The SaaS Renaissance Illusion: Why AI-Driven Growth May Mask Fundamental Business Model Threats

The SaaS industry stands at its most critical inflection point since the cloud revolution. While market indicators suggest a Renaissance - with many top SaaS stocks up 40-60% in recent months, high-profile AI launches like Salesforce's Agentforce, and ServiceTitan’s successful IPO - this surface-level optimism masks a fundamental transformation that will determine which companies survive the AI era.

To guide investors through this transformation and analyze the impact of disruptive technologies on business models, our firm has developed Advanced Growth Intelligence (AGI), a framework for unlocking durable growth. Drawing from our analysis of over 600 disruptive technology companies in private markets and systematic tracking of public SaaS companies with market caps exceeding $1B between 2022-2024, AGI provides a data-driven approach to evaluating companies in this rapidly evolving landscape. Our analysis shows companies scoring in the top quartile of our AGI framework consistently achieved "10X Club" status (companies valued at 10x or more of their ARR, where ARR is calculated as four times the last quarter's net sales) as of December 31st, 2024.

AGI evaluates a company across three dimensions:

1. Assess the fundamentals of a company at a granular level to measure core business strength and execution.

2. Gage the potential for GenAI to impact those fundamentals in either positive or negative directions.

3. Determine whether GenAI is a single point of failure that poses an existential threat to the company’s business model.

By applying the AGI framework, investors and founders gain insight into whether a company has established a Durable Growth Moat, the combination of intrinsic strengths and resilience factors that protect against competitive, technological, and market disruptions.

Evaluating this Moat is both more urgent and more difficult during moments of Discontinuity – such as the one we are experiencing with GenAI – because historical benchmarks and playbooks no longer apply.

To break down how this works, let’s use AGI to take a deeper dive into the impact of GenAI on the most emblematic SaaS companies: Salesforce.

The Context

The first half of 2024 was filled with pundits reading the last rites for SaaS, sure that generative AI would cause havoc for these companies. The sentiment surrounding Salesforce over the past year perfectly mirrors the outlook investors had for SaaS companies.

In late May, the company released Q1 earnings that fell well short of expectations, including growth that fell below 10% for the first time in decades. Investors punished the stock by driving it down 20% in one day – the biggest such drop in 20 years. Factors like a weak macroeconomy and the threat of GenAI were cited as reasons to be pessimistic about the company’s outlook.

Things seemed even gloomier in early September when Klarna announced it would rip out all SaaS platforms – including Salesforce – and replace them with AI applications. Four months after this announcement, Klarna reported 30% productivity gains but also faced significant integration challenges, suggesting the transition to AI-native solutions may be more complex than initially portrayed.

Flash forward three months and Salesforce’s stock is up a remarkable 28.62% on the year.

The comeback began a couple of weeks after the Klarna announcement at Dreamforce 2024 where Salesforce made a big splash by officially unveiling Agentforce, the evolution of its Einstein Co-pilot AI that introduced AI agents.

Agents had become the white-hot topic in tech by then because they can operate with true autonomy and take actions based on interactions and data they encounter. Going into 2025, Agents are poised to be a major theme, but the technology is still far from mature so the impact may be limited. The promise to customers is big productivity gains across the company. In December 2024, Google introduced Gemini 2.0, an advanced AI model designed for the "agentic era." Microsoft made its own major Agent announcement in October. Many other big players such as Workday and Hubspot are introducing Agents across their platforms. In Salesforce’s case, the market was ecstatic about Agentforce and its stock price went on a tear.

Here is the real challenge for investors: How can they know if the current SaaS “Renaissance” is real or just another hype bubble?

Part I: What AGI tells us about fundamentals today

Surviving the GenAI transition demands exceptional fundamentals. Think of this as a company’s traits to understand how it might behave in the coming disruptive evolution. The key is to understand the dynamics underlying these fundamentals and identify which metrics matter most.

Based on our comprehensive analysis of leading SaaS companies, we've identified critical benchmarks for top performers:

- Gross Revenue Retention must exceed 95% for enterprise and 90% for SMB segments. Klaviyo raised the bar for SMBs by exhibiting a 92% GRR in its S1 filing last year.

- Sustained new customer growth is essential, not just expansion revenue.

- R&D investment must exceed 18-20% of revenue with clear efficiency metrics.

- Strong balance sheets are required to fund the AI transition.

Salesforce's current metrics reveal several challenges:

• 92% GRR is below best-in-class SaaS metrics and suggests some challenges in core product stickiness.

• 9.1% ARR growth indicates possible headwinds in translating AI features into revenue acceleration, particularly given the growth deceleration in mature markets. The company has 6% growth in its core region, suggesting market saturation.

• R&D investment of 14.4% is relatively low and may constrain the pace of AI innovation, though this may be partially offset by the company's historic use of M&A to add new technologies and products.

• Overall, Salesforce has a 38.8% score on the Rule of 40 measuring stick, which falls just short of the elite benchmark. ServiceNow in comparison is at a Rule of 55, and many of the 10X club companies are Rule of 50 + (on a free-cash-flow basis).

This analysis suggests that while Salesforce remains a market leader, its fundamental metrics reveal vulnerabilities as the industry enters the generative AI era. The combination of below-benchmark GRR, decelerating growth in mature markets, and relatively conservative R&D investment indicates potential challenges in maintaining competitive advantages. Companies that successfully navigate the GenAI transition will likely need to demonstrate stronger fundamentals across all these metrics, particularly in terms of product stickiness and innovation capacity.

Looking ahead, we expect the gap between top performers and the rest of the market to widen, with top performers increasingly distinguished by their ability to maintain elite-level metrics while successfully integrating AI capabilities. The key question for investors is whether companies like Salesforce can strengthen their fundamentals quickly enough to keep pace with this evolving landscape.

Part II: GenAI’s Impact on Fundamentals

Now that we’ve unlocked the genetic code using AGI, we can use that to look ahead. SaaS companies such as Salesforce face a Darwinian moment due to the changes driven by GenAI. As Darwin wrote, genetics evolve according to how one’s environment changes.

The announcement of Agentforce represents Salesforce’s most ambitious attempt to adapt to this evolutionary tide. Investors must be prepared to ask the right questions and track the metrics that will reveal how well a company is navigating this transition.

Let’s zoom in again on Salesforce, which managed to make the biggest Agent-related splash at its Dreamforce conference. During its Q3 earnings report in early December, the company said it had closed 200 Agentforce-related deals – a small but promising number that analysts viewed as reason to be optimistic that GenAI will boost revenue growth.

Still, that is only part of the picture. Investors need to dig deeper and take a holistic and medium-term view (at least).

These companies have not fully evolved; they are in the process of evolving. To know if a SaaS company has a chance of achieving durable growth as it evolves, we need to look again at how those fundamentals are changing.

When looking at Salesforce, investors should be asking: Does Agentforce lead to an improvement or a deterioration in fundamentals?

For example, a part of the Agentforce announcement that received considerably less attention is how the company will charge for these agents. Rather than using its classic subscription model that requires companies to pay a subscription license fee and hope they reap the benefits later, Salesforce will charge $2 for each conversation these agents have.

This represents a dramatic business model shift for the biggest SaaS 1.0 company. Rather than using its classic subscription model that requires companies to pay license fees upfront, Salesforce is charging based on tasks. This shift threatens long-standing SaaS revenue predictability.

It also has the potential to put substantial pressure on Gross Margins, the metric that is emerging as one of the most critical indicators of the GenAI evolution. Based on early adopters like Snowflake and MongoDB, AI feature integration typically requires:

• 3-5% higher cloud computing costs

• 5-7% in usage inference costs

• Infrastructure scaling expenses

• Continuous model optimization costs

• API and integration expenses

These factors contribute to a potential 10-percentage point drop in gross margins as AI features scale. This margin pressure creates a critical challenge for SaaS companies transitioning to AI-first models.

Companies with massive scale like Salesforce may be better positioned to absorb these costs. With over 150,000 enterprise customers and significant bargaining power with infrastructure providers, Salesforce could potentially negotiate better rates and spread fixed costs across its customer base.

Still, the shift to usage-based pricing with Agentforce at $2 per conversation introduces new margin uncertainty that investors must monitor closely.
In a broad sense, Salesforce could evolve along three trajectories:

• Positive: Agentforce becomes a growth engine, revitalizing Salesforce’s fundamentals through upselling and premium pricing for GenAI features.

• Neutral: Agentforce stabilizes retention and leads to slightly declining margins as Salesforce is not able to pass through the entire cost uplift.

• Negative: Revenue deceleration and compressed margins.

By understanding the related change in fundamentals, investors and founders can carefully track this complex transition and make course corrections quickly in terms of the playbook for growth.

Beyond Agentforce, the resilience of Salesforce's fundamentals (e.g., its ability to integrate new pricing models, manage margins, and retain customer loyalty) will determine whether it thrives or struggles in the AI-driven SaaS landscape.

Part III: GenAI’s existential threat to SaaS

Even if a SaaS company like Salesforce shows promising adaptation through initiatives like Agentforce, generative AI could still trigger fundamental disruption by undermining the core premises that enabled SaaS companies to thrive in the first place. Success requires understanding and addressing four interconnected existential threats:

1. The Legacy Architecture Challenge

Salesforce's established architecture creates three critical vulnerabilities:

• Technical Debt Burden: Decades of accumulated code and integrations make rapid AI innovation difficult.

• Resource Competition: The broad product portfolio forces impossible tradeoffs in allocating AI development resources.

• Integration Complexity: Each product requires unique AI capabilities, multiplying the challenge exponentially.

This architectural challenge isn't just technical - it's existential. While startups can build AI-native architectures from scratch, Salesforce must simultaneously maintain legacy systems while building new ones.

2. The System of Record Disruption

As Microsoft CEO Satya Nadella recently highlighted on the BG2 podcast with Bill Gurley and Brad Gerstner: "SaaS business applications could collapse in the Agent era" because the value will move to AI applications rather than the platforms that enable them. "Once the AI tier becomes the place where all the logic is, then people start replacing the back ends. We are going to go pretty aggressively and try and collapse it all."

This stark assessment highlights three fundamental threats:

• Direct Data Intelligence: GenAI can extract insights directly from unstructured data, bypassing traditional CRM systems.

• Workflow Automation: AI agents can manage relationships and processes without needing traditional SaaS platforms.

• Value Layer Shift: The core value proposition moves from data organization to AI-powered insights.

This isn't just competition - it's the potential obsolescence of the traditional SaaS intermediary role.

3. The Vertical Specialization Imperative

While companies like Salesforce have broad horizontal data sets, the real AI advantage comes from deep, industry-specific data (“Data Moats”) that enables unique insights.

As I recently highlighted in the context of its S1 teardown, ServiceTitan exemplifies this specialization advantage through:

• Deep industry-specific data sets that enable superior AI implementations.

• Specialized workflow optimization for trade industries.

• High retention through deep domain expertise.

• Proven ability to expand into adjacent verticals while maintaining differentiation

This represents a fundamental challenge to horizontal platforms' traditional advantages of scale and scope. The winner may not be determined by data ownership alone, but by who can best transform their data access - whether broad or deep - into actionable AI insights.

The data moat dynamics when it comes to horizontal versus vertical are however nuanced. While vertical SaaS players often have deeper, industry-specific data, horizontal platforms like Salesforce have unique cross-industry insights that could prove valuable for AI applications.

4. The SMB Vulnerability

Finally, GenAI poses a particular threat to Salesforce's SMB segment (a key relay of growth):

• Price Sensitivity: SMBs are more likely to adopt cheaper, AI-native alternatives.

• Integration Value Gap: Smaller businesses gain less value from enterprise-level integrations.

• Growth Impact: With enterprise markets saturated, losing SMB growth threatens overall expansion.

This vulnerability is particularly concerning as the majority of new customer growth comes from these smaller businesses, while enterprise segments are already saturated.

Strategic Imperatives for Survival

To navigate these existential threats, SaaS companies – like Salesforce – must:

• Rebuild while Flying, i.e. strategically modernize architecture without disrupting existing operations. MongoDB exemplifies successful 'rebuilding while flying' through its Atlas transition. While maintaining its core database business, MongoDB gradually shifted workloads to the Atlas cloud platform, maintained 95%+ gross margins during the transition, achieved 50%+ cloud revenue without disrupting existing customers, and Integrated AI capabilities while preserving performance. This approach allowed MongoDB to transform its architecture while growing revenue by 47% year-over-year.

• Develop Vertical Depth: Create industry-specific AI capabilities that leverage unique data advantages.

• Leverage scale for better AI infrastructure costs.

The winners in this transition won't necessarily be the largest companies, but those that most effectively address these fundamental threats to their business model. For investors and operators alike, understanding these existential challenges is crucial for distinguishing between temporary AI-driven growth and sustainable competitive advantages.

Looking Ahead: Beyond the Renaissance

Rather than a true Renaissance, the SaaS market is entering a period of bifurcation where companies that successfully adapt their business models will thrive, while those that merely add AI features will struggle. Success requires complete business model transformation, not merely adding AI capabilities. This is about reimagining what software delivery and value creation look like in an AI-first world.

Key metrics for investors to track include (not exhaustive):

1. AI Feature Adoption Rate

• Percentage of customers using AI features

• Depth of feature utilization

• User engagement metrics

2. AI Revenue Impact

• Revenue from AI-powered features

• Revenue from AI-powered products

• Impact on total contract value

• Pricing model transition metrics

3. Margin Impact

• Changes in gross margin post-AI implementation

• Infrastructure cost scaling

• Training and inference costs

4. Customer Efficiency Gains

• Documented productivity improvements

• Time saved through automation

• Return on AI investment

5. AI Development Velocity

• Time from feature concept to deployment

• Innovation pipeline metrics

• R&D efficiency indicators

The investors and companies that recognize and act on this reality will be the ones that thrive in the next era of enterprise software. This requires careful attention to fundamentals while navigating the complex transition to AI-first business models.

Glossary

• ARR: Annual Recurring Revenue

• GRR: Gross Revenue Retention

• AGI: Advanced Growth Intelligence

• LLM: Large Language Model

• GenAI: Generative Artificial Intelligence