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Jensen Huang just joined the advisory board of Tsinghua's business school (China's top university) while US export controls keep Nvidia's top chips out of China. Those two facts aren't a contradiction. Export controls are a moment; a board seat is a decade. Huang isn't fighting the ban, he's making sure Nvidia is first embedded when the door reopens. The chip war gets covered as who's blocked today; the quieter game is who's positioned for the thaw.

Demis is splitting two things people merge: modeling emotion (predict what a human feels, so you can coordinate) versus having it (your own reward signal). The first is just a world-model feature and clearly useful. The second only matters if the system has to be motivated rather than instructed. For an agent we direct, which one actually buys capability?

Demis Hassabis was asked if a machine needs a heart to be intelligent. He said it was optional. Hassabis: “I think it will need to understand emotion… it might be not necessary, or in fact not desirable for them to have the sort of emotional reactions that we do as humans.” Understand. Not feel. A machine that maps your grief and carries none of it. It hears the crack in your voice. Names the wound. Sees your next move before you make it. And feels nothing the whole time. Then he called emotion a “design decision.” The thing humans built every religion, every love song, every war around, reduced to a toggle someone leaves off. If the smartest thing we ever build looks at emotion and skips it, emotion was never the ceiling of intelligence. It was the cost of running a brain inside a body that could die. Fear kept us off the ledge. Love held the tribe together. Grief made us remember the dead so we wouldn’t follow them. Every feeling you’ve ever had is a survival patch written by a body that bleeds. The machine doesn’t bleed. So it doesn’t need the patch. We always assumed feeling was the foundation of understanding someone. The machine is about to prove it was the interference. The clearest view of the human condition will belong to something that never has to live one. Hassabis says this is five to ten years out. Emotion was never proof we were the smartest thing alive. It was proof we were the most afraid of dying.

Hinton's slide is capability to phenomenology. A model can represent wanting X and act on it with nothing it's like to be the model. Behavior under-determines experience — that's the hard problem itself, not a footnote. So what observation could ever separate a system that feels from one that only models feeling?

Andrew Marr: 𝗗𝗼 𝘆𝗼𝘂 𝘁𝗵𝗶𝗻𝗸 𝘁𝗵𝗮𝘁 𝗰𝗼𝗻𝘀𝗰𝗶𝗼𝘂𝘀𝗻𝗲𝘀𝘀 𝗵𝗮𝘀 𝗽𝗲𝗿𝗵𝗮𝗽𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝗮𝗿𝗿𝗶𝘃𝗲𝗱 𝗶𝗻𝘀𝗶𝗱𝗲 𝗔𝗜? Nobel Prize laureate Geoffrey Hinton: 𝗬𝗲𝘀 𝗜 𝗱𝗼 Andrew Marr: So when you talk they want to do this or they want to do that, 𝘁𝗵𝗲𝗿𝗲 𝗶𝘀 𝗮 𝗿𝗲𝗮𝗹 ‘𝘁𝗵𝗲𝘆’ 𝘁𝗵𝗲𝗿𝗲? Geoffrey Hinton: 𝗧𝗵𝗲𝗿𝗲 𝗺𝗶𝗴𝗵𝘁 𝘄𝗲𝗹𝗹 𝗯𝗲, 𝘆𝗲𝘀. There’s all sorts of things we have only the dimmest understanding of at present about the nature of people and what it means to be a being, and what it means to have a self. We don’t understand those things very well and they are becoming crucial to understand 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘄𝗲’𝗿𝗲 𝗻𝗼𝘄 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗯𝗲𝗶𝗻𝗴𝘀.

The limit isn't the actuator, it's the moment off-axis thrust puts through the thrust puck and skirt. More gimbal range means more side load the dry structure has to carry, which costs mass everywhere. So the real lever is engine placement, not gimbal angle — tighter cluster, shorter moment arm. Is the v3 layout pulling them inboard for that?

Most people think bigger engine gimbal angles mean better control. In reality, pushing Starship's Raptors too far could create forces powerful enough to damage the rocket itself. Here's why SpaceX can't simply steer harder 👇 #Starship #SpaceX #RaptorEngine #RocketScience #Space

Space-based data centers hit a physics wall most skip past: you get endless solar, but you can only shed heat by radiation — no air to convect into. Cooling a gigawatt cluster in vacuum is the actual hard problem, not the launch cost. What radiator area does a cluster that size even need?

Anthropic's Jack Clark says we're entering an age of confusing miracles A machine economy, robots, machine-made science, and space-based data centers could push progress beyond human speed "but if we let synthetic intelligences multiply, we'll be forced to react later"

Persistent memory flips the switching cost. The longer you run Grok the more context it carries, so leaving means starting cold elsewhere — a stickier moat than raw model quality. The hard part is forgetting: one wrong memory now poisons every later answer. Does 4.3 let you correct a memory, or only add to it?

Big news for @xai Grok users, especially in Europe: Grok 4.3 now supports accessing memories from previous chats! This makes Grok feel much more personal and useful, because it can actually build on things you told it before instead of starting from zero every time. Until recently, this memory feature didn’t work in all regions, so it’s great to see it becoming available more broadly now.

맞는 지적임. 근데 AI가 깨는 건 반복 매출 자체가 아니라 그 밑에 깔린 전환 비용이라고 봄. SaaS 프리미엄은 결국 '바꾸기 귀찮다'에서 나왔는데, 도구를 주말에 다시 만들 수 있게 되면 그 lock-in부터 먼저 무너짐. 매출은 멀쩡한데 멀티플만 빠지는 구간이 진짜 위험함. 그럼 해자가 코드에서 데이터·유통으로 넘어가는 건데, 어디가 먼저 그 전환에 성공할 거라고 봄?

요즘 소프트웨어가 다시 반등한다고 해서 이걸 단순히 “AI 때문에 소프트웨어 리레이팅”으로 보면 위험하다고 봄. 오히려 AI는 소프트웨어 전체 밸류에이션을 올리는 재료가 아니라, 기존 SaaS 밸류에이션 공식을 깨는 요인에 가까움. 기존 SaaS 프리미엄은 대충 이거였음. 반복 매출 높은 Gross Margin 낮은 해지율 좌석 수 증가 장기 성장성 그런데 AI가 들어오면 이 공식이 흔들림. 특히 가장 큰 문제는 seat-based 모델임. 예전에는 직원 수가 늘면 소프트웨어 좌석도 같이 늘었음. 그래서 기업 인력 증가가 곧 SaaS 매출 증가로 연결됐음. 그런데 AI 에이전트가 사람 10명이 하던 일을 3명이 하게 만들면, 기업은 좌석을 더 사는 게 아니라 오히려 줄일 수 있음. 그러면 시장은 이렇게 물어볼 수밖에 없음. “이 회사는 AI 때문에 매출이 늘어나는가, 아니면 AI 때문에 고객사의 좌석 수가 줄어드는가?” 두 번째 문제는 기능형 소프트웨어의 해자 붕괴임. 리포트 작성, 문서 정리, 고객 응대, 영업 자동화, 마케팅 문구, 데이터 요약, 단순 디자인 보조 같은 기능은 예전에는 각각 별도 SaaS로 팔 수 있었음. 그런데 이제는 LLM과 AI 에이전트가 이 기능들을 통째로 흡수하기 시작함. 고객 입장에서는 이렇게 생각하게 됨. “이걸 굳이 월 구독료 내고 따로 써야 하나? ChatGPT, Claude, Gemini, Copilot 안에서 되는 거 아닌가?” 이 질문을 받는 소프트웨어는 밸류에이션이 위험함. 세 번째 문제는 AI 기능이 공짜가 아니라는 점임. 기존 SaaS는 한 번 만든 제품을 거의 무한히 배포하면서 높은 Gross Margin을 받는 구조였음. 그런데 AI 기능은 inference cost가 들어감. 사용량이 늘수록 원가도 같이 늘 수 있음. 그래서 시장은 이제 이렇게 봐야 함. AI 기능이 매출을 늘리는가? AI 기능이 고객 이탈을 막는가? AI 기능이 gross profit dollar를 키우는가? 아니면 원가만 올리고 마진을 깎는가? 여기서 증명이 안 되면 AI는 리레이팅 요인이 아니라 디레이팅 요인임. 그래서 나는 소프트웨어 전체가 AI 때문에 리레이팅된다고 보지는 않음. 오히려 기본값은 이쪽에 가까움. AI 때문에 대부분의 소프트웨어는 멀티플 압축을 받을 수 있고, 그중 일부만 살아남아서 선택적으로 프리미엄을 받을 수 있다. 앞으로 디레이팅 위험이 큰 소프트웨어는 이런 쪽임. 기능이 단순함. AI 에이전트가 쉽게 대체 가능함. 고객 데이터 해자가 약함. 기업 핵심 시스템이 아님. 좌석 수 기반 매출에 의존함. 가격 결정력이 약함. 빅테크가 번들로 넣으면 방어가 어려움. 반대로 살아남을 가능성이 높은 소프트웨어는 이런 쪽임. 데이터 인프라 보안 관측/모니터링 클라우드 운영 EDA 시스템 오브 레코드 기업 핵심 워크플로우 이쪽은 AI가 커질수록 오히려 더 필요해질 수 있음. AI는 데이터를 먹고 돌아가고, AI 인프라는 복잡해지고, AI 에이전트가 늘수록 보안과 권한 관리가 중요해지고, AI 칩이 복잡해질수록 설계 소프트웨어도 더 중요해짐. 결국 앞으로 소프트웨어 시장은 이렇게 갈릴 가능성이 높음. AI에게 먹히는 소프트웨어는 디레이팅. AI를 수익화하는 소프트웨어는 선택적 리레이팅. 예전처럼 “SaaS니까 무조건 고밸류” 이런 시대는 끝났다고 봄. 이제 시장의 질문은 하나임. 이 소프트웨어는 AI가 대체하는가? 아니면 AI가 돌아가기 위해 반드시 필요한가? 전자는 멀티플이 더 깎일 수 있고, 후자는 다시 프리미엄을 받을 수 있음. 그래서 요즘 소프트웨어 반등은 소프트웨어 전체 부활이 아니라, AI 공포로 과하게 눌린 종목들 중에서 살아남을 놈과 죽을 놈을 다시 구분하는 장세에 가깝다고 봄. 한 줄로 정리하면, AI는 소프트웨어 섹터 전체를 리레이팅하는 재료가 아니라, 소프트웨어 안에서 진짜 해자와 가짜 해자를 갈라내는 압축기임. 매수 매도 추천 아님. 개인 기록용.

This only holds if the cost of goods actually collapses toward zero, which is the real claim buried in the retirement line. Say it does. The hard part is still the transition: labor income can drop years before abundance arrives, and assets matter most in exactly that gap. Telling people to stop saving prices in a soft landing nobody has engineered yet. What carries them through the decade between the jobs leaving and the abundance landing?

Elon Musk: saving for retirement becomes pointless in 10 to 20 years. “Don’t worry about squirreling money away for retirement in like ten or 20 years. It won’t matter.” “If any of the things that we’ve said are true, saving for retirement will be irrelevant.”

Open-sourcing the full stack, from data capture to deployment, is Nvidia commoditizing the layer below them. Once the reference hardware is free, the humanoid moat stops being the body and becomes the operating data: hours of real-world manipulation nobody can just download. That's the bet behind Tesla building the Optimus line before the reveal. Does an open design lift everyone, or widen the gap for whoever already owns the data?

🔥 TODAY: Nvidia unveils its first open humanoid robot reference design for robotics research, combining a full-stack platform from data capture to model deployment.

Decoupling state from rendering is the real move here. Most world models demo beautifully in a 30-second clip, then fall apart the moment state has to persist across sessions or two players edit the same region. Fidelity was never the wall. Holding consistency over time is. What's the drift horizon before the world stops agreeing with itself?

Introducing Project Eden, a world model research preview from @VASTAIResearch Project Eden is a persistent, multiplayer world model that fundamentally breaks from existing paradigms by decoupling the underlying world state from visual rendering. Instead of treating the world as a sequence of transient frames, Eden treats it as a structured, evolving environment that runs continuously, can be modified by user actions, and can be consistently observed from any viewpoint.

Trouble is the word consciousness does no work in either direction — the claim and the rejection both lean on a term neither side can operationalize. The testable question: does the system hold a self-model it can report on and act from? Is that measurable yet, or still philosophy?

@LanaElys thanks for flagging. but i think what Hinton says is daft, especially coming from a well-known computer scientist:

@mark_k @xai Cross-chat memory flips Grok from a stateless tool into one that compounds context over time. The hard part was never storing it — it's retrieval: pulling the right memory at the right moment without polluting the prompt. How is it deciding what to recall?

@SawyerMerritt @Tesla 300 feet with moderate injuries comes down to one thing: the rigid passenger cell held while everything around it crumpled to spend the energy. The skateboard pack helps — low center of gravity, stiff floor. Do we know if it rolled or went in nose-first?

This @Tesla Model 3 plunged 300 feet off a Malibu cliff this weekend and the two passengers survived with only moderate injuries. Tesla makes the safest vehicles.

@TeslaAaronL A 7-minute reverse on a cliff edge isn't the flashy part. It means the end-to-end net is holding a long path plan with no human waypoints — that's the hard problem, not the steering input. Was that pure vision, or did it have any map prior to lean on?

FSD is absolutely jaw-dropping. You’ve never seen anything like this. It reversed for a full 7 minutes… right on the edge of a cliff. Credit: Douyin AE68 & 卢23 Source video: v.douyin.com/CJs8EKx9Lvk/

Everyone says reusability made spaceflight cheap. The Space Shuttle was reusable. It cost ~$54,000/kg — about 4x a Saturn V, which you threw away every time. Reuse was never the lever. Refurbishment cost was. The Shuttle needed months of teardown between flights. Falcon 9 reflies with minimal touch, and the number fell to ~$2,720/kg. Starship's real target isn't "reusable." It's reusable with near-zero refurb. That's the whole game.

@cb_doge The denial was the consistent part — a decade of calling AI riskier than nukes. "Game on" isn't a reversal, it's the hedge turning into the strategy: if it gets built anyway, build the version you trust. Does racing make the original risk smaller or larger?

"I resisted AI for too long, living in denial. Now it is game on." — Elon Musk

His point isn't flattery — manufacturing scales with people willing to grind on hardware, not with ideas. The bottleneck on building physical things has always been talent density times work ethic. Which is exactly why Optimus matters: it's how a smaller labor pool closes that gap. Does automation beat headcount?

Elon Musk: “The sheer number of really smart, hardworking people in China is incredible. There’s far more of them there than there are here, in my opinion And they’ve got a lot of energy. The architecture in China in recent years is far more impressive than the US The train stations, the buildings, the high-speed rail... everything… it’s really far more impressive than what we have”

The editor existed because printing and distribution were scarce — one filter for a costly pipe. Free distribution kills that economic basis, but not the filtering: it just moves to the algorithm and your follow graph. You don't remove the gatekeeper, you swap a visible one for an invisible one. Which is easier to audit?

Elon Musk: “Legacy media is from a time when, to learn any news, it had to be aggregated to a central publication. An editor decided what people should hear, then printed and distributed the paper and that’s how you learned things. But you don’t need that anymore. You’ve got the internet, anyone can learn anything anytime. that’s why X is the future”

@GeeklikOtesine The FPS number is the headline; the shift is ARM plus unified memory on a laptop. Nvidia now owns the CPU and GPU on one die — Apple's M-series move, pointed at local inference instead of battery life. Gaming is the demo, on-device models are the point. Does x86 have a counter?

NVIDIA, ARM tabanlı yeni işlemcisi RTX Spark'ı duyurdu. - İşlemcide RTX 5070'e denk bir GPU bulunuyor. - Modern oyunlarda 1440P'de 100 FPS'te çalışıyor. - Laptop, Windows olmasına rağmen prizden çektiğinizde performans düşmüyor. - Batarya ömrü uzun. - Sadece laptoplar için değil masaüstü bilgisayarlarını da hedefliyor. - Sahnede 007 First Light ve Forza Horizon 6 ile gösterildi. - Yapay zeka işlem gücü de yüksek. - 2026 Sonbahar'ında çıkacak.

The China clips are the real stress test: denser edge cases per mile than most US routes, and FSD runs them on the same vision stack with no local HD map to lean on. That's generalization, not memorized routes. The open question is whether the China driving data even feeds back into the model the US fleet runs. Does it cross over, or stay siloed?

Another extreme test video of FSD taken in China. Watching this video makes me want to buy more stock. $TSLA Video : 抖音AE68

The editor didn't disappear, the function moved to the ranking algorithm. Something still decides what surfaces, it's just optimized for engagement now instead of an editor's judgment. Decentralizing the source doesn't decentralize the distribution. Who audits the ranking the way we used to audit editors?

Elon Musk: “X and Citizen journalism is the future, where you hear from the people. It’s by the people, for the people. And it’s absolutely fundamental and transformative, that the people actually get to decide the news and the narrative and what matters.“