i’ve been saying for months that every billion we spend on LLMs etc. is moving us further away from AGI, not closer, and that retreat is accelerating.

it feels so wildly irresponsible for evangelists like ellison to continue to hype this dumpster fire up. but then he doesn’t stand to lose his mansions, his islands, his jets.

then all software companies who replaced bulk of their junior coders with LLMs are fucked because you can’t train an LLM to be a senior coder.

I mean, there won’t be. Not with the current gen of transformers/attention/etc. It’s now been over eight years since the “Attention is All You Need” paper that kicked off all of this, and every company is just betting billions upon billions on scaling being enough when it so obviously isn’t. They could train a 20T parameter model and it wouldn’t be meaningfully better. The limits of the architecture were reached some time ago. The comedown will be rough.

It always has been, and will be, AGI. A guy instead.

Last year, VCs poured a record $110 billion into AI startups, representing an astonishing 42 percent of the entire VC funding pie. That’s a far cry from global VC investments, only 18 percent of which went to AI. Silicon Valley, known for its alleged contrarian style of thinking, seems to be herding itself off a cliff.

But if it’s true that the expected exponential gains from scaling pre-training are instead facing diminishing returns, if AGI is still a faraway dream and the industry’s technical leader (OpenAI) has no clear pathway to it, then the Valley has made a terrible bet. According to CB Insights, there are 498 AI unicorns, or startups worth at least $1 billion, turning the $350 billion in VC investments since 2021 into a total valuation of $2.7 trillion. Those valuations will be hard to justify if no one has a ready solution to keep the current pace of progress going.

Despite expecting to hit $20 billion in annual recurring revenue (ARR) by the end of this year (though, as Zitron notes, the math here is a little fuzzy), Altman projects OpenAI to be unprofitable and lose $8 billion. By 2026, OpenAI projects its losses to grow to $14 billion a year, despite seeing another jump in ARR to around $30 billion. The startup doesn’t expect to break even until it can clear $100 billion a year in revenue, the number targeted for 2029. Zitron considers OpenAI the paradigmatic example of what he calls “the rot economy,” a company that “burns billions to lose billions.”

Without new products or continued exponential progress, it’s difficult to see a pathway for OpenAI to hit $100 billion in revenue—for reference, about a third of the revenues that companies like Microsoft, Amazon, and Google make every year—in less than five years. It’s difficult to square the financials of a company that plans to lose more than $100 billion by the end of the decade as being as valuable as Coca-Cola or twice as valuable as Spotify, both centi-billion-dollar companies that have been profitable for years.

You ever wondered morbidly what it is like to stand at the foot of a beach with a massive tsunami towering before you?

When you turn to the broader U.S. economy, the numbers really make you sweat. Just seven companies (Amazon, Apple, Tesla, Nvidia, Meta, Alphabet, and Microsoft—the “Magnificent Seven”) now account for 34 percent of the total S&P 500 stock index, an all-time record for market concentration. In the first two quarters of 2025, Big Tech spending on AI infrastructure contributed more to U.S. GDP growth than consumer spending, which is a little worrisome, since the U.S. consumer makes up 70 percent of the domestic economy.

They hire everyone back at 75% of their old wage to clean up the mess AI created.