Anthropic is still leading the way among enterprises, but not with its best models.
On Thursday, payments platform Ramp published its monthly index on AI spend among its enterprise users, and found that Anthropic has widened its lead in business AI adoption. According to the index, 43.5% of its U.S. business clients paid for Anthropic subscriptions or tokens, compared to 39.7% of businesses paying for OpenAI services.
However, most of that spend wasn't on its ultrapowerful new models, with only 6% of token spend being attributed to Fable 5, Anthropic's most capable (and expensive) model on the market.
- Despite Anthropic's lead, Ramp noted that Fable 5 was less popular with businesses than rival OpenAI's GPT-5.6 Sol, which comprised 25% of tokens used by OpenAI Ramp customers.
- "So with Fable 5, we’ve found a new upper bound for how much businesses are willing to spend on AI. Here, more performance is not worth the price tag," Ara Kharazian, lead economist at Ramp, said in the company's blog post.
Notably, other AI contenders saw significant gains: The share of models using serving platforms, like Hugging Face, such as those that provide open-source and Chinese models, saw gains in the past month, with 6.1% of Ramp customers that use AI buying into those platforms. Additionally, xAI saw its fastest growth month since July of last year, rising 0.94 percentage points month-over-month and now making up 4% of AI business spend.
Kharazian noted that both OpenAI and Anthropic, however, have seen slower adoption in recent months. "That’s not because new AI spenders are switching to open-source/Chinese models … it means more of their growth will have to come from existing businesses spending on AI, particularly the advanced spenders, and those businesses are increasingly spending on open-source," he wrote.
It's important to note that Ramp's data comes with a caveat: It only covers Ramp customers, which are largely startups and small businesses, though it has a growing list of enterprise clients as well. Ramp is also a relatively new company, only starting in 2019, and many large enterprises rely on legacy expensing and billing systems that Ramp doesn't account for.
Still, the data points to a growing trend that we're seeing among businesses: We are in a post-tokenmaxxing era, and these companies are trying to put a leash on their spending without quashing their AI ambitions entirely.
Our Deeper View #
Ramp's data may represent a reality check for the AI industry. Having the bulkiest, most expensive and most capable model may be the thing that helps these labs get an upper hand on benchmarks, but it doesn't mean much to the end user or enterprise buyer if it makes their wallets sting. Efficiency is quickly becoming the new paragon of AI, with companies like Microsoft, Meta and Google all shifting their attention to lightweight models. It's also why open-source models are growing in popularity, and why companies like Pathway are making strides with models as small as 150 million parameters. That's because the vast majority of enterprise tasks don't need multi-trillion parameter models. For things like writing emails, scheduling meetings or making documents, that kind of power is often overkill. And while the shift towards efficiency may sound like good news for easing the crunched compute market, it calls into question the blind devotion to scaling laws that companies like OpenAI and Anthropic have staked their missions on.