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If you’ve been paying attention to the AI discourse lately, you’ve probably been exposed to some variations of these narratives:
“OpenAI is losing the enterprise market to Anthropic” — somewhat true, as Anthropic’s been crushing both PLG (Claude Code) and top-down sales (AWS/GCP/Accenture partnerships)
“Once LLMs are commoditized, all value will accrue to applications that have the ‘context graph’” — a view Marc Benioff (CEO, Salesforce) understandably loves
But those views are only correct if you naively assume that businesses are static, compute supply is infinite, and OpenAI doesn’t make any adjustments to its strategy. Unfortunately, none of these assumptions hold in real life.
In fact, there’s mounting evidence that OpenAI’s enterprise will regain momentum in H1 2026 based on a new revenue / engagement model.
To understand this, we need to cover a bit of recent history.
The second half of last year (2025) was about OpenAI setting the stage for its ad business and driving inference costs low enough to sustain a free tier, which is 4x the size of Gemini and 20x Anthropic. So OpenAI took foot off the gas from enterprise, while focusing on lowering its inference costs to prevent its Free Tier from bankrupting OpenAI.
That diversion created an opening for Anthropic to capitalize, and capitalize it did with style. Kudos to Anthropic.
But now that inference costs are under control, and the ad infrastructure is in place, OpenAI is revving up its enterprise motions. It started with OpenAI launching a post-sales / consulting arm, partnering with PE firms to sell AI transformation, and poaching Denise Dresser (former Slack CEO) as the new CRO to focus on enterprise.
Then a few days ago, Sarah Friar - OpenAI’s CFO - published a blog post titled “A business that scales with the value of intelligence” that reads like an investor update, but is actually a sneak peak at OpenAI’s revenue model for years to come.
In particular, she suggests that OpenAI’s revenue model will increasingly move from selling tokens through APIs, toward outcome-based arrangements where OpenAI shares revenue with its customers. In short, OpenAI will increasingly pursue value based pricing, taking a fraction % of value created, instead of selling raw tokens.
Why now? For a while, it’s been known that selling tokens via API is a crappy business, as it gives more bargaining power to application-layer customers, while enabling competitors to copy you. This necessitated a new engagement / business model, where OpenAI (or Anthropic) gets an explicit cut of the value for every dollar that every agentic application earns.
But you can’t just raise prices on tokens, because it motivates customers to seek out alternatives, such as open source models. So what’s the right strategy to capture more value, avoid selling raw API tokens, while keeping customers happy?
In this post, I’ll cover:
OpenAI’s new engagement model with enterprise customers, which will maximize take rates and minimizes model provider churn
Two additional levers that OpenAI can pull to maximize model revenue
Why it will work
Signals from analyzing more than 400+ job openings at OpenAI to reverse engineer its org structure
Why the “foundation models are commoditizing” narrative may age poorly.
What this means for investors and founders who are concerned about building something that OpenAI or Anthropic will make obsolete.
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