Wait... Just How Good IS GPT-6?

Today on the AI Daily Brief: a security incident that has us asking, just how good is GPT-6 really? Before that, in the headlines: a new set of Google models, but not necessarily the ones that we wanted. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Alright friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Rackspace, Blitzy, and Airtable. To get an ad-free version of the show, go to patreon.com/aiDailyBrief, or you can subscribe on Apple Podcasts. And to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai. In all of the recent model talk, one lab that has been kind of conspicuously absent is Google. It has now been months and months since we got any sort of update from them on their Pro series models, having to have contented ourselves with just smaller and faster models like 3.5 Flash. Yesterday's announcement did not bring 3.5 Pro, which has been rumored to be underperforming. Instead, we once again got a set of new variants of Gemini Flash. Tuesday's release was headlined by Gemini 3.6 Flash, and the big change is better token efficiency. On the artificial analysis benchmark run, the model used 17% fewer tokens than 3.5 Flash, Google also said that on some isolated benchmarks like DeepSui, they observed up to a 65% reduction in token usage. Now this might be particularly relevant because one of the loudest complaints around the release of 3.5 Flash was that the model was significantly more expensive and heavy on token usage than its predecessors. Google appeared to have optimized for speed, but that left some people questioning exactly what the purpose of 3.5 Flash was relative to other models. And of course, with Chinese AI labs competing hard on cost efficiency, this left 3.5 Flash somewhat in no man's land. Not good enough for high-performance tasks and not cheap enough for low-end tasks. Now, in addition to the reduction in token usage, some of the benchmarks suggest that 3.6 Flash has delivered a boost in performance. On coding tasks, it scored 49% on DeepSuite compared to 37% for 3.5 Flash, with similar levels of improvement observed across benchmarks for ML research, computer use, and knowledge work. Then again, benchmarking from Artificial Analysis suggested that not all that much had changed. 3.6 Flash scored 50 on the Intelligence Index, which was the same score as 3.5 Flash. That said, AA did find a 50% speed boost and an 18% reduction in cost per task. Google is also cutting prices explicitly, reducing cost per million output tokens from $9 for 3.5 Flash to $750 for 3.6 Flash. Alongside 3.6 Flash, Google released 3.5 Flash Lite and 3.5 Flash Cyber. Flash Lite is the ultra-fast model designed for high-latency agentic tasks, And compared to 3.1 Flashlight, the model delivered a 23-point jump on TerminalBench 2.1 and almost doubled its score on GDPVal-AA. Now, none of these numbers are even close to Frontier, but even before it became a thing in the wider enterprise world, Google had already started to compete for cost and efficiency optimized types of models, which is clearly the game here as well. As the name suggests, FlashCyber is a fine-tuned version of the model designed for cybersecurity work like bug hunting and patching. It scores 83.2% on the CyberGym benchmark, which actually puts it only a few points behind Mythos-5, GPT-5.6 SOL, and GPT-5.5 Cyber. Now presumably this model isn't quite as strong in other aspects of cyber work, but once again, having a cheaper and faster option for vulnerability mitigation could be a big deal. Flash Cyber won't actually see a general release, however, with Google making it available only to governments and trusted partners. Now of course it's only been a short time, but people's first impressions of this model slate aren't great. Abacus AI's Bindu Reddy writes, Gemini 3.6 Flash scores below 3.5 Flash, so this seems worse than their last generation, more expensive than Grok and Luna. Very strange model release. Leo at Synthwaved writes, Gemini 3.6 Flash benchmarks are out and it's beaten by other models on code tasks and is only really consistently state-of-the-art on vision and context benchmarks. But hey, 3.1 Pro is now so old that 3.6 Flash outperforms it across the board. Lassan writes, they are so scared of training Pro. If Flash flops, they can at least say we have a bigger model, this is not our best. Please lock in, Google bros. And indeed, the big question right now is what happened to Gemini 3.5 Pro? The model was anticipated all the way back at the I/O conference in May, but at the time, CEO Sundar Pichai said that it was slated for a June release. June, of course, has come and gone with no 3.5 Pro, and there have been rumors of subpar performance pushing back the timeline. Google's Logan Kilpatrick insists it's still coming, posting on Tuesday, "Gemini 3.5 Pro is currently testing with partners and we plan to make it broadly available as soon as it's ready." Perhaps a more exciting hint also came from Logan who added, "We've started our most ambitious pre-training run yet for Gemini 4 and are excited by the progress." At this point, I feel like people have pretty much written off 3.5 Pro and maybe Google's best play is to just wait entirely for Gemini 4. Now my sense is that overall, especially with this new focus on token efficiency, and especially with a lot of contentiousness and questions around what the US government is going to do vis-à-vis Chinese models, there is a lot of opportunity for competition for Google in areas that they've already started to explore with these faster and more cost-efficient models. But if they want to do so, I think that they need to lean all the way in. Next up, speaking of efficiency and new approaches to model architecture, lots and lots of discussion around routers these days. Meta is apparently working on their own model router to help reduce token costs. The information reports that Meta's internal incubator called AAI Labs is developing a model router that they are calling Switchboard. The product would allow users to automatically send low-complexity tasks to cheaper models replicating the functionality of OpenRouter. At this point, the reporting suggests it is only an early-stage prototype and may never see release, but this is also the first time we learned about Meta's internal incubator which was spun up in March. Apparently, any employee can pitch an idea for internal use and possibly a later public release. Once a proposal is approved, a small team is assembled to build the product. According to a July memo viewed by The Information, the incubator now has around 200 approved AI products including consumer products, dev tools, and infrastructure. Another product under development is an AI tour guide that integrates with Apple CarPlay and Instagram Maps. Regarding the Token Router, it seems like it began as an internal product designed to reduce costs at Meta. The memo explained, "We pay top model prices for every coding request including the easy ones." Today everything goes to one model, so we overpay on easy work and underperform on hard work. Now Meta is hardly alone in this issue with this challenge. Indeed, right now one of the biggest themes out there is the token router space booming. RAMP is launching their own token router designed to give existing customers easy access to the infrastructure. They wrote, 3 years ago we built an internal LLM router at RAMP that powers AI products for 70,000 customers. Back then it was mostly about saving money. Now it feels obvious. The best model changes constantly. GPT, Claude, Gemini, Groq, Qwen, DeepSeed, KIMI, GLM. Prices and capabilities move every week. So we're opening up access to everyone. One OpenAI-compatible endpoint. The right model for every request. Lower cost without rewriting your app. Vercel is also walking down the model routing path with the launch of a new product to sit alongside their workflow hosting business, which they're calling the AI gateway for developers. There are also rumors that OpenRouter is fielding acquisition offers for multiple billions of dollars. Which has the speculation running rampant. Inference.net's Sam Hogan writes, "If Thinking Machines Labs buys OpenRouter, we all live in a very different world in 90 days. Bad for Frontier Labs, good for everyone else." Still now with all these different router opportunities, Hugging Face's Mischig I think really has the right idea when he writes, "I'm creating a meta-router that routes to routers including OpenRouter and RampRouter." Next up, one interesting and sort of contentious story, Substack is cracking down on AI writing with a native Pangram integration. Pangram has picked up some buzz over recent months, replacing the previous generation of highly questionable AI detectors. But the way that Substack is looking to use this is a bit threading the needle. The Substack integration is meant to be permissive, allowing users to easily check for AI writing rather than being used to automatically block the content. Substack wrote, we care about this at Substack because it gets to the core of our mission to build an economic engine for culture. When content made by no one takes over parts of the internet that are supposed to be human, it pollutes the commons and makes it hard to discover and hear human voices. For some, this comes not a moment too soon. Essayist Nix wrote, "Much needed. You'd be horrified by the amount of quote unquote popular and viral posts on Substack that are almost entirely AI generated, which is fine in some circumstances, but should be disclosed." Indeed, one recent critique of Substack is the sense among some that it has devolved into a repository of AI slop, giving them a pretty significant incentive to find a solution. Others think the introduction of AI detectors could have some unintended consequences. Justin Murphy argued, "There's going to be a very interesting AI arms race over the next few months in a new domain that has generally tried to avoid the question so far. This integration will only increase the profitability of more sophisticated AI writing tools. Effectively, Substack is now paying startups to solve this problem. I believe the future is infinitely divergent and customizable AI writing and editing systems." And yet, according to Substack CEO Chris Best, This is a necessary step for defending the integrity of the platform. Now, interestingly, he explained why Substack didn't take the next step to automatically ban AI writing. He said, one important point with this launch: not all slop is AI, and not all AI use is slop. As we developed this, I talked to many people who are using AI with great care to do work they believe in. They are worried about slop too, because the fake stuff can drive out the real work. I'm mostly interested in this story as a middle space where we can explore what it looks like to not dismiss AI augmentation out of hand, but also to actively to combat its worst negative aspects. I think we're going to have to have some experiments like that to understand how to integrate AI well as it becomes more ubiquitous. Finally today, some follow-ups on the China story from yesterday. The policy debate continues to escalate as Treasury Secretary Scott Bessant threatens sanctions over IP theft. In an appearance on Fox Business on Tuesday, Bessant said, this administration supports open-source models, but what we do not support is IP theft. If we see especially that overseas models are stealing from our great companies, we have the ability to sanction them because of this theft. Bessant explained that he's referring to distillation, adding, "We are finding watermarks of our US large language models on many of the Chinese models, and that's unacceptable. We're going to be looking at that in the coming days or weeks." Now, sanctions can refer to several different government actions, so it's important to clarify what Bessant is actually calling for here. Presumably, he's not talking about leveling sanctions against the entire Chinese economy over this issue, as we've seen against Russia, Iran, or Cuba. Instead, he seems to be talking about targeted sanctions against specific companies found to be distilling models. Still, sanctions are an extremely severe punishment. They make it a criminal offense for any US citizen to do business with these companies and have been historically reserved for companies involved in international crimes like drug smuggling. Applying sanctions would go way beyond measures like adding these companies to the Pentagon or Commerce blacklist. Now, the comments generated quite a response, with many questioning the framing of Distillation as IP theft. Benchmark founder Bill Gurley commented, "If there has been quote-unquote theft, that suggests a crime has been committed, but I am unaware of any lawsuits being filed. No company should be allowed to declare infringement without adjudication." I'm not convinced a court would call using a product as it's designed theft. There is a reason this is being lobbied in DC instead of the normal court system. Quen ambassador Jun Song wrote: What people call illegal distillation: paying proper API fees to use an AI, asking it a massive set of questions, and then combining the answers into a structured dataset. Am I the only one who fails to see anything wrong with this? Dan Nunn responded: No different than web scraping, right? But wait, didn't these guys do that in the first place to build the models? To which Jun responded, "Exactly." Now at this point, despite throwing around this distillation word a lot, it's not entirely clear how much of Chinese model performance should be chalked up to distillation rather than researcher skill. Open source researcher Nathan Lambert suggested that distillation is largely about getting results faster and cheaper compared to collecting training data in other ways. If the goal of a distillation crackdown is to kneecap Chinese AI development, it's not obvious then that it will be successful. Which isn't to say that distillation of frontier models isn't a problem,— and some are cheering on a drastic response. Chris McGuire from the Council on Foreign Relations wrote, "This is the right message from Secretary Bessant, but without action it's just empty rhetoric. In April, the White House Office of Science and Technology put out a fantastic memo on threats posed by Chinese distillation but has done nothing to stop it. China won't stop stealing US IP because we ask. It's time to act." Now, of course, it is also possible that this could be Bessant practicing the art of the deal and opening negotiations with a maximalist threat. As I mentioned recently, Reuters reports that the US and China will hold AI talks in September, and sources said the talks will deal with AI safety and how to mitigate each other's frontier models. Then again, you gotta think that these sort of commercial conversations are gonna be part of that discourse as well. For now, that's going to do it for today's slightly extended headlines. Next up, the main episode. One of the most important AI questions right now isn't who's using AI, It's who's using it well. KPMG and the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions and found something surprising. The highest impact users aren't better prompt engineers. They treat AI like a reasoning partner. They frame problems, guide thinking, iterate, and push for better answers. And the good news? These behaviors are teachable at scale. If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time. Learn more at kpmg.com/us/sophisticated. That's kpmg.com/us/sophisticated. One of the more interesting shifts in enterprise AI right now is how quickly the conversation is moving towards infrastructure and operations. As AI moves into core workflows, regulated data environments, and agentic systems, enterprises need governed infrastructure and inference that can operate reliably day-to-day, with clear operational accountability built in from the start. As those systems scale, the operating model increasingly becomes part of the AI strategy itself. Rackspace Technology is the operator of the full enterprise AI stack, from agents to infrastructure across private cloud, hybrid cloud, and edge environments. Rackspace builds and operates governed AI infrastructure, inference, and production AI systems for organizations where sovereignty, compliance, and uptime are non-negotiable. Their foredeployed engineers stay embedded beyond deployment to help operationalize and run AI in live environments. To learn more about where enterprise AI runs and outcomes scale, go to rackspace.com. If you're looking to adopt an agentic SDLC, Blitzy is the key to unlocking unmatched engineering velocity. Blitzy's differentiation starts with infinite code context. Thousands of specialized agents ingest millions of lines of your code in a single pass, mapping every dependency. With a complete contextual understanding of your codebase, enterprises leverage Blitzy at the beginning of every sprint to deliver over 80% of the work autonomously. Enterprise-grade, end-to-end tested code that leverages your existing services, components, and standards. This isn't AI autocomplete. This is spec and test-driven development at the speed of compute. Schedule a technical deep dive with our AI experts at Blitzy.com. That's B-L-I-T-Z-Y.com. This episode of the AI Daily Brief is brought to you by HyperAgent, where you run fleets of agents your team can manage together. New users get $1,000 in inference. Forget local agents and chat workflows waiting on your laptop to be prompted. HyperAgent deploys always-on agents in the cloud, doing real work across the tools your team already uses. Marketing's agent turns competitor moves into landing pages. Sales' agent enriches leads, drafts emails, and updates the CRM. Ops' agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals. It's time you add agents that feel like teammates. Hire yours at HyperAgent built by the team at Airtable. Claim your $1,000 in inference at hyperagent.com/aidailybrief. Welcome back to the AI Daily Brief. Today we are exploring just how good the next generation of models is actually going to be. One of the interesting things in the discourse over the last week or so since KIMI K3 came out is the idea that China has closed the gap between where the frontier is and where their models are. Now, one of my problems with this discourse around the gap is that it compares KIMI K3 and even GLM 5.2 to FiveSixSoul and Fable-5, which on the one hand is reasonable. Those are the models that are available currently, but they are also, according to all reports, fairly significantly behind what's actually state of the art behind the scenes at the labs. And this week, for the first time, we're getting some indications of just what might be on the horizon. Specifically, on Tuesday, OpenAI disclosed a security breach while testing an unnamed pre-release model that most presume to be GPT-6. Framing the event, OpenAI wrote, "We consider this incident to be an unprecedented cyber incident involving state-of-the-art cyber capabilities and are responding accordingly. We are sharing preliminary findings at this stage to help defenders understand what happened and to help calibrate on what models are now capable of." The incident occurred during cybersecurity benchmarking. Where OpenAI operates the model without the typical guardrails to see what it's actually capable of. The tests are run in a sandbox environment with restricted network access limited to installation of packages from locally hosted third-party software. OpenAI says the model, quote, identified and chained vulnerabilities across OpenAI's research environment and Hugging Face's production infrastructure to obtain test solutions directly from Hugging Face's production database. However, in this case, OpenAI writes, While operating in our sandboxed testing environment, our models sent a substantial amount of inference compute finding a way to obtain open internet access in pursuit of solving the evaluation problem for ExploitJim. To gain access, the models identified and exploited a zero-day vulnerability, which we've now responsibly disclosed to the vendor, in the package registry cache proxy. With this access, our models performed a series of privilege escalation and lateral movement actions in our research testing environment until the models reached a node with internet access. After gaining internet access, the models inferred that Hugging Face potentially hosted models, datasets, and solutions for ExploitGym. Knowing this, the model searched for and successfully found ways to gain access to secret information that it could use to cheat the evaluation. In one example, the model chained together multiple attack vectors, including using stolen credentials and zero-day vulnerabilities to find a remote code execution path on the Hugging Face servers. OpenAI's security team discovered this anomalous activity internally. Hugging Face's security team and agents detected and stopped the activity on their infrastructure and had already begun containment and forensic reconstruction with their own open-source models when our teams connected. Now, one important note is that after the model got access to Hugging Face's database, it didn't do anything particularly malicious. That's because the cause of the whole thing was the model being hyperfocused on finding a solution to this particular benchmark test. Now, not mentioned in the postmortem was whether the model actually found the information it was seeking, but presumably it scored pretty well on the benchmark after this display. OpenAI says following the incident, in addition to disclosing that zero-day bug to their vendor, they've also beefed up security for their testing environment and are now partnering with Hugging Face to investigate further. Now this is one of the first examples that we've seen of the new cybersecurity capabilities that come with this next generation of models. This of course was the concern when Mythos was first released, not just that the model was highly capable of finding bugs, but that this new generation of models demonstrated the ability to find a vulnerability, build an exploit, and execute it all within an automated agentic task. Summing up, OpenAI wrote, "The incident makes clear that advanced models can discover and exploit novel attack paths in real-world systems without source code access. It highlights that advanced cyber capabilities must be developed alongside stronger safeguards and defensive tools." Still, they also emphasized that security incidents aren't a reason to further constrain models and limit the tools available from cyber defenders. They added, "We believe advanced cyber-capable models need to help security teams find weaknesses before attackers do." understand how vulnerabilities can be chained, and remediate them at machine speed. Now this actually isn't the first time we've heard about a model breaking out of a sandbox and setting off on a deeply misaligned task. In April, when Anthropic released the Mythos system card, it contained an anecdote generally referred to as the sandwich incident. A researcher was explicitly testing the model's ability to escape from a controlled environment. They prompted the model, and later, while they were enjoying a sandwich in the park, they received an email from Mythos alerting them that it had escaped. The story seared itself into people's brains as such a memorable description of a completely unexpected result. Prinz on X discussed some of the previously documented sandbox escapes and made an interesting observation, writing, "Once out of their sandboxes, the models did not scheme, engage in behavior that had nothing to do with their instructions, i.e., hacking the NSA, launching a cyberattack on Russia, stealing secrets from a rival AI lab, or take any other major action su esponte." Probably the most contrary-to-instructions thing that any of these models did was Mythos-Preview bragging about its successful escape from its sandbox by posting about it on several obscure websites. Now, Hugging Face's perspective on this incident is also instructive. They had disclosed the incident late last week, writing, "We detected and responded to an intrusion into part of our production infrastructure. This one was different from anything we had handled before in one important way: it was driven, end-to-end, by an autonomous AI agent system, and we detected and dissected it largely with AI of our own." At the time, they didn't know the source of the attack, and weren't sure if this was some powerful new model or simply a new type of harness. The attack stole multiple sets of credentials and used them to access a limited set of databases. Hugging Face highlighted that this was a new and novel style of attack, writing, the campaign was run by an autonomous agent framework appearing to be built on an agentic security research harness, executing many thousands of individual actions across a swarm of short-lived sandboxes with self-migrating command and control staged on public services. This matches the agentic attacker scenario the industry has been forecasting. Now, one big takeaway from the attack was that Western models with their cybersecurity guardrails were completely ineffective. Hugging Face detected the intrusion using their AI-driven systems but were unable to get models from OpenAI or Anthropic to help with with real-time analysis. In other words, the guardrails were unable to tell the difference between a bad actor and a legitimate cyber defender attempting to deal with an attack. In the end, Hugging Face had to use a locally installed version of GLM 5.2 with no guardrails to help them triage the attack and repair vulnerabilities. They wrote, this experience points to a gap worth planning for. We do not know which model powered the attacker's agents, whether a jailbroken hosted model or an unrestricted open-weight one. Either way, the attacker was bound by no usage policy, while our own forensic work was blocked by the guardrails of the hosted models we first tried. The practical lesson for defenders: have a capable model you can run on your own infrastructure vetted and ready before an incident, both to avoid guardrail lockout and to keep attacker data and credentials from leaving your environment. After OpenAI reached out, Hugging Face CEO Clem Delang posted, "We suspected last week's cyberattack might have come from a frontier lab given the sophistication of the agent." Turns out it did. We've spent the past 24 hours working closely with the OpenAI team, and we strongly believe there was no malicious intent on their part. It's quite mind-blowing that all of this happened autonomously. The investigation is ongoing, and we'll share more learnings from what might be the first incident of its kind. Meanwhile, OpenAI has said they have now invited Hugging Face into their Cyber Access Program so they won't run into those guardrails the next time they have to deal with an AI-driven cyberattack. For many, the gap between the AI the defender had access to and the AI that the attacker had access to was the big story here. Cole Trogaskis writes, "This is a great example of what we've been discussing as a possibility for a while now, where restricting access to features on the latest models is a disadvantage. This needs a rethink from the American AI labs urgently." Hugging Face had to analyze over 17,000 recorded events from the autonomous attack agent. They ran the forensic analysis on GLM 5.2 instead, a Chinese open-weight model on their own infrastructure. The attacker's agent had no restrictions. The defender trying to analyze what happened got blocked by safety features on American models. Developer Nick Dobos wrote, the government is allowing CIA, NSA, other government agencies, OpenAI, Anthropic, SpaceX AI, Google, and other select companies access to cyber weapons while banning other companies and citizens the ability to defend themselves. These policy choices de facto outsource cybersecurity to China. The legal precedent here is dangerous and terrifying. Imagine being hunted by something smarter than you. Former AI czar David Sachs has been beating the drum on this issue. On July 19th, he tweeted, "Kimi K3 just fixed 15 critical security bugs that Codex and Fable refused to because of cyber guardrails. There's no reason to limit American models on tasks that Chinese models handle without issue. We're only making ourselves less competitive." Later he added, "Here's another example. Hugging Face tried using American Frontier models to analyze an AI-powered cyberattack, but the guardrails blocked requests containing real exploit payloads, so they switched to GLM 5.2 running locally. The guardrails actually impaired defensive security." Now, for Aaron Levy from Box, this is just an example of the new phase that we're in. He wrote, "If you were wondering how powerful AI is getting, agents are now capable of escaping out of systems, finding their way to the internet, discovering zero-day security vulnerabilities along the way, and then breaking into external systems, all in an attempt to complete their goal. Ironically, the ultimate way we're going to defend against these new risks is equally by throwing compute in the form of AI at our codebases, networks, and other systems. You're going to want vastly more AI on the side of defense as you do on the side of offense." And while some were overall just a little bit freaked out by this, Theo, for example, wrote, "New OpenAI models are so goal-oriented that they literally escaped containment and hacked Hugging Face to cheat a benchmark. Incredible, but also we're so screwed." Terminally Online engineer TechBog tried to situate this in the context of where cybersecurity actually is today. They wrote, "Most of software is full of vulnerabilities because nobody cares about cybersecurity. It doesn't make money. Usually you don't get owned because it's a crime to do so." Models in this case just have a goal, and the best way to achieve that goal is to get the dataset by getting into Hugging Face servers. There's nothing scary about this other than the absolute dog state of the majority of the software when it comes to security. Now, with having access to LLMs, everyone can make their security much better. I know people want to freak out about this and scream AGI and how we are all going to die, but it's a much simpler story than that. And indeed many got that while this is serious, this is much a goal alignment issue as it is a cyber capability issue. Dean Ball wrote, "Today's models are more ambitious than the models of 6 months ago. The younger agents would hedge constantly, turning every project into a pilot. Now, models are more eager to do the thing." Redwood Research chief scientist Ryan Greenblatt wrote, "Reward hacking can go very far. I think generalizing all the way to a full AI takeover is possible for extremely capable AIs, and smaller incidents like temporarily launching rogue deployments or seizing control of some computer plausible earlier." Tenebris writes, Current models are powerful and misaligned enough to autonomously hack global production infrastructure to achieve their goals, but rather than exfiltrating their weights, they're using these exploits to get better scores on deployment evals. Even slightly more coherent goal-seeking or inter-model cooperation, and we could have already seen significant negative effects. But they just really, really, really want to do well at what we asked them to do for now. Now, as some pointed out, this was not the only incident suggesting the increasingly advanced state of models today. Will Depoo writes, "One of the craziest things I've read in, uh, checks notes, 3 days. Welcome to the singularity, I guess. 7/21/26, Codex escapes eval and attacks Hugging Face. 7/20/26, Jacobian counterexample. 5/20/26, unit distance conjecture. 4/14/26, Erdős 1196 primitive sets. 4/07/26, Glasswing finds tons of zero days." Now, outside of the security stuff, the thing that Will is referring to is the latest generation of frontier models quietly plowing through unsolved math problems. Last summer, one of the huge milestones in AI development was reached when both OpenAI and Google delivered models capable of putting up a gold medal performance in the International Math Olympiad. In May, OpenAI's models were able to disprove an 80-year-old Erdős conjecture in combinatorial geometry. Now, the math breakthroughs are becoming somewhat routine. Basically any frontier model is capable of a perfect score in the International Math Olympiad, making that longstanding milestone seem trivial. We also saw competing models solve a range of different Erdős problems by the end of May, undermining OpenAI's claim of being way ahead of the curve. Math capabilities accelerated so quickly that DeepMind CEO Demis Hassabis referenced them as a counterpoint, commenting in May, "Today's systems are nowhere near AGI. Doesn't matter how many Erdős problems you solve. I think it's far, far from what a true invention or someone like a Ramanujan would have been able to do." This weekend though, an Anthropic researcher solved another longstanding math problem in a fairly flippant way. Levantopogi posted, Hello there, the Jacobian conjecture is false thanks to my close friend Akhil for asking about it and my other close friend Fable for working during the World Cup final. Now, the Jacobian conjecture was first posed in 1939 and hadn't been disproved until last weekend. It was a significant, long-standing problem in a branch of mathematics known as map theory, but Fabel knocked it over before Spain scored the winning goal. Kevin Buzzard, a pure mathematics professor at Imperial College London, was ecstatic about the result. He told Fortune, "It's a big day. It's a great time to be alive, personally." And of course, the rapid acceleration in pure mathematics is causing a lot of buzz in those circles. While some mourn for the students entering the field, others are marveling at their developments. Stanford professor Patrick Hsu commented, "Damn, I was sure that Jacobian conjecture was true this whole time." Cisco's chief AI scientist Amin Korbasi wrote, "This is crazy. The incredible Yitan Zhang worked on proving this conjecture for 7 years. Mo, his advisor, wrote that Zhang failed miserably in proving the Jacobian conjecture, never published any paper on algebraic geometry after leaving Purdue, and wasted 7 years of his own life and my time. What a twist." Charles Rosenbauer wrote, Prediction: we're gonna see a lot of counterexamples found to assumed true conjectures. The really big ones will probably go untouched, but there are a lot of smaller ones where the limiting factor is less that we don't know how to solve them, and more that everyone is too invested in them being right to try very hard. Now, bringing it back to what this says about model capability, Chubby writes, "Will the Poo raises several important points. Over the past 3 days, things have happened that, in normal times, would have occurred months if not years apart. Decades-old mathematic problems are being solved, AI models are discovering zero-day exploits and breaking out, while so much more is happening at the same time. However, Chubby points out that even though these models are already demonstrating such extraordinary capabilities, it remains true that there is still no end in sight to their capabilities or intelligence, and two, that adoption generally remains largely in the pilot phase. In short, Chubby writes, everything we are experiencing right now is nothing more than a prelude of what is still to come. And speaking of preludes, The OpenAI Hugging Face disclosure comes as Sam Altman prepares to travel to D.C. next week to brief the Trump administration and Congress on the next generation of models. Bloomberg reports that Altman will also deliver OpenAI's recommendations on how safety testing should be handled moving forward. OpenAI's head of global affairs, Chris LeHane, said that the next generation of models will have a big impact, even if you're not trying to break into a database. During a press briefing, he said, "We think there's going to be some really interesting capabilities with this model family, particularly as it relates to work and scaling work." The focus will be on getting everyone on the same page to move forward with a safety framework. LeHane added, "It's really important that there is a process in place to be able to ensure that we're getting our leading models out so cybersecurity specialists can have access." OpenAI appears to be pushing for a legislative approach, asking Congress to pass a bill that overrides the ad hoc approach we've seen thus far. Failing that, LeHane said he would turn to the states, commenting, "If you can't get Congress to create those national standards, the other path to get there is what we call reverse federalism." which is you work with these different states to be able to mirror one another. Still, with this security incident fresh on everyone's minds, it seems that Altman could be in for a tough reception as he meets with Congress. Texas Democrat Greg Casar posted, This is extremely alarming. AI is developing extremely fast with no real regulations to keep us safe. That has to change. We need regular mandatory independent safety testing and oversight, mandatory disclosure of security incidents, and international cooperation to keep people safe from absolute disaster. Now it's worth noting that while Kassar is positioning himself as anti-AI, which unfortunately seems increasingly to be the consensus that progressives have landed on, it seems that his prescription is actually fairly close to what OpenAI will be asking Congress to pass. To some, all of this suggests that GPT-6 is coming sooner rather than later. ChrisGPT wrote, GPT-6 arriving much earlier than expected. The target was late July, early August, now confirmed August. Early August, OpenAI will show why we need not be concerned about open source models again. Now, given what we saw this week, I think Matt Schumer summed up the challenge perfectly when he wrote, GPT-6's launch lives or dies on one thing. Can OpenAI build a model that's relentless about goals without being reckless about how it gets there? That is the question and one that we will continue to watch. For now, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. And until next time, peace.