Thread by @fchollet

The current climate in AI has so many parallels to 2021 web3 it's making me uncomfortable. Narratives based on zero data are accepted as self-evident. Everyone is expecting as a sure thing "civilization-altering" impact (& 100x returns on investment) in the next 2-3 years

Personally I think there's a bull case and bear case. The bull case is way way more conservative than what the median person on my TL considers as completely self-evident. And the actual outcome we'll see is statistically likely to lie in between, somewhat closer to the bear case

The bull case is that generative AI becomes a widespread UX paradigm for interacting with most tech products (note: this has nothing to do with AGI, which is a pipe dream). Near-future iterations of current AI models become our interface to the world's information.

The bear case is the continuation of the GPT-3 trajectory, which is that LLMs only find limited commercial success in SEO, marketing, and copywriting niches, while image generation (much more successful) peaks as a XB/y industry circa 2024. LLMs will have been a complete bubble.

So far there is *far* more evidence towards the bear case, and hardly any towards the bull case. *But* I think we're still very far from peak LLM performance at this time -- these models will improve tremendously in the next few years, both in output and in cost.

For this reason I believe the actual outcome we'll see is somewhere between the two scenarios. "AI as our universal interface to information" is a thing that will definitely happen in the future (it was always going to), but it won't quite happen with this generation of the tech.

Crucially, any sufficiently successful scenario has its own returns-defeating mechanism built-in: commoditization. *If* LLMs are capable of generating outsized economic returns, the tech will get commoditized. It will become a feature in a bunch of products, built with OSS.

As far as we know OpenAI made something like 5-10M in 2021 (1.5 years after GPT-3) and 30-40M in 2022. Only image generation has proven to be a solid commercial success at this time, and there aren't that many successful players in the space. Make of that what you will.

One thing I've found endlessly fascinating is to search Twitter for the most popular ChatGPT tweets, to gain insight into popular use cases. These tweets fall overwhelmingly into one category (like 80%). Can you guess what that is?

That's right, it's SEO/marketing engagement bait. ChatGPT has completely revolutionized the engagement bait tweet routine in these niches.

Some of it directly monetized (pay to unlock 10 ChatGPT secrets!), most of it is just trying to collect eyeballs.

Now, seeing such tweets is compatible with both the bull case and the bear case. If the tech is revolutionary, it *will* be used in this way. What's interesting to me is that ~80% of ChatGPT tweets with >2000 likes fall into this category.

This is consistent with the primary learning from the 2020-2021 class of GPT-3 startups (a category of startups willed into existence by VCs and powered by hype), which is that commercial use cases have been falling almost entirely into the marketing and copywriting niches

I think the actual potential of ChatGPT goes significantly further than that, though. It will likely find success in consumer products, and perhaps even in education and search.

Whatever happens, we will know soon enough. Billions of dollars are being scrambled to deploy ChatGPT or similar technology into a large number of products. By the end of the year we will have enough data to make a call.

Anyway, hype aside, I really believe there's a ton of cool stuff you can build with deep learning today. That was true 5 years ago, it's true today, and it will still be true 5 years from now. The tech is super valuable, even if it attracts a particularly extreme form of hype men

One last thought -- don't overindex on the web3 <> LLMs comparison. Of course web3 was pure hot air while LLMs is real tech with actual applications -- that's not the parallel I'm making. The parallel is in the bubble formation social dynamics, especially in the VC crowd.

The fact that investment is being driven by pure hype, by data-free narratives rather than actual revenue data or first-principles analysis. The circularity of it all -- hype drives investment which drives hype which drives investment. The influx of influencer engagement bait.

Most of all, the way that narratives backed by nothing somehow end up enshrined as self-evident common wisdom simply because they get repeated enough times by enough people. The way everyone starts believing the same canon (especially those who bill themselves as contrarians)