41–50 of 83 posts
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How would this be a bad thing?
Because everyone's retirement depends on the stock market. If you're unlucky and your portfolio vests the day after a massive crash, that can have a very meaningful impact on the rest of your life.
Whether or not you believe we will reach it in a fee years, we are certainly wayy closer today than we were even two years ago.
The possibility of genuine AGI obliterates all the financial or energy related worries, they pale in comparison to the ultimate impact of such a technology.
However, yes, if you believe AGI is not possible or won’t arrive in the coming decade then all the data center buildup seems foolish.
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The .COM bubble was more than a divot because .COMs in so many industries employed so many people. There was amazon.com, but also pets.com, lowermybills.com, gateway.com. But if our economy somehow loses access to AI (rationing due to wartime efforts? sabotage by a foreign nation? simply not enough grid power to turn them on at the price people are willing to pay?) I would probably need to hire more coders to get the…
AI is driving trades, materials, real estate, all sorts of downstream stuff. The rest of the economy is dead. Oracle is dead without OpenAI. Remember that unlike the dotcom, none of these companies are public. So when it pops, you’ll see private credit and PE funds implode, which could bring down banks with unhedged exposure. The headlines talk about JP Morgan (which likely has the risk managed), but regional banks g…
People really really don’t understand the implications of AGI. Whether or not you believe we will reach it in a fee years, we are certainly wayy closer today than we were even two years ago. The possibility of genuine AGI obliterates all the financial or energy related worries, they pale in comparison to the ultimate impact of such a technology. However, yes, if you believe AGI is not possible or won’t arrive in the…
Gotta love this argument. Top it off by saying anyone skeptical is a fool, because of course.
People really really don’t understand the implications of AGI. Whether or not you believe we will reach it in a fee years, we are certainly wayy closer today than we were even two years ago. The possibility of genuine AGI obliterates all the financial or energy related worries, they pale in comparison to the ultimate impact of such a technology. However, yes, if you believe AGI is not possible or won’t arrive in the…
"any day now" Gotta love this argument. Top it off by saying anyone skeptical is a fool, because of course.
It's not really even a question. It's an obvious boondoggle. The forecasted net new energy requirements for the AI buildout over the next couple of years are roughly equivalent to all of Western Europe's power demand today. That's absurd. It's a physical impossibility to bring that much power online that quickly. And the cost to get even close would make AI more expensive than just hiring knowledge workers to do the…
https://catenaa.com/markets/cryptocurrencies/hut-8-builds-fl...
And they'll also do "high-performance computing."
Yet, I think Sun's early 2000's vision "the network is the computer" is finally coming and these data centers will all end up becoming multi-use. Want access to apps running with 128GB of memory? Fine.. it'll just be on a thin-client with a data-center powering it (and everything else it does.)
It's not a bad model. As I've mentioned previously, on the client-side I think will be a new era of all-in-one modular SBCs (medium clients.) These can become thin-clients for really beefy applications too that don't have to be "local-first" and can thus be "cloud enabled."
It'd also be interesting to see crypto become more dynamic. Like making it super easy to issue a token for say an upcoming event, or better yet, a new invention looking for early adopters and supporters like Rodin Coils. The big data centers on the backend can make it secure. Just speculating. So the "big iron" compute won't ever be wasted, just repurposed dynamically.
All these mad-scientist inventions will come from unemployed geniuses and tin-foil hatters, some of whom may actually be right. Let's see if they can find a way to vastly speed up radioactive decay with lasers, but, letting the bankers be fine with it all.
People really really don’t understand the implications of AGI. Whether or not you believe we will reach it in a fee years, we are certainly wayy closer today than we were even two years ago. The possibility of genuine AGI obliterates all the financial or energy related worries, they pale in comparison to the ultimate impact of such a technology. However, yes, if you believe AGI is not possible or won’t arrive in the…
It's not really even a question. It's an obvious boondoggle. The forecasted net new energy requirements for the AI buildout over the next couple of years are roughly equivalent to all of Western Europe's power demand today. That's absurd. It's a physical impossibility to bring that much power online that quickly. And the cost to get even close would make AI more expensive than just hiring knowledge workers to do the…
> Wobbly assumption that increasing the size of these models yields better performance. I'm assuming you disagree that larger models are better? Can you expand on what indicates that AI will hit a wall in scaling given the evidence of the last 9 years of scaling transformers (or other models)? Where on the plot does the line go from exponential to flat?
(Incidentally, the line of skill improvement isn't "exponential". It's been incremental in improvements per generation, but generations have been coming thick and fast of late, and have grown in parameter count exponentially since 2017.)
Speaking more broadly, LLMs don't have to "hit a wall" in scaling to become uneconomical. If incremental improvement continues to come at exponential cost, however, then the fundamental value argument falls apart.
Setting all that aside, even presuming that model performance scales linearly with dimensionality, there are just fundamental limits to the size of the training corpuses. Quality training data is not unbounded and infinite. Given the same size corpus of training data, there's a hard theoretical limit to how much meaning and inference a model can wring out of it.
And then there are other issues with the whole business model. For one thing, it's predicated on continual full scale retraining to achieve even modest gains in skill and relevancy. Topical and targeted learning requires a full retraining. Etc cetera.
I think that the next generation of AI will lean more heavily on RL to be useful beyond a few months. I also think that the energy requirements of a particular technology are a good proxy to whether it's got a realistic future.
Earlier quoted context omitted.
> Wobbly assumption that increasing the size of these models yields better performance. I'm assuming you disagree that larger models are better? Can you expand on what indicates that AI will hit a wall in scaling given the evidence of the last 9 years of scaling transformers (or other models)? Where on the plot does the line go from exponential to flat?
In my experience the models havent gotten any better, just the hype.