I guess my point is that the scaling is coming from capital explosion and it isn’t guaranteed that this will continue scaling as it did (the impending hardware and infrastructure crunch basically guarantees this). The article is pretty strongly projecting this trend to continue but hasn’t acknowledged this factor.

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mapontosevenths 2 hours ago | parent | next [-]

> the scaling is coming from capital explosion and it isn’t guaranteed that this will continue scaling

Not at all. Have you tried local AI lately? Qwen 3.8 is legitimately much better than SoTA from 1 year ago (Opus 4.1). You can run it on a consumer GPU.

Qwen Next Flash runs on a DGX spark well and is as good as some things you need datacenter class GPU's to run even today.

Check out Artificial Analysis if you haven't. Sometimes you don't realize how fast these things are moving, but to compare things from a year ago to today is just wild.

They aren't just getting better, they're getting more efficient by many orders of magnitude.

https://artificialanalysis.ai/#intelligence

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Zambyte 3 hours ago | parent | prev | next [-]

> the impending hardware and infrastructure crunch basically guarantees this

I'm not sure I follow. Are you saying a hardware shortage is going to restrict AI development? Because it seems to me like it would just push us towards an even greater focus on efficiency, which is still pushing the Pareto Frontier.

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bunderbunder 3 hours ago | parent | prev | next [-]

This does rather remind me of how in the 80s we had a big boom in AI research, fueled in part by the development of new & expensive tools that everyone just had to have. And then it dissipated just as quickly when the companies who were pouring money into it started expecting to see a return on their investment.

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jmcgough 3 hours ago | parent [-]

Feels different now when the value of AI isn't just theoretical. Anthropic has like $80B ARR, and AI has really only transformed tech and a few limited spaces so far.

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bunderbunder 2 hours ago | parent | next [-]

Companies selling AI kit in the 80s also had revenue and great expectations.

But, then as now, what they didn’t have was profits.

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rowanG077 2 hours ago | parent | next [-]

Show me the pure AI company in the 80s that even approaches Anthropic in terms of revenue and profit excl R&D spend.

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throw310822 2 hours ago | parent | prev [-]

Not sure what job you're in, but I don't really understand where such takes come from. As someone who works in software/ IT, this stuff is pure gold- it's multiplying by a substantial factor not just the productivity but the abilities and intelligence of my entire team. This has a very measurable value in money, and what the company pays for it now is just a small fraction of the value it gets, measured in terms of the salaries you'd need to pay to get the same level of performance. Anthropic could easily charge 5 times as much and still be convenient.

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bunderbunder an hour ago | parent [-]

The technology people were developing in the 80s was also gold, in terms of its usefulness. Heck, much of it forms an essential part of the coding harnesses we all rely on nowadays.

But that didn’t stop the AI winter from coming. Because a technology being transformative does not protect it from over-investment. History perhaps suggests the opposite is true.

Nor did it prevent the AI companies of the 80s from going bankrupt when they got undercut by the commoditization that naturally came with the maturation of the technology.

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bertmuir an hour ago | parent [-]

What specific technology are you referring to?

	▲	bunderbunder 3 minutes ago \| parent [-]
		For the 80s, the symbolic AI techniques that went into expert systems.

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GolfPopper 2 hours ago | parent | prev [-]

>Feels different now when the value of AI isn't just theoretical.

We're coming up on four years since ChatGPT launched, and the only thing LLMs seem to excel at is the movement of imaginary little green pieces of paper.

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lenerdenator 3 hours ago | parent | prev | next [-]

I don't think it'll continue on linearly, but it will likely continue.

We're getting the Claude efficiency talks at work. We're looking at all sort of optimizations, including local models and graphify, to reduce spend. There are people dedicating their nights and weekends to dropping token consumption and cache rebuilding. A few of these will likely catch on and be brought into common use. That will relieve at least a bit of the pressure, and enable some of the scaling to continue.

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ctoth 3 hours ago | parent | prev [-]

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