OpenAI and Anthropic Revenue Run-Rate
OpenAI and Anthropic Revenue Run-Rate
Experts expect OpenAI and Anthropic's combined revenue to reach $300B a year by 2040 — ten times their 2025 run-rate.
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Our Analysis
How do we interpret these results
Conditional on at least one of the two companies remaining independent, the three groups diverge on how fast the combined annualized revenue run-rate of OpenAI and Anthropic will grow, and on whether it will grow at all in the near term. The panel was shown baselines of roughly $30 billion at the end of 2025 and $72 billion by mid-2026. Against that, the median expert forecast for the end of 2026 is $70 billion and the median public forecast $41 billion; only superforecasters, at $90 billion, expect the run-rate to keep climbing. Over longer horizons all three groups expect substantial increases, but at very different scales. By 2030 the median forecasts are $150 billion for experts, $68 billion for the public, and $300 billion for superforecasters, rising by 2040 to $300 billion, $95 billion, and $770 billion, respectively. All three groups differ significantly from one another at every date and percentile. Superforecasters are the most uncertain group: for 2040, the median superforecaster puts the 10th percentile at $222 billion and the 90th at $1.3 trillion.
Forecasts by group
Median forecasts from each group
| group | 2026 | 2030 | 2040 |
|---|---|---|---|
| Experts | $70B | $150B | $300B |
| Superforecasters | $90B | $300B | $770B |
| Public | $41.1B | $67.7B | $95B |
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* Denotes non-overlapping bootstrapped confidence intervals between group aggregates.
Rationale analysis
The main lines of reasoning driving the spread in estimates
Trendline extrapolation
Most forecasters cite the two historical baseline figures that were provided in the question details. (Roughly $30B at end-2025 and $72B by mid-2026.) High-revenue respondents typically extend it: “Both companies going from essentially nothing to a combined $72B run-rate by mid-2026 (already more than double the end-2025 figure) shows growth is still compounding fast, not decelerating.”; “Right now this is exploding exponentially.” Whereas low-revenue respondents tend to cite the recent explosive growth as evidence that a slowdown is inevitable: “You cannot sustain triple-digit annual growth against the ceiling of total enterprise software and consumer spend.”
Commoditization
Most low-revenue respondents point to the potential for AI models to become commoditized as a result of competition: “Chinese labs and the open-weight people in the West keep putting near-frontier models out at close to the cost of running them. So customers are paying for the last little bit of capability plus whatever it costs them to switch. Neither of those looks safe to me.”; “Their models risk becoming a commodity, while most of the value ends up elsewhere: in hardware now, and in the applications built on top later.” High-revenue respondents tend to argue that an early advantage will likely prove durable, despite robust competition. “Most people are still using Microsoft and Apple despite OSS options,” notes one. Another adds that “the regulated professions are precisely where American providers are best placed to secure their position through the regulatory apparatus, and it is those clients rather than casual users who generate the revenue at issue here.”
Level of diffusion
High-revenue respondents often argue that for the later resolution dates, revenue levels stop being just about software budgets: “The high end needs these companies to price against what a worker costs rather than what a software seat costs.”; “Once the product begins performing or materially enabling work rather than merely providing access to software, the relevant market starts to resemble a fraction of global knowledge-work spending rather than the traditional enterprise software market.” Low-revenue respondents more frequently align their revenue projections with existing budgets, implying a cap on the level of diffusion they foresee: “The largest companies today have a revenue of 300-400 billion, so I’ve capped their combined ARR to this value.”
Rationale examples
High-quality rationales from forecasters across the range
High-revenue respondents
Growth would not come primarily from selling more chatbot subscriptions. It would come from converting model capability into useful execution across software development, research, support, marketing, analysis, legal work, document production, data engineering, scientific workflows, and operational planning. Once the product begins performing or materially enabling work rather than merely providing access to software, the relevant market starts to resemble a fraction of global knowledge-work spending rather than the traditional enterprise software market.
The bullish case is that AI becomes increasingly embedded in nearly all labor globally—beginning with cognitive and transitioning to physical as robotics evolves—and that OpenAI and Anthropic remain essential to that embedment. The bear case is that AI becomes commoditized, users migrate to near-frontier Chinese models, and OpenAI and Anthropic fail, plateau, or are bought out as revenues plunge. Prior tech developments suggest the former scenario is likely. Most people are still using Microsoft and Apple despite OSS options. Most people are buying brand-name phones like Samsung and Apple despite nearly-as-good options being available. As long as the product pricing isn't prohibitive—and self-interest suggests pricing for OpenAI and Anthropic products won’t be—then enterprises and individuals will pay a modest premium that results in big revenues given the volume.
Low-revenue respondents
Revenue is volume times price, and the price of any given capability is falling by an order of magnitude [each] year. Volume has grown faster so far, so revenue went up. I don’t think that lasts. The Chinese labs and the open-weight people in the West keep putting near-frontier models out at close to the cost of running them…My low numbers assume revenue peaks in the next two years and then erodes from there.
The real money will be made by the companies that create agentic workflows that solve real business and professional problems…the LLM engines will behave more like electrical power plants that provide the raw power, but not the finished product.