phil beisel

Terafab may be the most essential vertical integration Tesla has ever undertaken— and it is truly non-optional. It will take years to build and will test even Elon’s speedrunning abilities to the limit, but that won’t stop him from trying. The breakthrough likely lies in overhauling the overall facility’s cleanroom model. By moving wafers in sealed pods with localized micro-environments, the fab no longer needs a monolithic ultra-clean space. Elon’s line about “eating cheeseburgers and smoking cigars” on the fab floor isn’t silly, it’s the practical reality of a radically simpler, cheaper, faster approach that could finally change the economics of chipmaking. This is all forced by the brutal “pinch” in chip supply. Tesla must produce on the order of 100–200 billion AI chips per year just to saturate its roadmap. That volume powers: FSD cars & Robotaxis (tens of millions of vehicles needing AI5 inference for near-perfect autonomy), Physical Optimus (scaling from thousands today to millions per year, each requiring AI5/AI6-level compute), Digital Optimus (the new xAI-Tesla software agents for digital/office automation, running massive inference clusters), Space-based data centers (AI7/Dojo3 orbital compute for GW-scale training and inference beyond Earth limits). AI5 delivers the ~10× leap for vehicles and early robots; AI6 shifts focus to Optimus + terrestrial DCs; AI7 goes orbital. No external foundry (TSMC, Samsung, etc.) can deliver that scale or timeline— hence the Terafab launch. Without it, the entire robotics + autonomy future hits a brick wall. Terafab isn’t optional; it’s the only way forward.

Elon says FSD 14.3 is coming. But if you’ve been following along, it was also “two weeks away” a few months ago. That’s drawn a lot of criticism, understandably. Let’s step back and talk about what’s actually going on: engineering reality. I’ve spent years running engineering teams at Apple and Rivian, and what you’re seeing here is not unusual. Not even a little. I’m not here to defend Elon or say communication couldn’t be better. It could. But what’s happening behind the scenes is far more ordinary than people think. First, understand what kind of company Tesla is. Tesla exposes more of its internal process than most companies— you’re watching how the sausage is made, often in real time. Compare that to Apple. Products appear at a moment in time, fully formed. What you don’t see are the features that slipped, were cut, or quietly postponed to make the deadline. Most companies communicate through layers of marketing at discrete events (e.g., NVIDIA GTC). That may include a CEO keynote—but it’s still tightly controlled. Tesla, largely via Elon, doesn’t. And that creates friction. Most people are used to being in the dining room. With Tesla, you’re watching the sausage get made whether you like it or not. If that makes you uncomfortable, this model will drive you crazy no matter how it’s explained. Now, about FSD 14.3— the so-called “reasoning” release. My view: when Elon originally referenced it, it was real. It was on a roadmap with a timeline. But then reality hit. Somewhere along the way, engineering discussions likely exposed a fork: ship what’s partially there, or go deeper and "do it right". That kind of shift happens constantly. Plans change. Timelines slip. This is normal engineering behavior, not dysfunction. The difference is: you’re seeing it. At companies like Apple, those decisions are invisible. Deadlines are protected by cutting scope. At Tesla, you’re watching the scope evolve in real time. On the technical side, 14.1 and 14.2 were already producing “reasoning tokens,” as Ashok (Tesla AI VP) noted. But producing tokens isn’t the same as using them effectively. 14.3 appears to be where those tokens actually start driving behavior, more human-like decision-making in edge cases. My guess is this is where things got more complicated. The work likely started to overlap with what xAI is doing. At that point, the question becomes: do you ship an interim solution, or integrate a more capable reasoning layer? That’s not a small decision. And it likely has downstream impact— potentially even on Robotaxi timelines— because these same reasoning challenges show up there too. So the team probably made a call: go deeper, even if it costs time. And here’s the part people underestimate: great engineering teams often convince themselves the extra work is worth it… and that it won’t take that much longer. They’re usually wrong on the timeline. But often right on the outcome. At this stage, FSD isn’t about raw safety (it seems to have nailed that)— it’s about behavior. Making decisions feel natural, human, predictable in edge cases. That’s a much harder problem. So if you’re following Tesla closely, the best thing you can do is understand the process and accept the messiness that comes with it. If you want tightly controlled messaging and polished delivery, companies like Apple exist for that. Tesla is something else entirely. Fire away.

Digital Optimus, Optimus, and FSD What’s going on here? A lot. xAI and Tesla’s AI team have been working on complementary, partially overlapping AI systems. Tesla AI has focused on vision-based intelligence for both FSD and Optimus, while xAI has focused on building a frontier model (an LLM) aimed at general intelligence. More recently, xAI has pushed deeper into what has been called Macrohard (aka Digital Optimus). Macrohard applies xAI’s intelligence layer, Grok, to human activity in the digital world, essentially operating computers the way a human would. The idea is that the AI can move through existing software environments and perform tasks that previously required direct human interaction. But Macrohard goes beyond simply navigating pixels. It is also about generating outcomes within those environments— producing results (pixels), not just interacting with interfaces. In that sense, Macrohard becomes a quasi vision-based AI system as well. Elon, effectively the technology head of both companies, sees the convergence. The decision now appears to be to combine efforts and focus each team on its relative strengths. Tesla’s vision-based AI team becomes central in integrating this perception stack with xAI’s “pure” intelligence model. The benefits are substantial. First, xAI advances its Digital Optimus concept— an AI capable of driving productivity directly in the digital world. At the same time, Tesla gains a powerful intelligence backbone: a reasoning engine layered on top of its vision systems. For Optimus, the implications are colossal. The robot is no longer just a physical-world machine driven by perception and action loops; it becomes a reasoning system as well. In other words, Optimus gains both embodiment and intelligence. That combination directly addresses the data patterns I discussed in my article this week. And that is a big deal. Of course, the impact extends to FSD as well. Many of the “last mile” problems in vehicle autonomy involve nuanced human intent and interaction. A reasoning layer makes those scenarios far more tractable. Talking to your car and having it genuinely understand what you want it to do becomes realistic. Further, Elon has suggested that this combined approach fits within an AI4 inference framework—delivering more intelligence per watt and reducing the need to wait for AI5-scale hardware to solve these larger problems. All in all, this is significant news. It may shift some timelines, and I suspect it may also explain why version 14.3 (the rumored “reasoning edition”) has not yet appeared. It may now be part of this broader combined effort.

Jensen Huang just called out every CEO who’s been firing people “because of AI.” Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive. Jensen's answer: "For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more." Read that again. The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing: They have no imagination. They have no vision for what comes next. They got handed the most powerful tool in human history and their FIRST instinct was to fire people. This is the CEO of NVIDIA. The company whose chips power every AI system on the planet. If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang. And he said the OPPOSITE. He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it. But here's where it gets really interesting... During the same interview, Jensen revealed something nobody's talking about: He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees. One to two billion per week. That's a $50 to $100 BILLION annualized run rate. For companies that most people think are burning cash and making nothing. The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong. Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real. So if the money IS real, why are other companies firing people? Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets. They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board. Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines. That number doesn't include the new chips, systems, or partnerships announced this week. And he's not cutting people. He's hiring. Because when you have imagination, more capability means MORE opportunity. Not less headcount. Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency." Jensen's response: You're out of imagination. He also said something that stuck with me. Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's. His answer: "I was just trying to make it through the shift." Biggest tip he ever got? Two, three dollars. Now he's building tech that increased computing demand by one million times in two years. He announced OpenClaw, which he says is as big as ChatGPT. And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet. When asked how long he plans to keep working? "I'm hoping to die on the job. And I'm not hoping to die anytime soon." This is a man who believes every single thing he's building. And his message to every CEO using AI to justify layoffs is simple... You're not innovating. You're surrendering. The technology wasn't built to shrink companies. It was built to make them limitless. If your leadership can't see that, the problem isn't AI. It's THEM.

The Terafab "Yield Buffer": Why 160k is the Real Number Elon just clarified the math on Terafab, and the 60% jump in wafer starts (from 100k to 160k per month) tells a massive story about the reality of 2nm manufacturing. In my original breakdown, I estimated 100k wafers/month to hit 100 million AI5 chips/year. That assumes a relatively mature yield (60%+). Elon’s response, "Probably more like 160k wafers/month, factoring in yield", is a reality check. The "Bleeding Edge" Tax: Launching a 2nm fab from scratch is historically difficult. By aiming for 160k wafers, Tesla is building in a massive safety margin. If initial yields are lower (closer to 35-40%), they still hit the 100 million chip target. Monthly Starts: 160,000 wafers Annual Capacity: 1.92 Million wafers The Goal: 100 Million "Good" Chips Required Net Yield: ~35% (The "Launch" yield) The Upside: If yields hit 65%, output jumps to ~190 Million chips/year The TSMC Benchmark: Matching the Giant To put 160k wafers/month in perspective, look at TSMC. As of early 2026, TSMC’s entire global 2nm capacity (spread across multiple "Gigafabs" in Hsinchu and Kaohsiung) is targeting roughly 100k to 140k wafers per month. By pushing for 160k, Elon is essentially saying that a single Tesla Terafab cluster aims to outproduce the entire world’s initial 2nm supply.

This is so on 🎯 . Jensen Huang, $NVDA

This is so on 🎯 . Jensen Huang, $NVDA

Jensen Huang just called out every CEO who’s been firing people “because of AI.” Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive. Jensen's answer: "For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more." Read that again. The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing: They have no imagination. They have no vision for what comes next. They got handed the most powerful tool in human history and their FIRST instinct was to fire people. This is the CEO of NVIDIA. The company whose chips power every AI system on the planet. If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang. And he said the OPPOSITE. He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it. But here's where it gets really interesting... During the same interview, Jensen revealed something nobody's talking about: He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees. One to two billion per week. That's a $50 to $100 BILLION annualized run rate. For companies that most people think are burning cash and making nothing. The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong. Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real. So if the money IS real, why are other companies firing people? Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets. They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board. Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines. That number doesn't include the new chips, systems, or partnerships announced this week. And he's not cutting people. He's hiring. Because when you have imagination, more capability means MORE opportunity. Not less headcount. Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency." Jensen's response: You're out of imagination. He also said something that stuck with me. Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's. His answer: "I was just trying to make it through the shift." Biggest tip he ever got? Two, three dollars. Now he's building tech that increased computing demand by one million times in two years. He announced OpenClaw, which he says is as big as ChatGPT. And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet. When asked how long he plans to keep working? "I'm hoping to die on the job. And I'm not hoping to die anytime soon." This is a man who believes every single thing he's building. And his message to every CEO using AI to justify layoffs is simple... You're not innovating. You're surrendering. The technology wasn't built to shrink companies. It was built to make them limitless. If your leadership can't see that, the problem isn't AI. It's THEM.