AI Companies Still Haven’t Delivered on Their Biggest Promises

In a recent podcast appearance, a prominent investor said that he had heard from multiple sources inside Anthropic that Dario Amodei and other leaders in that company felt that at some point in the future, they might be the only company left. It would just be them, governments, and the rest of us. Now, these comments on that podcast kicked off quite a firestorm of discourse about Anthropic and their role in AI and what their beliefs actually meant for the industry. It also generated that rarest of phenomenon, an appearance on social media from Anthropic CEO Dario Amodei himself. In his response post, Dario discusses his real views on regulatory capture, what he thinks the real root of AI's trust problems with people are, and what he thinks could actually address those trust problems in the long run. So did people find it enlightening, convincing? Did anyone's opinions actually change? And what does the whole conversation say about the AI discourse and Anthropic's place in it? 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, Blitzy, Robots and Pencils, and HyperAgent. 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. We got ourselves quite a Monday here, so strap in. First up comes a new model, and one that is sure to kick off a lot of debate. ZAI has dropped GLM-5.3. Now, you might remember that when ZAI released GLM-5.2 in June, it helped kick off this new wave of concern that we're still in right now that Chinese open weight models were closing the gap. Now, part of that was timing— the release came during the period where Fable-5 was locked behind government doors and OpenAI was delaying 5.6 for the same reason— but the feeling of Chinese models nipping on Western heels compounded with the release of KIMI-K3 the following month. Still, as has happened every other time, even acknowledging what these models are really good for, there has been a sense that they still are, ultimately, behind the frontier in pretty meaningful ways. So where does that leave us with GLM-5.3? Well, 5.3 is built on the same base model as 5.2, meaning it's not some massive multi-trillion parameter model. Still, ZAI claims that they've made some big advances purely by scaling reinforcement learning. On coding, GLM-5.3 scores 28.3% on TerminalBench 3.0. That puts it around 5 points behind the frontier with Fable-5 and GPT-5.6 SOL, but 11 points ahead of Kimi K3. The results on DeepSWE were less impressive, scoring 66.9%, which puts the model half a point behind Kimi K3, 3 points behind Fable, and 6 points behind 5.2 SOL. For agentic use, 5.3 scored state-of-the-art results on AutomationBench and GDPVal, just slightly inching out the US Frontier. Overall, it looks like that for high-level use cases like running agents and coding, 5.3 remains a bit behind the absolute state-of-the-art, but has squeezed a lot of performance out of a mid-sized model and may in some cases have overtaken KIMI-K3. Now, ZAI said improved cyber performance was one of the key focuses for their reinforcement learning run. They noted that during the Hugging Face attack, cyber defenders had been forced to use GLM 5.2 because the guardrails on frontier models had rendered them useless. In a WeChat post, ZAI said, if the powerful attack ability is spreading, the defensive ability cannot be limited to a few closed-source model companies. That makes their performance gains on cybersecurity benchmark Cyber Gym a bit more interesting, jumping 7 points from their predecessor to actually overtake Fable 5. Now, just to be super clear, Because of course, we're already seeing some people freak out about a Mythos-level cyber model released as open source. That is not what these benchmarks are saying, even if you take the benchmarks at face value. There is a difference in being Mythos or Fable level at finding vulnerabilities and being Mythos or Fable level at then doing something about it and/or autonomously executing cyberattacks. ZAI is also taking a phased approach to the release, testing the model with trusted partners before publishing the full weights. Now, at the time of recording, we don't yet have the full benchmark run from Artificial Analysis. So we don't have either a full barrage of independent tests, nor do we have a great gauge for token-adjusted cost. On a per-token basis, GLM 5.3 is less than a 10th of the cost of Fable or 5.6 SOL, and one-fifth the cost of Kimi K3. In limited testing, some users are finding that GLM 5.3 costs around two-thirds of Kimi K3 or Groq 4.6 for the same task. First testing for real-world performance leads to feedback that is a little mixed. ZAI claims to have detected over 1,000 critical and high-risk vulnerabilities in open-source repos. They also believe they found a potentially serious vulnerability in Cursor, which they privately reported to the team. But some of the other first tests are a little less clear. Developer Aditya tested it for game development tasks and found GLM 5.3 struggling a bit and clearly behind KIMI-K3. And another big complaint was that the model was painfully slow to the point of being unusable. Now, that could just be issues with excessive demand on release day rather than a fundamental problem, but that remains to be seen. Overall, it looks like ZAI delivered a solid improvement over GLM 5.2 and showed once again that a lot of performance can come simply from scaling reinforcement learning. Open model researcher Nathan Lambert used his review to argue that we should stop being surprised by strong performance out of Chinese labs. To risk a broad oversimplification, he wrote, ZAI seems to have strength in post-training when compared to KIMI, which is more of a pre-training masterpiece. Following this release, there have been a lot of discussions wondering how China can keep up so well. How can such a small model be matching the leading public American models? Are these results real? The simplest explanation, he continued, is that ZAI is very good at what they do. Lambert's point is that we need to stop writing off the Chinese labs as only performing well because of distillation or benchmark maxing, arguing that when we do so, we underestimate what they're actually capable of. Importantly, alongside this type of release, Wall Street analysts and people who aren't necessarily listening to the AI Daily Brief every day are finally beginning to update their priors and recognize that Chinese labs aren't in most cases selling frontier intelligence for pennies on the dollar the way they had previously assumed. The savings are definitely still there, but the pricing gap has contracted substantially. At this stage, cheaper US models like Grok 4-6, Muse Spark 1.2, or GPT-5-6 Luna are cost-competitive with the best models out of China. Wall Street is also recognizing that even Chinese models still need infrastructure. Now, that probably won't come as a huge surprise to you, but it is worth remembering that following the DeepSeek moment in early 2025, one of the big concerns was that cheaper models would invalidate large-scale GPU investment. In a recent note, however, Morningstar analyst Malik Khan wrote, if an enterprise were to consolidate its entire AI stack on OpenWeight models, it would still need cloud infrastructure to run those workloads, store data, manage security and access to resources, etc., all tailwinds to cloud infrastructure companies. I will certainly keep an eye on Five-3 as people test it further. But another bit of model news, or at least model innuendo and rumor, is that Anthropic has suggested that they won't release their next model, instead keeping it for internal use. The second edition of the Anthropic Risk Report included a discussion of unreleased models that are currently being used internally. As of mid-July, Anthropic had 3 significant unreleased models. The first 2 were Opus-5 and a model referred to as Model-1, with capabilities broadly in line with Mythos-5. Anthropic also disclosed a model known as Model-2, which they described as, quote, somewhat more capable than Mythos-5. However, Anthropic continued, our rough qualitative sense is that this model is a noticeable improvement on Mythos-5 for many tasks relevant to internal use, but does not display a capability jump to the degree observed from Claude Opus 4.6 to Mythos Preview. Anthropic said they have no plans for releasing this model publicly and have not run it through their usual suite of evaluations. ChrisGPT pointed out Anthropic's new model 2 doesn't just slightly outperform Mythos 5, as they stated in the report. It scores 62.8% on Anthropic's internal CoBenchmark V2 AI R&D benchmark versus 50.3% for Mythos 5 and 54.8% for Mythos Preview. Also, keep in mind the report's date is July 15th, so these reports are already a months-old snapshot of Anthropic's internal capabilities. Their current internal frontier is most likely further ahead. I think that is just important context to keep in mind as we discuss things like how far behind Chinese models are relative to the state of the art, especially in this new emerging paradigm of government involvement in U.S. state-of-the-art model releases. The gap between what we are using and what the labs have internally is somewhat wider than it's been in the past. Meanwhile, not wanting to be left out of the game, people are noticing that OpenAI staffers have started vague posting about Astra, Suggesting that that model, whatever it ends up being called officially, will be in our hands soon. Lastly today, we're staying on the big labs but moving over to their IPO plans. Anthropic has begun meeting with investors and investment banks, and reports have come in relaying some details that Anthropic has shared, as well as a few points they stayed away from. Anthropic told investors that revenue was up 14x compared to a year ago, reaching $11.5 billion in Q2. That annualizes out to $46 billion for the year. But no updated run rate was leaked to the press. The other big number is $2 trillion, which is the valuation that Anthropic investors said that they expect from the IPO. Anthropic themselves reportedly hasn't discussed valuation, so this is purely what investors have told the Financial Times. Still, if they achieve that, it would make Anthropic's debut valuation even larger than the $1.7 billion attached to SpaceX earlier this year. It would also mean more than a doubling in valuation from where they were when they raised funds in May. Investors said that they expect Anthropic to reach between $100 and $120 billion in revenue by the end of this year. So around 2.5 times where they are currently. Anthropic themselves are a little more modest with their forecasts. And while some investors are extrapolating out this type of growth rate indefinitely, Anthropic themselves are perhaps a little more modest with their forecasts, with Reuters reporting that they see revenue hitting between $190 and $200 billion by 2028. Now, the financial press is starting to poke holes in the valuation, with, for example, Fortune noting that public stocks typically trade on an earnings multiple, Not a revenue multiple. And assuming an average earnings multiple, they write, a $2 trillion Anthropic would need to post annual profits in the neighborhood of $59 to $79 billion to keep pace. Then again, Anthropic is not being valued as an average company, and we are really working without precedent. I think you can expect to see a lot more of this type of debate in the lead-up to this IPO because in the absence of clear precedent, all we have is opinion to be argued. Now, speaking of opinions and just how unique Anthropic is, Reported discussions of how singular Anthropic believes they are was actually what started the topic that I will address in the main episode. So for now, let's end the headlines and move on over into main. Hello everyone. One big change around AI is we've shifted our thinking from how we rank our pages to how do we become the source that AI trusts enough to answer with. At KPMG, they're seeing this firsthand. AI-generated results now surface answers directly, often without a single click. That's why they are increasingly focused on generative engine optimization, or GEO, structuring content so AI systems can retrieve it, understand it, and cite it as trusted authority. This is not just an SEO evolution, but a visibility mandate. And indeed, the GEO mandate from KPMG is simple. If AI is shaping decisions, your expertise needs to show up inside the answer. Read all about it at kpmg.com/us/geo. Again, that is kpmg.com/us/geo. 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Most companies are still figuring out how to start. Robots and Pencils is already launching and scaling. Agentic and generative AI in production at large enterprises in weeks. AWS Advanced Tier Pattern partner more than doubled in a year. And they're hiring. 50 open roles. If you're someone who knows this moment is different, who wants to be inside it, not watching it, this is worth a look. At Robots and Pencils, the best ideas win, and the team is purposefully kept super high quality. This is the kind of place you look back on as the best decision you ever made. Take a look at robotsandpencils.com/careers. 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. One of the things that I try to do on this show is bring you a wide-ranging and representative set of commentary around whatever the big events happening are. I think this is one of the best ways to understand the news on a deeper level, given that when it comes to how news impacts our world, it's not just the news itself, but how that interacts with how people perceive it. Today, however, this goes a step farther where the discourse is actually the news itself, as Anthropic's Dario Amadei has waded into the muck of X.com, for a rare public engagement around some concerns about Anthropic. The responses and interaction around this are more than just insider baseball psychodrama. Given the stakes of this particular conversation and the outsized role specifically Anthropic and OpenAI are playing in global economic discourse, the comments themselves in this case actually count as news. It started when investor Gavin Baker, CIO of Atreides Capital, appeared on the All-In podcast this weekend. In that interview, he said, internally, Anthropic is very confident. I have been told by multiple people I trust that Dario has said that Anthropic might be the only private company in the world at some point. Think about that. In this vision, an Anthropic maximalist vision, there's Anthropic and then there are governments and that's it. So there's a lot of confidence. I'm sure they have more advanced checkpoints than Fable up their sleeve. They've executed really, really well. I would probably take the under on them being the only private company in the world. I think it's gonna be a long time before they land a rocket. When former AI czar David Sachs said, "I might interpret that as a negative signal because it's so hubristic." This is getting into SPF land a little bit. Gavin followed up, "I would certainly discourage Dario from saying that ever again to anyone." And those comments might have just stayed on all in. Had Anthropics' Sholto Douglas not decided to wade in, he reshared that post on X and said, "Completely false. I like Gavin's takes, but whoever he heard this from is lying so that it fits the narrative some people so desperately want you to believe." The same people will try to convince you Anthropic has no moat, and a sentence later that it might become so powerful it could be the only company left. In fact, one of the things we are most worried about is economic concentration of power. There is no world where the government should let any company have that much influence. We need competition and capitalism. The AI market is literally the most competitive market in the world right now. Every single one of the largest companies on Earth is singularly focused on getting you smarter, cheaper models. If it all works out, we'll succeed in reducing the cost of everything to the cost of energy. This is awesome, but it threatens a lot of people's old moats. They're frightened. For sure, with AGI, capitalism gets super weird, and what a company even is might look different. Picked it up from there. Sholto, thank you for setting the record straight. Larger issue is that multiple very serious people in Silicon Valley have heard some variation of this and believe it to be true. And the reason it is believable to so many is that it is consistent with Dario's public messaging and what he outlined in the essay you shared. This technology might be dangerous for humans in multiple ways, could lead to extreme concentration of economic power, and therefore needs to be regulated thoughtfully. I agree with the potential risks, and I believe Dario makes all of these arguments in good faith. As discussed on the pod, if one agrees that AI might be dangerous, there are 2 ways to address this potential risk: either concentrate it in the hands of a chosen few companies and politicians via regulation, Or distribute it widely. Essentially boils down to whether one believes AI is too dangerous to concentrate or too dangerous to distribute. There are reasonable arguments on both sides, but I profoundly agree with Zuckerberg's statement that, quote, the notion that AI is so dangerous that the only safe path is an extreme concentration of power seems inherently problematic. Historically, hoping that an absolute power will benevolently provide for humanity if sufficiently enlightened has not led to safer positive outcomes. Continuing, Gavin writes, And, as Dario says in the aforementioned essay, quote, some may object that we can simply keep AIs in check with a balance of power between many AI systems as we do with humans. Continuing his own thoughts, Gavin says, I believe this is the best path forward. I want as many AIs as possible to maximize the odds that one shares my own particular values. And as Dario notes, no human has ever been able to take over the world. At this point, I think safe to say that Dario has lost the argument. His messaging has failed to result in his preferred regulatory path. The fact that the only solution to the recent incident where an unreleased advanced OpenAI model hacked Hugging Face was an open-source model likely ended any chance of strict near-term regulation. Essentially every major company other than Anthropic has signed Jensen's letter. However, Dario's messaging has been massively helpful to efforts to ban data centers here in America. I suspect we will see anti-data center advocacy groups running ads using clips of Dario warning about how dangerous AI could be for humans. His good faith efforts in favor of regulation are now increasing the odds that AI will not be beneficial for Americans and humans everywhere. I believe there is a reasonable chance AI might help us cure most forms of disease such that we have extended lifespans and can enjoy these long lives in an abundant Star Trek-like future. That is the future I want, and I think Dario is decreasing the odds of that future at this point. He is about to be the CEO of one of the most important public companies in the world, and given that the pro-regulatory effort has failed, at least for now, I respectfully think he should make an effort to be a more positive advocate for his own industry. And if I am wrong and we do need to regulate this technology, he will be a more effective advocate for this in the future, having been open-minded to the alternative. For the sake of clarity, I think Anthropic has deep competitive advantages and is an amazing company. Ironically, the main risk I saw to Anthropic a few months ago was nationalization as a result of Dario's own rhetoric and behavior. Now, Sholto and Gavin go on from there, but this is the point where Dario comes into the conversation. Now, just for some context, Dario is very rarely on social media. He has been very clear that he hates it. In fact, he has blamed it for a lot of people misinterpreting him. He currently follows zero people on X, and the last time he posted was June 10th to share his essay Policy on the AI Exponential. Before that, it was April 7th to announce Project Glasswing, and before that, January 26th to promote another essay of his, The Adolescence of Technology. Coming back to his response to Gavin, he says, Thanks, Gavin, for an especially thoughtful exchange. I don't usually spend much time on social media, but I wanted to engage here because it really brings out the heart of an important conversation. First, on regulation, I think that either concentrate it in the hands of a chosen few companies and politicians via regulation or distribute it widely is a false choice. I know that there is a sort of Silicon Valley shorthand where regulation equals regulatory capture equals concentration of power, but I've always found this to be an overly simplified picture of the world. Many people outside this bubble think of regulation as something that constrains corporate power and benefits ordinary people. I don't necessarily agree with that perspective either. Rather, I think it's complicated and really depends on what the regulation consists of. But in particular, I think that those in the regulation equals regulatory capture equals concentration of power frame often underrate the decentralizing power of objective and fair institutional processes. A crude analogy is that the formal court system can sometimes feel stuffy and elitist, but it does a much better job of defending the rights of vulnerable individuals than the alternative, mob justice. At their best, institutions can vest power in ideas rather than people and thereby decentralize that power. That is why Anthropic has always made its policy proposals very carefully. We try very hard to make proposals that disadvantage or slow down frontier AI companies while advantaging smaller competitors. California's SB 53, which we supported, and even the much-maligned SB 1047, which we were ambivalent on, completely exempt any company below a certain amount of revenue or model training costs from being covered at all. More recently, the testing process we've advocated for at CAISI and the White House involves more rigorous testing of frontier models than off-frontier models. Something that differentially advantages challengers. Similarly, the Pacing the Frontier letter envisions— or at least Anthropic's preferred implementation of it envisions— modulating the pace of the very best models while not constraining those who are catching up. This hurts the business interests of the frontier labs and helps challengers, including OpenWeights. Overall, my view is that AI is structurally a technology that tends to concentrate power, for reasons that have nothing to do with regulation— more to do with the extreme implications of the scaling laws. Open weights do help some with this, but are nowhere near a sufficient solution because they simply shift the concentration somewhat to those with the most compute and chips, which are roughly the frontier labs plus maybe hardware providers. By contrast, I think the right rules of the road can simultaneously A, address AI cyber/bio/alignment risks, B, institutionally constrain the power of the frontier AI companies, and C, leave room for open weights models while also addressing the specific risks that they bring. By the way, I do not think that the events of the last months have failed to result in my preferred regulatory path. The approach that the Trump administration is reported to be taking— pre-deployment testing for frontier models, and also testing of open weights models when they get closer to the frontier— is one that I am very supportive of. Though, of course, I have to see the details to be sure. I am also supportive of Demis Hassabis' ideas around a FINRA-like entity. This contrasts with 6 months ago when most of the industry was still pushing for preemption of all state regulation, and no apparent federal approach either. In his next post, he continues, second, on the messaging around AI, I do not agree that my messaging has been disproportionately negative. In fact, it has been equally balanced between risks and benefits. I've written one major essay about each, and even in interviews where I discuss the risks, I make sure to frequently mention the incredible benefits as well as proposing possible solutions to the risks. Short clips from my interviews that end up on social media tend to be disproportionately negative as that gets clicks. In fact, I wrote Machines of Loving Grace because I didn't feel the AI industry was painting an inspiring enough picture of how the technology could radically transform the world for the The bulk of the essay is devoted to refuting skepticism of AI's potential in health and biology, and showing why I think it will actually be possible to cure most human diseases in around 5 to 10 years, as crazy as it may sound to ordinary people and frankly to biologists as well. And if you read my most recent essay, Policy on the AI Exponential, I discuss concrete proposals for how to streamline the FDA process to make sure the deluge of AI-accelerated drugs isn't slowed down by the regulatory process. I feel the urgency here. I lost my father to hepatitis C only a few years before the development of direct-acting antivirals, which cures 95% of patients and probably would have cured him. I do agree that the public has a negative view of AI and that this is a big problem, but I don't think it is primarily caused by me or any other AI leader warning about AI's risks. I think it is fundamentally a crisis of trust. I think that ordinary people don't trust companies, governments, or the tech industry, and always suspect that we are cooking up some new ways to screw them over. The causes of this go back decades, and AI is just the latest iteration of it. I don't think that a glitzy marketing campaign with a positive spin, which some have advocated that Anthropic do, is the way to win back that trust. At this point, saying that AI will cure cancer is more of a cliché than it is inspiring, and most people think it is deceptive. The thing that will work is actually curing cancer. I think by far the most accurate criticism of AI companies, including Anthropic, is that we haven't yet delivered on our big promises to benefit the world. That is totally on us, and I think it's the criticism you should be making instead of all this stuff about messaging and marketing. We are, however, doing our best to fix this. Anthropic is ramping up efforts very quickly in biology and medicine, and we hope to have incredible results in the coming years and some early glimmers in the coming months. When we've actually accomplished something real, the whole world will hear about it as loudly as possible. You have my word on that. But until then, I don't want to make empty promises. And in the meantime, I feel compelled to speak honestly about the very real risks of AI and how to address them. Honesty is the right thing on the merits, and in terms of public credibility and trust, it is no worse than, and may in fact be better than, an approach that ignores or distracts from risks which people instinctively understand are real. All right, so very long, but I didn't want to summarize here given that the posts are the actual story. So one of the first types of reactions was basically more of this, please. Gavin himself responded, most of all, I think it is great that you are engaging here in such a constructive, thoughtful way. Open dialogue is a great way to build trust with the public, and these are difficult, weighty issues that deserve to be debated in the proverbial town square. Former OpenAI staffer Will Depoo says, I really think Dario should be writing a lot more in public. This was well done. Jessica Lessin, the founder of The Information, writes, with just 2 tweets, Dario Amadei did what he has struggled to do all year. He changed the narrative about himself and gave the chattering classes of tech a far more accessible message to quote regarding his views. And his trust comment is, I believe, exactly the point. Now, others weren't so sure that he actually changed the narrative, though. Capturing the feeling of many, Austin Allred retweeted a screenshot of Dario's second post with the highlight on the section, I do not agree that my messaging has been disproportionately negative, adding the comment, lol, LMFAO. Terminally Online Engineer highlighted that same line and said, are you kidding me? AI Commentator Hater wrote, oh, Dario, as usual, you blame everyone else for misunderstanding you or hyping your own words. It's almost like you say something reckless, then later try to gaslight people into thinking they misunderstood you. Former AI researcher Susan Zhang writes, a wall of text and an army of sycophants to thoughtfully not deny the original accusation of Dario says Anthropic might be the only private company in the world at some point. Oh, and by the way, Dario is totally not spreading P-doom or massive human disempowerment fears. No, no, no. You all hallucinated that and misread it all in bad faith. Shame on you, dear readers and users, for all of your own skill issues. PR expert Lulu Cheng Meservy actually discussed this section of Dario's post in more clinical terms. She writes, the argument in part 2 is an interesting insight into how Dario assesses his own messaging. When accused of negative messaging, qualitative, he rebuts that it's roughly balanced in large part because he's written one essay about benefits and one about risks, quantitative. Implication? He's taking an analytical approach to measuring positivity, but many others are simply going off vibes. So this won't be the last time he and his critics talk past each other. For my part, I don't think that it's just two parties talking past each other. I think this is an area where Dario is just wrong, or perhaps being willfully unwilling to understand the reality of the world we live in. In my view, you can, on the one hand, claim to know and understand that social media is always going to clip the most negative parts and then go and do an endless string of interviews with a seeming never-ending torrent of easily soundbiteable negative statistics. In the real world of public discourse, public perception is not shaped by a word counter of how many words in your positive essay versus how many words in your negative essay. And to argue such shows just a radical lack of understanding of the actual media environment in which you are going to have to operate as the leader of an extremely significant company. Now, if you have listened to even a handful of AI Daily Brief episodes, where I certainly agree is in that I believe that there absolutely is a fundamental trust gap between the broader world and the labs. That is not just a question of messaging, and that messaging cannot fix alone, but you still do have to understand how messaging plays into this. Others picked up on the idea that Dario didn't dispute Gavin's original claim, that he had said that Anthropic might be the only private company in the world at some point. ZephyrZ9 made that point, as did David Sacks, although I think that this is more tactical than anything else. Lulu again points out, going direct with a personal post was the escalation after a senior colleague, i.e., Sholto, already did the fact-checking and elevated the debate to the level of principles. So Dario was able to come in and focus on his beliefs rather than having to first debate the details of who said what when. Still, the bigger critique for many was around Dario's discussion on regulation. Sacks called Dario's characterization of Silicon Valley thinking that all regulation equals regulatory capture a straw man. Of course, he writes, treating all regulation as capture would be overly simplified, but almost no one holds that view. Meanwhile, Replit's Amjad Masad takes issue with Dario's argument that AI structurally centralizes power. He says, the argument that AI structurally centralizes power because it's currently compute hungry ignores 125 years of super exponential growth in compute price performance. Improvements in algorithms and continued hardware efficiency gains mean there is no reason to assume AGI-level capabilities will always require a data center to run. Scaling laws are not laws of physics. They're simply empirical relationships observed for particular architectures, objectives, datasets, etc. Change any one of those factors and you get a different scaling curve. Meanwhile, for others, the whole thing remains a little too abstract. Writes Silicon Data's head of research, Steve Howe, ultimately, the whole discussion can feel self-indulgent to anyone not already deeply inside the industry or AGI-pilled. A relatively small group in Silicon Valley is fully convinced of the accelerating or imminent arrival of systems capable of superintelligence, large-scale displacement, and maximal harm. To most others, this still looks like a large stretch. Which is, in fact, why some agree with Dario. That for AI to actually solve its trust issues, it's going to have to be a show, not a tell. Peter Yang writes, I 100% agree with Dario that using AI to cure diseases and speeding up the regulatory approval of AI breakthroughs in healthcare could bring 10x the benefit to humanity as everything else combined. Still, OpenAI's Angel Brodin writes, My personal opinion, it's an oversimplification to assume that any AI lab can solve the trust problem by doing something spectacular, like curing cancer or achieving some major medical breakthrough. Pharma has delivered some of the greatest improvements in human health and is still one of the least trusted industries. My friends who are ethically against AI don't feel that way because they don't think it has the potential to do great things. The concern is whether the company building the technology has their best interests at heart. Dario is correct that a glossy marketing campaign cannot and will not make people trust AI companies, but people also won't judge AI companies solely by their breakthroughs. They'll judge them by pricing, access, lobbying, opacity, how the economic gains are distributed, Who gets to participate in its benefits, and who ultimately holds the power? Dario argues that the public distrusts AI companies because society has spent decades losing trust in powerful institutions. But if that's true, the answer can't simply be asking people to place even more trust in a small number of powerful institutions to responsibly steward this technology. In an ideal future, AI should help distribute power, not just access to technology, but the agency and economic benefits that come with it. That means democratization, individual agency, broad access, affordability, and making increasingly capable intelligence available to more people at lower cost. Now, if at this point your head is spinning around and you're asking, yeah, but does anything actually get resolved here? The answer is, of course, no. But I do think that there are some important takeaways. First, people getting to debate things that were actually said rather than just suppositions that they're making or reports that they're getting makes for better discourse. Second, as much as he hates social media, these posts themselves show that there is sometimes value in participating in it, and one doesn't have to become Elon posting 50 times a day for that to be the case. Third, for Dario specifically, I would suggest that this length of communication might be a better fit for him than he realizes. It's very clear that he's frustrated that people don't read his full posts, but they're like 13,000 words long, man. People just don't have that sort of attention span anymore, and so of course they're just gonna grab the statistics. Same, unfortunately, with long interviews, even if long is 10 or 15 minutes. This medium of a few hundred words on X is actually much harder for people to take out of context than some other mediums are. Anyone can quickly and easily go check it out for themselves, and many people will. I don't expect, nor do I think, that Dario should all of a sudden become an inveterate tweeter, but this should absolutely be a tool in their communications toolkit. Fourth and finally, part of why this conversation feels useful and value-accretive is that by having it in public, other people at least get to participate through responses and reposts in a way that they don't with other mediums. Given that the stakes of AI are for everyone, And given that so much of the frustration is with the lack of agency around a future that is happening to us, the value of this sort of public conversation is pretty disproportionate. Obviously, I encourage you to go check it out for yourselves. Even if you're just making a lurker X account and never plan on posting it, it is worth it to keep track of this sort of discussion. For now, however, that's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always. Until next time, peace.