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Who's going to pay to run those models? They are currently running at a huge loss.
The models get more efficient every year and consumer chips get more capable every year. A GPT-5 level model will be on every phone running locally in 5 years.
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One of the reasons I like to swing by HN on the weekend is that the flavor of the comments is a lot spicier. For better or worse.
Is that a thing now?
Slightly different cohorts.
This AI bubble already has lots of people with their forks and knifes waiting to capitalize on a myriad of possible surpluses after the burst. There's speculation on top of _the next bubble_ and how it will form, even before this one pops.
That is absolutely disgusting, by the way.
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The self checkout machines at the supermarket can talk and make decisions. I don't see them revolutionising the world.
> I don't see them revolutionising the world. They revolutionized supermarkets.
I would really like to hear you explain how they revolutionized supermarkets.
I use them every day, and my shopping experience is served far better by going to a place that is smaller than one that has automated checkout machines. (Smaller means so much faster.)
Hell, if you go to Costco, the automated checkout line moves slower than the ones manned by experienced workers.
The singularity. I don't think most authors of articles like these understand what the AI build up is about, they think it's another fad tool.
I cant believe people still arent grasping the profound implications of computers that can talk and make decisions.
Speech to text and vice versa exists for over a decade. Where's the life altering application from that?
Indeed. I was using speech to text three decades ago. Dragon Naturally Speaking was released in the 90s.
GPUs still won't be cheap
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After heavy use, though? I don't think they mean aging out of being cutting edge but actually starting to fail sooner after being used in DCs.
They're mostly solid state parts. The parts that do wear out like fans are easily replaced by hobbyists.
> GPUs that have a 1-3 year lifespan In 10 years GPUs will have a lifespan for 5-7 years. The rate of improvement on this front has been slowing down faster then CPU.
> Most of the money is being spent on incredibly expensive GPUs that have a 1-3 year lifespan due to becoming obsolete quickly and wearing out under constant, high-intensity use.
So it isn’t entirely tied to the rate of obsolescence, these things apparently get worn down from the workloads.
In terms of performance improvement, it is slightly complicated, right? It turns out that it was possible to do ML training on existing GPGPU. Then there was spurt of improvement as they go after the low-hanging fruit for that application…
If we’re talking about what we might be left with after the bubble pops, the rate of obsolescence doesn’t seem that relevant anyway. The chips as they are after the pop will be usable for the next thing or not, it is hard to guess.
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I think you aren't understanding the meaning of the world bubble here. No one can deny the impact LLM can have but it still has limits. And the term bubble is used here as an economic phenomenon. This is for the money that openai is planning on spending which they don't have. So much money is being l poured here, but most users won't pay the outrageous sums of money that will actually be needed for these LLM to run,…
the real innovation is that neural networks are generalized learning machines. LLMs are neural networks on human language. The implications of world models + LLMs will take them farther
Even in 2002, my CS profs were talking about how GAI was a long time off bc we had been trying for decades to innovate on neural nets and LLMs and nothing better had been created despite some of the smartest people on the planet trying.