Excuse me, but the industries AI is disrupting are not lucrative - 0 views
-
Google’s Gemini. The demo video earlier this week was nothing short of amazing, as Gemini appeared to fluidly interact with a questioner going through various tasks and drawings, always giving succinct and correct answers.
-
that’s. . . not what’s going on. Rather, they pre-recorded it and sent individual frames of the video to Gemini to respond to, as well as more informative prompts than shown, in addition to editing the replies from Gemini to be shorter and thus, presumably, more relevant. Factor all that in, Gemini doesn’t look that different from GPT-4,
- ...24 more annotations...
-
Continued hype is necessary for the industry, because so much money flowing in essentially allows the big players, like OpenAI, to operate free of economic worry and considerations
-
The money involved is staggering—Anthropic announced they would compete with OpenAI and raised 2 billion dollars to train their next-gen model, a European counterpart just raised 500 million, etc. Venture capitalists are eager to throw as much money as humanely possible into AI, as it looks so revolutionary, so manifesto-worthy, so lucrative.
-
While I have no idea what the downloads are going to be for the GPT Store next year, my suspicion is it does not live up to the hyped Apple-esque expectation.
-
given their test scores, I’m willing to say GPT-4 or Gemini is smarter along many dimensions than a lot of actual humans, at least in the breadth of their abstract knowledge—all while noting even leading models still have around a 3% hallucination rate, which stacks up in a complex task.
-
A more interesting “bear case” for AI is that, if you look at the list of industries that leading AIs like GPT-4 are capable of disrupting—and therefore making money off of—the list is lackluster from a return-on-investment perspective, because the industries themselves are not very lucrative.
-
What are AIs of the GPT-4 generation best at? It’s things like:writing essays or short fictionsdigital artchattingprogramming assistance
-
While I personally wouldn’t go so far as to describe current LLMs as “a solution in search of a problem” like cryptocurrency has famously been described as, I do think the description rings true in an overall economic/business sense so fa
-
The issue is that taking the job of a human illustrator just. . . doesn’t make you much money. Because human illustrators don’t make much money
-
While you can easily use Dall-E to make art for a blog, or a comic book, or a fantasy portrait to play an RPG, the market for those things is vanishingly small, almost nonexistent
-
As of this writing, the compute cost to create an image using a large image model is roughly $.001 and it takes around 1 second. Doing a similar task with a designer or a photographer would cost hundreds of dollars (minimum) and many hours or days (accounting for work time, as well as schedules). Even if, for simplicity’s sake, we underestimate the cost to be $100 and the time to be 1 hour, generative AI is 100,000 times cheaper and 3,600 times faster than the human alternative.
-
Like, wow, an AI that can write a Reddit comment! Well, there are millions of Reddit comments, which is precisely why we now have AIs good at writing them. Wow, an AI that can generate music! Well, there are millions of songs, which is precisely why we now have AIs good at creating them.
-
Search is the most obvious large market for AI companies, but Bing has had effectively GPT-4-level AI on offer now for almost a year, and there’s been no huge steal from Google’s market share.
-
What about programming? It’s actually a great expression of the issue, because AI isn’t replacing programming—it’s replacing Stack Overflow, a programming advice website (after all, you can’t just hire GPT-4 to code something for you, you have to hire a programmer who uses GPT-4
-
Even if OpenAI drove Stack Overflow out of business entirely and cornered the market on “helping with programming” they would gain, what? Stack Overflow is worth about 1.8 billion, according to its last sale in 2022. OpenAI already dwarfs it in valuation by an order of magnitude.
-
The more one thinks about this, one notices a tension in the very pitch itself: don’t worry, AI isn’t going to take all our jobs, just make us better at them, but at the same time, the upside of AI as an industry is the total combined worth of the industries its replacing, er, disrupting, and this justifies the massive investments and endless economic optimism.
-
It makes me worried about the worst of all possible worlds: generative AI manages to pollute the internet with cheap synthetic data, manages to make being a human artist / creator harder, manages to provide the basis of agential AIs that still pose some sort of existential risk if they get intelligent enough—all without ushering in some massive GDP boost that takes us into utopia
-
If the AI industry ever goes through an economic bust sometime in the next decade I think it’ll be because there are fewer ways than first thought to squeeze substantial profits out of tasks that are relatively commonplace already
-
We can just look around for equivalencies. The payment for humans working as “mechanical turks” on Amazon are shockingly low. If a human pretending to be an AI (which is essentially what a mechanical turk worker is doing) only makes a buck an hour, how much will an AI make doing the same thing?
-
What’s written on the internet is a huge “high quality” training set (at least in that it is all legible and collectable and easy to parse) so AIs are very good at writing the kind of things you read on the internet
-
But data with a high supply usually means its production is easy or commonplace, which, ceteris paribus, means it’s cheap to sell in turn. The result is a highly-intelligent AI merely adding to an already-massive supply of the stuff it’s trained on.
-
Was there really a great crying need for new ways to cheat on academic essays? Probably not. Will chatting with the History Buff AI app (it was is in the background of Sam Altman’s presentation) be significantly different than chatting with posters on /r/history on Reddit? Probably not
-
Call it the supply paradox of AI: the easier it is to train an AI to do something, the less economically valuable that thing is. After all, the huge supply of the thing is how the AI got so good in the first place.