The conventional wisdom, well captured recently by Ethan Mollick, is that LLMs are advancing exponentially. A few days ago, in very popular blog post, Mollick claimed that “the current best estimates of the rate of improvement in Large Language models show capabilities doubling every 5 to 14 months”:
AI is not self-sustaining yet. Nvidia is doing well selling shovels, but most AI companies are not profitable. Stock prices and investor valuations are effectively bets on the future, not measurements of current success.
From this Forbes list of top AI companies, all but one make their money from something besides AI directly. Several of them rode the Web3 hype wave too, that didn’t make them Web3 companies.
We’re still in the early days of AI adoption and most reports of AI-driven profit increases should be taken with a large grain of salt. Some parts of AI are going to be useful, but that doesn’t mean another winter won’t come when the bubble bursts.
AI is absolutely self-sustaining. Just because a company doesn’t “only do AI” doesn’t matter. I don’t even know what that would really look like. AI is just a tool. But it’s currently an extremely widely used tool. You don’t even see 99% of the applications of it.
How do I know? I worked in that industry for a decade. Just about every large company on the planet is using some form of AI in a way that increases profitability. There’s enough return on investment that it will continue to grow.
This is like saying only computer manufacturers make money from computers directly, whereas everyone and their grandmas use computers. You’re literally looking at the news cycle about ChatGPT and making broad conclusions about an AI winter based solely on that.
Industries like fintech and cybersecurity have made permanent shifts into AI years ago and there’s no going back. The benefits of AI in these sectors cannot be matched by traditional methods.
Then, like I said in my previous comment, there are industries like security and video surveillance where object recognition, facial recognition, ALPR, video analytics, etc, have been going strong for over a decade and it’s still growing and expanding. We’ might reach a point where the advancements slow down, but that’s after the tech becomes established and commonplace.
There will be no AI winter going forward. It’s done.
You’re using “machine learning” interchangeably with “AI.” We’ve been doing ML for decades, but it’s not what most people would consider AI and it’s definitely not what I’m referring to when I say “AI winter.”
“Generative AI” is the more precise term for what most people are thinking of when they say “AI” today and it’s what is driving investments right now. It’s still very unclear what the actual value of this bubble is. There are tons of promises and a few clear use-cases, but not much proof on the ground of it being as wildly profitable as the industry is saying yet.
No I’m not
Machine learning, deep learning, generative AI, object recognition, etc, are all subsets or forms of AI.
It doesn’t matter what people are “thinking of”, if someone invokes the term “AI winter” then they better be using the right terminology, or else get out of the conversation.
There are loads and loads of proven use cases, even for LLMs. It doesn’t matter if the average person thinks that AI refers only to things like ChatGPT, the reality is that there is no AI winter coming and AI has been generating revenue (or helping to generate revenue) for a lot of companies for years now.