LLM Inevitabilism

I think this is both right and wrong. There was a good book that came out probably 15 years ago about how technology never stops in aggregate, but individual technologies tend to grow quickly and then stall. Airplane jets were one example in the book. The reason why I partially note this as wrong is that even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today.

A better example, also in the book, are skyscrapers. Each year they grew and new ones were taller than the ones last year. The ability to build them and traverse them increased each year with new technologies to support it. There wasn't a general consensus around issues that would stop growth (except at more extremes like air pressure). But the growth did stop. No one even has expectations of taller skyscrapers any more.

LLMs may fail to advance, but not because of any consensus reason that exists today. And it maybe that they serve their purpose to build something on top of them which ends up being far more revolutionary than LLMs. This is more like the path of electricity -- electricity in itself isn't that exciting nowadays, but almost every piece of technology built uses it.

I fundamentally find it odd that people seem so against AI. I get the potential dystopian future, which I also don't want. But the more mundane annoyance seems odd to me.

There are sound math reasons for skyscrapers topping out, mostly due to elevator capacity and the inability to effectively get people in and out of the floorspace as you go past a few hundred ft. There's no construction engineering reason you can't go taller - the Burj Khalifa, for example, is three times taller than a typical Western major city skyscraper - it just doesn't make economic sense unless you're a newly rich nation looking to prove a point.

> even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today

I think they pretty strongly do

The solution seems to be "just lower your standards for acceptable margin of error to whatever the LLM is capable of producing" which should be concerning and absolutely unacceptable to anyone calling themselves an Engineer

> The reason why I partially note this as wrong is that even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today.

The fundamental problem has already been mentioned: Nobody can figure out how to SELL it. Because few people are buying it.

It's useful for aggregation and summarization of large amounts of text, but it's not trustworthy. A good summary decreases noise and amplifies signal. LLMs don't do that. Without the capability to validate the output, it's not really generating output of lasting value. It's just a slightly better search engine.

It feels like, fundamentally, the primary invention here is teaching computers that it's okay to be wrong as long as you're convincing. That's very useful for propaganda or less savory aspects of business, but it's less useful for actual communication.

Was the problem that supersonic flight was expensive and the amount of customers willing to pay the price was even lower than the number of customers that could even if they wanted to?

From what I had read in passing and remember.

  - They were loud (sonic booms were nasty).

  - They were expensive to maintain and operate. Guzzlers. (Britain and France clung to them as a matter of pride/ego)

  - They were narrow and uncomfortable. I have seen videos where there is space only for one stewardess to walk. I had been inside of one in Seattle museum. Very cramped.

  - As you mentioned, ticket cost was high.

  - I suspect people traveled in these mostly for bragging rights.

> Nobody can figure out how to SELL it. Because few people are buying it.

Just picking one company who basically just does AI, OpenAI. They reported it has 20 million PAID subscribers to ChatGPT. With revenue projected above $12b dollars (https://www.theverge.com/openai/640894/chatgpt-has-hit-20-mi...).

I think what you meant to say is that costs are high so they can't generate large profits. but saying that they can't figure out how to sell it seems absurd. Is it Netflix level of subscribers, no. But there can't be more than a couple of hundred products that have that type of subscription reach.

Yeah, and with LLMs the thing I can't shake, however, is that this time it's pretty strongly (maybe parasitically) latched onto the aggregate progress of Moore's law. Few other technologies have enjoyed such relatively unfettered exponential improvement. It's like if skyscraper materials double in strength every n years, and their elevators approach teleportation speed, the water pumps get twice as powerful, etc., which would change the economics vs the reality that most of the physical world doesn't improve that fast.

Ok but isn’t 20 million subscribers out of what, 800 million or 1 billion monthly users or whatever they’re claiming, an absolutely abysmal conversion rate? Especially given that the industry and media have been proclaiming this as somewhere between the internet and the industrial revolution in terms of impact and advancement? Why can they not get more than 3% of users to convert to paying subscribers for such a supposedly world changing technology, even with a massive subsidy?

Because they give too much of it away for free? Most casual use fits into the very generous free tier.

>I think this is both right and wrong. There was a good book that came out probably 15 years ago about how technology never stops in aggregate, but individual technologies tend to grow quickly and then stall. Airplane jets were one example in the book. The reason why I partially note this as wrong is that even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today.

I don't see any solution to hallucinations, nor do I see any solution in sight. I think that could count as a concrete issue that would stop them.

Ok so the argument is that all the model builders either suck at business or they are purposefully choosing to lose billions of dollars?

As another commenter notes, because you get access to a lot of functionality for free. And other providers are also providing free alternatives. The ratio for their free/paid tier is about the same as YouTube's. And like YouTube, it's not that YouTube isn't providing great value, but rather that most people get what they need out of the free tier.

The better question is what if all LLM services stopped providing for free at all -- how many paid users would there then be?

> absolutely unacceptable to anyone calling themselves an Engineer

Isn’t that exactly what engineers do? Even very strong bridges aren’t designed to survive every possible eventuality.

They are purposely losing billions, this is a growth phase where all of the big AI companies are racing to grow their userbase, later down the line they will monetize that captured userbase.

This is very similar to Uber which lost money for 14 years before becoming profitable, but with significantly more upside.

Investors see the growth, user stickiness and potential for the tech; and are throwing money to burn to be part of the winning team, which will turn on the money switch on that userbase down the line.

The biggest companies and investors in the planet aren't all bad at business.

That's what a "margin of error" is. The margin of error of a bridge is predictable thanks to well-established techniques of physical analysis.

An LLM system, on the other hand, can fail because you moved some punctuation around.

You made this point in passing, but it's so relevant to LLMs I wanted to highlight it: The development and operational cost was heavily subsidized by the British and French governments, because having an SST was a point of national prestige.

No

I'm talking about engineering a bridge for 50 cars that collapses at 51, not engineering a bridge for 500 cars that is only expected to get 50

Engineering does require tradeoffs of course. But that's not what the minimum possible quality is

> even in the 70s people recognized that supersonic travel had real concrete issues with no solution in sight. I don't think LLMs share that characteristic today

I hate to dogpile on this statement but I can think of two major issues right now:

* Small context windows, and serious degradation when pushing the limits of existing context windows. A human can add large amounts of state to their "context window" every day.

* Realtime learning. My humans get smarter every day, especially in the context of working with a specific codebase.

Maybe the AI companies will figure this out, but they are not "same technique more processor power" kinds of problems.

Vision and everyday-physics models are the answer: hallucinations will stop when the models stop thinking in words and start thinking in physical reality.

You could say the same of Dropbox. Or Gmail.

True, although I don't think Dropbox or Gmail's operating costs to support those free users are anywhere near those of OpenAI.

Economic Concrete construction (what China specializes in) typically tops out at 30-40 floors, so the vast majority of buildings in Asia are that height, a sweet spot so to speak especially for residential (even in limited space HK).

A service like Gmail or Dropbox with low storage is close to free to operate. Same thing with iCloud - 50 gigs a month is what, 1 dollar? How is that possible?

Because 50 gigs is next to nothing, and you only need a rinky dink amount of compute to write files.

YouTube, on the other hand, is actually pretty expensive to operate. Takes a lot of storage to store videos, never mind handling uploads. But then streaming video? Man, the amount of bandwidth required for that makes file syncing look like nothing. I mean, how often does a single customer watch a YouTube video? And then, how often do people download files from Dropbox? It's orders of magnitude in difference.

But LLMs outshine both. They require stupid amounts of compute to run.

99% or more of software developers behave in ways that would be inconceivable in actual engineering. That's not to say there aren't software engineers, but most developers aren't engineers and aren't held to that standard.

In my companies, AI subscriptions and API access are now the biggest costs after salaries and taxes. Don't know what makes you think these services aren't attracting paid customers?

Code is not physical. While computation errors can have real effects, a lot of orgs and people are resilient about them.

I'd say the userbase has grown. You can't claim half a billion users and simultaneously say you're still trying to grow. This isn't a month-old technology now. And they still can't turn a profit. (edit: and by "you" i meant "they")

The way you phrased it reveals how these model providers have framed the conversation in their favor. Models don’t “think.”