Dwarkesh Patel recently announced a $20,000 blog prize for the big questions about AI, in part to help him identify and hire a research collaborator. I’m re-posting my submission (limited to 1,000 words or less) below, along with the original question I chose to answer.
Dwarkesh asks:
What’s the most plausible story where foundation model companies actually start making money? If you consider each individual model as a company, then its profits may be able to pay back the training cost. But of course, if you don’t train a bigger, more expensive model immediately, then you stop making money after 3 months. So when does the profit start? Maybe at some point scaling will plateau, but if progress at the frontier has slowed down, then the combination of distillation and low switching costs (cloud margins result from high switching costs) makes it really easy for open source to catch up to the labs, eating into their margins. So how do the labs actually start making money?
And here’s my response:
Foundation-model companies will most likely generate profit from first-party products (as opposed to APIs), using a two-pronged approach:
Acquire first-party-product customers by providing exclusive access to frontier models with differentiated capabilities;
Lock customers in via high switching costs.
In particular, labs will increasingly sell opaque managed agents to create data flywheels that protect capability leads.
How can foundation-model companies (and other AI-product companies) increase switching costs at the application layer?
For coding and enterprise products they have two effective approaches:
Increase the complexity of integration surfaces, integrating into more workflows/UIs and authenticating with more enterprise apps;
Manage more customer data such as interaction history, “memories” (and other derived data), or custom models (via fine-tuning or future forms of continual learning).
Software organizations in particular will resist these lock-in tactics and are incentivized to self-host agent-related data and code, as evidenced by the recent growth of open-source coding harnesses like OpenCode and Cline. However, as AI products advance and are rolled out across more business functions, customers will necessarily accept greater integration complexity, and will tolerate data lock-in if the underlying product is sufficiently capable.
For consumer products, customer motivation to avoid lock-in is even lower, as shown by the success of apps like ChatGPT which bind chat histories to a single model-provider. As such, all the normal consumer monopoly plays will likely work, such as personal-data lock-in and network effects from social products. We can also expect normal consumer revenue streams to work too, and we already see ChatGPT testing ads.
So normal consumer/SAAS profitability strategies should work in the AI-applications market. But where do foundation-model companies find their advantage?
Although capability advances diffuse quickly these days (it takes 3 months on average for open-weight models to catch up to the prior frontier), foundation-model companies can retain market-specific mindshare as long as their models are occasionally best-in-class and are otherwise state-of-the-art. These mindshare advantages can rapidly attract customers to first-party products, as demonstrated by the breakout growth of ChatGPT during the period of OpenAI’s chat-model supremacy and the similar growth of Claude Code during Anthropic’s reign in coding. As frontier labs continue to differentiate into various consumer and enterprise niches, they will increasingly hold mindshare advantages for longer durations.
We already see companies leveraging these advantages to draw customers towards first-party products, specifically by restricting API access to frontier models. Anthropic, for example, banned third-party harnesses like OpenCode from Claude Pro/Max subscriptions in early 2026 and recently introduced “priority-tier” API prices which also increase costs for third-party harness companies, pushing more users towards Claude Code.
Why not cut off API access entirely for frontier models? First, that would be financially burdensome given current revenue streams: ~70% of Anthropic’s revenue and ~20% of OpenAI’s come from the API, mainly from frontier models. Second, doing so would hurt customer acquisition - third-party products can draw customers towards first-party products. Therefore, while the market is still growing and switching costs are relatively low, foundation-model companies’ optimal strategy is to slightly subsidize first-party products relative to APIs, creating a gradient that slowly siphons market share from third-party competitors.
As application-layer markets saturate and are dominated by foundation-model companies, those companies will extend their capability leads by serving frontier models exclusively through opaque managed agents - AI-powered business solutions whose model activity is not auditable, and thus less prone to distillation.
For example, in the software domain, companies will sell agents who can send Slack messages and submit pull requests, but whose intermediate tool-use patterns are inaccessible. True, adversaries can learn some of the model’s policy by training on the agent’s work product, but real-world engineering (and other work) is full of necessary intermediate feedback loops, like running tests or deploying and monitoring services. These loops would be hidden, creating a closed data flywheel; as customers sign on, the company receives more and more exclusive real-world training data. Such flywheels would create decisive advantages in whichever niches a foundation-model company targets, and would be especially valuable capability differentiators in worlds where algorithmic progress stalls.
Opaque managed agents would also have complex integration surfaces and high switching costs. Just think of the difficulties involved in replacing a software development consultancy whose engineers are tightly integrated into a company’s relationships and workflows. Replacing a fleet of managed agents would be similarly costly, especially if memory/continual-learning features are included in the mix.
No foundation-model company has launched such opaque managed agents yet, but auditable managed agents just hit the market this month, with Anthropic’s “Managed Agents” product and OpenAI’s “Workspace Agents.” These can be seen as a grab towards high-switching-cost first-party products, and as groundwork for more opaque future applications. We’ve already seen labs hide their chain-of-thought traces, and it’s no stretch to imagine them hiding even more of their models’ activity if so incentivized. They may argue that such restrictions are the only safe way to interact with newer and more capable frontier models, and there is precedent for this messaging, such as in Anthropic’s restriction of Claude Mythos access to Project Glasswing participants, and in OpenAI’s delayed API release of gpt-5.3-codex for claimed security concerns.
To balance the data-flywheel benefits of opaque managed agents with the reduced revenue opportunities of restricting API access, companies will need to release models to their API after a calculated delay. As revenue share shifts towards first-party products and away from API services, their calculus will favor a gradual lengthening of API-release latencies, and opaque managed agents will gradually take center stage as primary profit-driving products. This approach, which creates a large customer base that is both willing to pay a premium for the niche capabilities afforded by improved data flywheels, and is disincentivized from costly migrations to competitor products, is the clearest and most likely path to profitability for foundation-model companies.