Ramp's July AI index: Anthropic's market share hit 43.5%, widening its lead over OpenAI; Fable 5 is only 6% of tokens businesses bought, likely due to high cost
Dear Colleagues: Today's letter includes my monthly update of Ramp AI Index, our flagship research using spend data from Ramp to track how American businesses are using AI.
RampAra Kharazian
Context & Ripple Effects
Ramp’s earlier data showed Anthropic winning roughly 73% of spending from companies buying AI tools for the first time, after a near-even January split with OpenAI. By March, the same dataset showed Anthropic’s paid-business adoption rising while OpenAI’s was nearly flat, making July’s wider share lead part of a sustained enterprise-procurement shift rather than an isolated reading.
The index also separates vendor share from token purchasing: Fable 5’s 6% share of business tokens indicates that purchase cost is constraining its use even as companies buy AI capacity.
First-order effects
Anthropic’s 43.5% share extends its lead over OpenAI in Ramp’s July index, strengthening Anthropic’s position with the businesses represented in Ramp’s spend data.
Fable 5 captures only 6% of business token purchases; Ramp attributes the limited usage to its high cost, placing pricing directly at the center of its enterprise adoption challenge.
Fable 5’s low token share makes cost per purchased token a more immediate competitive pressure than model availability alone, particularly for businesses scaling usage.
Third-order effects
If Ramp’s readings continue, enterprise AI procurement will increasingly favor a small number of vendors with demonstrated adoption momentum, reinforcing the revenue concentration already observed between Anthropic and OpenAI.
Usage-based AI competition is likely to be shaped as much by the cost of sustained business consumption as by model selection, with token economics determining which tools progress from trials to broad deployment.
The trend: Enterprise AI is moving from initial experimentation toward procurement decisions that reward vendor momentum and affordable high-volume usage.
Powerful enough to be banned, still only 11% of business spend. The reason given is price. In agent workloads, much of that cost comes from input tokens, often context the model never needed to see. We compress that context before inference. Same model, smaller bill.
A lot of replies from employees who say they aren't allowed to use Fable bc Anthropic is required to retain prompts for 30 days for U.S. government safety checks
might be bad chart/not account for consumer use but isn't this bad for R&D? given fable isn't recovering it's cost in API spend. also possible distilling into opis 5 justifies cost too but all around very odd
Cost per intelligence has never been more important, and it's exciting to see enterprises reach for grok 4.6 as a frontier model at a fraction of the cost
NEW from Ramp AI Index: disappointing adoption of Fable 5 We've heard several reasons from businesses...mainly Fable 5 is just too expensive. A model so powerful it was briefly banned, and yet businesses don't think it's worth the price. [image]
To register a little prediction, I think better and better models will be cost-efficient to fewer and fewer customers. Most people use AI to do stuff for which it really is already approaching a kind of “AGI for dummies.” So high end models will have to justify their cost by
If this analysis from @SemiAnalysis_ (ht @brotsquared) is correct, does Fable need to be commercially popular for it to be an economic success story? Is it possible that Anthropic wins at Opus-tier levels because of breakthroughs made at the Fable-tier? [image]
We'll see much faster commoditisation of LLMs from here. What does this mean? It simply means go long hyperscalers. Fable 5 adoption failed simply because it was too expensive. This means customers' willingness to pay premium for incremental performance is shrinking. Models [imag…
IMO this is 100% about the 30-day data retention policy that came with Fable. Businesses are saying no effing way and sticking with the models where that policy doesn't apply.