What is true on Energy?

How do you teach a humanoid robot to walk without programming every move? Reinforcement learning step by step 1. Build a simulation (MuJoCo or NVIDIA Isaac Lab) with joints motors sensors and a physics model. 2. Define the task: walk forward while keeping balance. 3. Write a reward function points for moving toward the target staying upright and matching the desired speed. Penalties for falls, wasted energy and unstable movements. 4. A neural network, the policy, reads the robot's state (joint positions, joint velocities, body orientation) and outputs target positions or torques for each joint. 5. An algorithm like PPO updates the policy over many episodes and millions of simulated time steps. 6. Close the sim-to-real gap with domain randomization, then test carefully in the real world.

🇺🇸 @ElonMusk’s two biggest companies, SpaceX and Tesla, report earnings this week. @SpaceX reports today, @Tesla tomorrow. Both are aggressively building for SI: last quarter alone, SpaceX put $15.8 billion of its $18.4 billion investment budget into it. That’s the kind of infrastructure spend that can actually scale superintelligence, robotics, and mass-to-space. Investors are also watching for any further alignment between the companies. Tesla’s new shareholder filing includes standard change-in-control provisions in Elon’s long-term stock awards (common in executive packages to protect incentives in a major deal). Any combination would still need the proper process, but the overlap in AI, chips, energy, and robotics keeps growing.

You're not ready for this. One of the most human-like humanoid robots has just entered production. Tesla says Optimus production has started on its high volume manufacturing lines in Fremont with the first production quality units coming off the line. Tesla says the current process is suitable for a commercial product, although the hardware and manufacturing methods will continue to evolve. The company has also outlined plans for a Fremont line capable of producing up to 1 million robots per year. That is a target capacity, not its current output. How quickly can Tesla move from early production to manufacturing humanoid robots at scale? And what production numbers will we see over the next 12 months? Source @ SawyerMerritt

The China state-aligned threat actor Storm-0940 used the agentic AI penetration testing framework ARTEX to automate attacks targeting hundreds of organizations globally across a broad range of industries. Microsoft Threat Intelligence assesses that Storm-0940 was responsible for the recently disclosed ConnectWise supply chain compromise of a multitenant application. https://t.co/VXovj0jI90 Our investigation found that Storm-0940's use of ARTEX accelerated the speed of compromised application abuse to authenticate to target environments through applications that had already been granted access, allowing the threat actor to operate without requiring an interactive user sign-in. AI-accelerated follow-on activity included reconnaissance, application credential changes, and attempts to modify privileges that could enable expanded or persistent access. Read our blog for a detailed analysis and indicators of compromise (IOCs), along with Microsoft Defender detections, protection recommendations, and guidance on investigating service principal sign-ins and unexpected application credential or privilege changes.

Most people just saw a humanoid robot throwing kicks and flipping off cars. But the H2-X has some technical details worth looking at. Here’s what the footage and the available specifications tell us about Unitree’s Kung Fu Legend Guardian. ▪️ Height: approximately 170 cm, according to the H2-X booth panel. ▪️ Weight: approximately 65 kg, according to the same panel. ▪️ Degrees of freedom: 31, covering the robot’s articulated body. ▪️ Joint layout: 6 DOF per leg, 7 per arm, 3 at the waist and 2 at the head. This breakdown is reported for the H2 and has not been independently confirmed for the H2-X. ▪️ Battery endurance: Unitree advertises approximately 8 hours. The operating conditions and runtime during intense movement have not been published. ▪️ Battery design: two battery modules are visible on the robot’s back. Their capacity has not been disclosed. ▪️ Joint torque: figures of up to 360 N·m for the legs and 120 N·m for the arms are associated with the H2. They are not confirmed H2-X specifications. ▪️ Computing power: up to 2,070 TOPS is reported for a high-end H2 configuration. Its use in the H2-X remains unconfirmed. ▪️ Sensors: binocular wide-angle vision, an inertial measurement unit and joint sensors are listed for the H2. The H2-X sensor package has not been fully documented. ▪️ Speed and payload: figures below 2 m/s, 7 kg continuous payload and 21 kg short-duration payload are reported for the H2, not confirmed H2-X ratings. ▪️ Recovery: the demonstration shows the robot getting back up after a vehicle runs over it. No standardized recovery test results have been published. ▪️ Autonomy: no detailed explanation of teleoperation, preprogrammed sequences or onboard decision-making has been released. One detail deserves attention: the booth lists the H2-X at around 170 cm and 65 kg, while many reports quote 182 cm and 70 kg from the older H2. The distinction matters when comparing the hardware. Unitree has yet to publish a complete H2-X datasheet covering torque, computing hardware, payload, battery capacity and autonomous operation. Which specification would you want Unitree to publish first? #Unitree #HumanoidRobots #Robotics #PhysicalAI #AI

General Motors is unfazed by the EV meltdown of the past few years, moving ahead with an all-new EV program expected to produce a new battery-powered mainstream SUV with about three years https://t.co/pBqYFlfn3S

Why is nobody talking about this? ToddlerBot is a 0.56 m, 3.4 kg humanoid robot built at Stanford by Haochen Shi, Weizhuo Wang, Shuran Song and C. Karen Liu. Its goal is to make robot learning cheap and easy to reproduce. The full hardware costs under 6,000 USD. Hardware - 30 active degrees of freedom: 7 per arm, 6 per leg, 2 in the neck, 2 in the waist - Dynamixel servos (XC330, XC430, 2XC430, 2XL430 and XM430 in the current open-source configuration) - 3D-printed structure: a full repair takes about 21 minutes of printing and 14 minutes of assembly - Jetson Orin NX 16 GB, two fisheye cameras, an IMU, two microphones and a speaker Sim-to-real - Zero-point calibration and motor system identification produce a digital twin of the robot - Walking: an RL policy trained in MuJoCo MJX and transferred to the real robot with no extra training, up to 0.25 m/s in any direction and 1 rad/s in place - Push-ups and pull-ups: keyframed motions, also transferred with no extra training Manipulation - RGB diffusion policies trained on 60 teleoperated demonstrations per task - Real-time onboard depth estimation at 10 Hz from the stereo fisheye cameras - A policy trained on one robot runs on another, and two robots tidied a room together Measured limits - Payload: 1,484 g, about 40% of its body weight - Endurance: 19 minutes of walking before motor heating affects stability - Durability: up to 7 falls before a part breaks - The cartwheel works, but its success rate is still low

Q3 Shareholder Update → https://t.co/u0UcDOuMNa Highlights – FSD Supervised attach rate grew to over 75% of new vehicle deliveries in NA – Unveiled the next-gen Roadster – Record deliveries in Canada, Australia, Japan & Taiwan – Tesla Semi now in volume production. Top variant can go up to 500 miles at a full 82,000 lbs while achieving efficiency of 1.7 kWh/mile – about a third of the energy a comparable diesel truck burns for the same load – Ended the quarter with the largest backlog for our passenger vehicles in 4 years! FSD Supervised – 4 new approvals since Q2 (Croatia, Slovenia, Czech Republic, Slovakia) – FSD is now live in nine EU countries & used by over 100,000 customers logging more than 160 million kilometers since initial approval Robotaxi – Deployed Cybercab into our Austin fleet – Hit nearly 2 million cumulative unsupervised miles, 8x growth in the quarter – Austin service now runs 6 am to midnight, with wait times continuing to come down as we add cars & improve the orchestration algorithms – Added Grok to Cybercab with Grok Bot & Connectors, letting you interface with your inbox, calendar, files, chats and tasks, all by voice. Transport, office & living room in one Robotics – Started production of @Tesla_Optimus on our high-volume lines in Fremont. These are the first production-quality bots we've built, proving that the current manufacturing process for Optimus is suitable for a commercial product SI Compute – On track for 400 MW of training compute by year end, feeding both vehicle & humanoid autonomy – Austin Fab construction continues, equipment commissioning planned for 2027 Battery – Battery supply is still the limiter on how fast we ramp vehicles globally, so we're expanding on every front: pack capacity in Berlin, cathode and lithium refining in Texas, LFP cells in Nevada – Shipped the first Megapacks built entirely with our own LFP cells – Increasing 4680 output to support ramp of Cybercab, Semi & Model Y Energy – Megafactory Texas is up & running, with Megapack 3 production started – Highest-ever order rate & backlog for energy storage Grid constraints & power availability are making Megapack the answer for SI infrastructure demand: it excels at supplying large data center loads, smooths out demand spikes, helps new projects meet grid connection rules & makes the grid itself more stable All with less local fossil fuel generation Supporting Infrastructure – Added 2,100+ net new Supercharger stalls, up 14% year over year

BOOOM 🚨 $TSLA's Q3 2026 update is out. Non-GAAP EPS came in at $0.48, ahead of the roughly $0.43 to $0.45 analysts expected 🔥 But the most revealing parts of the deck have little to do with EPS. They show a company steadily shifting its weight from cars to robots, robotaxis, and AI 🔥 🧠 FSD is now on more than three out of four new cars in North America. Over 75% of new North American deliveries included FSD in Q3, up nearly 20 percentage points from Q2. Active FSD subscriptions reached 1.74 million, up from 1.48 million last quarter and 1.04 million a year ago. That count includes both upfront purchases and monthly subscriptions, and it leaves out free trials. Europe is opening up too. Croatia, Slovenia, the Czech Republic, and Slovakia have approved FSD (Supervised) since the end of Q2, bringing the total to nine EU countries. More than 100,000 European customers have already driven over 160 million kilometers on it, or nearly 100 million miles. Tesla says FSD subscriptions helped lift revenue and that FSD is driving demand wherever it's available. Put those together, and software is becoming a real revenue stream that can grow across the cars already on the road, not just with new sales. The outlook points the same way, with Tesla expecting hardware profits to be joined by faster growth in SI, software, and fleet-based profits. 🤖 Optimus has moved from prototypes to a real production line. Optimus production has started on Tesla's high-volume lines in Fremont. The bots coming off that line are the first production-quality Optimus units Tesla has ever built, and Tesla says it has proven the current process is ready for a commercial product. The capacity table lists Fremont as "Early Ramp" and shows a second Optimus site already under construction in Texas. Tesla hasn't shared a capacity number for either one yet. This is the jump from prototypes to manufactured units, and that's the step that matters most for bringing costs down. Building a second site before the first one has even ramped suggests Tesla expects demand well beyond what Fremont can handle. The missing capacity figure is a reminder that the ramp is still early. 🚕 Cybercab is now giving rides in Austin, and unsupervised miles are growing fast. Tesla started Robotaxi rides with Cybercab in Austin on September 3. By the end of the quarter, the Robotaxi service had logged nearly 2 million cumulative unsupervised miles, eight times the total from three months earlier. Austin service now runs from 6am to midnight. Texas now shows more than 125,000 units of installed annual Cybercab capacity with "Production" status. Tesla points out that installed capacity isn't the same as the current production rate, and it says battery supply is still the main constraint on vehicle volumes. It's ramping 4680 cells specifically to support Cybercab and Semi. There's also a telling line in the outlook. Tesla says deliveries will depend partly on "allocation decisions between sale to customers and use for our owned and operated fleet." In plain English, Tesla is openly weighing how many cars to keep for its own network instead of selling them. With the tooling listed as in production, batteries look like what will set the pace for Cybercab. Inside the car, riders get Grok with voice access to their inbox, calendar, files, and tasks, and Tesla pitches the cabin as "a second living room." Tesla is clearly thinking about what riders do during the trip, not just the trip itself. ⚡ Tesla plans to grow its AI training compute by about 50% in a single quarter. Cortex 1 and Cortex 2 in Texas are rated at more than 105 MW and 120 MW, and Tesla has another 40 MW of compute on top of that, for roughly 265 MW in total. The goal is 400 MW by the end of the year, and Tesla says this compute trains autonomy software for both its vehicles and its humanoid robots. Tesla is also building its own chip fab in Austin and plans to start commissioning equipment there in 2027. It hasn't said yet which chips the fab will make. All of this shows up in the spending. R&D rose to $2.76 billion, up 69% from a year ago, and capex hit $4.73 billion, more than double last year's $2.25 billion. Even so, free cash flow stayed positive at $1.5 billion, and cash grew to $45.4 billion. Because one compute pool trains both FSD and Optimus, progress on one can feed the other. A chip fab is one of the boldest vertical integration moves Tesla has made, and staying cash-positive while building it makes that bet much easier to fund. 🔭 Tesla is building every piece of its next chapter at the same time. Robots are coming off a production line, driverless Cybercabs are carrying riders, FSD is turning into recurring software revenue, and the compute behind all of it is about to grow by half. Each piece makes the others stronger, and together they point to a Tesla whose future depends less on how many cars it sells and more on what its AI can do.

Today I’m excited to share that Optivolt is unstealthing with a $32M Series A. Solar growth continues to outpace all other forms of energy production, but the granularity of control we get from deployed systems hasn’t changed in decades. So we’ve added a brain inside solar panels, opening the door for a new unit of optimization and control.

ELON MUSK SAYS SPACEX CAN BRING POWER ONLINE 12 TO 18 MONTHS FASTER THAN ANYONE ELSE And he has a plan to add 20 gigawatts next year On the earnings call, Musk said SpaceX $SPCX is turning on about 220,000 Nvidia $NVDA GB300 chips every month, and that pace will speed up next year. He expects to reach close to 3 gigawatts of compute in the next few months. He says he's highly confident SpaceX adds another 10 gigawatts next year, for about 13 total. The bigger plan would add 20 gigawatts, and he says the odds of getting there are "rising rapidly." He kept the details of that plan private, calling it "highly proprietary" because if he spelled it out, others would try to do the same thing. One advantage he did share comes from rocket engines. Turbine blades and vanes are one of the biggest bottlenecks in the industry, running 12 to 18 months behind the rest of the turbine. SpaceX is now casting its own, using what Musk calls the most advanced casting capability on Earth. "That means that we can bring power online 12 to 18 months faster than anyone else."

Unitree Introducing: H2-X Kung Fu Legend Guardian🤖 Powerful performance to serve and guard you, with a maximum endurance of approximately 8 hours. Please use the robot safely and in a friendly manner. https://t.co/5RU5kDzSUK

Unitree Introducing: H2-X Kung Fu Legend Guardian🤖 Powerful performance to serve and guard you, with a maximum endurance of approximately 8 hours. Please use the robot safely and in a friendly manner. https://t.co/5RU5kDzSUK

🇺🇸The Department of War said it will install Indiana’s 1st advanced nuclear Micro Modular Reactor at Naval Weapons Station Crane no later than September 2028. The commercially owned unit is meant to give the Navy installation around-the-clock power off the commercial grid and is being run through the Army’s Janus Program with the Navy and the Office of the Under Secretary of War for Research and Engineering. Officials framed it as the 1st shore-based Navy microreactor and the opening move under Trump’s executive orders on advanced nuclear for national security.

Power is the BIGGEST constraint in the entire AI buildout right now. Morgan Stanley estimates that US data centers could require roughly 160 GW of power by 2029, but even after accounting for data centers under construction, additional utility grid access, behind the meter generation, and potential nuclear capacity, there could STILL be a 76.5 GW power shortfall. Think about how INSANE that is. We're talking about nearly HALF of projected US data center power demand potentially going unmet and here's why this matters so much. We can manufacture more GPUs, build better AI models, and raise hundreds of billions of dollars to finance new infrastructure. But none of that matters if you can't actually power the data centers. Electricity isn't something you can simply scale overnight. Building new transmission infrastructure, expanding grid capacity, securing interconnection agreements, and developing new power generation can take YEARS. And with AI workloads becoming increasingly compute intensive, demand for electricity is only going to accelerate as inference, autonomous agents, robotics, and physical AI continue scaling. This is exactly why I think POWER is becoming one of the most valuable assets in the entire AI ecosystem. And I think @elonmusk understands this better than almost anyone and it's also why I remain so bullish on neoclouds like $NBIS, $IREN, $SPCX. These companies are becoming incredibly important because they're helping solve one of the biggest bottlenecks in the AI industry turning massive demand for compute into actual operational capacity. The companies that can secure large amounts of power, develop data center campuses, deploy GPUs, and get that infrastructure operational FAST are going to have an enormous competitive advantage. And as power becomes increasingly scarce, the value of existing infrastructure with secured electricity and grid access could become significantly more valuable. You can't have an AI revolution without compute, and you can't have compute without POWER. If you want to see how I’m investing around the AI power bottleneck and the infrastructure companies I think benefit most, check out my portfolio below. https://t.co/MMkdjfqLsf

Spaculation: Might Digit V5 use LFP batteries? V5 has an unusually high 10:1 run-to-charge ratio: 90 minutes of runtime and only 9 minutes to recharge. Few battery chemistries are built for that charge rate. LFP is one of them. LFP cells are heavier than most chemistries, but they tolerate 0–100% state of charge, support fast charging, and are less prone to thermal runaway and fire, making them a safer fit for a safety-conscious company. Assuming a 48 V architecture (higher voltages raise safety concerns, and Agility emphasizes safe operation) and a nominal 1 kW operating load. 90 minutes of runtime then implies a 1.5 kWh pack. Recharging that pack in 9 minutes would take about 208 A. Current at that level typically needs liquid cooling, something the robot may already have for high-end compute and sensors. Combined with LFP cells, that would also help explain the large backpack and the extra weight (285 lb). thanks to Jordan @LimitingThe for suggesting LFP as a possibility in our exchange early this morning

据《每日电讯报》，微软退出 Nscale 英国埃塞克斯 Loughton 旗舰数据中心的锚定租户协议，因电网接入或要等到 2030 年代；此事冲击 Nscale 计划中约 350 亿美元估值的 IPO。 来源：BBN Times https://t.co/l1aokVMGyx #AInews #LetsConnect #蓝V互关 #浇朋友 #蓝朋友 #gm #蓝v互关 https://t.co/tZ1B34Pj5n

Anthropic just reported some absolutely RIDICULOUS numbers! According to the Financial Times, Anthropic generated $16.8 BILLION in revenue in Q3, up 46% from $11.5 billion in Q2 and that's nearly $5.3 billion in additional quarterly revenue in just THREE months. Anthropic also told investors it was profitable on an adjusted basis AND cash flow positive during the quarter. This is a pretty big deal because one of the biggest concerns surrounding AI companies has been whether they can actually turn this massive demand into a sustainable business, especially when they're spending billions of dollars training models and running inference. And this is where things get even more interesting. Anthropic is planning to spend MORE THAN $500 BILLION on computing and AI infrastructure over the next decade. Think about that for a second. That's an average of more than $50 billion annually, although the actual spending will likely vary significantly from year to year. All of that money has to go somewhere. We're talking about GPUs, memory, networking equipment, optical components, data centers, power infrastructure, and everything else required to support increasingly powerful AI models. This is exactly why I continue to be so bullish on the broader AI infrastructure trade.

Alef Aeronautics, the Silicon Valley startup behind the world's first street-legal flying car, has threw down a cheeky challenge to @Tesla 🔥 The company dropped a video showing its all-electric Model A lifting straight off the pavement and gliding smoothly over a lineup that included a Cybertruck and a Tesla Roadster. The timing could not have been sharper, landing the exact day Tesla showcased its next-gen Roadster. More than just a viral stunt, Alef confirmed the liftoff marked a massive milestone: its 1,000th recorded flight. CEO Jim Dukhovny greeted the newcomer with a memorable one-liner: "Welcome, Tesla, to the flying car race; we've been here alone for eleven years." He added that heavyweight competition is always a win because it validates consumer demand and sets an industry benchmark. What makes this rivalry fascinating is how differently each team plans to conquer gravity. Alef relies on distributed electric propulsion (DEP), using ambient air and internal propellers to hover and transition into forward flight. Tesla, meanwhile, plans to blast the Roadster airborne using cold-gas thrusters powered by compressed air. Yet beneath the surface, both contenders share surprising common ground. Each vehicle runs on an all-electric, all-wheel-drive powertrain, and both secured pivotal early backing from venture capitalist Tim Draper. Flying cars were supposed to stay trapped in retro sci-fi movies. Now, an agile startup and the titan of EVs are taking to the skies with completely different playbooks, and the race to own the air just shifted into high gear!

Huge thanks to @ParadisLabs, @ren_stocks, @pequityresearch, @StockSavvyShay, @zerohedge, and @jukan05, as many details were drawn from their work in the writing of this article. https://t.co/MWRfehTV4M

Roadster event livestream https://t.co/G0jU4mWfid

This is interesting. I think Nyobolt specializes in niobium tungsten oxide (NWO) batteries. From memory it's similar lithium titanate (LTO) in terms of super fast charging speeds and long battery life, but has slightly better energy density. Not much time to research at the moment. I'm about to watch the Roadster reveal. 😁

We’re excited to announce we’ve purchased two dedicated launches with @SkyrootA to deploy future spacecraft. Our aim is to expand solar energy generation to reduce reliance on fossil fuels and help save lives by supporting search-and-rescue operations at night. These launches give us assured schedule and capacity alongside our other planned launches from US sites. Following the FCC’s approval of our first demo mission, we remain focused on engaging with key stakeholders to earn the right to scale. Onwards! Full announcement: https://t.co/TjqwCHx8eF

China will be making a LOT of RAM in 2028 https://t.co/RiBFTe6JIC

GM Defense is prototyping a self-driving Chevy Silverado HD pickup truck for the U.S. Army. https://t.co/Si9IMxJ4af https://t.co/YeytGXp4hE

if you are building: - actuators - dexterous hands - training data for physical ai - self-driving labs - magnets - the deployment layer of robotics - reindustrialization - space manufacturing - energy and battery innovation - high-fidelity, physics-based world models - BCI for robot control - open-source frontier robot foundation models - anything frontier and sci-fi in physical AI welcome. this account is for you. tell me what you're working on 👇

UBS: “For C2027 we anticipate ~50GW of compute shipments (requiring ~57GW of powered data center including PUE factor) against current visibility for ~38GW of powered shell when excluding early-stage power plant projects. In C2028, this gap widens further with semis shipments of ~68GW (requiring ~76GW of powered shell) against visibility of ~50GW. On the surface, this is worrisome, but we do feel it is still possible to bridge the gap in both years.” $AMD $AVGO $NVDA $CIFR $WULF

"Energy Capacity = Economic Capacity" Former Intel CEO Pat Gelsinger on why America's power grid is the ceiling on growth: "We went through 10 to 15 years where essentially I was taking coal offline at the rate I was adding renewables, and essentially the nation was flatlined in terms of overall energy capacity." "So essentially our economic capacity as a nation, flat for fifteen years." "Even with that, we've gone to maybe increasing our national energy capacity 4% per year. Going from essentially zero to one." "Nuclear as base load is a fabulous thing that we want to go build more of. Unfortunately all of our renewables have deep dependencies in China." "Energy capacity somewhat dampens how euphoric we can get on AI. Why build a new data center and buy the million GPUs if I can't power them?" @PGelsinger @RaghuRaghuram @appenz

Terafab could bring Tesla’s experience building custom factory equipment into one of the world’s most demanding industries 🔥 During the X Takeover talk, @elonmusk described how he wants to get there: start with conventional equipment, learn the process, then build custom machines designed for much higher wafer throughput 🔥 Elon sees an opportunity to bring Tesla’s experience with custom manufacturing equipment into an entirely new industry: “I have no idea how to make chips, but we’ll figure it out. Got to start somewhere. I had no idea how to make cars or rockets as well. […] The equipment’s all pretty much coming from the same vendors. […] Whereas if you tour a Tesla factory, we got tons of custom equipment. We’re gonna start with conventional equipment and then make custom equipment that allows for much higher throughput on wafers.”

ENERGY ABUNDANCE: China struggles to consume all of its green power 🇨🇳⚡ About a quarter of China's solar and wind generation goes unrealized due to grid bottlenecks and other hurdles. That's 481 TWh in 2025 -- equivalent to the power output of Spain and the Philippines https://t.co/phgAp3kb2w

Former Intel CEO Pat Gelsinger with a16z's Raghu Raghuram and Guido Appenzeller on the next wave of semiconductor innovation and the physical constraints shaping the AI buildout: Drawing on his experience designing Intel's 386 and 486 processors, Pat explains how AI could transform chip design, but also why faster design alone won't solve the industry's biggest problems. They examine the bottlenecks in manufacturing, memory bandwidth, advanced packaging, and power, and why today's explosion of specialized AI chips may eventually consolidate around a smaller number of architectures. They also discuss the potential for new memory technologies, the shift from copper to optical networking, and why energy capacity could become a major constraint on AI growth. Finally, they revisit Pat's VMware years to ask what virtualization might look like when infrastructure is built for agents rather than humans. YouTube: https://t.co/ZJFvOky68H 1:05 Hired by Intel at 18 3:50 Arguing with Stanford professors 7:25 Will AI design the next gen chips? 8:45 Designing faster than fabs can build 11:55 Why GPUs are stuck with bad memory 13:40 Why 100 AI chips will collapse to a few 19:15 Agents make new chips cheap to run 22:10 No new memory chip since the 90s 27:20 Why 16-layer chip stacks are a fantasy 32:15 The physics that keeps memory next to compute 34:55 "I declared the death of copper" 38:05 AI traffic breaks the internet's design 42:40 "Energy capacity equals economic capacity" 46:25 The 130-year-old power war AI reopened 48:50 VMware for agents @PGelsinger @RaghuRaghuram @appenz

Former Intel CEO Pat Gelsinger with a16z's Raghu Raghuram and Guido Appenzeller on the next wave of semiconductor innovation and the physical constraints shaping the AI buildout: Drawing on his experience designing Intel's 386 and 486 processors, Pat explains how AI could transform chip design, but also why faster design alone won't solve the industry's biggest problems. They examine the bottlenecks in manufacturing, memory bandwidth, advanced packaging, and power, and why today's explosion of specialized AI chips may eventually consolidate around a smaller number of architectures. They also discuss the potential for new memory technologies, the shift from copper to optical networking, and why energy capacity could become a major constraint on AI growth. Finally, they revisit Pat's VMware years to ask what virtualization might look like when infrastructure is built for agents rather than humans. YouTube: https://t.co/ZJFvOky68H 1:05 Hired by Intel at 18 3:50 Arguing with Stanford professors 7:25 Will AI design the next gen chips? 8:45 Designing faster than fabs can build 11:55 Why GPUs are stuck with bad memory 13:40 Why 100 AI chips will collapse to a few 19:15 Agents make new chips cheap to run 22:10 No new memory chip since the 90s 27:20 Why 16-layer chip stacks are a fantasy 32:15 The physics that keeps memory next to compute 34:55 "I declared the death of copper" 38:05 AI traffic breaks the internet's design 42:40 "Energy capacity equals economic capacity" 46:25 The 130-year-old power war AI reopened 48:50 VMware for agents @PGelsinger @RaghuRaghuram @appenz

$AAOI is adding $BE fuel cells to power its Taiwan expansion because demand for 800G and 1.6T optics is growing faster than the grid can support. The interesting part is this is already a qualified hyperscale line so faster power directly means more transceivers shipped. https://t.co/mZwItisBgY