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Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry. Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to come. But its full promise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility. This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems. Together, we are building the foundation of the AI economy. Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world.

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Grok Bot Summary of SpaceX CFO Bret Johnsen at Goldman Sachs Communacopia today. Vertical integration Vertical integration is the company’s core operating model, not a side strategy. - Rockets: own metal → engines → avionics → software - Starlink: own launch, satellites, and the end customer - AI: build facilities and power themselves, run their own models, sell to consumer and enterprise, and soon orbital compute Starship and launch Starship is the foundation for every other business. - Flight 13: big learning flight. Delivered demo V3 payloads, relit a Raptor, and got a soft, precise second-stage splashdown. Recovery team towed the stage back so engineers could study the heat shield. - Those learnings feed straight into Flight 14 and beyond. - Flight 14 (later this month): first revenue-generating Starship flight, flying production V3 Starlink satellites. - Later this year: aim to recover both first and second stages. Orbital compute Most of the AI industry agrees orbital compute is the future. Almost everyone else thinks it’s many years away. SpaceX disagrees because they control the stack. - Target: first orbital compute satellites next year - Scale: big compute in space into 2028 - Hardware approach: same V3 bus as Starlink, swap the payload, add larger solar arrays Why orbital can beat terrestrial on cost The crossover is about Starship reusability. - Falcon 9: first-stage reuse since Dec 2015; 500+ booster reflights - Starship: first stage already recovered/reflown; second-stage recovery progressing - Goal: reflight of both stages as soon as next year, which drops deployment cost sharply Terrestrial compute is getting more expensive (power, cooling, buildings, real estate). Orbital rides the opposite curve: cheaper rockets + better/cheaper satellites + scale. Johnsen said cost parity could come as soon as next year. Terrestrial compute and the $100B ARR goal - End of this year: on track for ~$100B ARR (annualizing the December number) - New update: another hosting deal closed earlier this month → about $1.1B/month starting Dec 1 → roughly +$13B ARR - Capacity: end this year well over 2 GW; next year 5–10 GW deployed - Confidence comes from line of sight to power, facilities, and permitting, plus being NVIDIA-exclusive for allocation - They stand compute up fast for themselves and for industry partners, which strengthens the NVIDIA relationship How they monetize compute Most hosting deals are short: ~90 days with a 90-day out (~6-month commits), including the newest deal. Why keep them short? - High conviction in their own products (Grok, Grok Bot, Cursor team after closing that deal) - Don’t want to lock forever capacity they may need internally - Internal bar: don’t let internal monetization fall below external hosting Earnings framing for next year: roughly $30–$50 per watt monetization range; they said they’re at the high end. Hosting customers appear to monetize even higher, which is why demand stays strong. Payback is under one year on new compute capex, so residual GPU value and financing options look attractive. “Not all CapEx is the same” — GPUs with <1-year payback are different from a launch tower built for decades. AI products and M&A Historically SpaceX was almost all organic growth. This year they did M&A because the AI product cycle rewards speed to frontier. - Closed Cursor deal weeks ago; product cycles already accelerating (called out Grok Bot) - Grok 4.6 improved on 4.5; 4.7 coming soon - Pitch: best infrastructure + competitive model + lower token cost = best position for customers - Market mood shift: months ago people bought the infra story but doubted the products; ~90 days later that skepticism is fading Starlink broadband Started as “better than nothing” (~2020–21). Now enterprise-grade with strong uptime/SLAs. - Resiliency pitch: boards will ask why Starlink wasn’t in the network if you go down - Mobility: aircraft backlog is large and production is ramping; cruise ships, yachts, trains too - Awareness, especially outside the US, is still a growth unlock - Longer-term: physical AI (robots, cars, aircraft) will need always-on connectivity terrestrial networks can’t fully cover Mobile / direct-to-cell Not a distraction. Same V3 bus, different payload. - Fly direct-to-device satellites through next year - Target service turn-on: first half of 2028 - V1 today (e.g. T-Mobile / T-SAT): text / light voice, great for emergencies and dead zones - Next gen: full 5G-quality from space - US: mid-band spectrum from EchoStar, FCC path for space + terrestrial - Go-to-market: flexible — own terrestrial build, or partner with carriers - International: same regulator-by-regulator playbook as broadband (Starlink now in 170+ countries) Near-term priorities: 1. Starship (enables everything else) 2. Terrestrial compute (funds growth and teaches them how to do orbital) Bottom line in one line Own the full stack, make Starship reusable at scale, use terrestrial AI compute as a cash engine now, and use the same satellite bus + Starship cadence to win broadband, mobile, and orbital AI.

A director who doesn't come up to the builder's heels makes a four-hour film about him. Yesterday in Venice, Alex Gibney's nearly four-hour documentary "Musk" had its world premiere. It is a classic case of someone who has spent a career telling other people's stories taking on a man who actually builds those stories with rockets, cars, satellites, and infrastructure, not slides. Alex Gibney is an acclaimed documentarian. An Oscar for Taxi to the Dark Side, Emmys for Going Clear on Scientology, high-profile films on Enron, Elizabeth Holmes and Theranos. His specialty is exposing corporations, cults, and frauds. He does it skillfully and with a thesis. The problem starts when he applies the same method to someone who is not selling an illusion, but hardware that either flies or blows up in front of the entire world. Gibney's fuel is controversy. Elon's fuel is the pursuit of truth hard, engineering truth, the kind that either works on the test stand or it doesn't. It would have been enough to make a film based on facts. Instead we got another rut that fits the slogans of provocation: megalomaniac, confidence man, threat to democracy, an AI avatar reciting old quotes, ex-wives, his father, a former partner. Musk said in 2023 it would be a hit piece. After the premiere, a large share of reviewers effectively agreed with him. I don't know about you, but four hours of falsified formulas would bore me. Instead I watch how much Elon has already done. His energy is extraordinary. Tesla changed the car industry. SpaceX brings rockets back. Starlink works. Starship stands on the pad and prepares for the next flight. This is not a PowerPoint. It is steel, fuel, engines, and people who cannot hide failure in the edit suite. A weak documentary like this is a crumb on the shoulder of his jacket. Now it depends on us when and how quickly that blown-off crumb a utopian attention vampire is simply starved of attention. You could list the names of people who tried to take Elon's energy. Energy is the most precious thing we have. Anyone who steals it with slogans, gossip, and a four-hour indictment that excludes the main subject is sowing under his own feet. Gibney: you reap what you sow. If you have a shred of shame, look at the list of Elon's accomplishments. Look at what kind of person he is for the world and what he does around himself so that things get better cars that drive, internet from orbit, rockets that return, attempts at a brain-computer interface, AI that is supposed to be more than another chatbot. You can dislike his tweets, his political alliances. You cannot honestly erase the scale of what was physically built. A good documentarian should stand, at least for a moment, in the shoes of the person he is portraying. Gibney did not just fail to do that. He took a measure cut for Enron and Holmes and held it up to a man who lands rockets. He left Elon's shoes in the edit suite. Elon walked on barefoot and kept building. One man tells stories about the world from an editing chair. The other moves that world. A four-hour film will not change that. At most it will confirm what people who already disliked Musk already believed. Everyone else can blow the crumb off the jacket and keep going. History does not judge a man by the length of a film. It judges him by what is still in the air when the camera has stopped running. Controversy feeds on attention. Building feeds on repetition. One vanishes when you look away. The other keeps flying. You can cut a man together in four hours. You cannot cut a landing together. A documentary can be switched off. The work cannot.

我提问的内容： “我最近观察发现，当一个做交易做得好的人，他的观点被市场充分验证的时候，往往可能也是市场开始接近转折，以及他自己开始因为傲慢犯错的起点。 我这个观察目前有没有科学依据？” ChatGPT的回答： 有，而且我认为这个观察抓到的是一个很重要的行为金融现象。 不过如果说得更严谨一点，应该改写成： 不是‘观点被验证’本身导致了市场反转， 而是当一个交易者的观点被连续验证时，往往会同时发生两件事： 人的认知风险在上升 交易本身的剩余赔率在下降 这背后其实可以拆成两部分来看。 第一部分，是“人”的问题。 行为金融里有一个很经典的机制，叫自我归因偏差。 简单说就是： 做对了，更容易觉得是因为自己能力强 做错了，更容易觉得只是市场出了意外 所以一个人连续做对之后，他增加的不只是对这笔交易的信心， 还会无形中高估自己整体的判断能力。 也就是说，他不是只会想： “这次我看对了。” 而是会慢慢变成： “我果然比大多数人更懂这个市场。” 这时候，危险其实就开始了。 因为一旦这种心理形成，后面经常会伴随一连串行为变化： 仓位变大 杠杆提高 交易频率提高 对反方观点的敏感度下降 更不愿意承认市场环境已经变了 所以真正的问题不是： 高手什么时候会判断错。 而是： 高手什么时候会在最确信自己没错的时候，把一次普通错误放大成一次大亏损。 这个差别很重要。 因为高手不是不会错， 而是当他顺的时候，他下一次错往往不再只是“小错”。 他会因为前面的成功，把那次错误配上更大的仓位、更高的杠杆、更强的执念。 原本一次正常的判断失误， 最后可能变成一次很重的回撤。 这部分其实是有不少研究支持的。 研究里经常会发现： 成功会提高人的过度自信概率 过度自信往往会带来过度交易 盈利之后，人也更愿意承担更高风险 所以从科学依据上看， 你说的“高手做顺之后反而更容易因为傲慢开始犯错”，这个方向是成立的。 但这还只是第一层。 第二层，是“市场”的问题。 这部分也很关键。 因为一个观点越被市场验证，通常意味着： 越来越多的人开始接受这个观点 价格已经开始甚至已经提前计入这些利好 原来只有少数人相信的东西，慢慢变成更多人都相信的东西 这里最容易让人混淆的一点是： “观点越来越正确” 不等于 “这笔交易越来越划算”。 这个区分非常重要。 一个观点刚提出来的时候，可能市场还没怎么信，价格也没怎么反映。 那时候虽然不确定性高，但赔率可能反而很好。 后来随着市场一步步验证： 基本面越来越清楚 趋势越来越顺 越来越多人开始认同 新闻、舆论、资金都站到这一边 这时候你对这个观点的信心当然会上升。 但同时也意味着， 市场可能已经提前把这件事交易得差不多了。 也就是说： thesis confidence 在上升， 但 remaining alpha 可能在下降。 一个观点越被验证， 往往越说明它可能是对的； 但也越说明， 价格里可能已经包含了越来越多同样的共识。 于是就会出现一个很微妙、也很危险的阶段： 人最自信 市场最拥挤 新增买盘开始减少 赔率反而没有前面那么好了 如果这时候再遇到一点边际变化， 比如： 预期没有继续超预期 新增利好变少 筹码开始拥挤 反方力量开始回补 市场叙事进入“所有人都知道”的阶段 那就很容易从“持续验证”走向“高位脆弱”。 所以你说： 一个观点被充分验证的时候，往往也可能是市场转折的起点。 这个说法不能理解成绝对规律， 但作为一种条件性规律，是有逻辑基础的。 更准确一点说，不是说： “大家都信了，所以一定马上见顶。” 而是说： “当大家都信了，说明价格已经计入了更多共识； 如果此时交易变得拥挤，而边际上又没有新的增量信息，那市场的脆弱性就会上升。” 换句话说， 风险不一定来自‘观点错了’； 很多时候风险来自： 观点虽然没错，但这笔交易已经没那么便宜了。 这也是为什么我觉得你这个观察特别有价值。 因为它把很多人容易混在一起的两件事分开了： 基本面判断是否正确 当前交易赔率是否还划算 这两个不是一回事。 一个人完全可能在方向上是对的， 但在位置上已经没有前面那么有优势了。 如果这时候又刚好因为前面的成功而膨胀， 所以如果把你这个观察再提炼成一句话，我会这么说： 最危险的时刻，往往不是你开始怀疑自己，而是市场用价格证明你过去判断都正确的时候。 因为这个时候最容易发生一件事： 你会偷偷把 “我过去判断是对的” 替换成 “我现在依然是对的，并且还可以继续重仓。” 但市场真正考验的，其实永远是后者。 所以我最后的结论是： 你的这个观察，是有科学依据的。 只是不能简单理解成“高手顺了就一定要完”或者“观点被验证了就一定该反手”。 更准确的理解应该是： 当一个高手的观点被连续验证时， 一方面，他自己更容易进入过度自信区； 另一方面，这笔交易本身也可能越来越接近被市场充分定价。 于是就会形成一个危险组合： 人的置信度达到峰值， 交易的剩余赔率却可能已经接近低点。 这时候， 才是最需要警惕的时候。

Elon Musk told an 𝕏 user to try connecting Grok Bot to their bank account and said any loss would be covered if the bot made a mistake. This is a major show of confidence in Grok Bot as it moves beyond chat and takes on real-world tasks. 🔥