#大佬观点

this is how finding GPUs goes: 1. someone says they have GPUs 2. you ask & turns out they have a lead on an OEM that will ship in maybe Q1 ‘27. 3. you ask & turns out they haven’t found colo space, haven’t ordered networking. 4. you ask & the OEM is SMC with optics in the hot aisle. back of your mind you also know SMC always rugs & ships late. 4. you ask & turns out they’re racked with hydra and they don’t even have final seller’s rights. 5. you say you’ll do $4.9/hr 10% prepay for RFS Jan ‘27. effectively betting there’s a 60% chance it goes live and you get ‘em 6. jan comes, not live, you get 50% of your prepay back and is what it is. everyone’s pissed. other scenario: 1. guy says he has GPUs RFS tmrw 2. you ask and get test node for 1hr, it has infiniband & storage nodes on network 3. he says $10/hr, sign in next two hrs 100% prepay or piss off. 3. startup knows they’ll make $20/hr so they grit their teeth & snap call sign plus wire. BMC without of band access is given same day, everyone’s happy! speculative market for yotta compute available maybe Q1 maybe Q3 ‘27 in yucatan is completely different than nodes with immediate RFS. anything under $7/hr probably isn’t real.

The only way to interpret Lumentum’s management accepting this deal is that they have no faith in the current AI cycle. They think the market will collapse in two to three years.

What a Week! Anthropic Opens the Books, Intel Raises More Questions, Marvell and Lumentum? With @benbajarin and @jaygoldberg Chapters: (00:00) Welcome: A Packed Week for The Circuit (00:29) Anthropic’s S-1: Growth, Spending, and Existential Risk (03:46) Anthropic’s Bet on Scaling Laws (05:43) Enterprise Adoption and Anthropic’s Growth Engine (09:26) Our CIO Survey: AI ROI and the Skills Gap (12:16) How AI Budgets Grow Beyond IT (18:55) Anthropic Direct: Taking Control of Compute (20:23) Anthropic’s Multi-Chip Strategy (25:32) When Custom Silicon Becomes a Strategic Advantage (30:58) Does Anthropic Look More Like a Foundry Than a Software Company? (32:49) Compute Commitments, Cloud Discounts, and Token Economics (37:37) Anthropic’s Governance and Founder Control (41:23) Hyperscaler Earnings: Strong AI Demand, Mixed Results (47:39) Whose AI Flywheel Needs to Work the Most? (51:45) Google Search, AI Advertising, and Platform Risk (54:55) Intel’s Strong Quarter and Weak Guidance (56:47) Allocating Capacity: PCs Versus AI Servers (60:22) The PC Downturn and Intel’s Delayed Response (62:53) Intel’s Recovery: Better Foundations, Longer Timeline (66:02) Chiplets and the Challenge of Planning Across Nodes (68:24) Substrate Constraints and Supply Chain Planning (71:00) Rebuilding Confidence in Intel (74:20) Marvell–Lumentum: Making Sense of a Potential Deal (76:30) Why Optical Networking May Need More Vertical Integration (79:13) Will Optical Components Become Commodities? (82:25) NVIDIA’s Role and the Battle Against Broadcom (84:55) Closing Thoughts

We raised a $75M Series B to deploy intelligent robots now, led by @a16z, with participation from @usv and @neo. @tutorintel robots work real jobs across the United States, directed by human “tutors” and the foundation models we’ve trained on the work done. This round accelerates frontier model training, robot manufacturing, and deployment into industry. We’re in almost 4x as many sites as a year ago; nearly a third of this year’s deployments went to existing customers coming back for more; and we’re on track to have more than 1,000 robots under contract by the end of 2027. Our mission is to proliferate robotics for the common good. If you want to make robots reliably do things others think are impossible, for people who’ve never had access to this technology, join us.

Tutor Intelligence co-founder and CEO Josh Gruenstein is putting AI robots to work in American factories and warehouses, taking on the back-breaking jobs they can't hire for. Tutor builds its robots in Massachusetts and charges by the hour, with no upfront cost. When the AI gets stuck, a human "tutor" takes over and teaches it the task, creating training data along the way. Josh joins a16z's Martin Casado and Raghu Raghuram to discuss how Tutor goes from a first sales call to a working robot in about three months, why so many robot startups died building for a single customer, why anyone will be able to start a business in the physical world, and a whole lot more. 0:50 Deploy robots, then build the data 2:50 Why smart robots aren't enough 4:38 The ultimate systems problem 6:45 Why robot companies go vertical 9:07 How remote humans tutor robots 11:00 Will robots outgrow their tutors? 14:54 How AI labs sped up robotics 16:57 Meet Cassie and Sonny 20:55 What factory work actually looks like 23:18 A robot for $16 an hour 25:51 When robots become coworkers 29:04 Why robot startups keep dying 31:21 When 1% cheaper flips a market 34:52 Robots cost more to run than to buy 38:18 China's automation bet may backfire 42:02 Real-world RL on a 100-robot fleet YouTube: https://youtu.be/lXkjXSul6yg @joshgruenstein @tutorintel @martin_casado @RaghuRaghuram

Tutor Intelligence co-founder and CEO Josh Gruenstein is putting AI robots to work in American factories and warehouses, taking on the back-breaking jobs they can't hire for. Tutor builds its robots in Massachusetts and charges by the hour, with no upfront cost. When the AI gets stuck, a human "tutor" takes over and teaches it the task, creating training data along the way. Josh joins a16z's Martin Casado and Raghu Raghuram to discuss how Tutor goes from a first sales call to a working robot in about three months, why so many robot startups died building for a single customer, why anyone will be able to start a business in the physical world, and a whole lot more. 0:50 Deploy robots, then build the data 2:50 Why smart robots aren't enough 4:38 The ultimate systems problem 6:45 Why robot companies go vertical 9:07 How remote humans tutor robots 11:00 Will robots outgrow their tutors? 14:54 How AI labs sped up robotics 16:57 Meet Cassie and Sonny 20:55 What factory work actually looks like 23:18 A robot for $16 an hour 25:51 When robots become coworkers 29:04 Why robot startups keep dying 31:21 When 1% cheaper flips a market 34:52 Robots cost more to run than to buy 38:18 China's automation bet may backfire 42:02 Real-world RL on a 100-robot fleet YouTube: https://youtu.be/lXkjXSul6yg @joshgruenstein @tutorintel @martin_casado @RaghuRaghuram

Tutor Intelligence co-founder and CEO Josh Gruenstein is putting AI robots to work in American factories and warehouses, taking on the back-breaking jobs they can't hire for. Tutor builds its robots in Massachusetts and charges by the hour, with no upfront cost. When the AI gets stuck, a human "tutor" takes over and teaches it the task, creating training data along the way. Josh joins a16z's Martin Casado and Raghu Raghuram to discuss how Tutor goes from a first sales call to a working robot in about three months, why so many robot startups died building for a single customer, why anyone will be able to start a business in the physical world, and a whole lot more. 0:50 Deploy robots, then build the data 2:50 Why smart robots aren't enough 4:38 The ultimate systems problem 6:45 Why robot companies go vertical 9:07 How remote humans tutor robots 11:00 Will robots outgrow their tutors? 14:54 How AI labs sped up robotics 16:57 Meet Cassie and Sonny 20:55 What factory work actually looks like 23:18 A robot for $16 an hour 25:51 When robots become coworkers 29:04 Why robot startups keep dying 31:21 When 1% cheaper flips a market 34:52 Robots cost more to run than to buy 38:18 China's automation bet may backfire 42:02 Real-world RL on a 100-robot fleet YouTube: https://youtu.be/lXkjXSul6yg @joshgruenstein @tutorintel @martin_casado @RaghuRaghuram

Tutor Intelligence co-founder and CEO Josh Gruenstein is putting AI robots to work in American factories and warehouses, taking on the back-breaking jobs they can't hire for. Tutor builds its robots in Massachusetts and charges by the hour, with no upfront cost. When the AI gets stuck, a human "tutor" takes over and teaches it the task, creating training data along the way. Josh joins a16z's Martin Casado and Raghu Raghuram to discuss how Tutor goes from a first sales call to a working robot in about three months, why so many robot startups died building for a single customer, why anyone will be able to start a business in the physical world, and a whole lot more. 0:50 Deploy robots, then build the data 2:50 Why smart robots aren't enough 4:38 The ultimate systems problem 6:45 Why robot companies go vertical 9:07 How remote humans tutor robots 11:00 Will robots outgrow their tutors? 14:54 How AI labs sped up robotics 16:57 Meet Cassie and Sonny 20:55 What factory work actually looks like 23:18 A robot for $16 an hour 25:51 When robots become coworkers 29:04 Why robot startups keep dying 31:21 When 1% cheaper flips a market 34:52 Robots cost more to run than to buy 38:18 China's automation bet may backfire 42:02 Real-world RL on a 100-robot fleet YouTube: https://youtu.be/lXkjXSul6yg @joshgruenstein @tutorintel @martin_casado @RaghuRaghuram

Tutor Intelligence co-founder and CEO Josh Gruenstein is putting AI robots to work in American factories and warehouses, taking on the back-breaking jobs they can't hire for. Tutor builds its robots in Massachusetts and charges by the hour, with no upfront cost. When the AI gets stuck, a human "tutor" takes over and teaches it the task, creating training data along the way. Josh joins a16z's Martin Casado and Raghu Raghuram to discuss how Tutor goes from a first sales call to a working robot in about three months, why so many robot startups died building for a single customer, why anyone will be able to start a business in the physical world, and a whole lot more. 0:50 Deploy robots, then build the data 2:50 Why smart robots aren't enough 4:38 The ultimate systems problem 6:45 Why robot companies go vertical 9:07 How remote humans tutor robots 11:00 Will robots outgrow their tutors? 14:54 How AI labs sped up robotics 16:57 Meet Cassie and Sonny 20:55 What factory work actually looks like 23:18 A robot for $16 an hour 25:51 When robots become coworkers 29:04 Why robot startups keep dying 31:21 When 1% cheaper flips a market 34:52 Robots cost more to run than to buy 38:18 China's automation bet may backfire 42:02 Real-world RL on a 100-robot fleet YouTube: https://youtu.be/lXkjXSul6yg @joshgruenstein @tutorintel @martin_casado @RaghuRaghuram

We are about to experience the dot-com bust all over again. If this mania feels sustainable to you, you need to wake up and go touch some grass. AI will change the world, but it will be slowly over the next 20 years. Just like the internet. Hold on tight and keep cash on hand.

Whoa. Doubling down. Literally!! ~$200B of “backstopping” of which HALF is “residual value guarantees” which means more lease backing (now ~$500B) IF the leases don’t renew. Big Bet - Big Risk Future AI data center value is pure speculation! Bit it keeps the shell game going.

He who shapes power, data centers, chips and models into an economically feasible endpoint, disproportionately shapes AI/SI right now. Why? Power is THE binding constraint in AI/SI. Few really have it. Data centers don’t work without power. This is not about promises of power tomorrow. Or forecasted power in 2028. Power TODAY. Chips can’t operate profitably unless installed in a functional data center. If you can install chips, it’s because you’ve solved the power problem. Models aren’t economically viable unless run on cost effective infrastructure which requires a functional data center with low input costs and reliable management. This can’t happen without power. AI/SI ROI doesn’t pencil unless models generate profitable outputs which necessarily needs functional data centers as the critical input. Again, power. Ergo, he who can shape data centers, chips and models together into a tightly coordinated stack is someone who has solved power. Solving power allows you to disproportionately shape AI/SI right now.

“AI has provided ‘zero payoff’ to corporate America outside of the technology sector, according to Apollo Global Management Chief Economist Torsten Slok.” What Slok and many others are missing is that the massive boosts in software creation and reasoning are elemental, and can be extended across all knowledge work (work in front of a computer) and then across any work that requires intelligence over 5-10 years, and eventually manual labor and dexterity as it’s the software that’s holding back robots from being useful. Astra and upcoming robot foundation models will change this. Major tech firms don’t make big bets on fads and herding-they’re just the first to see the productivity boosts (revenue per employee sharply up) and this will extend to the Fortune 1000 over 5-10 years.