Some terrifying numbers in Ed Zitron’s post AI Is Too Expensive:

But LLMs are too expensive! They cost too much to run, and said costs appear to increase linearly with revenues. The more a user uses a product, the more it costs the company to run it, and the more capacity they can take up. The only way to capture any growth is to buy and install GPUs, which in turn requires you to build somewhere to put them, which takes time and money.

In effect, Microsoft, Google and Amazon are spending vast sums on computer power to service the likes of OpenAI and Anthropic. Those two companies are paying the bills through investment capital with no real route to profitability in sight.

What is almost certainly looks like is costs going up for end users through token based billing, which is likely to result in reduced usage of said tokens to make things affordable, which then means the whole pyramid collapses.

Be careful what you end up relying on, folks.

The whole article is full of quotable bits. Like this:

Any executive-level f*ckwit you’ve met in your life now has a seemingly-powerful tool that can burp up mimicry of open source software and, if you constantly prompt it, eventually get something half-functional onto some sort of web server. When you face bugs, it’ll try and fix them, sometimes also “fixing” (adding or deleting code) from elsewhere to be helpful, like when Cursor using Anthropic’s Claude Opus 4.6 model deleted an entire production database and all its backups. It will never, ever say no, even if it’s incapable, even if it has no thoughts, even if what you are asking is equal parts impossible and unreasonable in both its timescale and scope.

Or this:

Organizations aren’t burning millions or hundreds of millions of dollars a year on AI because it’s good, they’re doing it because they are run by people who do not know what the f*ck they’re doing.