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, 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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benedictevans 13 hours ago | parent [-]

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.

	▲	keeda 11 hours ago \| parent [-]
		That's fair, I may be conflating your takes on mobile data with others who've made the comparison to the telecom bubble, and if so, mea culpa! 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.