A Trillion Dollars (Potentially) Wasted on Gen-AI

Yes, indeed, that is why all we have done since the 90s is scale up the 'expert systems' we invented ...

That's such an a-historic take it's crazy.

* 1966: failure of machine translation

* 1969: criticism of perceptrons (early, single-layer artificial neural networks)

* 1971–75: DARPA's frustration with the Speech Understanding Research program at Carnegie Mellon University

* 1973: large decrease in AI research in the United Kingdom in response to the Lighthill report

* 1973–74: DARPA's cutbacks to academic AI research in general

* 1987: collapse of the LISP machine market

* 1988: cancellation of new spending on AI by the Strategic Computing Initiative

* 1990s: many expert systems were abandoned

* 1990s: end of the Fifth Generation computer project's original goals

Time and time again, we have seen that each academic research begets a degree of progress, improved by the application of hardware and money, but ultimately only a step towards AGI, which ends with a realisation that there's a missing congitive ability that can't be overcome by absurd compute.

LLMs are not the final step.

Read about the the No Free Lunch Theorem. Basically, the reason we need to "scale" so hard is because we're building models that we want to be good at everything. We could build models that are as good at LLMs at a narrow fraction of tasks we ask of them to do, at probably 1/10th the parameters.

https://en.wikipedia.org/wiki/Bitter_lesson

https://www.youtube.com/watch?v=DtePicx_kFY https://www.bbc.com/news/articles/cy7e7mj0jmro

The author did not say every dollar was wasted, he said that LLMs will never meet the current investment returns.

It’s very frustrating to see comments like this attacking strawmans and setting up Motte and Bailey arguments every time there’s AI criticism. “Oh but LLMs are still useful” and “Even if LLMs can’t achieve AGI we’ll figure out something that will eventually.” Yes but that isn’t what Sam and Andreesen and all these VCs have been saying, and now the entire US economy is a big gamble on a technology that doesn’t deliver what they said it would and because the admin is so cozy with VCs we’re probably all going to suffer for the mistakes of a handful of investors who got blinded by dollar signs in their eyes.

One thing to keep in mind, is that most of these people who go around spreading unfounded criticism of LLMs, "Gen-AI" and just generally AI aren't usually very deep into understanding computer science, and even less science itself. In their mind, if someone does an experiment, and it doesn't pan out, they'll assume that means "science itself failed", because they literally don't know how research and science work in practice.

Its all about scale.

If you spend $100 on something that didn't work out that money wasn't wasted if you learned something amazing. If you spend $1,000,000,000,000 on something that didn't work out the expectation is that you learn something close to 1,000,000,000x more than the $100 spend. If the value of learning is several orders of magnitude less than the level of investment there is absolutely tremendous waste.

For example: nobody qualifies spending a billion dollars on a failed project as value if your learning only resulted in avoiding future paper cuts.

While it doesn't seem we can agree on a meaning for AGI, I think a lot of people think of it as an intelligent entity that has 100% agency.

Currently we need to direct LLM's from task to task. They don't yet posses the capability of full real world context.

This is why I get confused when people talk about AI replacing jobs. It can replace work, but you still need skilled workers to guide them. To me, this could result in humans being even more valuable to businesses, and result in an even greater demand for labor.

If this is true, individuals need to race to learn how to use AI and use it well.

As a counter-point, LLMs still do embarrassing amounts of hallucinations, some of which are quite hilarious. When that is gone and it starts doing web searches -- or it has any mechanisms that mimic actual research when it does not know something -- then the agents will be much closer to whatever most people imagine AGI to be.

Have LLMs learned to say "I don't know" yet?

Boosters: it can answer PhD-level questions and it helps me a lot with my software projects.

Detractors: it can't learn to do a task it doesn't already know how to do.

Boosters: But actually it can actually sometimes do things it wouldn't be able to do otherwise if you give it lots of context and instructions.

Detractors: I want it to be able to actually figure out and retain the context itself, without being given detailed instructions every time, and do so reliably.

Boosters: But look, in this specific case it sort of does that.

Detractors: But not in my case.

Boosters: you're just using it wrong. There must be something wrong with your prompting strategy or how you manage context.

etc etc etc...

[1] https://en.wikipedia.org/wiki/Bloom's_taxonomy

Agreed. Has there been waste? Inarguably. Has the whole thing been a waste? Absolutely not. There are lessons from our past that in an ideal world would have allowed us to navigate this much more efficiently and effectively. However, if we're being honest with ourselves, that's been true of any nascent technology (especially hyped ones) for as long as we've been recording history. The path to success is paved with failure, Hindsight is 20/20, History rhymes and all that.

> I can't figure out what people mean when they say "AGI" any more

We've been asking "What is intelligence" (and/or Sentience) for as long as we've been alive, and still haven't come to a consensus on that. Plenty people will confidently claim they have an answer, which is great, but it's entirely irrelevant if there's not a broad consensus on that definition or a well defined way to verify AI/people/anything against it. Point in case...

> we appear to be past that. We've got something that seems to be general and seems to be more intelligent than an average human

Hard disagree specifically as it regards to Intelligence. They are certainly useful utilities when you use them right, but I digress. What are you basing that on? How can we be sure we're past a goal-post when we don't even know where the goal-post is? For starters, how much is Speed (or latency or IOP/TPSs or however you wish to contextualize it) a function of "intelligence"? For a tangible example of that: If an AI came to a conclusion derived from 100 separate sources, and a human manually went through those same 100 sources and came to the same conclusion, is the AI more intelligent by virtue of completing that task faster? I can absolutely see (and agree with) how that is convenient/useful, but the question specifically is: Does the speed it can provide answers (assuming they're both correct/same) make it smarter or as smart as the human?

How do they rationalize and reason their way through new problems? How do we humans? How important is the reasoning or the "how" of how it arrives at answers to the questions we ask it if the answers are correct? For a tangible example of that: What is happening when you ask an AI to compute the sum of 1 plus 1? What are we doing when we're asking to perform the same task? What about proving it to be correct? More broadly, in the context of AGI/Intelligence, does it matter if the "path of reason" differs if the answers are correct?

What about how confidently it presents those answers (correct or not)? It's well known that us humans are incredibly biased towards confidence. Personally, I might start buying into the hype the day that AI starts telling me "I'm not sure" or "I don't know." Ultimately, until I can trust it to tell me it doesn't know/isn't certain, I wont trust it when it tells me it does know/is certain, regardless of how "Correct" it may be. We'll get there one day, and until then I'm happy to use it for the utility and convenience it provides while doing my part to make it better and more useful.

Here i think it's more about opportunity cost.

> I can't figure out what people mean when they say "AGI" any more, we appear to be past that

What i ask of an AGI is to not hallucinate idiotic stuff. I don't care about being bullshitted too much if the bullshit is logic, but when i ask "fix mypy errors using pydantic" and instead of declaring a type for a variable it invent weird algorithms that make no sense and don't work (and the fix would have taken 5 minutes for any average dev).I mean, Claude 4.5 and Codex have replaced my sed/search and replaces, write my sanity tests, write my commit comment, write my migration scripts (and most of my scripts), and make refactor so easy i now do one refactor every month or so, but if it is AGI, i _really_ wonder what people mean by intelligence.

> Also, if anyone wants to know what a real effort to waste a trillion dollars can buy

100% agree. Pleas Altman, Ilya and other, i will hapilly let you use whatever money you want if that money is taken from war profiteers and warmongers.

We've got something that occasionally sounds as if it were more intelligent than an average human. However, if we stick to areas of interest of that average human, they'll beat the machine in reasoning, critical assessment, etc.

And in just about any area, an average human will beat the machine wherever a world model is required, i.e., a generalized understanding of how the world works.

It's not to criticize the usefulness of LLMs. Yet broad statements that an LLM is more intelligent than an average Joe are necessarily misleading.

I like how Simon Wardley assesses how good the most recent models are. He asks them to summarize an article or a book which he's deeply familiar with (his own or someone else's). It's like a test of trust. If he can't trust the summary of the stuff he knows, he can't trust the summary that's foreign to him either.

None of this is a binary, though. We already have AGI that is superhuman in some ways and subhuman in others. We are already using LLM's to help improve themselves. We already have job displacement.

That continuum is going to continue. AI will become more superhuman in some ways, but likely stay subhuman in others. LLM's will help improve themselves. Job displacement will increase.

Thus the question is whether this rate of change will be fast or slow. Seems mundane, but it's a big deal. Humans can adapt to slow changes, but not so well to fast ones. Thus AGI is a big deal, even if it's a crap stand in for the things people care about.

https://pcpartpicker.com/trends/price/memory/

If it pops it might end up being looked at in the lens of history as one of the largest backdoor/proxy wealth redistributions ever. The capex being spent is in large part going to fund the labor of the unwashed masses, and society is getting the individual productivity and efficiency benefits from the end result models.

I’m particularly thankful for the plethora of open source models I have access to thanks to all this.

I, individually, have realized indisputable substantial benefits from having these tools at my disposal every day. If the whole thing pops, these tools are safely in my possession and I’m better because I have them. Thanks .01%!!

(the reality is I don’t think it will pop in the classic sense, and these days it seems the .01 can never lose. either way, the $1tn can’t be labeled as a waste).

If all this went away tomorrow, what would we do with all the compute? Its not exactly general purpose infrastructure thats being built.

The real takeaway of the study was that workers were using their personal LLM accounts to do work rather than using the AI implementation mess their companies had shat out.

https://ai.vixra.org/pdf/2506.0065v1.pdf

The public has now caught up with that view. Familiarity breeds contempt, in this case justifiably so.

EDIT: It is interesting that in a submission about Sutskever essentially citing Sutskever is downvoted. You can do it here, but the whole of YouTube will still hate "AI".

He probably makes quite good money as the go to guy for saying AI is rubbish? https://champions-speakers.co.uk/speaker-agent/gary-marcus

https://arxiv.org/abs/1801.00631

Here are some of the points

Is deep learning approaching a wall? - He doesn't make a concrete prediction, which seems like a hedge to avoid looking silly later. Similarly, I noticed a hedge in this post:

Of course it ain’t over til it’s over. Maybe pure scaling ... will somehow magically yet solve ...

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But the paper isn't wrong either:

Deep learning thus far is data hungry - yes, absolutely

Deep learning thus far is shallow and has limited capacity for transfer - yes, Sutskeyer is saying that deep learning doesn't generalize as well as humans

Deep learning thus far has no natural way to deal with hierarchical structure - I think this is technically true, but I would also say that a HUMAN can LEARN to use LLMs while taking these limitations into account. It's non-trivial to use them, but they are useful

Deep learning thus far has struggled with open-ended inference - same point as above -- all the limitations are of course open research questions, but it doesn't necessarily mean that scaling was "wrong". (The amount of money does seem crazy though, and if it screws up the US economy, I wouldn't be that surprised)

Deep learning thus far is not sufficiently transparent - absolutely, the scaling has greatly outpaced understanding/interpretability

Deep learning thus far has not been well integrated with prior knowledge - also seems like a valuable research direction

Deep learning thus far cannot inherently distinguish causation from correlation - ditto

Deep learning presumes a largely stable world, in ways that may be problematic - he uses the example of Google Flu Trends ... yes, deep learning cannot predict the future better than humans. That is a key point in the book "AI Snake Oil". I think this relates to the point about generalization -- deep learning is better at regurgitating and remixing the past, rather than generalizing and understanding the future.

Lots of people are saying otherwise, and then when you call them out on their predictions from 2 years ago, they have curiously short memories.

Deep learning thus far works well as an approximation, but its answers often cannot be fully trusted - absolutely, this is the main limitation. You have to verify its answers, and this can be very costly. Deep learning is only useful when verifying say 5 solutions is significantly cheaper than coming up with one yourself.

Deep learning thus far is difficult to engineer with - this is still true, e.g. deep learning failed to solve self-driving ~10 years ago

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So Marcus is not wrong, and has nothing to apologize for. The scaling enthusiasts were not exactly wrong either, and we'll see what happens to their companies.

It does seem similar to be dot com bubble - when the dust cleared, real value was created. But you can also see that the marketing was very self-serving.

Stuff like "AGI 2027" will come off poorly -- it's an attempt by people with little power to curry favor with powerful people. They are serving as the marketing arm, and oddly not realizing it.

"AI will write all the code" will also come off poorly. Or at least we will realize that software creation != writing code, and software creation is the valuable activity

He wasn't wrong though.

> Yet deep learning may well be approaching a wall, much as I anticipated earlier, at beginning of the resurgence (Marcus, 2012)

(From "Deep Learning: A Critical Appraisal")

Indeed. Humans are a sucker for a quick answer delivered confidently. And The industry coalesced around LLM's once it was able to output competent, confident, corporate (aka HR-approved) english, which for many AI/DL/ML/NN researchers was actually a bit of a bummer. Reason I say that is because that milestone suddenly made the "[AGI is] always a decade away" to seeming much more imminent. Thus the focus of investment in the space shifted from actual ML/DL/NN research to who could convert largest pile of speculatively leveraged money into pallets of GPU's and data to feed them as "throw more compute/data" at it was a quicker/more reliable way to realize performance gains than investing in research did. Yes, research would inevitably yield results, but it's incredibly hard to forceast how long it takes for research to yield tangible results and harder still to quantify that X dollars will result in Y result in Z time compared to X dollars buys Y compute deployed in Z time. With the immense speculative backed FOMO and the potential valuation/investment that could result from being "the leader" in any given regard, it's no wonder that BigTech chose to primarily invest in the latter, thus leaving to those working in the former space to start considering looking elsewhere to continue actual research.

Is weird to me. Surely you recognise just as they don't know what they don't know (which is presumably the problem when it hallucinates), you must also have the same issue, there's just no old greybeard to wish you didn't have access.

My humble take on AGI is that we don't understand consciousness so how could we build something conscious except by accident? It seems like an extremely risky and foolish thing to attempt. Luckily, humans will fail at it.

"creating goods of actual value"

and

"creating goods of actual value for any price"

I don't think it's controversial that these things are valuable but rather the cost to produce use things is up for discussion, and the real problem here. If the price is too high now, then there will be real losses people experience down the line, and real losses have real consequences.

Especially given the humungous scale of infrastructure that the current approach requires. Is there another line of technology that would require remotely as much?

Note, I'm not saying there can't be. It's just that I don't think there are obvious shots at that target.