The AI Revolution Is Already Losing Steam

I was one of the people who signed up for plus that day. It took me a while to realizing that the voice feature was not the same one being demoed. I think it was a bad move to roll it out like that.

If a company figured out production ready automation, boots on the ground actually being replaced with prompts on the ground, they would probably trade at a P/E well over 100.

This article deals with the ginormous models being trained and run in places like meta or openai - but the real AI revolution is a mix of things and we've only barely scraped the surface of what we could do with this technology.

I say this not because the next ChatGPT will be incredible (which it might be) but because it's actually the open source community which amazes me with what has already been built, and runs locally on consumer hardware.

Criticism on the massive cloud models which are expensive to run - is not wrong, for now. But it also used to take an entire room with thousands and thousands of costly vacuum tubes to be able to calculate 2+2.

My point is hardware will get better, more efficient and more suitable for AI superficially, just as software will improve and optimize.

I bet we'll be able to run bigger and bigger models for lower costs in the long term - but even if running SOA models will remain a premium, the local models still provide incredible value and that's where a lot of the revolution will (and already does) come from.

Chamath was talking about this near the end of the All In Podcast released yesterday. He was talking about development and energy cost vs. revenue and even more importantly profit.

My gut feeling is that small large language models and ever decreasing model size and computational resource use on edge devices will lead to great outcomes in a few years, but that is only my hunch.

Right now, I get the most use out of pipelined systems like Perplexity that use an LLM to understand what you want and produce search terms, do the search and set up a temporary RAG type system with search results, identify the most useful retrieved text, and then feed this into another LLM that formats and presents a report that contains links back to source material. I find this very useful, and incidentally makes the Internet “fun” again.

Also right now, my favorite play activity is writing a lot of code (Python, Racket, Common Lisp) to do small tasks using APIs fronting LLMs (mostly running locally with Ollama).

I can see the use of image-gen integrated into photoshop and krita. Codepilot and other code specific llms have not been useful at all.

That said I do find stuff like ChatGPT and Gemini useful enough to always be running a client I can send prompts to. It works great as a guide for things you know nothing about and can guide you on what resources to actually look for when you do actually care about having correct information.

I can see it being useful for things like self study and writing. For writing not in the same sense as copilot, I think it will be equally useless for anyone that is doing anything serious but it can help in exploration.

To me we have already reached the peak of what this type of 'AI' can do. Don't expect things to get much better. My mind is not going wild with the possibilities. Useful yes, revolutionary? no.