What is Discontinuity?
August 2024
Generative AI is following the classic hype cycle. After almost two years of unrealistic expectations and euphoria, a rising tide of disappointment is mounting because GenAI has not transformed the world overnight.
The past few weeks have seen reports that corporate spending hasn't delivered ROI, that GenAI startups aren’t building sustainable businesses, and that investors have driven the valuations of early-stage startups in this sector beyond reason.
While these reports are factually correct, the framing of this analysis is fundamentally wrong. To GenAI skeptics, I want to send a clear message:
GenAI is not disruption. It’s discontinuity.
The distinction between the two is more than just academic. GenAI is the start of a transformation that 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. Faced with the unknown, too many people are going with their gut to make decisions.
Instead, this moment demands a long-term view to map out which investments and technologies will make sense at which moment and why. Investors and founders must develop a rigorous multi-scale time frame for their analysis to know which questions to ask about emerging GenAI businesses, how to identify the most resilient startups, and to recognize how this technology will impact existing businesses across all markets.
Breaking from the past
Almost 15 years ago, I was working in the traditional financial analysis industry and growing increasingly frustrated. When it came to evaluating innovative companies, everyone seemed stuck in silos and trying to understand innovation based on previous generations of technologies and industries. I believed a more holistic approach was needed —one that started with a clear understanding of the business model (starting with the revenue model) —with subsequent financial analysis built on that foundational step. Numbers always tell the truth, but only have an impact when the numbers that actually matter are surfaced. In the case of new technologies, this gets complex, and it is easy to depart from rigorous financial analysis on the pretext that there is no precedent.
I founded my firm, D’Ornano + Co, based on that conviction. Since then, we’ve built our practice around the philosophy that traditional tools and thinking are insufficient for analyzing disruptive technologies.
Over the past 18 months, a growing share of the deals we analyzed have involved GenAI, from infrastructure to horizontal and vertical applications. I found this space to be fascinating.
I have detected a pattern that has been hard to break. The expectations about growth and returns remain tied to historic patterns, and so is the timing: GenAI should already be transforming earnings figures of those who adopt it; this is just over-hyped. Really?
Sure, bets are being placed in areas of GenAI that are not mature enough to deliver impactful products. FOMO proves to be overwhelming. Many bad investments will be made in companies unable to build sustainable business models in the long run. A part of that is perfectly normal and is linked to the nature of Venture Capital: fund innovation. Of course, adequate diligence will help avoid — we hope — some of those.
To illustrate why this thinking is problematic, I want to pull the lens back to focus on discontinuity versus disruption.
When disruption comes along, it disrupts the linear trend and changes the curve, increasing or decreasing it by, let’s say, +10%, +20%, +50 %, and so on. The curve becomes steeper or less steep, but it is based on the same fundamentals that created the curves in the first place. That’s not to say that navigating disruption is easy, but leaders and investors can deploy existing tools to analyze the risks and opportunities and understand how the game has changed.
Discontinuity stops the curve and creates a new curve that has an entirely different profile. The curve can continue in the same direction and at the same speed, but it can also collapse or accelerate in unexpected ways. In geology, discontinuity is a term that is used to designate structural breaks, which are usually unhealed. But when they heal, they exhibit high tensile strength. The same applies in the business world.
All the previous strategies, tools, and historical lessons are ripped away. In this dynamic, discontinuity introduces a break from the past, which can lead to something very positive or to failure. But it’s philosophically and fundamentally very different from disruption, which does not introduce such clear break points.
Such moments present a fundamental challenge that goes beyond the tech: People’s brains are not framed for discontinuity.
Several years ago, John Kao, the Turing Fellow at Yale’s Center for Collaborative Arts and Media and a noted innovation thinker, wrote an essay discussing the dilemmas leaders face when confronted with the disruption versus discontinuity paradigm: “Optimizing for disruption and discontinuity requires different skillsets and mindsets. Addressing discontinuity requires creativity and the ability to create a bridge from today to an uncertain future. It requires the ability to generate meaningful ideas that can serve as beacons for a journey into the unknown.”
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 realign or emerge to enable something new.
From what I have seen in the deals that I’ve worked on and conversations with top private equity investors, they are not yet fully recognizing the discontinuity. And this is understandable given that the asset class has largely been built on predictable, stable cash flows in its investable targets.
But today, they are missing the larger story of GenAI.
The GenAI Discontinuity
Though critics like to mock Silicon Valley when bubbles emerge, tech leaders have proved to be remarkably prescient about what will happen. What they tend to get wrong is when it will happen.
During the dot-com bubble, founders and investors rushed to create and fund companies based on the assumption that businesses and consumers would change their habits in a blink and the old economy would be washed away in a few months. Telecom companies in the US raised billions in debt to build networks that lay unused in the ground, creating a glut of “dark fiber” across the country. When the bubble burst, the “New Economy” became a punchline.
Amid the rubble of the dot-com bust, 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. Companies that furnished electricity needed a reason for people to use it, so they built streetcars. Then they needed more riders on the streetcars, so they built amusement parks at the ends of the lines. To increase demand for electricity in homes, they designed appliances such as refrigerators and washing machines. As residential buildings became electrified, people stayed up later doing things like reading.
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.
VCs saw a parallel with the dot-com bust: The real transformation would unfold over a much longer timescale as all the pieces were put into place. 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.
This is where we are with GenAI. While we are not seeing a GenAI “bust,” the clouds forming around it are obscuring its discontinuity.
GenAI represents a fundamental break because its impact could go much further than tech disruptions such as the cloud, smartphones, or the social Web. The scale of GenAI is likely to rival that of the internet, and most certainly exceed it, because its ability to leverage both structured and unstructured data removes previous limitations on the types of data that can be used.
However, this technology is still in its infancy. The LLMs that enable GenAI are evolving rapidly, and many elements have not stabilized. Open or Closed LLMs? Big or small models? Nobody can say for sure. Meta announced recently that it had started working on Llama 4, expected to be 10 times larger than Llama 3. Mistral has unprecedented traction. The race is still on, it seems. The capex surge resulting from these developments is to be weighed against the asymmetric risk of being absent from the next platform shift.
At the application level, software companies are still trying to understand what data they may have that could be tapped for GenAI, a process that could require huge investments to create the right dataflows.
Meanwhile, the infrastructure needed for GenAI is only just being put into place. That includes a scramble to deliver the processing power and a boom in the data center construction. This explosion in data centers has a secondary effect: a surge in energy consumption. Goldman Sachs projects that AI will increase data center power usage by 160% by 2030.
Given the uncertainty around all parts of this GenAI discontinuity, applications built on top of existing foundational models are shaky. We are still in an experimentation period for many adopting companies. So many things need to be figured out, and making sweeping judgments about GenAI based on short-term ROI is premature.
Any meaningful impact will not become apparent for another 2 to 4 years. The real transformation will take even longer.
This is not a rationale to stand on the sidelines and wait passively to see how things play out. Execs and investors need to ensure their business does not lose ground before reaping the benefits. GenAI has the power to transform many non-tech companies into tech companies, an evolution many industries should start recognizing now. This is already happening at full speed in the B2B services space, a preferred space for Private Equity investors.
Now is the time to take the first steps. Investors should start by acknowledging that the way they have seen the world in terms of returns and timing, and the structure of their approach, is not adapted to this new era.
Instead, investors must begin to grapple with the ways these different silos within the GenAI tech stack — data centers, real estate, chips, energy, software — are blending to create a new framework. The same applies to asset classes, with boundaries blurring among Venture Capitalists, Private Equity Investors, Asset Managers, and, of course, Hyper-scalers.
Investors must learn to conceive of what has not yet happened and to imagine what will happen. Navigating discontinuity requires an ability to abstract to see how all these pieces might eventually align and what consequences could be unleashed.
Discontinuity leaves investors in an uncomfortable position, with everything they’ve known lost. Therefore, very rigorous measurement is needed. When an investor assigns a startup a value of 100X versus 10X, they need to be 10 times more rigorous in their due diligence.
GenAI is not a mysterious black box. Every element can be measured at a granular level. No investment is free of risk, but it is possible to make smart, disciplined decisions by identifying the important business levers of GenAI and measuring them to create confidence. A more strategic and informed approach will orient capital to the right businesses that are fostering this discontinuity and those that are transforming themselves in preparation for this huge opportunity. For more mature businesses, GenAI will enable unprecedented productivity gains, helping expand margins in a challenging macroeconomic context.
GenAI requires a fundamental change in investing and in how leaders should think about their businesses going forward. Only decoding this discontinuity will allow for higher returns and long-term value creation.