I think Anthropic and OpenAI have found product-market fit

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I think Anthropic and OpenAI have found product-market fit
27th May 2026
Anthropic are strongly rumored to be about to have their first profitable quarter. Stories are circulating of companies surprised at how expensive their LLM bills are becoming from usage by their staff. I think this is because OpenAI and Anthropic have both found product-market fit.

Enterprise customers are now paying API prices
I think they’ve found product-market fit
And they’re ramping up
The AI-failure stories around this are pretty thin
We also know the labs are spending a lot
API revenue is becoming less important
April is a new inflection point

Enterprise customers are now paying API prices
I currently subscribe to the $100/month Max plan from Anthropic and the $100/month Pro plan from OpenAI. If you are a heavy user of coding agents these plans are a fantastic deal. I just ran the ccusage tool on my laptop to get an estimate of how much I would have spent if I were to pay for API tokens in the past 30 days and got:

$1,199.79 for Anthropic Claude Code
$980.37 for OpenAI Codex

That’s $2,180.16 worth of tokens for $200—not bad at all! I’m a moderately heavy user of these tools, but I’m certainly not running agents every hour of the day and night.
I had assumed that companies making extensive use of agents were getting similar discounts. It turns out I could not have been more wrong about that.
I haven’t been able to track down the exact date, but at some point in the last six months Anthropic switched their Enterprise plan (originally “Claude seats include enough usage for a typical workday” back in August 2025) to $20/seat/month plus API pricing for usage. This story about the change from The Information is dated Apr 14, 2026, but cites an Anthropic spokesperson claiming that the pricing change occurred in November 2025. Existing customers are finding out about the change as they renew their contracts.
OpenAI made a similar pricing change in April. The Codex rate card (Internet Archive copy) currently says:

Note: On April 2, 2026, we updated Codex pricing to align with API token usage, instead of per-message pricing. This change was applicable to new and existing Plus, Pro, ChatGPT Business and new ChatGPT Enterprise plans.
On April 23, 2026, we made this update for all existing ChatGPT Enterprise plans as well, inclusive of Edu, Health, Gov, and ChatGPT for Teachers.

It’s a little harder to decode as they quote prices in “credits”, but as far as I can tell those credit costs are an exact match for the API token costs listed for those models.
All of which is to say that as of April 2026 the “Enterprise” cost for both OpenAI Codex and Anthropic Claude Code/Cowork is the same as the listed API price.
GPT-5.5 (released April 23rd) is 2x the API price of GPT-5.4. Opus 4.7 (April 16th) is around 1.4x the price of Opus 4.6 when you take their new tokenizer into account.
So April saw both leading model companies release new frontier models with a higher API price, and both companies now have measures to lock their enterprise customers (who tend to sign year-long deals) at those API prices, not the previous extreme discounts.
I think they’ve found product-market fit
Why these sudden aggressive moves on pricing? Both Anthropic and OpenAI are planning to IPO, but I suspect there’s a more important factor here: I think they’ve finally found product-market fit, with the coding/general-purpose agent products embodied by Claude Code/Cowork and Codex.
Tools like ChatGPT are wildly popular, but that wild popularity has been difficult to turn into revenue. In February OpenAI boasted more than 900 million weekly active users for ChatGPT, but only 50 million—5.6% of that—were paying consumer subscribers.
Charging $10-$20/month per user is an OK business, but you’d need 1-2 billion subscribers sticking around for four years to cover $1 trillion in infrastructure.
Companies spending $200+/month/user will get you there a whole lot faster—and as noted above, as a power-user I’m at ~$1,000/month in API costs per vendor already.
Coding agents really did change everything. These are tools which burn vastly more tokens, but are also quickly becoming daily drivers for the work carried out by extremely well-compensated professionals. Right now that’s still mostly software engineers, but a coding agent is a tool that can automate anything you can do by typing commands into a computer... so they are clearly applicable to a much wider set of skilled knowledge workers.
As I’ve discussed on this site at length, the models released in November 2025 elevated agents to being genuinely useful. We’ve had six months to get used to that idea now—it’s no wonder companies are beginning to spend real money on this technology.
You could argue that ChatGPT achieved product-market fit when it became the fastest-growing consumer app in history back in February 2023... but it certainly wasn’t making any actual money back then. Coding agents plus enterprise pricing marks the point when these companies start making very real revenue. Maybe even enough to start covering their costs!
And they’re ramping up
As further evidence that enterprise agents represent product-market fit for these companies, consider their open job listings.
OpenAI have 703 open jobs right now, of which I’d categorize 229 (32.6%) as relating to enterprise sales and support—account executives, “Go To Market”, “Forward Deployed Engineers” and the like.
Anthropic have 390 open jobs, 105 (26.9%) of which look enterprisey to me.
It’s pleasingly ironic that these AI labs have picked a business model with such a heavy demand on human labor—enterprise sales contracts don’t close themselves without a whole lot of humans in the mix!
(I ran this analysis by scraping their job sites with Claude Code, then having it use Datasette’s JSON API to pipe that data into Datasette Cloud where I used Datasette Agent for the analysis, exported here. Dogfood!)
The AI-failure stories around this are pretty thin
I started digging into this in response to a growing volume of stories claiming that large companies were sounding the alarm because their AI usage costs had grown so large.
The most widely cited of these stories appear quite overblown to me.
The most discussed has been Uber, based on this report where CTO Praveen Neppalli Naga indicated that Uber had “maxed out its full year AI budget just a few months into 2026”, mostly thanks to Claude Code.
Given that Claude Code only got really good in November it’s entirely unsurprising to me that a budget set in 2025 may have failed to predict demand for that tool in 2026!
That Uber story was further fueled by comments made by Uber’s COO, Andrew Macdonald, on the Rapid Response podcast. I tracked down the segment and there really isn’t much there. Here’s what Andrew said:

But then you sometimes go and talk to your senior engineering leaders and you’re saying, OK, how many projects that were on the cutting room floor got moved above the line because of the productivity gains because 25% of our code commits were via Claude Code last quarter?
That link is not there yet, right? I think maybe implicitly there’s more that is getting shipped. But it’s very hard to draw a line between one of those stats and, OK, now we’re actually producing like 25% more useful consumer features, right? And that line is hard to draw.

Somehow this fragment turned into headlines like Uber’s COO says it’s getting harder to justify the money spent on AI tokenmaxxing, because the market for stories about AI failures remains enormous.
The other popular story around this is Microsoft starts canceling Claude Code licenses, ostensibly to encourage their engineers to dogfood their own Copilot CLI agent instead—but The Verge reporter Tom Warren says “sources tell me the decision is also a financial one”, triggered by the June 30th end of Microsoft’s financial year.
I think both of these stories support my “product-market fit” hypothesis. The best advice I ever heard on pricing a product was that your customer should suck air through their teeth and then say yes. Uber’s budget overrun and Microsoft’s seat cancellations look like that effect playing out in practice.
We also know the labs are spending a lot
The big AI labs spend billions of dollars on both training and inference. Credible figures are hard to come by, but we did get one huge hint as to the figures involved from, oddly enough, the recent SpaceX S-1:

[...] in May 2026, we entered into Cloud Services Agreements with Anthropic PBC (“Anthropic”), an AI research and development public benefit corporation, with respect to access to compute capacity across COLOSSUS and COLOSSUS II. Pursuant to these agreements, the customer has agreed to pay us $1.25 billion per month through May 2029 [...]

The Anthropic announcement said that this deal meant they could “increase our usage limits for Claude Code and the Claude API”, heavily implying that Colossus is being used for inference, not model training.
Anthropic already have vast amounts of compute from other providers. The fact that they’re willing to spend $1.25 billion per month for extra capacity from just one of their vendors hints at how big these inference budgets have become.
API revenue is becoming less important
Over the past two years my impression has been that OpenAI made more of their income from subscription revenue while Anthropic made more from their API.
Anthropic’s API revenue was historically quite dependent on a small number of large API customers—this VentureBeat story from August 2025 quotes “sources familiar with the matter” suggesting that just Cursor and GitHub Copilot were responsible for $1.2 billion of the company’s then-$4 billion revenue.
Today Anthropic are rumored to hit $10.9 billion in the second quarter, potentially even operating at a profit for the first time.
This pivot-to-Enterprise suggests that the labs have realized that the real money lies in cutting out the middlemen. Anthropic’s Claude Code directly competes with Cursor and Copilot. No wonder Cursor are investing in their own models!
April is a new inflection point
I’ve called November 2025 the November inflection point because that was when GPT-5.1 and Opus 4.5, combined with their respective coding agent harnesses, got good—good enough that we’ve spent the last six months adapting to agent systems that can reliably get useful work done.
I think April 2026 is a new inflection point where the revenue implications of this have started to land, to the benefit of the frontier AI labs and with material impacts on the budgets of large companies.
We’ll know for sure how real this moment is when the S-1 documents for the upcoming Anthropic and OpenAI IPOs give us some real, audited numbers to get our teeth into.

Posted 27th May 2026 at 4:38 pm · Follow me on Mastodon, Bluesky, Twitter or subscribe to my newsletter

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Anthropic and OpenAI are posited to have achieved product-market fit, primarily driven by the success of their coding and general-purpose agent products. This success is evidenced by the fact that enterprise customers are now directly paying API prices for their usage, shifting the revenue model significantly. This change is supported by data indicating that while consumer applications like ChatGPT achieved immense popularity, translating that popularity into sustainable revenue was challenging. The author suggests that the key inflection point was the emergence of coding agents embodied by tools like Claude Code/Cowork and Codex, which, despite consuming more tokens, have become essential daily drivers for highly compensated professionals, such as software engineers, thereby generating real business value.

The pricing structure has undergone significant adjustments. The author notes that previous models based on per-message pricing have evolved to align with API token usage for enterprise plans. For instance, Anthropic switched its Enterprise plan to include usage-based pricing, and OpenAI updated its Codex rate card to align with API token usage across various enterprise plans as of April 2026. This synchronization implies that the enterprise cost for models like Claude Code/Cowork and Codex now reflects the underlying API token costs. Furthermore, the release of new frontier models, such as GPT-5.5 and Opus 4.7, increased API prices, and both companies implemented measures to lock in enterprise customers at these API prices rather than previous substantial discounts, which the author argues reflects a realization of true market value.

This shift is further demonstrated by the increased focus on enterprise engagement by the AI labs. Evidence suggests a ramping up of enterprise sales and support roles at both companies, with OpenAI listing numerous positions focused on enterprise sales and deployment, and Anthropic also showing a significant number of enterprise-focused job openings. This indicates that securing these contracts requires substantial human interaction, supporting the hypothesis that agents represent a market fit by creating a heavy demand for human-driven sales and support infrastructure.

The author addresses concerns regarding alleged AI failures, such as inflated usage costs, by suggesting that prominent stories are often overblown. For example, reports regarding Uber's budget overrun stemmed from anticipating demand for tools like Claude Code in 2026, which the author notes was based on a less mature tool. Similarly, Microsoft's decision to alter Claude Code licenses was likely driven by financial considerations rather than just encouraging dogfooding. The author posits that these real-world impacts on budgets, seen in both the Uber and Microsoft scenarios, align with the principle that customers ultimately accept the product based on real-world utility and financial constraints, rather than speculative cost projections.

The overall market dynamic is also shifting toward API revenue dominance. Historically, Anthropic's API revenue was concentrated among a few large customers, while OpenAI focused more on subscription revenue. The pivot toward enterprise services allows the labs to focus on cutting out intermediaries, as direct agent tools like Claude Code directly compete with established tools like Cursor and GitHub Copilot. The author concludes that the time of November 2025 marked an inflection point for agent utility, and April 2026 represents the inflection point where the revenue implications of this utility are beginning to materialize for the AI labs and have material effects on large companies' budgets.