[libraryofbabel]

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 degree.

* he emphasizes over and over how we have still have no idea how all of this is going to work when the dust settles. I think that’s kind of a historian’s move as well. When you look at what people were saying during the early days of the web, for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong questions. The implication is that we are probably asking the wrong questions about AI too.

* Nonetheless his thesis about the commoditization of models is actually a fairly strong concrete prediction. i’m not sure if I agree with it entirely, but I do keep it in mind every time I look at the valuation of leading AI labs.

* he continually makes the point that a chat bot is barely a product and that AI labs have so far had very little success in delivering products above that layer… with the exception of coding agents, of course.

[pingou]

I just got a bit triggered by the "hype" word. What if the hype was real? It is easy to say that nobody knows how all of this is going to work, and I would say it is a prudent thing to say, but there is value in making a bold prediction from the start instead of just updating your view to respond to change. In one case you are predicting stuff, in the other, just reacting.

But I absolutely agree that in hindsight we are often asking the wrong questions about each new technology.

I keep seeing on HN that AI is a hype, and many here are anti AI (which I get, as a programmer AI made my job less interesting, and I'm even worried about losing it), but where has AI underdelivered?

[elvis10ten]

The hype is in what AI delivers (at least so far). I would never create a PR without an AI review. I will ask an AI to write code for me from time to time.

But it still has huge gaps in quality. And from time to time, it shows me that it doesn’t really understand things. You might point out that how is that any different from your mediocre engineer. But for most people skilled enough, you can easily know the difference when someone doesn’t really know something.

With AI, you discover this after reading several pages being dumped on you by people being “more productive” with AI.

[pingou]

Ok so the hype would be people saying AI can currently do something well and autonomously when it cannot (or not consistently enough), and it is easy to prove them wrong.

But I feel like people are more hyped about what the AI will be able to do soon rather than what it can do now.

I think AI does understand things (depending on your definition), how else could we communicate and ask it a question if it didn't? I mean we're quite far from Eliza here.

And yes, often their answer would be so wrong that we think it is impossible that AI understands anything, but this jagged intelligence doesn't prove, at least to me, that there isn't some understanding. At what point do we say that AI understands things? What if we can reduce 99% of those dumb failures, would we then say than AI understands?

[GolfPopper]

>I think AI does understand things (depending on your definition), how else could we communicate and ask it a question if it didn't?

https://en.wikipedia.org/wiki/Chinese_room

[roenxi]

That doesn't really respond to the question though - there is a quite reasonable argument that the Chinese room as a system 'understands' things.

The issue that is hit immediately is we don't have a definition or test of understanding that AI doesn't clear easily. Then on top of that we can't even really be sure that we ourselves are understand things given all the tricks that our minds play with memory and perception. There is precious little evidence that the people around us understand things, they seem to be guessing. It is completely unclear if a Chinese room has or doesn't have a property if we rule out all the tests that check for it as not really counting. But all the tests we can do suggest it does understand, because engineers can implement Chinese rooms now and they even turn out to be more reliably artistic/capable of novel thinking/creative than humans. Anything that tests understanding they can do.

[LtWorf]

> and it is easy to prove them wrong

No, they just say you are using the wrong model or something.

If it's a coworker dumping reviews of crap code on you at work, the incentive is to blanket approve everything because otherwise you're just the grumpy old man who is resisting innovation. No matter that the code makes no sense at all and the tests aren't actually testing what they should test.

[GolfPopper]

>where has AI underdelivered?

Other than the stock market (which seems decoupled from reality at the moment), where has AI delivered?

The only use case where I see anything resembling AI delivering on it's promises is software, and my personal experience with that is that everything that comes out of the teams using AI is destructively broken. (Where they used to be able to deliver software that worked, even if it wasn't ideal, now they reliably make things worse and their stuff doesn't work when used.)

[Viliam1234]

I agree, especially the juxtaposition of "we have still have no idea how all of this is going to work when the dust settles" and "hype". If we don't know, then there is a chance it isn't a hype.

For example, now it may seem that the models are becoming mere infrastructure, and the value moves up to apps and data. But if the models of tomorrow become able to write the apps themselves, then the value moves back. I won't need to pay some to write me a wrapper for the LLM, if the LLM will be able to write the same wrapper, maybe even better because it will be customized for my needs. The app providers are currently profiting from the gap between "what a software company can do using the AI" and "what the AI can do unaided", but that gap is going to shrink, possibly to zero.

[zetsurin]

> for example, almost all of their predictions weren’t just wrong… in hindsight, given how the future played out, they were asking the wrong question

Do you have an example of this? My (poor) memory remembers "it's going to change how people buy things", was the big deal at the time, and it seems like it was a great prediction.

[libraryofbabel]

Well, yes, but as the other commenter says, that’s a very broad general statement akin to something like “AI will change knowledge work“. That’s certainly true, but how? What are the details? What kind of companies are going to be the winners and what kind will be losers, or end up with commodity margins, like the telcos did after the mobile revolution? What is the pricing structure going to look like?

I suppose a concrete example in 1997 would be that a lot of companies thought the future of e-commerce was setting up a store on AOL, that people would use while sitting down at a desktop PC. Obviously it didn’t turn out quite that way. Furthermore, the Internet enabled new kinds of ways to buy things that weren’t even envisioned in the pre-Internet pre-smartphone world: think Airbnb and Uber.

Predictions are hard, especially about the future. Most predictions reflect the worldview and biases of the time in which they are made: think about all the vintage sci-fi from the 60s 70s and 80s that actually reads or looks kind of retro now. Similarly, our predictions of the future will look kind of retro and strange to someone living in the 2030s or 2040s. If studying history has any lesson to teach us, it’s really just this: that the past is an alien world with alien moods of thinking, and that our moment in time will look similarly alien to people in the future who choose to look back and analyze it closely.

This isn’t an argument that we should stop trying to make predictions. We need to, but it is an argument for humility, and also for questioning all your assumptions that you might be importing.

[akiselev]

That's a very vague prediction that took decades to bear fruit. The concrete predictions behind the investments into companies like Pets.com and Webvan failed. It took the survivors like Ebay/Paypal and Amazon to build the digital payment and shipping infrastructure over decades until cultural acceptance hit critical mass.

[keeda]

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.

[benedictevans]

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]

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.

[StilesCrisis]

> new technology has always created new jobs... but all those new jobs required a higher skill level

The industrial revolution didn't seem to require any particular special skill at all. Just anyone who was willing to tend to a machine all day. (Maybe that's a parallel...)

[keeda]

You're right actually, what I really meant to say is a "higher-level skill" rather than a "higher skill-level." Higher-level skills don't necessarily mean a more difficult skill, they're usually just at a higher level of abstraction.

Specifically, the 3 dimensions along with new jobs required new skills were: a) cognitive, b) technical or c) social skills. I guess tending to a machine was a mix of a) and b), because even if the controls were straightforward, it probably required some understanding of the underlying mechanisms.