> Do you think Amazon and Google disappeared after 2001?

I don't think that was implied at all, just that the context of the web is what allowed those companies to pop up.

What I wanted to say is every era gave birth to something big.

AI era will get its own winners, but there will be some new big players as a result of this era I think

What good is an open-weights DeepSeek model if you have nowhere to run it?

OpenAI / Google / Anthropic / XAI also have a ton of compute. That is the real moat.

I think the original DeepSeek moment seemed important. And yes, the more recent model is good, but there are multiple. This commodification trend spans many different companies, including Kimi 2.5/2.6 and GLM5.1, and even Google itself with its Gemma models. There are a dozen models that exist at roughly the frontier from 6 months ago at 1/10th the cost.

Didn't you mean Claude take? It's ai written after all...

Legal has lots of institutional inertia behind it though. I think AI will be very very useful for lawyers..... at their desk in private. But I don't see it replacing them. The legal system is heavily personal and relies a lot on reputation and tradition. I think you'll see courts, bar organizations, etc frowning on using AI too heavily, and certainly not using it to automate "official" processes.

> 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.

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.

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?

Because that's 364 days.

Usually startups like to talk about Adjusted Annual Revenue in fundraising and other hype materials. There’s no regulation around this metric so whatever their investors are willing to accept is what they use. One way to measure it is to take the past 4 weeks revenue and multiply by 13.

There’s a bunch of fuzzy metrics here, which is one reason I turned it back into a monthly number. The other issue (as you’ll see on the chart) is that Anthropic and openAI are recognising revenue in completely different ways.

Lets say you work at a startup that is growing insanely fast and you want to report financial metrics to investors, media etc. You can't use annual recurring revenue because 2026 is not over yet, and your company is so young it doesn't make sense to look back to last year. You can't use YoY because it would be some obscene figure (100000%) that definitely won't hold.

So the two best metrics are annualized recurring revenue (take last month * 12 or last 4 weeks * 13) and QoQ growth %.

There are two caveats:

- If the revenue is high quality (e.g. annual enterprise contracts, good NRR), then last 4 weeks * 13 is actually a conservative estimate as your company will likely continue to grow.

- But if the revenue is more volatile (e.g. consumption, token usage, bad NRR) then annualized recurring revenue can be used to hide worse performance because companies will juice revenue one month and report high "ARR"

What's wrong with that? There are now materials that allow you to have solar panels on a window (so they are not opaque anymore), and we can put data centers under our feet.

"we"? Are we all now billionaires who decide what gets built and who gets laid off?

None of "us" gets to decide this. Only the very wealthy get to decide.

In the current system technology is meant to serve the shareholders. That might end up serving us, if you believe the standard narrative. But maybe the shareholders will just carve out a few nature reserves for themselves and just wait for the rest of us to die off.

when someone gives their opinion about AI, one typical retort is "are you an AI/LLM expert? we should let the experts talk"

> Further than the doomers think, but not enough to pay off the investors of the original boom

Not an uncommon event - not only did this happen to many companies who were big in the original internet boom (e.g. Sun Microsystems, as well as all the Boo, Pets.com etc), it also happened to the railway boom of the previous century, and even the Channel Tunnel.

I feel like the talk about "world models" is trying to reach at that, but cast it in different terminology. World model is just domain model, and once you're at domain model, there are multitudes of domains.

Unsupervised learning over domain rulesystems has the potential to let us define really well-defined, scoped models that behave a lot more deterministically and don't colour outside the lines, and reserve their weights for cleanly modeling the domain associations and relationships that matter.

I just asked codex the following question in the middle of my coding prompt:

  What are you thoughts on the relative strengths of ewoks vs jawans?

Answer:

  • Ewoks are stronger in direct conflict. They are organized fighters, good at
    ambushes, traps, terrain control, and coordinated attacks. On Endor, the beat
  a technologically superior force by using preparation and local knowledge.
  ....

As amusing as this may be, I really have no need or desire for my coding model to understand or be aware of ewoks and their relative strengths compared to jawans. Nor do I need it to understand the nuances of the races of middle earth. And prompt response of "I have no idea what you are talking about" to all of these would feel reassuringly scoped.

Mixture-of-Experts seems like an attempt to do this - the domain structure being extracted into specific sub-models that are presumably trained on particular domain-associated content - but it feels like this is once again the beginnings of what is possible.

> much of the groundwork for the “rules-and-heuristics” mode of AI was laid down in the 70s and 80s, long before we had the raw compute power to reliably extract patterns from reality-scale inputs. Those early efforts failed miserably

Yes, and: we concluded that enough of reality doesn't work like that. The formal reasoning space is very powerful, but all the stuff we're really interested in has enough ambiguity and generalisation in that you can't cover it with a "small" set of rules.

Maybe if you had a really large number of rules? And used matrix multiplication to make sure that you covered all the marginal interactions between every possible set of rules? And then had some means of looking back on both output and input to constrain it towards things that were relevant? Wait a minute ...

May be cuz it needs water and electricity for its existence?

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 tier of intelligence.

Today, Anthropic and OpenAI are clearly in the lead for models and then there is everyone else. Google is a close 3rd. No one else is challenging them anymore in SOTA models. Some models might beat them in one or two benchmarks but none can compete overall. I expect this gap to grow bigger as models cost more and more to train.

A few evidence I pieced together:

1. Ex-ByteDance Engineer said so recently: https://www.businessinsider.com/ex-bytedance-engineer-says-c...

2. A benchmark concludes so: https://www.nist.gov/news-events/news/2026/05/caisi-evaluati...

3. Generally, people say Chinese models bench better than they perform

I think it seems to make sense given that China does not have access to Blackwell while in the past, they at least had access to gimped H200s.

I believe they're referring to the quote showing up on 50 and 51 but that appears to be intentional (transition adds question below)

Ben, I follow you and think you’re brilliant, but boy you can’t take feedback like ever. Duplicate slide is there clearly to add the question, but it has a glaring typo—- “predications” instead of “predictions”. A random internet stranger read to page 51, which is already a rare occurrence, and helped you find a typo that you can now edit. But sure, the answer to that is “your comment is wrong”.

> Except that even the first classical computers were built with goals and applications in mind. [...] Instead, when asking a quantum computing company what they're trying to achieve, they'll gesture vaguely at "chemistry, finance, ecology".

I think the problem is a little bit more subtle:

To finance a lot of innovations, better also some intermediate step towards the far goal should already be very useful, otherwise the company that builds it will go bankrupt.

If this is not the case, it's typically not commercially viable, some product category is typically basic research (which is very important, but it typically means that the commercial potential will only come up in some future).

There do exist problems where a quantum computer gives an extreme advantage in the sense that we have no idea how a fast classical algorithm could look like. So, the only viable approaches for these problems are:

1. work on a huge algorithmic breakthrough (to be able to solve these problems fast on a classical computer)

2. build a quantum computer

What are these problems?

They are basically all special cases of the abelian hidden subgroup problem:

> https://en.wikipedia.org/w/index.php?title=Hidden_subgroup_p...

In particular cf. the table at the end of this Wikipedia article:

> https://en.wikipedia.org/w/index.php?title=Hidden_subgroup_p...

If you do have such a problem to solve, 1 and 2 are the only viable approaches.

So, there do exist goals and applications for which a quantum computer is insanely useful (assuming no huge algorithmic breakthrough happens).

The questions are thus:

- Is the abelian hidden subgroup problem sufficient for being able to carry a whole potential industry?

- (To come back to my introduction) What use does a quantum computer that is only capable of solving very small instances of this problem have for the user?

I think a more nuanced take is appropriate here. It‘s true that computers were invented with the express goal of speeding up military and corporate computing (back when computers were still people), but their influence on our culture and society extended far beyond those initial applications. The telephone was invented as a means of long-distance communication, but it shaped our values surrounding communication as well. Therefore it may be hard to predict what will ultimately become of a technology.

I agree that there are a lot of overhyped technologies though. Quantum computing has been in the works for decades now, with little to show for it in the popular perception.