Voices.

Elon Musk

SpaceX/xAI/Tesla — orbital compute, Optimus, robotaxi, power; the single most market-moving account

• “Robotaxi works 168 hours a week x.com/robotaxi/statu…”

  Nov 12, 12:12 AM ET

• “Tesla self-driving now approved in Finland x.com/teslaeurope/st…”

  Nov 11, 12:11 PM ET

• “Turns out that the Starlink software, built for managing the movement of vast amounts of data through space and to the ground, is also very well suited to training super intelligence”

  Nov 10, 5:34 AM ET

Jensen Huang

Sets the roadmap every other index builds against

Not yet covered

Sam Altman

OpenAI — capex, compute deals, IPO

• “People should always be in control of the future. OpenAI is against the notion that it would be acceptable for people to be relegated to a role of doing things that “AI systems think makes sense to reward in humans”. People should not cede control of the future to superintelligence, and that requirement should ultimately constrain AI progress.”

  Nov 3, 8:40 PM ET

• “6.1 Terra is here! It is good at a lot of things, but we are especially excited for this new era of computer use. x.com/OpenAI/status/…”

  Oct 26, 4:37 PM ET

• “I am very grateful Aleksander accepted our request to come take this on; the safety and security challenges in front of us as we confront superintelligence are immense, and he has an excellent set of skills to work on this. x.com/aleks_madry/st…”

  Oct 20, 5:10 PM ET

Dario Amodei

Anthropic — the Sep 14 warning rotated $ from chips to software in one day

• “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.”

  Oct 20, 10:26 PM ET

• “Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.”

  Sep 23, 2:43 PM ET

• “We Must Pace the Frontier: I’ve written a new essay on why the AI industry should slow down, with a three-part plan for doing so. Anthropic is unilaterally committing to the first of these steps. We’ll provide third-party evaluators with permanent, employee-level access to our systems, so that they can verify adherence to our safety measures, report on incidents, and assess models’ alignment during training. You can read the full post here: darioamodei.com/post/we-must-p…”

  Sep 12, 10:01 AM ET

Satya Nadella

Microsoft — largest AI capex, OpenAI's landlord

• “2/ Underneath our 1P experiences is a common platform, starting with Foundry, which is becoming an increasingly important driver of our growth: · Foundry revenue more than tripled YoY · Number of customers building with multiple models grew 5X YoY · Revenue from open source models increased 10X YoY We're also bringing Foundry and Fabric closer together, so customers can build agents right next to their data. And with Microsoft IQ, we're making the rich enterprise context underneath Microsoft 365 + Foundry + Fabric available to agentic workloads. Nearly 60K customers are accessing context across Microsoft IQ, up 25% QoQ.”

  Oct 28, 6:53 PM ET

• “3/ We’re also helping customers secure and govern their agents at scale. Over 50M agents are now in A365, up 30% QoQ. We are bringing together Windows + Agent 365 so agents can work within secure boundaries on-device using MXC. Agents like OpenAI Codex, Anthropic Claude Code, and Meta Muse are all adopting it. And with Perception, we’re separating intelligence from authority, enforcing identity, permissions, monitoring and containment outside the model itself. We’re encouraged by the feedback from preview customers.”

  Oct 28, 6:53 PM ET

• “4/ When it comes to infrastructure, we have a fungible fleet that allows us to dynamically allocate capacity to the highest value opportunities across compute, data, Foundry, and Copilot. We are bringing capacity online faster, and getting more from the infrastructure we already have. Azure growth was broad-based across enterprise and SMB, spanning both core cloud services and AI infra. I have never been more confident in the opportunity ahead of us.”

  Oct 28, 6:53 PM ET

Mark Zuckerberg

$115–135B capex with no cloud to sell — the marginal buyer

• “Muse Ember, Muse Spark 1.3, and Muse Wonder are now available on Google Cloud.”

  Nov 9, 4:49 PM ET

• “Releasing Muse Ember today -- our smallest model that delivers higher intelligence per dollar than others in its class. Built for token-heavy work that agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing. Available today on the Meta Model API and in Muse Code.”

  Nov 9, 12:54 PM ET

• “Upgraded the models for Muse and Meta Al, bringing Active Memory and Active Focus from Muse Wonder to everyone. In Muse, these new specialized models are 2.4x less likely to forget critical info and are 67% better at self correcting and completing everyday tasks which means less repeating yourself and fewer dead ends. More on the models: research.meta.ai/blog/specializ…”

  Nov 6, 1:08 PM ET

Peter Diamandis

The convergence voice — energy bottleneck, physical AI, space; Brad's named source

• “In Ghana, a WhatsApp-based AI math tutor has shown promising learning gains at a reported marginal cost of about $5 per student. Personalized tutoring has long been out of reach for many families. We're seeing signs of accessibility happening.”

  Nov 13, 10:30 AM ET

• “When a human expert dies, they take decades of experience to the grave. AI agents now keep every lesson and spend their downtime auditing and fixing their own memories. Expertise just became immortal; at least that's what Anthropic's research is showing.  Most companies have no idea what that will do to them.”

  Nov 12, 5:03 PM ET

• “NVIDIA is under investigation for allegedly funneling $8 billion in GPUs to China with the stock dropping 6%, perhaps handing Congress a national security excuse to come after the entire AI industry. Meanwhile, Anthropic launched its IPO roadshow at $1.3 trillion. A thousand AI agents proved Fermat’s Last Theorem in 30 hours, Elon says 20-30 million robots could outproduce every working American, and Delaware is drafting a law to let AIs run companies. -- Fable 5.2 scored 16.4% on GDPX, 30% ahead of Opus 5.5, and designed working chip circuits from prompt to Verilog simulation. -- SpaceX tracking $100B annual revenue by December, with Elon saying $500B to $1T possible next year. -- AI autonomy time horizons doubling every 2 months. -- Emad predicts security clearances for frontier lab researchers and a ban on Chinese models in regulated industries.”

  Nov 12, 3:34 PM ET

Dylan Patel

The chips/fabs/HBM supply chain, read by every desk

• “Nvidia Board needs to chase down Charlie Kawwas or Johny Srouji. They are the only somewhat viable CEO replacements.”

  Nov 12, 1:11 PM ET

• “The funny thing is that Perplexity is primarily using Anthropic models for our use cases, but Anthropic sucks at product, and they're not really receptive to any feedback on their product Same with OpenAI”

  Nov 11, 3:42 PM ET

• “What I love about Perplexity is that they're very fast on any feature request. Even though they did not invent the category, they are fast followers and implement any change that's needed in less than 24 hours. They even got the name from us back in February. pic.x.com/UvhlEYkTEl”

  Nov 11, 3:42 PM ET

Ben Thompson

Strategy of the platforms

Not yet covered

David Cahn

The capex-vs-revenue arithmetic

Not yet covered

Alex Wang

Data, labs, talent

Not yet covered

Leopold Aschenbrenner

Timelines, compute scaling, power

Not yet covered

Epoch AI

The numbers on compute, training runs, chips

Not yet covered

Ramez Naam

Energy — the bottleneck, stated by an energy person

• “In biology, we often lack the raw data that would allow AI to one shot breakthroughs through computation. We probably lack the right sensors and instruments to even collect the data at scale. AI will be enormously helpful in moving biology forward. It's still gated on instruments, data, experiments, trials.”

  Nov 13, 5:53 PM ET

• “For quite a while to come, we're going to wonder if any digital incident is an AI enabled cyber attack. And some of them will be. But complex systems fail for many reasons. pic.x.com/OaBT3hG8ZS”

  Nov 13, 5:41 PM ET

• “The HuggingFace incident was good for AI safety and security. This is how it goes with every new technology. We make mistakes. We learn. We improve safety. x.com/joedaroo/statu…”

  Nov 13, 4:44 PM ET

Brad Gerstner

The investor voice the CEOs answer to on earnings calls

• “Jensen is a friend & legend. But he was not a one man show. He built an exceptional company of 42k people (31k in R&D alone) led by brilliant leaders like Jonah, Arjun, Sameer, Ashish, Gilad, Andrew, Dwight, Deb, Jeff, Ian, Colette, etc (look them up on Claude) - they run the key parts of the business day in and day out that makes Nvidia, Nvidia. These are the unsung heroes who help architect & do the daily impossible work to deliver the most sophisticated AI products in the world that have put America in the lead at this critical moment in time. That didn’t change today. Despite the noise - they showed up to deliver the Blackwell & Rubin GPUs & Vera CPUs that power the world. And are following the same playbook to make sure they deliver the fully baked Rubin Ultra in 2027 & Feynman in 2028 ahead of expectations. And no better interim leader than Colette Kress who knows every nook & cranny of the business. She will make the trains run on time; allocate capital to its highest & best use; and provide the steady hand that the business needs to make important decisions. Not spinning this as happy or good news - just offering a dose of realism. Silicon Valley is not run by 10 rock stars. Founders are essential but the great ones attract legends in their own right who compound the magic. Thousands of people driven by a mission bigger than all of us. And nowhere is that more true than Nvidia. 🇺🇸🦾🚀”

  Nov 12, 8:03 PM ET

• “Plenty of people strangely shit on leading US labs while touting Chinese OS models. I’m for max competition & open source - but I am happy that Haiku (ANT), Luna (OAI), Muse Spark (Meta), Gemini & Grok are bringing cheap capable models to US biz to compete w Chinese tokens. 🇺🇸🤖 x.com/cohere/status/…”

  Oct 26, 2:58 PM ET

• “Congrats to Team @AnthropicAI on another important milestone! Altimeter looks forward to continuing our partnership w major participation in the IPO on top of our existing ownership. Day 1. 🇺🇸🚀 sec.gov/Archives/edgar… pic.x.com/AFtxJmwv6z”

  Oct 26, 9:25 AM ET