Curious how you think about catalyzing a modular middle without freezing it in place. The risk seems less “can we subsidize this?” and more “can we do it without locking in incumbents and killing the iteration pressure that makes the model work.”
I tried to think of the policy angle for this and the best I could do was identify modular elements Musk is building in house and get a CAPEX subsidy to anyone who wants to replicate it on US soil, but that feels messy.
I don’t think it’s smart to assume we’ll get there without a market intervention though. As the article notes, China was deliberate in building it. Our capital markets still fire on more cylinders than the rest of the world combined - the government has to correctly incentivize the move in this direction.
Ryan, This “modular middle” framing feels bigger than manufacturing. It’s really the integration layer that converts abundant primitives into reliable, repeatable capability.
In enterprise AI, models, GPUs, and agent tooling are quickly becoming the primitives. The bottleneck is the organizational modular middle: clear decision rights, workflow redesign, permission/risk boundaries, measurement, and feedback loops that let teams learn fast without breaking trust or compliance. When that layer is missing, pilots multiply but yield stays low, rework, stalled adoption, brittle reliability, and ROI that never stabilizes.
Your point that winners “own system architecture” lands here too. The advantage won’t come from access to the “best model” as much as owning the integration operating system that turns AI into shippable work.
One human-side yield issue I keep seeing: if teams feel threatened or punished for experimentation, iteration slows to a crawl no matter how good the tools are.
I am curious where you’re seeing credible “middle-layer” builders emerge in the U.S. (hardware or software)? The suppliers/partners who can co-design early, iterate quickly, and then scale without resetting the learning curve.
Good to read "There is tremendous reason for optimism"
Great post Ryan.
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Ryan, you’re missing the elephant in the room: NVIDIA the most valuable company ever, and its foundry, TSMC
Curious how you think about catalyzing a modular middle without freezing it in place. The risk seems less “can we subsidize this?” and more “can we do it without locking in incumbents and killing the iteration pressure that makes the model work.”
I tried to think of the policy angle for this and the best I could do was identify modular elements Musk is building in house and get a CAPEX subsidy to anyone who wants to replicate it on US soil, but that feels messy.
I don’t think it’s smart to assume we’ll get there without a market intervention though. As the article notes, China was deliberate in building it. Our capital markets still fire on more cylinders than the rest of the world combined - the government has to correctly incentivize the move in this direction.
Your piece perfectly connected all the missing puzzle pieces in my mind. Thank you so much!
Ryan, This “modular middle” framing feels bigger than manufacturing. It’s really the integration layer that converts abundant primitives into reliable, repeatable capability.
In enterprise AI, models, GPUs, and agent tooling are quickly becoming the primitives. The bottleneck is the organizational modular middle: clear decision rights, workflow redesign, permission/risk boundaries, measurement, and feedback loops that let teams learn fast without breaking trust or compliance. When that layer is missing, pilots multiply but yield stays low, rework, stalled adoption, brittle reliability, and ROI that never stabilizes.
Your point that winners “own system architecture” lands here too. The advantage won’t come from access to the “best model” as much as owning the integration operating system that turns AI into shippable work.
One human-side yield issue I keep seeing: if teams feel threatened or punished for experimentation, iteration slows to a crawl no matter how good the tools are.
I am curious where you’re seeing credible “middle-layer” builders emerge in the U.S. (hardware or software)? The suppliers/partners who can co-design early, iterate quickly, and then scale without resetting the learning curve.