Why behind AI: The private market opportunity
I've previously covered the impact of AI on the public markets, and today we'll turn to the most interesting growth opportunity right now: private AI-native companies. The information comes from the Redpoint Market Update.
In my previous deep dive, I noted that the flood of money into private markets has been both a positive and a negative:
Many of the AI-native companies mostly exist to steal money from their investors in order to fund a LARP founder lifestyle. It's harsh but it's true. Still, for those that are genuinely great new entrants in their categories, there are many interesting attributes to those orgs that are not seen in their incumbent competitors.
While there are obvious cases of companies pushed hard with media exposure despite offering unoriginal, pointless, and ultimately insecure products (Cluely, Delve), there are also plenty with a genuine shot at being generational.
To be fair, the odds of a generational AI-native company emerging are obviously high when almost every new company receiving venture capital today is AI-native.
More interestingly, for the existing crop of massive private companies (Anthropic, OpenAI, Databricks, SpaceX), the funding flowing into late-stage rounds has dwarfed anything we're seeing in the public markets. An IPO today in cloud infrastructure software is seen as a burden rather than the most exciting outcome for a company. This dynamic might finally change in the next 12 months if the "private whales" take the leap into the public markets, starting with SpaceX in the summer.
This will be critical to reshaping where venture dollars flow, as the top 20 deals in 2025 captured almost half of available liquidity. While there are fair justifications given the compute demands of the whales, realistically this is not beneficial for the broader startup ecosystem, if not outright detrimental.
The flip side is that the time to scale to an outstanding valuation is shortening dramatically. Whether this makes a material difference (nobody is paying $50B to acquire Cursor, and xAI had to be bailed out through SpaceX) is a different topic.
Well, I guess there's one person willing to pay even $60B.
This is a deceptively simple framework, but I think it’s a fair way of looking at things in the current context.
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The problem for the application layer is defending why anyone should pay for workflows that can either be replicated internally or handled within the model provider itself. Harvey’s legaltech challenge is a clear example.
The problem for infrastructure plays (and cloud infrastructure software specifically) is whether their most important market (enterprise) sees meaningful benefits from adopting their platform. Cursor vs. Claude Code illustrates this well: both are valid approaches, with Cursor scaling coding agent access across organizations at enterprise-grade reliability and security, while Claude Code takes a different path to the same buyer.
Although it's easy to be bearish on the application layer, that view is mostly driven by investors being limited to public markets (the SaaSpocalypse) versus private markets, where we're seeing accelerated outcomes and growth at an extraordinary pace.
The most interesting metric in this new application layer remains ARR per employee. For all intents and purposes, AI-native orgs run at significantly higher productivity per employee, which is also why the deep headcount cuts at public companies haven't drawn much pushback (most agree, if silently, that those orgs are deeply bloated).
Large incumbents in cloud infrastructure software have struggled to offer obvious "best-in-class" products across most of the AI infra challenges like agent security and observability. This leaves significant room for new AI-native companies to enter and separate themselves from the pack.
Regardless of how much we try to highlight the wider ecosystem, at the center of the AI scaling era there are two companies driving the majority of growth: OpenAI and Anthropic. The race for who scales the most in run-rate this year is wide open and highly volatile.
The competition is not sitting still on fundraising, but in practical terms we have not yet seen real challengers emerge from the neolabs. The most interesting one to track is Safe Superintelligence, thanks to Ilya Sutskever, arguably the individual who has had the biggest impact on deep learning progress in AI over the last 15 years. His primary "opponent" is Demis Hassabis, the single most motivated leader in AI research today, running not just DeepMind but also Isomorphic Labs. Funnily enough, OpenAI was co-founded by Musk as the counterpoint to Demis.
Between the SaaSpocalypse and the significant liquidity in private markets, it's fair to say things can be perceived as frothy.
The counterargument is that the growth rate justifies the premium. While that's clearly a risky bet (stalled adoption would be brutal on valuations), it does make sense.
The "growth slowdown" could also come from a different angle than weaker customer demand: overcrowding in certain markets. High valuations only make sense if someone is the obvious winner of a category, and the difficulty curve to become one is much higher today.
Frothy or not, the opportunity to enter the market from a funding perspective is still very much open, and the same goes for talented individuals looking to move to an AI-native company. That said, the window for the highest returns is likely closing in the next 12 months.