The two things being true may as well be: LLM will not be transformative and the LLM business is mangled in terms of ROIC vs CoC.
In fact given the behavior of AI companies, I actually consider it more likely then not that the effects of the technology is severely over-hyped. I for one do not trust the words of the people who mangle their business in terms of ROIC and CoC. Why should I?
The world looks pretty different today. Even if the model maker companies go out of business (I’m skeptical), the model weights would stick around (many are open freeware already).
I know many software engineers who haven’t written code this year. AI chatbots are regularly used as alternatives to searching manually by many people. Agents are becoming a valuable new enterprise tool, and now consumers tech consumers are hopping on board.
Even if you eliminate 100% of the people involved in creating software, that's still not enough money (and of course, that's not going to happen because someone has to know what to build).
Everyone is adding agents to enterprise stuff, but an overwhelming majority of the general population now hate AI for most things -- especially the "AI support" these companies are using. I think most people would rather suffer through overseas call centers with absolutely terrible representatives than deal with AI support (studies seem to indicate 80+% prefer a human to AI for support in general).
AI as a search engine is useful, but a very different animal. Proficient users want a way to check the AI like Google's AI search does (though the links sometimes don't agree with the summary), but these run very small models (8b or so) with the RAG backend doing the real work.
How many competitors does this space need? Companies can build their own proprietary RAG search engines, but users would almost always prefer the company make that public data available to Google and just use one well-optimized search engine instead of dozens of bad copies.
What about profitability? Google enshittified their search to increase retention and ad time, but AI search should reduce retention/ad time AND costs a lot more money to run too meaning it should lower their bottom line. If that weren't enough, their RAG system is almost certainly more replaceable by users with alternatives than their traditional search system. This seems like all downside for Google.
The currently well-served market for AI is not software engineering, it's approximately all of white-collar work, ranging from accounting and law, through medicine, general office work, to school administration, education, NGOs and governance.
Not everyone is going to just publicly brag about their AI use, but it's an open secret everyone is either using LLMs for half their work, or - if for some reason they're not busy enough to arrive at this idea on their own - under pressure to start using them.
If this the criteria we are using form something being transformative, then being transformative is truly unremarkable in this context, to the point of being a distraction.
If LLMs are "transformative" in the same sense as counting "alchemy because of the scientific funding that went into various attempts led into many vital discoveries", what's going to be our version of "actually we can turn lead into gold now, we just have better things to do with the capability"?
Isn't it vibecoding? Turn out it's not really gold
We do have the means today to turn literal lead into literal gold.
The energy is better spent on almost anything else and the gold is radioactive afterwards, but we can do it.
The closest analogy I'd have for vibecoding is combustion engines. Historical antecedent was a toy, early industrial ones took a lot of fuel and were only useful to pump water out of the coal mines that supplied that fuel, but kept getting improved until they made a critical quality leap that took them from "slightly worse than a horse" to "marginally better than a horse" and then there were suddenly a lot of unemployed farriers.
But literal-lead-to-gold took a while longer than that, and a different set of inventions behind it.
The #1 and #2 apps are non-coding, pure consumer AI applications (Meta Muse and Momo).
How do you add them? Inside the model has tons of problems. From user context makes a bit more sense, but how do you decide and how do you ensure the context is actually beneficial for the company paying for the ads (advertisers are very sensitive about what content gets subconsciously associated with their brand).
How do you make sure a human sees them? This is a very hard problem even in normal ads and seems even more problematic with chatbots.
How do you attract advertiser dollars? Ad spending is zero-sum. You must somehow convince advertisers that your chatbot is a better ad platform than Youtube, Facebook, etc.
The only AI solution that seems reasonable is something like Google's AI search because the RAG backend ensures Google can control where ads show effectively through deterministic means (based on the websites it pulls). Google also has the ad network, consumer base, and provable humans to drive up ad value (this is without mentioning that Gemini falls behind on coding/math benchmarks, but seems to be better at "normie" interactions).
None of this helps any of the AI startups and running all this extra stuff for the same ad revenue cuts Google's profit margins too.
You're right. I didn't even need AI to get my point across.
I mean, if self education isn't a "real use", I wonder why all those libraries decided to dedicate all that shelf space to nonfiction books?
Wikipedia could probably save a lot of hard drive space too.
I'm by no means citing that AI is perfect, or that there are no problems or challenges ahead, but the self-lobotomizing AI doomerism/rejection is turning otherwise intelligent people into a horde of anti-AI lemmings that are afraid to apply the critical thinking they believe is being lost by AI users.
These things are puppeteering blender to model things for gods sake. Are you all blind?
Yeah, yeah, can you shut the fuck up? I work in a successful LLM startup. More successful than OpenAI and Anthropic if you measure by profit and not ability to con people into investing hundreds of billions into developing the promised machine god. We train models for specific use cases and rent them out. I know better than anyone there are real uses. But "use Blender and make a slopshit model that any 3D artist would be fired for if they presented to their boss", ditto the slop videos, is not it. It's fucking sickening listening to the way people talk about these models.
The trendline is clear, but not in the way you think it is. Scaling vertically has basically stopped working already. Generalization plateaued more than a year ago. Since then the gains have been training models for iterating in a harness, training in objective reward-verified domains like math, training in specific tasks. All of which are useful, and which the field of ML has been demonstrating the unreasonable effectiveness of for at least 30 years when DeepBlue beat Kasparov. But code quality has stopped meaningfully improving; what actually improved is the ability to fix compile errors in a loop, giving you the worst code you've ever seen that does compile. If the trendline were significantly upwards, we would be seeing something approximating code that isn't absolute dogshit. But that's not what we're seeing at all.
Where? I can't find one company that did "AI layoffs" and their app/services got better. Give me just one good example please
Also computers. DEC and IBM and Gateway and whatever didn't extract the economic benefits of digitization. Even Apple extracts a vanishing fraction of the economic benefits the iPhone has created.
They try to get around this by charging you more after you've invested in your tools, but even then you can only go so far (though it's astonishing how far the big guys have managed to get regardless)
People seem to have forgotten the bust in railroads and Internet (etc), even though those technologies were transformative:
* https://en.wikipedia.org/wiki/Technological_Revolutions_and_...
One thing I'm wonder about: previous busts left useful infrastructure behind (rail, fibre) that could still be used. If/When the AI/LLM companies go bust, what will be left over afterwards and how useful will it be?
Many many idle GPUs.
I would bet that if all those GPUs go idle, they stay housed and cooled in the data center they were originally installed in, for better or for worse.
Yes, but what use will they have? Rail lines and dark fibre could be used post-bust (and don't depreciate all that quickly).
And that's at day 0. Because LLMs are not going away, thank to open weight models, and the race for capabilities has left so, so many low-hanging fruits unpicked, we'd have a decade or more of useful R&D to do even if LLM capabilities suddenly plateaued tomorrow and never improved.
I mean, look at Jev making round in the industry now - this is just a single example of a low-hanging fruit that took many years for someone to bother to pick up and market a bit. There's many, many more of these just laying around.
Don't focus on the hardware. The models themselves are tremendously valuable. These tools would've been considered alien technology just 15 years ago.
-LLMs profoundly change society -The LLM business is mangled in terms of ROIC vs CoC
Such a business already exists - airlines.
This nuance is what many here refuse/find difficult to understand.