OpenAI: Three Engines, One Platform
Similar to the exercise we did on Anthropic, we wanted to do a similar deep dive on OpenAI. In our view, both companies have meaningful overlap but also important differences in technology, strategy, and market positioning. The exercise for both was a hypothetical: what would an initiation note look like on these companies, given neither is public yet? We focused on the business and technology fundamentals and the path to revenue growth using data we could find, or was able to triangulate from numerous sources.
The Scale
When it comes to OpenAIs scale, there is perhaps no single technlogy (experience?) that has diffused faster than ChatGPT. 900 million weekly active users. 50 million paid consumer subscribers. 9 million-plus business users. 7 million workplace seats. 4 million developers building on the platform. Revenue that went from 2 billion dollars in 2023 to 6 billion in 2024 to approximately ~15-20 billion in 2025 recognized revenue. An annualized run rate likely to be in the range of 20-30 billion by year end. And a fresh 110 billion dollar raise at an 840 billion dollar post-money valuation, with management guiding to 280 billion dollars-plus in revenue by 2030.
We think it is interesting how early OpenAI has architected for five parallel monetization vectors: consumer subscriptions, enterprise seats, developer APIs, advertising, and infrastructure. Not all five are at scale yet, advertising in particular is still nascent, but all five are in market and generating signal within three years of meaningful commercial launch. Most technology companies take a decade or more to reach this breadth.
The Shape of the Numbers
OpenAI has 50 million paid subscribers out of 900 million weekly active users. That is a mid-single-digit paid conversion rate. For context, Spotify runs roughly 44 percent paid conversion. Netflix is essentially 100 percent since the free tier went away. Even at the low end of consumer internet, most mature platforms convert 5 to 10 percent of actives into paying users over time. OpenAI is at roughly 5.5 percent today with a product that is still rapidly improving and expanding use cases every month.
If paid conversion moves to 8 to 10 percent against a growing WAU base, the subscription revenue opportunity alone could reach 15 to 20 billion dollars annually before you layer on enterprise, API, or advertising revenue. The current subscription run rate is already approximately 10 billion dollars, and what makes this particularly compelling is that the consumer story is the smallest of the three engines we underwrite. On subscriptions, we think the argument that most consumers won’t be enticed by a subscription is fair if the only valuable use case is essentailly “search.” But that is not what we believe AI evolves into, particularly in the age of agentic AI. While we do think most mainstream consumers will be free users, montized by ads (very well), we also think a much higher percent of consumers will subscribe to at least some tier given the broader value AI will bring.
The Enterprise Signal
The enterprise data is where we spent the most time and where the thesis becomes most differentiated. Business users grew from 150,000 in early 2024 to more than 9 million by February 2026. CIO surveys from late 2025 show more than half of CIOs naming OpenAI as their primary large language model, roughly double the nearest competitor. More than 90 percent report net spending increases for AI, and the vast majority are funding it with new budget rather than cannibalizing existing IT spend.
The reality is that most enterprises are still early. Our checks suggest most organizations are somewhere between crawl and walk in their AI deployment journey. Agents for complex use cases could take two to five years to reach production. This is a positive for the thesis because it means the revenue ramp has a very long runway. The current trajectory is the beginning of a multi-year expansion cycle, and enterprise will become the largest revenue contributor by 2030 by a wide margin.
The Margin Story Most People Miss
There is a widespread narrative that OpenAI is a money-losing company that needs to grow into profitability. The reality is more nuanced. Compute margins, which measure inference-specific unit economics, expanded from 35 percent in January 2024 to 70 percent by October 2025. Inference is already profitable at the model layer. Company-level losses arise because training the next generation of frontier models is funded from the same compute budget during a period of exponential scale-up. Adjusted gross margin, which includes training cost amortization, sits in the low-to-mid 30s percent range today and is projected to expand toward 58 to 65 percent by 2028 to 2029. The profitability framing is better understood as a demand forecasting problem rather than a structural question. That distinction matters for valuation.
The 850 Million (and growing) Free Users
If OpenAI has 900 million WAU and 50 million paid subscribers, that leaves roughly 850 million free-tier users generating no subscription revenue today. At even 2 to 3 dollars per user annually in advertising revenue, that free tier represents 1.7 to 2.6 billion dollars in annual ad revenue from a user base that is already engaged and growing. OpenAI’s initial CPM rates are running roughly three times the digital advertising industry average, suggesting premium pricing power from the start. We are intentionally leaving advertising out of our base case because the thesis works without it, but the scale of the free-tier opportunity is hard to ignore as optionality.
The Valuation Math
At 840 billion dollars post-money on approximately 13 billion in 2025 recognized revenue, OpenAI is valued at roughly 65x current revenue. By traditional metrics, this looks expensive. Against 280 billion dollars-plus in 2030 management guidance, it compresses to roughly 3x. The question we spent the most time on was whether that 2030 figure is achievable. After triangulating across company disclosures, enterprise survey data, developer ecosystem growth, and our proprietary consumer model, we believe it is. We also believe OpenAI requires a multi-tier valuation framework rather than a single revenue multiple, because SaaS subscription revenue, consumption-based API revenue, and infrastructure economics carry fundamentally different margin profiles and comparable sets.
How This Compares to Our Anthropic Work
We published a separate institutional thesis on Anthropic, and the two analyses are worth reading together because they illuminate different paths through the same market. Both companies operate in what the framing as a Cournot oligopoly, a three-to-four player frontier market with high barriers and positive margins in equilibrium. Both face the same underlying inference economics where profitability is a demand forecasting problem rather than a structural threshold. And both benefit from the same structural tailwind: enterprise AI budgets that are overwhelmingly incremental rather than cannibalistic of existing IT spend.
Where they diverge, at least for now, is on the consumer front. OpenAI is a consumer-first platform that is pulling enterprise behind it. A large percent of current revenue comes from individual subscriptions, and the 900 million WAU base creates a lead generation funnel for workplace deployment that no competitor can replicate. We also believe OpenAI has some structural first mover advantages in the consumer market that are not fully appreciated. Anthropic is enterprise-first and developer-first, with roughly 30 to 35 percent of revenue from enterprise and a developer tool in Claude Code that is already generating meaningful run-rate revenue on its own. OpenAI monetizes breadth across five simultaneous vectors. Anthropic monetizes depth with a tighter product surface and a multi-rail compute strategy that gives it infrastructure optionality.
The margin trajectories also differ in composition even if they converge in destination. OpenAI’s compute margins have expanded faster on a larger revenue base, but Anthropic’s gross margin bridge from roughly 50 percent today toward 58 to 77 percent by 2028 reflects a different cost structure with less consumer infrastructure overhead. We think both companies reach healthy margins by decade end, but through different paths, and that distinction matters for how you build a position in the category.
The broader point is that this is not a winner-take-all market. We believe both OpenAI and Anthropic can sustain leading positions in a durable oligopoly, and that the analytical vocabulary we developed across both reports, the intelligence utility classification, the three-tiered valuation decomposition, the Cournot equilibrium framing, applies to the category as a whole rather than to any single name. Subscribers to the Dilligence Stack have access to both theses and the framework connecting them.
What is in the full report
Our three-engine thesis framework (consumer monetization, enterprise and API, inference economics) with detailed analysis of each
A proprietary consumer revenue model projecting subscription and advertising revenue through 2040
Denominator bridge explaining the relationship between WAU, modeled consumer actives, ad-eligible DAU, and paid subscribers
Full metric taxonomy separating recognized revenue, annualized run rate, adjusted gross margin, and compute margin with precise definitions
Revenue segment bridge table decomposing estimated revenue by segment across 2025, 2027, and 2030 with source tags on every line
Intelligence utility valuation framework with three-tiered comparable set analysis and applied multiples
Consumer growth cycle analysis with historical parallels to Google and Meta platform economics
Enterprise adoption analysis informed by CIO surveys and channel checks across 25-plus organizations
Detailed analysis of upside optionality in advertising, hardware, and third-party infrastructure
“What Must Be True” milestone framework for 2027 and 2030
“What Would Make Us Wrong” section covering model commoditization, enterprise deceleration, engagement plateau, compute cost, and competitive share loss scenarios
Cournot oligopoly competitive structure analysis