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INSTEAD OF WATCHING NETFLIX TONIGHT. Spend 1 hour with this. Claude AI FULL COURSE that teaches you how to BUILD and AUTOMATE anything. The people who watch this tonight will wake up tomorrow with a new skill. Watch it and bookmark it now.

🚀 CrossWall 高速大流量机场 每天一毛钱，看遍全世界 · 超级实惠：48/年(月285G) 132/年(月1200G) · 闪电速度：国际高速线路，4K秒开不卡顿 · 全球畅行：13地域40+节点，解锁Netflix/TikTok/Disney+等流媒体与AI · 多端支持：手机/电脑/平板全端通用

INSTEAD OF WATCHING AN HOUR OF NETFLIX TONIGHT. This 60-minute Cambridge lecture by Demis Hassabis will teach you more about the future of AI than most people will learn in the next 5 years. Bookmark it and give it an hour, no matter what.

🔴 I'M BUILDING THIS OUT OF HATE! 🔴 I'll be honest. I don't love education. I never have. I grew up inside it, then started my career building more of it. I lasted long enough to learn how it really works, and I left swearing never again. You know the shape of it. A syllabus older than the students. Forty kids taught like they're one kid. Nine hours of school, four of tuition, then a room full of us mugging up the same shit until we could repeat it on cue. It was never built to find smart people. It finds good memorisers. Your Netflix knows you better than your school ever did. And if you didn't fit it, you were told you were the problem. You weren't. The system was just bad at you. I only know it can be different because of design. In design, nobody asks where you studied. You open the portfolio. It either blows them away or it doesn't. The degree doesn't count. The work does. It's the fairest test I know, because it asks one honest question. Can you actually do the thing? For years that was true for design and almost nothing else. So that's what we're building @zero_university for. Proof of work, for everyone. Not a grade. Not a profile you wrote about yourself. A real record of what you can actually do, and how you got good at it. Something you can't fake. Picture what that unlocks. No take-home assignments. No seven rounds of interviews to slowly find out what your work already shows. You stop proving yourself from scratch in every room. You walk in already proven. And we built Zero so we only win when you do. Most schools treat students as customers, so they're built to chase more of them, not to make the ones they have any better. We flipped it. You're not who we sell to. You're what we build. So we'll never trade you a degree, a hostel, and a nice story about your life for four years and a fat bill. Zero is free. And our beta users get a guaranteed interview, the one promise your college couldn't make you after all those years and all that money. This is the first launch. The thing I hated most turned out to be the thing I'm here to build. I'm writing this from a treadmill in Vietnam, and I haven't felt this alive in years.

Nicolai Tangen, CEO of Norges Bank Investment Management pressed IBM CEO Arvind Krishna directly on whether AI is a bubble (Save this). And Krishna responded with what has become known inside financial circles as the $8 trillion math problem. A single gigawatt of AI data center capacity filled with accelerators, liquid cooling, and power infrastructure costs roughly $60 to $80 billion to build and populate. The industry has committed to more than 100 gigawatts of buildout globally. That is $6 to $8 trillion in capital expenditure and because AI grade hardware depreciates on a five-year cycle, that entire sum must be effectively replaced and refreshed every five years. To service the interest on $8 trillion in capital at a conservative 10% borrowing rate, the AI ecosystem would need to generate approximately $800 billion in annual profit, a number that currently exceeds the combined net income of every large technology company in the world. Goldman Sachs estimates $7.6 trillion in aggregate AI CapEx between 2026 and 2031 alone, and Reuters Breakingviews has flagged that even if the capital is available, physical bottlenecks power permits, land, cooling infrastructure, and electrical grid connections mean that half of the planned data center projects are being cancelled or delayed before they ever go live. Krishna also raised a second, structurally distinct concern that markets have largely ignored. He argued that the largest foundation models, GPT, Gemini, Claude, Llama are converging toward commodity status. When a product is a commodity, switching costs collapse. When switching costs collapse, pricing power evaporates and margins compress regardless of how much capital was spent building the capability. Morningstar's equity research team conducted a review of 132 technology companies in 2026 and found that AI had caused moat rating downgrades across roughly 40 major stocks concentrated in enterprise software, IT services, and SaaS with Adobe, Salesforce, Workday, and ADP among the companies whose competitive moats have materially weakened. The implication is that the companies spending the most on AI model development may be building an asset that is simultaneously the most expensive to produce and the most difficult to monetize with durable margins. This bear case is serious but it is also incomplete and that is what makes Krishna's framing so important to understand precisely. When pressed further, Krishna explicitly said he does not believe there is an AI bubble in the technology itself only in a subset of the infrastructure capital that is being deployed against speculative assumptions rather than proven demand. He draws the same analogy, the fiber optic overbuild of the late 1990s. Dozens of companies went bankrupt laying cable that nobody was using. And yet that exact "wasted" infrastructure became the physical backbone of every cloud company, every streaming service, every mobile network, and every modern AI training cluster that followed. The builders lost, the infrastructure won. And the companies that were built on top of it, Amazon, Google, Netflix, Salesforce compounded for two decades. The question, as Krishna framed it, is not whether AI is real. It is which capital deployment earns a return versus which gets stranded and crucially, whether you own the stranded assets or the companies built on top of them. On winners, Krishna was direct that distribution is the moat on the consumer side, and enterprise is wide open. The data supports this, Meta with 3.3 billion daily active users across Facebook, Instagram, and WhatsApp is building AI into a distribution network that no startup can replicate at any cost. Meanwhile, the productivity evidence arriving in real time is beginning to challenge the bear case's revenue projections. Jensen Huang just showed on stage at Computex that GitHub commits, the universal measure of global software output nearly tripled in the first months of 2026, effectively converting $3 trillion in developer salaries into $9 trillion in productive output. That is measurable, real time economic value already flowing through the system and it feeds directly back into token demand in a compounding loop that Krishna's static CapEx math does not fully capture.

En vez de 2 horas de Netflix esta noche, mira esta clase magistral de 40 min del fundador de una empresa china de IA valorada en más de $20B La explicación más clara que he visto sobre enjambres de agentes y sistemas de IA a gran escala.

instead of watching 2 hours of Netflix tonight, watch this 12-minute Nvidia Computex 2026 keynote from Jensen Huang it's the clearest explanation I've seen of where AI agents, robots, and personal computers are actually heading useful whether you've never built an agent or have been using Claude, Chatgpt, Kimi every day for the past year and if you want to see how China is already running 1 trillion parameter — the complete guide to 7 free Kimi Skills is waiting below too.