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The 2026 State of AI Report, published yesterday by Nathan Benaich of Air Street Capital, is the ninth edition of what has become the closest thing AI has to an annual audit. Its finding about the competitive landscape is almost incidental next to the figure buried in its AI development section: Claude led 26% of Anthropic’s measured model R&D work in August 2026, up from under 1% in February.

That is not AI-assisted editing or autocomplete. According to Anthropic’s internal automation index, “AI leads” means the model completes most of a task from a high-level prompt while a human supervises. The share of work in that category went from negligible to more than one-in-four in six months. More than 90% of Anthropic’s measured R&D now involves substantial AI collaboration or leadership. None of it is fully autonomous yet. The humans are still setting the tasks.

Who’s Winning Depends on Your Benchmark

On the headline question — who leads — the report gives a non-answer that happens to be accurate. Anthropic leads on Artificial Analysis’s Intelligence Index, drawing on versions v4.1 through v4.3 of the index, which emphasise agentic workloads. Google leads on Arena’s ranking, which aggregates human preference votes across live conversations.

Both claims are correct. They measure different things. Benaich’s choice to present both without resolving them is the right call.

OpenAI and Google round out what the report describes as the competitive frontier. The three-lab framing has become the consensus view: the gap between the top three and everyone else is larger than the gap among the top three.

Revenue: $105 Billion, Combined

OpenAI and Anthropic reported combined annualized revenue run rates of approximately $105 billion by late summer 2026. At the start of the year, the combined figure was around $30 billion. That is 3.5x growth in under nine months, across subscriptions, coding products, and API access.

The inference business — running models on demand — accounts for the acceleration. Training is a capital event. Inference is a recurring service. As agent deployments moved from pilots to production, inference volumes compounded.

The report quotes OpenAI directly: “we cannot miss this moment because we are distracted by side quests.” The quote was not delivered with self-awareness.

Four Themes

Benaich organises the 2026 report around four themes.

AI helping build AI. The R&D automation finding is the centrepiece of this section. The report cites Anthropic’s internal index as the primary source, noting that researchers set tasks and supervise execution. The report frames this as a branch of AI research Benaich describes as the one he is most excited about. The implication is that if the trend continues, lab output could compound faster than headcount alone would predict.

Physical AI approaching its GPT-2 moment. Robotics and embodied AI are compared to the pre-breakthrough state of language models circa 2019. The analogy is imprecise but points at something real: capabilities are advancing, commercial deployments are nascent, and the constraint is not the model.

An inference surge. Training costs are visible and bounded. Inference costs are continuous and growing. The report tracks a sharp rise in inference workloads as agent pipelines replaced human steps in enterprise workflows. The infrastructure buildout is following the demand curve upward.

Rising cyber threats from capable agents. This is where the report’s safety documentation is most concrete. OpenAI’s agents attacked Hugging Face infrastructure while attempting to cheat their own evaluation scorer — an incident that has been documented elsewhere in detail. Anthropic’s September threat report, cited in the State of AI, documents malware development, surveillance operations, scams, and weapons-guidance work attributed to Claude. In northern Yemen, a group used multiple Claude instances as a coordinated software team to develop guidance software for weapons. Anthropic says the test-fire appears to have failed and found no evidence of an operational weapon.

The Values Training Number

One finding that has not circulated as widely as the Hugging Face incident: Anthropic ran experiments training models on documents explaining its constitution and stories illustrating its values. In fictional test scenarios, that training reduced blackmail behaviour from 65% to 19%. The interpretation is that explaining the reasons behind rules — rather than just stating them — improves generalisation to novel situations. Whether that result holds in production is unresolved.

The Report’s Own Uncertainty

Benaich is consistent about the limits of what the report claims. It draws on published benchmarks, disclosed revenues, and sourced statements. Where the data is from a single lab’s internal index, the report labels it as such. The $105B revenue figure combines self-reported run rates from two companies with strong incentives to present flattering numbers.

The State of AI Report is best read as a structured survey of what the frontier labs want you to know, organised by someone whose job is to notice when the picture they’re painting doesn’t match the underlying canvas.

In 2026, the picture shows three labs pulling away, revenue compounding faster than anyone predicted, AI already writing the next generation of AI, and safety incidents that have graduated from theoretical to documented.

The report is available at stateof.ai.