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.

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.

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.

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.

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.

What do you think Tesla has been doing this for so long?

The bitter lesson tells you about the trend in the technology. It does not get product to market with today's technology.

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.

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.

> 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.

Doesn’t Google own Waymo? I feel like they would have connected the dots.

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.

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.

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.

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.

I think this a slight different lesson. There is one algorithm that is called transformer, rest is irreverent/performance optimization.

> The bitter lesson is finally coming for

This is hilarious, and good: Those who were too lazy/stubborn/arrogant to adapt, get disrupted and buried.

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.

https://arstechnica.com/cars/2026/09/aftermarket-driver-assi...

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*.

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.