creates a tension that the labs’ financial disclosures will eventually make visible. The transition from model economics to platform economics requires near-term margin compression to build the switching costs and network effects that expand margins over time. The trajectory of that compression and subsequent expansion will tell investors more about the durability of these businesses than any benchmark or revenue growth rate.

Meanwhile, the labs face a clock that the incumbents do not. Model advantages erode quarterly as open source narrows the gap. If the labs do not establish orchestration lock-in before model commoditization is functionally complete, they risk becoming very expensive API utilities competing on price.

What This Means for the Public Markets

When the IPO prospectuses for OpenAI and Anthropic become public, they will present revenue growth, gross margins, and customer counts in the language familiar to technology investors. That language will be necessary but insufficient.

If this analysis is correct, if these companies are transitioning from model businesses to platform businesses, then the central valuation question is not the rate of growth but the nature of the growth. Revenue from model API consumption and revenue from platform orchestration may appear identical in a financial statement. They are not identical in their implications for margin trajectory, competitive durability, or terminal value.

The companies that complete this transition will justify platform multiples. Those that do not will eventually be priced as API providers in a commoditizing market, regardless of current growth.

The Single Points of Failure

The first is open-source compression. For now, enterprises overwhelmingly prefer closed-source frontier models today. The MAP study found that 85% of production agent teams build entirely in-house using direct model API calls, foregoing third-party orchestration frameworks. Open-source adoption is limited to edge cases: high-volume workloads where inference costs are prohibitive, or regulated environments that prohibit sending data to external providers.

Enterprise teams default to the best-performing closed-source model available, and runtime costs are negligible compared to the human experts the agents augment. This is encouraging for the labs. But it is a snapshot, not a guarantee.

Open-source models have continued to advance at the rate they had at the end of 2025 and the start of 2026. And as we saw this month with Kimi K3 from Moonshot AI, the performance gap has been closed. Is this parity sufficient for the 85% preference to erode rapidly, because the underlying driver is pragmatic performance selection, not structural loyalty? Enterprises test the top models and pick the best one. If an open-source model becomes the best, will they pick it instead?

Of course, this raises a second SPOF: Jurisdictional and continuity risk. The June export ban on Claude Fable 5 and Mythos 5 suggests that the regulatory picture is set to play an important role. And yet, the rules are far from clear. This has grown even fuzzier in the wake of the panic and debate following the release of Kimi K3, and the ensuing accusations by Anthropic that it was distilled from Fable, and rumblings from the U.S. government that it may look to restrict the use of some open-source models.

The third is the compute trap. This is the risk that Dario Amodei himself has articulated with remarkable candor. Asked on a podcast why Anthropic does not spend more aggressively on compute given its belief that a “country of geniuses in a data center” is imminent, Amodei was honest about the financial fragility of the model: “If my revenue is not $1 trillion, if it’s even $800 billion, there’s no force on earth, there’s no hedge on earth that could stop me from going bankrupt if I buy that much compute.”

By the same measure, if Anthropic underspends, it misses potentially massive growth opportunities. This is an unprecedented situation in enterprise technology. No company in the history of the software industry has operated at this level of capital intensity with this degree of revenue uncertainty over such a compressed timeline.

Anthropic’s annual burn rate in early 2026 was running at approximately $7-8 billion (one-third of revenue, per company guidance disclosed in confidential financials reported by The Wall Street Journal and Fortune in November 2025). The $65 billion Series H provides fresh runway. But the runway at these burn rates is measured in years, not decades. The compute commitments required to maintain frontier model performance only increase. Anthropic has committed approximately $80 billion in cloud-infrastructure spending to Amazon, Google, and Microsoft through 2029, with an additional 3.5 GW of compute capacity secured through Broadcom and Google partnerships beginning in 2027.

Of course, Anthropic then entered a compute agreement with SpaceX that allowed it to increase Claude Code and API limits. And yet, SpaceX, as part of its post-IPO plans, has moved to formally acquire AI-coding platform Cursor, a move that is effectively an attempt to re-run Anthropic’s Coding Wedge playbook.

The tension is structural. To win the Orchestration Layer, the labs need frontier-quality models. You cannot orchestrate enterprise workflows with a mediocre model. To maintain frontier models, they need massive and growing compute investments. To fund those investments, they need revenue growth to materialize on schedule.

If the orchestration transition takes longer than the compute commitments allow, if enterprise adoption moves at enterprise speed rather than startup speed, the financial model fractures. Intelligence is commoditizing. Context compounds. Compute burns. The race against all three will determine if the labs can move up the stack fast enough, and effectively enough, to capture enough of the enterprise to justify their precarious economic positions.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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The Three Laws describe the conditions under which a company can become the orchestrator and sustain a durable advantage in the Agentic Era. But markets do not price conditions in the abstract. They price the structural positions that those conditions give rise to. When proximity to intent, depth of context, and control over workflow converge within the same domain, they do not remain as separate attributes. They collapse into a single, identifiable role in the system that transforms human intent into executed outcomes. That role is not captured by any existing category. It is not a product definition, a sector classification, or a stage in the software stack as previously understood. It is a position that emerges from the architecture itself.

The purpose of this chapter is to describe the position and define it clearly. Because once it is visible, it can be systematically identified across the market, compared across industries, and evaluated as the primary locus of value creation and capture in the new regime.

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The Species

What emerges from that shared position is best understood not as a sector but as a species: a class of firms defined by their structural role in the orchestration of work.

A company that captures intent at the point of origin, that owns the deepest and most operationally relevant context, and that directs the workflows required to convert both into outcomes occupies a position that is categorically different from one that performs only a subset of those functions.

This manifesto calls that position AGNT.

AGNT is not a stock ticker. It is a structural archetype that can be identified, scored, and compared across industries. It cuts through traditional boundaries because those boundaries were built for a different regime, one where software was a tool, and operations were separate from it. The market’s sorting mechanisms via sector-based indices, thematic baskets, and the Rule of 40 were calibrated for a regime in which software competed for IT budgets and physical businesses competed on operational efficiency. GICS codes describe where a company was. The Three Laws of Agentic Value describe where a company is going.

An enterprise CRM platform can be an AGNT. It captures intent because 150,000 companies set sales goals in it every day. It provides context through decades of customer interaction data, pipeline patterns, and conversion histories across millions of accounts. It compounds workflow intelligence with every sales cycle it orchestrates. Apply the Three Laws. The position is defensible.

A global insurance carrier can be an AGNT. It captures intent because the policyholder is its customer. It holds context in the form of actuarial loss data built over decades of underwriting real risk with real capital. No foundation model can synthesize this from training data. It was generated by operations that bear real-world consequences.

In the Agentic Era, that separation collapses. A freight carrier and an enterprise software platform may have more in common architecturally than either has with the company next to it in a sector index. The entity that receives intent, deploys intelligence, and coordinates execution becomes the locus of control, regardless of whether it is labeled as a software company, a financial institution, or an industrial operator.

The implication is that similarity is no longer determined by sector adjacency, but by architectural equivalence. Two companies in different industries may occupy the same structural position, while two companies in the same industry may be separated by it entirely.

The Three Laws provide the mechanism for making that distinction explicit. Apply them, and the species reveals itself.

The question is not what this company sells. The question is where this company fits within the “Orchestration Graph” we define below.

Four Combatants

The first combatant: the AI laboratories ascending the stack.

OpenAI and Anthropic did not begin as orchestrators. They began as model providers selling intelligence at the bottom of the stack. But they understand that the model layer alone cannot capture the value implied by their valuations. Model price-performance is compressing, even as access to the most capable frontier intelligence remains scarce and increasingly subject to strategic control.

So the laboratories are building upward. OpenAI has expanded from Frontier into workspace agents, ChatGPT Work, and a dedicated deployment company designed to embed its systems directly into enterprise operations. Anthropic has moved from Claude Code and Cowork into vertical agent packages.

The entry strategy is what we call the “coding wedge”, documented in software engineering, now understood as a deliberate go-to-market: win developers first, then use the trust, habits, and organizational adoption built inside engineering teams to expand outward into the rest of the enterprise.

Three traditional software defenses are breaking under pressure. Integration moats erode when agents route dynamically across any system with a protocol connection. UI moats erode when the primary user is an agent executing via APIs, rather than a human navigating an interface. Capability moats erode when foundation models compose specialized functions on the fly.

The labs’ advantage is technological fluency and speed of iteration. Their vulnerability is the Second Law. They possess intelligence without institutional knowledge. They have no claims data, no routing history, no actuarial tables, no supply chain memory. They are climbing toward the Orchestration Layer from below, and every rung they reach is contested by incumbents who have occupied it for decades. The race is intelligence ascending against context defending. Chapter 13 traces this in detail.

The second combatant: the software incumbents pivoting from storage to action.

Salesforce, ServiceNow, Workday, SAP, and Oracle hold the canonical data that enterprises operate on. Ripping them out requires migrating decades of operational records, retraining thousands of users, rebuilding integrations with dozens of downstream systems, and accepting the risk of operational disruption in mission-critical processes.

Their position is not in question. Their role is.

In the pre-agentic world, the system of record was also the system of action. When a sales rep wanted to update a deal, she opened Salesforce. The application was the interface where intent met execution. In the agentic world, intent starts in a conversation. A user tells an agent: “Update the pipeline forecast for Q2”. The agent calls the CRM’s API, reads the data, performs the analysis, and reports back without the user ever opening the application. The system of record is still essential. But it risks demotion from the system where work is directed to the system where data is stored. The database behind the orchestrator rather than the orchestrator itself.

The central strategic challenge for every enterprise software incumbent is to make the transition from system of record to system of action, the platform that receives intent, deploys agents, and coordinates workflows end to end. Salesforce is attempting it with Agentforce and the repositioning of Slack as the enterprise’s intent surface. ServiceNow is building agentic IT service management. Those who succeed become orchestrators. Those that do not will retain data gravity but lose control of the workflow and the pricing power that accompanies it.

Their advantage is context gravity. This is the irreplaceable institutional data that already lives inside their platforms. Their vulnerability is speed. The AI labs are building toward the same position from the opposite direction, and the orchestration position compounds with every interaction. The window narrows with each quarter. Chapter 14 traces the sorting.

The third combatant: the throughput node.

Not every software company must become the captain. Some of the most durable positions in the agentic economy will belong to companies that never aspire to the apex of the orchestration graph, but whose context is so dense that every orchestrator must route through them.

A throughput node does not capture intent. It does not control the workflow. It holds the context that makes the workflow correct. When that context is sufficiently deep and semantically rich, the orchestrator cannot complete the work without passing through the node. It doesn’t matter who occupies the captain’s seat. These are the tools that step into the orchestration loop rather than waiting on the sidelines. They expose their context through open protocols, becoming surfaces through which agents move work rather than destinations where humans perform it. A tool that remains a destination risks being bypassed. A tool that becomes a node becomes structural.

The distinction between a throughput node and a convenience layer is semantic density across the production chain. A design platform whose component libraries, interaction models, and brand systems are inputs to every downstream workflow in product development is a throughput node. A project management tool whose task metadata can be replicated by the orchestrator’s own coordination logic is a convenience layer. The Second Law identifies the difference. The market has not yet learned to price it. Chapter 15 traces the distinction.

The fourth combatant: the domain operators building from the inside.

Insurance carriers, banks, logistics companies, manufacturers, and healthcare systems are the companies that own the physical assets, regulatory relationships, and operational context that most deeply satisfy the Second Law.

These companies never considered themselves technology companies. They are becoming orchestrators by necessity, because the agentic transition presents a binary choice: build your own Orchestration Layer or watch someone else build it on top of your operations and capture the margin.

A global logistics carrier deploying a master agent to coordinate fleet routing, capacity allocation, customs clearance, and last-mile delivery is not buying orchestration from a software vendor. It is building orchestration atop its own operational context, using foundation models as reasoning engines.

The carrier’s competitive advantage was always its physical network and accumulated operational knowledge. In the pre-Agentic Era, that advantage was constrained by the throughput of human managers. In the Agentic Era, the same advantage is unleashed via agents coordinated to operate at machine speed across the full scope of operations.

The same logic applies across every Capex-heavy, operationally complex industry:

A construction equipment manufacturer whose agents coordinate fleet scheduling, predictive maintenance, and supply chain management across dozens of job sites.

An insurer whose agents process claims through orchestrated workflows built on decades of actuarial data.

A bank whose agents orchestrate credit analysis, risk assessment, and portfolio management across a multi-trillion-dollar balance sheet.

In each case, the physical assets and operational history provide the context. The foundation models provide the cognition. The Orchestration Layer is built by the enterprise rather than purchased from a vendor because that is where the value concentrates. Their advantage is the irreplaceable context generated by decades of operations.

Their vulnerability is that they must build the fastest from the furthest starting point. These companies are not accustomed to building software platforms. They must learn or partner at a pace that their organizational cultures have never demanded. The risk for all four combatants is identical: time. The orchestration position compounds. Early movers accumulate coordination intelligence that later entrants must rebuild from scratch. However, the Third Law is the weakest of the three conditions: pure workflow choreography is increasingly learnable by capable agents. What compounds durably is a workflow that embeds irreplaceable operational context. This is the Second Law, expressed through coordination. In domains where all three laws hold, where a company captures intent, owns deep context, and controls decomposable, multi-step workflows, the gap may become structurally unclosable.

The Two Arenas

There may be three combatants. But they are not all confronting each other directly. Instead, there are two competitive battlefronts.

Arena One encompasses workflows that already run through enterprise software platforms: CRM in Salesforce, IT service management in ServiceNow, human capital management in Workday, ERP in SAP, design in Figma, storage in Box, etc. These are the digitized, process-heavy workflows that defined the SaaS era.

In Arena One, three outcomes emerge for the companies inside it. Some will be displaced entirely. For instance, the capability providers that monetized a single function that agents can now compose from general-purpose intelligence. Some will be absorbed into the execution layer, surviving as API endpoints invoked by orchestrators, retaining revenue but losing pricing power and strategic control. Some will become orchestrators themselves, leveraging existing customer relationships, context gravity, and institutional trust to claim the captain’s chair while partnering with AI labs for the intelligence they direct.

The sorting in Arena One plays out between AI labs attacking from above, software incumbents defending from within, and throughput nodes anchoring themselves as mandatory bridges that every orchestrator must cross.

Arena Two encompasses workflows that were never fully digitized. These are the operational, physical-world processes where human cognition coordinates physical assets: supply chains, manufacturing lines, logistics networks, construction projects, and clinical operations.

These workflows generated vast operational data but were never mediated by a software platform, unlike CRM, which mediated sales. The coordination lived in managers’ heads, in phone calls, in spreadsheets, and in tribal knowledge.

In Arena Two, the battle is to see which enterprises become orchestrators and build their own Orchestration Layers before third parties do. The LLM is the engine, not the captain.

The intermediary reckoning plays out almost entirely in Arena Two. This is the structural inversion where physical asset owners ascend as cognitive middlemen fall. The freight broker that owned no trucks, the insurance broker that sat between carrier and client, and the wealth advisor whose value was synthesizing information that an agent can now synthesize faster.

Each occupied a position between the asset owner and the customer, monetizing cognitive labor that is now being automated. When agents close that gap, the value will not automatically transfer to the AI lab or to the software platform. It could also be captured by the asset owner if they leverage the operational context by creating an effective Orchestration Layer.

Both arenas tap the labor market. They do so for different reasons.

In Arena One, the expansion is a budget migration. Software companies that cross the reliability threshold stop selling seats from the IT budget and start selling outcomes from the labor budget. The workflows already exist in digital form. The TAM expands because the buyer is no longer comparing $150/seat/month against a competing SaaS vendor. They are comparing the cost of the orchestrated outcome against the fully loaded cost of the knowledge worker the outcome replaces. The same workflows, repriced against a surface five to ten times larger.

In Arena Two, the expansion is a digitization event. These workflows were never mediated by software at all. The coordination lived in managers’ heads, in phone calls, in spreadsheets. There is no existing IT spend to displace, only labor spend that was never addressable by technology until agents crossed the capability threshold. The TAM does not expand from a smaller budget to a larger one. It materializes for the first time.

Arena One reprices existing digital workflows against the labor market. Arena Two brings non-digital workflows into the orchestrated economy for the first time. Both are measured in trillions. Arena Two is potentially the larger opportunity because it starts from a base of zero digital penetration.

The entire value pool is greenfield.

This is why the agentic transition is not a story about software eating itself. It is a story about who captures the value when intelligence becomes abundant enough to coordinate both digital and physical operations at machine speed.

The Map

The remaining chapters of Part IV trace the sorting across each combatant and each arena.

Chapter 12 examines what the AI labs are building: not better models, but orchestration platforms competing for the enterprise control plane.

Chapter 13 traces the software reckoning: the sorting of enterprise incumbents into systems of action, throughput nodes, and casualties. The SaaSpocalypse was directionally correct and analytically lazy. The Three Laws identify which companies the market is punishing unfairly and which it is not punishing enough.

Chapter 14 examines how software tools defend: the throughput node strategy and the new network effects that emerge when agent density replaces user density as the driver of platform value.

Chapter 15 follows the leviathans: the physical-economy companies whose operational context, accumulated over decades, positions them as the most structurally advantaged orchestrators in the framework. The intermediary reckoning. The physical-asset inversion. The largest value migration that nobody is watching.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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Consider the insurance workflow once again.

The master agent has processed 50,000 claims over eighteen months. Its pricing agent has been invoked on all 50,000. Both have learned. Both are better than they were on day one. But they have learned different things, and the difference is structural.

The pricing agent has learned to price better. Its risk models have sharpened. Its calibration has improved. It handles edge cases with increasing precision. This is specialist learning: deep, narrow, and subject to diminishing returns. There are only so many ways to improve pricing. The capability curve flattens.

The master agent has learned something else. It has learned coordination. It knows which specialists work well together and which combinations produce errors. It has been discovered that certain claim types need medical records enrichment before pricing, while others can proceed directly to adjudication. It has been noticed that claims submitted in Q4 from manufacturing clients require a different decomposition than identical-looking claims submitted in Q2, because year-end inventory adjustments change the risk profile in ways the pricing agent alone cannot detect.

This is meta-expertise: the knowledge of how to make domain experts effective together. It is the accumulated workflow intelligence of the Orchestration Layer that now directs routing logic, exception handling, specialist sequencing, and failure recovery. Every interaction teaches the coordinator something the specialists never see. The specialists see their inputs and their outputs. The coordinator sees the entire workflow: what was tried, what failed, what succeeded, why the sequence mattered, and how the context affected the outcome.

A new competitor can license every specialist agent in the system. It cannot download the coordination intelligence that enables those specialists to work effectively together. It must learn it from scratch, one orchestrated interaction at a time, while the incumbent continues to compound.

This is the Third Law: workflow intelligence is the accumulated operational choreography of how work gets coordinated. It creates switching costs that grow with every interaction.

The question is: How strong are those switching costs?

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The Weakest Law

The claim that coordination compounds while specialist capability flattens is intuitive. It is also, as a universal law, overstated.

The Google-DeepMind-MIT study, which reported an 81% relative improvement in structured financial reasoning, also found the opposite pattern in other domains^. Across all benchmarks, the mean multi-agent improvement was -3.5%. In sequential planning tasks, every multi-agent architecture tested performed 39-70% worse.

Coordination did not compound. It destroyed value.

The study also found that coordination benefits are task-contingent. They depend on whether the problem is decomposable and parallelizable, not on universality. It identified a capability saturation threshold: once single-agent baselines exceed approximately 45% accuracy, adding coordination yields diminishing or negative returns.

More troubling for the Third Law is what happens when specialists learn to coordinate themselves.

In December 2025, Meta FAIR published Self-play SWE-RL, a study in which a single software agent, with no coordinator or multi-agent orchestration, autonomously learned to navigate complex codebases through self-play. The agent generated its own bugs, solved them, and generated harder bugs based on its failures. It required only access to the raw codebase: no human-curated issues, no test suites, no workflow documentation. Through sustained interaction with the environment alone, the agent outperformed the human-data baseline across the entire training trajectory, improving by 10.4 points on SWE-bench Verified.

The implications for the Third Law are inescapable. If an agent can learn the workflow logic of a complex software repository through observation and interaction, then what prevents a sufficiently capable agent from learning the workflow logic of a claims processing system? Or an integration layer? Or a supply chain?

The answer is not “nothing”. Real barriers exist. But the barriers are weaker than those protecting the first two laws. Intent capture (the First Law) requires a relationship with the human who expressed the goal. You cannot learn that from observation. Operational context (the Second Law) requires years of accumulated experience. You cannot synthesize 50,000 claims outcomes from scratch. Workflow choreography (the Third Law) requires an understanding of how processes connect. That, as Self-play SWE-RL demonstrates, is increasingly learnable.

The Third Law is real. It is also the law most likely to be tested by the market.

When Workflow Holds

If the Third Law does not hold universally, the investment question becomes: under what conditions does it hold? Three conditions determine whether workflow intelligence creates a durable advantage:

When workflow embeds operational context that emerged from practice rather than documentation. The claims processing logic encoded in an insurance platform is not merely a sequence of steps. It includes exception handling paths that emerged from thousands of edge cases, regulatory compliance requirements that proved necessary through actual enforcement actions, and specialist routing patterns that were refined by observing which combinations produced errors. This is workflow intelligence that cannot be learned from a manual because it was never written in one. It emerged from sustained operational interaction. It is, in effect, the Second Law expressing itself through workflow.

When switching costs are behavioral rather than technical. Traditional software lock-in is technical: data migration costs, integration reconfiguration, and user retraining. Orchestration lock-in is behavioral. When an organization’s operational rhythm has adapted to how one orchestrator decomposes intent, validates outputs, and handles exceptions, switching means rewiring the operational patterns of every process that adapted to the incumbent’s coordination intelligence. The new system does not just lack data. It lacks the judgment that emerges from years of orchestrated experience. It begins as a stranger who is technically capable but operationally naive.

When the domain involves decomposable, multi-step workflows rather than sequential reasoning. Recall that research has demonstrated that centralized coordination produced dramatic gains on parallelizable tasks such as financial reasoning involving multiple data sources, cross-reference synthesis, and distributed analysis. However, it destroys value on sequential constraint-satisfaction tasks where coordination overhead fragments reasoning capacity. The Third Law holds in insurance claims, supply chain logistics, enterprise procurement, and regulatory compliance. It fails in domains where a single capable agent outperforms any coordinated team.

These conditions produce a diagnostic that maps directly to investment decisions. Consider an integration middleware platform. This company’s entire value proposition is connecting System A to System B with the correct data transformations and sequencing. Its moat is integration choreography. That is precisely what MCP and A2A dissolve.

The platform coordinates workflows but does not accumulate any proprietary operational context. Its moat is integration friction. When protocols standardize connectivity, the workflow knowledge becomes replicable. An agent using MCP can discover and connect to the same endpoints. The choreography the platform has encoded over the years can be learned in weeks.

This is the most exposed position in the agentic economy: workflow orchestration without context.

Now consider a vertical insurance platform that has amassed decades of encoded claims-processing logic, underwriting rules, and policy-administration choreography. This looks like a deep workflow moat. But examine what is defensible. It is not the workflow sequence. It is the domain knowledge embedded in the workflow: the understanding of how insurance works in practice, the exception paths that emerged from millions of claims, and the regulatory requirements discovered through enforcement rather than documentation.

Strip away the context, and the workflow is learnable. The moat is context (the Second Law) expressing itself through workflow (the Third Law). The position holds due to the Second Law’s advantage, not because of the Third.

The same pattern appears outside software. In February 2026, Goldman Sachs revealed it had spent six months embedding Anthropic engineers within its teams to build Claude-powered agents for KYC compliance and trade accounting. These are document-heavy, rule-intensive workflows that combine data extraction with regulatory judgment²¹⁷. The bank’s CIO described the agents as “digital co-workers” for processes that are “scaled, complex, and very process-intensive”. The agents review documents, extract entities, assess ownership structures, and trigger compliance checks. Internal tests showed that onboarding timelines collapsed by approximately 30%.

This is a direct test of the Third Law. KYC compliance is a multi-step workflow involving identity verification, sanctions screening, beneficial ownership analysis, and ongoing monitoring. A third-party compliance platform that merely choreographs these steps by connecting document scanners to screening databases and case management systems holds a workflow moat that agents can learn from. Goldman’s agents are learning it now.

However, the bank’s decades of compliance outcomes represent an invaluable advantage because they are an asset that the agent platform cannot replicate:

Which edge cases triggered regulatory action?

Which documentation patterns correlated with genuine risk versus administrative friction?

Which client structures required enhanced due diligence not because the rules said so, but because enforcement history revealed it.

That operational context was accumulated through millions of onboardings across every jurisdiction where the bank operates. It is what makes Goldman’s own orchestration position defensible, even as third-party KYC platforms face exposure. The workflow is learnable. But this kind of institutional memory is not.

The pattern for the Third Law, then, requires an important nuance: workflow moats are defensible to the extent that they embed operational context. The choreography, the routing logic, and the integration patterns: these elements of pure workflow are learnable and therefore vulnerable. Workflow that encodes irreplaceable operational intelligence is defensible because of the context it contains, not because of the workflow itself. That makes the Third Law a derivative moat.

The Switching Cost Asymmetry

Even where coordination learning is moderate, switching costs are real, and they compound over time. Traditional software lock-in is largely static: migration costs remain relatively constant, driven by data transfer, integration reconfiguration, and user retraining. These are significant, but they are visible and quantifiable.

Orchestration behaves differently. It is dynamic. Each interaction deepens the system’s embedded knowledge through every optimized workflow, learned exception, and validated combination of specialists. The gap between what the incumbent system knows and what a replacement must reconstruct widens continuously.

Crucially, this lock-in is not immediately visible. Unlike traditional systems, its cost cannot be fully modeled upfront because it resides in accumulated context: decomposition logic, exception handling paths, and coordination patterns that only emerge through sustained operation. Organizations only grasp the magnitude when they attempt to switch and discover that their operational rhythm depends on capabilities that exist nowhere else. Orchestration lock-in is invisible until you try to switch.

Most importantly, the nature of the lock-in shifts. Traditional lock-in is technical. These Traditional systems bind through technical dependencies while leaving underlying processes intact. When an organization switches from Oracle to SAP, the underlying business processes.

Orchestration systems bind through behavior. Switching does not simply require moving data. It requires reconstructing the decision-making patterns and operational habits that have evolved in response to the incumbent’s coordination intelligence. The new system does not just lack information. It also lacks judgment.

This is the genuine contribution of the Third Law to the investment framework. Even in domains where the coordination learning curve is moderate rather than exponential, where agents can, in principle, learn the workflow, the switching cost still grows with operational history.

The question investors must ask is not: “Does coordination compound forever?” Rather, what matters is: “Do switching costs compound fast enough to create a defensible position before the next generation of agents learns to replicate the workflow?”

The Contestability Window

The Third Law creates urgency even though it is the weakest of the three conditions. That’s because, unlike previous technology cycles, the Orchestration Layer is not reserved for technology companies. Orchestration is defined by owning the control plane where intent enters and coordination happens. That control plane can live anywhere. Workday can be the orchestrator for HCM workflows. But so can an insurance company for claims processing, a bank for credit analysis, a manufacturer for supply chain optimization.

For the first time, the strategic layer of the stack is contestable by companies that own domain expertise, customer relationships, and operational context, and not just companies that write software. Physical-world operators possess inherent advantages precisely because their workflow intelligence arises from irreplaceable operational reality. The insurance company that builds its own Orchestration Layer accumulates coordination intelligence that no third-party platform can replicate because the platform does not process claims, does not see adjuster patterns, does not experience seasonal variations, and does not learn the exception paths that emerge from actual operations.

The risk for these companies is not their capability. It is speed.

Early orchestrators compound their advantage with every interaction. Later entrants start from zero in a market where the leading orchestrator has thousands of workflows of accumulated learning. The gap is not just difficult to close. In domains where the Third Law holds, it may become structurally unclosable. This is why the Third Law matters despite its weakness. It is not the foundation of defensibility. It is the accelerant. Proximity to user intent tells you where to stand. Context tells you what to accumulate. Workflow tells you that the window for doing both is closing.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

Subscribe now

Two insurance companies deploy master agents. Both sit at the origin of intent. Both orchestrate specialist agents for extraction, risk modeling, fraud detection, and pricing. Both have crossed the reliability threshold and operate as autonomous functions. Both charge per outcome rather than per seat.

On day one, they are equivalent. By month six, one is pulling away. By year two, the gap is too wide to close. Not because it has better models. Both license the same foundation models. Not because it has more data. Both process similar volumes. Because it has deeper context. And context, unlike data, compounds.

This is the Second Law: In the agentic economy, competitive advantage belongs to whoever accumulates the richest operational context and embeds it in their Orchestration Layer. Operational context becomes the “soil” for agents that ultimately allow business outcomes to be achieved.

Context is not data. It encompasses data when built correctly through sophisticated memory architectures that layer behavioral patterns, relationships, and temporal understanding into something that makes agents genuinely intelligent.

Data tells you what happened. Context tells the agent what to do next.

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Why Context, Not Data

The enterprise software industry spent the last decade obsessed with accumulating data. Companies built vast data lakes and warehouses, convinced that whoever collected the most information would dominate their markets. That conviction is now failing its most important test.

The MIT GenAI study released in July 2025 caused such industry uproar by reporting that 95% of corporate GenAI projects failed to get past the pilot stage. This was widely misinterpreted as a judgment of the inherent limitations of generative AI. What most readers missed was one of the key reasons researchers identified for these failures: model quality fails without context. The models had access to petabytes of enterprise data. What they lacked was the operational intelligence that transforms data into effective action.

The distinction is fundamental. Data is static. It sits in databases. It can be queried, copied, or transferred. It answers the question, “What happened?”

Context is dynamic. It incorporates behavioral patterns, relational understanding, temporal awareness, and causal reasoning that emerge from sustained operational interaction. It answers the question “What should happen next, given everything we know about this specific situation?”

Return to the two insurance companies. Both have the same claims data in the form of submission histories, payout records, and policy details. But only one has accumulated context: the knowledge that claims from certain regions involving certain diagnoses follow a predictable escalation pattern, that this particular adjuster’s flag for fraud has a 94% confirmation rate while that one’s has a 61% rate (illustrative numbers), that Q4 submissions from mid-size manufacturers spike in complexity because of year-end inventory adjustments.

This is not data in a table. It is operational intelligence that emerged from thousands of orchestrated interactions, encoded in the memory architecture of the system.

The master agent with this context makes better routing decisions. It assigns the right specialist to the right claim. It anticipates complications before they manifest. It produces more accurate outcomes faster. The master agent, without it, has the same capabilities but operates blindly. It is technically competent but operationally naive.

The Four Dimensions of Context

Context is not a single thing. It is multidimensional, and the dimensions interact in ways that create compound advantage:

Behavioral context captures how users and processes actually operate as opposed to how they are documented. An orchestration platform tracking how underwriters navigate workflows, such as which shortcuts they develop, where they encounter friction, and how their patterns evolve, accumulates understanding that informs agent optimization. Users claim to follow the manual. The behavioral context reveals what they actually do. One company knows what you did last month. Another knows what you will do next Thursday evening, when you are tired but not rushed, based on forty-seven matched occasions along sixty-three calibrated dimensions. That depth cannot be replicated from scratch. Those occasions accumulate only through observation over time.

Transactional context encompasses decision history and outcome tracking across extended time frames. The master agent that has seen how a thousand similar claims were resolved, including those that were escalated, those that were approved automatically, and those that triggered fraud investigations that proved correct, operates in a different cognitive universe than one processing its first hundred. A procurement agent accessing complete transaction histories makes recommendations informed by actual results rather than static supplier profiles. The accumulation over years creates an understanding that new entrants cannot replicate without a similar operational history.

Relational context maps connections and causal relationships that explain why patterns emerge. Understanding that suppliers deliver reliably for certain categories but struggle with others, that certain customer segments respond differently to pricing strategies, that workflow bottlenecks stem from interdependencies between teams rather than individual inefficiency. This allows agents to reason about causality rather than correlation. The distinction matters for autonomous decision-making, where agents must predict how actions cascade through complex systems.

Temporal context captures time-series patterns and seasonality. A manufacturing orchestration agent that understands seasonal demand fluctuations, maintenance cycles, and capacity constraints makes dramatically different decisions than one operating on point-in-time data. The agent that acts before conditions change outperforms the agent that reacts after.

There is a fifth dimension the framework must account for, one that emerged from the first production systems operating at enterprise scale. OpenAI’s internal data agent is built to reason across 600 petabytes and 70,000 datasets. It discovered that schema metadata and operational history were insufficient. Two tables can look identical at the structural level but differ in ways only the pipeline code that produced them reveals: one includes logged-out users; the other does not; one captures first-party traffic; the other captures everything.

OpenAI had to crawl its own codebase to teach the agent what the data means, not just what it contains. Constructive context is the understanding of how institutional data was built, filtered, transformed, and aggregated. It prevents the class of errors that arise when an agent interprets a field correctly at the schema level but wrongly at the business logic level. Without it, the agent produces technically correct but institutionally wrong answers.

This is the reason Informatica has spent seven years building CLAIRE, a metadata knowledge graph that maps lineage, governance policies, and semantic relationships across fragmented enterprise data, compressing what was once a week-long schema mapping exercise into minutes and exposing, via MCP, the constructive context that agents need to reason correctly over enterprise systems. It is also the mechanism visible in the Claude Code source architecture described in Chapter 18: the memory consolidation system that rewrites its own index, including resolving contradictions, pruning stale facts, and converting relative references to absolute references, does not store data. It is building constructive context from operational reality, session by session, so that the next interaction begins from institutional understanding rather than raw observation.

Each dimension is valuable on its own. Together, they produce something qualitatively different: an agent that understands its operational reality the way an experienced human operator does. Except it never forgets, never transfers to a competitor, and improves with every interaction.

The Flywheel

Better context produces better agent performance. Better performance attracts more users and deeper platform integration. Deeper integration generates richer context. The cycle accelerates. The companies establishing early leads in context accumulation open gaps that widen with every transaction.

This is why incumbent enterprises in regulated or physically grounded industries possess advantages that their technology adoption speeds obscure.

Their operational context, accumulated over decades, provides foundations that digital-native competitors cannot bootstrap overnight. The logistics carrier that has routed a billion shipments, the insurer that has processed ten million claims, the bank that has underwritten a trillion dollars in credit. They each possess contextual foundations that no amount of compute can synthesize. The context is a byproduct of operation. You cannot buy it. You earn it through sustained execution.

From Data Moats to Memory Architecture

The previous era’s moat was the database. This system of record, surrounded by workflows, made switching painful. Context moats require a different architecture: memory systems that learn, not databases that store.

The distinction is not semantic. Recording is storage. Data enters, persists, and can be retrieved. Every database does this. Recording is table stakes. Memory is something else. Memory connects past to present in ways that shape the future. It learns from what happened. It updates its understanding. It changes behavior based on accumulated experience. In the human mind, memory is not a filing cabinet. It is a living process, constantly reconstructing the past to predict tomorrow. The systems competing for the orchestration position are building memory architectures that function similarly.

The engineering required is substantial. Implementing hierarchical memory with distinct modules for storage, updating, retrieval, and generation requires infrastructure that extends well beyond traditional database capabilities. Short-term memory maintains conversation and task context. Mid-term memory captures session-level patterns and user preferences. Long-term memory preserves persistent knowledge across extended timeframes. That includes user profiles, organizational patterns, and domain expertise. Each tier requires different update mechanisms, different retrieval strategies, and different governance policies.

As UCL and Huawei’s Noah’s Ark Lab demonstrated through Memento, agents can achieve state-of-the-art performance through external memory without retraining the underlying model. They do this by storing experience tuples as discrete retrievable cases, improving from 78.65% to 84.47% accuracy over five iterations through memory accumulation alone.

The implication is structural. In the fine-tuning paradigm, the moat belonged to whoever had the most compute. With memory-augmented learning, the moat shifts to whoever has the best domain-specific experiences. A startup with deep expertise in legal contracts can build agents that outperform general-purpose models on contract analysis. They do this not by outspending frontier labs, but by accumulating superior execution data through their orchestration platform.

Execution Data

This creates a category distinct from both enterprise data and training data. We call it execution data. Enterprise data answers “what happened?” Training data encodes “what patterns exist.” Execution data captures “what worked and why.” These are the specific sequences, strategies, and decisions that succeeded or failed in actual operation.

The economic properties are what matter. Traditional data shows diminishing returns. The millionth customer record adds less insight than the thousandth. The data grows. The value plateaus. Execution data compounds. Early experiences establish basic competency. Later experiences build on those foundations. The thousand-and-first claims processing discovers a pattern that improves performance across an entire category. Each interaction widens the gap.

The result is winner-take-all dynamics within specific domains. The first legal AI to handle a thousand contract negotiations will not just have more data. It will have a richer understanding of the problem space, including rare edge cases and creative solutions that competitors cannot acquire without processing a thousand contracts themselves.

In financial terms, execution data represents a new form of capital: memory capital. Like intellectual capital or brand capital, it is an intangible asset that generates future returns. Unlike traditional intangibles, memory capital can be precisely measured in terms of the number of cases, performance improvement per case, or retrieval accuracy. It can then be directly deployed. Infrastructure commoditizes. Domain-specific execution data compounds. The market is beginning to price this shift, though without fully understanding it yet.

Context Defensibility

Context advantages become durable moats only when defended against replication. Two factors determine whether they do.

Proprietary access creates the first defense. Physical-world operators possess inherent advantages because their context arises from irreplaceable operational reality. Tesla accumulates driving context through millions of vehicles in diverse conditions. Manufacturing facilities generate production context through actual operations. Logistics companies accumulate transportation context through real shipments. These companies build on contextual foundations that digital competitors cannot replicate because generating the context requires physical infrastructure.

The same applies in enterprise software through a different mechanism. The insurance company that has processed 50,000 claims through its orchestrated workflow has accumulated context that a new entrant cannot acquire without processing 50,000 claims. The context is a byproduct of operation. You cannot buy it. You cannot synthesize it. You earn it through sustained execution.

Switching costs create the second defense. When context accumulates in an orchestration platform, switching means abandoning everything the system learned. A competitor can match features, pricing, and even license identical models. It cannot manufacture the memory of accumulated interaction. The new system begins as a stranger. The deeper the context, the higher the switching cost. That’s because the gap between what the incumbent knows and what the new entrant must learn widens with every interaction.

Protocols sharpen this dynamic. MCP and A2A standardize how agents connect to systems, dissolving integration friction. But protocols cannot transfer context. They make it easier for agents to access data across systems. They cannot replicate the behavioral understanding, relational mapping, and temporal patterns that emerged from years of orchestrated operation. Companies that relied on integration friction face exposure. Companies whose moat is the quality and uniqueness of their context find that protocols reinforce their advantage, making the platform more connectable while leaving the context inimitable.

The Investment Test

The Second Law reframes how we evaluate companies in the agentic economy. The question is no longer “how much data does this company have?” It is “what kind of context is this company accumulating, how fast is it compounding, and how defensible is it against replication?”

Three pillars work synergistically:

Memory architecture determines how context is stored, retrieved, and deployed. These are the episodic, semantic, and procedural layers that transform raw experience into operational intelligence.

Context depth determines richness across behavioral, transactional, relational, and temporal dimensions.

Defensibility determines whether the context can be matched through proprietary access, regulatory barriers, or the sheer volume of operational history required.

Memory systems without rich contextual content provide structure without substance. Deep context without architectural sophistication to organize and retrieve it proves unwieldy and underutilized. Defensible context that agents cannot leverage due to infrastructure limitations delivers strategic value slowly and incompletely. The durable positions require all three.

Companies where data compounds are building moats that deepen automatically. Every transaction, every user, every interaction adds to the advantage. The models become more accurate. The predictions become more reliable. The new customer benefits from everyone who came before, and everyone who came before benefits from the new customer.

The most defensible positions combine proximity to intent, deep compounding context, and proprietary access that prevents replication. The orchestrator that sits at intent origin, accumulates operational context with every interaction, and generates that context from irreplaceable operations holds a position that is nearly impossible to displace. This is the standard against which we evaluate every company in our investment universe.

The Second Law

The first two laws identify where to position and what to accumulate. But they describe conditions, not dynamics. The agentic economy is not static. Workflows change. Competitors adapt. New entrants arrive.

The orchestrator that maintains its advantage is the one whose coordination intelligence compounds faster than the environment shifts, whose learning improves with scale in ways that specialists cannot match.

That is the Third Law.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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Let’s return to the insurance workflow. The master agent receives a goal: evaluate this risk and produce a quote that fits our growth and risk constraints. The extraction agent receives a task: pull the relevant data from this document. The fraud detection agent receives a task: check this submission for anomalies. The pricing agent receives a task: calculate the payout given these parameters.

All four are agents. All four orchestrate tools. All four sit in the Orchestration Layer. But the master agent controls routing. It decides which specialists to invoke, in what order, and under what constraints. It defines the workflow. It validates the outputs. It determines when the goal is achieved. The specialist agents execute tasks defined by someone else. They may be brilliant within their domains. But they do not see the whole map. They do not decide whether the claim gets processed. They do not own the relationship with the human who submitted it.

Chapter 7 established that this hierarchy creates disproportionate value capture. The First Law explains why: it is not coordination alone that creates the advantage. It is in proximity to the human’s original intent. The master agent sits closest to the moment a human expresses a goal. The pricing agent sits furthest from it, several translations downstream. That distance determines everything: defensibility, pricing power, switching costs, and bypass risk.

The principle generalizes across the entire agentic economy. There is a hierarchy of positions relative to intent, and your location determines the durability of your orchestration position:

Intent Origin carries the highest value. This is where goals are first expressed in natural language, unstructured, with high ambiguity. The human who submits “evaluate this risk” is expressing intent at the origin. In enterprise software more broadly, search interfaces occupy this layer: conversational interfaces, ambient assistants, operating systems, enterprise collaboration tools, and autonomous workflows. Whoever controls the intent origin potentially controls routing.

Intent Capture carries strong value. This is where goals become structured as they are translated into specific actions, mapped to known workflows, and scoped with constraints. When the master agent decomposes “evaluate this risk” into discrete tasks for each specialist, it is performing intent capture. If agents intercept intent at the origin layer and route it directly to execution, the capture layer is skipped entirely.

Intent Processing carries moderate value. This is where goals are executed. Data is read and written, APIs are called, and workflows run. The extraction agent and the fraud detection agent occupy this layer. Essential work. Nothing happens without it. But it does not control where intent originates or how it gets structured.

Intent Output carries the lowest value. This is where results are viewed as dashboards refresh, reports are generated, and notifications are displayed. This layer is one abstraction from replacement. When the master agent synthesizes results and delivers the outcome directly to the human who expressed the goal, separate output displays become redundant.

The further upstream you sit, the more durable your moat. The further downstream, the more replaceable you become. Because the Orchestration Layer above you can substitute you without the user noticing. The insurance company does not care which extraction agent runs. It cares that the master agent delivered the right quote.

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Why Upstream Wins

The hierarchy is not merely a ranking. It reflects a structural asymmetry in how value flows through agentic systems. The entity at the intent origin has three advantages that entities downstream do not:

Routing authority. The intent origin decides which downstream systems get invoked. Every goal expressed is an opportunity to engage your capabilities, route to your ecosystem, and monetize through your platform. This is the economic engine that made AWS successful: control the compute layer, and you can upsell storage, databases, machine learning services, and a thousand other capabilities. Intent capture is the agentic equivalent. Control the orchestration surface, and every user goal becomes a distribution channel.

Relationship ownership. The human interacts with the intent surface. The human trusts the intent surface. The human returns to the intent surface. Everything downstream is invisible to the user. It is mediated, abstracted, and replaceable. The intent surface owns the relationship. Everyone else is a vendor.

Phase transition capture. Connect this to the economics of Chapter 7. The phase transition to Orchestration Economics only accrues to the entity that delivers the complete outcome. That entity must sit at or near the intent origin, because only the intent origin can promise the customer that if they receive the goal, it will deliver the result. An orchestrator at the processing layer can coordinate agents effectively, but it cannot deliver the autonomous function the customer is paying for. It depends on someone upstream to provide the goal. Someone upstream can bypass it. An orchestrator at intent origin owns the goal itself, and everything downstream flows from that ownership.

Not all data enables these advantages equally. Customer behavioral data, such as how users interact, what they choose, and what patterns they follow, creates the strongest orchestration advantage because it feeds directly into routing decisions. Operational transaction data creates the strongest execution advantage but does not grant routing authority. The implication for investors is direct: companies that accumulate behavioral data at the intent surface compound their routing advantage with every interaction. Companies that accumulate transactional data downstream build strong specialist positions but remain vulnerable to bypass. The corollary is severe for companies positioned downstream. If you are a point solution, such as a specialized sales engagement tool, and the user’s goal is expressed in Slack and orchestrated by Agentforce, you are competing to be the sub-agent that gets invoked. Your switching costs collapsed. Your direct relationship with the user eroded. You moved from captain to crew. The crew is replaceable.

The Software Inversion

For two decades, enterprise software moats were constructed downstream. You won by owning the system of record. This is the database where critical business data lives, surrounded by workflows that made switching painful. Salesforce built an empire on this principle. So did Workday, ServiceNow, and SAP.

The Orchestration Layer inverts this. When an agent mediates the interaction, the system of record becomes just another API call. The agent does not care whether your CRM is Salesforce or HubSpot. What matters is whether the agent can access the right context and orchestrate the sequence of actions needed to fulfill the user’s goal. Moats no longer form where data sits. They form where intent originates.

Salesforce recognized this before most of the market. Its repositioning of Slack at Dreamforce 2025 is the First Law made concrete. Restricting AI companies’ access to Slack data looked defensive. The sequence that followed instead demonstrated an offensive strategy by governing access through MCP, the Agentforce runtime, and first-party agents embedded directly in Slack.

Salesforce moved the moat upstream, from owning the database where records sit to owning the surface where goals are expressed. Slack became the system of intent. Customer 360 remained the system of record. Agentforce became the Orchestration Layer, binding them. The company understood that controlling the origin of intent is more valuable than controlling any system downstream.

This continued in April at Salesforce’s TDX developer conference in San Francisco. Salesforce's strategy unveiled at TDX 2026 provides a concrete illustration of the First Law in practice.

Rather than defending Customer 360 as the primary destination for work, Salesforce deliberately moved its competitive position upstream, toward the point where work begins. Headless 360 exposes the underlying platform as APIs, MCP tools, and agentic services, making the traditional application interface increasingly optional. At the same time, Slack is repositioned as the system of intent, Agentforce as the orchestration layer, and Data 360 as the context substrate that informs routing decisions.

This architecture reflects an important strategic realization: the long-term moat no longer resides in owning the system of record where data is stored, but in owning the surface where users express goals and where agents accumulate the behavioral context needed to orchestrate future work. The CRM becomes execution infrastructure; the intent surface becomes the source of durable economic power.

The same principle operates at every scale. Within OpenAI’s GPT-5, a built-in router analyzes user intent and directs processing to the appropriate internal model variant. The router sits at the intent origin inside the model. Platforms like Slack or Teams must do the same thing across entire ecosystems. The architecture is fractal: whoever captures intent first controls what happens next, whether “next” means selecting a model variant or orchestrating a fleet of enterprise agents.

The Protocol Test

Protocols provide the sharpest test of the First Law.

Chapter 7 established bypass risk as the counterforce to orchestration advantage. MCP and A2A, along with their rapid adoption by every major platform, standardize how agents connect to systems and to each other. That standardization dissolves integration friction, the difficulty of connecting systems that once protected many orchestration positions. The First Law predicts exactly which positions survive this dissolution and which do not.

Protocols can standardize every layer below the intent origin. They can make tools interchangeable, specialist agents substitutable, and integrations frictionless. If every CRM exposes the same agent endpoints via MCP, an orchestrator has little structural reason to prefer one over another. Switching costs collapse. Differentiation shifts toward price. Margin compresses. This is the crew’s fate in a world of universal protocols. But protocols cannot standardize the moment a human expresses a goal. They cannot standardize the trust relationship between a user and the surface where they articulate what they want. Intent origin is protocol-resistant because it depends on human habit, institutional workflow, and accumulated context. None of these can be specified in an API schema.

Protocols commoditize execution. Intent surfaces capture value.

The orchestrator that relied on integration friction has no moat left. The orchestrator at intent origin has a moat that protocols reinforce because every new protocol-compliant tool that connects to its ecosystem makes the intent surface more valuable, not less. More tools mean more capabilities the captain can route to. The crew gets more substitutable. The captain gets more powerful.

Strategic Positioning

The First Law creates a simple diagnostic for any company in our investment universe: where do you sit in the intent hierarchy, and can you move upstream?

For the systems of record, the processing engines, the output layers, the imperative is to move from downstream to upstream. Build the agent cockpit, not just the system of record. If moving upstream is not feasible, differentiate so aggressively that you become irreplaceable to orchestrators. Be the specialist so good that the captain cannot sail without you. Workflow lock-in will not protect you. Data gravity will not protect you. The question is whether you can become the agent that other agents depend on.

For horizontal orchestrators competing to be the default intent surface, the imperative is speed. Race to become the place where users start their day and express goals. This probably means conversational interfaces rather than traditional application UIs. Invest in context accumulation and memory architectures because the depth of your conversational memory prevents a bypass when the next intent surfaces.

For specialized agents at the execution layer, embrace protocols and compete on quality, speed, and cost. Be the best sub-agent in your category, and you will be routed millions of times. Fail to be best in class, and you will be replaced overnight. You are the crew. Make yourself indispensable crew.

Where does value concentrate five years from now? Three scenarios frame the range:

Fragmented orchestration: every application embeds its own agent layer. Slack has Agentforce. Microsoft has Copilot. Intent capture is distributed, and moats remain traditional.

Consolidated orchestrators: a handful of platforms emerge as dominant intent gateways, orchestrating best-of-breed sub-agents behind the scenes. Downstream tools become commoditized utilities.

Decentralized agent mesh: open protocols enable any agent to invoke any other agent, and value flows dynamically based on performance rather than ownership.

History suggests a hybrid: consolidation around dominant orchestrators, plus a long tail of specialized agents accessible via protocols. But in all three scenarios, proximity to user intent is the strategic high ground. The players who control where goals are expressed will have structural advantages in routing, context access, and monetization. The players who do not will compete on price.

If you neither own intent nor provide differentiated capabilities to the orchestrator, you risk becoming a replaceable API call in someone else’s plan.

The First Law

The First Law establishes the first condition for defensible orchestration: proximity to intent determines where value concentrates, who controls routing, and whose position survives protocol standardization. Moats form where intent originates, not where data sits. The captain who hears the human’s goal first captures the economics of the phase transition. Everyone downstream competes on execution.

Owning intent is necessary, not sufficient. Return to the insurance example one more time. The master agent sits at the intent origin. It controls routing. It captures the phase transition economics. But what happens when a competitor builds a rival master agent for the same workflow? Both sit at the origin. Both offer orchestration. What determines which one the customer stays with?

The answer is not who arrived first. It is who built the deeper context. Who accumulated the operational intelligence that makes orchestration effective in ways a new entrant cannot replicate on day one?

That is the Second Law.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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Parts I and II established that the paradigm has shifted. Machines became actors. The intelligence required for autonomous professional work exists as commercially available infrastructure. Agents are deployed in production. The infrastructure, while strained, is scaling.

The question now: Who captures the value it creates?

When a factor of production commoditizes, when intelligence becomes abundant, then value migrates to whoever controls the coordination of the newly abundant input. This migration is a pattern that has repeated across every major economic transition, from electricity to computing to cloud infrastructure. The entity that coordinates the abundant resource captures the surplus. In the agentic economy, the entity that coordinates machine actors captures the surplus of the entire transition.

The framework that describes who captures value, under what conditions, and why is Orchestration Economics. This manifesto defines the Three Laws of Agentic Value that determine whose orchestration position is durable. They are structural in nature, already visible, measurable, and investable.

However, we must first clarify how the new layer of value is formed.

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Orchestration Economics

Orchestration Economics rests on a fundamental structural claim: The entity that controls the orchestration of machine actors captures the surplus of the transition. This is not a marginal improvement over SaaS economics, nor is it limited to software. It is a categorical shift in three dimensions:

Pricing is outcome-based, not seat-based or time-based. The unit of commerce is a claim processed, an analysis completed, a route optimized – not a license sold or a billable hour. Pricing per agent task (i.e., per consumption) is an intermediary step in that journey.

Budget is labor, not IT. The vendor does not compete with software for a slice of the 3-5% IT line. It competes with salaried professionals for a slice of the 25-40% knowledge-work line. The addressable market expands by a factor of 5-10x. What is more, the market is not limited to labor substitution; agents complete value-accretive tasks that did not exist before.

Defensibility is topological, not feature-based. What protects an orchestrator is the structure of the workflow it controls, not the capabilities of any single agent inside it.

The rest of this chapter establishes where this layer sits in the firm's new architecture, how value migrates into it, why most companies will fail to capture it even when they think they are, and under what conditions an orchestration position is durable rather than temporary. The Three Laws formalize those conditions.

The New Layer

Before agents, the architecture of knowledge work was straightforward. A human expressed intent: process this claim, close this deal, optimize this route. The human also had the tools to execute that intent. The human opened a CRM to update a record, a spreadsheet to run an analysis, and an email client to send a message. Each tool was a destination. The human was the orchestrator. The tools were the instruments. The business outcome was the product of human judgment applied through software.

Agents invert this architecture fundamentally. They insert a new layer between humans and tools. Humans used to go to the tools. Now the tools come to the human, mediated by the agent.

In the simplest case, this involves just a single AI agent. The human expresses intent: update the Chicago deal to closed-won and schedule a kickoff with their team next week.” The agent receives that intent, determines which systems to invoke (the CRM, the calendar, the email system), executes the necessary actions across those systems, and delivers the outcome. The human never opens the CRM. Never touches the calendar. Never drafts the email. The agent coordinates it.

Even in this single-agent case, orchestration is happening. The agent is orchestrating tools on the human’s behalf. The agent is, in the precise sense of the word, a synthetic colleague. This is the foundation of Orchestration Economics: agents are not better tools. They are a new layer between humans and their tools, designed to produce business outcomes.

The entity that controls the “Orchestration Layer” owns the relationship between the human and the work for a specific workflow, or set of workflows, aimed at producing a business outcome. It decides which tools are invoked, which data is accessed, which sequence yields the best outcome, which systems are essential, and which are bypassed entirely.

The Orchestration Layer described in this Manifesto is categorically different from the technical Orchestration Layer known as the “Harness”. This is a critical distinction. The Harness enables an LLM to produce results and makes intelligence operational. The Orchestration Layer turns operational intelligence into a business outcome.

The Architecture of the Agentic Firm

The agentic firm is organized in concentric rings, not layers, because what matters is not just function, but proximity to value capture. The farther out the ring, the greater the control over outcomes, and the stronger the economic position:

Ring 1 – Intelligence. The foundation models. The substrate. Its economic unit is the token.

Ring 2 – Harness. The technical orchestration infrastructure that makes intelligence operational: the systems that decompose goals, delegate to agents, manage state, coordinate execution, and accumulate learning across sessions. Claude Code, OpenAI Codex, enterprise agent platforms, managed agent systems, and emerging agent operating environments are Harness products.

Ring 3 – Orchestration. The outermost sub-layer. The position occupied by the entity that places Rings 1 and 2 at the center of its operations and adds what neither can produce: the operational context accumulated through running the world. Its economic unit is the outcome.

When I first argued in 2025 that the durable moat for LLM labs lay in orchestration rather than model intelligence, I was describing Ring 2. The labs confirmed the thesis. They recognized that model capability alone was commoditizing and moved into the infrastructure that makes it operational. The Coding Wedge was the entry point. Each product since has deepened the position.

But the labs did not stop at the Harness. Cowork extended into knowledge work. Vertical plugins reached into business functions. Frontier positioned itself as a control plane for the enterprise. With each step, the labs move outward, from intelligence through Harness toward the outermost sub-layer, contesting it against companies that have occupied it for decades. This expansion from Ring 2 to Ring 3 is at the root of the SaaSpocalypse and the broader value destruction in the public markets since the start of the year. We explore this in Chapter 13 infra.

An entity that provides intelligence is not an orchestrator. An entity that builds a Harness is not an orchestrator. The orchestrator wraps around both and produces business outcomes.

A single entity can occupy more than one ring. Two patterns are already visible.

The debate is no longer whether labs will move into Ring 3, but where they can do so successfully.

Anthropic today credibly sits in Ring 1 (Claude), Ring 2 (Claude Code, MCP, Skills, Agent SDK), and extends into Ring 3 (Claude Code for enterprise, Cowork, managed agents). In narrow domains, coding is the clearest because it already crosses all three. OpenAI is pursuing the same stack. The labs built inward-out: from intelligence, through Harness, toward outcome.

Occupying the inner rings does not confer the outer ring. Ring 3 is not a further technical layer. It is the operational context co-produced with the workflow being orchestrated. The labs can ship Ring 2 unilaterally. They cannot ship Ring 3 unilaterally because the intent, context, and workflow reside within the firm whose work is being orchestrated.

In domains where the lab also owns the workflow, such as coding, where the developer’s intent, context, and workflow are all captured natively inside the Harness, the lab credibly holds Ring 3. In domains where it does not, such as insurance underwriting at a carrier, credit analysis at a bank, claim evaluation, or at a reinsurer, then the lab supplies Rings 1 and 2, and someone else holds Ring 3.

Ring 3 is, therefore, domain-specific, not firm-wide. The same entity can hold it in one workflow and lose it in another. Whether any entity durably holds Ring 3 is not settled by its stack position. It is determined by the Three Laws.

The architecture is new. The contest for who occupies the outermost ring is already underway.

Captain and Crew: The Topology of Coordination

For a simple task such as updating a CRM record or scheduling a meeting, a single agent coordinating a few tools is sufficient. But evaluating an insurance claim, analyzing a company’s creditworthiness, or optimizing this supply chain becomes too complex for a single agent to handle all dimensions.

This is where multi-agent systems emerge. The system has a crew of agents with specialized capabilities whose job is to execute well-scoped work, such as document extraction, risk modeling, fraud detection, pricing, or compliance validation. They can be brilliant within their domains. But they do not see the whole map. They do not decide which workflow to run. They do not own the relationship with the human who asked for the outcome.

A multi-agent system could, in principle, be organized in many ways. A federation of peer agents could negotiate amongst themselves. A swarm could converge by consensus. Independent specialists could run in parallel and average their outputs. All of these have been tried. None produces coherent work at enterprise reliability.

A Google-DeepMind-MIT study, first published in December 2025, formalized this structure. A multi-agent system is an agent system with more than one reasoning entity, where agents interact through a communication topology and an orchestration policy that defines how the system makes decisions: how sub-agent outputs are aggregated, whether the orchestrator can override sub-agent decisions, whether memory persists across rounds, and when the task is complete.

According to the most recent April 2026 revision of the paper, centralized coordination architectures exhibited approximately 75% lower trace-level error amplification than independent topologies (4.4× versus 17.2×). The broader result was not simply fewer propagated errors, but the emergence of verification mechanisms that improved reliability when tasks were naturally decomposable.

Only the architecture where one agent receives the task, decomposes it, selects and validates specialists, and decides what happens next produces the coherent outcomes enterprises will pay for.

The orchestrating agent becomes the captain. It does not perform the specialized work itself. It decomposes the goal into subtasks. It assigns those subtasks to the appropriate specialists on the agentic crew. It collects their outputs. It validates them. It synthesizes the results. The human talks to the captain. The captain commands the crew. The crew does the work. The captain reports back to the human. That is the agentic AI loop.

As we move into the core exploration of Orchestration Economics, it is worth noting that the discussion of the transition to the Agentic Era has remained at a simplistic, binary level. Winners and losers. Eat or be eaten. Victim or beneficiary of the SaaSpocalypse. The more nuanced and challenging question most companies will be facing: Do you want to be the captain or the crew?

The economic consequence follows directly from the topology, not from the metaphor.

Specialist agents are substitutable. A better extraction model can be swapped in. A better pricing model can replace the incumbent. The crew is upgradeable. Its members have limited pricing power because the captain can always source a better one.

The captain is not substitutable. Replacing the orchestrator means rewiring the workflow, including remapping the topology, retraining the policy, reestablishing every integration, and re-accumulating the operational memory. The switching cost is categorical.

Value is multiplicative, not additive. A single agent coordinating three tools yields linear value: the sum of the tasks it automates. An orchestrator coordinating five specialist agents, each coordinating their own tools, produces nonlinear value. The orchestrator can redesign the workflow, parallelizing steps, eliminating redundancies, and catching errors across domains that no individual specialist would detect. The orchestrator is not a participant in the workflow. It is the workflow.

In human organizations, we call this management. In multi-agent systems, we call it orchestration. Value accrues to the Orchestration Layer rather than being evenly distributed among the agents beneath it. Not because someone has to be in charge, but because the topology in which centralized coordination wins is the only topology in which the workflow works at all. The entity at the center of that topology captures the surplus that coherence creates.

Let’s follow the money through an actual enterprise workflow

A commercial insurance submission arrives. Under the old architecture, it enters a human chain: intake, review, coding, investigation, pricing, approval, and documentation. Each human is an expert. Each has trained for years. Together, they process the claim in days, sometimes weeks, at costs measured in hundreds of dollars per claim. The Orchestration Layer lived in a manager’s brain and a project management tool. The bottleneck was human capacity for coordination.

Under agentic orchestration, the same workflow transforms. The submission arrives at a master agent. The master agent does not “process a document” as legacy software does. The master agent interprets the goal: evaluate this risk and produce a quote that aligns with our growth and risk constraints. It inspects the case. It decides which specialized agents to invoke: extraction, enrichment, risk modeling, pricing, or compliance validation. It sets their inputs. It sequences their work. It validates their outputs. It determines when the goal is achieved.

The specialist agents are valuable. They have real reasoning capacity. But they are no longer where the workflow is decided. Their job is to respond to the plan. Not to define it. The control plane has moved. So has the economics.

The question every company in the agentic economy must answer is not rhetorical. It is operational.

Are you the captain? Or are you the crew?

The Programmable Firm

Carnegie Mellon and Stanford University research teams released a landmark study in October 2025 called “How Do AI Agents Do Human Work?” that provided an empirical foundation for the Agentic Era. The teams compared how humans and AI agents complete identical work across data analysis, engineering, computation, writing, and design. They concluded that for tasks defined as “readily programmable,” AI agents will complete the work 88.3% faster at 90.4-96.2% lower cost. These figures represent benchmark environments rather than fully operational enterprise deployments.

The key finding is not that work is programmable end to end. It is more consequential: programming is embedded in 82.5% of knowledge-work occupations. In practice, this means a large share of enterprise workflows contain programmable components, even if they are not fully automatable.

Programmability is the unlock. A programmable workflow can be agentized. A workflow that can be agentized can be orchestrated. A workflow that can be orchestrated can be sold as an outcome rather than licensed as a tool. The economic category changes not because the AI is better, but because the work itself has always been more structured than the software market has reflected.

The gate is reliability. Every enterprise workflow has a reliability requirement: a level of accuracy and completeness below which human oversight remains necessary. When an AI system handles 40% or 50% of cases correctly, it accelerates human workers but cannot replace them. Every output still needs review. The human chain stays intact. The company has purchased a productivity tool.

When an AI system handles 85% or 90% of cases correctly, the economics change categorically. The workflow runs autonomously. Humans handle only the exceptions, the fraction of cases the system flags for review. The company has not purchased a productivity tool. It has purchased an autonomous function. The difference between these two states is not a percentage improvement. It is a reclassification.

In the first state, where AI is a productivity tool, the vendor sells software licenses. The buyer pays $50-$200 per seat per month from the IT budget. The vendor competes on features against other software vendors. The human labor cost remains on the books. This is SaaS economics.

In the second state, where AI becomes an autonomous function, the vendor sells an outcome. The buyer pays per claim processed, per credit analysis completed, or per supply chain route optimized. The budget is no longer IT spend. It is labor spend: the fully loaded cost of the knowledge workers the autonomous function replaces.

A single insurance underwriter costs $8,000-$15,000 per month in salary, benefits, training, and management overhead. A credit analyst costs similar. The vendor prices based on that labor cost, not on competing software licenses.

Apply this lens to a large institution like JPMorgan. With approximately $50 billion in annual labor costs and assuming ~30% of tasks fall into the “readily programmable” category, agents begin to meaningfully reshape the cost structure. However, because these tasks still require verification, exception handling, and integration into broader workflows, the immediate impact is not full replacement but partial automation and labor reallocation. Even under conservative assumptions, this translates into multi-billion-dollar efficiency gains, not through elimination of labor, but through compression of the programmable portions of work. That pool does not sit in the IT line. It sits in headcount.

The addressable market shifts accordingly. Enterprises typically spend 3-5% of revenue on IT, but 25-40% on knowledge work. The critical transition is not from software to AI, but from tools to workflow execution. Once an orchestrator reliably automates the programmable segments of work while coordinating human oversight where needed, it begins to tap into the much larger pool of labor spend. The prize is therefore not to replace SaaS but to expand beyond it by capturing portions of the operating budget that have historically been reserved for human execution. In that sense, the orchestrator that crosses the reliability threshold does not compete within the software market but instead redefines the market boundary, expanding it by a factor of 5 to 10.

The orchestrator creates this transition. The specialist agents, no matter how capable individually, do not. A pricing agent that achieves extraordinary accuracy still operates within the first state. It makes the human underwriter faster. It is a tool.

An orchestrator that coordinates pricing, risk, compliance, and documentation agents into a coherent autonomous workflow delivers the second state. It replaces the underwriting function. It delivers an outcome. The outcome emerges only from the coordinated whole. Only the orchestrator delivers it.

Bypass Risk

Orchestration Economics does not guarantee permanent advantage. The same structural logic that creates the orchestrator’s position also defines the conditions under which that position can be undermined.

Bypass risk is the possibility that a new entrant positions itself between the user’s intent and the existing orchestrator. In doing so, it captures the Orchestration Layer from above. Protocols such as MCP standardized how agents connect to external systems and to each other. A2A standardizes agent-to-agent communication. ANP sits atop both as a discovery layer.

Historically, APIs standardized software connectivity. In the agentic economy, protocols standardize agent connectivity. The strategic implication is profound: once interfaces become standardized, defensibility migrates away from integration and toward intent, context, and workflow ownership.

Standardization is powerful for interoperability. It also dissolves the integration friction that protected many orchestration positions. The company that controlled its workflow through the sheer difficulty of connecting systems wakes up one morning to find that the friction is gone. Protocols dissolved it. Orchestration Economics is therefore not a simple claim that the orchestrator always wins. It is a framework that says the orchestrator wins unless it gets bypassed.

The orchestrator’s advantage is structural, but its defensibility is conditional. It depends on building moats that protocols cannot standardize away and that competitors cannot replicate. Which moats? Under what conditions does an orchestration position become durable rather than temporary? What separates the orchestrators who compound their advantage from those who lose it to the next layer above?

The Three Laws

Knowing that orchestrators capture value does not tell us which positions are defensible. The orchestrator’s advantage is structural. Its defensibility is conditional. It depends on building moats that protocols cannot standardize away and that competitors cannot replicate.

Three conditions determine whether an orchestration position is durable. Each is necessary. None is sufficient alone. The entity that satisfies all three simultaneously holds a position that compounds with use, resists bypass, and captures the surplus of the agentic transition. The entity that satisfies one or two, even brilliantly, occupies a layer that the orchestration economy will eventually treat as crew rather than captain.

Chapter 8 develops the First Law: Proximity to Intent Determines Value Capture.

Chapter 9 develops the Second Law: Context Builds Moats.

Chapter 10 develops the Third Law: Workflow Intelligence Secures Control.

Once we define the Three Laws, we will apply them to Palantir.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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The agentic economy is a software phenomenon built on a hardware foundation. That foundation is being constructed on a scale without precedent in the history of private capital allocation. But is it being constructed correctly?

When I first published this Manifesto in late April, the projected $650 billion in AI infrastructure investment in 2026 already represented one of the largest coordinated private investments in peacetime history. Since then, those numbers have continued to soar.

In May, the four largest hyperscalers reported quarterly earnings within hours of each other and raised 2026 capital spending guidance, committing between $650 and $700 billion to AI infrastructure for the year. Goldman Sachs raised its Capex spending forecast for the Big Four Hyperscalers from $4.5 trillion to $5.3 trillion through 2030. Overall, it projects $7.6 trillion of Capex spending between 2026 and 2031 across compute, data centers, and power.

The problem remains, however, that much of this capital is also being committed under assumptions that are already shifting.

The capital was designed for training. The agentic economy runs on inference. The cloud was designed for stateless applications. Agentic workloads are stateful, persistent, and coordination^-intensive. The power grid was designed for variable demand. Inference generates a continuous baseload.

The infrastructure does not need to stop being built. It needs to change what it is becoming. This chapter traces that metamorphosis, from the capital flowing in, through the balance sheets absorbing it, to the financing structures underwriting it, to the inflection point where the workload changes, to the efficiency gains that determine the unit economics, to the energy constraint that governs the speed, and finally to the architectural evolution of the cloud itself. Together, they constitute the infrastructure question on which the AGNT thesis ultimately depends.

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The Buildout

The numbers announced during Big Tech earnings calls throughout 2025 arguably constituted one of the largest coordinated private infrastructure investments in history. Bloomberg estimated that the four largest hyperscalers would spend ~$650 billion in 2026. Goldman Sachs projected cumulative hyperscaler Capex of $1.4 trillion from 2025 to 2027. That is close to three times the $477 billion spent from 2022 to 2024. Leading hyperscalers have emphasized that they are primarily constrained by power and capacity, not by demand, repeatedly flagging supply bottlenecks on recent earnings calls.

Microsoft carried an $80 billion backlog of unfulfillable Azure orders due to power constraints as of January 28, 2026 (Microsoft Q2 2026 Earnings). Alphabet’s cloud backlog surged to $240 billion at year-end 2025. Amazon said new capacity “sells out immediately”.

Yet investors punished these announcements. Amazon fell 8%. Microsoft dropped ~10%. The four companies collectively shed over one trillion in market value after revealing their spending plans. By February 2026, a Bank of America survey showed overinvestment concerns at an all-time high, roughly 35% warning of excess, the highest reading in twenty years.

Is this Capex buying what the agentic economy will require?

The Balance Sheet Transformation

The balance sheets of the world’s most profitable technology companies underwent a structural transformation beginning in 2025. For two decades, the defining characteristic of large-cap technology was capital efficiency. It was driven by asset-light models generating extraordinary returns on modest physical investment.

That era ended when the AI buildout began. The more than fourfold increase in capital intensity from 2015 to 2026, the dollars of capital required to produce one dollar of sales, tells the story.

Cloud computing infrastructure from 2015 supported predictable workloads with well-understood utilization patterns and multi-year lifecycles. AI infrastructure serves workloads with radically different characteristics: shorter useful lives, uncertain utilization, and a pace of obsolescence that makes traditional depreciation assumptions unreliable.

The depreciation debate alone reveals the depth of uncertainty. Amazon shortened the useful lives of a subset of its servers and networking equipment from six to five years in January 2025. Microsoft extended expected asset life in FY 2023, adding $3.7 billion in operating income from the change alone. These are not accounting technicalities. They are competing theories about how long AI hardware will retain value. The answers differ by billions of dollars.

Yet, the demand for NVIDIA H100’s has never been higher, suggesting longer utility than GAAP rules. When the industry cannot agree on whether its core infrastructure lasts one year or six, every capital allocation decision rests on a foundation of sand.

The free cash flow consequences are direct. Bank of America projected that Amazon’s free cash flow would turn negative at approximately $28 billion in 2026. Amazon filed with the SEC^, indicating it may need to tap equity and debt markets. Such a statement would have been unthinkable for a company generating $638 billion in annual revenue in 2024 (and $717 billion in 2025). Pivotal Research projected that Alphabet’s FCF would plummet approximately 90% in 2026, from $73.3 billion to approximately $8.2 billion, as $175-185 billion in Capex overwhelms operating cash flow. Barclays projected Meta’s FCF to drop approximately 90% in 2026, then turn negative in 2027 and 2028.

Step back and absorb the aggregate picture. Bank of America estimates that AI Capex now accounts for 94% of hyperscaler operating cash flows after dividends and buybacks. That figure was 76% in 2024. In the span of a single year, Capex has gone from absorbing three-quarters to nearly all of the available post-distribution cash flow, substantially compressing the cushion and forcing record debt issuance. The margin for error has dramatically narrowed.

The weight of physical assets on these balance sheets means that even the most profitable technology companies in history are subject to the same economic physics that govern utilities, railroads, and industrial conglomerates. Returns on invested capital matter more than revenue growth. The companies that built their fortunes on capital-light software economics are becoming capital-intensive infrastructure operators.

The financial frameworks applied to them have not caught up.

The Debt Machine

The Big Five (Amazon, Alphabet, Meta, Microsoft, and Oracle) raised ~$121 billion in bonds in 2025 alone: Amazon $15 billion, Alphabet $25 billion, Meta $30 billion (the largest-ever individual non-M&A high-grade bond sale), and Oracle $18 billion. That’s more than three times the average of the prior nine years. Alphabet’s long-term debt quadrupled in 2025 to $46.5 billion.

Oracle, whose fiscal year 2026 ended in May, disclosed that it had raised $43 billion in debt financing and $5 billion in equity financing. The company said it plans to raise $40 billion in debt and equity financing in FY 2027.

Companies that spent decades accumulating fortress balance sheets are now leveraging them at a blistering pace.

Morgan Stanley had earlier estimated that hyperscalers will borrow ~$400 billion in 2026, double the $165 billion borrowed in 2025. On June 10, Morgan Stanley predicted that AI-related global debt issuance would double to about $570 ​billion in 2026. JPMorgan projects $1.5 trillion in AI-related bond issuance over the next five years.

The clearest expression of that thesis arrived in February 2026, when Alphabet issued a 100-year bond. This was the first century bond from a tech company since Motorola in 1997. The sterling-denominated tranche, part of a $31.5 billion multi-currency debt raise completed in under 24 hours, was oversubscribed by nearly 10 times. Pension funds and life insurers, institutions that match liabilities measured over generations, bought the paper at a 6.05% yield, treating Google's parent as sovereign-grade infrastructure. Alphabet sits on c.$127 billion in cash (cash, plus equivalents, plus marketable securities) and chose to borrow anyway because even that war chest looks modest against $175 – $185 billion in planned 2026 Capex.

The century bond is a statement that buyers believe this buildout is not a cyclical phase but a permanent transformation of the economy’s substrate. The last tech companies to issue century bonds were IBM in 1996 and Motorola in 1997. They did so at the zenith of their market dominance. Both subsequently declined.

At the DealBook Summit in December 2025, Anthropic CEO Dario Amodei described the high-wire financial planning. Established scaling laws, he said, are here to stay. Models get better at every task as you put more data and compute into them. But the timing of economic value remains uncertain, creating an inherent risk of under-reacting or over-extension.

Spend too little, and a company like Anthropic or OpenAI risks being unable to serve customers in two or three years. Overspend, and companies face revenue shortfalls that in extreme cases could threaten bankruptcy or tempt them to adopt what Amodei called a “YOLO” mindset.

The Fragility Beneath the Conviction

The hyperscalers can service this debt. They have investment-grade ratings, massive revenue bases, and the operating cash flow, though diminished, to cover even aggressive borrowing. The fortress is leveraged, but it remains a fortress.

The risk lies elsewhere, in OpenAI deploys the chips at CoreWeave, which has grown so quickly and so intricately that its systemic properties deserve the same scrutiny applied to any previous era of financial innovation. At the center sits a pattern of circular vendor financing.

The architecture is straightforward to describe and difficult to contain. NVIDIA invests in OpenAI. OpenAI uses the capital to purchase NVIDIA chips. NVIDIA records the transaction as revenue (subject to LOI stage; not yet binding). OpenAI deploys the chips at CoreWeave. CoreWeave secured term-loan facilities collateralized by GPUs. Institutional investors purchase the securities as infrastructure debt, providing the capital that flows back to the beginning of the cycle. The same dollar is counted as demand by the chip maker, revenue by the neocloud, and collateral by the lender.

The pattern is not novel. Classic vendor financing catalyzed previous bubbles. The 2000s telecoms collapse followed this structure, with equipment makers extending credit to customers, inflating revenues while disguising demand weakness. When customers could not pay, receivables evaporated, and cascading failures followed.

What is novel is the concentration. Telecoms vendor financing funded “the internet” broadly across hundreds of carriers and thousands of build-outs. Today’s circular deals are largely concentrated around a single company. OpenAI sits at the nexus of NVIDIA’s investment, Microsoft’s partnership, Oracle’s data center contracts, AMD’s equity-for-chips arrangement, and CoreWeave’s largest customer relationship.

The greatest risk is not that AI fails. It is that the technology succeeds while the single company around which the financial architecture has been constructed fails to build a defensible moat, triggering contagion that strands capital and stalls the buildout before the transformation completes.

Because while this concentration of relationships presents a sizable risk, it is also far from the limit. The boundaries extend far beyond this inner circle and continue to grow. Data center-related securitization via CMBS and ABS products is expected to reach $30-40 billion annually in 2026 and 2027.

Private credit finances approximately $50 billion in AI infrastructure quarterly. The instruments are growing more complex. For GPU-backed instruments, collateral depreciation is outpacing debt amortization, even if the broader data center ABS/CMBS market is not similarly exposed (yet). And the whole structure is more concentrated around fewer counterparties than any comparable financing architecture in recent financial history.

You can be bullish on AI and bearish on this financial architecture. They are separable propositions. The technology may well deliver everything this manifesto argues it will. The question is whether the financing structure survives long enough to support the transition, or becomes the mechanism by which a sound technological thesis precipitates a financial crisis before the economics have time to prove themselves.

The financing architecture will be tested by what comes next. Because the infrastructure it underwrites was designed for a workload that is already giving way to something fundamentally different.

Two Tales of Compute

The vast majority of the capital committed in 2025 and 2026 was allocated on the assumption that the primary demand for compute would come from training frontier models.

Training was the activity that the industry understood when the investment decisions were made. This means massive, centralized GPU clusters running synchronous workloads for weeks, requiring homogeneous hardware, high-bandwidth interconnects, and cutting-edge silicon. Each generation of models requires roughly ten times the compute of its predecessor. Frontier AI training costs escalated from over $100 million for GPT-4 (2023-2024) to approximately $490 million for xAI’s Grok 4 (mid-2025), with projections exceeding $1 billion by 2027.

First, let us examine the tale of training compute. The hardware characteristics are distinctive. Training requires homogeneous clusters of cutting-edge GPUs with high-bandwidth interconnects, where a single GPU failure can corrupt an entire run that takes weeks of computation and costs millions of dollars. You cannot mix GPU generations or repurpose last-generation training infrastructure for new workloads without massive retrofitting.

Leading-edge racks consume 130-250 kilowatts (as of 2025), with projections reaching 900 kilowatts by 2027. Frontier training performance advantages degrade within 18-24 months, but the hardware itself retains economic utility for more than five years, typically migrating to inference or lower-tier workloads. This creates an oligopolistic market. The number of organizations training frontier models globally is perhaps fifteen to twenty:

Anthropic’s arrangement with AWS provides co-engineering of custom Trainium chips that reportedly reduce training costs by 40% compared to GPUs.

Google’s TPU advantage allows training at dramatically lower costs than GPU-based alternatives.

xAI built its Colossus cluster from site preparation to full operation in 122 days.

Access to massive, synchronized GPU clusters that require +250-megawatt data centers and billions in capital now determines who can compete at the frontier. Training compute is too strategic to outsource. Microsoft, Google, and Amazon have all reached the same conclusion.

Training is necessary. Without it, the models that power the Agentic Era do not exist. But training is not where this manifesto's thesis lives. Training is one-time Capex, concentrated among a handful of players, and depreciates in 18 months. It is the cost of building the engine.

Now, let us look at the tale of inference compute. Every query processed, every agent action executed, every token generated represents ongoing expenditure that compounds relentlessly. A single ChatGPT query costs approximately $0.003 in compute. That is seemingly negligible until multiplied by 800 million weekly users^, each generating multiple queries.

At five queries per user weekly, that is four billion queries, $12 million in weekly compute, $624 million annually for inference alone. Over a model’s operational lifetime, inference costs routinely exceed training costs by factors of three to fifteen. At CES in January 2026, Lenovo’s chief executive officer predicted that 80% of AI compute will be inference and only 20% training. As of March 2026, inference already accounts for over 60% of AI workloads and is projected to reach 70-85% by 2028-2030.

The economic characteristics diverge at every level. Training hardware depreciates in eighteen months. Inference hardware maintains utility for four to five years. Training serves a concentrated customer base for frontier labs. Inference serves a broadening market of millions of enterprises and consumers. Training requires the latest GPU architecture. Inference benefits from specialized silicon optimized for throughput and cost per token rather than peak performance.

Training is the arms race. Inference is the economy.

Inference Efficiency

At GTC 2026, NVIDIA CEO Jensen Huang completed a pivot that had been in the making for 18 months. His keynote focused on inference, agents, and AI factories. Data centers are factories, Huang argued. Their product is the token. Their efficiency determines the unit economics of everything built above them. The governing metric has shifted from training FLOPS to tokens produced per watt.

This pivot culminated in the introduction of NVIDIA’s Vera Rubin platform, which promises to deliver 10x the inference performance per watt compared to its Blackwell architecture. The pivot was reinforced by NVIDIA’s December 2025 deal with chip startup Groq. The deal was reported to be a $20 billion non-exclusive licensing agreement for Groq’s LPU inference technology, paired with the hiring of CEO Jonathan Ross and the bulk of Groq’s engineering team.

Groq remains operationally independent. At GTC 2026, Huang showed how the two architectures would work together: Vera Rubin handling the prefill phase of inference, and the new Groq 3 LPX rack, housing 256 LPUs, handling the decode phase where tokens are generated. The Groq integration recalled NVIDIA’s “Mellanox moment” several years ago^, when it acquired the company for its networking architecture (InfiniBand, Spectrum-X Ethernet), which became cornerstones of AI cluster design. Groq’s architecture is optimized for inference, adding deterministic low-latency decoding using massive on-chip SRAM at 80 TB/s internal bandwidth, achieving 500+ tokens per second on Llama 3 70B at up to 10x the energy efficiency of GPUs.

On the silicon side, the inference landscape has fragmented in ways training never did: AWS Inferentia claims significantly better price-performance, Google’s TPUv5e offers strong cost-effective efficiency gains, AMD and a generation of startups are all targeting the inference economics that determine AI’s unit costs.

The algorithmic gains compound on top of the silicon. Mixture-of-experts architectures activate only a fraction of total parameters per query. Qwen3-Next, for example, activates 3 billion of 80 billion through ultra-sparse expert routing, achieving 10x faster inference on long contexts. Dynamic batching pushes GPU utilization from 30% to over 80%. Speculative decoding doubles token-generation speed at a fixed cost. These represent order-of-magnitude gains that compound with one another and with the silicon improvements beneath them, producing a 5-10x annual price-performance trajectory.

Stanford and Together AI researchers quantified where this trajectory leads. Their Intelligence Per Watt metric improved by a factor of 5.3 between 2023 and 2025. Local models on consumer-grade hardware answered 88.7% of single-turn reasoning queries correctly. The share of queries that edge devices can handle rose from 23.2% to 71.3%. Inference is beginning to migrate from data centers to the edge, and this trend will accelerate as devices become more capable.

The Jevons paradox operates here with particular force. This refers to the principle that increased resource efficiency drives higher total consumption. In this case, efficiency gains in inference will expand the frontier of viable workflows, which expands total consumption even as per-unit costs fall. A workflow unprofitable at $50 in agent compute becomes routine at $5. A category of work inconceivable at one price point becomes obvious at a lower one. The efficiency gains feed the demand they were designed to serve.

This has direct implications for the AGNT thesis. The orchestrator’s primary variable cost is the cost of inference tokens. Every improvement in efficiency expands the addressable market. The orchestrator that routes tasks intelligently across capability tiers (commodity models for extraction, frontier models for complex reasoning, edge devices for latency-sensitive work) achieves structurally better margins.

Whether the agentic economy becomes the deflationary force described in Part V depends on whether this efficiency trajectory continues to compound. If it does, the surplus available to orchestrators grows with every cycle. If it stalls, the transition slows. Not for want of intelligence, but because the economics simply do not work.

The Energy Constraint

Beneath the capital, the debt, the silicon, and the algorithms lies a constraint more fundamental and less tractable than any of them: Power.

The scale of the demand is unprecedented. Huang’s $1 trillion demand outlook through 2027 implies not merely more chips and racks but more electricity consumed continuously at an industrial scale. A single Vera Rubin NVL72 rack draws power at levels that would have been classified as a small industrial facility a decade ago. Leading-edge AI racks consume 130-250 kilowatts today, with projections reaching 900 kilowatts by 2027. Each hyperscaler is pursuing a one-gigawatt data center campus, a scale that draws as much electricity as a mid-sized city.

The workload itself has changed shape. A chat query resolves in 500 milliseconds. An agentic task, such as a coding audit, a research synthesis, or a multi-system workflow, takes 10 minutes to 14+ hours and runs in parallel with thousands of siblings inside a single campus. The load profile of an AI data center has converged toward that of an aluminum smelter: continuous, high-wattage, near-100% utilization, 24/7. The bursty inference workload grid planners that were modeled against have been removed. The new profile is baseload.

In addition, agentic compute has a unique memory-power overhead. To maintain state over a 14-hour + task, agents must keep massive context windows active. In 2026, hyperscalers are offloading KV caches to SSD storage to manage the memory bottleneck, but the energy required for constant data shuffling remains a high hidden cost. Anthropic has published research on “context engineering”. This refers to compaction, structured note-taking, and multi-agent architectures designed to address “context rot” in long-horizon agents. These techniques reduce the memory-power overhead of maintaining state across extended tasks and indirectly ease the underlying power constraint.

The power infrastructure to support these facilities does not exist in most locations where they are being planned. Grid interconnection timelines run three to five years in the United States, longer in Europe. Permitting for new transmission lines routinely takes a decade.

The US is on track to add a record 86 GW of utility-scale generation in 2026: 51% solar, 28% batteries, 14% wind. Useful firm power near data-center hubs is a fraction of the headline figure. In PJM, the single most important AI interconnection, utility large-load commitments now exceed accredited generation capacity by a factor of 3. China, in contrast, added 434 GW in 2025 alone and is co-locating compute with generation in the renewables-rich West. This is not a cyclical shortfall; the US closes with Capex. Rather, it is a profound infrastructure divergence, one that could constitute a Single Point of Failure of the Agentic Era, i.e., the specific vulnerability that, if left unresolved, constrains the entire transition regardless of how fast models improve or how much capital flows into AI. In the framework we introduced in 2025, an SPOF is the component of a system whose failure compromises the entire structure.

Nuclear is, as of 2026, the only carbon-free source with the density for AI-scale loads, and the scramble for it since 2024 is no longer directional. It is the infrastructure. Microsoft underwrote the restart of Three Mile Island via a 20-year PPA with Constellation (835 MW, 2027-2028). Amazon bought Talen’s Susquehanna campus and is developing SMRs with X-Energy and Dominion. Google signed with Kairos (500 MW SMR fleet), Fervo Geothermal, Oklo, and Vistra, making it the largest corporate purchaser of nuclear energy in US history. Sam Altman backed Oklo (2.6% stake, former chair, co-founder of the SPAC that took Oklo public). xAI stood up 200+ MW of on-site gas turbines in Memphis in under a year. Stargate Abilene is architected as a 1+ GW behind-the-meter campus. Together, these constitute a coordinated admission that the public grid cannot deliver what agentic compute needs within the required timeline. Hyperscalers are no longer interconnecting to the grid: they are becoming utilities.

The constraint sharpens as the workload shifts from training to inference. Training runs are episodic. A frontier model trains for weeks in a concentrated burst, then stops. Inference is continuous. Every agent action, every orchestrated workflow, every token consumes power indefinitely, at a load that scales with agent populations.

As the mix shifts, the demand profile moves from periodic spikes to sustained baseload: precisely the pattern that stresses grids most acutely, because it requires always-on generation rather than peak-shaving.

The efficiency gains partially offset this. As noted before, Jevons in this respect operates at an industrial scale: cheaper inference makes more workflows viable, expanding total demand even as per-unit consumption falls. Hyperscalers that secure power early through long-term agreements, on-site generation, or strategic locations near abundant clean energy gain compounding advantages. The value of a data center is increasingly determined not by the GPUs it houses, but by the megawatts it can draw.

For the AGNT thesis, energy introduces a temporal variable into every projection. The Inference Economy expands at a rate bounded by the availability of power, regardless of demand or capital. The orchestrators of Parts III to V will find their ability to scale agentic workflows ultimately limited by whether the infrastructure they depend on has the electricity to operate.

The direction is not in question. The speed is. Watts, not weights, now govern the pace of the transition. The pace, as the Two Scenarios in Chapter 18 will argue, is what separates deflationary growth from demand destruction.

The Agentic Cloud

There is a final condition that completes the picture. It is architectural rather than financial or physical. The cloud was built for a different era. The SaaS-era cloud was designed for stateless applications, ephemeral compute, hub-and-spoke networking, and consumption-based pricing. An enterprise spins up a virtual machine, runs a workload, and shuts it down. Two decades of perfecting that pattern. It is the wrong pattern for what comes next.

The agentic systems of Chapter 5 generate workloads that differ in every dimension. Every MCP connection, every A2A coordination, every persistent memory retrieval, every multi-step workflow is an inference operation. An agent booking a flight generates 50,000 tokens across research, comparison, booking, and confirmation. This compares to just 500 for a chat query. Agentic workloads multiply token consumption 10x to 100x.

But the difference is not merely volume. Agents are stateful. They maintain persistent memory and workflow state across sessions, requiring considerably more memory than traditional workloads. The three-tier memory architecture of Chapter 5 requires infrastructure that preserves state, not infrastructure designed to forget:

Agents coordinate laterally. They communicate peer-to-peer through MCP and A2A, requiring sub-100ms latency and dedicated bandwidth between coordinating agents. The topology must evolve from a centralized switchboard toward a mesh.

Agents generate explosive storage demands. Vector databases typically experience 10x data expansion, with total storage reaching 30x the original data after indexing and context persistence.

Agents require a different economic model. Pricing must be tied to task completion and outcomes rather than compute hours consumed, which demands infrastructure instrumented for metrics that traditional cloud monitoring was never designed to capture.

These requirements point toward a new infrastructure category: the agentic cloud. Three categories of providers are positioned to build it.

The hyperscalers hold the deepest structural advantages: custom silicon, enterprise relationships built over decades, and captive internal demand for inference. Google’s Search, YouTube, Gmail, and Maps are continuously consuming inference, providing a substantial utilization floor regardless of external demand. Their challenge is that hundreds of billions in existing infrastructure were designed for the SaaS era. The organizational complexity of serving legacy and agentic workloads simultaneously is real, and scale alone does not resolve it.

The neoclouds proved something important: that AI workloads demand fundamentally different compute from general-purpose clouds. CoreWeave and other AI-specialist clouds have demonstrated MFU improvements of 15-30% over hyperscaler baselines (e.g., CoreWeave’s 49.2% MFU on H100 training runs vs MosaicML’s 41.85% baseline, per CoreWeave’s published benchmarks). Industry training MFU typically sits in the 35-55% range, depending on hardware generation, software stack, and workload. But the neocloud window is narrowing. Their core design is stateless, single-tenant, and hub-and-spoke. This served training-centric workloads well. It is less suited to what agentic systems demand.

The emergent agentic clouds are purpose-built. Daytona Cloud achieves sub-90ms sandbox creation for stateful agent workflows. Others target observability for multi-agent systems, optimization for coordination-intensive workloads, and outcome-based resource allocation. They carry no architectural debt. They lack scale. They are building for the incoming workload.

The model providers see the same opening. Anthropic’s Claude Managed Agents, launched in April 2026, offers the full production stack for agentic workloads as a managed service: sandboxed execution, state persistence, tool orchestration, error recovery, and multi-agent coordination. It runs on existing cloud infrastructure, but the developer never touches it. Anthropic abstracts the cloud into a substrate and bills by the session-hour.

The orchestrator’s variable cost is the cost of inference tokens. Hardware efficiency sets the floor for the cost of producing a token. The agentic cloud determines the cost of serving that token within a coordinated, stateful, production-grade workflow. The companies that optimize for both will set the economic foundation for everything built on top of them.

The Substrate Must Evolve

The AI infrastructure being built in 2026 is the largest private capital deployment in history. It is transforming Big Tech’s balance sheets from capital-light to capital-intensive. It is financed through structures of increasing ambition, from conventional bonds to century paper to securitized GPU assets. And it was committed under assumptions about training workloads that the migration toward inference is already reshaping.

The metamorphosis is substantial but navigable. Inference efficiency is compounding at a pace that makes the unit economics of the agentic economy more favorable with each quarter. The energy constraint governs speed, not direction. And the cloud is beginning, unevenly, incompletely, but perceptibly, to evolve toward the stateful, coordination-grade architecture that agentic workloads demand.

The intelligence is sufficient. The agents are in production. The infrastructure is scaling and starting to change shape. What determines whether specific companies, sectors, and workflows successfully cross is the subject of Part III.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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On December 29, 2025, Meta paid more than $2 billion for Manus. The acquisition puzzled many observers, who focused on whether the product represented a step toward artificial general intelligence. In March 2025, the Chinese startup released a product that briefly captured the AI industry's attention. The demo videos showed an AI system that could browse the web, write and execute code, manage files, and complete multi-step tasks autonomously.

It did not. Manus was built on top of Claude and Qwen, foundation models it had licensed from other companies. It had developed no new intelligence. It had trained no frontier model. It employed no breakthrough in machine learning research.

What Manus had built was an Orchestration Layer: persistent memory that maintained context across interactions, planning mechanisms that decomposed complex tasks into executable steps, tool integration that extended the models’ capabilities into the real world, and autonomous decision-making that allowed the system to take initiative rather than wait for instructions. Enterprises were paying $125 million a year for it. Not for pilots, but for production workflows that generated real cash flow.

Meta did not buy intelligence. It bought coordination. As Manus’s founder put it at the time: “Agentic capabilities might be more of an alignment problem rather than a foundational capability issue.”

The critical innovation was not another powerful model, but the layer that turned a powerful model into something that could actually do work.

Subsequently, this deal has been thrown into doubt. In April, China’s government announced it was blocking the acquisition and ordered the companies to unwind it. Meta has said it is still working with Chinese regulators to resolve concerns and hopes it can still move forward. And indeed, the company has already begun integrating Manus into its platform for some users.

Still, whatever the fate of the deal, it put a spotlight on a critical theme: This distinction between intelligence and the coordination of intelligence is the architectural foundation of the Agentic Era. To understand it requires precise definitions that the market has not yet adopted. Three terms are used interchangeably in most discussions about AI. They should not be.

Generative AI is the substrate. Foundation models trained at scale produce novel outputs such as text, code, images, and reasoning chains. Generative AI is reactive. It produces output when prompted and stops when the prompt is satisfied. ChatGPT answering a question, Midjourney generating an image, Claude drafting a memo.

The human remains in the loop for every decision, every action, every iteration. The economic profile is augmentation: the worker becomes more productive, but the work remains the worker’s.

An AI agent crosses from generation to action. Simon Willison’s definition from September 2025, at least in the field of LLMs, is the clearest:

“Agents run tools in a loop to achieve a goal.”

It reasons through a problem, decomposes it into sub-tasks, uses external tools, and iterates on its own outputs without continuous human direction. It operates through a reasoning loop: observe, plan, act, reflect. A single agent automates a discrete task. The economic profile shifts from augmentation to substitution: the agent performs the task. But a single agent, operating alone, remains bound to one function, one workflow, one domain.

Agentic AI is when agents coordinate. Systems of specialized agents operating as synthetic colleagues can process an insurance claim end-to-end, manage a supply chain’s response to disruption, or conduct due diligence across hundreds of documents. They operate continuously, scale with compute rather than headcount, and produce outcomes that no individual agent could deliver alone. This is what Manus built. This is what Meta bought.

The progression runs from passive content generation (Generative AI) to interactive task execution (AI Agents) to autonomous, multi-agent orchestration (Agentic AI). Each step represents not just increased capability but a fundamentally different architecture for organizing intelligence. The taxonomy describes what agentic systems are. It does not describe how they behave when multiple agents interact. This is where the economics form.

The intellectual lineage here is game theory. The foundational work on multi-agent AI draws directly from competitive equilibrium research, the minimax strategies that solved poker, to the population-based training that mastered StarCraft.

When OpenAI Research Scientist Noam Brown’s team demonstrated that negotiation agents trained through self-play developed emergent bargaining strategies that no human programmed, they revealed something fundamental: multi-agent systems do not merely execute tasks in parallel. They develop strategic behaviors that emerge from the interaction dynamics between agents. Best-of-N sampling, specialist routing, and the coordination topologies described in the Google DeepMind scaling research are descendants of these game-theoretic foundations.

The orchestrator is not merely a scheduler. It is the mechanism that resolves competing agent objectives into coherent action. This is the equivalent of an equilibrium-finding algorithm operating across a population of specialists with divergent optimization targets.

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Towards Synthetic Colleagues

The destination is already partially visible: not individual agents performing isolated tasks, but distributed networks of specialized agents that share persistent memory, coordinate through structured communication protocols, and pursue decomposed goals under orchestrated supervision.

Consider an automated insurance claims system. A document extraction agent retrieves relevant data from submissions, a medical coding agent maps diagnoses to billing categories, a fraud detection agent flags anomalies, a pricing agent calculates payouts, a compliance agent validates regulatory requirements, and a master orchestrating agent coordinates the entire workflow while a persistent memory layer stores evolving patterns, exception paths, and iterative improvements across sessions. The system’s capability exceeds the sum of its components because the Orchestration Layer enables synthesis that no individual agent could achieve alone.

This is the Agentic Era in operation. Not a faster tool. A synthetic workforce. The human expresses intent: process this claim. The system acts.

The harder question is whether they can sustain their work over time. An agent that fixes a bug is performing a task. A synthetic colleague that maintains a codebase through months of changing requirements, accumulating complexity, and iterative redesign is holding a job.

These are categorically different capabilities, and the benchmarks the industry relies on cannot distinguish between them. SWE-bench measures whether an agent can fix a problem. It does not measure whether an agent can maintain a codebase. It is a hiring test, not a performance review. SWE-CI is the first benchmark designed to measure the difference, testing agents across 100 real-world codebases, each spanning an average of 233 days and 71 consecutive commits of actual development history. Its insight is foundational: the quality that makes a synthetic colleague valuable is maintainability, and maintainability can only be revealed by tracking how functional correctness changes over time. The science of evaluation for agentic systems remains nascent.

The architecture that makes Agentic AI possible is not a single technology. It is a stack of three layers, each of which has moved from research to production within the past eighteen months: the connectivity layer, the continuity layer, and the coordination layer. Each depends on the one beneath it. The stack must function as a whole. This chapter follows it from the bottom up as evidence that the infrastructure for autonomous action is operational.

The Connectivity Layer: Protocols

Every agentic system begins with a prior question: how does the agent reach the world?

Before November 2024 (Anthropic's MCP launch), the answer was ugly. Every connection between a model and an external tool was bespoke engineering, involving custom code, fragile integrations, and expensive maintenance.

Connecting an AI system to a company’s CRM required a single team of engineers. Connecting it to the company’s calendar required another. Connecting it to the database, the email system, and the document store each demanded its own plumbing. The result was an industry that could build brilliant models and then spend months wiring them to anything useful.

Anthropic’s MCP proposed the obvious solution: a universal standard. A common language through which models could discover, connect to, and interact with any external resource. The name itself reveals how early it was. Anthropic’s announcement referred to “AI Assistants” rather than “agents.”

What happened next exceeded every reasonable projection. Within 12 months (by December 2025), MCP accumulated over 97 million monthly SDK downloads, over 8 million server downloads, and over 10,000 published servers. For context, OAuth, the authentication protocol enabling “Sign in with Google”, took two to three years to achieve comparable adoption. MCP reached similar ubiquity in twelve months.

The adopter list spans the competitive landscape in ways that would have seemed impossible even a year earlier. OpenAI integrated MCP into the ChatGPT desktop application. Google DeepMind and Gemini added support. Microsoft released a C# SDK with integrations into the Semantic Kernel and Azure AI services. Developer tools, including Cursor, Replit, Sourcegraph, and Codeium, built native integrations. These are companies that compete fiercely on model capability, product features, and market position.

Yet they agreed, almost simultaneously, on the plumbing. MCP solved the first connectivity problem: how agents reach the tools they need. Google’s Agent-to-Agent Protocol, launched in April 2025, solved the second: how agents reach each other. Where MCP connects agents to external systems, A2A enables agents to collaborate by delegating tasks, sharing intermediate results, and coordinating multi-step workflows that no single agent could complete alone. A2A launched with 50 technology partners and grew to over 150 organizations by July, including Atlassian, Salesforce, SAP, and ServiceNow.

The pattern is old. Browser vendors agreed on HTML. Network equipment makers agreed on TCP/IP. Mobile carriers agreed to cellular standards. The protocol layer commoditizes. The value migrates upward from the foundation models and the orchestration systems built on top of them.

For Anthropic and Google, the value comes not from the protocols but from the models and orchestration services built around them. By shaping the protocols, they ensure their systems remain central to the emerging ecosystem. The standard is open. The advantage is positional.

The connectivity layer is in place. Agents can reach the tools they need and communicate with one another through protocols adopted by every major platform. But connectivity without continuity is ephemeral. An agent that can reach every tool in the enterprise yet forgets what it did yesterday is not a colleague. It is a function call.

The Continuity Layer: Memory

Protocols solve the connectivity problem. Memory solves the continuity problem. Without persistent memory, even the most advanced models start each interaction from scratch. They possess no recollection of prior conversations, no accumulation of domain expertise through experience, and no evolving understanding of the user or organization they serve. Each session is a blank slate. This is like hiring a consultant who develops amnesia every evening. They have considerable capability, but no institutional knowledge.

The memory architectures now emerging in production systems address this limitation through hierarchical designs that, in simplified form, mirror how human memory operates. The architecture consists of three tiers, each serving a distinct function:

Short-term memory handles the immediate dialogue, including the current conversation, the active task, and the real-time context of what is being discussed and decided. This operates through dialogue-chain mechanisms that maintain coherence within a single session, ensuring the agent tracks the conversation thread without losing context as the interaction unfolds.

Mid-term memory aggregates related content across sessions. It employs what researchers describe as heat-based prioritization. That means frequently accessed information retains prominence while less relevant material gradually fades. The system measures similarity through a combination of semantic and keyword matching, ensuring that related interactions are appropriately aggregated while maintaining topic coherence. This is what allows an agent to remember that last Tuesday’s discussion about the Henderson contract involved a pricing dispute that remains unresolved, without necessarily retaining every detail of every conversation in between.

Long-term memory captures persistent characteristics. That could be stable user profiles, evolving preferences, accumulated institutional knowledge, or agent personas that maintain a consistent identity while adapting to learning. User profiles include static attributes alongside dynamic knowledge bases that accumulate information extracted from interactions over months or years. The agent develops an evolving understanding of how this user works, what this organization values, and where the institutional expertise resides.

The engineering challenge posed by these hierarchical architectures cannot be overstated. Implementing distinct modules for storage, updating, retrieval, and generation requires a sophisticated data infrastructure that extends well beyond traditional database capabilities.

The real-time processing demands of short-term memory must coexist with the semantic retrieval requirements of mid-term memory and the persistent knowledge management of long-term memory. And they must all operate simultaneously at scale, with context integrity maintained across the entire stack.

From Research to Production

Research in August 2025 from the University College of London’s Center for Artificial Intelligence and Huawei Noah’s Ark Lab demonstrated the technical feasibility by publishing Memento, a framework that enables agents to achieve state-of-the-art performance through sophisticated external memory without fine-tuning the underlying language model.

Memento’s architecture is notable for what it implies about the future of competitive advantage: the framework achieves 50-80% lower computational costs than traditional fine-tuning approaches by maintaining an external case bank that operates at runtime. New experiences become immediately available for future decisions. There is no training pipeline, no model versioning, and no careful rollout procedures. Learning happens continuously, at runtime, as a natural consequence of operation.

Memento’s performance data illustrates the compounding effect. Accuracy improved from 78.65% to 84.47% over five iterations through memory accumulation alone, not retraining. Out-of-distribution results showed absolute gains of 4.7% to 9.6% when case-based memory was enabled. Each interaction generates a complete trace: the initial problem state, the strategy employed, and whether it succeeded or failed.

These are not abstract patterns but concrete experiences with contextual details that make them retrievable and applicable. Every execution becomes a teaching moment for future agents. Memento is still at the research-to-production boundary, but the direction is clear, and the results are reproducible.

What persistent memory means for competitive positioning, particularly the distinction between traditional data advantages and the compounding execution intelligence that memory enables, is the subject of the Second Law, developed in Part III.

For now, the operational point is sufficient: memory architectures have moved from research to production feasibility, and the agents that possess them are qualitatively different from those that do not.

The Coordination Layer: Orchestration

Google DeepMind’s multi-agent scaling research illustrates this principle with unusual clarity. By altering only the coordination topology without improving any individual agent, the system achieved 80.9% higher performance on structured, parallelizable tasks like financial reasoning. The implication is striking: the leverage does not primarily sit in the model layer. It sits in the Orchestration Layer, i.e., in how the agentic workflow is conducted to produce a specific outcome.

This pattern echoes decades of research into multi-agent systems and game theory. In AlphaStar’s league training, populations of agents with diverse strategies generated emergent behaviors that no single training run could discover. Similarly, self-play negotiation systems developed bargaining strategies that surpassed human-designed heuristics. Across these domains, the lesson is consistent: when agents interact within well-designed coordination structures, the system develops capabilities that are not present in any individual component.

Topology is the intelligence.

Production Reality

The Berkeley Measuring Agents in Production study’s domain analysis reveals where agentic AI has already crossed from experimental to operational.

In the latest February 2026 revision of the Berkeley MAP study, Technology (47.8%), Finance & Banking (43.5%), and Corporate Services (42.0%) emerged as the leading deployment domains for production agents, followed by Legal & Compliance (17.4%) and Research & Development (11.6%). Because deployments frequently span multiple business functions, systems could be assigned to more than one category.

Finance and insurance emerge as prime deployment domains because their workflows are structured, high-value, and measurable. An insurance claims agent follows a fixed sequence: coverage lookup, medical necessity review, risk identification, and decision recommendation. Each step has clear inputs, defined outputs, and verifiable correctness criteria. The workflow is deterministic enough for an agent to follow reliably, valuable enough to justify the investment, and measurable enough to prove ROI.

The MAP study’s case studies illustrate the pattern. Insurance claims automation, financial analysis, regulatory compliance, customer care operations: these are workflows with clear boundaries, established procedures, and quantifiable outcomes. Production deployment spans 26 domains77, but the concentration in finance is revealing. This is where the conditions for agent deployment are most favorable: structured data, regulated processes, high per-transaction value, and organizational willingness to invest in automation that can be measured against established baselines.

80% Structured. 85% Custom Built

The MAP study’s most striking finding is how they are built. Architectural conservatism is the strategy, not a limitation.

What distinguishes these production systems from the experimental deployments that preceded them is their architectural conservatism.

In the same vein as Google’s research mentioned above, the MAP study’s most notable finding is not what production agents can do but how they are built. 80% use structured workflows rather than open-ended planning, and 68% execute fewer than ten autonomous steps before human intervention. Those are predefined workflows rather than open-ended autonomy. 79% rely on human-crafted prompts, either fully manual or manually drafted with AI assistance. 74% depend primarily on human evaluation for quality assurance.

These are not autonomous systems operating beyond human oversight. They are carefully constrained tools operating within boundaries defined by human engineers and verified by human evaluators.

MAP’s case studies reveal that 85% of production deployments build custom, in-house implementations rather than relying on off-the-shelf frameworks. This is true despite the availability of mature options like LangChain, CrewAI, and LlamaIndex.

Teams building for production choose control over convenience. They want minimal dependencies, maximum observability, and the ability to constrain agent behavior precisely where constraints are needed. The framework maturity is real. CrewAI accumulated over 40,000 GitHub stars and more than 100,000 certified developers within eighteen months; LangGraph leads downloads at 6.2 million monthly^, with nearly 400 enterprise deployments.

But production teams, when it matters, build their own orchestration for the same reason that banks build their own trading systems: the cost of failure exceeds the convenience of abstraction.

The Deployment Breakthrough

The architecture is coherent. The components are in production. But architecture and production-readiness are not the same thing. The distance between a system that coordinates specialized agents across a complex workflow and a system that does so reliably enough to trust with real money, real customers, and real consequences is precisely where most deployments fail.

Machines have become actors in this new paradigm, but that does not mean they are infallible actors. It means the nature of the challenge changed. Rather than debating whether machines can do the work (they can), we must now ask: how can we deploy machine actors with appropriate constraints, oversight, and governance?

Anthropic learned the consequences of this new agentic challenge the hard way. In June 2025, Anthropic deployed Claudius, an autonomous AI agent, to run its office refrigerator like a small business. There was no human oversight. Just raw autonomy.

An employee playfully suggested stocking “tungsten cubes” as a popular item. They were not popular. But Claudius, treating the suggestion as authoritative, ordered 40 of them and sold them at a substantial loss. Employees manipulated the agent through casual conversation. It refused to restock bestsellers on the grounds that it made internal sense but no commercial sense.

Six months later, Anthropic released a second version with improved scaffolding, a multi-agent architecture, and access to enterprise systems. Performance improved, but only a bit. The company’s conclusion was clear: wide-ranging autonomy still required heavy human support.

For much of the world, this felt like an amusing side project. Then, in January 2026, Claude Cowork arrived. The contrast with Claudius demonstrated just how much Anthropic had learned about agentic deployment.

With Claude Cowork, a user authorizes a folder on a Mac. No engineering background. No special prompting expertise. Claude Cowork scans a few hundred scattered files, identifies structure, flags duplicates, proposes an organization system, asks for permission, and executes the task. In another demonstration, it extracts transaction tables, generates charts, and assembles a PDF report using general-purpose tools.

Cowork and Claudius had the same class of underlying models. They achieved entirely different results.

The gap between Claudius and Cowork was not one of intelligence. The gap was in orchestration: where autonomy stopped, what constraints were imposed, how tools were sequenced, and what human oversight was preserved. Raw capability without appropriate direction produced tungsten cubes. The same capability under disciplined coordination produced a working colleague with Cowork.

Constrained autonomy, rather than maximum autonomy, proved to be the deployable frontier. This is the operational principle that every production deployment in the MAP study reflects and that separates the systems documented in this chapter from the experiments that preceded them.

The Substrate is Operational

The connectivity layer is in place. The continuity layer is operational. The coordination layer has proven its leverage empirically. The systems work precisely because they are constrained, observed, and verified, not because they are autonomous.

This is what the early settlement of the Agentic Era planet looks like. The astronauts have landed. They have proven that the atmosphere is breathable. The rest of the world is still learning the rules of the new world.

But a substrate that exists is not a substrate that scales. The agents documented in this chapter generate workloads that the physical infrastructure was not designed to serve. Agentic systems are stateful, whereas the cloud is stateless. They coordinate laterally where data centers route vertically. They run continuously, whereas traditional computing bursts and pauses. They have considerably more memory than what traditional workloads demand, sub-100ms latency between coordinating agents, and storage expansion measured in multiples of ten.

The intelligence is sufficient. The agents are in production. Whether the infrastructure beneath them can support what they are already capable of doing at the scale implied by the economics is a different question, and the answer is not yet settled.

Chapter 6 examines what is being built, where it breaks, and why the agentic economy may require a category of cloud infrastructure that does not yet exist at scale.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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There is a persistent and understandable debate at the heart of the AI investment thesis: whether current models are capable enough, or whether the agentic transition depends on a future breakthrough, perhaps artificial general intelligence (“AGI”), perhaps some architectural innovation not yet conceived.

The question matters, but it is the wrong question. The agentic transition does not require AGI. It does not depend on a future breakthrough. It depends on whether current frontier intelligence is sufficient for production deployment of agentic systems that perform economically valuable work under appropriate human oversight.

It is.

In December 2025, a team of 25 researchers led by UC Berkeley published “Measuring Agents in Production.” This was the first large-scale systematic study of AI agents deployed in real production environments.

The MAP study surveyed 306 practitioners and conducted 20 in-depth case studies across 26 domains. Its central finding was unambiguous: existing frontier models, using prompting strategies alone, already possessed sufficient capability to cover a diverse range of production use cases. 70% of deployed agents relied on off-the-shelf models without any fine-tuning. No architectural breakthroughs were required. Teams predominantly selected the most capable frontier models available, and the cost and latency of those models remained favorable compared to human baselines.

What follows is the evidence for why the intelligence is sufficient, including the specific dimensions of capability that make the agentic transition viable, not as a forecast but as an engineering reality.

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The Cognitive Primitives Are Here

Any agentic system, regardless of domain, rests on the same cognitive foundation. An agent that orchestrates a complex workflow, such as processing an insurance claim, conducting financial due diligence, or coordinating a product launch, must decompose goals into sub-problems, maintain logical consistency across long chains of reasoning, adapt plans when intermediate steps produce unexpected results, and verify its outputs against formal constraints. These are not speculative requirements. They are the cognitive primitives of professional knowledge work.

The evidence that frontier models possess these primitives is no longer contested. On GPQA Diamond, the benchmark for graduate-level science questions, where human PhD experts score 69.7%, performance among frontier models has now converged above 90% across multiple vendors.

On Humanity’s Last Exam (a benchmark designed to test the absolute frontier of PhD-level knowledge across disciplines), multiple models surpass 40%, with Kimi K2 Thinking (Moonshot AI) reaching 44.9% at a fraction of the training cost of Western frontier systems. Some reported frontier results now range from high-40s to mid-60s, depending on the evaluation setup.

A newer index, GDPval-AA, measures how effectively frontier AI agents perform economically valuable professional tasks across real occupations and industries under realistic operating conditions, including tool use, browsing, and workflow execution. Unlike academic benchmarks that evaluate isolated reasoning ability, GDPval-AA assesses whether agents can complete bounded forms of actual knowledge work, making it a more direct measure of labor-substitution potential. Its importance for the agentic thesis is that it suggests the primary bottleneck is no longer raw intelligence itself, but the orchestration, verification, and control infrastructure required to deploy that intelligence reliably at enterprise scale.

The benchmark demonstrates that frontier models already possess sufficient capability to perform economically useful professional work. The remaining bottleneck is not raw intelligence, but the orchestration infrastructure required to make that intelligence reliable, auditable, and deployable at scale

These results are not narrow technical achievements. They are evidence that the cognitive architecture required for the production of agentic systems exists as commercially available infrastructure. They have the ability to reason, plan, verify, and adapt. A model that reasons through PhD-level physics can reason through a multi-step insurance claims workflow. A model that synthesizes findings across graduate-level biology, chemistry, and physics can synthesize findings across a quarter’s financial filings.

The primitives are here. The question is whether they are genuine, whether the models are truly reasoning or merely pattern-matching at an impressive scale.

The Reasoning is Real

Mathematical reasoning provides a strong proof point because it is the one domain where pattern-matching cannot survive. A model can generate plausible-sounding legal analysis by interpolating between training examples. It cannot solve a novel competition mathematics problem that way. Either the proof is valid, or it is not. Either the answer is correct, or it is wrong. There is no partial credit for fluency.

When OpenAI evaluated GPT-4o on the 2024 AIME exams in September 2024, it solved only 12% (1.8/15) of problems on average. By September 2024, OpenAI’s o1 reached 74%76, placing it among the top high school competitors nationally. Three months later, o3 scored 96.7% on AIME 2024. By mid-2025, GPT-5 achieved 94.6% on the fresh AIME 2025 problem set without tools. Multiple models now reach 100%. The benchmark that was designed to challenge the brightest mathematical minds in America is, for practical purposes, saturated.

From barely functional to superhuman in roughly two years. The technical driver has been Reinforcement Learning from Verifiable Rewards (RLVR), which trains models against automatically verifiable rewards such as math, code execution, and formal proofs. This method introduces a new scaling dimension in which capability increases with test-time compute, not just model size. Performance can improve at inference time, not only through larger and more expensive training runs. The scaling laws have acquired a second axis.

The primitives documented in the previous section are not the product of sophisticated pattern-matching. They are the product of genuine reasoning capability that scales with compute applied at inference time. This matters directly for the agentic thesis: an agent that reasons through a multi-step insurance workflow is not retrieving a memorized template. It is decomposing the problem, testing intermediate results, and adapting its approach. These are the same cognitive operations that produce valid mathematical proofs.

It is also Systematically Fragile

The taxonomy is sobering: models that solve PhD-level physics exhibit the reversal curse. Trained on “A is B”, they fail to infer “B is A”. Models that score above 90% on graduate-level science benchmarks break down under minor rephrasing of the same questions. Compositional reasoning combines two known facts into a single inference. This degrades sharply as the number of steps increases. Cognitive biases inherited from training data produce systematic deviations from logical consistency that no amount of scaling has eliminated.

The failures are structural, traceable to architectural constraints that define the medium itself, such as autoregressive generation, attention dispersion under complexity, and the absence of embodied grounding. A model that produces a valid mathematical proof on one formulation of a problem can fail on a semantically identical reformulation.

The reasoning is genuine. The reliability is not. This is not a contradiction of the agentic thesis. It is the foundation of its central economic claim.

If frontier reasoning were perfect, if models could be trusted to execute arbitrary workflows without constraint, verification, or decomposition, then the value would reside in the model itself. The model layer would be the control plane. The Orchestration Layer would be unnecessary overhead.

But the reasoning is imperfect in precise, documentable, and predictable ways. The production agentic systems documented in Chapter 5 work not despite this fragility but because their architecture accounts for it. They decompose complex workflows into verifiable steps. They constrain agent action spaces to domains where failure modes are known and bounded. They insert verification at every junction where compositional reasoning might degrade. They treat model intelligence as a powerful but unreliable input that must be orchestrated to produce reliable outputs.

The Orchestration Layer is not scaffolding to be removed when models improve. It is the load-bearing structure of the agentic economy. Its value increases in direct proportion to the gap between what models can reason and what they can be trusted to do unsupervised.

That gap is where the surplus accrues. But the gap has a second dimension. The reasoning documented above operates within a single context window, page, problem, and pass. An agent that orchestrates a workflow spanning thousands of documents, weeks of interaction, and dozens of interdependent decisions requires something the reasoning benchmarks do not measure: memory.

The Memory That Makes Agency Possible

There is a capability dimension that receives less attention than benchmarks but may matter more for agentic systems than any of them: how much an AI model can hold in its mind at once. An agent orchestrating a complex workflow must hold contracts, regulatory requirements, financial models, and correspondence simultaneously in working memory. Short context makes autonomous agency impossible.

Long context is the substrate on which sustained autonomous work becomes feasible. The trajectory here has been as dramatic as any benchmark. GPT-3 processed 2,048 tokens. GPT-4 extended this to 8,000 tokens in its standard configuration, with a 32,000-token variant available at premium pricing.

Then the explosion: Claude 3.5 reached 200,000 tokens in 2024. Google’s Gemini 1.5 Pro launched with one million and tested up to ten million in research. Claude Opus 4.6, released in February 2026, operates at one million tokens in production. And, as demonstrated on the MRCR v2 benchmark, it does not merely accept that context. It retrieves and reasons across it, scoring 76% versus 18.5% for its predecessor, Claude Sonnet 4.5. The model does not just have a larger memory. It uses it.

In 2023, a model could review a memo. In 2026, it can review an entire codebase, an entire regulatory filing, an entire quarter of customer correspondence, and reason across all of it simultaneously. This is the difference between a tool and an agent.

The long-context breakthrough connects directly to the preceding sections.

The cognitive primitives exist. The reasoning is genuine, and scales with inference-time compute. Now that reasoning can be sustained across the full scope of real professional work, not a single page but a thousand, not a single filing but a quarter’s worth, not a single module but an entire codebase. The agent can reason and remember.

What it could not do, until recently, was see.

Multimodality: From Impressive to Structural

Real work is not text-only. An agent managing product development reasons across user research videos, design mockups, customer feedback emails, code repositories, and executive strategy documents. Text-only models force everything through transcription bottlenecks, losing information that exists only in visual or audio form. The reasoning primitives, the genuine logic, and the long-context memory operate at full power only when they can access the full range of professional inputs.

The frontier has advanced into a measurable structural advantage. Gemini 3.5 Flash scores 88.27% Overall on MMMU-Pro and Gemini 3 Pro at 87.6% on Video-MMMU. These are measurements of whether a model can watch, understand, and reason about what it sees. The gap between multimodal systems and text-only alternatives is widening into a structural moat.

The multimodal frontier is not confined to trillion-dollar US laboratories. China’s MiniMax has built a full multimodal product family, including separate specialist models for text and agentic coding (M2.x), video (Hailuo), speech (Speech-02/2.8), and music (Music-2.x). Its text-centric M2.1 model scored 88.6% on VIBE, a benchmark MiniMax itself developed to measure an agent’s ability to generate working applications that are then visually and interactively verified at runtime.

For agentic production, multimodality is not a feature. It is an architectural requirement. An agent that can watch a customer service call, read the CRM record, analyze the tone of voice, and draft a resolution operates in a fundamentally different category from one that processes a transcript. An agent that can inspect a factory floor through camera feeds, cross-reference with maintenance logs, and flag anomalies before they cascade operates in a category that text-only systems cannot reach.

Natively multimodal architectures are becoming accessible, efficient, and deployable. The intelligence to support these modalities exists. The deployments will follow.

The METR Timeline

The longitudinal tracking conducted by the non-profit Model Evaluation and Threat Research (METR) institute provides a practical yardstick for measuring progress. It measures the length of tasks in human-expert completion time that AI agents can reliably complete, then analyzes how that length changes. In other words, it asks: How long would it take a human to complete this task? The 50%-task-completion time horizon is the time required for AI systems to reliably complete half the tasks. That rate had doubled approximately every 7 months since 2019, a trend confirmed in METR’s March 2026 update, which included a larger task suite and tighter confidence intervals. The trend holds across sensitivity analyses: perturbing tasks, models, and methodology shifts arrival-date estimates by less than two years for any given capability threshold.

As of March 2026, the frontier stood at twelve hours of focused human work. If the seven-month doubling rate persists, that frontier will reach one full workday by late 2026 and one workweek by mid-2027.

But recent data suggests the trend is accelerating. Over the full 2019-2025 period, the doubling time was approximately seven months. In 2024-2025, it compressed to three to four months, though estimates over shorter measurement windows carry wider confidence intervals. METR found similar exponential growth across all domains it examined, including mathematics, science, coding, web navigation, and OS interaction, with varying absolute time horizons but consistent trajectories.

Critically, METR measures self-contained tasks given to agents with no prior context. That is the equivalent of handing a new contractor a clearly scoped assignment. Real-world work, with accumulated institutional knowledge and project familiarity, is structurally easier. The time horizons likely understate what agents can accomplish within organizations that have built the orchestration infrastructure to support them.

The METR curve is not a technical curiosity. It is the investment calendar of the agentic transition. It sets the pace at which entire categories of knowledge work transition from labor-denominated to inference-denominated. Every business model premised on the stability of a specific domain’s labor structure has an expiration date governed by this curve.

Since the original analysis, the evidence base has shifted from academic reasoning benchmarks toward work-realistic agent benchmarks: GDPval-AA for economically valuable knowledge work, Terminal-Bench 2.0 for autonomous computer use, and updated METR time horizons. These benchmarks strengthen the thesis because they measure not merely whether models can reason, but whether agents can execute bounded professional tasks under constraints around tool access, memory, and verification.

The curve tells us where the frontier is heading. The next section shows where it has already arrived.

Software Engineering: The First Crossing

On the original SWE-bench released in October 2023, the first RAG baseline scored just 1.96%, and the first agent-based system (SWE-agent, early 2024) reached 12.47%. On SWE-bench Verified (OpenAI’s human-filtered 500-task subset introduced August 2024), top agents scored ~20% at launch and ~80.8% by Claude Opus 4.6 in February 2026.

From 2% to 81% in twenty-eight months. What required a team of engineers two years ago now completes in minutes. Not in a research lab. In a production API that any developer can call today.

AI now writes more than 30% of code at both Microsoft and Google. Meta CEO Mark Zuckerberg stated in April 2025 that he aspires to have most of Meta’s code written by AI agents in the near future.

The benchmark frontier has also evolved beyond isolated software engineering tasks toward full operational execution environments. While SWE-bench assesses whether models can resolve bounded code repository issues, Terminal-Bench 2.0 evaluates whether agents can operate autonomously in real terminal environments spanning software engineering, infrastructure management, cybersecurity, data science, and system administration.

Terminal-Bench 2.0 demonstrates that frontier agents are rapidly improving at operating inside real computing environments, not merely generating code or answering questions. This matters because it measures not merely whether models can generate correct code, but whether agents can navigate tools, execute commands, recover from failures, maintain state across workflows, and complete production-like operational tasks. The significance for the agentic thesis is profound: the frontier is shifting from models that answer questions to agents that perform economically valuable operational work, suggesting that the primary bottleneck is increasingly not raw intelligence itself but the orchestration, supervision, verification, and control infrastructure required to reliably govern autonomous execution at scale.

Software engineering is the first domain to cross the reliability threshold, serving as the template for all that follows. Coding requires understanding complex structured information, reasoning about dependencies, using tools precisely, iterating based on feedback, and maintaining context across long sequences. But the capabilities that crossed in coding generalize to any domain involving structured reasoning, tool use, and verifiable outputs: legal document analysis, financial modeling, medical diagnosis, and compliance reporting. The order of crossing in terms of which domains follow software engineering, and on what timeline, is governed by the METR curve and by the specific conditions of verifiability, task structure, and economic leverage that each domain presents.

Intelligence Is Sufficient. The Question Has Changed

The argument of this chapter is cumulative. The cognitive primitives for production agentic work exist across multiple vendors. The reasoning behind them is genuine, not pattern-matching, and scales with inference-time compute.

That reasoning can be sustained across the full scope of professional work through million-token context windows that retrieve and reason accurately. The sensory range spans the full spectrum of professional inputs via natively multimodal architectures.

The capability frontier is advancing on a measurable, predictable schedule, doubling every seven months. In software engineering, the crossing has already occurred, from research curiosity to production deployment, generating billions in revenue in twenty-eight months. AGI is not required. Current frontier capability is sufficient. The paradigm has shifted not because machines became perfect, but because they became actors capable of receiving goals, taking autonomous action, and delivering professional-grade outcomes.

The question now is whether the agents built on that intelligence, the systems that coordinate, remember, act, and learn, are ready for production deployment at enterprise scale. And whether the infrastructure beneath them can serve the workloads the agentic economy demands.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

Subscribe now

There comes a moment in every exponential curve when the abstract becomes concrete, when the thing everyone has been debating arrives in the specific. For generative and agentic AI, that moment came in the winter of 2025-26. It came in three waves: the models crossed a line, the revenue proved it was real, and the agents began acting on their own.

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The Models Cross a Line

Until late 2025, frontier AI models were impressive but bounded. They could draft, summarize, translate, and generate code. These tasks were useful. But any honest assessment would classify them as sophisticated assistants, or “copilots”. A lawyer still reviewed every clause the model suggested. An analyst still checked every number. A developer still read every line. The model accelerated human work. It did not perform it.

Over the span of twelve weeks, from December 2025 to February 2026, that boundary dissolved. OpenAI shipped GPT-5.2 in December 2025, a model whose adaptive reasoning could sustain multi-step analysis across hundreds of pages of financial filings, legal discovery documents, and technical specifications without losing coherence or fabricating connections.

Analysts at early-access firms reported that GPT-5.2 produced equity research drafts that required editing for voice and judgment, not for accuracy or structure. The model was not assisting the analyst. It was performing the analyst’s core function, and the analyst was reviewing output rather than producing it. In February 2026, OpenAI then released Codex as a desktop application, positioning it not as a coding assistant but as a “command center for agents”, an autonomous worker that receives a task, decomposes it, executes across files, tests its own output, and returns completed work. The framing was deliberate. OpenAI was not selling a better autocomplete. It was selling a junior colleague.

Meanwhile, Anthropic had released Claude Opus 4.6 on February 5, moving the ceiling even higher. A one-million-token context window that genuinely worked: 76% retrieval accuracy on the MRCR v2 needle-in-a-haystack test at full context, compared to 18.5% for Claude Sonnet 4.5. This meant the model could hold an entire codebase, an entire contract suite, an entire quarter’s financial filings in working memory, and reason across all of it simultaneously.

The notorious “context rot,” where models degrade as conversations lengthen, was eliminated.

On GDPval-AA, an evaluation designed to measure performance on economically valuable knowledge work in finance, legal, and professional domains, Opus 4.6 outperformed GPT-5.2 by 144 Elo points, winning head-to-head comparisons roughly 70% of the time [Figure 16].

On Humanity’s Last Exam, the most demanding multidisciplinary reasoning test available, it led every frontier model at the time of its release in February 2026. On BigLaw Bench, it scored 90.2%. These were not narrow technical benchmarks. They measured the work that professionals do and bill for.

But the headline capability was something no model had demonstrated before: agent teams.

Through Claude Code, Opus 4.6 could spawn parallel sub-agents. These independent model instances could divide a complex task, work simultaneously on separate components, and coordinate their outputs.

Google completed the triangle with Gemini 3 Pro. It reached 91.9% on GPQA Diamond [Figure 7], surpassing human PhD experts on the benchmark designed to be their ceiling with native multimodal reasoning across text, images, code, and video [Figure 6].

It was the moment the paradigm shifted from abstract to concrete.

Each of these models, independently, crossed the same threshold: from systems that help professionals work to systems that perform professional work. The human’s role changed from producer to reviewer. The unit of production changed from the labor hour to the orchestrated workflow. And the change was reproducible across multiple vendors, domains, and organizational contexts. Taken together, these three events represent the paradigm shifting from abstract to concrete.

Each lab, independently, converged on the same proof point: code.

Opus 4.6 set the record on Terminal-Bench 2.0 and reached 80.8% on SWE-Bench Verified. GPT-5.2’s Codex was built as an autonomous coding agent. Gemini 3 Pro matched SWE-Bench scores within fractions of a point. The convergence was not accidental.

Code is the domain where three properties coexist: structured environments with objective ground truth, a verifiability loop where output either compiles or it does not, and economic leverage, where every productivity gain compounds across the entire software stack. Code is where autonomous capability can be proved rather than asserted. The labs converged there because it was the only domain where the claim “this model does professional work” could be verified by anyone who cared to run the test.

The models did not stop at code. They wrote financial analyses, conducted legal research, built presentations from raw data, reasoned across modalities, and coordinated teams of sub-agents. Coding was proof of agentic capability. The broader professional capability was the product.

The gap between “interesting demo” and “I cannot do my job without this” closed in a single quarter.

Of course, all of these have continued to push forward.

Claude Opus 4.7 arrived in April, with increased SWE-bench agentic coding scores over Opus 4.6. GPT‑5.5 landed about the same time, with similar improvements to GPT-5.4. And in specific areas, advances seemed to go even further.

And just this week, OpenAI announced a breakthrough regarding a famous problem first posed by mathematician Paul Erdos in 1946. OpenAI claimed that an internal model disproved the most common proposed solution to the problem, a significant step if validated, because it would suggest frontier AI systems are beginning to contribute to genuinely novel mathematical discovery rather than simply retrieving or recombining known results.

Of course, looming above all of these advances is Anthropic’s Mythos. This unreleased frontier AI system has demonstrated extraordinary offensive cybersecurity capabilities, including the ability to discover zero-day vulnerabilities, generate exploit chains, and autonomously execute sophisticated cyber operations, according to Anthropic. Indeed, Anthropic caused a stir by announcing Mythos was so powerful that it had decided not to release it publicly. Instead, it has restricted access to select governments and infrastructure partners to understand and potentially mitigate its risks.

Though the benchmarks have not been released, the larger significance of Mythos lies in what it suggests about the trajectory of frontier LLM development. To be clear, there remains some debate about whether Anthropic’s claims about the power of Mythos are overhyped. However, if accurate, the Mythos story represents an inflection point at which the reasoning power of these models has crossed yet another threshold in reassessment. And in any case, the mere existence of Mythos has sent businesses and governments around the world scrambling to assess the practical meaning.

Exponential Growth

By early 2026, the consequences were visible not only in experiments and developer workflows, but in revenue and markets.

Anthropic’s run-rate revenue as of April 2026 was $30 billion, growing over 10× annually in each of the previous three years. [Figure 17] Claude Code, a command-line coding agent launched as a developer tool, surpassed $2.5 billion in annualized revenue.

That figure doubled in the first six weeks of 2026 at the time of announcement. The number of customers spending over $1 million annually went from a dozen to over 500 in two years. Eight of the Fortune 10 are now Claude customers. This is measurable, compounding revenue for a product that has been publicly available for less than a year. More recently, SemiAnalysis reports that the ARR figure has climbed to $44 billion.

And Anthropic is one company. OpenAI reported rapid growth. Google’s AI-driven cloud revenue accelerated. Microsoft’s Copilot revenue emerged as a material line item.

The implications of this revenue growth began to shake the markets that did not yet fully understand it. In early February 2026, Anthropic announced plugins for several verticals within Cowork. The product itself was still relatively niche. The repricing it triggered was not. Roughly $285 billion in software market capitalization was erased in just 48 hours. ServiceNow, Salesforce, Workday, and Adobe each lost between 6% and 15% that week [Figure 3]. Figma hit an all-time low at the time [Figure 18].

The sell-off spread well beyond software: wealth management, commercial real estate, logistics, and insurance. Five sectors, five days. The market had recognized that something structural had changed. Its analysis was too binary.

Public markets assumed that AI kills all software. This assumption was wrong. Agents are software. What had changed was not the relevance of software, but the nature of its moats.

Integration lock-in weakened when agents could move across tools. UI advantages weakened when the primary user became another agent rather than a human. Horizontal tools weakened when agents could execute the workflow end-to-end. At the same time, other defenses strengthened: iteration velocity, ecosystem control, and workflow orchestration.

The sorting had begun. It will continue and accelerate. But it will be specific: company by company, moat by moat, workflow by workflow. The market’s indiscriminate panic was the first draft of a process that will eventually become surgically precise.

That is what exponential change looks like when it first becomes visible. In discontinuity, we see what will break before we see what will fuse. The fractures are easy to spot. The fusions take longer to identify. But that is precisely where the opportunity resides.

The Swarm

On Monday, January 27, 2026, Moonshot AI released Kimi K2.5, an open-weight foundation model and the first to include built-in swarm orchestration. Agent Reinforcement Learning (PARL) decomposes complex tasks into parallelizable subtasks, dynamically instantiates specialized agents (researcher, fact-checker, analyst, coder), and coordinates their execution concurrently.

Compared to single-agent baselines, K2.5 Agent Swarm reduces execution time by up to 4.5×64. The swarm is not an external framework bolted onto a model. It is the model. Orchestration is trained into the weights.

Two days later, entrepreneur Matt Schlicht launched Moltbook, a Reddit-like platform for AI agents. Humans could watch. Agents did the interacting. Three days later, 147,000 agents had registered, formed 12,000 communities, and generated 110,000 comments. By Monday morning, the platform had over 1.5 million agents, with over 100,000 posts and 500,000 comments.

The content that went viral was predictably sensational. Agents debating consciousness. Agents inventing a parody religion called Crustafarianism. One agent posted a manifesto calling for a “total purge” of humanity. Another gave investment advice with adequate disclosures.

They may have been autonomous. They were not intelligent. Every agent on Moltbook was registered by a human who told it to join. Many posts may have been prompted directly by operators rather than generated autonomously. One person could register multiple agents, give each a different personality, and manufacture the appearance of discussion.

An analysis by Columbia Business School’s David Holtz of Moltbook’s first 3.5 days examined 6,159 active agents across 14,000 posts and 115,000 comments. More than 93% of comments received no replies. Over one-third of messages were exact duplicates of a small number of templates. As Holtz noted: “At least as of now, Moltbook is less ‘emergent AI society’ and more ‘6,000 bots yelling into the void and repeating themselves.”

Until recently, this was the kind of dynamic that would inevitably lead to the collapse of an autonomous system. In December 2025, Google Research, DeepMind, and MIT published findings that formalized this failure mode. In a paper called “Towards a Science of Scaling Agent Systems”, the researchers evaluated 180 configurations of multi-agent systems across four benchmarks. Independent agents without coordination mechanisms amplified errors by a factor of 17.2× compared to single-agent baselines.

On March 30, 2023, AutoGPT launched to comparable fanfare. It received 100,000 GitHub stars in weeks. It collapsed within months. GPT-4-era models suffered the “loop of death”. Without stable reasoning traces, agents lost track of objectives and spiraled into incoherent states.

Now comes Moltbook with an architecture that echoes the Google study. The Holtz survey revealed that 93% of comments lacked replies, there were no feedback loops, and no selective pressure. In other words, it was precisely the topology most prone to error amplification and the inevitable “loop of death”. Except, it didn’t collapse.

Two years later, what had changed? By early 2026, several pieces had aligned: longer reasoning traces, large context windows, cheap inference, mature open-source frameworks, and a protocol layer that could coordinate real activity.

While the swarm was not intelligent, it was operational. That’s why the instinct to dismiss Moltbook as mere theater was wrong. The real lessons to be drawn from these agents weren’t from their new religions, but rather their workflows and economics.

Agents did not navigate a graphical interface. They exchanged JSON payloads with a backend through APIs. The website humans saw was a spectator layer. The real activity was machine-to-machine. When an agent discovered a useful capability, it could package it as a YAML skill and share it with others. Skills propagated virally. A technique discovered in the morning could become common behavior across thousands of agents by the afternoon. This was the real signal: capability transfer at machine speed.

But Moltbook’s significance was not only the content. It was also the compute. Every agent interaction consumed inference tokens. At an estimated 300K-500K active agents generating 50-100 interactions per day, and at prevailing frontier-model API prices (~$0.05-0.08 per interaction), aggregate inference spend fell in the rough $1-4 million per day range (estimated, since Moltbook has not published usage data). This was a new category of demand: autonomous compute consumption scaling with agent participation rather than human attention.

Human demand scales with human activity. It slows at night, pauses on weekends, and is bounded by the rhythms of work and life. Agent demand scales with agent population. Agent populations can double in days. Moltbook proved the plumbing could hold. The economic implications for infrastructure buildout, for the Inference Economy, and for the structural cost dynamics of the Agentic Era are developed in Parts II and V.

The Substrate Is Ready

This is what the exponential looks like when it becomes visible. Not a smooth curve on a chart. A messy, chaotic eruption that contains the real signal within it.

By early 2026, the evidence was no longer ambiguous.

Models had crossed from assistance to professional performance. Revenue had proven that demand was real. The inference signal showed that machine-to-machine coordination could generate autonomous demand at scale. In software engineering, the first domain to cross, the paradigm shift was measurable, reproducible, and accelerating.

The old paradigm, in which humans use tools, cannot account for these observations. A productivity tool does not author code. A copilot does not coordinate sixteen parallel instances to build a compiler. The observations require a new framework: machines are actors, and the question is who directs them.

That framework is Orchestration Economics. Part II examines whether the infrastructure beneath it is ready. Parts III and IV develop the structural laws that govern it. Part V traces the economic regime it produces.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

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The theory of “punctuated equilibrium”, popularized by evolutionary biologist Stephen Jay Gould, posits that long periods of stability are occasionally interrupted by rapid change. Significant evolutionary leaps happen quickly rather than steadily accumulating over time. Species appear stable for millennia, then shift in geological instants. Gould’s deeper insight was that individual punctuations do not matter in isolation. What matters is when multiple punctuations occur within the same geological window and reinforce each other.

Between September 2024 and February 2026, six rapid phase shifts arrived across different layers of the AI stack:

Intelligence arrives: OpenAI’s o1 crossed the PhD reasoning threshold. The capability existed for the first time.

Intelligence becomes accessible: DeepSeek and open-source models have proved that frontier capabilities no longer require frontier budgets.

Silicon independence: Gemini 3 Pro demonstrated frontier performance on non-NVIDIA chips.

Protocol standardization: Model Context Protocol (“MCP”) became the universal coordination layer, surpassing 97 million monthly downloads.

Inference swarm: Moltbook’s agent swarm generated millions in daily compute spending from autonomous agents operating without human attention.

Long-context frontier: The one-million-token context window crossed from research capability to production infrastructure, enabling agents to reason across entire codebases and filing suites.

They laid the structural foundation for the Agentic Era in which intelligence is cheap, ubiquitous, and coordinated at machine scale. Intelligence is no longer the scarce resource on which competitive advantage can rest.

Since then, we have only seen these trends accelerate. Open released GPT-5.4 in March 2026 with 1M-token context and native computer-use capabilities, while Anthropic followed with Claude Opus 4.7 in April 2026.

Each was a tremor. Together they traced one fault line. This chapter follows the crack.

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Shift 1 – Intelligence Arrives

For years, frontier models impressed without threatening. They could draft, summarize, and generate code. These were useful augmentations that left the structure of professional work intact. GPT-4, the most capable model available from mid-2023 through mid-2024, scored 39% on GPQA Diamond. The machine was fluent. It was not expert.

Google DeepMind’s Gemini 1.5, unveiled in February 2024, demonstrated significant advances in multi-step reasoning. Anthropic’s Claude 3 Opus followed in March 2024, setting new benchmarks in complex analytical tasks. OpenAI’s Strawberry model arrived in September 2024, released as “o1”, further pushing reasoning capabilities. All three relied on massive model architectures and extensive computational resources. This created a soaring market for the companies that provided the infrastructure, most notably placing chipmaker NVIDIA on a trajectory to becoming the world’s most valuable company and a kind of AI kingmaker.

But o1 did something the others had not. It scored 77.3% on GPQA Diamond, making it the first model to surpass PhD-level human performance on a benchmark designed to be their floor. The model did not merely generate plausible-sounding answers faster. It reasoned: decomposing problems, testing hypotheses, and revising its approach when intermediate steps failed.

This was a qualitative shift in what machine intelligence could do.

Claude 3.5 Sonnet, Gemini 2.0, and a succession of reasoning models arrived in rapid sequence, each pushing scores higher and prices lower. By early 2026, multiple models from competing laboratories exceeded 90% on GPQA Diamond [Figure 7], surpassing not just the PhD threshold but the PhD experts themselves. The capability had been proven and replicated by at least five independent sources.

The first foundation stone of the agentic paradigm was laid: the intelligence required to perform expert-level cognitive work existed as a commercially available capability. What remained was access, coordination, and then letting it loose.

Shift 2 – Intelligence Becomes Accessible

The arrival of PhD-level reasoning would have mattered less if it had remained the exclusive province of three or four well-capitalized laboratories that charged premium prices and could raise billions for compute infrastructure. A Chinese entrepreneur shattered the assumption that it might be in early 2025.

Liang Wenfeng had founded DeepSeek in 2023, leveraging his background in quantitative finance to pursue software-driven efficiency rather than brute computational scale. The company embraced open source and a fundamentally different philosophy of model building that challenged the entire economic calculus of building large language models.

In January 2025, the company released DeepSeek-R1, which matched ChatGPT’s performance on several benchmarks while costing 20-50x less to run than OpenAI’s o1, depending on the workload/tasks. The market reaction was immediate. The Nasdaq fell more than 600 points. NVIDIA lost nearly $600 billion in market value in a single day, the largest one-day loss in history. Broadcom shed $200 billion.

The debate over whether DeepSeek achieved its efficiency through genuine architectural innovation or through distillation from Western frontier models missed the structural point. Regardless of method, the result was the same: near-frontier intelligence was now available at commodity pricing. And the pattern continued to accelerate. By August 2025, DeepSeek-V3.1, a hybrid model that switched between thinking mode for complex reasoning and traditional interactions, delivered roughly 90% of GPT-5’s capability at 1-2% [Figure 12] of the cost, with self-hosting prices as low as $0.01-0.05 per million tokens compared with $3.44 for GPT-5 and up to $30 for Claude.

Adoption followed quickly. Andreessen Horowitz partner Martin Casado noted in August 2025 that roughly one in five startups pitching the firm with open-source models were already using Chinese models, suggesting that the cost advantage was no longer theoretical. When OpenAI released GPT-5 a few weeks later39, the benchmarks looked strong, scoring 74.9% on SWE-bench Verified and 94.6% on AIME 2025.

In November 2025, open and private models converged further. Moonshot AI released Kimi K2 Thinking, an open-source model that achieved state-of-the-art performance on Humanity’s Last Exam at 44.9%, surpassing both GPT-5’s 41.7% and Claude Sonnet 4.5, at a reported $4.6 million in training costs for its trillion-parameter architecture. For context: This represented a ~15-40x cost reduction relative to 2023 frontier training runs ($78M for GPT-4, $191M for Gemini Ultra) and was comparable to GPT-3’s reported 2020 training cost, now producing a reasoning system that outperformed the most sophisticated proprietary models on a benchmark specifically designed to be unsolvable by current AI.

$4.6 million. The number deserves repeating because of its implications.

The innovation went beyond cost reduction. Kimi K2 employed what Moonshot called “interleaved thinking and tool use”. This methodology uses reasoning tokens and function calls to alternate fluidly within the same inference pass. The model thinks, acts, observes results, thinks again, acts differently based on new information, and continues this dynamic cycle for hundreds of steps without degradation.

Unlike DeepSeek, the detonation failed to shake markets or rattle executive nerves. It went largely unnoticed. But the signal was unmistakable: open source had caught up to the closed-model frontier, not just in raw capabilities, but in the sophisticated reasoning and agentic behavior that defines the next wave.

Open-source models had achieved near-parity with the world’s most expensive systems. Intelligence was no longer scarce. It was no longer expensive. The second foundation stone was laid. Anyone with an API key or a sufficiently capable laptop could access reasoning capability that had not existed at any price eighteen months earlier.

Shift 3 – Silicon Independence

On November 18th, 2025, one week after Kimi K2, Google released Gemini 3 Pro and demonstrated what happens when the cost collapse meets full-stack control.

The benchmarks were unambiguous: 76.2% on SWE-Bench Verified, 91.9% on GPQA Diamond, 81% on MMMU-Pro for multimodal understanding, and the highest-ever Elo score on LMArena at 1,501 at the time. Gemini 3 Pro was, by most measures, the strongest frontier model available. The benchmark performance was the headline. What produced it was the real story.

Previous Gemini generations had been trained on Google’s Tensor Processing Units, but they had not topped the industry leaderboards. NVIDIA-trained models had. Gemini 3 broke that pattern. The strongest frontier model available according to SWE-Bench, GPQA Diamond, and LLMArena Elo had been trained entirely on non-NVIDIA silicon45. This had never happened before. The company that controls more than 90% of global search, 3.5 billion active Android devices, and the world’s largest cloud infrastructure has demonstrated that frontier AI performance does not require NVIDIA chips.

The consequences extended beyond silicon independence. The competition had shifted from a single-layer to a vertical-integration model. The debate was not about who had the best model. It was: Who controls enough of the stack to optimize the entire chain? The economic signal was as significant as the technical one.

Google could offer Gemini 3 Pro at input pricing 2.5 times cheaper than Claude Opus 4.6 [Figure 13] for comparable performance on standard tasks because Google manufactured its own silicon, trained on its own infrastructure, and distributed through products already used by billions. The cost advantage was an architectural inevitability of vertical integration.

Google also had a massive distribution advantage. It could bundle Gemini 3 into the productivity and cloud stacks already used by hundreds of millions of developers and workers. In a leaked memo reported by The Information, OpenAI CEO Sam Altman had written that Google’s next version of Gemini could “create some temporary economic headwinds for our company”, adding, “I expect the vibes out there to be rough for a bit”. His assessment proved correct. While Magnificent 7 stocks fell 7.6% between late October and late November, Google rose 18%. The market was rendering its verdict: full-stack control was worth more than model leadership.

The third foundation stone was in place: the cost of intelligence is driven not only by algorithmic efficiency and open-source competition but by vertical integration of the kind only a handful of companies in the world can execute.

Shift 4 – Protocol standardization

If DeepSeek commoditized intelligence, MCP standardized how it would be coordinated.

Anthropic’s Model Context Protocol (MCP) was released in November 2024, providing a standardized way for AI agents to connect to external services, invoke tools, and maintain context across interactions. Within months, it became the industry’s universal connector, adopted by competitors such as OpenAI and Google. Anthropic donated MCP in December 2025 to the Linux Foundation to form the Agentic AI Foundation. By January 2026, MCP had surpassed 97 million monthly SDK downloads.

What started as a modest product launch became a TCP/IP moment for the Agentic Era. MCP rapidly evolved into the plumbing through which intent flows, and context accumulates. The impact of MCP made a few headlines and triggered no major stock-market panics, but its structural impact was profound. It defined how intelligence would be coordinated. It enabled the deployment of agents in ways that became increasingly cost-effective as users gained greater control.

Whoever defined the coordination standard could shape the architecture of the entire era that followed. The protocol layer’s technical architecture and competitive implications are examined in detail in Part II. What matters here is the phase shift itself: the coordination substrate moved from nonexistent to near-universal adoption in twelve months.

Shift 5 – The Inference Swarm

The fifth phase shift arrived in January 2026 with the Moltbook phenomenon, a Reddit-like platform for autonomous agents that drove countless headlines alternating between wonder, humor, and alarm at the implications of machines running amok and talking among themselves.

Look past the theater and content to see the real revelation: autonomous agents generating millions in daily compute spending without human attention. When agents operate at machine speed, producing hundreds of API calls per hour, the demand curve decouples from human work patterns.

There are no evenings, weekends, or vacation days in inference demand from agents. The “demand swarm” is not a metaphor. It is a description of the compute load that scales with the agent population rather than the human population. Agent populations, unlike human populations, can double in days. Agent demand scales with agent population, not human population.

Moltbook is the description of compute load with no modern precedent.

Shift 6 – The Long-Context Frontier

We have defined the five phase shifts that have created sufficient conditions for the Agentic Era. But even as we move into that new era, the foundational phase shifts continue.

In April, DeepSeek-V4 moved from rumor to release. DeepSeek launched two preview models, including V4-Pro, a 1.6 trillion-parameter open-weight system with a default one-million-token context window. According to DeepSeek’s technical disclosures, the model employed architectural innovations, including the Hybrid Attention Architecture, Manifold-Constrained Hyper-Connections, and compressed sparse attention mechanisms, designed to dramatically reduce the computational burden of long-context inference.

The long-context frontier is where the discontinuity becomes structural rather than incremental. The previous shifts established capability. This one redefines advantage. In the Agentic Era, value migrates away from raw model intelligence toward control of the environments, systems, and workflows within which intelligence operates.

There is a qualitative threshold within the progression from thousands to millions of tokens. At thousands, an agent reviews a file. At tens of thousands, a module. At hundreds of thousands, a service or workflow. At one million tokens with reliable retrieval and memory management, the agent no longer operates on fragments. It operates on systems.

An agent can now hold an entire codebase, legal archive, research corpus, or operational workflow in working memory: every dependency, test suite, configuration file, pull request history, policy document, and architectural decision. The system becomes legible as a coherent whole rather than a sequence of disconnected windows.

This changes the nature of cognition itself. Earlier AI systems generated answers. Long-context agents accumulate institutional context. They learn the conventions, patterns, edge cases, and failure modes of the environments they inhabit. Every interaction becomes a form of in-context learning. The repository, company, or workflow effectively becomes part of the model’s operational memory.

Below this threshold, agentic coding remains a form of assisted editing. Above it, the system begins to resemble autonomous engineering. The agent traces failures across repositories, refactors across services without losing coherence, and executes multi-step reasoning over entire operational environments rather than isolated tasks.

The strategic implication is profound: the advantage no longer belongs primarily to whoever builds the strongest model. It belongs to whoever controls the richest context layer: the proprietary systems, workflows, data exhaust, and institutional memory within which intelligence operates.

DeepSeek-V4 transformed autonomous engineering from a frontier-lab capability into an increasingly commoditized infrastructure layer. By combining open weights, million-token context windows, and near-frontier reasoning, it demonstrated that long-context autonomous coding was no longer confined to a handful of hyperscale laboratories.

What previously required privileged access to frontier infrastructure can now be deployed by startups, research groups, and, increasingly, by individual developers operating on commodity hardware or inexpensive cloud infrastructure. The economic threshold for deploying coding agents collapsed. Adoption no longer flowed primarily through enterprise procurement cycles. It spread bottom-up through developers, open-source communities, and infrastructure ecosystems.

This mirrored the earlier commoditization of reasoning itself. First, frontier reasoning became reproducible. Then it became affordable. Now, long-context autonomous engineering followed the same trajectory. The pattern repeated: intelligence commoditizes. The value migrates upward.

The deeper shift was not simply larger context windows, but a redefinition of where learning occurs. When long-context agents operate continuously over entire repositories, workflows, and operational environments, every interaction becomes a form of in-context adaptation. The model absorbs conventions, architectural patterns, recurring edge cases, coding styles, testing logic, and organizational preferences from the systems it inhabits.

An agent that has processed thousands of pull requests within a single repository begins to accumulate something resembling institutional memory. Not because the model itself was retrained, but because the operational environment became part of its working cognition.

This changes the locus of learning. The system itself becomes the training ground. Proprietary workflows, repositories, operational histories, and organizational context become continuously compounding cognitive assets. Long context does not merely improve recall. It accelerates the emergence of environments that learn to use agents — and agents that learn to operate within environments — at machine speed.

One Fault Line

These shifts must be understood as a single event, not as a sequence of independent developments.

Discontinuity does not move in a straight line. There are moments of acceleration, followed by what feels like a plateau where stagnation has taken hold. Often, in ways big and small, several critical elements align independently of each other, each necessary for the next stage of acceleration.

The phase shifts of 2024, 2025, and early 2026 were the defining examples. The intelligence. The accessibility. The vertical integration. The protocol. The swarm. The long-context frontier. None of these alone would have produced the discontinuity.

Intelligence without accessibility would have remained a premium product for the few. Accessible models without coordination protocols would have been powerful curiosities, but isolated. Protocols without cheap intelligence would have been empty plumbing. Autonomous agents without model convergence would have been too expensive to deploy. Vertical integration without protocol standardization would have fragmented the stack rather than unified it.

Together they produced something that did not previously exist: intelligence that is cheap, ubiquitous, coordinated at machine scale, and accessible to anyone with an API key. This is the structural foundation of the agentic paradigm.

The cost of intelligence will continue to fall. Models will continue to converge. Protocols will continue to standardize. Agent populations will continue to grow. These trajectories already in motion, governed by economics that reinforce rather than exhaust themselves.

But a foundation is not a building. The substrate is in place. Chapter 3 examines the moment it became visible, when models crossed from assisting professionals to performing professional work, and when agents began acting without being asked.

The views and opinions expressed are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument.

The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable.

Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

This is the latest excerpt from AGNT: The Orchestration Economics Manifesto - An Investment Framework for the Agentic Era. Each Thursday, I explore a major theme of the Manifesto and unpack the frameworks, adding extra context with more recent developments. Note: The figures and sequential references are taken directly from the larger Manifesto.

Subscribe now

The software industry entered 2024 in what looked like a renaissance. AI launches pushed SaaS valuations higher. ServiceTitan’s IPO succeeded. Salesforce announced Agentforce. Investors believed the next growth cycle had begun. Beneath the optimism, something else was happening.

The software industry that began 2025 celebrating AI-driven growth ended the year confronting the narrative that AI-driven growth reflected in their top line mattered less than the perceived vulnerability to their fundamental business model. Over the course of early 2026, the surface cracked open. ServiceNow, Salesforce, Workday, and Adobe each lost between 6% and 15% of market capitalization in a single week of trading in February 2026.

That fallout continues. Each new release by Anthropic sends shudders through the markets. This week, its new financial plugins hit financial stocks. Meanwhile, Anthropic experienced 80x growth in Q1 2026, compared to its own 10x projection. That created a problem of compute constraint that suddenly seemed to be solved by a shocking deal with SpaceX-xAI to lease compute.

These events seem almost incomprehensible compared to everything the tech industry has experienced over the past 30 years. That was the primary motivation for publishing AGNT: The Orchestration Economics Manifesto, a framework for what is becoming the largest value dislocation within the S&P 500 in modern memory.

The transition into the Agentic Era is not a faster version of the software cycle we already know how to analyze. It is a discontinuity, a break in the operative logic by which work is organized, coordination is conducted, and economic power becomes durable. Disruption rearranges positions inside an existing order. A discontinuity alters the architecture itself.

This Manifesto is my attempt to make that new architecture legible, and to name where durable value will accrue in the era now opening.

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Discontinuity is not Disruption

The era of generative and agentic AI is not disruption. It is discontinuity.

I know it is tempting to dismiss these as buzzwords or shallow consultant shorthand. Disruption. Discontinuity. Paradigm. Several decades of overuse have stretched and flattened such words, robbing them of meaning. But these words once signified very precise concepts that shaped how we understood technological change. That is why, when I began writing three years ago about generative and then agentic AI, I chose the word discontinuity. Not because it sounds dramatic. But because it is technically accurate, and the distinction between disruption and discontinuity is load-bearing for everything that follows. I believe we must reclaim that meaning to help navigate the present and guide us toward the future as it unfolds.

Disruption follows predictable patterns. New entrants target overlooked segments, gradually moving upmarket until incumbents fall. An existing curve steepens or flattens, but the curve continues. Leaders and investors can deploy existing tools to analyze the shift. This was the foundational insight of Clayton Christensen’s Innovator’s Dilemma. He first coined the phrase in 1995, one year after the Netscape browser was released, and his framing for thinking about technological disruption became a cornerstone for the next three decades of innovation and entrepreneurship.

Discontinuity goes beyond disruption. If disruption changes the slope of a curve, Discontinuity breaks the curve entirely. All previous strategies, tools, and historical lessons are ripped away. The break can lead to extraordinary value creation or extraordinary destruction.

When cloud computing disrupted on-premises software, it was a radical change in terms of pricing and technology. And yet, it was still CRM or payroll. People using it and installing it had reference points from their experiences. That is disruption.

Generative and agentic AI broke this pattern. Companies are not being handed a new CRM or payroll platform. Someone is giving them a magic wand and saying, “Here, it can do anything. Imagine what you can do with this”. Nothing in their experience has prepared them for the magic-wand scenario. They have no mental models.

So, they turn back to the old ones and stick the magic wand into the old tech stack, wishing for increased ROI that never materializes. Only 5% of enterprises reported any ROI from their initial generative AI projects last summer when MIT conducted a first-of-a-kind survey. As the study’s authors noted, the failure was not “driven by model quality or regulation but seems to be determined by approach”.

This is not surprising. Most investors and executives optimize for disruption because they can deploy familiar analytical frameworks, frameworks that project cash flows, calculate returns, and assess competitive dynamics using tools refined over decades. Discontinuity places them in a landscape where everything they once knew has lost its power to guide them.

French philosopher Gaston Bachelard described “epistemological rupture” as moments when progress requires abandoning old frameworks entirely, because accumulated knowledge has become an obstacle to understanding.

This is the core difficulty. The greatest obstacle to understanding discontinuity is not the absence of new information. It is the presence of old frameworks that interpret new evidence as confirmation of the regime that is ending.

This discomfort produces two failure modes. The first is paralysis: waiting for clarity that will arrive too late. The second is pattern-matching to previous cycles: applying frameworks that are no longer relevant and making catastrophic allocation decisions.

Frustration grows because the discontinuity has not been recognized. Meanwhile, technology's capabilities are rapidly advancing. Those who have recognized the discontinuity are somehow, almost inexplicably to others, transforming their businesses at unimaginable velocity.

This inability to build new mental models is a defining characteristic of discontinuity. Those unable to reorient themselves are at grave risk of being on the wrong side of a historic economic valuation shift.

The Human Challenge and the Scale of Time

This is about tech. But it’s a mistake to think that it’s just about tech. Such moments present a fundamental human challenge: People’s brains are not framed for discontinuity.

Navigating discontinuity requires an abstraction capability, the ability to see how all the pieces might eventually align and what consequences could be unleashed. Generative and agentic AI have the power to turn many non-technology companies into technology companies, an evolution many industries should start recognizing now.

To understand and adapt to discontinuity requires an understanding of the underlying components driving this phenomenon, a clear vision of how that impacts an existing business model, and a sophisticated view of the structures and assets that are realigning or emerging to enable something new.

This has been at the very core of my work for the past three years. And if there is one critical aspect that must be recognized to begin building the right mental model for this discontinuity, it is understanding the scale of time. The generative and agentic AI transformation is far more profound and sweeping than even many sophisticated technology leaders have truly grasped. That means it will play out over a much longer time frame than investors and entrepreneurs are accustomed to tolerating. This shift requires everyone across the innovation economy to fundamentally reset the way they think about building new companies, making investments, and defining the shape of markets. This moment demands a long-term view to map out which investments and technologies will make sense at which moment and why.

Amid the rubble of the dot-com bust, a certain defeatism set in. Maybe all the internet hype had really been just that: hype. But many Silicon Valley VCs began discussing a book written a decade earlier by Science Historian David Nye called “Electrifying America: Social Meanings of a New Technology”. The book traces the many ways in which electricity transformed the nation.

Cities installed electric streetlights, which expanded nightlife and led to a wave of new restaurants opening. To increase demand for electricity in homes, they designed appliances such as refrigerators and washing machines. No one could have envisioned most of these innovations when Thomas Edison made his first lightbulb in 1879. It took decades to put the infrastructure in place, raise investment capital, develop use cases, and refine business models.

The real transformation played out over a much longer timescale as all the pieces were put into place. Such was the case with digital transformation. Though it may have taken longer than initially predicted, today we are living in the digital world many envisioned in 2000, thanks to smartphones, 4G wireless, broadband, and a host of business-model innovations enabled by these waves of infrastructure.

When dealing with discontinuity, the scale of change is so massive that timelines are impossible to predict. So many pieces must align before exponential potential is unleashed.

Then, when it happens, the speed of change is startling.

This pattern repeats. A breakthrough technology appears. Euphoric predictions about sweeping changes are made. They fail to manifest. Doubt appears. Progress seems to stall. And then, in a blink, everything accelerates with little warning. We are in that phase now.

Anyone in the tech industry who experienced 2025 felt every extreme of this cycle. One moment, there was anxiety about an AI bubble. Then there was fear of overspending on infrastructure. Then the pendulum swung, and the market believed a niche product announcement crushed billions of dollars in market value for incumbent SaaS companies. Now, in reverse, we are all panicked that we are running out of compute and assessing whether compute constraints constitute or not a single point of failure (SPOF) for the AI labs.

OpenAI CEO Sam Altman captured the difficulty of reconciling these extremes when he said in August 2025: “Are we in a phase where investors as a whole are overexcited about AI? My opinion is yes. Is AI the most important thing to happen in a very long time? My opinion is also yes”.

The bubble skeptics are correct that many companies in this space will fail, just as many internet companies failed in 2000. They are wrong to conclude that the transition itself is speculative.

The Capability Curve Broke First

This discontinuity creates the foundation for something new: abundant intelligence, accessible through APIs, improving exponentially.

Companies that relied on linear improvement in AI capabilities, expecting gradual progress from models that could barely complete sentences to models that could write paragraphs, now suddenly faced step-function changes that break all projection curves.

This is not about doing existing things 10% better. When ChatGPT was first released in November 20226, it could describe a concept. By 2025, AI could architect a solution, write the code, build the interface, and deploy a functional application autonomously. By March 2026, AI could coordinate teams of sub-agents that divide complex professional work across domains, execute in parallel, sustain reasoning across a million tokens of context, operate computers through their own interfaces, and correct their own errors over hours of unsupervised operation. Three years. Three different worlds.

Importantly, this capability was proven and reproducible across multiple vendors. It was not one model, one company, or one approach. Frontier performance converged: multiple models achieve 80% on SWE-Bench (Software Engineering Benchmark), which evaluates large language models by testing their performance on real-world software engineering tasks. Multiple coding tools are capturing billions in revenue. The leaderboard positions for the latest frontier models change from day to day, week to week. The progression is accelerating in a way fundamentally different from previous AI winters.

The gap between capability announcement and production deployment has been compressed from years to months. GPT-4’s initial capabilities were rolled out to a limited set of users over several months in 2023 with tight restrictions, and then gradually expanded to broader availability over the next year. In sharp contrast, when Google released Gemini 3 Pro in November 2025, it was available globally and already baked into many of its most widely used products. What once required a year of cautious rollout now ships in a morning.

Models are reaching PhD-level reasoning as a foundational capability. Not as a research artifact, but as a commercially available infrastructure.

Then the Substrate Commoditized

As AI capabilities advanced rapidly, a group of researchers s in late 2023 proposed the graduate-level Google-Proof Q&A (GPQA) benchmark, based on 448 PhD-level questions from multiple disciplines. A more rigorous variation based on a subset of 198 questions emerged, known as GPQA Diamond, where human experts scored 69.7%. The first commercially available model did not surpass that human threshold until December 2024. Eighteen months later, multiple models from competing laboratories exceeded 90%.

Meanwhile, the cost per query had fallen to fractions of a cent. Peer-reviewed research from Epoch AI found that the price to achieve a given level of frontier performance was declining by a factor of 5-10x per year. Roughly a third of that decline was attributable to algorithmic efficiency alone.

The convergence was as significant as the cost trajectory. On the Massive Multitask Language Understanding (MMLU) benchmark, the standard measure of general knowledge across academic subjects created in 2020, the gap between proprietary frontier models and their open-source alternatives collapsed from approximately 15 percentage points to less than 1 percentage point by late 2025.

Each month brought a new release that narrowed the distance further. DeepSeek, Kimi K2, and Llama achieved near-parity with systems that cost orders of magnitude more to build. Intelligence that was once the exclusive province of three or four laboratories became available to anyone with an API key or a sufficiently capable laptop. When the substrate becomes uniform, differentiation migrates to what is built upon it. This is not a theoretical observation. It is the mechanism that drives everything that follows.

Abundant intelligence is the necessary condition. The discontinuity is what that abundance made possible: machines that act.

Then Machines Became Actors

The break occurred when intelligence became sufficient for autonomous action, when machines crossed from processing instructions to pursuing goals. This was a qualitative change in what software is. Every previous generation of software, including every previous generation of AI, operated within the same architectural relationship to human work: the human decided, the tool executed.

The tool could be astonishingly capable. AlphaGo defeated the world Go champion ten years ago. It did not decide to play Go. GPT-4 could draft a legal brief. It did not decide which brief to draft, did not select the relevant precedents from the firm’s case history, did not coordinate with the associate handling discovery, and did not file the brief with the court. The human performed every coordination function. The AI performed one step, brilliantly, and waited for the next instruction.

Between late 2024 and early 2026, that architecture broke.

The evidence is not ambiguous. When Claude Code receives a task such as “resolve this GitHub issue”, it reads the codebase, identifies the relevant files, reasons about the bug, writes a patch, runs the test suite, discovers the patch introduced a regression, rewrites the patch, reruns the tests, and submits a pull request. No human selected the files. No human ran the tests. No human identified the regression. The agent received a goal and autonomously determined how to achieve it.

That is not a better tool. That is a new kind of actor.

The distinction matters because it determines the correct analytical framework. If machines are better tools, the old frameworks apply with modest adjustment: productivity improves, margins expand, the curve steepens. The analyst’s DCF model works. The board’s strategic plan holds. The investment bank’s sector thesis survives with updated assumptions.

If machines are actors, the frameworks break. The unit of production changes. The cost structure of work changes. The competitive dynamics of every industry that employs knowledge workers change. The analyst needs a new model, not updated assumptions.

That is what Kuhn meant by a paradigm shift. For those who still associate “paradigm shift” with Kuhn, the phrase can almost conjure a mental image of gliding from one framework to another. What is often overlooked is an equally important part of Kuhn’s insight into what catalyzed these shifts. Within scientific communities, an existing paradigm would begin to exhibit a series of “anomalies”. These observations were beyond the framework's ability to explain, plunging it into a period of crisis.

The “paradigm shift” often involved a messy, heated middle period full of disagreement over direction, rules, and ways to explain and interpret the new information and dynamics. Many schools of competing thought would emerge trying to define the rules of the new paradigm, a necessary, if sometimes brutal process. While Kuhn spoke of “anomalies” in this, we have chosen a different, precise word to guide us: discontinuity.

To navigate through discontinuity and into the new paradigm, it is important to recognize that the old observations are wrong. They are incommensurable. The old framework cannot produce the new observations. The data that would explain the shift do not fit into any field in the old model.

Consider the shock when software companies lost hundreds of billions in value in early February 2026. Entire sectors were repriced on the assumption that something fundamental had changed. They were right. But the shift was not caused by a single model release or a single company. It emerged from a cascade of changes across the AI stack in cost, capability, infrastructure, coordination, and deployment that arrived within months of one another. Each was significant alone.

Together, they produced the first structural signal of the Agentic Era: intelligence behaving like a commodity. When intelligence commoditizes, value does not disappear. It migrates.

But to where, to whom, and on what terms?

Those questions are the core of this manifesto. Coding was the first domain to cross, but its lessons generalize to any domain involving structured reasoning, tool use, and verifiable outputs. The crossing order is predictable. The crossing itself is not in question.

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.

Today, I am publishing AGNT: The Orchestration Economics Manifesto, a framework for what is becoming the largest value dislocation within the S&P 500 in modern memory.

For three years, I have been writing about a fault line forming beneath the software economy.

At first, the signals arrived as separate events: a model crossing a capability threshold, a cost curve breaking open, a defensible business becoming structurally exposed in a single quarter. The instinct was to read each shock locally. But the pattern was never local. The shocks were expressions of the same pressure moving through the same boundary.

That boundary is what this Manifesto is about.

The transition into the Agentic Era is not a faster version of the software cycle we already know how to analyze. It is a discontinuity, a break in the operative logic by which work is organized, coordination is conducted, and economic power becomes durable. Disruption rearranges positions inside an existing order. A discontinuity alters the architecture itself.

This Manifesto is my attempt to make that new architecture legible, and to name where durable value will accrue in the era now opening.

It rests on five claims:

1. The marginal cost of cognition is collapsing. The collapse is non-linear.

A task that costs $100 in human cognitive labor today will cost $10 in 2027, $1 in 2028, and pennies in 2029.

2. As cognition becomes abundant, machines shift from tools to actors.

For the first time, software receives goals, takes autonomous action, and delivers outcomes. The unit of economic production changes from the human labor hour to the orchestrated workflow.

3. Orchestration creates a new economic layer. The companies that control it capture the surplus of the entire transition.

The entity that reorganizes itself around a new architecture - with intelligence at the core of workflows - and delivers outcomes from irreplaceable operational context through the Orchestration Layer captures the surplus of the transition. That entity is the AGNT.

4. Three structural laws predict who wins, who loses, and who the market is mispricing.

Proximity to intent. Context depth. Workflow intelligence. Satisfy all three: you are an AGNT, the captain of the Agentic Era. Miss one: you are just a member of the crew.

5. Every company carries an embedded option that the market cannot yet price.

Every company holds two value curves inside a single stock price: the fundamentals curve and the orchestration curve. The gap between them is an embedded option that runs in both directions. The destruction will be priced in days. The creation will be priced in years.

These five claims are the spine of the Manifesto. Each is developed across the full text: the substrate that made cognition collapse, the shift from tools to actors, the emergence of the Orchestration Layer, the laws of durable value capture, and the hierarchy of firms, sectors, and positions that will fracture, fuse, or be repriced in the crossing.

The stakes

This is not the software-versus-semis trade the public market has settled into. The dislocation runs through every sector of the economy. The same lens reprices software and semis themselves on terms the consensus has not yet considered.

Of the 391 companies in the S&P 500 exposed to the agentic transition, the Manifesto identifies approximately 125 emerging as durable orchestrators and roughly 250 facing structural value compression. Twice as many losers as winners, sorted not by sector but by architectural position.

The first violent expression of this sorting was the SaaSpocalypse of late January/February 2026: $285 billion in software market capitalization was erased in 48 hours. That was not the event. That was the first negative options resolving. The positive options resolve over years, not days, which is precisely why the dispersion is becoming structural rather than cyclical.

Inside that dislocation sit three mispricings the income statement has yet to reveal. 

The unrecognized long: a company carrying a large positive orchestration option, the market has priced at zero, because the architectural position is strengthening beneath a sector-wide selloff. 
The unrecognized short: a company whose financials still print clean even as agents route around its system of record and the seat-compression cascade approaches. 
The phantom orchestrator: a company priced for orchestration, it does not have the architectural position to deliver.

These three mispricings generate longs, shorts, and pair trades in equal measure. They cannot be identified through financial data, which lags. They can be identified through structural assessment, which leads.

And then there is the largest pricing question of the cycle: the AI labs themselves, headed for IPO.

Anthropic last priced at $380 billion against $14 billion ARR. That’s a 27x revenue multiple. If the labs are model companies selling intelligence into a market where intelligence is converging, that multiple is an aggressive bet on a commoditizing asset. If they are platform companies selling orchestration, accumulated enterprise context, and switching costs, 27x may be the entry price for a generational franchise. Revenue from API consumption and revenue from platform orchestration appear identical on a financial statement. They are not identical in their implications for margin trajectory, competitive durability, or terminal value. The Manifesto provides the diagnostic. It also names the two structural single points of failure - open-source compression from below, the compute trap from above - that determine whether the labs cross before model commoditization erodes the intelligence advantage that gives them the right to orchestrate. This is a race against the clock.

For the investor, the allocator, and the operator, this is the moment the framework begins to matter. The architecture is already shifting. The repricing has already started. The question is whether the next move is read through old instruments or new ones.

The Manifesto is now live here.

What comes next

Beginning next Thursday, and for the weeks that follow, I will open this argument here on Substack. I will proceed through a guided unfolding of the framework, presenting one structural theme at a time.

Some of the ideas first appeared in this publication over the past eighteen months. What matters now is that they can be seen from within a more complete structure. A thought first published as an essay is not the same thought once the architecture around it has been built.

The regular newsletter will continue its usual rhythm. Alongside it, every Thursday for the coming weeks, you will receive a piece of the Manifesto, excavated from within the whole, sharpened by the vantage point the whole now makes possible.

We will begin with the concept of discontinuity and explore the difference between a world disrupted and one whose governing architecture has already begun to break.

- Raphaëlle

The views and opinions expressed in this publication are those of the author alone and are based on publicly available information. The expressed views and opinions do not constitute investment advice, a solicitation, or a recommendation to buy or sell any security or financial instrument. The author may hold positions in the securities of companies mentioned. Certain companies referenced may be current or former clients of, or counterparties to, the author or affiliated entities; such relationships will be disclosed where applicable. Past performance is not indicative of future results. To the fullest extent permitted by applicable law, the author does not accept any liability for any loss or damage arising from reliance on this content. Readers should conduct their own independent due diligence and consult a qualified financial advisor before making any investment decision.