How accurate have Ed Zitron's AI skeptic predictions been?

Like I said, "This makes it much harder to evaluate how credible the new information is."

Next 12 months: Oct 2026, Nov 2026, Dec 2026, Jan 2027, Feb 2027, Mar 2027, Apr 2027, May 2027, Jun 2027, Jul 2027, Aug 2027, Sep 2027.

So, if we're being pedantic, I'm not wrong until Oct 1st 2027 or later. But I'm also wrong if it happens this month ;)

The good news is that, while the number of fabricated papers have accelerated, so too is our ability to detect fabrications. Also, fabricated papers were a massive problem well before AI was useful in research. I think it's fair to characterise AI as our saviour here instead of the villain.

The broader issue pertains to the [Replication Crisis.](https://en.wikipedia.org/wiki/Replication_crisis) For decades, journals relied mostly on the honour system. Many people think peer review requires validating the data. It does not. It's basically a cursory check that the claimed methods are valid. The data is usually not validated. In fact, the data is usually not even produced for the journal or peer reviewers at all. This has become such a large issue that research began to sample studies to see if they could replicate the results given the claimed methods. A shocking number of papers could not be replicated. Scientists are loathe to claim this proves fraud, but when one has carefully written 50 pages of dense research methodology to arrive at statistically significant findings, it's clear that they would know if their experiments were replicable.

There is also another major issue with academic research as it exists: findings are (almost) only ever published when they confirm the hypothesis. Researchers begin from an often very biased perspective about the way they believe the world works (sociology is one of the worst for this), and work backwards from their conclusion. They might conduct dozens or hundreds of experiments, all while fail and are never published. Then they land on an experiment which can be massaged to reflect the message, and that is published. The fact is that all those "failures" are equally insightful research, and it should all be published for a variety of reasons.

There are also major issues re how research is gated and funded, and how many hobby researchers are never given the opportunity to publish at all - even if they could afford to pay the journal bribe.

Long story short, this entire industry was on the verge of collapse prior to AI. I cannot imagine it could get any worse, and I'm hopefully that AI will enable us to easily screen fraudulent and non-replicable research in the future. I expect 98% of papers to be rejected.

$800M a year is less than 1% of their quoted revenues at $100B+ / year. Claiming credits as revenue would be tax fraud. Credits to clients for services are counted as debits against

There are zero serious companies collecting $1000 on revenue and sending $999 as a cost of goods sold to Anthropic/AI. It would be unprofitable to even run a proxy to Anthropic on such thin margins. But I digress.

No company was banking $100 and keeping $99 in the "before times" either. These are fantasy numbers not even the most highly optimized software company produced. As an example, Slack famously went public in 2019 and it had revenue of $401M with a gross margin of ~79%, meaning they were pulling in $316M in gross profit. That is the figure before labor, administration, R&D, sales & marketing, etc. They actually operated on a net loss after factoring for those expenses, despite their high gross margin, which is common in high growth startups (Amazon famously ran losses or marginal profits until decades after their founding because they continuously reinvested in expansion).

Credits reduce revenue by all basic accounting standards. You can accuse these companies of fraud, it is within the realm of possibility, but it would also be You are making conflicting arguments at the same time. There exist startups that are able to generate gross profit with some consumption of AI services, they are also able to invest nearly 100% of their capital into AI to generate those profits without needing to spend on traditional labor, and yet AI is not sustainable. By your own circular logic it is of course sustainable, but by grounded logic, you have to understand any business that goes from zero 4 years ago to $100B+ in annual revenue today with double digit growth rates is offering the world something of value. Anyone who has tried AI sees some value in it. There is some revenue and profit to be made here. Betting against that in the long term will just lose you money and sanity.

From anything i read/watched of him, the shorthand is "it doesn't work". When taking more than a few words to explain, it means it's not replacing humans altogether, because the output is low-quality and needs human quality control. In the end, he's not arguing LLMs are not working (as in returning 500 in your browser), but that the promise of autonomous agents replacing humans for increased productivity is a lie and doesn't work.

I overall agree with his interpretation, but i'm afraid he might be wrong in the conclusion that noone is going to replace human labor with shittier agent slop. As long as we humans don't really have regulations and choice for proper quality of service, companies might just in fact enshittify everything with slop and make more profits while we suffer from lower-quality products and support.