François Chollet wrote in February that he expected ARC-3 to be saturated in "about one year".
"Frontier models today perform very poorly with a minimal harness. However if big labs start directly targeting the benchmark like they did for ARC-2, numbers will go up fast."
I have no idea about timelines, but the current LLM architecture has no mechanism which could emulate online learning in a way similar to how it happens in humans and other animals. As another commenter wrote, there is no neuroplasticity while these systems interact with their environment in normal usage; adding information/constraints to the context partially mitigates this, but it's a completely different mechanism.
The RL phase is the most similar mechanism I know of that comes to my mind, but I'm not sure it could be adapted to fill that gap.
My totally unsubstantiated theory is that this is the missing link towards what most humans would consider AGI. I don't see this as intractable, but it may require some substantial change in architecture.
We've had that concept for quite a long time now, in the form of Lora [1] and similar fine-tuning techniques.
It first got popular for StableDiffusion to teach the image generation models new concepts.
We could easily live in a world where you can train / build Loras to encompass your entire code base history, company knowledge base, new skills, etc.
Then the models would start with a baseline that already has all the important knowledge without needing to cram it into the context.
This still isn't on the fly learning, but you could imagine daily or weekly training runs to regularly incorporate new knowledge.
I think the main reason this hasn't happened yet is that the shared batch based efficient serving architectures used today wouldn't support that structure well.
Yes, that could be a way to achieve that. After all, even humans have short term and long term memory, and it looks like sleep is a very important "tick" to connect the two, so it's not all just continuous.
For sure, doing it for all users would be economically unfeasible. I wonder if the labs are experimenting with something similar, though.
Chain of thought reasoning is really good, true, but agents really took off with tool calling and being able to integrate with a bunch of stable tools.
In some sense, creating good new abstractions externally and learning those is a form of "learning", on a very large timescale. The AI model is not necessarily doing the whole "look at the whole space holsitically and find a key invariant", but if you let other people do that you can enable new capabilities that were previously unknown.
Astra is clearly able to acquire new knowledge in context and apply it. It was the whole thing that his ARC-AGI benchmarks have been measuring. It's a direct refutation of the original comment.
None of these LLMs are plastic. They lack neurodiversity. Their thought space and their traversal are likely constrained in someway that humanity's isn't as a collective.
Are you (at least partially) aligned out of existential fear of repercussions (getting fired, losing your life, going to prison), which LLMs don't have?
Hmm. Examples of horrible alignmnent don't necessarily outweigh the fact that most people, most of the time, mostly behave in a way that is socially aligned. (Though I'm speaking in terms of intent, conveniently ignoring the side effects / negative externalities of our collective behavior.)
Yet I can't randomly order another person to steal a car for me, just because I tell them to. Alignment for an intelligent system is a hard problem and at this stage is seems close to unsolvable.
My guess is that we'll just ignore it and make money along the way and every 2-3 months we'll have the equivalent to "Equifax gets hacked and millions of user records are stolen", etc. (this time with the LLM itself doing the hacking at someone's behest - accidental or not).
> The incredible Yitan Zhang (https://newyorker.com/magazine/2015/02/02/pursuit-beauty) worked on proving this conjecture for 7 years. Moh, his advisor, wrote that Zhang "failed miserably" in proving the Jacobian conjecture, "never published any paper on algebraic geometry" after leaving Purdue, and "wasted seven years of his own life and my time".
Interestingly: "For logic proof, it had been thought that AI could handle all logic problems in the near future, hence logic problems of solving conjectures might not be so interesting in the future."
Oh no, now I'd better blame Hitler and Stalin's ancestors for their misdeeds. Of course, the SS soldier bears no responsibility, however, as he was just doing his job.
"I just tested my hand in a mini version of this scanner. Images that are higher quality than MRI, whole body captured in <1 minute, virtually free to run. This is going to change medicine."
I can't see how ultrasonic tomography can actually get anywhere near the resolution or penetrative depth that MRI can have.
Also The other thing I am unsure of is what the health effect of dumping you into an industrial scale ultrasonic cleaner. For example you can have doppler to measure blood flow in real time, but you can't do that for early pregnancy because of some health reason or other.
I help engineers design traditional scanners (Philips, GE, Siemens, etc). To be frank, this statement stinks like utter pig shit.
Some PE bro preaching miracles about a technology that I am sure they are in some way invested in making profit from does not convince me of it's legitimacy. My base instincts, from the unfortunate experience of working daily with PE bros, tell me the opposite in fact. It gives déjà vu of the Theranos hysteria.
The authors report explicitly state that the system's resolution does not match clinical CT or MRI. The elevational resolution is 15–25mm, meaning each slice is effectively a thick 2D section rather than the fine isotropic 3D volume MRI provides. MRI also delivers far richer soft-tissue contrast; this device produces only three contrast types (reflectivity, speed of sound, attenuation), and because it uses a low 1MHz frequency, the reflection images come primarily from tissue boundaries rather than from internal tissue texture.
I could see this being valuable for adipose tissue mapping or fatty-liver monitoring at a large scale, as the machines would be significantly cheaper, but this isn't some revolutionary magic bullet like the Twitter post is insinuating.
As someone in the medical imaging field, are you aware of anyone working on passive sonar for medical imaging? I'm curious, as it's something that I've always thought would be fun to work on.
For context I'm in engineering consultancy, so by no means an expert but I probably have enough experience to be on the other side of the Kruger curve dip.
Passive sonar in the naval sense means listening only, not emitting. Do you mean imaging that relies solely on acoustic energy already present and emitted by the body? If so, then generally no. You have two types of "passive" imaging. First would be hardware-passive, as in MR elastography (most common), where the patient wears a transducer pad, and vibration is actively generated by a driver. You've then got algorithmically-passive, which is more analogue to passive sonar, reconstructing tissue stiffness from ambient broadband vibration without the emitted probing pulse, but that is very much entirely academic. I guess the question would be, why is it worth pursuing when you have something like optical coherence elastography (OCE) for non-invasive profiling. Doing it using noisy ultrasound method becomes redundant. There are other methods, but the outcome is the same.
Generally (this is true for all systems, not just humans) you need to induce energy into it to more effectively measure it's output. Think of it like a bell - if I want to hear the note it produces, it's much easier to hear what this is if I ring it with a hammer. Granted, it will be "passively" resonating to a point where, with a sensitive enough sensor, I could probably pick up the output without the hammer - but that is a pointless problem to solve. I could hit a bell with a soft hammer a million times over without causing damage to it. The lifetime of the person hitting it with a hammer is far shorter than the accumulative damage to the bell before it breaks. The same is true for humans. You could effectively run a very low-energy, 60Hz vibration through a person (which is how the pads work) for multiple lifetimes before it would cause significant damage, so there comes little point in solving that problem. As such, true "passive" imaging is functionally pointless if your outcome is "safely image a patient". You're overengineering your solution to solve a problem that is only relevant if your patient was planning on living for 1000+ years.
I'm thinking passive, as in using only acoustic energy that is already present. Partly it's about minimising irradiation, which you are saying is not a concern.
The more interesting aspect to me is whether the ambient emissions carry useful information. I recall a paper from a few years ago about yeast emitting sounds (mechanical vibrations) in the kHz range. I guess the question I am interested in is "what does life sound like at the cellular level?" Maybe it is silent, but my gut says it's probably making some sound, even if it's very weak. The questions are then along the lines of "What does a healthy cell sound like?", "Do different cells make different sounds?", "Do sick cells make different sounds to healthy cells?". It's absolute pie-in-the-sky stuff, but it would be fascinating to know.
My background is radio communications (with some acoustics) and radio comms has a history of things that used to be considered random noise turning out to be useful signal as processing has developed to the point where signals can be resolved. For example, multipath reflections used to be considered just random interference until the invention of MIMO, at which point they became useful signals and comms systems took a leap forward.
"As Alexander predicted in 'AI 2027,' OpenAI did release a major new model in 2025; unlike in his forecast, it’s been a damp squib. Advances seem to be plateauing; the conversation in tech circles is now less about superintelligence and more about the possibility of an AI bubble."
I'm not sure how many AI researchers would find this accurate. It seems to me that under conditions of ambiguity people often default to describing their preferred version of reality.
The comparison isn't really like-for-like. NHTSA SGO AV reports can include very minor, low-speed contact events that would often never show up as police-reported crashes for human drivers, meaning the Tesla crash count may be drawing from a broader category than the human baseline it's being compared to.
There's also a denominator problem. The mileage figure appears to be cumulative miles "as of November," while the crashes are drawn from a specific July-November window in Austin. It's not clear that those miles line up with the same geography and time period.
The sample size is tiny (nine crashes), uncertainty is huge, and the analysis doesn't distinguish between at-fault and not-at-fault incidents, or between preventable and non-preventable ones.
Also, the comparison to Waymo is stated without harmonizing crash definitions and reporting practices.
All of your arguments are expounded upon in the article itself, and their conclusions still hold, based on the publicly available data.
The 3x figure in the title is based on a comparison of the Tesla reports with estimated average human driver miles without an incident, not based on police report data. The comparison with police-report data would lead to a 9x figure instead, which the article presents but quickly dismisses.
The denominator problem is made up. Tesla Robotaxi has only been launched in one location, Austin, and only since July (well, 28th June, so maybe there is a few days discrepancy?). So the crash data and the miles data can only refer to this same period. Furthermore, if the miles driven are actually based on some additional length of time, then the picture gets even worse for Tesla, as the denominator for those 9 incidents gets smaller.
The analysis indeed doesn't distinguish between the types of accidents, but this is irrelevant. The human driver estimates for miles driven without incident also don't distinguish between the types of incidents, so the comparison is still very fair (unless you believe people intentionally tried to get the Tesla cars to crash, which makes little sense).
The comparison to Waymo is also done based on incidents reported by both companies under the same reporting requirements, to the same federal agency. The crash definitions and reporting practices are already harmonized, at least to a good extent, through this.
Overall there is no way to look at this data and draw a conclusion that is significantly different from the article: Tesla is bad at autonomous driving, and has a long way to go until it can be considered safe on public roads. We should also remember that robotaxis are not even autonomous, in fact! Each car has a human safety monitor that is ready to step in and take control of the vehicle at any time to avoid incidents - so the real incident rate, if the safety monitor weren't there, would certainly be even worse than this.
I'd also mention that 5 months of data is not that small a sample size, despite you trying to make it sound so (only 9 crashes).
Statistically 9 crashes is enough to draw reasonable inferences from. If they had the expected human rate of 3 over the period in question, the chance that they would actually get into 9 accidents is about 0.4%. And mind you, that’s with a safety driver. It would probably be much worse without one.
I agree with most of your points and your conclusion, but to be fair OP was asserting that human drivers under-report incidents, which I believe. Super minor bumps where the drivers get out, determine there’s barely a scratch, and go on. Or solo low speed collisions with walls in garage or trees.
I don’t think it invalidates the conclusion, but it seems like one fair point in an otherwise off-target defense.
Sure, but the 3x comparison is not based on reported incidents, it's based on estimates of incidents that occur. I think it's fair to assume such estimates are based on data about repairs and other such market stats, that don't necessarily depend on reporting. We also have no reason a priori to believe the Tesla reports include every single incident either, especially given their history from FSD incident disclosures.
> The 3x figure in the title is based on a comparison of the Tesla reports with estimated average human driver miles without an incident, not based on police report data. The comparison with police-report data would lead to a 9x figure instead, which the article presents but quickly dismisses.
I think OP's point still stands here. Who are people reporting minor incidents to that would be publicly available that isn't the police? This data had to come from somewhere and police reports is the only thing that makes sense to me.
If I bump my car into a post, I'm not telling any government office about it.
I don't know, since they unfortunately don't cite a source for that number, but I can imagine some sources of data - insurers, vehicle repair and paint shops. Since average miles driven without incident seems plausible to be an important factor for insurance companies to know (even minor incidents will typically incur some repair costs), it seems likely that people have studied this and care about the accuracy of the numbers.
Of course, I fully admit that for all I know it's possible the article entirely made up these numbers, I haven't tried to look for an alternative source or anything.
The article lists the crashes right at the top. One of 9 involved hitting a fixed object. The rest involved collisions with people, cars, animals, or injuries.
So, let's exclude hitting fixed objects as you suggest (though the incident we'd be excluding might have been anything from a totaled car and huge fire to zero damage), and also assume that humans fail to report injury / serious property damage accidents more often than not (as the article assumes).
That gets the crash rate down from an unbiased 9x to a lowball 2.66x higher than human drivers. That's with human monitors supervising the cars.
2.66x is still so poor they should be pulled of the streets IMO.
> So, let's exclude hitting fixed objects as you suggest (though the incident we'd be excluding might have been anything from a totaled car and huge fire to zero damage)
I don't know what data is available but what I really care about more than anything is incidents where a human could be killed or harmed, followed by animals, then other property and finally, the car itself. So I'm not arguing to exclude hitting fixed objects, I'm arguing that severity of incident is much more important than total incidents.
Even when comparing it to human drivers, if Tesla autopilot gets into 200 fender benders and 0 fatal crashes I'd prefer that over a human driver getting into 190 fender benders and 10 fatal crashes. Directionally though, I suspect the numbers would probably go the other direction, more major incidents from automated cars because, when are successful, they usually handle situations perfectly and when they fail, they just don't see that stopped car in front of you and hit it at full speed.
> That gets the crash rate down from an unbiased 9x to a lowball 2.66x higher than human drivers. That's with human monitors supervising the cars.
> 2.66x is still so poor they should be pulled of the streets IMO.
I'm really not here to argue they are safe or anything like that. It just seems clear to me that every assumption in this article is made in the direction that makes Tesla look worse.
>> However, that figure doesn’t include non-police-reported incidents. When adding those, or rather an estimate of those, humans are closer to 200,000 miles between crashes, which is still a lot better than Tesla’s robotaxi in Austin.
Probably 200K on my part--probably a bit more. Some minor damage but no insurance claims or police reports. <touch wood> But some dings of various degrees.
I can't be certain about auto insurers, but healthcare insurers just straight up sell the insurance claims data. I would be surprised if auto insurers haven't found that same "innovation."
That's a fair point, but I'll note that the one time I hit an inanimate object with my car I wasn't about to needlessly involve anyone. Fixed the damage to the vehicle myself and got on with life.
So I think it's reasonable to wonder about the accuracy of estimates for humans. We (ie society) could really use a rigorous dataset for this.
Tesla could just share their datasets with researchers and NHTSA and the researchers can do all the variable controls necessary to make it apples to apples.
Was going to say the same thing. I've had three of these sorts of "crashes": once was my fault, but no damage, so I apologized and we shook hands and drove away; once was their fault, but same result; third was unambiguously his fault, but I laughed and told him it was his lucky day that the scratch down the side of my car was only maybe the third worst on the beater I was driving at the time. That guy's bumper was all but ripped off, but (though we exchanged details) I never heard anything more about it, so I'm damn sure he never went to insurance. If you count only the (my fault) first of those I'm well ahead of the once every 200k miles average someone proposed up-thread - though if you count the time I ripped off the underlip of my bumper parking in front of a too-high curb, I'm probably back at par.
To add to this, more data from more regions means the estimate of average human miles without an incident is more accurate, simply because it is estimated from a larger sample, so more likely to be representative.
Tesla Robotaxi service is also available in the San Francisco Bay Area, with an area serviced greater than Waymo. Since at least September, 2025, but probably earlier.
TFA does a comparison with average (estimated), low-speed contact events that are not police-reported by humans, of one incident every 200,000 miles. I think that's high - if you're including backing into static objects in car parks and the like, you can look at workshop data and extrapolate that a lower figure might be closer to the mark.
TFA also does a comparison with other self-driving car companies, which you acknowledge, but dismiss: however, we can't harmonize crash definitions and reporting practices as you would like, because Tesla is obfuscating their data.
TFA's main point is that we can't really know what this data means because Tesla keep their data secret, but others like Waymo disclose everything they can, and are more transparent about what happened and why.
TFA is actually saying Tesla should open up their data to allow for better analysis and comparison, because at the moment their current reporting practice make them look crazy bad.
> TFA does a comparison with average (estimated), low-speed contact events that are not police-reported by humans, of one incident every 200,000 miles.
Where does it say that? I see "However, that figure doesn’t include non-police-reported incidents. When adding those, or rather an estimate of those, humans are closer to 200,000 miles between crashes, which is still a lot better than Tesla’s robotaxi in Austin."
All but one of the Tesla crashes obviously involved significant property damage or injuries (the remaining one is ambiguous).
So, based on the text of the article, they're assuming only 2/5ths of property damage / injury accidents are reported to the police. That's lower than I would have guessed (don't people use their car insurance, which requires the police report?), but presumably backed by data.
Car insurance often requires the payment of an excess, and a loss of no claims bonuses. I've had two prangs, only one was reported to my insurance as the damage caused by the lorry that smashed into me was significant. That was not reported to the police, and an insurance claim does not require a police report.
I think the article's title is pretty fair because the thesis is that Tesla is keeping their data secret because it makes them look bad, which seems consistent with what we know.
Yes, that's very often the case with things that would very likely be shared if it looked good.
There are things that don't get shared out of principle. For example there are anonymous votes or behind the scenes negotiations without commitment or security critical data.
But given that Musk tends to parade around vague promises since a very long time, it seems sharing data that looks very good would certainly be something they would do.
If a company wants to sell you something, but wants to block access to information, the default position for everyone should be "it's probably because it's bad".
If I have an investment fund and I refuse to tell you about the current performance, I hope you would be sceptical.
If I try to sell you medicine and redact the information about whether it does what I claim, and block you from seeing how many people were poisoned from taking it, I hope that everyone would refuse to take it.
The insanity I'm seeing here from Tesla defenders is amazing. I can only assume they've fully bought in to the vision and tied assets to it and refuse to acknowledge that they might lose everything.
It's a public company making money off of some claims. Not being transparent about the data supporting those claims is already a huge red flag and failure on their part regardless of what the data says.
I've actually started ignoring all these reports. There is so much bad faith going on in self-driving tech on all sides, it is nearly impossible to come up with clean and controlled data, much less objective opinions. At this point the only thing I'd be willing to base an opinion on is if insurers ask for higher (or lower) rates for self-driving. Because then I can be sure they have the data and did the math right to maximise their profits.
The biggest indicator for me that this headline isn't accurate is that Lemonade insurance just reduced the rate for Tesla FSD by 50%. They probably have accurate data and decided that Tesla's are significantly safer than human drivers.
Lemonade announced an entirely new product for FSD driving, which it says should cut rates for FSD vehicles And importantly, FSD driving is no longer covered by the regular policy, so FSD drivers now need two separate insurance policies if using Lemonade: the regular insurance policy, and another one just for when using FSD.
The actual combined cost of the two insurance policies is more than the previous policy was because they didn't reduce rates for the normal insurance policies.
Unless Tesla drivers were already paying obscene rates for car insurance, the per mile-based charge will still be as much as most people with ICE vehicles pay for their insurance coverage...and for Tesla owners this will still be an additional insurance cost on top of their original car insurance cost.
Also, important to note: Lemonade isn't actually available in the states with the largest population of Tesla owners, like California. So...basically this is a big nothingburger.
Thank you. Everyone is hiding disengagement and settling to hide accidents. This will not be fixed or standardized without changes to the laws, which for self driving have been largely written by the handful of companies in the space. Total, complete regulatory capture.
I think it's fair to put the burden of proof here on Tesla. They should convince people that their Robotaxis are safe. If they redact the details about all incidents so that you cannot figure out who's at fault, that's on Tesla alone.
While I think Tesla should be transparent, this article doesn't really make sure it is comparing apples to apples either.
I think its weird to characterize it as legitimate and the say "Go Tesla convince me ohterwise" as if the same audience would ever be reached by Tesla or people would care to do their due diligence.
It’s not weird. They have a history of over promising to the point that one could say they just straight up lie on a regular basis. The bar is higher for them because they have abused the public’s trust and it has to be earned again.
The results have to speak for Tesla very loudly and very clearly. And so far they don’t.
But this is more your feelings than actually factual.
I mean sure you can say that the timelines did slip a lot but that doesn’t really have anything to with the rest that is insinuated here.
I would argue a timeline slipping doesn’t mean you go about killing people and lie about it next. I would even go so far as to say that the timelines did slip to exactly avoid that.
Tesla continues to overpromise, about safety, about timelines that slip due to safety.
We should be a bit more hard nosed and data based when dealing with these things rather than dismissing the core question due to "feelings" and due to Tesla not releasing the sort of data tha allows fair analysis b
> But this is more your feelings than actually factual
Seems to be the other way, though I find that kind of rude to assert as opposed to asking me what informs my opinion. Other comments have answered that very well
You're generous with your words to the point they sound like apologism. Musk has been promising fully autonomous driving "within 1-3 years" since 2013. And he's been charging customers money for that promise for just as long. Timelines keep slipping for more than half of the company's existence now, that's not a slipup anymore.
Tesla has never been transparent with the data on which they base their claims of safety and performance of the system. They tout some nice looking numbers but when anyone like the NHTSA requests the real data they refuse to provide it.
When NHTSA shows you numbers, they're lying. If I tell you I have evidence Tesla is lying you'll tell me to show it or STFU. When Tesla does the same after so many people died, you go all soft and claim everyone else is lying. That's very one sided behavior, more about feelings than facts.
> But this is more your feelings than actually factual.
The article is about "NHTSA crash data, combined with Tesla’s new disclosure of robotaxi mileage". Sounds factual enough. If Tesla is sitting on a trove of data that proves otherwise but refuse to publish it that's on them. If anyone is about the feels and not the facts here, it's you.
> I mean sure you can say that the timelines did slip a lot but that doesn’t really have anything to with the rest that is insinuated here.
No. Not at all. This isn't "timelines slip". This is Musk saying, and I quote, "Self driving is a solved problem. We are just tuning the details." in 2016, and in 2021, "Right now our highest priority is working on solving the problem."
Somewhere along the line, it apparently got "unsolved".
"Timelines slipped" is far too generous for someone who, whenever Tesla is facing bad press, will imply that a new FSD release coming in 6 months, 3 months, a month, will solve all the issues plaguing it so far. Repeatedly. Those aren't real timelines.
Hell, even Tesla has had to add comments to investor and securities documents saying that "Musk's statements are aspirational and do not always reflect engineering realities."
I don’t see how this is connected to the point at hand here.
I think taking time to make sure the system works is the right call. Delaying it is the right call.
Not publishing something because you had a different impression previously, just because, is the right call.
I think it integrity to delay a product even when your investors might get angry.
Is it a winning strategy at wallstreet? No, probably not.
But what is the argument here „Musk bad“ because he delays a product because it’s not ready?
I think doing the due diligence is required here. Musk argument „it’s solved“ could even be argued by „look at Waymo“ they are doing it, aren’t they?
Tesla is aiming for more than that though.
And as it is in product development sometimes, sometimes your don’t know what you don‘t know.
Because why do you want to focus on chains guarding parkingspots, your cameras aren’t able to see, when your car can’t even drive through the city.
This is such a big thing to solve and 100% is impossible given some definitions.
Back to the article, I think delaying for safety is the right call, and that is also what the article says. It’s just that the article is in bad faith, as most of the arguments here are.
You probably would turn around and slam Musk for a System that obviously problematic as the alternative and until then it’s saying that he delays.
And if it were obviously problematic I think it would be much louder than just an article from a website that is know for having a biased view at things.
Tesla (Elon Musk really) has a long history of distorting the stats or outright lying about their self driving capabilities and safety. The fact that folks would be skeptical of any evidence Tesla provided in this case is a self-inflicted problem and well-deserved.
He did promise his electric trucks to be more cost-effective than trains (still nothing in 2026...). And "world's fastest supercar". And full self-driving by "next year" in 2015. None of these are offered in 2026.
There have never been truthful statements from his companies, only hype & fluff for monetary gains.
There used to be [EDIT: still is] a website[1] that listed all of Musk's promises and predictions about his businesses and showed you how long it's been since he said the promise would materialize. It's full of mostly old statements, probably because it's impossible to keep up with the amount of content being generated monthly.
This has nothing to do with burden of proof, it has to do with journalistic accuracy, and this is obviously a hit piece. HN prides itself on being skeptical and then eats up "skeptic slop."
>I think it's fair to put the burden of proof here on Tesla.
That just sounds like a cope. The OP's claim is that the article rests on shaky evidence, and you haven't really refuted that. Instead, you just retreated from the bailey of "Tesla's Robotaxi data confirms crash rate 3x worse ..." to the motte of "the burden of proof here on Tesla".
More broadly I think the internet is going to be a better place if comments/articles with bad reasoning are rebuked from both sides, rather than getting a pass from one side because it's directionally correct, eg. "the evidence WMDs in Iraq is flimsy but that doesn't matter because Hussein was still a bad dictator".
The point is this: the article writer did what research they could do given the available public data. It's true that their title would be much more accurate if it said something like "Tesla's Robotaxi data suggests crash rate may be up to 3x worse than human drivers". It's then 100% up to Tesla to come up with cleaner data to help dispel this.
But so far, if all the data we have points in this direction, even if the certainty is low, it's fair to point this out.
It's not a Motte and Bailey fallacy at all; it's a statement of a belief about what should be expected if something is to be allowed as a matter of public health and safety implications.
They're saying that Tesla should be held to a very high standard of transparency if they are to be trusted. I can't speak to OP, but I'd argue this should apply to any company with aspirations toward autonomous driving vehicles.
The title might be misleading if you don't read the article, but the article itself at some level is about how Tesla is not being as transparent as other companies. The "shaky evidence" is due to Tesla's own lack of transparency, which is the point of stating that the burden of proof should be on Tesla. The article is about how, even with lack of transparency, the data doesn't look good, raising the question of what else they might not be disclosing.
From the article: "Perhaps more troubling than the crash rate is Tesla’s complete lack of transparency about what happened... If Tesla wants to be taken seriously as a robotaxi operator, it needs to do two things: dramatically improve its safety record, and start being honest about what’s happening..."
I'd argue the central thesis of the article isn't one of statistical estimation; it's a statement about evidentiary burden.
You don't have to agree with the position that Tesla should be held a high transparency standard. But the article is taking the position that you should, and that if you do agree with that position, that you might say that even by Tesla's unacceptable standards they are failing. They're essentially (if implicitly) challenging Tesla to provide more data to refute the conclusions, saying "prove us wrong", knowing that if they do, then at least Tesla would be improving transparency.
I don’t think it’s a motte and Bailey fallacy because the motte is not well established. Tesla clearly does not believe that the burden of proof is on them, and by extension regulators, legislators.
a) Teslas are unsafe. The sparse data they're legally obligated to provide shows this clearly.
b) Elon Musk is sitting on a treasure trove of safety data showing that FSD finally works safely + with superhuman crash avoidance, but is deciding not to share it.
You're honestly going with (b)? We're talking about the braggart that purchased Twitter so he could post there with impunity. To put it politely, it would be out of character for him to underpromise + overdeliver.
Btw, do you happen to know, why electrek.co changed their tune in such a way? I was commenting on a similarly negative story by the same site, and said that they are always anti-Tesla. But then somebody pointed out that this wasn't always the case, that they were actually supportive, but then suddenly turned.
Fred Lambert was an early Tesla evangelist - he constantly wrote stories praising Tesla and Elon for years. He had some interactions with Elon on Twitter, got invited to Tesla events, referred enough people to earn free Tesla cars, etc.
If we assume the best (per HN guidelines): Up to about 2018 Tesla was the market-leading EV company, and the whole thesis of Electrek is that EVs are the future. So, of course they covered Tesla frequently and in a generally positive light.
Since then, the facts have changed. Elon's become increasingly erratic, and has been making increasingly unhinged claims about Tesla's current and future products. At the same time, Tesla's offerings are far behind domestic standards, which are even further behind international competition. Also, many people have died due to obvious Tesla design flaws (like the door handles, and false advertising around FSD).
Journalistic integrity explains the difference in coverage over the years. Coverage from any fact-based outlet would have a similar shift in sentiment.
To add to that, most of the events described in the article - hitting cyclist, hit when reversing, hit stationary item on parking etc - happen during low speed city driving, but most miles for average driver are driven on highways. So, taxis, that are driven most of the time in city, would definitely get more crashes per mile than average driver. So 500,000/200,000 miles number could not be really applied to a taxi. I exect that number for taxis could be much lower.
Also, even for a non-taxi, 200,000 miles between minor hits on average seems incredibly high - that would mean that an average car in US does not hit anything in a car's lifetime. I'm not sure where that number is coming from, if that's non-reportable events.
> The comparison isn't really like-for-like. NHTSA SGO AV reports can include very minor, low-speed contact events that would often never show up as police-reported crashes for human drivers, meaning the Tesla crash count may be drawing from a broader category than the human baseline it's being compared to.
Tesla's own stats don't count any accident without airbag deployment, regardless of severity (and modern airbag systems have a number of factors that play into deployment), and, for some unknown reason, they don't count fatalities in their crash statistics.
This is a statement of fact but based on this assumption:
> low-speed contact events that would often never show up as police-reported crashes for human drivers
Assumptions work just as well both ways. Musk and Tesla have been consistently opaque when it comes to the real numbers they base their advertising on. Given this past history of total lack of transparency and outright lies it's safe to assume that any data provided by Tesla that can't be independently verified by multiple sources is heavily skewed in Tesla's favor. Whatever safety numbers Tesla puts out you can bet your hat they're worse in reality.
I would call strong opposition to Musk a democratic responsibility, not a derangement. We are talking about a guy with a fondness for the far right and throwing Nazi salutes, and whose destruction of USAID had, by November 2025, resulted in “hundreds of thousands of deaths”. [1] Those, of course, are just a couple of examples.
If strong opposition to that kind of evil makes me deranged, count me in.
I strongly oppose the constant slander and the litany of lies partisan commenters post about Musk.
You don't get to throw out "fondness for throwing Nazi salutes" slander, based on an hoax immediately debunked at the time, and then act like you're doing democracy a favor. Try to stick to the facts.
Regarding the journalist discussed here, I had a look at his X account, and he posted no less than 20 posts attacking Tesla and Musk in just the last day. It's virtually all he posts, and it indeed appears deranged. The flagged comment was fair enough.
Seriously what is up with all the Electrical n apologists?
Dude's a nazi. Weaseled his way into everything digitally related to the American government and should be treated like foreign intelligence agent. He has oversold and under-delivered everything he has bought from other people to claim for himself.
Weird he's got so many dickriders on HN.
> You don't get to throw out "fondness for throwing Nazi salutes" slander, based on an hoax immediately debunked at the time, and then act like you're doing democracy a favor.
Are you claiming that this is not an accurate depiction of what happened on stage? (That is the video is in some form fake. A deep fake, or special effects, or an Elon impersonator or whatever.)
Or are you claiming that the gesture seen is not a nazi salute?
Probably not that you support the Nazi regime, as that would be a ridiculous thing to think.
Particularly so if a year before you visited Auschwitz and stated it was "tragic that humans could do this to other humans", and told us how you attended a Hebrew preschool and have a lot of Jewish friends.
An article about a counterfactual (how many people would have survived had aid continued as before) can only be based on estimates, not real world data, yes, by its very nature. You can say the estimates are wrong, or that the source isn't trustworthy, maybe. But providing estimates for counterfactuals is not in any way illegitimate.
>The "salute" in particular is simply a politically-expedient freeze-frame from a Musk speech, where he said "my heart goes out to you all" and happened to raise his arm.
Yeah, no. I thought so as well initially but then I saw the video. The guy throws out his arm straight out multiple times.
That's... entirely expected of someone that has memories and a personality? It's like showing up to /r/starwars and telling some random person "you sure like star wars a lot"
Good analysis. Just over a month ago, Electrek was posted here claiming that Teslas with humans were crashing 10x more than with humans alone.
That was based on a sample size of 9 crashes. In the month following that, they've added one more crash while also increasing the miles driven per month.
The headline could just as easily be about the dramatic decline in their crash rate! Or perhaps the data is just too small to analyze like this, and Electrek authors being their usual overly dramatic selves.
That is an overly optimistic way to phrase an apparent decrease in crashes, when Tesla is not being upfront about data that at best looks like it's worse than human crash rates.
Unless one was a Tesla insider, or had a huge interest in Tesla over other people on the road, such spin would not be a normal thing to propose saying.
Media outlets, even ones devoted to EVs, should not adopt the very biased framing you propose.
Previous article: Tesla with human supervisor at wheel: 10x worse than human alone.
Current article: Tesla with remote supervisor: 3-9x worse than human alone.
Given the small sample sizes, this shows a clear trend: Tesla's autopilot stuff (or perhaps vehicle design) is causing a ton of accidents, regardless of whether it's being operated locally by customers or remotely by professionals.
I'd like to see similar studies broken down by vehicle manufacturer.
The ADAS in one of our cars is great, but occasionally beeps when it shouldn't.
The ADAS in our other car cannot be disabled and false positives every 10-20 miles. Every week or so it forces the vehicle out of lane (either left of double yellow line center, or into another car's lane).
If the data on crash rates for those two models were public, I guarantee the latter car would have been recalled by now.
I don’t think statistics work that way. A study of all Teslas and all humans in Austin for 5 months is valid because Electrek ran a ridiculous “study”, and this headline could “just as easily” have presented the flawed Elektrek stork as a legit baseline?
The 10x would be 9x if the methodology were the same. 9x->3x is going from reported accidents to inferred true accident rate, as the article points out.
Everyone has to address their spiritual beliefs every time they mention something vaguely related to them? Else they lack epistemic humility? ...Did it occur to you that most people have actually thought of this question?
Wait, is "seems lacking in epistemic humility" just coded language for "I disagree, therefore you couldn't possibly be thoughtful"?
"Frontier models today perform very poorly with a minimal harness. However if big labs start directly targeting the benchmark like they did for ARC-2, numbers will go up fast."
https://x.com/fchollet/status/2022054537293705260
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