After Certainty

AI investing is a high dimensional system with many degrees of freedom — the old heuristics don’t work anymore

There was a time when growth stage investing was as simple as looking at ARR growth rate, net dollar retention and magic number and spitting out a valuation.

It was never that simple, of course, but there’s something to be said for the fact that it was extremely obvious when something was working. The job of the investor was to assess a) The size of the market, b) The durability of growth (a function of market size and product-market fit), and c) Management quality (the most critical piece of all).

It wasn’t just that the input metrics were straightforward, but the private market and public market moved in lockstep. Snowflake, Datadog and Crowdstrike were premier SaaS companies in the private market, and they turned into banger public companies. The metrics that mattered in the private market also mattered in the public.

Fast-forward to 2026, the degrees of freedom have expanded phenomenally. Deep tech and hard tech are in vogue, which have high barriers to entry and require hundreds of millions of dollars to compete. There is tremendous debate — and no certainty — about where value accrues across foundation models, app layer companies, services roll-up companies, GPU companies, power companies, and so much more.

At the same time, the public market and the private market might as well be different universes. It’s not that they evaluate companies differently, but that the menu itself just isn’t the same. The private market is a smorgasbord of hundreds of types of risks to take. The public market has semis and software, and there seems to be a consensus around buying the former and selling the latter. Lastly, there are the Mag7 which are countries, not companies. And they are propping up public semis and the broader AI ecosystem with their firehose of demand and capital.

Given the inherent complexity of navigating this market, we have entered a phase where the market is hyper-reactive. A front page article in the Wall Street Journal can send stocks tanking, even when there is no new information. A Substack post from Citrini Research can drive the market for a week. Investors are overloaded with information. And a lot of it is contradictory or confusing, especially when it comes to the gap between AI capabilities (startlingly good and getting better) and AI adoption (painfully slow and difficult, though accelerating, especially around code).

Zooming out, there are three independent sources of volatility:

1. Inherent uncertainty that emerges from our lack of deep understanding of AI capabilities and where exactly they are going (even people inside the labs seem to get caught by surprise when there are new capability unlocks)

2. A paradigm shift in investing from clear and well understood business models (cloud and mobile), into new paradigms where long-term business models are far from certain (AI and hardware / deep tech)

3. A capital market environment where most of the action is happening in private markets (which are illiquid and opaque) or inside of seven behemoth public ones (where decision making is game theoretic and requires second-order thinking)

In the face of this complexity, there are two options for investors. Option 1 is to really try to unpack the complexity, and make targeted forecasts on which companies will win in the long-term. For example, if coding agents turn out to be the most powerful technology to come out of this LLM era, what industries will these agents disrupt, and how? This strategy is opinionated and exposed to the possibility of error. Option 2 is to try and simplify the complexity through an overarching narrative, such as AGI. But narratives are inherently fragile, and the more we try to encapsulate in a single narrative, the more fragile that narrative becomes.

I’m increasingly convinced that reactive and hyperbolic market behavior is a psychological defense mechanism against inherent market uncertainty. Instead, the best way for investors to navigate this uncertainty is to embrace it. This is what finance as a discipline is all about — taking calculated risks against an uncertain future. It is not the case that uncertainty now is temporary. Yesterday’s certainty was the real mirage. Investing in today’s market requires a higher degree of fortitude — belief in entrepreneurs, willingness to go to war alongside them, willingness to risk it all on a future that is unknown.

The days of Benjamin Graham — when you got paid to own companies at a discount to asset value — are dead. That was the value era. The days when you paid to own high growth companies with a strong Rule of 40 are dead too. That was the growth era. The AGI paradigm is an attempt to recreate the sense of certainty we used to have as investors — one that has slipped through our fingers. Instead, I think we are entering the era of uncertainty, where there is no option but to take real hard risk, and be right.

The early days of cloud and mobile was also a time of great uncertainty, and this gave birth to DoorDash and Uber and AirBnB and Stripe and Instagram. Uncertainty is good for entrepreneurship, it is good for creativity. For those who embrace it, rather than fight it, there will be much opportunity in the years ahead.