Congratulations to Team USA, you did not ‘beat China’ but 2nd place is still awesome. Great job, China, you got us this time, three perfect scores is crazy.
You’ve all done a fantastic, amazingly hard thing, and as someone who tried hard to join you and only got as far as the [, year censored because oh man I am old] USAMO and would probably have gotten 0/45 on this IMO if I had taken it today, and know what it is like to practice for the USAMO in a room with multiple future IMO team members that must have thought I was an idiot, let me say: I am always in awe.
What matters is that Google and OpenAI have LLMs with gold medal performances, each scoring exactly the threshold of 35/42 by solving the first five of the six problems.
This is up from Google’s 28/42 performance last year, which was previously achieved with a longer time frame. The methods used by both are presented as being more general, whereas last year’s version was a more specialized effort.
The new scores were a 92nd percentile result at the event.
Google did this under official collaboration with the IMO, announced on Monday as per the IMO’s request. OpenAI did it on their own, so they announced a bit earlier, so we are taking their word on many details.
This was not expected. Prediction markets thought gold this year was unlikely.
What matters more is how they did it, with general purpose LLMs without tools, in ways that represent unexpected and large future gains in other reasoning as well.
The more I think about the details here, the more freaked out I get rather than less. This is a big deal. How big remains to be seen, as we lack details, and no one knows how much of this will generalize.
Jxmo (replying to Ravid): if they did well, you’d be complaining that they overfit.
Ravid Shwartz: That’s true, because they are 👽
Rohit: This isn’t a gotcha. Any problem that we fundamentally focus on deeply enough is one that AI will be able to solve. The question, as ever, is whether that solution is likely to carry over to other domains.
I disagree, I think this is a gotcha in the positive sense. People took ‘the AIs that weren’t aimed at this problem that are publicly released are only doing okay relative to the best humans, and have not proven themselves the best yet’ to be ‘look at the pathetic AIs,’ one day before we learned that, well, actually, in a way prediction markets did not expect.
I do think people need to update their models of the future.
It would be non-trivial for non-math person to achieve the same score. We have spent some time to carefully check the solutions. Regardless, the prompts are very general and can be applied to other models. We will release an automatic agent soon.
Jun Wu: They added a lot of steps in order to solve 5 problems. They didn’t publish the details on how these steps were done beyond the concepts.
I don’t have time to investigate how ‘legit’ the Gemini 2.5 Pro solutions are, including in terms of how much you have to cheat to get them.
We achieved this year’s result using an advanced version of Gemini Deep Think – an enhanced reasoning mode for complex problems that incorporates some of our latest research techniques, including parallel thinking. This setup enables the model to simultaneously explore and combine multiple possible solutions before giving a final answer, rather than pursuing a single, linear chain of thought.
To make the most of the reasoning capabilities of Deep Think, we additionally trained this version of Gemini on novel reinforcement learning techniques that can leverage more multi-step reasoning, problem-solving and theorem-proving data. We also provided Gemini with access to a curated corpus of high-quality solutions to mathematics problems, and added some general hints and tips on how to approach IMO problems to its instructions.
We will be making a version of this Deep Think model available to a set of trusted testers, including mathematicians, before rolling it out to Google AI Ultra subscribers.
Google’s answers were even in nice form.
IMO President Dr. Gregor Dolinar: We can confirm that Google DeepMind has reached the much-desired milestone, earning 35 out of a possible 42 points — a gold medal score. Their solutions were astonishing in many respects. IMO graders found them to be clear, precise and most of them easy to follow.
Colin Fraser: has anyone actually read these LLM IMO proofs? I read one of the Google ones and it’s good. I find the OAI version of the same one impenetrable. The Google one is also kind of hard to read but possible.
Ernest Davis (6th in US Math Olympiad once, just short of the IMO): Second: The proofs produced by DM-IMO and by every single earlier LLM, whether correct or incorrect, are written in a smooth, elegant style. They could be cut and pasted into a journal article or into a textbook with little or no editing. The worst you can say of them is that they are sometimes verbose.
By contrast, OpenAI-IMO writes proofs in the style of an informal spoken presentation who is not very practiced or competent at giving informal presentations, and regularly mutters reassurances to themselves that they’re on the right rack.
Miles Brundage: OAI one got RL’d to within an inch of its life.
What else did they say about how they did this?
DeepMind: With Deep Think, an enhanced reasoning mode, our model could simultaneously explore and combine multiple possible solutions before giving definitive answers.
We also trained it on RL techniques that use more multi-step reasoning, problem-solving and theorem-proving data.
Finally, we pushed this version of Gemini further by giving it:
🔘 More thinking time
🔘 Access to a set of high-quality solutions to previous problems
🔘 General hints and tips on how to approach IMO problems
That sounds mostly rather general. There’s some specialized IMO context, but orders of magnitude less than what IMO competitors devote to this.
Elon Musk: While a notable milestone, this is already borderline trivial for AI.
Um, Elon, no, and I remind you that Grok 4 got 11.9%. Which for a human would be super impressive, but seriously, borderline trivial?
Noam Brown (OpenAI): Congrats to the GDM team on their IMO result! I think their parallel success highlights how fast AI progress is. Their approach was a bit different than ours, but I think that shows there are many research directions for further progress.
OpenAI claimed its victory first, right after the closing ceremony and before the party, whereas Google DeepMind waited to announce until the following Monday.
The most impressive thing about OpenAI’s result is that they claim this is not an IMO-specific model, and that it uses only general-purpose techniques.
Alexander Wei (OpenAI): I’m excited to share that our latest @OpenAI experimental reasoning LLM has achieved a longstanding grand challenge in AI: gold medal-level performance on the world’s most prestigious math competition—the International Math Olympiad (IMO).
We evaluated our models on the 2025 IMO problems under the same rules as human contestants: two 4.5 hour exam sessions, no tools or internet, reading the official problem statements, and writing natural language proofs.
Why is this a big deal? First, IMO problems demand a new level of sustained creative thinking compared to past benchmarks. In reasoning time horizon, we’ve now progressed from GSM8K (~0.1 min for top humans) → MATH benchmark (~1 min) → AIME (~10 mins) → IMO (~100 mins).
Second, IMO submissions are hard-to-verify, multi-page proofs. Progress here calls for going beyond the RL paradigm of clear-cut, verifiable rewards. By doing so, we’ve obtained a model that can craft intricate, watertight arguments at the level of human mathematicians.
Besides the result itself, I am excited about our approach: We reach this capability level not via narrow, task-specific methodology, but by breaking new ground in general-purpose reinforcement learning and test-time compute scaling.
In our evaluation, the model solved 5 of the 6 problems on the 2025 IMO. For each problem, three former IMO medalists independently graded the model’s submitted proof, with scores finalized after unanimous consensus. The model earned 35/42 points in total, enough for gold! 🥇
HUGE congratulations to the team—@SherylHsu02, @polynoamial, and the many giants whose shoulders we stood on—for turning this crazy dream into reality! I am lucky I get to spend late nights and early mornings working alongside the very best.
Btw, we are releasing GPT-5 soon, and we’re excited for you to try it. But just to be clear: the IMO gold LLM is an experimental research model. We don’t plan to release anything with this level of math capability for several months.
Still—this underscores how fast AI has advanced in recent years. In 2021, my PhD advisor @JacobSteinhardt had me forecast AI math progress by July 2025. I predicted 30% on the MATH benchmark (and thought everyone else was too optimistic). Instead, we have IMO gold.
If you want to take a look, here are the model’s solutions to the 2025 IMO problems! The model solved P1 through P5; it did not produce a solution for P6. (Apologies in advance for its … distinct style—it is very much an experimental model 😅)
Lastly, we’d like to congratulate all the participants of the 2025 IMO on their achievement! We are proud to have many past IMO participants at @OpenAI and recognize that these are some of the brightest young minds of the future.
Noam Brown (OpenAI): Today, we at @OpenAI achieved a milestone that many considered years away: gold medal-level performance on the 2025 IMO with a general reasoning LLM—under the same time limits as humans, without tools. As remarkable as that sounds, it’s even more significant than the headline.
Typically for these AI results, like in Go/Dota/Poker/Diplomacy, researchers spend years making an AI that masters one narrow domain and does little else. But this isn’t an IMO-specific model. It’s a reasoning LLM that incorporates new experimental general-purpose techniques.
So what’s different? We developed new techniques that make LLMs a lot better at hard-to-verify tasks. IMO problems were the perfect challenge for this: proofs are pages long and take experts hours to grade. Compare that to AIME, where answers are simply an integer from 0 to 999.
Jacques: Most important part of the IMO Gold achievement. Were you surprised by this? Did you not update all the way to avoid likelihood of surprise?
Indeed. Purely getting the gold medal is surprising but not that big a deal. The way they got the result, assuming they’re reporting accurately? That’s a really big deal.
Noam Brown (resuming): Also this model thinks for a *longtime. o1 thought for seconds. Deep Research for minutes. This one thinks for hours. Importantly, it’s also more efficient with its thinking. And there’s a lot of room to push the test-time compute and efficiency further.
…
Importantly, I think we’re close to AI substantially contributing to scientific discovery. There’s a big difference between AI slightly below top human performance vs slightly above.
This was a small team effort led by @alexwei_. He took a research idea few believed in and used it to achieve a result fewer thought possible. This also wouldn’t be possible without years of research+engineering from many at @OpenAI and the wider AI community.
What about Problem 6? Did the programs submit incorrect solutions?
Note that if you are maximizing, then when time runs out if you have anything at all then yes you do submit the best incorrect solution you have, because you might get you partial credit, although this rarely works out.
Daniel Litt: One piece of info that seems important to me in terms of forecasting usefulness of new AI models for mathematics: did the gold-medal-winning models, which did not solve IMO problem 6, submit incorrect answers for it?
Alexander Wei: On IMO P6 (without going into too much detail about our setup), the model “knew” it didn’t have a correct solution. The model knowing when it didn’t know was one of the early signs of life that made us excited about the underlying research direction!
If one person gets to say ‘Not So Fast’ about this sort of thing, Tao is that one person.
It is entirely fair to say that if you don’t disclose conditions in advance, and definitely if you don’t disclose conditions after the fact, it is difficult to know exactly what to make of the result. Tao’s objections are valid.
Terence Tao: It is tempting to view the capability of current AI technology as a singular quantity: either a given task X is within the ability of current tools, or it is not. However, there is in fact a very wide spread in capability (several orders of magnitude) depending on what resources and assistance gives the tool, and how one reports their results.
One can illustrate this with a human metaphor. I will use the recently concluded International Mathematical Olympiad (IMO) as an example. Here, the format is that each country fields a team of six human contestants (high school students), led by a team leader (often a professional mathematician). Over the course of two days, each contestant is given four and a half hours on each day to solve three difficult mathematical problems, given only pen and paper. No communication between contestants (or with the team leader) during this period is permitted, although the contestants can ask the invigilators for clarification on the wording of the problems. The team leader advocates for the students in front of the IMO jury during the grading process, but is not involved in the IMO examination directly.
The IMO is widely regarded as a highly selective measure of mathematical achievement for a high school student to be able to score well enough to receive a medal, particularly a gold medal or a perfect score; this year the threshold for the gold was 35/42, which corresponds to answering five of the six questions perfectly. Even answering one question perfectly merits an “honorable mention”.
But consider what happens to the difficulty level of the Olympiad if we alter the format in various ways, such as the following:
One gives the students several days to complete each question, rather than four and half hours for three questions. (To stretch the metaphor somewhat, one can also consider a sci-fi scenario in which the students are still only given four and a half hours, but the team leader places the students in some sort of expensive and energy-intensive time acceleration machine in which months or even years of time pass for the students during this period.)
Before the exam starts, the team leader rewrites the questions in a format that the students find easier to work with.
The team leader gives the students unlimited access to calculators, computer algebra packages, formal proof assistants, textbooks, or the ability to search the internet.
The team leader has the six student team work on the same problem simultaneously, communicating with each other on their partial progress and reported dead ends.
The team leader gives the students prompts in the direction of favorable approaches, and intervenes if one of the students is spending too much time on a direction that they know to be unlikely to succeed.
Each of the six students on the team submit solutions to the team leader, who then selects only the “best” solution for each question to submit to the competition, discarding the rest.
If none of the students on the team obtains a satisfactory solution, the team leader does not submit any solution at all, and silently withdraws from the competition without their participation ever being noted.
In each of these formats, the submitted solutions are still technically generated by the high school contestants, rather than the team leader. However, the reported success rate of the students on the competition can be dramatically affected by such changes of format; a student or team of students who might not even always reach bronze medal performance if taking the competition under standard test conditions might instead reach reliable gold medal performance under some of the modified formats indicated above.
So, in the absence of a controlled test methodology that was not self-selected by the competing teams, one should be wary of making overly simplistic apples-to-apples comparisons between the performance of various AI models on competitions such as the IMO, or between such models and the human contestants.
Related to this, I will not be commenting on any self-reported AI competition performance results for which the methodology was not disclosed in advance of the competition.
EDIT: In particular, the above comments are not specific to any single result of this nature.
The catch is that this is about grading the horse’s grammar, as opposed to the observation that the horse can talk and rather intelligently and with rapidly improving performance at that.
Thus, while the objections are valid, as long as we know the AIs had no access to outside tools or to the internet (which is confirmed), we should seek the answers to these other questions but the concerns primarily matter for comparisons between models, and within a reasonably narro (in the grand scheme of things) band of capabilities.
I also would note that if OpenAI did essentially do the ‘team thinks in parallel’ thing where it had multiple inference processes running simultaneously on multiple computers, well, that is something AIs can do in the real world, and this seems entirely fair for our purposes the same way humans can fire multiple neurons at once. It’s totally fair to also want a limited-compute or one-thread category or what not, but that’s not important right now.
To use Tao’s metaphor, if you took 99.99% of high school students, you could fully and simultaneously apply all these interventions other than formal proof assistants and internet searches or hints so clear they give you the first point on a question, and you still almost always get zero.
Nat McAleese: 17 M U.S. teens grades 9-12, ~5 US IMO golds in practice but ~20 kids at gold-level. So IMO gold is one-in-a-million math talent (for 18 year olds; but I bet next Putnam falls too). 99.9999th percentile.
As a former not only math competitor but also Magic: The Gathering competitor, absolutely all these details matter for competitions, and I respect the hell out of getting all of those details right – I just don’t think that, in terms of takeaways, they change the answer much here.
In other words? Not Not So Fast. So Fast.
OpenAI chose not to officially collaborate with the IMO. They announced their result after the IMO closing ceremony and prior to the IMO 2025 closing party. Those who did collaborate agreed to wait until the following Monday, which was when Google announced. By going first, OpenAI largely stole the spotlight on this from Google, yet another case of Google Failing Marketing Forever.
A question that was debated is, did OpenAI do something wrong here?
Mikhail Samin claimed that they did, and put their hype and clout ahead of the kids celebrating their achievements against the wishes of the IMO.
OpenAI’s Noam Brown replied that they waited until after the closing ceremony exactly to avoid stealing the spotlight. He said he was the only person at OpenAI to speak to anyone at the IMO, and that person only requested waiting until after the ceremony, so that is what OpenAI did.
Not collaborating with the IMO was a choice that OpenAI made.
Mikhail Samin: AI companies that chose to cooperate with the IMO on assessment of the performance of their models had in-person meetings with IMO people on July 16. It was agreed there that announcements of AI achievements should be made on 28 July or later.
A quote from someone involved: “I certainly expect that if OpenAI had contacted the IMO in advance and expressed interest in cooperating in the assessment of their work, they would have been able to be included in that meeting, so I suppose that unless there was a major miscommunication somewhere, they effectively ended up choosing, by default or otherwise, not to cooperate with the IMO on this, and so not to be aware of what ground rules might have been agreed by those who did cooperate.”
Demis Hassabis (CEO DeepMind): Btw as an aside, we didn’t announce on Friday because we respected the IMO Board’s original request that all AI labs share their results only after the official results had been verified by independent experts & the students had rightfully received the acclamation they deserved.
We’ve now been given permission to share our results and are pleased to have been part of the inaugural cohort to have our model results officially graded and certified by IMO coordinators and experts, receiving the first official gold-level performance grading for an AI system!
Noam Brown: ~2 months ago, the IMO emailed us about participating in a formal (Lean) version of the IMO. We’ve been focused on general reasoning in natural language without the constraints of Lean, so we declined. We were never approached about a natural language math option.
Over the past several months, we made a lot of progress on general reasoning. This involved collecting, curating, and training on high-quality math data, which will also go into future models. In our IMO eval we did not use RAG or any tools.
Before we shared our results, we spoke with an IMO board member, who asked us to wait until after the award ceremony to make it public, a request we happily honored.
Jasper: DeepMind got a gold medal at the IMO on Friday afternoon. But they had to wait for marketing to approve the tweet — until Monday. @OpenAI shared theirs first at 1am on Saturday and stole the spotlight.
In this game, speed > bureaucracy. Miss the moment, lose the narrative.
Clarification: I’ve been told by someone at Google that their IMO results are still being verified internally. Once that’s done, they plan to share them officially—curious to see their approach. Another source mentioned that the IMO committee asked not to publicly discuss AI involvement within a week after the closing ceremony. Things just got a bit more interesting.
Daniel Eth: “In this game, speed > bureaucracy. Miss the moment, lose the narrative.” Honestly, disagree. If GDM beats OpenAI, then the narrative will shift once that’s public.
I have reflected on this. It is not the main thing, the results are the main thing. I do think that on reflection while OpenAI did not break any agreements or their word, and strictly speaking they do not owe the IMO or the kids anything, and this presumably net increased the focus on the kids, this still does represent a meaningful failure to properly honor the competition and process, as well as offering us the proper opportunities for verification, and they should have known that this was the case. I do get that this was a small team’s last minute effort, which makes me more understanding, but it’s still not great.
Fig Spirit: then again, assuming Myers is correct about his impression of the “general coordinator view”, seems like the kind of thing that OpenAI could have known about *ifthey cared, no? by e.g. talking to the right people at the IMO… which imo is not asking much! and looks like others did?
Thus, I was careful to wait to write this until after Google’s results were announced, and have placed Google’s announcement before OpenAI’s in this post, even though due to claimed details by OpenAI I do think their achievement here is likely the more meaningful one. Perhaps that is simply Google failing marketing again and failing to share details.
Ultimately, the reason OpenAI stole my spotlight is that it harkens something general and new in a way that Google’s announcement doesn’t.
With Google sharing its results I don’t want to wait any longer, but note Harmonic?
Harmonic Math: This past week, Harmonic had the opportunity to represent our advanced mathematical reasoning model, Aristotle, at the International Mathematics Olympiad – the most prestigious mathematics competition in the world.
To uphold the sanctity of the student competition, the IMO Board has asked us, along with the other leading AI companies that participated, to hold on releasing our results until July 28th.
So please join us live on @X next Monday, July 28th at 3PM PT and hear from our CEO @tachim and Executive Chairman @vladtenev about the advent of mathematical superintelligence (and maybe a few surprises along the way).
This would be a weird flex if they didn’t also get gold, although it looks like they would have done it in a less general and thus less ultimately interesting way. On the flip side, they are not a big lab like Google or OpenAI, so that’s pretty impressive.
Andrew Curran: OpenAI’s new model has achieved gold level at the International Math Olympiad in a stunning result. It is a reasoning model that incorporates new experimental general-purpose techniques. This has happened much sooner than was predicted by most experts.
Noam Brown (OpenAI): When you work at a frontier lab, you usually know where frontier capabilities are months before anyone else. But this result is brand new, using recently developed techniques. It was a surprise even to many researchers at OpenAI. Today, everyone gets to see where the frontier is.
JGalt Tweets: When will an AI win a Gold Medal in the International Math Olympiad? Median predicted date over time
July 2021: 2043 (22 years away)
July 2022: 2029 (7 years away)
July 2023: 2028 (5 years away)
July 2024: 2026 (2 years away)
Final result, July 2025: 2025 (now). Buckle up, Dorothy.
Some people did expect it, some of whom offered caveats.
Greg Burnham: Pretty happy with how my predictions are holding up.
5/6 was the gold medal threshold this year. OAI’s “experimental reasoning LLM” got that exactly, failing only to solve the one hard combinatorics problem, P6.
My advice remains: look beyond the medal.
Now, this is an LLM, not AlphaProof. That means LLMs have improved at proofs. I didn’t expect that so soon.
Though, FWIW, P3 is a bit of an outlier this year, at least for humans: over 15% of humans got it, higher than any P3 in the last 10 years.
But “the big one” remains whether the AI solutions show qualitatively creative problem-solving.
LLMs could already grind out “low insight” sol’ns to hard AIME problems. If OAI found a way to train them do that for olympiad proof-based problems too, that’s new, but less exciting.
So, clear progress, but not *toosurprising. I’ll keep my takes tempered until looking at the AI solutions in depth, which I hope to do soon! Above excerpts from my preregistered take on the IMO here.
Mikhail Samin: As someone who bet back in 2023 that that it’s >70% likely AI will get an IMO gold medal by 2027:
the IMO markets have been incredibly underpriced, especially for the past year.
(Sadly, another prediction I’ve been >70% confident about is that AI will literally kill everyone.)
The AIs took the IMO under the same time limits as the humans, and success was highly valued, so it is no surprise that they used parallel inference to get more done within that time frame, trading efficiency for speed.
Andrew Curran: These agentic teams based models like Grok Heavy, the Gemini Deep Think that just won gold, and the next gen from OpenAI are all going to use about fifteen times more tokens than current systems. This is why Pro plans are north of $200. Essentially; Jensen wins again.
[from June 14]: Claude Opus, coordinating four instances of Sonnet as a team, used about 15 times more tokens than normal. (90% performance boost) Jensen has mentioned similar numbers on stage recently. GPT-5 is rumored to be agentic teams based. The demand for compute will continue to increase.
I just want to register a prediction, I’m 80% confident the inference run cost over $1M in compute.
Mostly because if they could do it for $1M they would, and they would be able to do it for $1M before they can do it for less.
Jerry Tworek (OpenAI): I’m so limited by compute you wouldn’t believe it. Stargate can’t finish soon enough.
Sure, you solved this particular problem, but that would never generalize, right? That part is the same hype as always?
Near Cyan: you wont believe how smart our new frontier llm is. it repeatedly samples from the data manifold just like our last one. but this time we gave it new data to cover a past blindspot. watch in awe as we now sample from a slightly different area of the data manifold.
there may lay a prize at the end of the hyperdimensioanl checkered rainbow, but it’s likely not what you think it is.
i really thought someone would have done something original by now. of course, if anything was ~truly~ cooking, it shouldn’t be something i’d know about… but the years continue to pass
and, right right we have to finish *this phaseso that we have the pre-requisites. and yet.
David Holz (CEO MidJourney): noooo money can’t be dumb it’s so green.
Near Cyan: it is for now! but some of it may turn a dark crimson surprisingly quickly.
Nico: What do you make of [the OpenAI model knowing it didn’t have a correct solution to problem 6]? Sounds pretty important.
Near Cyan: seems cool i bet they have some great data.
A grand tradition is:
AI can do a set of things [X] better than humans, but not a set of things [Y].
People say [X] and [Y] are distinct because Moravec’s Paradox and so on.
AI lab announces that [Z], previously in [Y], is now in [X].
People move [Z] from [Y] to [X] and then repeat that this distinct category of things [Y] exists because Moravec’s Paradox, that one task was simply miscategorized before, so it’s fine.
Or: AI can do the things it can do, and can’t do the things it can’t do, they’re hard.
Yuchen Jin: OpenAI and DeepMind models winning IMO golds is super cool, but not surprising if you remember AlphaGo beat Lee Sedol.
What’s easy for AI can be hard for humans, and vice versa. That’s Moravec’s Paradox.
So yes, AI can win math gold medals and beat humans in competitive coding contests. But ask it to act like a competent “intern” across a multi-step project without messing things up? Still a long way to go.
To get there, models need longer context windows, far less hallucination (a single one can derail a multi-step task), and likely a new learning paradigm. RL with a single scalar +1/-1 reward at the end of a long trajectory just isn’t informative enough to drive actual learning.
I mean, it probably can’t even do real math, right?
Kevin Buzzard (Mathematician, Imperial College): I certainly don’t agree that machines which can solve IMO problems will be useful for mathematicians doing research, in the same way that when I arrived in Cambridge UK as an undergraduate clutching my IMO gold medal I was in no position to help any of the research mathematicians there.
It is still entirely unclear whether things will scale from machines being able to do mathematics which can be solved using high school techniques to machines being able to help with mathematics which can only be solved by having a deep understanding of modern research ideas.
This is a big open question right now.
Hehe: What most people don’t realize is that IMO (and IOI, though to a different extent) aren’t particularly hard. They’re aimed at high schoolers, so anyone with decent uni education should be able to solve most of them.
Daniel Litt: I’m sorry, this is nonsense. Vast majority of strong math majors can’t do 5/6 IMO problems. It’s a specific skill that getting a math major doesn’t really train you for.
So yes, we still do not know for sure if being able to do [X] will extend to doing [Y], either with the same model or with a future different model, and [X] and [Y] are distinct skills such that the humans who do [X] cannot yet do [Y] and training humans to do [Y] does not give them the ability to do [X]. However please try to think ahead.
Daniel Litt: An AI tool that gets gold on the IMO is obviously immensely impressive. Does it mean math is “solved”? Is an AI-generated proof of the Riemann hypothesis clearly on the horizon? Obviously not.
Worth keeping timescales in mind here: IMO competitors spend an average of 1.5 hrs on each problem. High-quality math research, by contrast, takes month or years.
What are the obstructions to AI performing high-quality autonomous math research? I don’t claim to know for sure, but I think they include many of the same obstructions that prevent it from doing many jobs:
Long context, long-term planning, consistency, unclear rewards, lack of training data, etc.
It’s possible that some or all of these will be solved soon (or have been solved) but I think it’s worth being cautious about over-indexing on recent (amazing) progress.
To briefly expand on the point about timescales: one recent paper I wrote solved a problem I’ve been thinking about since 2017. Another was 94 pages of extremely densely-written math, aimed at experts.
We don’t know much yet about how the best internal models work, but I don’t think it’s clear that getting capabilities of that level is “only” an engineering problem. That said, I do think it’s pretty likely that many or all of these issues will be solved within the span of my mathematics career.
That is all entirely fair. An IMO problem is measured in hours, not months, and is bounded in important ways. That is exactly the paradigm of METR, and the one being talked about by Noam Brown and Alexander Wei, that we have now made the move from 10 minute problems to 100 minute problems.
That does not mean we can yet solve 10,000 minute or 1 million minute problems, but why would you expect the scaling to stop here? As I discussed in the debates over AI 2027, it makes sense to think that these orders of magnitude start to get easier rather than harder once you get into longer problems. If you can do 100 minute problems that doesn’t mean you can easily go to 1000 or a million, but if you can go 1 million, I bet you can probably do 1 billion without fundamentally changing things that much, if you actually have that kind of time. At some point your timeline is ‘indefinite’ or ‘well, how much time and compute have you got?’
David White: the openai IMO news hit me pretty heavy this weekend.
i’m still in the acute phase of the impact, i think.
i consider myself a professional mathematician (a characterization some actual professional mathematicians might take issue with, but my party my rules) and i don’t think i can answer a single imo question.
ok, yes, imo is its own little athletic subsection of math for which i have not trained, etc. etc., but. if i meet someone in the wild who has an IMO gold, i immediately update to “this person is much better at math than i am”
now a bunch of robots can do it. as someone who has a lot of their identity and their actual life built around “is good at math,” it’s a gut punch. it’s a kind of dying.
like, one day you discover you can talk to dogs. it’s fun and interesting so you do it more, learning the intricacies of their language and their deepest customs. you learn other people are surprised by what you can do. you have never quite fit in, but you learn people appreciate your ability and want you around to help them. the dogs appreciate you too, the only biped who really gets it. you assemble for yourself a kind of belonging. then one day you wake up and the universal dog translator is for sale at walmart for $4.99.
the IMO result isn’t news, exactly. in fact, if you look at the METR agent task length over time plot, i think agents being able to solve ~ 1.5 hour problems is coming right on time. so in some way we should not be surprised. and indeed, it appears multiple companies have achieved the same result. it’s just… the rising tide rising as fast as it has been rising.
of course, grief for my personal identity as a mathematician (and/or productive member of society) is the smallest part of this story
multiply that grief out by *everymathematician, by every coder, maybe every knowledge worker, every artist… over the next few years… it’s a slightly bigger story
and of course, beyond that, there is the fear of actual death, which perhaps i’ll go into more later.
this package — grief for relevance, grief for life, grief for what i have known — isn’t unique to the ai age or anything like that. i think it is a standard thing as one appreaches end of career or end of life. it just might be that that is coming a bit sooner for many of us, all at once.
i wonder if we are ready
I am very confident we are not ready. If we are fortunate we might survive, but we definitely are not ready.
Mechanize: Automating math would generate less than 1% as much value as automating software engineering.
Perhaps AI labs should focus less on chasing gold medals and focus more on the hard problem of automating SWE.
T11s: this is pretty reductionist? innovations in math uniquely enable lots of software (eg cryptography made ecommerce possible)
Deedy: Quant trading is a lot of math and accounts for $50-100B in revenue.
Never confuse costs and benefits #RulesForLife, and never reason from a price change.
(This defines ‘math’ rather narrowly as advanced Real Math that mathematicians and maybe quants and other professionals do, not the kind of math that underlies absolutely everything we do all day, since Fake Math is already mostly automated.)
The value of automating is not determined by how much we spent on it before it got automated. The value is determined by how much additional value we get out of something when we automate it, which might involve a lot more production and very diffuse benefits.
Back in February 2022, Eliezer Yudkowsky bet with Paul Christiano about IMO performance by 2025. The results were not super clear cut if you look at the details, as Christiano was in large part doubting that the hardest problem would be solved and indeed the hardest problem was #6 and was not solved, but a gold medal was still achieved.
So I think we have Paul at <8%, Eliezer at >16% for AI made before the IMO is able to get a gold (under time controls etc. of grand challenge) in one of 2022-2025.
Separately, we have Paul at <4% of an AI able to solve the "hardest" problem under the same conditions.
…
How [I, Paul, would] update
The informative:
I think the IMO challenge would be significant direct evidence that powerful AI would be sooner, or at least would be technologically possible sooner. I think this would be fairly significant evidence, perhaps pushing my 2040 TAI [transformational AI] probability up from 25% to 40% or something like that.
I think this would be significant evidence that takeoff will be limited by sociological facts and engineering effort rather than a slow march of smooth ML scaling. Maybe I’d move from a 30% chance of hard takeoff to a 50% chance of hard takeoff.
If Eliezer wins, he gets 1 bit of epistemic credit.These kinds of updates are slow going, and it would be better if we had a bigger portfolio of bets, but I’ll take what we can get.
This would be some update for Eliezer’s view that “the future is hard to predict.” I think we have clear enough pictures of the future that we have the right to be surprised by an IMO challenge win; if I’m wrong about that then it’s general evidence my error bars are too narrow.
If an AI wins a gold on some but not all of those years, without being able to solve the hardest problems, then my update will be somewhat more limited but in the same direction.
At this point, we have a lot of people who have updated far past 40% chance of transformational AI by 2040 and have 40% for dates like 2029.
If we take all of OpenAI’s statements at face value, think about what they actually did.
Sam Altman: we achieved gold medal level performance on the 2025 IMO competition with a general-purpose reasoning system! to emphasize, this is an LLM doing math and not a specific formal math system; it is part of our main push towards general intelligence.
when we first started openai, this was a dream but not one that felt very realistic to us; it is a significant marker of how far AI has come over the past decade.
we are releasing GPT-5 soon but want to set accurate expectations: this is an experimental model that incorporates new research techniques we will use in future models. we think you will love GPT-5, but we don’t plan to release a model with IMO gold level of capability for many months.
Sheryl Hsu (OpenAI): Watching the model solve these IMO problems and achieve gold-level performance was magical.
The model solves these problems without tools like lean or coding, it just uses natural language, and also only has 4.5 hours. We see the model reason at a very high level – trying out different strategies, making observations from examples, and testing hypothesis.
It’s crazy how we’ve gone from 12% on AIME (GPT 4o) → IMO gold in ~ 15 months. We have come very far very quickly. I wouldn’t be surprised if by next year models will be deriving new theorems and contributing to original math research!
I was particularly motivated to work on this project because this win came from general research advancements. Beyond just math, we will improve on other capabilities and make ChatGPT more useful over the coming months.
Sebastien Bubeck: It’s hard to overstate the significance of this. It may end up looking like a “moon‑landing moment” for AI.
Just to spell it out as clearly as possible: a next-word prediction machine (because that’s really what it is here, no tools no nothing) just produced genuinely creative proofs for hard, novel math problems at a level reached only by an elite handful of pre‑college prodigies.
Winning a gold medal at the 2025 IMO is an outstanding achievement, but in some ways, it might just be noise that grabbed the headlines.
They have recently developed new techniques that work much better on hard-to-verify problems, have extended TTC to several hours, and have improved thinking efficiency.
Jerry Tworek (OpenAI): Why am I excited about IMO results we just published:
– we did very little IMO-specific work, we just keep training general models
– all natural language proofs
– no evaluation harness
We needed a new research breakthrough and @alexwei_ and team delivered.
Diego Aud: Jerry, is this breakthrough included in GPT-5, or is it reserved for the next generation?
Jerry Tworek: It’s a later model probably end of year thing.
Guizin: Agent 1.
Jerry Tworek: I’m so limited by compute you wouldn’t believe it. Stargate can’t finish soon enough.
Going back to Tao’s objections, we know essentially nothing about this new model, or about what Google did to get their result. Given that P3 was unusually easy this year, these scores are perhaps not themselves that terribly impressive relative to expectations.
Can we trust this? It’s not like OpenAI has never misled us on such things in the past.
In terms of the result being worthy of a 35/42, I think we can mostly trust that. They shared the solution, in its garbled semi-English, and if there was something that would have lost them points I think someone would have spotted it by now.
In terms of OpenAI otherwise cheating, we don’t have any proof about this but I think the chances of this are quite low. There’s different kinds of deception or lies, different parts of OpenAI are differently trustworthy, and this kind of lie is not in their nature nor do they have much incentive to try it given the chance it gets exposed, and the fact that if it’s not real then they won’t be able to pay it off later.
The place where one might doubt the most is, can we trust that what OpenAI did this time is more general, in the ways they are claiming?
Gary Marcus: The paradox of the OpenAI IMO discussion is that the new model scored only slightly better than DeepMind’s system from last year (as @NeelNanda5 notes); but that we assume that the new model is far more general.
Yet we have not yet seen any direct evidence of that.
It can barely speak english.
The ‘barely speak English’ part makes the solution worse in some ways but actually makes me give their claims to be doing something different more credence rather than less. It also should worry anyone who wants to maintain monitorable chain of thought.
Then again, one could say that the version that does it better, and more naturally, is thus more important, for exactly the same reasons.
Vladimir Nesov: [GDM’s] is even more surprising than OpenAI’s entry (in its details). Since it can now write proofs well automatically (even if it costs a lot and takes a lot of time), in a few months regular reasoning models might get enough training data to reliably understand what proofs are directly, and that’s an important basic ingredient for STEM capabilities.
We only have OpenAI’s word on the details of how this went down. So what to think?
I am mostly inclined to believe them on the main thrust of what is going on. That doesn’t mean that this result will generalize. I do give them credit for having something that they believe came out of a general approach, and that they expect to generalize.
Still, it’s reasonable to ask what the catch might be, that there’s always going to be a catch. Certainly it is plausible that this was, as Miles suggested, RLed to within an inch of its life, and it starting to be unable to speak English is the opposite of what is claimed, that it is losing its generality, or things are otherwise going off the rails.
The thing is, to me this doesn’t feel like it is fake. It might not be a big deal, it might not transfer all that well to other contexts, but it doesn’t feel fake.
To wrap up, another reminder that no, you can’t pretend none of this matters, and both the Google and OpenAI results matter and should update you:
Cole Wyeth: The headline result was obviously going to happen, not an update for anyone paying attention.
Garrett Baker: “Obviously going to happen” is very different from ‘happens at this point in time rather than later or sooner and with this particular announcement by this particular company’. You should still update off this. Hell, I was pretty confident this would be first done by Google DeepMind, so its a large update for me (I don’t know what for yet though)!
Your claim “not an update for anyone paying attention” also seems false. I’m sure there are many who are updating off this who were paying attention, for whatever reason, as they likely should.
I generally dislike this turn of phrase as it serves literally no purpose but to denigrate people who are changing their mind in light of evidence, which is just a bad thing to do.
cdt: I think it was reasonable to expect GDM to achieve gold with an AlphaProof-like system. Achieving gold with a general LLM-reasoning system from GDM would be something else and it is important for discussion around this to not confuse one forecast for another.
X claimed that David Chavalarias, “who spearheads the ‘Escape X’ campaign”—which is “dedicated to encouraging X users to leave the platform”—was chosen to assess the data with one of his prior research collaborators, Maziyar Panahi.
“The involvement of these individuals raises serious concerns about the impartiality, fairness, and political motivations of the investigation, to put it charitably,” X alleged. “A predetermined outcome is not a fair one.”
However, Panahi told Reuters that he believes X blamed him “by mistake,” based only on his prior association with Chavalarias. He further clarified that “none” of his projects with Chavalarias “ever had any hostile intent toward X” and threatened legal action to protect himself against defamation if he receives “any form of hate speech” due to X’s seeming error and mischaracterization of his research. An Ars review suggests his research on social media platforms predates Musk’s ownership of X and has probed whether certain recommendation systems potentially make platforms toxic or influence presidential campaigns.
“The fact my name has been mentioned in such an erroneous manner demonstrates how little regard they have for the lives of others,” Panahi told Reuters.
X denies being an “organized gang”
X suggests that it “remains in the dark as to the specific allegations made against the platform,” accusing French police of “distorting French law in order to serve a political agenda and, ultimately, restrict free speech.”
The press release is indeed vague on what exactly French police are seeking to uncover. All French authorities say is that they are probing X for alleged “tampering with the operation of an automated data processing system by an organized gang” and “fraudulent extraction of data from an automated data processing system by an organized gang.” But later, a French magistrate, Laure Beccuau, clarified in a statement that the probe was based on complaints that X is spreading “an enormous amount of hateful, racist, anti-LGBT+ and homophobic political content, which aims to skew the democratic debate in France,” Politico reported.
RFK Jr. is targeting a little-known program that underpins childhood immunizations in the US.
US Secretary of Health and Human Services Robert F. Kennedy Jr. testifies before the Senate Committee on Health, Education, Labor, and Pensions on Capitol Hill on May 20, 2025 in Washington, DC. Credit: Getty | Tasos Katopodis
This story was originally published by ProPublica.
Five months after taking over the federal agency responsible for the health of all Americans, Robert F. Kennedy Jr. wants to overhaul an obscure but vital program that underpins the nation’s childhood immunization system.
Depending on what he does, the results could be catastrophic.
In his crosshairs is the Vaccine Injury Compensation Program, a system designed to provide fair and quick payouts for people who suffer rare but serious side effects from shots—without having to prove that drugmakers were negligent. Congress created the program in the 1980s when lawsuits drove vaccine makers from the market. A special tax on immunizations funds the awards, and manufacturers benefit from legal protections that make it harder to win big-money verdicts against them in civil courts.
Kennedy, who founded an anti-vaccination group and previously accused the pharmaceutical industry of inflicting “unnecessary and risky vaccines” on children for profits, has long argued that the program removes any incentive for the industry to make safe products.
In a recent interview with Tucker Carlson, Kennedy condemned what he called corruption in the program and said he had assigned a team to overhaul it and expand who could seek compensation. He didn’t detail his plans but did repeat the long-debunked claim that vaccines cause autism and suggested, without citing any evidence, that shots could also be responsible for a litany of chronic ailments, from diabetes to narcolepsy.
There are a number of ways he could blow up the program and prompt vaccine makers to stop selling shots in the US, like they did in the 1980s. The trust fund that pays awards, for instance, could run out of money if the government made it easy for Kennedy’s laundry list of common health problems to qualify for payments from the fund.
Or he could pick away at the program one shot at a time. Right now, immunizations routinely recommended for children or pregnant women are covered by the program. Kennedy has the power to drop vaccines from the list, a move that would open up their manufacturers to the kinds of lawsuits that made them flee years ago.
Dr. Eddy Bresnitz, who served as New Jersey’s state epidemiologist and then spent a dozen years as a vaccine executive at Merck, is among those worried.
“If his unstated goal is to basically destroy the vaccine industry, that could do it,” said Bresnitz, who retired from Merck and has consulted for vaccine manufacturers. “I still believe, having worked in the industry, that they care about protecting American health, but they are also for-profit companies with shareholders, and anything that detracts from the bottom line that can be avoided, they will avoid.”
A spokesperson for PhRMA, a US trade group for pharmaceutical companies, told ProPublica in a written statement that upending the Vaccine Injury Compensation Program “would threaten continued patient access to FDA-approved vaccines.”
The spokesperson, Andrew Powaleny, said the program “has compensated thousands of claims while helping ensure the continued availability of a safe and effective vaccine supply. It remains a vital safeguard for public health and importantly doesn’t shield manufacturers from liability.”
Since its inception, the compensation fund has paid about $4.8 billion in awards for harm from serious side effects, such as life-threatening allergic reactions and Guillain-Barré syndrome, an autoimmune condition that can cause paralysis. The federal agency that oversees the program found that for every 1 million doses of vaccine distributed between 2006 and 2023, about one person was compensated for an injury.
Since becoming Health and Human Services secretary, Kennedy has turned the staid world of immunizations on its ear. He reneged on the US government’s pledge to fund vaccinations for the world’s poorest kids. He fired every member of the federal advisory group that recommends which shots Americans get, and his new slate vowed to scrutinize the US childhood immunization schedule. Measles, a vaccine-preventable disease eliminated here in 2000, roared back and hit a grim record—more cases than the US has seen in 33 years, including three deaths. When a US senator asked Kennedy if he recommended measles shots, Kennedy answered, “Senator, if I advised you to swim in a lake that I knew there to be alligators in, wouldn’t you want me to tell you there were alligators in it?”
Fed up, the American Academy of Pediatrics and other medical societies sued Kennedy last week, accusing him of dismantling “the longstanding, Congressionally-authorized, science- and evidence-based vaccine infrastructure that has prevented the deaths of untold millions of Americans.” (The federal government has yet to respond to the suit.)
Just about all drugs have side effects. What’s unusual about vaccines is that they’re given to healthy people—even newborns on their first day of life. And many shots protect not just the individuals receiving them but also the broader community by making it harder for deadly scourges to spread. The Centers for Disease Control and Prevention estimates that routine childhood immunizations have prevented more than 1.1 million deaths and 32 million hospitalizations among the generation of Americans born between 1994 and 2023.
To most people, the nation’s vaccine system feels like a solid, reliable fact of life, doling out shots to children like clockwork. But in reality it is surprisingly fragile.
There are only a handful of companies that make nearly all of the shots children receive. Only one manufacturer makes chickenpox vaccines. And just two or three make the shots that protect against more than a dozen diseases, including polio and measles. If any were to drop out, the country could find itself in the same crisis that led President Ronald Reagan to sign the law creating the Vaccine Injury Compensation Program in 1986.
Back then, pharmaceutical companies faced hundreds of lawsuits alleging that the vaccine protecting kids from whooping cough, diphtheria, and tetanus caused unrelenting seizures that led to severe disabilities. (Today’s version of this shot is different.) One vaccine maker after another left the US market.
At one point, pediatricians could only buy whooping cough vaccines from a single company. Shortages were so bad that the CDC recommended doctors stop giving booster shots to preserve supplies for the most vulnerable babies.
While Congress debated what to do, public health clinics’ cost per dose jumped 5,000 percent in five years.
“We were really concerned that we would lose all vaccines, and we would get major resurgences of vaccine-preventable diseases,” recalled Dr. Walter Orenstein, a vaccine expert who worked in the CDC’s immunization division at the time.
A Forbes headline captured the anxiety of parents, pediatricians, and public health workers: “Scared Shotless.” So a bipartisan group in Congress hammered out the no-fault system.
Today, the program covers vaccines routinely recommended for children or pregnant women once Congress approves the special tax that funds awards. (COVID-19 shots are part of a separate, often-maligned system for handling claims of harm, though Kennedy has said he’s looking at ways to add them to the Vaccine Injury Compensation Program.)
Under program rules, people who say they are harmed by covered vaccines can’t head straight to civil court to sue manufacturers. First, they have to go through the no-fault system. The law established a table of injuries and the time frame for when those conditions must have appeared in order to be considered for quicker payouts. A tax on those vaccines — now 75 cents for every disease that a shot protects against — flows into a trust fund that pays those approved for awards. Win or lose, the program, for the most part, pays attorney fees and forbids lawyers from taking a cut of the money paid to the injured.
The law set up a dedicated vaccine court where government officials known as special masters, who operate like judges, rule on cases without juries. People can ask for compensation for health problems not listed on the injury table, and they don’t have to prove that the vaccine maker was negligent or failed to warn them about the medical condition they wound up with. At the same time, they can’t claim punitive damages, which drive up payouts in civil courts, and pain and suffering payments are capped at $250,000.
Plaintiffs who aren’t satisfied with the outcome or whose cases drag on too long can exit the program and file their cases in traditional civil courts. There they can pursue punitive damages, contingency-fee agreements with lawyers and the usual evidence gathering that plaintiffs use to hold companies accountable for wrongdoing.
But a Supreme Court ruling, interpreting the law that created the Vaccine Injury Compensation Program, limited the kinds of claims that can prevail in civil court. So while the program isn’t a full liability shield for vaccine makers, its very existence significantly narrows the cases trial lawyers can file.
Kennedy has been involved in such civil litigation. In his federal disclosures, he revealed that he referred plaintiffs to a law firm filing cases against Merck over its HPV shot in exchange for a 10 percent cut of the fees if they win. After a heated exchange with Sen. Elizabeth Warren during his confirmation proceedings, Kennedy said his share of any money from those cases would instead go to one of his adult sons, who he later said is a lawyer in California. His son Conor works as an attorney at the Los Angeles law firm benefiting from his referrals. When ProPublica asked about this arrangement, Conor Kennedy wrote, “I don’t work on those cases and I’m not receiving any money from them.”
In March, a North Carolina federal judge overseeing hundreds of cases that alleged Merck failed to warn patients about serious side effects from its HPV vaccine ruled in favor of Merck; an appeal is pending.
The Vaccine Injury Compensation Program succeeded in stabilizing the business of childhood vaccines, with many more shots developed and approved in the decades since it was established. But even ardent supporters acknowledge there are problems. The program’s staff levels haven’t kept up with the caseload. The law capped the number of special masters at eight, and congressional bills to increase that have failed. An influx of adult claims swamped the system after adverse reactions to flu shots became eligible for compensation in 2005 and serious shoulder problems were added to the injury table in 2017.
The quick and smooth system of payouts originally envisioned has evolved into a more adversarial one with lawyers for the Department of Justice duking it out with plaintiffs’ attorneys, which Kennedy says runs counter to the program’s intent. Many cases drag on for years.
In his recent interview with Carlson, he described “the lawyers of the Department of Justice, the leaders of it” working on the cases as corrupt. “They saw their job as protecting the trust fund rather than taking care of people who made this national sacrifice, and we’re going to change all that,” he said. “And I’ve brought in a team this week that is starting to work on that.”
The system is “supposed to be generous and fast and gives a tie to the runner,” he told Carlson. “In other words, if there’s doubts about, you know, whether somebody’s injury came from a vaccine or not, you’re going to assume they got it and compensate them.”
Kennedy didn’t identify who is on the team reviewing the program. At one point in the interview, he said, “We just brought a guy in this week who’s going to be revolutionizing the Vaccine Injury Compensation Program.”
Downing did not respond to a voicemail left at his law office. HHS didn’t reply to a request to make him and Kennedy available for an interview and declined to answer detailed questions about its plans for the Vaccine Injury Compensation Program. In the past, an HHS spokesperson has said that Kennedy is “not anti-vaccine—he is pro-safety.”
While it’s not clear what changes Downing and Kennedy have in mind, Kennedy’s interview with Carlson offered some insights. Kennedy said he was working to expand the program’s three-year statute of limitations so that more people can be compensated. Downing has complained that patients who have certain autoimmune disorders don’t realize their ailments were caused by a vaccine until it’s too late to file. Congress would have to change the law to allow this, experts said.
A key issue is whether Kennedy will try to add new ailments to the list of injuries that qualify for quicker awards.
In the Carlson interview, Kennedy dismissed the many studies and scientific consensus that shots don’t cause autism as nothing more than statistical trickery. “We’re going to do real science,” Kennedy said.
The vaccine court spent years in the 2000s trying cases that alleged autism was caused by the vaccine ingredient thimerosal and the shot that protects people from measles, mumps, and rubella. Facing more than 5,000 claims, the court asked a committee of attorneys representing children with autism to pick test cases that represented themes common in the broader group. In the cases that went to trial, the special masters considered more than 900 medical articles and heard testimony from dozens of experts. In each of those cases, the special masters found that the shots didn’t cause autism.
In at least two subsequent cases, children with autism were granted compensation because they met the criteria listed in the program’s injury table, according to a vaccine court decision. That table, for instance, lists certain forms of encephalopathy—a type of brain dysfunction—as a rare side effect of shots that protect people from whooping cough, measles, mumps, and rubella. In a 2016 vaccine court ruling, Special Master George L. Hastings Jr. explained, “The compensation of these two cases, thus does not afford any support to the notion that vaccinations can contribute to the causation of autism.”
Hastings noted that when Congress set up the injury table, the lawmakers acknowledged that people would get compensated for “some injuries that were not, in fact, truly vaccine-caused.”
Many disabling neurological disorders in children become apparent around the time kids get their shots. Figuring out whether the timing was coincidental or an indication that the vaccines caused the problem has been a huge challenge.
Devastating seizures in young children were the impetus for the compensation program. But in the mid-1990s, after a yearslong review of the evidence, HHS removed seizure disorder from the injury table and narrowed the type of encephalopathy that would automatically qualify for compensation. Scientists subsequently have discovered genetic mutations that cause some of the most severe forms of epilepsy.
What’s different now, though, is that Kennedy, as HHS secretary, has the power to add autism or other disorders to that injury table. Experts say he’d have to go through the federal government’s cumbersome rulemaking process to do so. He could also lean on federal employees to green-light more claims.
In addition, Kennedy has made it clear he’s thinking about illnesses beyond autism. “We have now this epidemic of immune dysregulation in our country, and there’s no way to rule out vaccines as one of the key culprits,” he told Carlson. Kennedy mentioned diabetes, rheumatoid arthritis, seizure disorders, ADHD, speech delay, language delay, tics, Tourette syndrome, narcolepsy, peanut allergies, and eczema.
President Donald Trump’s budget estimated that the value of the investments in the Vaccine Injury Compensation Program trust fund could reach $4.8 billion this year. While that’s a lot of money, a life-care plan for a child with severe autism can cost tens of millions of dollars, and the CDC reported in April that 1 in 31 children is diagnosed with autism by their 8th birthday. The other illnesses Kennedy mentioned also affect a wide swath of the US population.
Dr. Paul Offit, a co-inventor of a rotavirus vaccine and director of the Vaccine Education Center at Children’s Hospital of Philadelphia, for years has sparred with Kennedy over vaccines. Offit fears that Kennedy will use flawed studies to justify adding autism and other common medical problems to the injury table, no matter how much they conflict with robust scientific research.
“You can do that, and you will bankrupt the program,” he said. “These are ways to end vaccine manufacturing in this country.”
If the trust fund were to run out of money, Congress would have to act, said Dorit Reiss, a law professor at University of California Law San Francisco who has studied the Vaccine Injury Compensation Program. Congress could increase the excise tax on vaccines, she said, or pass a law limiting what’s on the injury table. Or Congress could abolish the program, and the vaccine makers would find themselves back in the situation they faced in the 1980s.
“That’s not unrealistic,” Reiss said.
Rep. Paul Gosar, an Arizona Republican, last year proposed the End the Vaccine Carveout Act, which would have allowed people to bypass the no-fault system and head straight to civil court. His press release for the bill—written in September, before Kennedy’s ascension to HHS secretary—quoted Kennedy saying, “If we want safe and effective vaccines, we need to end the liability shield.”
The legislation never came up for a vote. A spokesperson for the congressman said he expects to introduce it again “in the very near future.”
Renée Gentry, director of the George Washington University Law School’s Vaccine Injury Litigation Clinic, thinks it’s unlikely Congress will blow up the no-fault program. But Gentry, who represents people filing claims for injuries, said it’s hard to predict what Congress, faced with a doomsday scenario, would do.
“Normally Democrats are friends of plaintiffs’ lawyers,” she said. “But talking about vaccines on the Hill is like walking on a razor blade that’s on fire.”
While Dębiak won 500,000 yen and survived his ordeal better than the legendary steel driver, the AtCoder World Tour Finals pushes humans and AI models to their limits through complex optimization challenges that have no perfect solution—only incrementally better ones.
Coding marathon tests human endurance against AI efficiency
The AtCoder World Tour Finals represents one of competitive programming’s most exclusive events, inviting only the top 12 programmers worldwide based on their performance throughout the previous year. The Heuristic division focuses on “NP-hard” optimization problems. In programming, heuristics are problem-solving techniques that find good-enough solutions through shortcuts and educated guesses when perfect answers would take too long to calculate.
All competitors, including OpenAI, were limited to identical hardware provided by AtCoder, ensuring a level playing field between human and AI contestants. According to the contest rules, participants could use any programming language available on AtCoder, with no penalty for resubmission but a mandatory five-minute wait between submissions.
Final leaderboard results for the 2025 AtCoder World Finals Heuristic Contest, showing Dębiak (as “Psyho”) on top. Credit: AtCoder
The final contest results showed Psyho finishing with a score of 1,812,272,558,909 points, while OpenAI’s model (listed as “OpenAIAHC”) scored 1,654,675,725,406 points—a margin of roughly 9.5 percent. OpenAI’s artificial entrant, a custom simulated reasoning model similar to o3, placed second overall, ahead of 10 other human programmers who had qualified through year-long rankings.
OpenAI characterized the second-place finish as a milestone for AI models in competitive programming. “Models like o3 rank among the top-100 in coding/math contests, but as far as we know, this is the first top-3 placement in a premier coding/math contest,” a company spokesperson said in an email to Ars Technica. “Events like AtCoder give us a way to test how well our models can reason strategically, plan over long time horizons, and improve solutions through trial and error—just like a human would.”
For example, she said Tesla “clearly recognized that mode confusion is an issue—this is where people, for example, think the car is in Autopilot and don’t understand that the Autopilot has disengaged,” she told the court.
Cummings also referred to the deposition of Tesla autopilot firmware engineer Ajshay Phatak. Phatak’s deposition told the court that the company did not keep good track of Autopilot crashes prior to 2018, and Cummings pointed out that “it was clear they knew that they had a big problem with people ignoring the warnings. Ignoring the hands-on requests. And…as you know, prior to this accident. It was known to Tesla that they were having problems with people ignoring their warnings.”
Mendel Singer, a statistician at Case Western University School of Medicine, was very unimpressed with Tesla’s approach to crash data statistics in his testimony. Singer noted that he was “not aware of any published study, any reports that are done independently… where [Tesla] actually had raw data and could validate it to see does it tend to make sense” and that the car company was not comparing like with like.
“Non-Teslas crashes are counted based on police reports, regardless of safety system deployment,” Singer said. Further, Tesla kept misleading claims about safety on its website for years, Singer pointed out. When asked whether he would have accepted a paper for peer review from Tesla regarding its reports, “that would have been a really quick and easy rejection,” he said.
While it’s possible that Tesla will still settle this case, we may also see the trial carried out to its conclusion.
“The plaintiffs in this instance have already received compensation from the driver of the Tesla in question, apparently in a decent amount. My understanding is that this makes them much less likely to take the kinds of offers Tesla has been making for settlements, and this is more about the justice,” said Edward Niedermeyer, author and long-time Tesla-watcher.
“That said, the judge in the case has made some frustrating rulings around confidentiality on key issues, so it’s possible that may be in Tesla’s favor. They could also just up their settlement offer enough to be impossible to refuse,” Niedermeyer said.
Donald Trump’s first big crypto win “nothing to crow about,” analyst says.
Donald Trump is expected to sign the GENIUS Act into law Friday, securing his first big win as a self-described “pro-crypto president.” The act is the first major piece of cryptocurrency legislation passed in the US.
The House of Representatives voted to pass the GENIUS Act on Thursday, approving the same bill that the Senate passed last month. The law provides a federal framework for stablecoins, a form of cryptocurrency that’s considered less volatile than other cryptocurrencies, as each token is backed by the US dollar or other supposedly low-risk assets.
The GENIUS Act is expected to spur more widespread adoption of cryptocurrencies, since stablecoins are often used to move funds between different tokens. It could become a gateway for many Americans who are otherwise shy about investing in cryptocurrencies, which is what the industry wants. Ahead of Thursday’s vote, critics had warned that Republicans were rushing the pro-industry bill without ensuring adequate consumer protections, though, seemingly setting Americans up to embrace stablecoins as legitimate so-called “cash of the blockchain” without actually insuring their investments.
A big concern is that stablecoins will appear as safe investments, legitimized by the law, while supposedly private companies issuing stablecoins could peg their tokens to riskier assets that could tank reserves, cause bank runs, and potentially blindside and financially ruin Americans. Stablecoin scams could also target naïve stablecoin investors, luring them into making deposits that cannot be withdrawn.
Rep. Maxine Waters (D-Calif.)—part of a group of Democrats who had strongly opposed the bill—further warned Thursday that the GENIUS Act prevents lawmakers from owning or promoting stablecoins, but not the president. Trump and his family have allegedly made more than a billion dollars through their crypto ventures, and Waters is concerned that the law will make it easier for Trump and other presidents to use the office to grift and possibly even obscure foreign bribes.
“By passing this bill, Congress will be telling the world that Congress is OK with corruption, OK with foreign companies buying influence,” Waters said Thursday, CBS News reported.
Some lawmakers fear such corruption is already happening. Senators previously urged the Office of Government Ethics in a letter to investigate why “a crypto firm whose founder needs a pardon” (Binance’s Changpeng Zhao, also known as “CZ”) “and a foreign government spymaker coveting sensitive US technology” (United Arab Emirates-controlled MGX) “plan to pay the Trump and Witkoff families hundreds of millions of dollars.”
The White House continues to insist that Trump has “no conflicts of interest” because “his assets are in a trust managed by his children,” Reuters reported.
Ultimately, Waters and other Democrats failed to amend the bill to prevent presidents from benefiting from the stablecoin framework and promoting their own crypto projects.
Markets for various cryptocurrencies spiked Thursday, as the industry anticipates that more people will hold crypto wallets in a world where it’s fast, cheap, and easy to move money on the blockchain with stablecoins, as compared to relying on traditional bank services. And any fees associated with stablecoin transfers will likely be paid with other forms of cryptocurrencies, with a token called ether predicted to benefit most since “most stablecoins are issued and transacted on the underlying blockchain Ethereum,” Reuters reported.
Unsurprisingly, ether-linked stocks jumped Friday, with the token’s value hitting a six-month high. Notably, Bitcoin recently hit a record high; it was valued at above $120,000 as the stablecoin bill moved closer to Trump’s desk.
GENIUS Act plants “seeds for the next financial crisis”
As Trump prepares to sign the law, Consumer Reports’ senior director monitoring digital marketplaces, Delicia Hand, told Ars that the group plans to work with other consumer advocates and the implementing regulator to try to close any gaps in the stablecoin legislation that would leave Americans vulnerable.
Some Democrats supported the GENIUS Act, arguing that some regulation is better than none as cryptocurrency activity increases globally and the technology has the potential to revolutionize the US financial system.
But Hand told Ars that “we’ve already seen what happens when there are no protections” for consumers, like during the FTX collapse.
She joins critics that the BBC reported are concerned that stablecoin investors could get stuck in convoluted bankruptcy processes as tech firms engage more and more in “bank-like activities” without the same oversight as banks.
The only real assurances for stablecoin investors are requirements that all firms must publish monthly reserves backing their tokens, as well as annual statements required from the biggest companies issuing tokens. Those will likely include e-commerce and digital payments giants like Amazon, PayPal, and Shopify, as well as major social media companies.
Meanwhile, Trump seemingly wants to lure more elderly people into investing in crypto, reportedly “working on a presidential order that could allow retirement accounts to be invested in private assets, such as crypto, gold, and private equity,” the BBC reported.
Waters, a top Democrat on the House Financial Services Committee, is predicting the worst. She has warned that the law gives “Trump the pen to write the rules that would put more money in his family’s pocket” while causing “consumer harm” and planting “the seeds for the next financial crisis.”
Analyst: End of Trump’s crypto wins
The House of Representatives passed two other crypto bills this week, but those bills now go to the Senate, where they may not have enough support to pass.
The CLARITY Act—which creates a regulatory framework for digital assets and cryptocurrencies to allow for more innovation and competition—is “absolutely the most important thing” the crypto industry has been pushing since spending more than $119 million backing pro-crypto congressional candidates last year, a Coinbase policy official, Kara Calvert, told The New York Times.
Republicans and industry see the CLARITY Act as critical because it strips the Securities and Exchange Commission of power to police cryptocurrencies and digital assets and gives that power instead to the Commodity Futures Trading Commission, which is viewed as friendlier to industry. If it passed, the CLARITY Act would not just make it harder for the SEC to raise lawsuits, but it would also box out any future SEC officials under less crypto-friendly presidents from “bringing any cases for past misconduct,” Amanda Fischer, a top SEC official under the Biden administration, told the NYT.
“It would retroactively bless all the conduct of the crypto industry,” Fischer suggested.
But Senators aren’t happy with the CLARITY Act and expect to draft their own version of the bill, striving to lay out a crypto market structure that isn’t “reviled by consumer protection groups,” the NYT reported.
And the other bill that the House sent to the Senate on Thursday—which would ban the US from creating a central bank digital currency (CBDC) that some conservatives believe would allow for government financial surveillance—faces an uphill battle, in part due to Republicans seemingly downgrading it as a priority.
The anti-CBDC bill will likely be added to a “must-pass” annual defense policy bill facing a vote later this year, the NYT reported. But Rep. Marjorie Taylor Greene (R.-Ga.) “mocked” that plan, claiming she did not expect it to be “honored.”
Terry Haines, founder of the Washington-based analysis firm Pangaea Policy, has forecasted that both the CLARITY Act and the anti-CBDC bills will likely die in the Senate, the BBC reported.
“This is the end of crypto’s wins for quite a while—and the only one,” Haines suggested. “When the easy part, stablecoin, takes [approximately] four to five years and barely survives industry scandals, it’s not much to crow about.”
Ashley is a senior policy reporter for Ars Technica, dedicated to tracking social impacts of emerging policies and new technologies. She is a Chicago-based journalist with 20 years of experience.
NASA astronauts Mike Fincke (left) and Zena Cardman (right), the pilot and commander of NASA’s SpaceX Crew-11 mission to the International Space Station, view a Falcon 9 rocket ahead of their spaceflight. Credit: SpaceX
Welcome to Edition 8.03 of the Rocket Report! We are at an interesting stage in Europe, with its efforts to commercialize spaceflight. Finally, it seems the long-slumbering continent is waking up to the need to leverage private capital to drive down the costs of space access, and we are seeing more investment flow into European companies. But it is critical that European policymakers make strategic investments across the industry or companies like PLD Space, which outlined big plans this week, will struggle to get off the launch pad.
As always, we welcome reader submissions, and if you don’t want to miss an issue, please subscribe using the box below (the form will not appear on AMP-enabled versions of the site). Each report will include information on small-, medium-, and heavy-lift rockets, as well as a quick look ahead at the next three launches on the calendar.
Avio celebrates freedom from Arianespace. Representatives from Italy, Germany, and France met at the European Space Agency headquarters last week to sign the Launcher Exploitation Declaration, which officially began the transfer of Vega C launch operation responsibilities from Arianespace to the rocket’s builder, Avio, European Spaceflight reports. “It is a historic step that reinforces our nation’s autonomy in access to space and assigns us a strategic responsibility towards Europe,” said Avio CEO Giulio Ranzo. “We are ready to meet this challenge with determination, and we are investing in technologies, expertise, and infrastructure to ensure a competitive service.”
A breaking of long-term partnerships … In addition to securing control over the full exploitation of the Vega launch vehicle family, Italy, through Avio, is also investing in what comes next. The country has committed more than 330 million euros to the development of the MR60 methalox rocket engine and two demonstrator vehicles. These, along with the MR10 engine being developed under the Vega E programme, will support Avio’s preparation of a future reusable launch vehicle. Historically, France, Germany, and Italy have worked together on European launch vehicles. This appears to be another step in breaking up that long-term partnership toward more nationalistic efforts.
PLD Space outlines grand ambitions. PLD Space, Spain’s sole contestant in the European Launcher Challenge, unveiled its long-term strategy at the company’s Industry Days event this week, Payload reports. The company is targeting a production rate of 32 Miura 5 launchers annually by 2030. To achieve this output, PLD plans to deepen its vertical integration, consolidate its supplier network, and begin to serialize its manufacturing process beginning in 2027.
Building up the supply chain … The company’s production plans also call for the parallel development of Miura Next, a heavy-lift vehicle capable of bringing 13 tons to orbit. However, the company will start with the Miura 5 vehicle, which PLD expects to launch for the first time from French Guiana in 2026. Since the beginning of 2024, PLD has invested a total of 50 million euros in its Miura 5 supply chain, consisting of 397 industrial partners, many of which are located in Spain and other European countries. These plans are great, but sooner or later, the 14-year-old company needs to start putting rockets in space. (submitted by EllPeaTea)
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New consortium will study space plane. A UK-based space and defense consultant group, Frazer-Nash, will lead a program to design a vehicle and its integrated systems with the goal of building and flying a Mach 5-capable aircraft at the edge of space by early 2031. This so-called INVICTUS program was funded with a 7 million-euro grant from the European Space Agency and is seen as a stepping stone toward developing a reusable space plane that takes off and lands horizontally from a runway.
Seeking to lead a new era of flight … Over 12 months, INVICTUS has been tasked to deliver the concept and elements of the preliminary design of the full flight system. It will attempt to demonstrate the efficacy of hydrogen-fueled, precooled air-breathing propulsion at hypersonic speeds, technology that will ultimately enable horizontal take-off. “With INVICTUS, Europe is seizing the opportunity to lead in technologies that will redefine how we move across the planet and reach beyond it,” said Tommaso Ghidini, head of the Mechanical Department at the European Space Agency. (submitted by Jid)
ESA backs North Sea launch site. A private company developing a launch site in the North Sea, EuroSpaceport, has secured support from the European Space Agency. The company, founded five years ago, is developing a sea-based launch platform built on a repurposed offshore wind turbine service vessel, European Spaceflight reports. Rockets are envisioned to launch from a position 50 to 100 km offshore from the port of Esbjerg, in Denmark.
Seeing the forest for the trees … On Wednesday, EuroSpaceport announced that it had signed an agreement with the European Space Agency and Polish rocket builder SpaceForest to support the first launch from its Spaceport North Sea platform. The company will receive support from the agency through its Boost! Program. SpaceForest has been a recipient of Boost! funding, receiving 2.4 million euros in October 2024. SpaceForest said the mission will be used to verify the launch procedures of its Perun rocket under nominal suborbital conditions. (submitted by EllPeaTea)
Amazon and SpaceX, best frenemies? Maybe not, but for the time being, they appear to be friends of convenience. A Falcon 9 rocket launched from Florida’s Space Coast early on Wednesday with a batch of Internet satellites for Amazon’s Project Kuiper network, thrusting a rival one step closer to competing with SpaceX’s Starlink broadband service. With this launch, Amazon now has 78 Kuiper satellites in orbit, Ars reports. The full Kuiper constellation will consist of 3,232 satellites to provide broadband Internet service to most of the populated world, bringing Amazon in competition with SpaceX’s Starlink network.
Launch is not cheap … Kuiper is an expensive undertaking, estimated at between $16.5 billion and $20 billion by the industry analytics firm Quilty Space. Quilty has concluded that Amazon is spending $10 billion on launch alone, exceeding the company’s original cost estimate for the entire program. Amazon has booked more than 80 launches to deploy the Kuiper constellation, but the company didn’t turn to SpaceX until it had to. A shareholder lawsuit filed in 2023 accused Amazon founder Jeff Bezos and the company’s board of directors of breaching their “fiduciary duty” by not considering SpaceX as an option for launching Kuiper satellites. The plaintiffs in the lawsuit alleged Amazon didn’t consider the Falcon 9 due to an intense and personal rivalry between Bezos and SpaceX founder Elon Musk. Amazon bowed to the allegations and announced a contract with SpaceX for three Falcon 9 launches in December 2023 to provide “additional capacity” for deploying the Kuiper network.
NASA targets end of July for Crew-11. NASA said Monday that it and SpaceX were targeting July 31 for the flight of SpaceX’s Crew-11 mission to the orbiting outpost, Spaceflight Now reports. The mission is led by NASA astronaut Zena Cardman. She will be flying along with fellow NASA astronaut Mike Fincke, Japan Aerospace Exploration Agency (JAXA) astronaut Kimiya Yui and Roscosmos cosmonaut Oleg Platonov.
Pushing Dragon reuse … The mission was moved up from its previously planned August launch window to create more room in the manifest for the arrival of the Cargo Dragon flying the CRS-33 mission. That Dragon will perform a boost of the space station as a demonstration of some of the capabilities SpaceX will use on its US Deorbit Vehicle currently in work. Crew-11 will fly to the orbiting outpost on Crew Dragon Endeavour, which will be its sixth trip to the ISS. This will be the first Crew Dragon spacecraft to fly for a sixth time.
SpaceX won’t use Johnston Atoll for rocket cargo tests. Johnston Atoll, an unincorporated US territory and Pacific island wildlife refuge with a complicated military history, will no longer become a SpaceX reusable rocket test site, Popular Science reports. “The Department of the Air Force has elected to hold the preparation of the Johnston Atoll Environmental Assessment for a proposed rocket cargo landing demonstration on Johnston Atoll in abeyance while the service explores alternative options for implementation,” Air Force spokesperson Laurel Falls said.
Taking a toll on the atoll … Located roughly 860 miles southwest of Hawaii, Johnston Atoll has served as a base for numerous US military operations for over 90 years. Despite this, the atoll remains a home for 14 tropical bird species as part of the Pacific Remote Islands Marine National Monument. The site had been under consideration for tests as part of a military program to deliver cargo around the planet, using suborbital missions on rocket such as SpaceX’s Starship vehicle. The Johnston Atoll plans included the construction of two landing pads that were met with public backlash from wildlife experts and indigenous representatives. (submitted by Tfargo04)
Blue Origin confirms ESCAPADE is up next. On Thursday, Blue Origin said on social media that the second launch of its New Glenn rocket will carry NASA’s ESCAPADE mission as its primary payload. This launch will support ESCAPADE’s science objectives as the twin spacecraft progress on their journey to the Red Planet. Also onboard is a technology demonstration from @Viasat in support of @NASASpaceOps’ Communications Services Project.
Left unsaid was when the launch will occur … The social media post confirms a report from Ars in June, which said the ESCAPADE spacecraft was up next on New Glenn. Previously, the company has said this second launch will take place no earlier than August 15. However, that is less than one month away. Late September is probably the earliest realistic launch date, with October or November more likely for the second flight of the company’s large rocket.
Next three launches
July 19: Falcon 9 | Starlink 17-3 | Vandenberg Space Force Base, California | 03: 44 UTC
July 21: Falcon 9 | O3b mPOWER 9 & 10 | Cape Canaveral Space Force Station, Florida | 21: 00 UTC
July 22: Falcon 9 | NASA’s Tandem Reconnection and Cusp Electrodynamics Reconnaissance Satellites | Vandenberg Space Force Base, California | 18: 05 UTC
Eric Berger is the senior space editor at Ars Technica, covering everything from astronomy to private space to NASA policy, and author of two books: Liftoff, about the rise of SpaceX; and Reentry, on the development of the Falcon 9 rocket and Dragon. A certified meteorologist, Eric lives in Houston.
CarPlay Ultra takes over the main instrument display as well as the infotainment.
Aston Martin is the first automaker to adopt Apple’a CarPlay Ultra, which takes over all the displays in the car. Credit: Michael Teo Van Runkle
Aston Martin is the first automaker to adopt Apple’a CarPlay Ultra, which takes over all the displays in the car. Credit: Michael Teo Van Runkle
For the 2025 model year, Aston Martin’s user interface took a major step forward across the lineup, with improvements to the physical controls and digital infotainment, as well as updated gauge cluster layouts. However, the big news dropped in the spring, when Aston and Apple announced the launch of CarPlay Ultra, the next generation of Apple’s nearly ubiquitous automotive operating system.
Ultra extends beyond the strictly “phone” functions of traditional CarPlay to now encompass more robust vehicular integration, including climate control, drive modes, and the entire gauge cluster readout. Running Ultra, therefore, requires a digital gauge cluster. So far, not many automakers other than Aston have signaled their intent to join the revolution: Kia/Hyundai/Genesis will adopt Ultra next, and Porsche may come after that.
Before future partnerships come to fruition, I spent a week with a DB12 Volante to test Ultra’s use cases and conceptual failure points, most critically to discover whether this generational leap actually enhances or detracts from an otherwise stellar driving experience.
Setup
The following gallery will take you through the setup process. Michael Teo Van Runkle
Connecting to Ultra via Bluetooth takes a minute or two longer than traditional CarPlay and includes more consent screens to cover the additional legal ramifications of the operating system sharing data with the car, and vice versa. Apple restricts this data to multimedia info, plus real-time speed and engine status, vehicle lights, and similar functions. Specifically, neither the iPhone nor third-party apps store any vehicle data after disconnecting from the car, and the car doesn’t keep personal data once the iPhone disconnects, either.
What about Siri? I generally keep Siri turned off so that accidental “Hey, Siri” activations don’t constantly interrupt my life—but by pushing the DB12’s steering wheel button, I could test simple tasks that went just about as well as typical for Siri (read: don’t expect much “Apple Intelligence” quite yet). Standard Siri data sharing with Apple therefore applies when used with Ultra.
I tested Ultra with an iPhone 16 Pro, but the software requires an iPhone 12 or newer and the latest iOS 18.5 update. As a type of simple failure exercise, I turned my phone off while driving more than once. Doing so reverts both the gauge cluster and infotainment screen to Aston’s native UI, the former almost instantly and the latter just a few seconds later. However, once I turned my phone back on, I struggled to reactivate either traditional CarPlay or Ultra until I forgot the device in my Bluetooth settings and started over from scratch. This held true for every attempt.
We didn’t love the fact that there was some latency with the needles on the dials. Michael Teo Van Runkle
Once initiated, though, Ultra fired up straightaway every time. Much faster than the typical lag to boot up traditional CarPlay. In fact, as soon as I unlocked the doors but before entering the DB12, the gauge cluster showed Ultra’s Apple-style readouts. These configurable designs, which Apple developed with Aston’s input, include a classic analog-style gauge view as well as layouts that allow for minimized data, navigation, and stylistic choices selectable through the center console screen or by swiping the haptic button on the DB12’s steering wheel.
Call me old-fashioned, but I still enjoy seeing a tachometer, speedometer, drive modes, and fuel level versus range remaining and a digital speed—especially on an engaging performance vehicle like the DB12 Volante. Apple might be skilled at making new tech easy to use, but it’s hard to beat the power of millions of minds adapting to analog gauges over the past century or so. And in this case, Ultra’s tach(s) showed a bit of latency or lag while ripping that 671-hp twin-turbo V8 up through the revs, something I never noticed in the native UI.
It’s much more holistic now
Ultra’s biggest improvements over preceding CarPlay generations are in the center console infotainment integration. Being able to access climate controls, drive modes, and traction settings without leaving the intuitive suite of CarPlay makes life much easier. In fact, changing between drive modes and turning traction control off or down via Aston’s nifty adjustable system caused less latency and lagging in the displays in Ultra. And for climate, Ultra actually brings up a much better screen after spinning the physical rotaries on the center console than you get through Aston’s UI—plus, I found a way to make the ventilated seats blow stronger, which I never located through the innate UI despite purposefully searching for a similar menu page.
There are different main instrument UIs to choose from, like this one. Michael Teo Van Runkle
Some specific functions do require dipping out of Ultra, though, including changing any audio settings for the spectacular Bowers & Wilkins sound system. I also found two glitches. Trying to bring down the DB12 Volante’s convertible top cued up a “Close trunk separator” alert, but the only way to close the trunk separator is via the same button as the convertible top. So instead, the windows only went up and down repeatedly as I tried to enjoy open-top motoring. This happened both in Ultra and without, however, so it could just be an Aston issue that Ultra couldn’t fix.
Plus, over the course of my eight days with Ultra, I experienced one moment where both the infotainment and gauge cluster went totally black. This resembled GM’s Ultium screen issues and lasted about 30 seconds or so before both flickered to life again. At first, I suspected an inadvertent attempt to activate nighttime driving mode. But again, this could have been an Aston issue, an Apple issue, or both.
Running around Los Angeles, I never found a spot with zero reception (I run e-sims, both Verizon and AT&T simultaneously, for this very reason), but I did purposefully enter airplane mode. This time, Ultra stayed active, and regardless, Apple assured me that essential functions, including navigation, can pre-load offline data for planned route guidance. But at the very worst, as with the phone turning off or battery dying, Ultra can simply revert to the onboard navigation.
Using Ultra regularly seemed to deplete my iPhone’s battery slightly more quickly than normal, and I noticed some warming of the iPhone—though without a controlled experiment, I can’t say with certainty whether these two symptoms happened quicker than simply running traditional CarPlay or Bluetooth. And in reality, most cars running Ultra (for Aston and beyond) should come equipped with wireless charge pads and plenty of USB-C ports anyhow to keep those batteries topped up. On hot summer days in LA, though, my iPhone seemed to get warmest while using inductive charging and Ultra simultaneously, to my admittedly unscientific touch.
Apple Maps is the only map that is allowed to go here in CarPlay Ultra. Michael Teo Van Runkle
For commuters who brave traffic using Advanced Driver Assistance Systems (ADAS), Ultra seemed to work smoothly with the DB12’s lane departure warnings, steering corrections, and adaptive cruise control—though I typically turn all this off via Aston’s handy single button, which helps to stave off frustration. This introduces a loophole or gap in regulations, however, whether CarPlay Ultra needs to meet the ISO’s ASIL-D standards or achieve some kind of National Highway Traffic Safety Administration certification.
Traditional CarPlay stuck with infotainment and basic “phone” functions, but now that the iPhone essentially accesses and displays ADAS, drive modes, and traction setting information, where does regulated consumer safety come in? And where does liability rest, in the event of a driver aid or corrective maneuver going awry? Somehow, this question seems most likely to wind up on the desk of an insurance adjuster sooner rather than later.
Can we try it in an EV?
For me, some disappointment arose from being unable to cue up either Waze or Google Maps in Ultra’s gauge cluster navigation screens rather than strictly Apple Maps. But in many ways, I suspect that Ultra might work even better when (or if) Hyundai/Kia/Genesis introduce compatible EVs, rather than Aston’s (so far) more classic ICE vehicles. And not just because the modern futurist aesthetic matches better, either, but more so thanks to the improved accuracy of range, charging, and navigation features.
The center infotainment screen’s integration with vehicular functions, therefore, stands out as much more of a pro for Aston Martins than Ultra’s gauge cluster readout, enhancing the driving experience through a more intuitive UI that decreases time spent glancing away from the road. For those who want to skip out on Ultra, it’s also worth noting that the iPhone allows for the choice to stick with traditional CarPlay only as well. However, I suspect car buyers will eventually begin to expect Ultra, even if the added jump to vehicular control represents somewhat less of a massive leap than simply picking between models equipped with CarPlay or not.
It’s unclear whether other automakers will find the advantages worthy of converting to Ultra, including Rivian, which offers neither CarPlay nor Android Auto, or GM, which skipped out on CarPlay for EVs. On the other hand, automakers may also decide to hesitate before handing over further control to Apple now that the Apple Car is officially dead. And in that regard, Ultra might just represent the final straw that inspires further improvements to proprietary user interfaces across the industry as well.
In response to the white label program ending, a Reddit user who claimed that their organization spent 300,000 pounds (about $402,500) a year on licensing through a VMware white-label partner, said:
I now have 6 months to design / procure / build a new multi region service provider virtualisation platform to support millions in revenue and an additional 12 months to migrate all our VMware clients.
I’m just astonished.
In a statement to The Register, Broadcom encouraged CSPs cut from VMware’s channel to work with authorized partners to “ensure a smooth transition for customers who seek to renew a service at the end of their current term,” but it offered no incentive or resources.
“Stronger execution”
News of additional partner cuts follows last month’s debut of VMware Cloud Foundation (VCF) 9.0. The blog post by VMware partner Interactive posited that Broadcom is paring down its CSP partner program in relation to VCF 9.0, which it said “underpins a small number [of] hyperscale private cloud platforms in each region.”
In a statement to The Register explaining the changes, Broadcom said:
Broadcom’s strategy since closing the VMware acquisition has been to drive simplification, consistency, and innovation across the VMware Go To Market ecosystem, including VMware Cloud Service Providers (VCSPs).
Recent changes to this ecosystem are consistent with this strategy. Broadcom is focusing more and going deeper with the VCSPs who have demonstrated commitment to their cloud services built on VMware. This will enable us to deliver greater value, stronger execution, and a more streamlined experience for Broadcom’s VMware customers of all sizes and enable a truly competitive offering to the hyperscalers through our CSPs.
When Broadcom dropped those resellers last month, there was concern that its partner reductions were too extreme. At the time, Gartner VP analyst Michael Warrilow, for example, told The Register: “Broadcom seem intent on destroying what was one of the most successful partner ecosystems in the industry.” Sumit Bhatia, co-author of the book Navigating VMware Turmoil in the Broadcom Era, told Ars Technica that he expected the partner cuts to result in higher pricing for VMware customers.
As Broadcom continues to whittle away at VMware’s remaining partner base, the impacts of a smaller partner program will become harder to ignore, particularly for small-to-medium-sized businesses. The change aligns with the perception that Broadcom is mostly interested in conducting VMware business with large customers, despite repeated claims that its VMware changes benefit “customers of all sizes.”
Researchers from the Google Threat Intelligence Group said that hackers are compromising SonicWall Secure Mobile Access (SMA) appliances, which sit at the edge of enterprise networks and manage and secure access by mobile devices.
The targeted devices are end of life, meaning they no longer receive regular updates for stability and security. Despite the status, many organizations continue to rely on them. That has left them prime targets by UNC6148, the name Google has given to the unknown hacking group.
“GTIG recommends that all organizations with SMA appliances perform analysis to determine if they have been compromised,” a report published Wednesday said, using the abbreviation for Google Threat Intelligence Group. “Organizations should acquire disk images for forensic analysis to avoid interference from the rootkit anti-forensic capabilities. Organizations may need to engage with SonicWall to capture disk images from physical appliances.”
Lacking specifics
Many key details remain unknown. For one thing, the attacks are exploiting leaked local administrator credentials on the targeted devices, and so far, no one knows how the credentials were obtained. It’s also not known what vulnerabilities UNC6148 is exploiting. It’s also unclear precisely what the attackers are doing after they take control of a device.
The lack of details is largely the result of the functioning on Overstep, the name of custom backdoor malware UNC6148 is installing after initial compromise of the devices. Overstep allows the attackers to selectively remove log entries, a technique that is hindering forensic investigation. Wednesday’s report also posits that the attackers may be armed with a zero-day exploit, meaning it targets a vulnerability that’s currently publicly unknown. Possible vulnerabilities UNC6148 may be exploiting include:
CVE-2021-20038: An unauthenticated remote code execution made possible by a memory corruption vulnerability.
CVE-2024-38475: An unauthenticated path traversal vulnerability in Apache HTTP Server, which is present in the SMA 100. It can be exploited to extract two separate SQLite databases that store user account credentials, session tokens, and seed values for generating one-time passwords.
CVE-2021-20035: An authenticated remote code execution vulnerability. Security firm Arctic Wolf and SonicWall reported in April that this vulnerability was under active exploitation.
CVE-2021-20039: An authenticated remote code execution vulnerability. There have been reports that this vulnerability was under active exploitation to install ransomware in 2024.
CVE-2025-32819: An authenticated file deletion vulnerability that can be exploited to cause a targeted device to revert the built-in administrator credentials to a password so that attackers can gain administrator access.
Dark matter particle and antiparticle collisions could make some stars immortal.
For a star, its initial mass is everything. It determines how quickly it burns through its hydrogen and how it will evolve once it starts fusing heavier elements. It’s so well understood that scientists have devised a “main sequence” that acts a bit like a periodic table for stars, correlating their mass and age with their properties.
The main sequence, however, is based on an assumption that’s almost always true: All of the energy involved comes from the gravity-driven fusion of lighter elements into heavier ones. However, three astrophysicists consider an alternative source of energy that may apply at the very center of our galaxy— energy released when dark matter particles and antiparticles collide and annihilate. While we don’t even know that dark matter can do that, it’s a hypothetical with some interesting consequences, like seemingly immortal stars, and others that move backward along the main sequence path.
Dark annihilations
We haven’t figured out what dark matter is, but there are lots of reasons to think that it is comprised of elementary particles. And, if those behave like all of the particles we understand well, then there will be both regular and antimatter versions. Should those collide, they should annihilate each other, releasing energy in the process. Given dark matter’s general propensity not to interact with anything, these collisions will be extremely rare except in locations with very high dark matter concentrations.
The only place that’s likely to happen is at the very center of our galaxy. And, for a while, there was an excess of radiation coming from the galactic core that people thought might be due to dark matter annihilations, although it eventually turned out to have a more mundane explanation.
At the extreme densities found within a light year of the supermassive black hole at the center of our galaxy, concentrations are high enough that these collisions could be a major source of energy. And so astronomers have considered what all that energy might do to stars that end up in a black hole’s orbit, finding that under the right circumstances, dark matter destruction could provide more energy to a star than fusion.
That prompted three astrophysicists (Isabelle John, Rebecca Leane, and Tim Linden) to try to look at things in an organized fashion, modeling a “dark main sequence” of stars as they might exist within a close proximity to the Milky Way’s center.
The intense gravity and radiation found near the galaxy’s core mean that stars can’t form there. So, anything that’s in a tight orbit had formed somewhere else before gravitational interactions had pushed it into the gravitational grasp of the galaxy’s central black hole. The researchers used a standard model of star evolution to build a collection of moderate-sized stars, from one to 20 solar masses at 0.05 solar mass intervals. These are allowed to ignite fusion at their cores and then shift into a dark-matter-rich environment.
Since we have no idea how often dark matter particles might run into each other, John, Leane, and Linden use two different collision frequencies. These determine how much energy is imparted into these stars by dark matter, which the researchers simply add as a supplement to the amount of fusion energy the stars are producing. Then, the stars are allowed to evolve forward in time.
(The authors note that stars that are thrown into the grasp of a supermassive black hole tend to have very eccentric orbits, so they spend a lot of time outside the zone where dark matter collisions take place with a significant frequency. So, what they’ve done is the equivalent of having these stars experience the energy input given their average orbital distance from the galaxy’s core. In reality, a star would spend some years with higher energy input and some years with lower input as it moves about its orbit.)
Achieving immortality
The physics of what happens is based on the same balance of forces that govern fusion-powered stars, but produces some very strange results. Given only fusion power, a star will exist at a balance point. If gravity compresses it, fusion speeds up, more energy is released, and that energy causes the star to expand outward again. That causes the density drop, slowing fusion back down again.
The dark matter annihilations essentially provide an additional source of energy that stays constant regardless of what happens to the star’s density. At the low end of the mass range the researchers considered, this can cause the star to nearly shut off fusion, essentially looking like a far younger star than it actually is. That has the effect of causing the star to move backward along the main sequence diagram.
The researchers note that even lighter stars could essentially get so much additional energy that they can’t hold together and end up dissipating, something that’s been seen in models run by other researchers.
As the mass gets higher, stars reach the point where they essentially give up on fusion and get by with nothing but dark matter annihilations. They have enough mass to hold together gravitationally, but end up too diffused for fusion to continue. And they’ll stay that way as long as they continue to get additional injections of energy. “A star like this might look like a young, still-forming star,” the authors write, “but has features of a star that has undergone nuclear fusion in the past and is effectively immortal.”
John, Leane, and Linden find that the higher mass stars remain dense enough for fusion to continue even in proximity to the galaxy’s black hole. But the additional energy kept that fusion happening at a moderate rate. They proceeded through the main sequence, but at a pace that was exceptionally slow, so that running the simulation for a total of 10 billion years didn’t see them change significantly.
The other strange thing here is that all of this is very sensitive to how much dark matter annihilation is taking place. A star that’s “immortal” at one average distance will progress slowly through the main sequence if its average distance is a light year further out. Similarly, stars that are too light to survive at one location will hold together if they are a bit further from the supermassive black hole.
Is there anything to this?
The big caution is that this work only looks at the average input from dark matter annihilation. In reality, a star that might be immortal at its average distance will likely spend a few years too hot to hold together, and then several years cooling off in conditions that should allow fusion to reignite. It would be nice to see a model run with this sort of pulsed input, perhaps basing it on the orbits of some of the stars we’ve seen that get close to the Milky Way’s central black hole.
In the meantime, John, Leane, and Linden write that their results are consistent with some of the oddities that are apparent in the stars we’ve observed at the galaxy’s center. These have two distinctive properties: They appear heavier than the average star in the Milky Way, and all seem to be quite young. If there is a “dark main sequence,” then the unusual heft can be explained simply by the fact that lower mass stars end up dissipating due to the additional energy. And the model would suggest that these stars simply appear to be young because they haven’t undergone much fusion.
The researchers suggest that we could have a clearer picture if we were able to spend enough time observing the stars at our galaxy’s core with a large enough telescope, allowing us to understand their nature and orbits.
Physical Review D, 2025. DOI: Not yet available (About DOIs).
John is Ars Technica’s science editor. He has a Bachelor of Arts in Biochemistry from Columbia University, and a Ph.D. in Molecular and Cell Biology from the University of California, Berkeley. When physically separated from his keyboard, he tends to seek out a bicycle, or a scenic location for communing with his hiking boots.
“Tesla has partnered with Baidu [a Chinese search and AI group] but Baidu can’t disclose all the data points to Tesla,” Duo adds. “The real-world data is definitely more valuable.”
Home field advantage
While BYD might have home turf advantage when it comes to data collection and security, Wang’s late pivot to driverless functionality has created some risks for the group.
One is question marks over financial sustainability. Price wars among Chinese carmakers are putting margins and the industry’s balance sheet under strain as Beijing demands more action to protect suppliers in the world’s largest car market.
It has also opened up some rare gaps in BYD’s otherwise formidable vertical integration. Its market leadership has also enabled it to pressure suppliers for price cuts and extended payment terms, allowing it to rigorously control costs.
But according to Chris McNally, an analyst with US investment bank Evercore, the God’s Eye platform uses software and hardware partners, including Momenta, a Chinese group backed by General Motors in the US, and some chips from Nvidia.
BYD’s executive vice-president Stella Li said competition with Tesla in EVs and autonomous technology would accelerate innovation, ultimately making BYD a “better’” company.
Credit: Joel Saget/AFP/Getty Images
BYD’s executive vice-president Stella Li said competition with Tesla in EVs and autonomous technology would accelerate innovation, ultimately making BYD a “better’” company. Credit: Joel Saget/AFP/Getty Images
For years, the risks associated with reliance on US-made chips in particular have hovered over the Chinese car sector—plans for driverless systems could be held back at any moment by US export controls or sanctions.
“Given the geopolitical environment, no one will invest in a technology with such a high risk that they’re still relying on foreign technology,” says Raymond Tsang, an automotive technology expert with Bain in Shanghai.
However, these vulnerabilities might not persist. Analysts believe BYD will soon develop most of its driverless systems in house and increasingly swap out Nvidia chips for those made by Beijing-based Horizon Robotics. “This is the BYD way to drive costs down,” McNally says.
It would also be consistent with a broader shift towards self-reliance in key technologies, in response to Washington’s steadily increasing restrictions on technology exports to China.
Yuqian Ding, a veteran Beijing-based auto analyst with HSBC, says that while BYD has not talked about developing a robotaxi service, executives have made “very clear” their plans to develop in-house all the important software and hardware needed for autonomous vehicles.
Wang, the BYD boss, has also previously indicated to analysts that the company has all the tech and know-how to develop robots, in another potential long-term challenge to Musk.
“With more than 5 million scale per annum, they can do everything,” Ding says, adding: “That’s the ultimate goal … Their target is much closer to Tesla.”
In an interview with the Financial Times this year, BYD’s executive vice-president Stella Li said competition with Tesla in EVs and autonomous technology would accelerate innovation, ultimately making BYD a “better” company.
“In the future, if you are not producing an electric car, if you’re not introducing technology in intelligence and autonomous driving, you will be out,” she warned.
Additional reporting by Gloria Li in Hong Kong
Graphic illustration byIan Bottand data visualisation by Ray Douglas
