Why OpenAI's $157B valuation misreads AI's future (Oct 2024)

One point I’ll agree on is his final one: that the true big players haven’t even been founded yet. Right now, the AI hype seems to still revolve around the dream of replacing humans with machines and still magically making Capitalism work in the process, which is something I (and other “contrarians”) have beaten to death in other threads. That said, what these companies have managed to demonstrate is that transformer-based predictive models are a part of the future - just not AGI.

If I were a VC, I’d be looking at startups that take the same training techniques but apply them in niche fields with higher success rates than general models. An example might be a firm that puts in the grunt work of training a foundational model in a specific realm of medicine, and then makes it easier for a hospital network to run said model locally against patient data while also continuously training and fine-tuning the underlying model. I wouldn’t want to get into the muck of SaaS in these cases, because data sovereignty is only going to become an ever-thornier issue in the coming decades, and these prediction models can leak user data like a sieve if not implemented correctly. Same goes for other narrow applications, like single-mode logistics networks or on-site hospitality interfaces. The real money will be in the ability to run foundational models against your own data in privacy and security, with inference at the edge or on-device rather than off in a hyperscaler datacenter somewhere.

Then again, I could be totally wrong. Guess we’ll all find out together.

I can't admit to myself there's any open question as to if there is any long-term value.

I expect within 2 years, this will seem like a non-controversial idea, and it won't bring in a ton of assumptions about the speaker.

I have invested much time and effort making sure local models are a peer to remote ones in my app, and none, including DeepSeek's local models, are remotely close to the things needed to make that flow work.

EDIT: Reply-throttled, so answering replies here:

- The machine is building the machine: Telosnex, a cross-platform Flutter app

- it can do 90% of the scope, especially after I wrote precanned instructions for doing e.g. property-based testing.

- Things it's done mostly wholesale: -- secure iframe environment, on all 6 platforms, to: execute JS in, or render react components it wrote. -- completely refactoring my llama.cpp inference to use non-deprecated APIs.

- Codebase is about 40K real lines of code. (I have to think this helps a lot I doubt that ex. from scratch it would be able to build a Flutter app that used llama.cpp.)

- $30/day!?! -- Yeah, it's crazy, its up an order of magnitude from my most busy days when I just copy-pasted back and forth. It reads as much code as it wants, and you're doing more work literally, so it adds up.

- $20/day is realistic average

- Lines added per day +55%, lines deleted per day +29%, files changed per day 9 -> 21 https://x.com/jpohhhh/status/1881453489852948561