Earlier quoted context omitted.
Why is Zitron’s repute evaluated entirely on the basis of failed predictions? Predictions are incredibly hard. AI enthusiasts and thought leaders have made so many demonstrably incorrect predictions it’s hard to keep track. Based on this metric, Altman and Amodei should never be taken seriously again.
Isn't that pretty much his whole thing, confidently telling us what will happen? Of course his reputation should suffer if his predictions are wrong, as should those of the others whose correctness : confidence ratio is too low. (But if their reputation/power/relevance mainly comes from things other than their punditry, we can't really stop 'taking them seriously' altogether.)
How accurate have Ed Zitron's AI skeptic predictions been?
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Re: How accurate have Ed Zitron's AI skeptic predictions been?
#932Re: How accurate have Ed Zitron's AI skeptic predictions been?
#933Earlier 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?
Anthropic admitted last year to losing money on inference, it had negative margins. The margins have improved and are now positive but there’s still a significant cost. If plans aren’t being subsidized it would mean that the margin on inference is ~99%+ which would mean OpenAI and Anthropic should be wildly profitable but both are still losing money. So, it’s mathematically impossible that they’re not subsidizing pla…
No they did not. Dario has repeatedly stated if they stopped training new models, they would be very profitable.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#934Things in general I think he's right about: 1. Revenue if anthropic and openai is unlikely to grow to the high levels they need to pay for their commitments. Many of their heavy users (coding) will eventually offset a lot of usage to more efficient and cheaper open weight models. I know of people in a company I was at that spend thousands of dollars a month on tokens. I am sure that what they're using it for can be s…
This reads to me as incredible cope. Maybe not both of these companies but certainly one of them will be enormously valuable, and there will always value in the frontier models even if much of the practical usage can be done locally
Neither appears to be on track to long-term profitability specifically once you take into account depreciation on CAPEX.
On top of this the “productivity gains” from AI across the board seem to be a very mixed bag. The pitch from these companies has been huge gains in productivity and automation and although there is anecdotal evidence some people are able to do that, the broader studies seem to show marginal gains in most cases.
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#935Re: How accurate have Ed Zitron's AI skeptic predictions been?
#936Re: How accurate have Ed Zitron's AI skeptic predictions been?
#937Earlier quoted context omitted.
Yes, there are good examples of this working, anti-competitive as it may be. What if, in the DoorDash example, the pizza shop was itself venture backed and selling pizza at a loss to win customers and using this 20k revenue as a basis to raise money?
What if the same VCs were backing both and gearing up for an IPO?
Re: How accurate have Ed Zitron's AI skeptic predictions been?
#938Earlier quoted context omitted.
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"
I think when cornered, he does say it works for coding and some other tasks. But to be honest it also makes a lot of bugs when writing code. I don't agree with everything he thinks, but I think I agree with him more than I do Sam Altman or Jensen.
If he says AI is changing work in some places but not-at-all in most… that’s defensible. Or at least we can look at that. Did he say it about the llm offerings from one or two providers or about (broad gesture) all AI efforts, everywhere? Etc.
Certainly it’s changing sw development work. It’s less clear, most everywhere else.