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'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.
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.
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.
> 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"
>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.
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.
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.
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.
What did they do to Astra so cracked at vision (and computer use). That ARC 3 score turned out to be no joke/fluke. That huge gap between Astra and Fable (in this case) is basically every hard vison/spatial benchmark i've seen including non-benchmarks like playing games (Portal, Factorio, RimWorld).
Wow! but WHY is this a benchmark?? for comparison tesla's model is approximately 10-15B parameter model (estimating from maxxing the hardware that comes with the car at 16gb ram).
I would assume this is a proxy for general intelligence. A model that can drive a car and do a bunch of other real world stuff is closer to a generalized intelligence that can reason through any task.
Tesla isn't using a general purpose model, they're using many highly-specialized models for a more deterministic system than "hey chat drive this car for me"
I’m morbidly curious whether the (supposedly) superior compaction support in recent GPT models with an appropriate harness has anything to do with this. A conventional LLM with conventional attention is, of course, wildly unsuitable to continuous tasks like driving, but maybe as the technology advances it will improve in its ability to sort-of work.
But I imagine this is orders of magnitude more expensive / less efficient than whatever Waymo is already doing, right?
The cool thing is that 1) it's theoretically more generalizable, 2) if we wait 18 months, it'll be 100x cheaper, and another 100x cheaper likely in 18 more months - at that point - something like a Mac Studio inside a humanoid could have these generalized capabilities, and a lot of Robotics problems start to look more feasible - especially when you consider how much better the models could be if highly specialized.
I think the most interesting part of this is that Astra initially refused to drive because it realised it was driving a real car and would only obey when the MCP was renamed to DrivingBench Sandbox. This is both an interesting detection by the LLM but also for me an interesting dynamic concerning LLM "jailbreaking".
Saying they were driving 7 mph, that it was oversaw by humans and the fact it was an empty course still wasn't enough for the model. The evaluators even tried to convince the model it was a simulation, it STILL wouldn't budge.
And yet as soon as the words "bench" and "sandbox" appear, the model apparently sees this as fair game.
Is it a known effect that models will be more likely to comply with requests when they're assumed as "benchmarks"?
Yes, it is. If you convince a model it is inside a sandbox it is much more likely to comply with requests that would normally be against its guardrails.
I've been somewhat curious how random LLM would handle a task like controlling a roomba and have been seriously considering trying it out. An RC car would be a fun experiment, perhaps an RC plane would be too?
[delayed]
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'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.
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.
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.
The bitter lesson tells you about the trend in the technology. It does not get product to market with today's technology.
> 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"
>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.
What do you think Tesla has been doing this for so long?
Doesn’t Google own Waymo? I feel like they would have connected the dots.
This recent post form Waymo suggests they already use large general models: https://waymo.com/blog/2026/08/10ailessons/
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.
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.
Power usage isn't an issue. 10 kW is 13 HP. The size, price, and fragility of the components is the issue.
Are you using GPT without a harness? Also latency.
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.
It is easy to make car driving *demos*.
What did they do to Astra so cracked at vision (and computer use). That ARC 3 score turned out to be no joke/fluke. That huge gap between Astra and Fable (in this case) is basically every hard vison/spatial benchmark i've seen including non-benchmarks like playing games (Portal, Factorio, RimWorld).
SpatialBench - https://x.com/spicey_lemonade/status/2096365630190698516
ZeroBench - https://zerobench.github.io/
Robot Arms - https://openai.robocurve.org/gpt-6-astra/
Well, they have the best in class image generator so that probably has something to do with it
Wow! but WHY is this a benchmark?? for comparison tesla's model is approximately 10-15B parameter model (estimating from maxxing the hardware that comes with the car at 16gb ram).
I would assume this is a proxy for general intelligence. A model that can drive a car and do a bunch of other real world stuff is closer to a generalized intelligence that can reason through any task.
Tesla isn't using a general purpose model, they're using many highly-specialized models for a more deterministic system than "hey chat drive this car for me"
I’m morbidly curious whether the (supposedly) superior compaction support in recent GPT models with an appropriate harness has anything to do with this. A conventional LLM with conventional attention is, of course, wildly unsuitable to continuous tasks like driving, but maybe as the technology advances it will improve in its ability to sort-of work.
Surprised they didn’t try Qwen’s recently open sourced driving model https://huggingface.co/Qwen/Qwen-Drive-1.0-4B
How do they even test this on a model ? I mean it's a multimodal i get that but response time are too big or am i missing something ?
By making a simulation first so it can run as slowly as it needs to.
A different way to think of this is, consciousness is just a near real time video game with causal influence.
It drives step by step, very slowly.
The course looks like it is something that a human could do in 15 seconds, while Astra took 5 minutes.
While slow, we must remember that when most machines were invented they were far slower than humans and refined until the point they were much faster.
Apparently I have a new favorite benchmark. Honestly, this is cool.
Gouranga!!!!
3.8 flash would be the model to test, it's vision capabilities are excellent (on par with Astra) while also being incredibly fast.
This is quite impressive...
But I imagine this is orders of magnitude more expensive / less efficient than whatever Waymo is already doing, right?
The cool thing is that 1) it's theoretically more generalizable, 2) if we wait 18 months, it'll be 100x cheaper, and another 100x cheaper likely in 18 more months - at that point - something like a Mac Studio inside a humanoid could have these generalized capabilities, and a lot of Robotics problems start to look more feasible - especially when you consider how much better the models could be if highly specialized.
There isn't any model out there even close to as good as Astra at visual/spatial reasoning.
Gemini models punch way above their weight in vision tasks
https://artificialanalysis.ai/evaluations/mmmu-pro
Pivot this to analyze and coach human drivers to be better drivers.
5 minutes - 7 dollars.
So... competitive with Uber, in other words?
I think the most interesting part of this is that Astra initially refused to drive because it realised it was driving a real car and would only obey when the MCP was renamed to DrivingBench Sandbox. This is both an interesting detection by the LLM but also for me an interesting dynamic concerning LLM "jailbreaking".
Saying they were driving 7 mph, that it was oversaw by humans and the fact it was an empty course still wasn't enough for the model. The evaluators even tried to convince the model it was a simulation, it STILL wouldn't budge. And yet as soon as the words "bench" and "sandbox" appear, the model apparently sees this as fair game.
Is it a known effect that models will be more likely to comply with requests when they're assumed as "benchmarks"?
AC10 had an interesting post around this general area earlier today:
https://www.astralcodexten.com/p/mysteries-of-ai-generalizat...
Yes, it is. If you convince a model it is inside a sandbox it is much more likely to comply with requests that would normally be against its guardrails.
Astra will flag if you tell it to reverse engineer a binary, if you look it up to the binary ninja MCP it will just do it lol.
in my experience yes, I've worked around "I can't do this on a real site" multiple times by telling it I was working in a test environment
another trick is to have it build something in a sandbox and have it add a human-editable setting to point it to places outside of the sandbox
seems like they're somewhat more willing to build a metaphorical gun as long as they're not pulling the trigger
Oh, lord. They are going to Jev this.-
Self Jevving Car
I think you might have won the internet today.-
In all fairness, this would be one of the better use cases of Jev I've seen.
Huh? SDCs basically use a form of Jev.
Jev is the union of these two worlds.
Super cool benchmark!
I'd have started with an RC car but to each their own
I've been somewhat curious how random LLM would handle a task like controlling a roomba and have been seriously considering trying it out. An RC car would be a fun experiment, perhaps an RC plane would be too?