Comment by yumraj

8 hours ago

Given the fast churn of the models, how does it work out?

Won’t the silicon etched model already be 1 or more versions behind by the time the silicon comes out.

Though if it’s cheap enough, there certainly can be a market for cheaper model inferences.

I think the real value here is not as a customer-facing agent/chatbot but for for automated processes. Think of all the companies out there that have LLMs doing simple tasks like categorizing customer feedback emails. For such tasks, you don't gain much from better models, so if you could run it 10x cheaper on a slightly older model, it would absolutely be worth it. Pretty much any place people are currently running a flash model could benefit from this since they're already deciding that speed+price is worth using a less capable model.

I find speed alone would be a game changer for current models. I hardly find any task anymore that the current frontier models can't do with max reasoning after several rounds of feedback (provided sufficient instruction and the right harness). But waiting an hour or more for reasoning to finish is getting really cumbersome. If they could do the same in seconds (and for cheap of course), I'm pretty sure we'd pretty soon see major software companies pop up that are run by a single human.

  • Can you give some examples of these tasks that require an hour or more of reasoning?

    • The recent maths prompts did. The 'you should find a breakthrough' one was several blocks of reasoning, each taking 90 minutes or so

I think this would make sense for consumer hardware, not for AI companies.

AI companies constantly update/change stuff, new models come out, new requirements, etc.

But if you ship an "ai-powered" dishwasher, it can come with the chip built-in to do computer vision and precisely target each spot, and will be sold as-is with no updates.

  • Makes sense. Actually to expand, I believe this can make a lot of sense for industrial robots and such which have a more or less fixed job and latency matters more, so a well tested model may be more valuable than need to keep updating them

  • You don’t need this chip to do that. Computer vision has used machine learning for decades. The task you’re describing is pretty rudimentary and an off the shelf model with a control system would do it way cheaper.

    • Think of a HomePod. 99% (and likely much more) of what people are asking is super simple.

    • It was just a random example, you could think of it as being a lot more complex (detect which type of food it is, what detergent to use, how much water, remember patterns, learn over time, adapt, etc.)

  • subscription "ai-powered" dishwasher with personalized user ads, most of the chip dedicated to "personalized" not spots.

    • So local personalized ads?

      Not sure if that's better or worse than online personalizaed ads...

There’s some kind of tradeoff between speed, cost, and quality for every application. I would be perfectly happy with a model 6 months old that was 50x faster for many uses. Right now I use either Opus (for smart stuff) or Flash without thinking (for fast stuff). I would take an even dumber model for more speed (lower latency in particular).

Perfect for consumers. You buy it and then you need to buy a new one in a couple of years. If they can make them affordable they'll sell like hotcakes.

  • And the second hand market. I'd love to see this integrated into motherboards like RAM. Someone could have a motherboard with 4 sticks of different AI with various models. Swap, change and trade.

That’s not going to be true forever. As models mature, we will hit diminishing returns. Major improvements will come annually rather monthly - matching the roughly annual release of new processors. Model ROM’s will likely get integrated into die packages just like DRAM now.

Already models have gotten really good at a lot of things.

A lot of people would probably be happy to stick with the same model for a year or two if it’s 10x faster and cheaper.

And perhaps older models can become cheaper over time as newer models come out on new silicon for a higher price. That incentivizes people to stick with older models.

I'm still using Opus for most daily task because Fable is too expensive.

If they begin etching Fable into silicon now and release it 2-3 years later, i can see the market for it

This is a feature for most local use cases. You don't want all your work flows to start failing because of a model update.

They expect a sort of breakpoint at which each subsequent model version will only be marginally better than the previous ones, thus allowing them to retain their value for some time. Their business doesn’t work if each year the new model demolishes the previous one in terms of performance.

pretty much everything is “1 or more versions behind” by the time it comes out. the question is whether or not it’s still useful? at some point, presumably not every application will need the latest cutting edge huge model.

There isn't a fast churn in the underlying pretrained model, nor RL. It's mostly orchestration around the model. Said another way you could just pretrain and RL for longer.

Also I believe there is both a market for extremely fast local inference with current model performance and that such fast inference would unlock unforeseen usecases. Especially as TPS approaches early computer clock cycles and data rates.

You could take your silicon chip and have it re-etched only with model diffs for an upgraded version.