111–120 of 189 posts
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> To quite Ilya Sutskever: Who made Ilya Sutskever, or any other LLM-bonehead the Grand Prophet of Humanity? Why the fuck is his opinion on that relevant? Of course he will shill for data centers.
when someone gives their opinion about AI, one typical retort is "are you an AI/LLM expert? we should let the experts talk"
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I've made the semi comparison myself, but the amount of capital required to build a SOTA model today is clearly nowhere near enough to lead to a monopoly. I'm aware that telecoms networks are standardised (I was once a telecoms analyst), but that isn't a precondition for a commodity.
Just like how starting a chip fab was relatively easy back in the 80s and 90s. There were dozens of chip fab companies in the 80s. It turns out that fabs follow Rock's Law which is that the capital cost to build a new fab doubles every 4 years. This means it will quickly get rid of the less competitive players. This is not dissimilar to the LLM scaling laws where you need a magnitude more compute to get unlock a new…
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I don't think it's fair to consider writing an analysis (even a favorable one) about a topic as _shilling_ for it. It's not like he was pushing shitcoins or minting NFTs. I have always been (and remain!) bearish on crypto but it absolutely was something that couldn't be ignored a few years ago. Even if you came to the conclusion that it was bunk, there was significant enough fervor that any technologist needed to rec…
He actually was pushing NFTs but has since deleted his tweet: https://news.ycombinator.com/item?id=26434769
I made it very clear that I thought NFTs were a speculative bubble. I never suggested anyone should buy any crypto-related instrument. The idea that I was ‘shilling for crypto’ is something you would only say if you’re an idiot, as the OP and a few others on this thread clearly are.
You can find the 4 versions of Benedict's deck here: https://www.ben-evans.com/presentations I appreciate the temporal view into this thinking. My interpretation: Nov 2024: Don’t dismiss this; it may be the next platform shift. But the actual questions are still unsettled: scaling, usefulness, deployment, and business model. May 2025: The model layer is already showing signs of commoditization, so the important quest…
Thanks for the summary. I do love Benedict‘s work; I find he’s one of the few commentators who consistently strikes a balance between taking the transformative potential of AI seriously while not falling over into hype. Some things that stand out: * He’s really good with his historical analogies, especially looking at previous transformations like the early Internet and mobile; no surprise given that he has a history…
Like, yes, the telecom bubble was a clear case of overbuilding and the AI data center "bubble" looks a lot like that... but this overlooks that the fiber capacity being laid back then far outstripped the demand, whereas all the compute providers today have been desperately crunched for capacity, despite investing almost a trillion in CapEx -- to the tune of almost a trillion dollars more of backlog -- for multiple quarters now.
Or yes, historically new technology has always created new jobs... but all those new jobs required a higher skill level along dimensions that current AI models are already good at, meaning we've never had a technological revolution quite like this.
Or yes, prior technological revolutions consigned incumbents to irrelevancy, primarily due to shifts in technical platforms... but then today's business leaders are 1) very well educated about what happened to their predecessors, 2) very paranoid about the same thing happening to them, and hence 3) are actively making moves to capitalize on the next platform shift.
I also think his dismissal of chatbots is a bit premature. It is precisely because chatbots operate via an extremely simple, flexible and natural modality, i.e. a conversation -- entirely unconstrained by the form factor necessitated by any app -- that their infinite use-cases have become unleashed.
My take is that the AI labs are actively exploiting this extreme flexibility to surface valuable use-cases -- one of the hardest parts of innovation -- at which point they can simply slap an agent on top of them. Which is, yet again, simply a chatbot, except one that can actually do useful things for you and hence can be charged for a lot more money.
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Thanks for the summary. I do love Benedict‘s work; I find he’s one of the few commentators who consistently strikes a balance between taking the transformative potential of AI seriously while not falling over into hype. Some things that stand out: * He’s really good with his historical analogies, especially looking at previous transformations like the early Internet and mobile; no surprise given that he has a history…
Agreed, I appreciate his historical perspective, but I think one critical mistake his posts make is implying, largely because the parallels to history have been similar so far, that history will repeat. Like, yes, the telecom bubble was a clear case of overbuilding and the AI data center "bubble" looks a lot like that... but this overlooks that the fiber capacity being laid back then far outstripped the demand, where…
I think one of the things that the usage data shows us is that chatbots absolutely do not have infinite use cases - most users only use them a day or two a week or less.
You can find the 4 versions of Benedict's deck here: https://www.ben-evans.com/presentations I appreciate the temporal view into this thinking. My interpretation: Nov 2024: Don’t dismiss this; it may be the next platform shift. But the actual questions are still unsettled: scaling, usefulness, deployment, and business model. May 2025: The model layer is already showing signs of commoditization, so the important quest…
I didn’t know there were a sequence of these decks; thanks — it’s helpful to think of them as updating snapshots in time. The main thing that stands out to me on these graphs is just . how . early we still are - looking at industries like legal which in my mind are certainly going to be massively disrupted, and seeing the very low usage rates vs. tech (which still shows less than a quarter of tech people using AI dai…
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He actually was pushing NFTs but has since deleted his tweet: https://news.ycombinator.com/item?id=26434769
No, I didn’t. I made my account private and stopped using twitter. And the actual tweet says the opposite - that NFTs would need to develop some kind of cultural grounding for them to become a investment, which they didn’t have at the time and never got, and without that this would just be a speculative bubble, which is exactly what happened. I made it very clear that I thought NFTs were a speculative bubble. I never…
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No, I didn’t. I made my account private and stopped using twitter. And the actual tweet says the opposite - that NFTs would need to develop some kind of cultural grounding for them to become a investment, which they didn’t have at the time and never got, and without that this would just be a speculative bubble, which is exactly what happened. I made it very clear that I thought NFTs were a speculative bubble. I never…
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You don't know how to use Search, and this is beyond anything worth engaging with.
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No, I've never said that, and never thought that. The actual text in that tweet is: " The blockchain can't lie, but you can lie to the blockchain" You don't know how to use Search, and this is beyond anything worth engaging with.
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Agreed, I appreciate his historical perspective, but I think one critical mistake his posts make is implying, largely because the parallels to history have been similar so far, that history will repeat. Like, yes, the telecom bubble was a clear case of overbuilding and the AI data center "bubble" looks a lot like that... but this overlooks that the fiber capacity being laid back then far outstripped the demand, where…
I didn’t make any comparison at all with the fibre bubble, for precisely that reason. The comparison is with mobile data, which was and is always behind capacity. I think one of the things that the usage data shows us is that chatbots absolutely do not have infinite use cases - most users only use them a day or two a week or less.
But I also do disagree with the take that usage patterns indicate a fundamental shortage of use-cases. Yes, everyone reports WAU instead of DAU because WAU numbers look much more impressive, but I think the extreme shortage of compute plays a major role in this. I suspect all the AI labs are deliberately holding back from pushing AI adoption too much because of this. (Google execs have even made comments internally to this effect.) Note that even at such low frequency of usage all the model providers are desperately strapped for compute, which means there is insanely high demand from some quarters.
One way how capacity limitations could impact adoption is that the free-tier models are not as good as the frontier ones, so the free users come away less impressed with AI capabilities, leading to lower regular usage. This problem is larger than it appears, because it can take a long time to figure out how to get AI to work for your use-case, and people simply have not experimented nearly enough, partially due to first impressions. On the other hand, most companies seem to be OK with huge tokenmaxxing bills!
It seems to me the AI players are all playing a delicate balancing game across three fundamental dimensions: adoption, monetization, capacity. That is, they are simultaneously 1) pushing free / cheap AI usage as much as possible to hook users, capture market share and suss out new use-cases, while 2) carefully allocating token quotas for the most lucrative use-cases to satisfy investors, and 3) balancing available compute between those two competing priorities. I suspect as the compute bottleneck is alleviated and frontier models become more accessible cheaply, we'll see way higher DAU numbers.