Venture Capital is Transforming
It has to Reimagine AI Every Few Weeks
## Editorial
### Venture Capital is Transforming: It Has to Reimagine AI Every Few Weeks
This week’s video is mostly about AI. Andrew and I recorded it earlie this week. But as he is traveling this is a good week for my writing to be about venture capital.
AI refuses to sit still.
Every few weeks, the frame changes. A model gets better. A company raises a round that would have looked impossible a year ago. A frontier lab turns into a platform. A data center becomes a strategic asset. A robot, drone, biology tool, coding agent, or math result makes the last mental model feel too small.
That is why this week I want to look at AI through the eyes of venture capital.
Not because venture is the whole story. It is not. Workers, cities, energy grids, universities, militaries, regulators, public markets, and families all have a stake in what happens next. But venture is the part of the system whose job is to fund constant reinvention before the rest of society has agreed what the new thing is.
That job has become both harder and more important.
AI is no longer a software category. It is a civilization category event. It is beginning to reshape robotics, defense, biology, finance, commerce, manufacturing, education, media, and the public markets themselves. It is also moving at a speed that makes consensus investing especially dangerous. By the time a theme is easy to describe, the next version of it is already forming.
Venture exists for that kind of uncertainty.
The basic venture bargain is simple and brutal. Most investments will fail. A few will work. The very largest winners have to pay for everything else. See Alessandra Caggiano’s essay about that. Every serious venture investor knows this. The point is not to be right most of the time. The point is to own enough of the exceptional companies when they arrive.
That sounds obvious, but it matters because the AI cycle is testing the bargain at unusual scale.
Look at what is happening around us. OpenAI is reportedly preparing to file confidentially for an IPO. SpaceX has filed with a prospectus that bundles launch, Starlink, X, xAI, AI data centers, Starship, and eventually orbital compute into a single controlled-company story. Cerebras has turned inference speed and memory bandwidth into a public-market narrative. Blackstone and Google are financing TPU capacity as infrastructure. Anthropic is reportedly paying SpaceX $15 billion a year for access to Musk’s data centers. OpenAI is selling Guaranteed Capacity to enterprises and offering YC startups $2 million of model tokens in exchange for equity exposure.
That is not just a product cycle. It is a capital cycle. It is civilizational to venture investors. They sink or swim on playing in these waters.
The companies at the center of AI need money for models, chips, energy, data centers, talent, distribution, deployment teams, regulatory strategy, and, increasingly, physical infrastructure. The old software venture model was cleaner. Build the product, find distribution, raise capital, expand margins, go public or sell. The new model is heavier. AI companies may need to look like cloud providers, semiconductor buyers, defense contractors, research labs, systems integrators, and media platforms at the same time.
Concentration is a feature
That is why concentration is not a bug in this market. It is part of how the system works. Up until now I have been critical, pointing out the challenges it represents for early stage investing. But on reflection, if liquidity can be large and fast (and the IPO filings suggest it might) then there may be returns from concentration that can fund the next decade of venturte capital.
The Fund CFO piece this week makes the point plainly. Venture is concentrated, but not dead. A large share of the dollars is going into a small number of very large rounds, especially around AI and infrastructure. That can look unhealthy if you expect venture dollars to spread evenly. But venture has never really worked that way. The returns are concentrated because the outcomes are concentrated.
The question is whether the concentration is thoughtful.
Series B is one place to look for the answer. Seed rounds can be about imagination. Series A can still be about early proof. By Series B, the market is asking whether the company can become legible to growth capital. Does the product matter? Is the market large enough? Can the team recruit? Can the company finance the next stage? Can it become one of the few companies that pays for the whole portfolio?
Rob Hodgkinson’s State of Venture work is useful here because it shows where that judgment is landing. SignalRank’s announced 2026 Series B investments are heavily weighted toward AI, with defense, robotics, and fintech making up much of the broader concentration. That is not random enthusiasm. It is capital trying to identify where intelligence becomes infrastructure, where software becomes physical capability, and where the next large public-market stories might come from.
AI, robotics, defense, and bio are the obvious places to watch because they change the world directly. They do not merely produce better dashboards. They change labor, manufacturing, war, medicine, logistics, energy demand, and national competitiveness. That is exactly the kind of change venture is supposed to fund before it is comfortable.
The best outcomes then recycle capital back into the system.
This is the part people outside venture often miss. A SpaceX, an OpenAI, a Stripe, a Databricks, or a future AI-robotics-bio giant does not only enrich its earliest investors. If it goes public, returns capital, creates liquidity, and gives employees and funds realizations, it helps refill the system that funds the next generation of seed and Series A companies. The outliers are not separate from the ecosystem. They are how the ecosystem pays for its mistakes and starts again. And because of the 2020, 2021, 2022 deployments with no outcomes, the liquidity crisis is large and real. We may be seeing the solution play out.
So - mea culpa - concentration is not automatically bad. It can be dangerous, of course. Crowded trades can become foolish. Mega-rounds can hide weak economics. Public-market narratives can outrun reality. But concentration around genuine infrastructure shifts can also be exactly how the market builds the next layer.
SpaceX and xAI are a good example of the tension.
Om Malik’s read of the SpaceX prospectus is useful because it brings the fantasy back to economics. Starlink is not just a story about rockets. It is a broadband business with real revenue, real operating income, and a huge amount of installed network capacity. That cash engine makes it possible to tell a much larger story about Starship, V3 satellites, AI infrastructure, Mars, and orbital compute. Public investors are being asked to buy both the present business and the future ‘myth’. (according to Om). Maybe a future plan?
That is venture logic taken public.
A real business supports a much larger claim on the future. Most such claims will disappoint. A few will become the platform on which the next decade is built.
The same logic is now showing up in compute. OpenAI’s Guaranteed Capacity turns model access into reserved infrastructure procurement. The YC token deal turns compute into startup financing. Tomasz Tunguz’s pricing piece shows the subsidy phase ending as capex and margins matter again. Blackstone’s TPU cloud and Anthropic’s reported Colossus commitments show AI capacity becoming a contracted industrial asset. Cerebras shows that public investors will underwrite specialized AI infrastructure if the story and the economics are strong enough.
This is why seed and Series A may be attractive right now. These investments represent a basket from which future concentrated plays will emerge.
Generally when concentration moves later, early capital can become more interesting. If the largest funds and public-market buyers are focused on the companies that have already become legible at Series B and beyond, then seed and Series A investors get to do what venture is supposed to do: find the next weird thing before it is obvious. That does not make it easy. It may make it better. Of course Sequoia, Andreessen, General catalyst and others are also investing earlier and with less of a filter than ever. So it is a very competitive early stage landscape.
Early stage managers have to ask what does AI make newly possible? Which workflows collapse? Which physical systems become programmable? Which scientific processes accelerate? Which defense capabilities get rebuilt around autonomy? Which biology companies become data and model companies? Which teams can use agents to do work that previously required a much larger organization? Tomasz Tunguz is a good example.
Most answers will be wrong.
That is fine. They are supposed to be.
The discipline is not to avoid failure. The discipline is to make sure the failures are small enough and the winners are large enough. As Alessandra points out, Venture is a power-law business because innovation is a power-law process. The world does not change evenly. It changes when a small number of companies make a new behavior, market, or infrastructure layer possible.
That is also why this week’s work and agency pieces still matter. Chamath’s software-reset argument is really a warning about what happens when agents replace low-end SaaS workflows. Vas’s Forward Deployed Engineering guide explains the labor model that follows: audit the workflow, build evals, deploy inside existing systems, and start with the smallest useful unit of autonomy. Dan Shipper’s Every essay adds the lived version. Andy Hall’s Free Systems classroom piece pushes it one step further: evals are not just an enterprise deployment tool, they are a civic skill for students and citizens who need to interrogate models rather than surrender judgment to them. Tomasz Tunguz adds the interface layer: headless systems do not eliminate UI; they make many interfaces possible, some disposable and some worth keeping. Automation does not simply erase expert work. It floods the world with cheap competence and increases the value of people who can frame problems, judge outputs, build harnesses, and decide what matters now.
Startups will be built around that shift.
Some will sell agents. Some will sell infrastructure. Some will sell the deployment layer. Some will use AI, robotics, defense technology, or bio to build companies that look nothing like SaaS. Some will fail because the models improve too fast. Some will fail because the incumbents absorb the feature. Some will fail because the customer was never real. A few will become enormous because they understood the new shape of work before everyone else did.
That is the point.
Venture capital is not supposed to wait until the future is settled. It is supposed to fund the argument.
The public unease around AI is real. The Wall Street Journal’s AI rebellion story captures something important. People are worried about jobs, children, data centers, power bills, fraud, surveillance, and whether the future is being built around them rather than with them. Ken Griffin’s comments at Stanford make the labor question concrete. Richard Murphy’s response to Standard Chartered’s language about lower-value human capital makes the distribution problem explicit.
Venture cannot ignore that. If AI creates abundance while making people feel disposable, the politics will become hostile. If the technology reorganizes work, then the financing system also has to help build companies that preserve agency, create new kinds of work, and make the benefits visible.
That is not philanthropy. It is good investing.
The best companies do not merely exploit a transition. They make the transition usable. They turn capability into products, trust, distribution, and social permission. In this cycle, that may matter as much as the model itself. It is one reason I grmace at Anthropics antics on price-changes and alienating some of its customers.
AI needs to be reimagined every few weeks because the frontier is moving that quickly .
Venture capital needs to fund constant innovation because no central planner, incumbent, or committee can know in advance which version of the future will work. But this is not random, good early stage investors tend to be able to do it repeatedly.
Most investments will fail. The big winners will pay for everything. Series B shows that the market is deploying thoughtfully into the places where intelligence is becoming infrastructure. Concentration is natural when the outcomes are concentrated.