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AI eats the world (Spring 26) [pdf]

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Re: AI eats the world (Spring 26) [pdf]

#131

Post by duskdozer »

lorecore wrote: post #117
benedictevans wrote: post #113

Earlier quoted context omitted.

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…

[flagged]

That quote is actually from "Scott Rackey" and not the OP: https://web.archive.org/web/20220603095222/https://twitter.c...

Re: AI eats the world (Spring 26) [pdf]

#132

Post by tovej »

benedictevans wrote: post #122
tovej wrote: post #121

Earlier quoted context omitted.

One of the graphs has two series: net revenue for one company, gross revenue for another. Absolutely ridiculous. And that's just one example. You also haven't adjusted for inflation in your graphs that span multiple decades. Not to mention that the graphs themselves are not related to what you're discussing most of the time. You're just pointing at random historical developments and seemingly claiming they imply some…

All of these points are simply wrong. I charted the revenue reported by Anthropic and OpenAI as gross and net because those are the numbers they disclose. Anthropic does not report net revenue nor give us any way to calculate that, and the same in reverse for OpenAI. It would be great if we had GAAP revenue, but we don't. This is what we have, and it still tells an important story. What are we supposed to do - just n…

It is not industry practice to actually give the sources then? Just list vague names in a short "Sources:Companies" note? I now see the inflation indication as well. Which is not on the y-axis label, but in the "Sources:"-note. I will concede that. But what the hell my guy, why is it down there.

If "financial analysis" is less rigid in sourcing requirements than grade school, what are you guys even doing. If you have the source, it's not very difficult to make a bibliography (or even write the year/publication with the author/publisher), and not doing so only serves to hinder the reader. If this is industry standard it means your entire industry is terrible at sourcing, does not want the reader to verify claims, or both.

And I've also definitely seen financial slide decks with actual sources cited. So I'm inclined to hope there are people in your industry who actually respect the reader's time.

Here's some more picks (and some more reasons you should list your sources): Your graphs based on surveys don't report error margins. You never list what a 100% is very precisely (what's the sample/population). In one graph, you don't label the y-axis at all (except for a 0 at the bottom)!

And finally, I was rude, yes. But I was only matching your energy. And I did show constraint there. I didn't make a veiled threat, did I?

Re: AI eats the world (Spring 26) [pdf]

#133

Post by aleph_minus_one »

thrance wrote: post #130
arexxbifs wrote: post #129

> Imagine asking “What will be changed by the internet?” in 1997 Pretty much all of the stuff that was suggested back then or earlier: Shopping, advertising, video conferencing, collaboration, software distribution, media consumption, banking, finance and of course communication overall. Most of these ideas weren't exactly new in 1997, but go back to services like CompuServe and even Douglas Engelbart's Mother of All…

Knowing why we're trying to build something is a good smell test to segregate promising tech from snake oil, in my experience. Take quantum computers for example, a lot of the time people will compare that to the dawn of classical computing, with claims such as "we can't know yet what we'll be able to achieve, we have to build it first!". Except that even the first classical computers were built with goals and applic…

> 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?

Re: AI eats the world (Spring 26) [pdf]

#134

Post by menaerus »

nmfisher wrote: post #33
dist-epoch wrote: post #23

Earlier quoted context omitted.

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.

antirez running (quantized) DeepSeek V4 Pro on a Mac Studio M3 Ultra with 512GB of RAM: https://bsky.app/profile/antirez.bsky.social/post/3mlzwmvlov... It's much closer than you think. We're going to see specialized hardware in the next 24 months capable of running 2025-era frontier models. That's big.

2-bit quantization? That's a lot of signal being removed. Considering how quickly the AI models are progressing in their capabilities (still exponential curve), I will not want to use the 2025 model in two years time. Similarly, how I don't want to use llama-3 or old Anthropic model from 2023 or 2024. Newer models are so much better that it makes it very difficult to ignore.

Once and if the advancements with the AI models slow down, only then IMHO it will become feasible to design the specialized HW for general-purpose consumption and general-purpose workloads.

Re: AI eats the world (Spring 26) [pdf]

#135

Post by menaerus »

aurareturn wrote: post #123
benedictevans wrote: post #112

Earlier quoted context omitted.

Yes, I wrote about Rock’s Law too, but we don’t know that this is how these models will develop

Evidence point to the same type of scaling law. Compute for a training run grows 4-5x every year.[0] I'm sure this will slow down but the premise remains that weaker competitors will not be able to maintain this pace. We already see labs like Cohere, Mistral, Inflection AI, Adept, Character.ai, and others bow out of the frontier race. I'm also skeptical that Meta, xAI can catch up. Even Google has trouble keeping up.…

You lay out some good arguments but I agree with both: the models relative to few years back really did become the commodity because today you could take the non-frontier model, maybe self-host it or pay the much less price per M tokens to get the performance of a ~2-year old frontier model. At the same time I do think that we are getting into the monopoly/duopoly/tripoly with the frontier models for all the reasons you already mentioned, and this scares me a little bit.

Re: AI eats the world (Spring 26) [pdf]

#136

Post by thrance »

aleph_minus_one wrote: post #133
thrance wrote: post #130

Earlier quoted context omitted.

Knowing why we're trying to build something is a good smell test to segregate promising tech from snake oil, in my experience. Take quantum computers for example, a lot of the time people will compare that to the dawn of classical computing, with claims such as "we can't know yet what we'll be able to achieve, we have to build it first!". Except that even the first classical computers were built with goals and applic…

> 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…

I am familiar with these applications. Indeed, your questions are very relevant. I've personally decided a long time ago that no, these applications are insufficiently useful to justify the billions invested in quantum computing, and the billions more that will be required to build anything remotely capable. So far, nothing's come up to make me reconsider.

Re: AI eats the world (Spring 26) [pdf]

#138

Post by wao0uuno »

ocimbote wrote: post #40

tl;dr; > "What happened the last time that everything changed?" Honestly, I'm glad we hear more of the commoditization of AI, and I hope that the comparison of AI with water or electricity will become mainstream and that the states (as in nation states) will understand that sooner rather than later and act accordingly.

How the fuck can you put ai on par with water or even electricity?

Re: AI eats the world (Spring 26) [pdf]

#139

Post by aurareturn »

menaerus wrote: post #135
aurareturn wrote: post #123

Earlier quoted context omitted.

Evidence point to the same type of scaling law. Compute for a training run grows 4-5x every year.[0] I'm sure this will slow down but the premise remains that weaker competitors will not be able to maintain this pace. We already see labs like Cohere, Mistral, Inflection AI, Adept, Character.ai, and others bow out of the frontier race. I'm also skeptical that Meta, xAI can catch up. Even Google has trouble keeping up.…

You lay out some good arguments but I agree with both: the models relative to few years back really did become the commodity because today you could take the non-frontier model, maybe self-host it or pay the much less price per M tokens to get the performance of a ~2-year old frontier model. At the same time I do think that we are getting into the monopoly/duopoly/tripoly with the frontier models for all the reasons…

Lower intelligence LLMs can be a commodity, yes. But these won't make much money, if at all. At the end of the day, it costs the same to inference a 1T frontier model and a 1T free model.

OpenAI and Anthropic don't compete in the LLM commodity market. Hence, I had a problem with slide 22.

Re: AI eats the world (Spring 26) [pdf]

#140

Post by pjc50 »

jkhdigital wrote: post #48
kannanvijayan wrote: post #37

This is a reasonably well-examined take of the situation. On the technical side, one of the additional things I've had on my mind is the potential that these mega models are in fact hiding a ton of inefficiency. The approach of simply shoving higher dimensionality and more parameters into largely tweaks to the current models has delivered results, but it feels like "mainframe" era of computing to me. Throwing reams o…

Right, the crazy thing is that 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 mostly because the rules had to be populated manually and in a ridiculously space-inefficient format (compared to the density of information in model weight…

> 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 ...

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