The bitter lesson is finally coming for the self-driving cars. The vision stack, 3D maps, lane selection grammar, occupancy networks, it’s maybe all about to give way to a single GPT looking at camera feeds and predicting the next steering wheel adjustment.
It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
You might be interested to learn that the bitter lesson has already been grok'd by generations of autonomous car company engineers, and many or all have incorporated learned components (at minimum) in all their vehicle stacks.
There's also a very tangible limitation of the bitter lesson.
If, over time, compute climbs, and so compute-bound data-driven general architectures beat bespoke architectures (this is the bitter lesson), then it is not necessarily true that the most general architecture now beats all available bespoke architectures now (or even in the near/mid future - the crossover point is "eventually").
Bitter lesson is most tangible for long-running research directions. Sometimes you need something working as best as possible now.
Yeah. Every major self driving model that I’m aware of is fully e2e at this point. Going from fused sensor output to control+debug vectors.
This is more generalised.
But also since there’s a huge volume of data it’s too expensive to just keep scaling compute up (per car overhead) so there are necessary tricks involved.
I do think having a large model that can do this means that a small specialised model could be distilled form it though. Which is probably the most feasible path to production IMO.
A later entrant can potentially side step those investments if their now is later. Since self driving car ventures aren’t profitable yet and need to make up their investments over time, thats a real risk for them.
But what might happen imo, is that these huge models might be much better at learning from data in an unsupervised manner, so a model based on Astra might become a much better driver in a short period of time (and given a smaller set of training data) - this is due to it understanding much more of the inputs, and being able to draw conclusions from it much more efficiently, thus the information available for training it is greater per sample.
Then the big model can teach a small model to become almost as good a driver.
This might be substantially more efficient way to train stuff, and might be fairly quick and straightforward.
In practical terms, I feel this means we can see huge jumps in capability overnight. And this is a general indicator of AI progress, not only in this narrow scope.
You're making a "might" argument for a more general, more CPU/Data intensive process, which I just call "bitter lesson". I wouldn't even say "might" i'd just say "yeah, eventually so!"
Astra is the first chat model with really strong spatial reasoning. Gemini is nowhere close. Hard to say what google has going on internally, but if they have an astra like model I doubt they’ve had it for very long.
This recent post form Waymo suggests they already use large general models: <a href="https://waymo.com/blog/2026/08/10ailessons/" rel="nofollow">https://waymo.com/blog/2026/08/10ailessons/
I believe every single company in that industry has connected those dots since years. I don’t understand how you all seem to believe that’s an original idea, they all have models already
I'm not sure how you take that from the original article. My 4 year old would drive that course in an automatic car, if only he could reach the pedals. Heck, he's done harder things at Lego land.
I wouldn't let him loose on the road though.
I think, at the very least, the guardrails would have to deterministic, ideally with super human senses, for people to accept self driving cars on the road.
Nope, no "deterministic guardrails" for you. The domain is simply far too broad and unstructured to allow for that.
Unless you mean "a typical AI with all the computation constrained sufficiently to always unfold the same exact way, given the same input". In practice, that just kicks the can to "given the same input" street.
The noise in the system is going to come from the input plane. Which is, I remind you, facing the real world. It's full of noise.
Almost certainly, there are 4 year olds who ride small motorbikes etc. I think the main problem is that they can't really be held accountable if they have an accident.
16 year olds are bad enough on the road: <a href="https://www.hallandalelaw.com/wp-content/uploads/2013/07/IIHS.CHART_.CRASHESBYAGEGROUP.png" rel="nofollow">https://www.hallandalelaw.com/wp-content/uploads/2013/07/IIH...
Yeah cause every car needs 8xH200 pulling 10kW to run a VLM at realtime speeds. Would be unfortunate if 4G dropped out under some trees while using the API after all.
Steady 10KW load means 40 less miles after an hour of driving if your EV gets 4mi/kwh. That kind of draw would use up nearly 1/6th of my EV's battery in an hour.
At the same time it's pretty crazy to look at my Ford E-Transit's power draw while on the interstate and think about just how many electronics I could power with the 20-60KW of power draw it takes to maintain 65mph on a relatively flat stretch of highway.
lol. Wait until your cloud frontier LLM stalls / disconnects due to load / interference while your car is on highway OR making unprotected left turn OR approaching pedestrians.
> The bitter lesson is finally coming for the self-driving cars.
Maybe, but the opacity level of models is not acceptable for cars. "Why did it drive under the semi?" "Model said to." "Why did the model say to?" "shrug"
But if the model is an LLM, you actually COULD ask it why it drove under the semi, and it would give you an answer. Now, you may argue that it will just be generating a whole new, backwards-rationalized post-hoc explanation of its own behavior given the logs that it managed to take before the crash. But then I ask you: how do you think a person explains why they did what they did after a crash? I direct you to all of the unsettling split-brain neuroscience literature demonstrating that humans are incorrigible backwards rationalizers who make for unreliable witnesses.
> Maybe, but the opacity level of models is not acceptable for cars.
That depends on actual performance of the model. I would prefer an opaque model with clearly superhuman driving abilities to a human, or to a non-opaque model with worse performance.
No self-driving cars that aren't transparent about exactly how they work. (Ideally, no anything that isn't transparent about exactly how it works.)
This take can perhaps appear to make sense in a situation when clearly superhuman opaque AI models don't yet exist. But once they do, good luck convincing people that they should not save lives or reduce their personal risks, just because they always supposedly need an explanation for any accidental deaths, lol.
In our scenario (self-driving), the one who would be ultimately "held accountable" would not be the computer, or the company, but the person who died after singing a waiver/EULA and getting into a statistically superhuman autonomous car, then having a stroke of incredibly bad luck. Such events will happen, but they will be very rare.
Sounds really expensive. I think OpenAI and Anthropic should really not dismiss making smaller capable models that they can license out in this space on the other hand.
Tesla's already solved this - their vision model does this phenomenally well.
And they've demonstrated adding a sidecar LLM to it as well, mostly for these kinds of "read these 3 street signs, what should i do next?" sort of situations.
The same Tesla that pulled radar to go vision only and a person was killed because the vision model didn't recognize a truck? <a href="https://www.bbc.com/news/technology-36680043" rel="nofollow">https://www.bbc.com/news/technology-36680043
From this year <a href="https://www.autoweek.com/news/a70794762/nhtsa-escalates-probe-tesla-fsd/" rel="nofollow">https://www.autoweek.com/news/a70794762/nhtsa-escalates-prob...
From that same article - "The agency said it has identified nine crashes potentially linked to the issue, including one fatality and two involving injuries. It is also reviewing six additional crashes that may be related."
Fifteen crashes - though not to be trivialized - is not a damning number at all in this context. What's more, per the article it's unconfirmed that the crashes are related, so it's hardly fitting to dismiss Tesla's approach based on this.
I think it's great that serious efforts are being made in different approaches to autonomous driving - and in this thread's context, it seems possible that Tesla's approach might eventually be revealed as the optimal approach given modern AI.
Only for 2024 or maybe late 2023 cars and later (HW4). The older the car is the worse the FSD software is because the old hardware can't run the latest software.
Right, and over time the number of HW4+ cars will increase, right?
I can't speak to FSD's quality on HW3, I never had FSD on my Model 3. I did have EAP and it worked really well on highways.
But what is your argument? newer tech will bring improved outcomes? I'm sure HW5 will be even better. But that doesn't change the fact that FSD (on HW4 vehicles of which there are tons), is really good.
Anecdotes. Only accident rate per mile driven (compared to human drivers' rate) is a relevant comparison. I don't think that any self-driving system will ever be absolutely perfect (all such complex non-linear systems are to some degree probabilistic and chaotic), but as long as the accident rate is lower than the human accident rate, I would consider it solved.
> Only accident rate per mile driven (compared to human drivers' rate) is a relevant comparison
This is such an insane take I see all the time from self-driving boosters
If a self driving car glitches out and crashes in some edge case pathological scenario we don't just accept that as totally fine because its hidden under big statistics
The reason why a crash happened does matter, its not just about aggregate statistics
As a thought experiment if I have a perfect self driving system but I add some code that purposefully crashes 1 in 10 million rides are you ok riding in it since the aggregate statistics look good?
> As a thought experiment if I have a perfect self driving system but I add some code that purposefully crashes 1 in 10 million rides are you ok riding in it since the aggregate statistics look good?
Do I know about the purposefully added harmful code? If yes, I would demand you remove it, because why not. If I don't know about the code, I would be OK with it, since it's clearly still more safe than the alternative and apparently cannot be made even better.
You seem to be neglecting the important part of that scenario where you're other option you have to compare it to is a human driver that will randomly get it a crash at some higher rate.
You're making it sound like the obvious answer is the irrational one.
It's Lego mindstorm logic level to drive a car on the motorway, so per mile is an absolutely insanely bad metric in general.
Per mile inside cities or other difficult scenarios are what may get close to an actually meaningful metric. That's why Tesla is very misleading and waymo is much more legit.
Cute, but no. Can't compare a self-driving system that's only willing to work in a subset of conditions humans drive in, to humans driving in all those conditions.
You added "totally" - as others have said, I don't know if anyone will claim a perfect system with no fatalities, enough stats show that it is already much safer than human drivers.
And the true third party validation is that insurance companies are starting to offer lower premiums the more you use FSD. So their risk models are showing enough improvement that they're putting their money where their mouths are.
The idea that Tesla's FSD is not ready for the mainstream is quite outdated, given that tons of Tesla owners are already using it daily, not just your early adopter types.
Note that the article has no info on whether it's FSD or Autopilot and whether they contributed to the fatal crashes. "Verified engaged" means ADAS was active at some point in the interval from T-30s to the end of the accident. The total number of collisions is not normalized by the miles driven.
It also has no info on what hardware+software version was in use. The older cars are significantly less capable but there are far more of them on the road.
>It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
But this is a bit of a ridiculous take, no?
You don't need Astra for self-driving. Astra is able to build complex 3D worlds, do your taxes, shop for you, and, apparently, drive a car. A self-driving car just needs to be able to drive a car. By the time you trim down Astra to just have the minimum capabilities needed to drive a car, you'll be looking at the same models these self-driving car companies already use. Then you get to deal with the actual hard problems, like handling failure cases (which will still be present with Astra).
>The vision stack, 3D maps, lane selection grammar, occupancy networks, it’s maybe all about to give way to a single GPT looking at camera feeds and predicting the next steering wheel adjustment.
Self-driving cars have been able to do this for a long time. The problem is that it isn't robust enough given the context. I mean, if Astra can drive a car with a single camera, then presumably Astra can drive the car even better with multiple cameras, and even better than that with 3D maps, etc. And when you start to consider the expectation of performance of these systems, you realize that these features really can't be omitted. If you're a company producing self-driving cars, then you do not want to face a lawsuit for you car killing someone because it physically would have never been able to see what it was doing because it lacked a camera.
I think the real gain here is that something like Astra can be used to help build these autonomous stacks. If it is able to drive itself, then it is able to generate novel data, analyze large quantities of data, and use context that isn't typically available when processing this data to make improvements to the actual autonomy stack which is ultimately responsible for driving the car. But thinking that these car companies are going to run an LLM in a car and call it a day is just naive.
The key thing Astra is doing is a loop... (my understanding) To figure out where things are... It's basically use more compute, self-driving cars are usually using on-device hardware where a "loop" might be a little too risky especially if it takes too long on local hardware... I wouldn't want my AI driving model to be over the air either, yikes in the case of lag or network outages.
No, no - all that “useless” knowledge is the good stuff. There is no clean interface boundary for driving a car, because the only interface that has been enforced is “if a human can navigate this situation, it’s fine”. Real world driving situations can be arbitrarily complicated, and if you want >human level driving, you need human level semantic understanding of the world around you. If you see a kid about to throw a model airplane across the street in front of you, you have to bring all your “useless” world knowledge with you to recognize that as a developing hazard. If you’re supposed to bring your passenger to the city building on main and you encounter construction outside with a detour sign saying “for tax dropoff park in rear”, suddenly all of your useless knowledge about the English language, what taxes are, and the likely goal of your passenger given their destination become useful.
As somebody working near the field, I do enjoy the fun of dreaming bespoke vision and autonomy algorithms (if I didn’t, I wouldn’t work in the field to begin with!). But I would drop it all in a heartbeat for a robot that works well. Robust, resilient robots would be such an incredible advance that the ‘how’ doesn’t matter. All of the nonsense from the current AI hype cycle would be worth it if it cashed out in Robots That Actually Work.
I think robotics AI revolution will come just a few years after the knowledge work AI revolution. We already have very promising robotics systems in active development.
And that would be the incorrect conclusion. Yes, cheaper is better, but worse and more expensive is still in the running if your boss doesn't have to deal with the human aspect and the tasks still get done. Early cars were worse than horses, but they still won out because there wasn't the biological aspect to contend with. Think about it, a human has all sort of mushy human crap to deal with. They're going to come in hung over or just tired from the weekend/last night, all sad because their mom/brother/sister/partner got cancer/died and get into fights/trouble with HR over something a coworker did and have lower output. A magic box you can put the same tasks into and get sufficiently good output back out, and not have to give it time off because it's Christmas/their daughter's ballet recital, that you can spin up 30 copies of and spin them back down with no remorse is worth way more than simply being able to pay the box $18/hr vs $20/hr to a real live human. That's why businesses are salivating at the idea of AI/robots. Not because they'll eventually be cheaper.
The robot loses an arm because your factory is unsafe? vs a human losing an arm?
What we're not ready for is replacing GDP as the important metric. There have long been known problems with GDP, and robots are only going to make that worse. A robot maid, purchased once, saves, say 20/hrs a week in household chores. That's a meaningful quality of life upgrade, but doesn't result in the GDP bump that getting a raise and hiring a service to clean your house does.
How do you justify all these data centers when models are becoming good enough to run locally and other models are being directly "burned" into chips (model on chip)? Open weight models are almost as good as the closed ones now and they are free/uncensored.
The only thing DCs will still be need for is training, everything else will be done locally on your own hardware.
Open models are still behind February's Mythos.
Whether they can narrow that gap in the future, or OpenAI and Anthropic widen the gap with access to more compute and their better models assisting in the research process, remains to be seen.
At this time I see no reason to believe these data centers won't be in high demand.
Extremely unlikely seeing what the Chinese have been able to do with the limited resources they have. The creativity in finding improvement such as what deepseek has released is incredible. At this point it's a bet on the looser if you think the open models won't catch up and surpass the closed ones.
Probably not. In humans, the visual processing circuitry is very different from the circuitry for language processing. There is no reason to believe GPTs will be effective at it.
If I'm reading the chart correctly, it took over 5 minutes to drive 135m at a cost of nearly $8.00 in tokens. I don't think that's really in the realm of practical yet.
Self-driving tech is more about reducing liability than the driving itself. The lidars and 3D maps and world models and everything else is needed to get reliability from 99.9% to 99.99% on public roads. This isn’t a SaaS product where the target is to be “good enough” at the cheapest cost.
Oh god, this move-fast-break-things thinking is going to kill so many people. We already have aftermarket problems with people adding in untested, unregulated self-driving features.
I'm no expert, but I think the future is more about extremely low latency and low power chips with LLMs etched directly onto them. You can create specialized chips that function as "neurons" in a larger system, generating the needed reactions with a very clearly defined set of constraints.
Think this dramatically simplifies the problem. AI existed before GPTs and the AI in self-driving is optimized for self-driving and the latency you already mentioned.
Regardless of how the AI is architected, you aren't going to be able to use a generic LLM like Qwen to perform reliable self-driving, you need a highly optimized, highly specific AI.
Can you please explain what does it mean by bitter lesson in this context specifically? I keep seeing this term here. I know there is an article of the same title but I still don't understand.
valine · · focus · HN ↗
It’s mostly a latency problem at this point. The models are too big to run locally, but given that open-weight models like Qwen already exist, an open-weight, low latency equivalent to Astra can’t be too far out.
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There's also a very tangible limitation of the bitter lesson.
If, over time, compute climbs, and so compute-bound data-driven general architectures beat bespoke architectures (this is the bitter lesson), then it is not necessarily true that the most general architecture now beats all available bespoke architectures now (or even in the near/mid future - the crossover point is "eventually").
Bitter lesson is most tangible for long-running research directions. Sometimes you need something working as best as possible now.
AlphaSite · · focus · HN ↗
This is more generalised.
But also since there’s a huge volume of data it’s too expensive to just keep scaling compute up (per car overhead) so there are necessary tricks involved.
I do think having a large model that can do this means that a small specialised model could be distilled form it though. Which is probably the most feasible path to production IMO.
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torginus · · focus · HN ↗
Then the big model can teach a small model to become almost as good a driver. This might be substantially more efficient way to train stuff, and might be fairly quick and straightforward.
In practical terms, I feel this means we can see huge jumps in capability overnight. And this is a general indicator of AI progress, not only in this narrow scope.
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VBprogrammer · · focus · HN ↗
I wouldn't let him loose on the road though.
I think, at the very least, the guardrails would have to deterministic, ideally with super human senses, for people to accept self driving cars on the road.
ACCount39 · · focus · HN ↗
Unless you mean "a typical AI with all the computation constrained sufficiently to always unfold the same exact way, given the same input". In practice, that just kicks the can to "given the same input" street.
The noise in the system is going to come from the input plane. Which is, I remind you, facing the real world. It's full of noise.
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aerhardt · · focus · HN ↗
What's your ARR, anyway?
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AlphaSite · · focus · HN ↗
And the disinclination of these companies to push the weights of their cutting edge models into people’s cars where they can be dumped.
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Marha01 · · focus · HN ↗
When the models stop improving, we will get model-specific ASICs that are much more power-efficient.
moffkalast · · focus · HN ↗
Soo, never? Granted Cerebras is a thing, if the process can be commoditized.
At the moment the area of edge inference at speed seems pretty bleak though.
tintor · · focus · HN ↗
It is easy to make car driving *demos*.
JoshTriplett · · focus · HN ↗
Maybe, but the opacity level of models is not acceptable for cars. "Why did it drive under the semi?" "Model said to." "Why did the model say to?" "shrug"
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Marha01 · · focus · HN ↗
That depends on actual performance of the model. I would prefer an opaque model with clearly superhuman driving abilities to a human, or to a non-opaque model with worse performance.
JoshTriplett · · focus · HN ↗
<a href="https://knowyourmeme.com/memes/a-computer-can-never-be-held-accountable" rel="nofollow">https://knowyourmeme.com/memes/a-computer-can-never-be-held-...
No self-driving cars that aren't transparent about exactly how they work. (Ideally, no anything that isn't transparent about exactly how it works.)
Marha01 · · focus · HN ↗
In our scenario (self-driving), the one who would be ultimately "held accountable" would not be the computer, or the company, but the person who died after singing a waiver/EULA and getting into a statistically superhuman autonomous car, then having a stroke of incredibly bad luck. Such events will happen, but they will be very rare.
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atonse · · focus · HN ↗
And they've demonstrated adding a sidecar LLM to it as well, mostly for these kinds of "read these 3 street signs, what should i do next?" sort of situations.
matt_heimer · · focus · HN ↗
Not sure that counts as phenomenally well.
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danielklnstein · · focus · HN ↗
Fifteen crashes - though not to be trivialized - is not a damning number at all in this context. What's more, per the article it's unconfirmed that the crashes are related, so it's hardly fitting to dismiss Tesla's approach based on this.
I think it's great that serious efforts are being made in different approaches to autonomous driving - and in this thread's context, it seems possible that Tesla's approach might eventually be revealed as the optimal approach given modern AI.
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atonse · · focus · HN ↗
I can't speak to FSD's quality on HW3, I never had FSD on my Model 3. I did have EAP and it worked really well on highways.
But what is your argument? newer tech will bring improved outcomes? I'm sure HW5 will be even better. But that doesn't change the fact that FSD (on HW4 vehicles of which there are tons), is really good.
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ex-aws-dude · · focus · HN ↗
This is such an insane take I see all the time from self-driving boosters
If a self driving car glitches out and crashes in some edge case pathological scenario we don't just accept that as totally fine because its hidden under big statistics
The reason why a crash happened does matter, its not just about aggregate statistics
As a thought experiment if I have a perfect self driving system but I add some code that purposefully crashes 1 in 10 million rides are you ok riding in it since the aggregate statistics look good?
Marha01 · · focus · HN ↗
Do I know about the purposefully added harmful code? If yes, I would demand you remove it, because why not. If I don't know about the code, I would be OK with it, since it's clearly still more safe than the alternative and apparently cannot be made even better.
parineum · · focus · HN ↗
You're making it sound like the obvious answer is the irrational one.
gf000 · · focus · HN ↗
Per mile inside cities or other difficult scenarios are what may get close to an actually meaningful metric. That's why Tesla is very misleading and waymo is much more legit.
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atonse · · focus · HN ↗
And the true third party validation is that insurance companies are starting to offer lower premiums the more you use FSD. So their risk models are showing enough improvement that they're putting their money where their mouths are.
The idea that Tesla's FSD is not ready for the mainstream is quite outdated, given that tons of Tesla owners are already using it daily, not just your early adopter types.
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nater5000 · · focus · HN ↗
But this is a bit of a ridiculous take, no?
You don't need Astra for self-driving. Astra is able to build complex 3D worlds, do your taxes, shop for you, and, apparently, drive a car. A self-driving car just needs to be able to drive a car. By the time you trim down Astra to just have the minimum capabilities needed to drive a car, you'll be looking at the same models these self-driving car companies already use. Then you get to deal with the actual hard problems, like handling failure cases (which will still be present with Astra).
>The vision stack, 3D maps, lane selection grammar, occupancy networks, it’s maybe all about to give way to a single GPT looking at camera feeds and predicting the next steering wheel adjustment.
Self-driving cars have been able to do this for a long time. The problem is that it isn't robust enough given the context. I mean, if Astra can drive a car with a single camera, then presumably Astra can drive the car even better with multiple cameras, and even better than that with 3D maps, etc. And when you start to consider the expectation of performance of these systems, you realize that these features really can't be omitted. If you're a company producing self-driving cars, then you do not want to face a lawsuit for you car killing someone because it physically would have never been able to see what it was doing because it lacked a camera.
I think the real gain here is that something like Astra can be used to help build these autonomous stacks. If it is able to drive itself, then it is able to generate novel data, analyze large quantities of data, and use context that isn't typically available when processing this data to make improvements to the actual autonomy stack which is ultimately responsible for driving the car. But thinking that these car companies are going to run an LLM in a car and call it a day is just naive.
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Marha01 · · focus · HN ↗
Is this a serious question? Use your imagination...
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sejje · · focus · HN ↗
Also to do the things humans don't even want to do.
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sejje · · focus · HN ↗
I don't want them working for my company, at least. I want my workers safe & sound.
gf000 · · focus · HN ↗
It will surely not devolve into the ultimate class war like Elysium and similar.
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fragmede · · focus · HN ↗
The robot loses an arm because your factory is unsafe? vs a human losing an arm?
What we're not ready for is replacing GDP as the important metric. There have long been known problems with GDP, and robots are only going to make that worse. A robot maid, purchased once, saves, say 20/hrs a week in household chores. That's a meaningful quality of life upgrade, but doesn't result in the GDP bump that getting a raise and hiring a service to clean your house does.
user43928 · · focus · HN ↗
Not to mention construction, infrastructure, agriculture, manufacturing, logistics...
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AI hype cycle? It's working today.
It's optimizing ML model graphs for me while I type this, and it already cut inference time from 30s to 18s.
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Some people act like there was no way for the AI labs to make back the $800B being invested in data center construction this year.
If we look at global GDP, it's $126T, and even a 5% productivity gain would correspond to $6T.
Is that impossible? Is it guaranteed to all crash? I don't think so.
sschueller · · focus · HN ↗
The only thing DCs will still be need for is training, everything else will be done locally on your own hardware.
This bubble will burst and it will be ugly.
user43928 · · focus · HN ↗
Whether they can narrow that gap in the future, or OpenAI and Anthropic widen the gap with access to more compute and their better models assisting in the research process, remains to be seen.
At this time I see no reason to believe these data centers won't be in high demand.
sschueller · · focus · HN ↗
Extremely unlikely seeing what the Chinese have been able to do with the limited resources they have. The creativity in finding improvement such as what deepseek has released is incredible. At this point it's a bet on the looser if you think the open models won't catch up and surpass the closed ones.
user43928 · · focus · HN ↗
Rumored breakthroughs in efficiency were reported a few times.
rayiner · · focus · HN ↗
SoftTalker · · focus · HN ↗
ed_balls · · focus · HN ↗
binlog · · focus · HN ↗
miltonlost · · focus · HN ↗
<a href="https://arstechnica.com/cars/2026/09/aftermarket-driver-assist-under-federal-probe-following-fatal-crashes/" rel="nofollow">https://arstechnica.com/cars/2026/09/aftermarket-driver-assi...
boplicity · · focus · HN ↗
Razengan · · focus · HN ↗
This is hilarious, and good: Those who were too lazy/stubborn/arrogant to adapt, get disrupted and buried.
chris_money202 · · focus · HN ↗
Regardless of how the AI is architected, you aren't going to be able to use a generic LLM like Qwen to perform reliable self-driving, you need a highly optimized, highly specific AI.
smusamashah · · focus · HN ↗