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What if A.I. doesn't get better than this?

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Re: What if A.I. doesn't get better than this?

#111

Post by rossdavidh »

mgfist wrote: post #26
rossdavidh wrote: post #15

What happens is they go out of business: "these firms spent five hundred and sixty billion dollars on A.I.-related capital expenditures in the past eighteen months, while their A.I. revenues were only about thirty-five billion." DeepSeek (and the like) will prevent the kind of price increases necessary for them to pay back hundreds of billions of dollars already spent, much less pay for more. If they don't find a way…

DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.

Matt Levine of Bloomberg said he believes most big hedge funds have a proprietary LLM (and various other machine learning models), which they use for their own purposes anyway, and therefore it was relatively straightforward for them to get into the business.

Re: What if A.I. doesn't get better than this?

#112

Post by player1234 »

bbqfog wrote: post #9

AI is so new and so powerful, that we don't really know how to use it yet. The next step is orchestration. LLMs are already powerful but they need to be scaled horizontally. "One shotting" something with a single call to an LLM should never be expected to work. That's not how the human brain works. We iterate, we collaborate with others, we reflect... We've already unlocked the hard and "mysterious" part, now we just…

Powerful but we don't know how to use it? If it is as powerful as all you true believers spout the usefulness would be self evident and that would be the display of its power.

But apparently it is powerful just because you say so, and then something, something ... business model ...

Re: What if A.I. doesn't get better than this?

#113

Post by bbqfog »

player1234 wrote: post #112
bbqfog wrote: post #9

AI is so new and so powerful, that we don't really know how to use it yet. The next step is orchestration. LLMs are already powerful but they need to be scaled horizontally. "One shotting" something with a single call to an LLM should never be expected to work. That's not how the human brain works. We iterate, we collaborate with others, we reflect... We've already unlocked the hard and "mysterious" part, now we just…

Powerful but we don't know how to use it? If it is as powerful as all you true believers spout the usefulness would be self evident and that would be the display of its power. But apparently it is powerful just because you say so, and then something, something ... business model ...

It does incredible things today that we wouldn't have thought possible 5 years ago.

Re: What if A.I. doesn't get better than this?

#115

Post by mgfist »

ben_w wrote: post #70
mgfist wrote: post #26

Earlier quoted context omitted.

DeepSeek is also undercutting itself. No one is making a profit here, everyone is trying to gobble market share. Even if you have the best model and don't care to make a dime, inference is very expensive.

> inference is very expensive I am surprised that this claim keeps getting made, given the observed prices. Even if one thinks that the losses of big model providers are due to selling below operating costs (rather than below that plus training costs plus the cost of growth), then even big open-weights models that need beefy machines, look like they eventually* amortise the cost so low that electricity is what matter…

Running a local model is not an apples comparison. Yes, if you run a small model 24/7 without a care for output latency and utilization is completely static with no bursts, then it can look cheap. But most people want output now, not in 10 hours. And they want it from the best models. And they want large context windows. And when you combine that with serving millions of users, it gets complicated and expensive.

Re: What if A.I. doesn't get better than this?

#116

Post by ben_w »

mgfist wrote: post #115
ben_w wrote: post #70

Earlier quoted context omitted.

> inference is very expensive I am surprised that this claim keeps getting made, given the observed prices. Even if one thinks that the losses of big model providers are due to selling below operating costs (rather than below that plus training costs plus the cost of growth), then even big open-weights models that need beefy machines, look like they eventually* amortise the cost so low that electricity is what matter…

Running a local model is not an apples comparison. Yes, if you run a small model 24/7 without a care for output latency and utilization is completely static with no bursts, then it can look cheap. But most people want output now, not in 10 hours. And they want it from the best models. And they want large context windows. And when you combine that with serving millions of users, it gets complicated and expensive.

When you combine that with serving millions of users, it also gets amortised over several million users.

> But most people want output now, not in 10 hours.

At 65t/s, that's 2.5 million tokens output.

Re: What if A.I. doesn't get better than this?

#117

Post by xnx »

eddythompson80 wrote: post #59
reactordev wrote: post #50

Earlier quoted context omitted.

Not all AI is LLMs. That's just what's most prevalent right now. There's still great work being done by models that don't "speak" but "perform". The issue is they need to be trained to perform like you said. The more tools like Claude Code are used, the more training they receive as well. I do think we'll see a plateau (if we haven't reached it already) of diminishing returns and we'll seek out new algorithms to impr…

> The more tools like Claude Code are used, the more training they receive as well. What do you mean? A model doesn't improve because it's being used more. Are you saying Anthropic invests more into Claude Code the more people use it? Or are you saying they collect its output and train it on it?

They probably mean https://en.wikipedia.org/wiki/Reinforcement_learning_from_hu..., but I don't think that is a huge factor for Claude Code.

Re: What if A.I. doesn't get better than this?

#118

Post by beacon294 »

ml_more wrote: post #102

We did a test of GPT5 yesterday. We asked it to generate a synopsis of a scientific topic and cite sources. We then checked those sources. GPT5 still hallucinated 65% of the citations. It did things like: Make up the paper title Make up the authors for a real paper title Mix a real title and a real journal If it can't even reference real papers it certainly can't be trusted to match up claims of fact with real source…

Google already did this, give free gemini deepresearch a spin. It's not perfect, but I have a feeling you'll be surprised if this is your honest impression.

Re: What if A.I. doesn't get better than this?

#119

Post by mgfist »

ben_w wrote: post #116
mgfist wrote: post #115

Earlier quoted context omitted.

Running a local model is not an apples comparison. Yes, if you run a small model 24/7 without a care for output latency and utilization is completely static with no bursts, then it can look cheap. But most people want output now, not in 10 hours. And they want it from the best models. And they want large context windows. And when you combine that with serving millions of users, it gets complicated and expensive.

When you combine that with serving millions of users, it also gets amortised over several million users . > But most people want output now, not in 10 hours. At 65t/s, that's 2.5 million tokens output.

Yes, but usage is not uniform even when you have millions of users. It smooths the usage lines, but the peaks and troughs become more extreme the more users you have. At 3am usage in the US goes down to effectively 0. Maybe you can use the compute for Asia customers, but then you compete with local compute that has far better latency.

Then you have seasonal peaks/troughs, such as the school year vs summer.

When you want 4 9s of uptime and good latency, you either have to overprovision hardware and eat idling costs, or rent compute and pay overhead. Both cost a lot.

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