The Intelligence Layer

The current business model of a frontier lab is unstable. The common wisdom goes something like this: you rack up massive losses on each new generation of models until one finally becomes good enough to recursively improve itself. At that point, everything pays for itself retroactively.

A deployed model may eventually cover its own costs. But then the next model comes and eats the surplus before the last one has time to look profitable. Each generation should fund itself and the next one. right?

So what comes next? I think the current generation of models are intermediate models (everything between here and AGI). They are not the final product. They are expenses that bought the labs two things: proof that LLMs can perform valuable work, and a live experimental surface for discovering where intelligence can be monetized. “But the world does not reward raw intelligence on its own”.

That brings us to the next phase: capturing the labor margin of professional knowledge work. You can already see it happening. Anthropic and OpenAI are moving toward joint ventures with the very firms that sell professional services. The labs are not spinning up their own consultancies or private equity funds. What they want is simpler and smarter. They want to become the intelligence layer behind a body they do not have to build.

A frontier lab has three things: compute, weights, a few hundred exceptional engineers.

Theoretically speaking, to actually deliver the work of a top-tier professional services firm, you need thousands of operators, deep vertical expertise, regulatory licenses, client relationships, compliance infrastructure, and distribution built over decades. Labs have none of that. Building it would take years and starve the training pipeline. So instead they can license the intelligence and take a minority equity stake in someone else’s body. The operator runs the business. The lab keeps the inference revenue, owns a piece of the upside, and avoids building the operating company itself.

But why would the operator give the lab equity at all? Why not just rent the model and keep the margin? Because renting the public model is not the same as getting the lab’s frontier roadmap, embedded engineers, deployment priority, and feedback loop. The equity stake is the price of being close to the source of capability before it commoditizes. And why would the customer pay? Because the unit being priced is the deliverable, not the token. A pitchbook costs thousands in analyst and MD time. The model will draft one for a few dollars. The margin does not live in the token. It lives in the MD’s hour.

The same logic applies across any regulated knowledge work vertical where the customer buys judgment, speed, and liability management rather than software directly.The lab does not need the customer to pay more for intelligence. It needs the customer to keep paying professional-services prices while the cost of delivery collapses underneath.

The obvious objection is that a limited number of engineers and senior operators plus a model cannot replace the huge teams firms use today. But huge teams are not pure productivity machines. They are coordination machines. Once a firm grows large enough, a shocking amount of the marginal hour is spent moving information between people: status meetings, alignment calls, spreadsheet reconciliation, deck review, version control theater, and the quiet bureaucracy of keeping everyone pointed in the same direction.

A model does not create its own coordination layer. A small forward-deployed team plus a model pays far less of the coordination tax that consumes a large firm. It does not need to replace every person. It only needs to produce the same deliverable with fewer people, fewer handoffs, and less internal drag.

The customer is willing to pay something between the cost of the old giant team and the cost of the new small one. The gap is the surplus, and it gets split three ways: between the customer, the operator, and the lab. The lab’s share is what could fund the next training run. But that gap only stays open if the model deployed inside the venture is meaningfully better at the vertical than a fresh open-weights model anyone can rent. This is where the feedback loop becomes everything.

The model works on real vertical problems. That work generates proprietary signal: workflow traces, edge cases, corrections, evaluation data, user preferences, failure modes, and tacit process knowledge. That signal makes the model better at the vertical. A better model creates more surplus. More surplus funds deeper deployment. Deeper deployment generates more signal. The exact form of the feedback matters less than whether the loop exists at all. I think the surplus in regulated knowledge work verticals is large enough to leave room for high margins. And I think this is the clearest path by which frontier labs become economically durable before AGI.

The money will not show up as token revenue. It will show up as captured labor margin: software costs hidden inside professional services pricing, protected by proprietary workflow data, and compounded through vertical deployment.

All this is speculative.