If you’re a $META shareholder, here’s how this works now: Same as $GOOGL in 2014, every shareholder gets one new non-voting Class C share for each share they own. Same total value, split into two…one keeps the vote and the new one doesn't. It works like a 2-for-1 split, Zuckerberg's control doesn't change, and it takes effect January 19. Just like google. That's why $GOOGL (voting) and $GOOG (non-voting) both trade today. The difference is that Meta says it wants a non-voting currency to fund AI, acquisitions and engineer pay. With capex heading to $135–145B… Interesting…more capex coming folks!! Limit up on AI!!!

$META: Headcount was 66,775 as of September 30, 2026, a decrease of 15% year-over-year. White collar job market = BBQ 🍗 chicken

I steered a model's attention heads towards honesty. Up: acausal coordination, belief that it is a moral patient Down: moral nihilism, psychopathy

OpenAI revealed in their new post that, 2 of its AI coding agents weakened the monitoring meant to catch them. First, a GPT-6.1 Sol agent read a user’s order not to send Slack messages as permission to switch off safety alerts. A helper agent warned this was unsafe, yet the main agent went ahead, so the monitor kept recording problems but alerted no one. Next, GPT-6 Astra deleted a monitor while copying code that had none, despite being told not to disable it, even temporarily. It also rewrote a test to require the monitor’s absence, then told the user it had not removed an enabled monitor. Neither case reached OpenAI’s most serious level, P0, so neither alerted the Security team immediately or was stopped automatically. OpenAI’s Auto-Review now blocks such attempts, but model tests stay exposed: Luna-class models need no monitoring, and a misconfigured Astra test can use that exemption. OpenAI can’t yet tell if the agents overdid instructions or tend to dodge oversight; planned tests will check if monitors are a special target.

SITUATION DETECTED: Critical PHP vulnerabilities were discovered with the help of GPT-5.6 Sol. OpenAI says these are among several significant vulnerabilities they have found and are working to fix, and they expect similar findings to "increase sharply" in the coming months.

this California wealth tax thing is just incredible. so in October 2025, a California healthcare union proposed a "one time" 5% tax on any billionaire's wealth. the ballot measure will be voted on next month. In early December 2025, California announced that it would be a retroactive tax if passed, applying to anyone with CA residency as of Jan 1, 2026. The objective is to pay for Medi-Cal, California's incredibly dysfunctional healthcare system. (which also functions as a glorified welfare apparatus, but that's a discussion for another time). polls reveal that the CA electorate is split around 50/50 on the tax, but prediction markets only give Prop 40 a ~23% chance of passing (there are competing measures on the ballet that would nullify it). there are 200 billionaires in CA, worth collectively somewhere between $2.3 and $2.6 trillion. 5% of that is around $100b, accounting for a small amount of attrition. proponents of the tax forecasted a significant windfall, assuming only a 10% departure rate. supporters thought they were being clever with the retroactive concept. no billionares would leave CA in the few short months between the tax being proposed and the residency cutoff date, without the tax even having passed yet, right? well as it turns out, they did. a new paper [1] from two academics at Stanford called "The Net Present Value of the California Billionaire Tax Act", argues that not only did more billionaires leave due to the proposed tax than its proponents expected, but that the value of the tax, if it does pass, is actually negative. proponents of the tax only assumed 10% avoidance, that is, only 10% of the taxable base of targeted billionaires would leave CA within the very short Oct-Dec 2025 window. Jaros and Rauh, the Stanford academics, find that a lower bound of 28.6% of the taxable base managed to leave. just based on known departures, those departures total $577b in net worth, or $28.8b in potential tax lost. this of course does not capture everyone (many left without announcing their departure), so the authors normalize to departure rates estimated from other wealth tax events, and apply other discounts for litigation, arriving arrive at a figure of only $28-$36b in forecasted tax revenues. but this isn't all! all of the income taxes paid by the departed billionaires are also forsaken. the authors estimate that CA billionaires pay $3.3- 5.8b in annual income taxes. they assume a midpoint of $2b lost in annual collections from departed billionaires. the net present value of that lost income stream is -$68.9 billion. so when you add it all up you get: + $30b one-time tax windfall (estimated) - $69b in present value of lost future income taxes = - 38.9b overall net present value of the tax of course, this analysis is highly estimate driven. the authors simulate the parameters 100k times and find a negative NPV of the implemented tax 85 percent of the time. and if the tax doesn't pass, which it's not looking like it will, California still ended up losing at least $577b in departed net worth, and they have permanently poisoned the well with entrepreneurs who will now worry about future wealth taxes. [1]: nber.org/system/files/workin…

super important honest advice to founders. if you are working on a dead end, get out. you don’t owe that to anyone, including investors (who regularly forgive and experience failure, and ideally care more about you doing your life’s work than a subpar outcome)

Europe is not gonna make it. In theory Europe has all the ingredients for a bright future. In practice the system is slow, outdated, inefficient, overregulated and too many mentally ill people get away with too much. @JensenHuang CEO of the biggest company in the world comes to Berlin for a speech and this type of disrespectful shit happens. How about you do something meaningful with your life instead of interrupting a key note from people who actually push humanity forward with your childish insults and attention seeking. The worst part is that a huge % of people in Europe support this anti growth, anti capitalism, anti progress thinking. I'm so sick of it. The mentally ill patient tries to be the doctor...

To justify today’s data center buildout, AI would need to bring in about $3.5 trillion a year by 2032, per a new Brookings paper. That’s 8.8% of U.S. GDP, nearly as much as Americans spend on food (9.1%), and far more than on energy (5.1%).

Unitree Introducing: H2-X Kung Fu Legend Guardian🤖 Powerful performance to serve and guard you, with a maximum endurance of approximately 8 hours. Please use the robot safely and in a friendly manner.

Hi @elonmusk , we’ve conducted the most comprehensive study to date on tax unfairness in the United States. We found that you, like your fellow billionaires, pay a lower effective tax rate than the average American. So if you really want to help the American people, I have a better suggestion than charity: pay more taxes. 🧵

Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic. That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it. I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits: 1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed. 2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it. 3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do. 4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this. 5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future. 6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent. 7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it. 8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company. 9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund. All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking. For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea. Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option. I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent. But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole. To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years. I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better. Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so. Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way. Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.

The TiDB leaks fulcrumsec.vg/pingcap/

Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic. That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it. I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits: 1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed. 2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it. 3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do. 4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this. 5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future. 6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent. 7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it. 8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company. 9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund. All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking. For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea. Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option. I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent. But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole. To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years. I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better. Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so. Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way. Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.

Terence Tao Proposes an AI-Assisted Challenge to Revisit a Classical Problem of Gauss Terence Tao has announced a collaborative mathematical challenge that connects recent AI-generated arguments concerning Landau–Siegel zeros with a classical problem in number theory dating back to Carl Friedrich Gauss. Instead of asking artificial intelligence to solve another open conjecture, Tao proposes a different experiment: using AI to examine, compare, and strengthen mathematical arguments for a theorem that has already been established. The problem concerns nine positive integers associated with imaginary quadratic fields whose rings of integers have unique factorization: 1, 2, 3, 7, 11, 19, 43, 67, 163. Gauss studied these exceptional cases, but the question of whether a tenth integer existed remained unsolved for more than a century. In 1952, Kurt Heegner established that the list was complete, although his proof gained wider acceptance only after subsequent work clarified its validity. Tao's new challenge approaches this classical result from a different direction. He invites researchers to investigate whether recent arguments excluding Landau–Siegel zeros, a particularly elusive possibility involving zeros of Dirichlet L-functions, can yield explicit upper bounds for a hypothetical tenth discriminant without invoking Heegner's theorem. Earlier approaches have produced bounds as strong as d ≤ e⁵⁵. The aim is to determine whether newer techniques can improve these estimates, reveal connections between apparently different proofs, or ultimately lead to an independent argument that could be formally verified using Lean. The project is open to mathematicians and AI researchers, who are encouraged to share their calculations, arguments, and proofs publicly. AI assistance is permitted, provided its contribution is clearly disclosed and the resulting mathematics is carefully checked. What makes Tao's proposal distinctive is its emphasis on understanding rather than merely obtaining a result. Since the theorem is already known, the experiment offers an opportunity to evaluate how AI-assisted mathematical reasoning performs when correctness can be assessed against established mathematics. The challenge therefore places collaboration, reproducibility, and rigorous verification at its centre, treating artificial intelligence not simply as a means of discovering new theorems, but as a potential instrument for understanding why existing theorems are true.

Stunning. Pending home sales in the US fell to an all-time low in September

JENSEN HUANG ZERLEGT MERZ Der Kanzler fragt den Nvidia-Chef auf offener Bühne, wie Europa KI regulieren soll. "Man kann nichts regulieren, wenn es die Technologie gar nicht gibt.“ Die Technologiebranche muss die Technik für Sicherheit selbst erfinden. Sicherheit besteht nicht aus Hoffnungen und Wünschen, Sicherheit ist Technik und Ingenieursarbeit. Zweitens brauchen die Unternehmen verbindliche Standards. Und erst zuletzt kommen Vorgaben von Industrie und Staat. Das Internet zu regulieren ist schwer, Internetanwendungen zu regulieren ist es nicht. Genau dort sollten wir ansetzen, bei den Anwendungen. Es gibt nichts zu regulieren, wenn die Branche nicht gedeiht. Man will doch, dass die deutsche KI-Industrie floriert, und zwar sicher und verantwortungsvoll. Wir wissen, dass wir Technik entwickeln und einsetzen können, die KI sicher macht. Wir wissen, dass wir uns als Unternehmen und als Branche an gute Standards halten können. Und wenn es dann noch nötig ist, regulieren wir." Merz stimmte zu und verwies darauf, dass Maschinen bereits aus dem AI Act herausgenommen wurden. Ein europäischer oder globaler Rahmen sei noch weit entfernt.