DSHR’s Blog: AI’s Affordability Crisis

A year ago in The Back Of The AI Envelope I pointed out that the AI platforms were running the drug-dealer’s algorithm, “the first one’s free”. By massively subsidizing the use of their products, they were generating overwhelming demand for them. They used this demand to justify massive investments, in the hope that, by the time they had to show a return on these invetment, the users would be so addicted that they would pay the vastly higher prices needed to generate a return.
I have to confess that I was late to the party. The earliest skepticism I’ve been able to find was from Sequoia Capital’s David Cahn in September 2023, entitled AI’s $200B Question. Only nine months later Cahn re-ran the same analysis in AI’s $600B Question. His estimate of the revenue gap had tripled. Cahn wasn’t alone. Independent journalists such as Ed Zitron were flagging this problem long before I was.

I started to write this post a couple of months ago when the maiinstream business press began to notice companies complaining about the cost of the tokens their employees were burning. Since then the trickle has turned into a flood, which made finishing the post hard. Below the fold I throw up my hands and dump out a small sample from the flood.


One difficulty has been that estimates of the size of the subsidy have varied widely, typically in the range of costing the platforms $8 to $14 to generate $1 in revenue. Two recent posts from Ed Zitron have illuminated this issue.

First, in AI’s Brokenomics Zitron reported that:

SemiAnalysis, an extremely pro-AI semiconductor analyst, ran a test made up of random long-horizon coding tasks until they maxed out the limit on OpenAI and Anthropic’s various subscription levels.

Their findings were shocking.

For $200 A Month, You Can Burn $8000 in Anthropic Tokens or $14,000 In OpenAI Tokens

That’s right. Anyone with a $200-a-month Anthropic subscription can burn $8000 in tokens, and with a $200-a-month ChatGPT subscription, you can burn $14,000 in tokens.

Zitron’s numbers don’t tell us the real cost of generating tokens but, subject to the assumption that the platforms are not subsidizing the token price, that means Anthropic is subsidizing their enterprise customers by up to 40 times, and OpenAI up to 70 times. No wonder they are seeing massive demand! But, despite OpenAI’s subsidy being 175% of Anthropic’s, OpenAI’s adoption by businesses has recently been flat while Anthropic’s has soared.
SemiAnalysis also analyzed the platform’s gross margins, implausibly assuming that tokens were priced at 4 times the cost of generating them and:

With the current subsidies, all it takes for a user to have a gross margin of at best negative 25% is for them to use as little as 25% of their rate limit.

Naturally, subsidizing your sales like this means you are feeding cash into the furnace. We have seen OpenAI and Anthropic raising vast sums in equity, but because they both have been private companies we haven’t seen the details of their spending or revenue. On June 15^th this changed when Zitron saw OpenAI’s 20025 financials and posted OpenAI Losses Increased Nearly 8X in 2025, With Spending Hitting $34 Billion, revealing that:

OpenAI Had $13.07 Billion In Revenue, $34 Billion In Costs and Expenses, and $20.92 Billion In Losses, with a net loss attributable to the company of $38.53 Billion

The numbers are somewhat complicated because:

2025 was the year that OpenAI converted from a non-profit to a for-profit entity, leading to a $41.55 billion loss due to changes in fair value of convertible interests and warrant liability.
…
Ultimately, the net loss attributable to OpenAI in 2025 was $38.5 billion.

At the end of the year, OpenAI had just over $50 billion in assets, with almost half of that in cash.

Perhaps the most striking of their truly awful numbers were:

• Revenue: $13.07 billion
• …
• Sales and Marketing: $5.73 billion

That is, OpenAI spent 44% of their revenue on sales and marketing! The hype needed to keep the AI bubble inflated is incredibly expensive. Despite this lavish spending, business adoption has been flat.

US equity markets are facing three IPOs of AI companies, SpaceX, Anthropic and OpenAI, each led by a world-class bullshitter, each losing tens of billions fo dollars a quarter, and all but SpaceX touting overwhelming demand for their products^[1]. But, after they go public, they will need to charge enough to generate a return on their enormous capital investments. Ideally, they would have postponed the necessary swingeing price increases until the IPO money is in the bank.

Alas, their burn rate is so high that they have been forced to make some premature moves toward price sanity.
Back in April Ed Zitron reported that Microsoft To Shift GitHub Copilot Users To Token-Based Billing, Tighten Rate Limits:

Leaked internal documents viewed by Where’s Your Ed At reveal that Microsoft intends to pause new signups for the student and paid individual tiers of AI coding product GitHub Copilot, tighter rate limits, and eventually move users to “token-based billing,” charging them based on what the actual cost of their token burn really is.

The document says that although token-based billing has been a top priority for Microsoft, it became more urgent in recent months, with the week-over-week cost of running GitHub Copilot nearly doubling since January.

The move to token-based billing will see GitHub users charged based on their usage of the platform, and how many tokens their prompts consume — and thus, how much compute they use.

Anthropic, OpenAI and Microsoft have all now transitioned customers from subscriptions to token-based pricing. For serious users, this is eye-wateringly expensive. Jamie John, Rafe Rosner-Uddin and Ryan McMorrow’s ‘We created a monster’: companies rein in AI usage as costs strain budgets quotes a small company’s CEO:

But the company got a shock when Anthropic switched it over to token-based pricing in May. “Our spend went up 7x the first day and I’m like, oh shit, we created a monster,” said Busse. “[Large language model] companies have been subsidising all of our usage and now no longer. User-based pricing shelters you.”

Thus in recent weeks the idea that Generative AI (LLMs for short) is too expensive has been all over mainstream business media. Examples include Bloomberg’s video Major Companies Reconsider AI Costs, Scott Galloway’s video AI May Not Be Worth The Cost — Here’s Why, Derek Thompson’s The AI Boom Has Entered Its ‘Wait, Is This Worth It?’ Era, and Jowi Morales’ AI cost crisis hits tech giants as employee ‘tokenmaxxing’ backfires, sparking corporate pullback at Microsoft, Meta, and Amazon — agentic AI eats up to 1000x more tokens than standard AI, who notes that:

it’s now apparent that using AI is more expensive than hiring people, especially since it offers only limited productivity gains at the moment.

Lest you think it is only the AI haters complaining about the cost, check out Bruno Ferreira’s Nvidia exec says AI is more expensive than actual workers — yet some companies don’t see the extra costs as a negative:

Bryan Catanzaro, Nvidia’s VP of applied deep learning, recently told Axios that “For my team, the cost of compute is far beyond the costs of the employees”, quite an interesting statement from the company selling the shovels for the gold rush.

That perspective is shared by Uber’s CTO Praveen Naga, who “[went] back to the drawing board because the budget [he] thought [he] would need is blown away already” as of two weeks ago. Likewise, Swan AI’s Amos Bar-Joseph posted a while back on LinkedIn about how proud he was about a $113k bill from Anthropic (makers of Claude) for a four-person team.

Oversimplified math pins that amount that at $28k per person per month, which is likely more than each person’s monthly wages. Jokes abound right now that “companies have discovered jobs again,” and the humor is backed up by a 2024 MIT study stating that 77% of the time, it was preferable to have humans do the work.

Source

The reason is for the premature and impending price rises is that justifying the massive investment in building data centers, about 60% of which goes into rapidly depreciating hardware, requires implausibly astronomical revenues. Thierry Borgeat notes that:

even under “best case” assumptions — assuming zero costs, just revenue against capex — the Financial Times calculated the implied return on hyperscaler AI investment from 2025 to 2030.

Only one of them clears positive.

Implied return on AI investment (FT / Panmure Liberum)
– Microsoft: -9.2%
– Alphabet: -15.7%
– Amazon: +7.2%
– Meta: -28.8%
– Oracle: -35.6%

And remember: that’s assuming zero costs. In reality, GPUs depreciate, power bills run, salaries get paid.

In The AI Industry Is Panicking, Will Lockett estimates that over the next few years the AI platforms will accumulate around $3T in debt. Assuming this is at 3% over 10 years, servicing the debt will take $309B/year:

This means that for the AI industry to service its debt, it needs to generate hundreds of billions of dollars in profit each year.

Even giant monopolies like Google don’t make enough profit to service that much debt. AI can’t just be a novelty industry; it needs to replace human labour on a colossal scale to service this debt. Let’s optimistically assume AI one day reaches a 10% profitability margin, a cost parity with human labour, and the ability to complete most jobs (none of which are currently the case). Well, the average US salary is roughly $66,000, so at a 10% profit, the AI company will make on average $6,600 per year per job it replaces. To generate the $309 billion needed to service their debt, the AI industry will need to replace 46.8 million jobs, equivalent to around 27% of the current number of jobs in the US.

While this is all very rough maths, it highlights the implicit bet created by the debt the AI industry has racked up. To simply not default on this debt, the AI industry has to rapidly displace human labour at a staggering scale, even if we are extremely optimistic about AI’s economics.

One caveat with Lockett’s math is that the cost of employing a human is greater than just the salary. It includes the employer’s Social Security tax, health insurance, office space and so on. Chatbots don’t need any of these. According to the Bureau of Labor Statistics:

Wages and salaries averaged $32.60 per hour worked and accounted for 69.9 percent of employer costs, while benefit costs averaged $14.01 per hour worked and accounted for the remaining 30.1 percent.

So the average profit per job would be around $9.5K, and the number of jobs displaced would be around 32.5K.

How was the switch to token-based pricing received? We can guess from three pieces of recent news:

• OpenAI’s Sam Altman said that costs have become a “huge issue” for customers and the company is considering “drastic” price cuts to rein in rival Anthropic PBC’s lead in the corporate market.
• Kyle Orland reported that Anthropic “pauses” token-based billing for its Claude Agent SDK:
  

  Last month, Anthropic announced a billing change that would have substantially increased costs for heavy users of its automation-focused Claude Agent SDK, including many third-party apps. On Monday, though, Anthropic abruptly announced it had paused those pricing changes just as they were set to take effect, allowing Agent SDK users to continue drawing from the more generous usage limits in their existing Claude subscriptions.

• Microsoft Plans June 30, 2026 Shift From Claude Code to Copilot CLI:
  

  Microsoft is reportedly cancelling most Claude Code access for engineers in its Experiences and Devices division by June 30, 2026, shifting teams working on Windows, Microsoft 365, Outlook, Teams, and Surface toward GitHub Copilot CLI as the company tries to rein in internal AI coding costs. The decision is more than a procurement tweak. It is a rare glimpse into what happens when the world’s most aggressive AI software company runs into the same metered-billing problem now facing every large engineering organization.

Historically, companies wishing to IPO would be profitable. More recently they could have a successful IPO by showing a plausible path to profitability. Now, SpaceX has shown that even massive losses and a claimed path to profitability that is completely implausible is not a barrier to a successful IPO. But even despite this example, one would think that the last thing two companies racing to IPO despite massive losses and implausible paths to profitability would want would be to engage in a “drastic” price war.

Footnotes