Edward Benjamin Zitron (born 1986/1987) is an English author, podcaster, and public relations specialist.
So why do we carr exactly?
How accurate have Ed Zitron's AI skeptic predictions been?
871–880 of 1001 posts
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#872Earlier quoted context omitted.
I feel like too many people have a binary vision of the world, i.e. you're either "pro AI" or "anti AI". Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase. He's basically saying that (1) the current data center investments are based on unrealistic revenue projections and (2) hyperscalers are using accounting tricks to move around "money" in a circular way to make it look like…
If you ever listen to more than a couple of Zitron interviews, it's clear that he's not the "reasonable centrist" viewpoint on AI. He's the doom-and-gloom guy. Maybe there is someone out there positive on AI itself and calling out reasonable objections to some of the extreme things. But that's not Zitron.
The center is not always the reasonable viewpoint. And sometimes the doom-and-gloom guy is correct.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#873In case you find this site difficult to read:
document.getElementsByTagName('main')[0].style.maxWidth = '39em';Re: How accurate have Ed Zitron's AI skeptic predictions been?
#874Earlier quoted context omitted.
I feel like too many people have a binary vision of the world, i.e. you're either "pro AI" or "anti AI". Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase. He's basically saying that (1) the current data center investments are based on unrealistic revenue projections and (2) hyperscalers are using accounting tricks to move around "money" in a circular way to make it look like…
> Ed Zitron doesn't say that AI doesn't work, that its impact on the world will not increase He's said variants of both of these things before! That's the point OP makes too, people say "He doesn't say that" or "But what about this" when he's said a million, sometimes contradictory things.
Ed's whole cornerstone thesis is that "AI doesn't work." Like, if I had to sum him up in a short sentence, that's what it'd be
that or "Angry but doesn't know much"
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#875Earlier quoted context omitted.
Why do everyone assume they are subsidized? When we seemingly have no idea what it costs? Maybe average subscription is breaking even and token spend is pretty much pure profit? Case in point, claude code seems hell bent on increasing usage at all cost. Which makes sense in the growing phase (get people hooked) but it does not make sense given the hardware shortage. So, which is it?
> Why do everyone assume they are subsidized? It's not an assumption when the companies hosting the models publicly announce that they're subsidizing costs.
Have they actually announced that they're charging less than the marginal cost of inference, or that the revenue they're taking in doesn't make up for the cost of training each newer and better model?
The former does not pass the smell test when there are random providers selling tokens for competitive open weight models.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#876Earlier quoted context omitted.
Oh c’mon. You must be trolling. Great, now we have have scientific papers with formally correct English, but data and sources of which are fabricated and most of the paragraphs are utter nonsense. And publications are drowning on these. Oh thanks for changing the world!
AI has been great for scientific research. It writes code, it can help solve difficult mathematical problems, you can bounce ideas off of it, you can learn background info on a scientific question quickly, etc. I haven't noticed any uptick of trash papers in quality journals, or even on the arXiv. I know submissions are way up to all the journals, but peer review appears to still be working to filter out obvious junk…
I am not saying LLM-based software is not useful. For instance, you can use it to verify claims made on HN:
What we find is sobering. Submission volume has risen by 42% since November 2022. At the same time, submission writing quality also began to decline at the end of 2022, with Flesch Reading Ease (a standard measure of writing quality) 1.28 standard deviations (SD) lower in January 2026 relative to January 2021. Submissions that are heavily AI-generated account for nearly all of these trends. The quality of reviews has also dropped sharply since November 2022, driven by an increase in heavily AI-generated reviews. These reviews, in addition to being of worse quality, are also narrower in their emphasis, focusing more on theory and less on data. In short, more research is being submitted, with more AI writing that is lower in quality—not better.[1]
Peer review is unpaid work that we do (often on nights and weekends) because peer review on our own work is so valuable. Spending hours going through a submission and then realizing that there are hallucinated citations is infuriating as it is a waste of our time! If you haven’t spent enough time with your work to even get the references correct, then a.) why should we spend time reviewing it for you, and b.) what are you hoping to accomplish with the submission in the first place? Learning from reviews requires reflecting on your work, and you need to spend time with your work in order to do this.[2]
[1] https://pubsonline.informs.org/doi/10.1287/orsc.2026.ed.v37....
[2] https://geospatialml.com/posts/reviewing-ai-slop/
[3] https://arstechnica.com/science/2026/08/peer-review-is-overw...
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#877Earlier quoted context omitted.
> The prediction was early, overestimated how long of a tail RIM would experience, but how wrong was it? Was the prediction of some utility? Wrong enough that the utility is seriously diminished. Predicting a specific quantity dropping to a specific level at a specific date is a lot more valuable than saying “those guys are cooked”. And even if some minuscule utility existed: why should predictors be so coddled by th…
> why should predictors be so coddled by their observers? Because prediction is hard and no one is so good at it that they won't make errors like that.
They’ll never get better if not held accountable.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#878Earlier quoted context omitted.
If you ever listen to more than a couple of Zitron interviews, it's clear that he's not the "reasonable centrist" viewpoint on AI. He's the doom-and-gloom guy. Maybe there is someone out there positive on AI itself and calling out reasonable objections to some of the extreme things. But that's not Zitron.
The center is not always the reasonable viewpoint. And sometimes the doom-and-gloom guy is correct.
Right, but as the article linked lays out in detail, Zitron has been repeatedly and consistently objectively incorrect.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#879Earlier quoted context omitted.
Telling me that quite a few of Zitron's predictions turned out to be accurate, while many others were completely off base, means he's throwing darts on a dartboard. Why should I listen to him? I'm sure I can make a lot of accurate predictions. It doesn't mean I'm worth listening to, if people can't distinguish the accurate from the inaccurate in the moment. Reminds me of a tactic I've seen often amongst both critics…
Everyone is always throwing darts at a dartboard when making predictions. I don’t know why you think this is a critique that applies exclusively to Zitron.
Because he presents his predictions with extreme confidence, and asserts that anyone who disagrees must be crazy.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#880Consider two propositions: P1. AI is useful powerful and (P1a) will continue to get more useful and powerful at the same rapid pace it's been improving 2. AI companies are very profitable, and (P2a) will be wildly profitable (eg $30T TAM) in the next few years These are completely separate. But in practice people seem to be either proAI (both true) or anti AI (both false). P1 is clearly true and I find it hard to tak…
I can certainly see one happening without the other, but I'd expect P1 and P2 to be correlated at least some of the time?
P1 is probably necessary but not sufficient for P2, which would show up as a correlation of a sorts. Maybe I should've specified even further, if the tech works (P1 and P1a) then yeah somebody is going to make a lot of money but not necessarily OpenAI/Anthropic. They could go the way of AOL/Time Warner (probably not but again who knows). Microsoft and Amazon survived the dotcom bubble and thrived, Meta came after it.