I recently attended a seminar at work lead by openAI (whom my company is paying for tools) which was billed as an opportunity to learn more about using AI to do our jobs more effectively. I attended mostly because I assumed there would be some technical discussions about the technology (which was largely absent) and maybe some interesting demos showing how someone used openAI’s product to solve technical problems (also absent). Instead, I was treated to a bizarre presentation, which felt strangely paternalistic and maybe even a little desperate? In order of events:
- The presentation opened with a discussion of the (impressive) scale of the data centers that openAI will be deploying + a little bragging about sora 2 (I promise you none of the scientists or engineers present give a shit about sora 2)
- It proceeded to a gentle haranguing focused on how we should not resist using AI, and that in every organization AI will become more popular as a few high performers learn how to use it to get ahead (ok, some demos would be great, openAI’s tools have been available for months, now would be a great time to show how a co-worker has used it solve a complex problem)
- Some discussion about how scientists and engineers tend to be bad at using AI relative to manager’s/procurement people/ executives/lawyers and others with what I would characterize as paper pushing roles where accuracy isn’t actually that important.
- Which finally devolved into a q&a. The most charitable questions went something like the following: Hi I am a $tpye_of_physical_scientist I love using your tool to help write python code, but it is completely worthless for helping me solve any kind of problem that I don’t already understand very well. For example, here is a tomography technique that I am aware of people using in another industry that I am mostly unfamiliar with. Right now, my approach to using this would be to read papers about how it works, try to implement it and maybe contact some other experts if I can’t figure it out. Wouldn’t it be great if I could just upload the papers about this technique to your bot and have it implement the new technique, saving myself weeks or months of time. But if you try this basic approach you usually end up with something that doesn’t work and while the bot might be able to give some superficial explanation of the phenomenon, it doesn’t add much to me just doing the background research / implementation myself and comes off as feeling like a waste of time. The response to these questions was usually some variation of the bot will get better as it scales and that you should be patient with it and make sure that you are prompting it well so that it can lead you to the correct solution.
Which brings to my primary point: which is that I am someone who has consistently tried to use AI at work in order to be effective, and while it helps somewhat with code creation, it isn’t a particularly useful research tool and doesn’t save me very much time. Apparently my co-workers are having much the same experience.
It really seems to me that openAI and their boosters believe (or would have me believe that they believe) that transformers really are all that you need and at some point in the near future they will achieve a scale where the system will rapidly go from being able to (actually) help me do my job to being able to comfortably replace me at my job. And the truth is that I just am not seeing it. It also seems like a lot of others aren’t either, with recent warnings from various tech leaders (Sam Altman for instance, by the way what possible motive for making Ai bubble statements unless it’s an attempt to prevent employees from leaving to start found their own startups).
I have been very inclined to think that this whole industry is in a bubble for months, and now that the mainstream press is picking up on it, it’s making me wonder if I am totally wrong. Id be interested if others (especially anyone with more actual experience in building these things) can help me understand if I either just suck at using them or if my “vibes” about the current state of the industry are totally incorrect. Or if there is something else going on (ie. can these things really replace enough customer service or other jobs to justify the infrastructure spend outs).
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Notes -
AVs seem like an incomparable category. I couldn't pinpoint the beginning of AV hype the same way you can point to the Transformer architecture for LLMs, but the early examples of AVs 10-15 years ago I recall were pretty impressive. It was like 80% of the way to human parity right from the get-go; it made sense that people were predicting a rapid replacement of human drivers, because they'd made such an impressive start. (I appreciate that AV efforts probably existed long before this but I think it's a fair starting point)
And then over the next decade AV capabilities crept up to human levels at like 1% per year. There were no significant breakthroughs, no evidence of rapid progress, and as you state it is only now that we're getting commercially available taxis in specific locations. Even when Waymo started rolling out proper AV taxis in some cities, it did not signal a sudden leap forward in capabilities as you might expect.
Contrast to LLMs. GPT-1 came out in 2018, a year after the Transformer paper, with GPT-2 following a year later. GPT-2 was impressive compared to previous language generators, but still only perhaps at 33% of the level of an average human. with 3 it jumped up to 50%, 3.5 went further, while 4 was perhaps at the 80% level that AVs started at. Every few months since then has since more and more large leaps, such that current models are winning mathematical competitions and are measured at PhD level in a huge variety of domains.
Chart the progress of both technologies, and they'll look completely different. It's fair to think at some point natural limits will stop the endless scaling of LLM capabilities, but thus far extrapolating a straight line has worked pretty well. AVs never even had a line to extrapolate from.
Imagine you're planning a vacation. Your dream vacation is Hawaii; your second choice is Myrtle Beach, but that would only be about half as fun. So you call a travel agent, and find out that you unfortunately don't have enough money for a flight to Hawaii. On the other hand, you could drive to Myrtle Beach, which wouldn't be nearly as expensive. Now suppose the travel agent calls you back and offers you the following proposition: "You can't afford to fly to Hawaii, but I've found a reduced rate ticket that will get you 95% of the way there for only 20% of the full price. Granted, it doesn't quite get you to Hawaii, but isn't getting 95% of your dream vacation better than settling for Myrtle Beach, which is only worth half?"
This is obviously nuts, because getting 95% of the way to Hawaii puts you somewhere in the middle of the Pacific Ocean. It's pretty obvious that if you can't get all the way to Hawaii then you're better off going somewhere else entirely. 80%, or 90%, or whatever of a marketable product is no product at all. 80% autonomous cars are regular cars with fancy cruise control (which is itself only used a small percentage of the time), and 80% of whatever AI is aiming for is fancy, expensive, inefficient Google. And saying you're 80% of the way there is more or less meaningless when it comes to technology investment. It's a vague term that has no bearing on actual numbers; it certainly doesn't mean that you're 80% of the way there time-wise or that you've spent 80% of what's necessary to get to 100%, just as the last 5% of the way to Hawaii costs four times as much as the first 95%.
In 2020, The Information estimated that the AV industry had spent $16 billion on research through 2019. Their conclusion was that the whole enterprise was a money pit and that they'd never be able to climb out of. Car and Driver put this in perspective by noting that they could have given every licensed driver in America two brand new Ford-F150s and still have cash to spare. OpenAI's recent projections for 2025 predict $7.8 billion in operating losses and a $13.5 billion net loss. One company in one year manages to spend half the money that the entire AV industry spent in a decade. And incidentally, the amount of money spent on AV research has actually gone up since then, yet you admit yourself that the improvements haven't exactly been dramatic.
AI companies want to spend another trillion or so in the next five years. Will it get them to that magic 100% mark where they can actually sell something for a profit? Nobody knows, but if it can't, I'm willing to guess that the industry's proposed solution will be to spend more money. The point I'm trying to make is that the amount of money they want to spend simply does not exist, and even if it did spending it is not justifiable to someone who eventually expects to turn a profit. If the amount being spent were on par with AVs I'd be more optimistic, but it's exponentially larger. There's going to be a point where the funding isn't going to be there, VC firms are going to have to eat their losses, and there will be a bear market in tech investment where AI is practically a dirty word. This isn't like AVs where the amount of money involved is small enough that companies can quietly make small gains that take years rather than months; it's significantly worse.
Your travel analogy is awful - it is often very valuable to solve 80% of a problem. A better analogy would be if your travel agent offered you a brand-new cheap teleportation device that had a range of "only" 80% of the way to Hawaii, but you had to purchase a flight for the last 20%. Which would obviously be great! AVs are the exception here, since you need to actually solve 99% of the driving problem for them to be useful (telepresent drivers "stepping in" can help a bit, but you don't want to depend on them).
Uh, and I don't think $64 per licensed driver in America is going to buy them two Ford-F150s. You might want to check Car and Driver's math. (What is with people being unable to properly divide by the population of the US? Does their common sense break down when dealing with big numbers?) Amusingly, I've never seen GPT4+ make this magnitude of a mistake.
Anyway, we should (and will) be taking the next decade to put smart models absolutely everywhere, even though they sometimes make mistakes. And that's going to be expensive. The major risk of AI investment is definitely not the lack of demand. As OP mentioned, the risk really is the lack of "moat" - if you only have to wait a year for an open-source model to catch up with GPT, why pay OpenAI's premium prices?
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Your first two paragraphs just appear to be quibbling over definitions. I don't really care what measurement scheme you use, abandon percentages if you find them useless. The point of the comparison is to show that the advancement in AI capabilities is on a completely different planet to AVs.
As for the comparison of investment, it seems trivial to point out that the difference in magnitude is due to the potential markets. If a company invented Level 5 self driving cars tomorrow, what would they get? You could take away human taxi drivers and truck drivers and some other logistics, and start taking a big chunk of the consumer car market. For a time at least, since other companies would be able to copy you pretty quickly. I'm assuming a lot of companies in that market plan to licence the technology for their revenues, rather than trying to take direct control. Certainly a big market, which likely explains a lot of the valuation for your Teslas and Ubers, but not unlimited.
The impact of a company announcing AGI tomorrow would be unimaginable, even if we assume a slow takeoff with limited recursive self-improvement.
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Got a source for that?
$16B divided by 230M is under $70. That is more than enough for two sets of F150 wiper blades for every licensed driver in America, but only if we don't splurge on Rain-X.
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