> Of course, ai will continue to improve. But it is also likely to get more expensive…Microsoft’s share price took a beating last week as investors winced at its enormous spending on the data centres underpinning the technology. Eventually these companies will need to demonstrate a return on all that investment, which is bound to mean higher prices.
That’s not how it works, and I’m surprised this fallacy made it into the Economist.
Commodity producers don’t get to choose the price they charge by wishful thinking and aspirational margins on their sunk costs. Variable cost determines price. If all the cloud companies spend trillions on GPUs, GPU rental price (and model inference cost) will continue going down.
Indeed, cheaper and cheaper AI for the same level of performance has been even more consistent empirically than improvement in frontier model performance.
> I’m surprised this fallacy made it into the Economist.
Fallacies in the Economist, you say without irony?
From TFA:
> Yet investors risk misdiagnosing the industry’s troubles.
Oh, I think investors are asking, "where is the actual gain in capability and/or productivity?" Because they don't see it. And huge lay-offs don't prove it.
> Of course, AI will continue to improve.
This is begging the question. However, for the sake of argument, let's assume that AI continues to improve.
The thing we call AI is a long way from improving robotics. It's most prevalent practical value right now is improved search and code/text information assistance. But these improvements themselves have proven to be far from perfect.
The only monetisable path that we have seen in search and information assistance is advertising. The AI boom walks and quacks like a duck: a bubble, and a dawning generation of new facets of enshittification.
Good instinct. A lot of the day to day is debugging nitty things, reconciling small differences in results, trying not to make dumb mistakes. Almost all attempts to do very smart theoretical novel work fail, often because of extremely mundane engineering and data issues.
There’s a great quote from Nick Patterson of RenTech who says that the most sophisticated technique they generally used was linear regression, and the main thing was avoiding stupid mistakes:
“I joined a hedged fund, Renaissance Technologies, I'll make a comment about that. It's funny that I think the most important thing to do on data analysis is to do the simple things right. So, here's a kind of non-secret about what we did at renaissance: in my opinion, our most important statistical tool was simple regression with one target and one independent variable. It's the simplest statistical model you can imagine. Any reasonably smart high school student could do it. Now we have some of the smartest people around, working in our hedge fund, we have string theorists we recruited from Harvard, and they're doing simple regression. Is this stupid and pointless? Should we be hiring stupider people and paying them less? And the answer is no. And the reason is nobody tells you what the variables you should be regressing [are]. What's the target. Should you do a nonlinear transform before you regress? What's the source? Should you clean your data? Do you notice when your results are obviously rubbish? And so on. And the smarter you are the less likely you are to make a stupid mistake. And that's why I think you often need smart people who appear to be doing something technically very easy, but actually usually not so easy.”
Moved to NYC. Have a good team at my new job. Satisfied with my income. Have enough free time. Made a lot of good friends really fast, and now I see a rotating cast of them 3-7 days a week. Happy with my apartment. Have an east facing window so I don't have to set an alarm to wake up in the morning, I wake with the sunrise. Getting plenty of exercise and walking a 8-12k steps a day.
Overcomplicated take. Burn out comes from lacking a feeling of forward progress and tractability to your problems, regardless of current objective state.
That is part of it but there is also something to be said about what is going on biochemically IMO. Even if you are feeling forward progress and comfortable about the scope of your problems, if you give yourself no time to rest and get out of a subconciously anxious state, that isn't very good.
Anxiety is meant to have your senses heightened to perhaps hear the tiger stalking you and encourage you to seek out a safer environment where you can comfortably rest. You aren't built to be in an anxious state for such extended periods of time. The tiger would have gotten you by then, with the way this system was designed. You aren't built to constantly run from the tiger.
A useful angle: does doing this thing make me more able to enjoy it over time (eg by increasing the subtlety and dimensionality of perception), or less able to enjoy it over time (eg by desensitization)?
This is the practical reason to favor “thick desires” over thin ones: they slope upward over time.
Per the author’s links, he warned that deep learning was hitting a wall in both 2018 and 2022. Now would be a reasonable time to look back and say “whoops, I was wrong about that.” Instead he seems to be doubling down.
> expert in human language development and cognitive neuroscience, Gary is a futurist able to accurately predict the challenges and limitations of contemporary AI
I'm struggling to reconcile how these connect and he has been installed as Head of AI at Uber. Reeks of being a huckster
>...held the position briefly after Uber acquired his company, Geometric Intelligence, in late 2016. However, Marcus stepped down from the directorship in March 2017,
Indeed. A mouse that runs through a maze may be right to say that it is constantly hitting a wall, yet it makes constant progress.
An example is citing Mr Sutskever's interview this way:
> in my 2022 “Deep learning is hitting a wall” evaluation of LLMs, which explicitly argued that the Kaplan scaling laws would eventually reach a point of diminishing returns (as Sutskever just did)
which is misleading, since Sutskever said it didn't hit a wall in 2022[0]:
> Up until 2020, from 2012 to 2020, it was the age of research. Now, from 2020 to 2025, it was the age of scaling
The larger point that Mr Marcus makes, though, is that the maze has no exit.
> there are many reasons to doubt that LLMs will ever deliver the rewards that many people expected.
That is something that most scientists disagree with. In fact the ongoing progress on LLMs has already accumulated tremendous utility which may already justify the investment.
a contrarian needs to keep spruiking the point, because if he relents, he loses the core audience that listened to him. That's why it's also the same with those who keep predicting market crashes etc.
The same can be said about hucksters of all stripes, yes.
But maybe not contrarians/non-contrarians? They are just the agree/disagree commentators. And much of the most valuable commentary is nuanced with support for and against their own position. But generally for.
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
I think it would help if either side could be more quantitative about their claims, and the problem is both narratives are usually rather weaselly. Let's take this section:
>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
But they do generalize to some extent, and my limited understanding is that they generalize way more than expected ("emergent abilities") from the pre-LLM era, when this prediction was made. Sutskever pretty much starts the podcast saying "Isn’t it straight out of science fiction?"
Now Gary Marcus says "limited capacity for transfer" so there is wiggle room there, but can this be quantified and compared to what is being seen today?
In the absence of concrete numbers, I would suspect he is wrong here. I mean, I still cannot mechanistically picture in my head how my intent, conveyed in high-level English, can get transformed into working code that fits just right into the rather bespoke surrounding code. Beyond coding, I've seen ChatGPT detect sarcasm in social media posts about truly absurd situations. In both cases, the test data is probably outside the distribution of the training data.
At some level, it is extracting abstract concepts from its training data, as well as my prompt and the unusual test data, even apply appropriate value judgements to those concepts where suitable, and combine everything properly to generate a correct response. These are much higher-level concepts than the ones Marcus says deep learning has no grasp of.
Absent quantifiable metrics, on a qualitative basis at least I would hold this point against him.
On a separate note:
> "AI will write all the code" will also come off poorly.
On the contrary, I think it is already true (cf agentic spec-driven development.) Sure, there are the hyper-boosters who were expecting software engineers to be replaced entirely, but looking back, claims from Dario, Satya, Pichai and their ilk were were all about "writing code" and not "creating software." They understand the difference and in retrospect were being deliberately careful in their wording while still aiming to create a splash.
Several OpenAI people said in 2023 that they were surprised by the acceptance of the public. Because they thought that LLMs were not so impressive.
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".
In what way do you consider that to be the case? IBM's Watson defeated actual human champions in Jeopardy in 2011. Both Walmart and McDonald's notably made large investments shortly after that on custom developed AI based on Watson for business modeling and lots of other major corporations did similar things. Yes subsidizing it for the masses is nice but given the impressive technology of Watson 15 years ago I have a hard time seeing how today's generative AI is science fiction. I'm not even sure that the SOTA models could even win Jeopardy today. Watson only hallucinated facts for one answer.
When Watson did that, everyone initially was very impressed, but later it felt more like it was just a slightly better search engine.
LLMs screw up a lot, sure, but Watson couldn't do code reviews, or help me learn a foreign language by critiquing my use of articles and declination and idiom, nor could it create an SVG of a pelican riding a bicycle, nor help millions of bored kids cheat on their homework by writing entire essays for them.
What exactly are they doing? I've seen a lot of hype but not much real change. It's like a different way to google for answers and some code generation tossed in, but it's not like LLMs are folding my laundry or mowing my lawn. They seem to be good at putting graphic artists out of work mainly because the public abides the miserable slop produced.
Any fool could have anticipated the eventual result of transformer architecture if pursued to its maximum viable form.
What is impressive is the massive scale of data collection and compute resources rolled out, and the amount of money pouring into all this.
But 10 years ago, spammers were building simple little bots with markov chains to evade filters because their outputs sounded plausibly human enough. Not hard to see how a more advanced version of that could produce more useful outputs.
Any fool could have seen self driving cars coming in 2022. But that didn't happen. And still hasn't happened. But if it did happen, it would be easy to say:
"Any fool could have seen this coming in 2012 if they were paying attention to vision model improvements"
Everyone who lives in the show belt understands that unless a self driving car can navigate icy, snow-covered roads better than humans can, it's a non-starter. And the car can't just "pull over because it's too dangerous" that doesn't work at all.
That works fine. Self driving doesn’t need to be everything for all conditions everywhere.
Give me reliable and safe self driving for Interstate highways in moderate to good weather conditions and I would be very happy. Get better incrementally from there.
I live solidly in the snow belt.
Autopilot for planes works in this manner too. Theoretically a modern airliner could autofly takeoff to landing entirely autonomously at this point, but they do not. They decrease pilot workload.
If you want the full robotaxi panacea everywhere at all times in all conditions? Sure. None of us are likely to see that in our lifetime.
We do not. We have much better cruise control and some rudimentary geofenced autonomy. In our lifetimes, neither you nor I will be able to drive in a car that, based on deep learning training on a corpus of real world generated data, goes wherever we want it to whenever we want it to, autonomously.
> But for API use, the models are easily substituted, so market share is fleeting. The LLM interface being unstructured plain text makes it simpler to upgrade to a smarter model than than it used to be to swap a library or upgrade to a new version of the JVM.
Agree that the plain text interface (which enables extremely fast user adoption) also makes the product less sticky. I wonder if this is part of the incentive to push for specialized tool calling interfaces / MCP stuff - to engineer more lock in by increasing the model specific surface area.
Sure, every business owner has incentives that point to delivering a worse product (eg cheaper pizza ingredients increase margins). For most businesses there is a strong counteracting incentive to do a great job so the customer returns next week.
The key variable is how long that gap of time is. In the online dating example, if the dating app does a sufficiently great job you will never return. A milder version: if the used car salesman gives you great value, you might be back in 10 years. This creates very weak incentives for good service, so more predatory tactics dominate.
That’s not how it works, and I’m surprised this fallacy made it into the Economist.
Commodity producers don’t get to choose the price they charge by wishful thinking and aspirational margins on their sunk costs. Variable cost determines price. If all the cloud companies spend trillions on GPUs, GPU rental price (and model inference cost) will continue going down.
Indeed, cheaper and cheaper AI for the same level of performance has been even more consistent empirically than improvement in frontier model performance.
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