9/5/25, 1:45 PM
Cristóbal Valenzuela
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Cristóbal Valenzuela
Value Accrues to the Extremes (or How Outliers Drive Evolution)
September 2025
I think we are all slowly realizing most economic value in AI flows to silicon
and research/product labs. The middle disappears.
The picks-and-shovels economy in AI trends toward zero. A big mistake was
to think AI will play out similarly to how the internet's story developed. But
the truth is that the internet needed thousands of companies to build
infrastructure. AI needs perhaps a dozen. The internet required distributed
infrastructure because value creation was distributed: millions of websites,
thousands of services. AI concentrates value because only a very small
number of companies globally can afford and know how to do frontier model
training.
Part of this has to do with the dynamics of how AI is built and the reality of
what models effectively do to the economy they sit in. Specifically: a) training
models is very hard and expensive so only a handful of companies do it well
b) models deprecate fast so the value lies in iterating with new capabilities
and building workflows more than being first on a benchmark for a week, c)
models eat the product layer as fast as they make major progress , and d)
research labs are product labs, because they have every incentive to be so.
With those conditions in mind, the most valuable and interesting AI
companies build their own tools because value lies in the integration, not the
components. When you outsource critical infrastructure, you outsource your
ability to understand. You lose the compound learning from failed
experiments. You lose the adjacencies. The unexpected discoveries from
controlling the full stack.
I think this is playing out similarly to how it played out with SpaceX. SpaceX's
advantage is not just knowing how systems connect. It's having the
institutional memory of 10,000 failed experiments that no competitor can
replicate. Every custom component represents hundreds of iterations that
taught them something essential. When Blue Origin buys off-the-shelf
components, they're buying someone else's assumptions. SpaceX's value
lies in knowing why each bolt matters, how each system interlocks, and
having the capability to go arbitrarily deep when needed. They make their
own bolts not because they must, but because understanding requires
building. Knowledge compounds through integration. You shoould only buy
or integrate tools when they don't interfere with your organizational learning
rate or disrupt your long-term assumptions.
https://cvalenzuelab.com/extremes
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9/5/25, 1:45 PM
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Cristóbal Valenzuela
The value in AI isn't headcount or compute alone. It's organizational
knowledge and custom infrastructure that lets you test the right ideas in
minutes, not months, and iterate furiously. IOW, organizational knowledge is
the product. The speed of iteration is the moat. The gap between discovery
and deployment collapses when your research directly becomes your
product. Every research breakthrough can be immediately productizable and
every product generates research data. When your way of doing things, your
picks-and-shovels, is your product differentiation, why would you let
someone else capture part of value?
We often get product demos or pitches on internal tools that could seem
relevant to Runway. While at a superficial level these are interesting, if they
are critical we will tend to build our own. I think most labs think very similary.
Not from arrogance, but from experience: when something becomes
essential, you must own it completely. The moment you depend on someone
else's infrastructure for a core capability, you've capped your potential. I
think there are a lot of transitional businesses today. Their margins will
compress to zero as the extremes expand their scope.
This is mostly a consequence of speed of iteration and velocity of AI
progress. A consequence of the value of compound learning rates. Every
system you build internally becomes a platform for five other potential
innovations. Every system you buy becomes a dependency that limits your
solution space. The expectation of building exceeds buying when iteration
speed is your primary advantage. Control the entire stack from silicon to
serving. You can't optimize what you can't see.
The best strategy mirrors the market structure: bet on the extremes. Either
you're investing in companies making the chips or companies making the
future. Everything else is temporary. The middle doesn't just get squeezed. It
disappears entirely. Organizational knowledge is the product. The speed of
iteration is the moat.
© 2024 Cristobal Valenzuela.
https://cvalenzuelab.com/extremes
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