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.
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.
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.
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.
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.
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.
What's your ARR, anyway?
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.
Not sure that counts as phenomenally well.
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.
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.
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?
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're making it sound like the obvious answer is the irrational one.
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.
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.
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"
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.
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.)
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.
And the disinclination of these companies to push the weights of their cutting edge models into people’s cars where they can be dumped.
When the models stop improving, we will get model-specific ASICs that are much more power-efficient.
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.
Is this a serious question? Use your imagination...
Also to do the things humans don't even want to do.
I don't want them working for my company, at least. I want my workers safe & sound.
It will surely not devolve into the ultimate class war like Elysium and similar.
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.
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.
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.
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.
Rumored breakthroughs in efficiency were reported a few times.
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.
https://arstechnica.com/cars/2026/09/aftermarket-driver-assi...
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.
This is hilarious, and good: Those who were too lazy/stubborn/arrogant to adapt, get disrupted and buried.
It is easy to make car driving *demos*.