[Bloedcoins]

You need to ask 'when'.

ChatGPT shows a clear path forward. Feedback loop (consistent improvement), tooling which leverages all of llms powers, writing unit tests automatically and running code (chatgpt can run python already, when will it able to run java and other langauges?)

And its arleady useful today for small things. Copilot is easier and more integrated than googling parameters or looking up documentation.

UIs/IDEs like curser.ai are a lot more integrated.

What you see today is just the beginning of a something, potentially big.

[EZ-E]

I respect your opinion and you could be right, but I don't buy it so far. While integrations have improved, for the LLM models everything relies on, we don't see major advances anymore. Compare the jump from GPT3.5 to 4, vs the next iterations, it still suffers from the same limitations LLMs have (context length, overconfidence, hallucinations). Maybe I'm too impatient.

[Bloedcoins]

From a research point of view, context length got a lot better in the last year and continues to become better.

Chatgpt just released new voice mode.

It took over a year to get GitHub Copilot rolled out in my very big company.

People work left and right to make it better. Every benchmark shows either smaller models or faster models or better models. This will not stop anytime soon.

Flux for Image generatin came out of nowhere and is a lot better with faces and hands and image description than anything before it.

Yes the original jump was crazy but we are running into capacity constrains left and right.

Alone how long it takes for a company to buy enough GPUs, building a platform, workflows, transition capacity into it, etc. takes time.

When i say AI will change our industry, i don't know how long it takes. I guess 5-10 years but it makes it a lot more obvious HOW and the HOW was completly missing before GPT3. I couldn't came up with an good idea how to do something like this at all.

And for hallucinations, there are also plenty of people working left and right. The reasoning of o1 is the first big throw of a big company to start running a model longer. But for running o1 for 10 seconds and longer, you need a lot more resources.

Nvidias chip production is currently a hard limit in our industry. Even getting enough energy into Datacenters is a hard limit right now.

Its clearly not money if you look how much money is thrown at it already.