Transcription
Elon would always say, "You have no boss. Your boss is data." And it's perfect. Do you know what perfect on a factory floor does for you as far as efficiency? It makes you impossible to beat. Impossible. Because it's perfect.
I I don't think many people understand it. The actual value of Tesla is actually Tesla 1, their internal AI. Once the learning accelerates, you get into a quasi monopoly position.
Welcome to Connecting the Dots, the channel where we follow the breadcrumbs to predict where disruptions will take us. I've been covering Tesla's engineering for years. I called Optimus before it [music] was real. I predicted the reinvention of the assembly line. I'm all in because of Optimus, [music] and I'm as bullish as they come. But I always keep an understated tone. There's always problems ahead, but this time [music] I'm breaking my own rule. I can't remain understated because what I found made my engineering brain light up like never before. [music] Tesla is about to unleash some seriously alien grade tech. In 2026, I expect a trickle. But after that, a tsunami will arrive. A tsunami of engineering [music] so advanced it seems inhuman. And in fact, it is. This AI-driven technology is above anything any company [music] outside Tesla and Elon's other companies can currently create. For the first time since loading up for Optimus, I've looked at Tesla and [music] thought, I wish I held other shares so I could sell them and buy some more stock. But I'm running ahead of myself.
This video started by realizing that Tesla has its own internal Palunteer. Palunteer is valued at $180 billion and rightly so. They bring order to chaos, connecting masses of dirty data into a single living system. And Tesla built something similar years earlier, only it didn't stop at data. It connected the physical world itself, factories, robots, cars. And while researching for this video, I realized that quietly in the background, that system has grown, learned, and is about to wake up. What's coming next could take Tesla's technology into alien mode, building things no human engineer ever could. But where did I find this? How far does it go? And why do I think Tesla 1 is Tesla's most valuable asset yet? Let's find out. If you enjoy my deep dives, please like and subscribe. It really helps the channel reach more people. And patrons, keep this channel going, so please consider supporting the channel. Every little bit helps. Now, buckle up and get ready because Tesla is about to disrupt itself. And disruptions are never boring.
Palunteer 101, turning chaos into dominance. Before discussing Tesla 1, we need to understand Palunteer because the parallels are striking. Palunteer started working with intelligence agencies and the military after 9/11. The problem they tackled was massive. Dozens of agencies had pieces of information scattered across incompatible systems. The CIA knew something. The NSA knew something else. The FBI had a third piece. Nobody could see the full picture. Terrorists were slipping through the cracks, not because the data didn't exist, but because no one could connect it. Palunteer built software that integrated everything into a single living breathing model. Suddenly analysts could spot patterns, abnormal behaviors, unusual transactions, connections that were invisible when data stayed siloed.
Once they had a proven product working for government agencies, Palunteer decided to enter the private sector because every large organization faces the same problem, data chaos. Their flagship product, Palunteer Foundry, does this for companies. It takes all of a company's scattered data, spreadsheets, databases, sensors, customer records, manufacturing logs, and weaves it together into one unified digital twin of the entire operation. Here's the problem most companies face. Sales data lives in one system, manufacturing in another, supply chain in a third, R&D in a fourth. When someone needs information from another department, they send emails and wait. Worse, people don't even know what vital data exists in other groups or who to contact. Questions that span departments either go unanswered or take weeks to resolve. Palunteer Foundry fixes this. And here's where it gets powerful. It enables insights from seemingly unrelated data. Take a major airline. They have dozens of disconnected systems. Flight schedules, weather data, maintenance logs, crew scheduling, passenger bookings, fuel prices, gate availability. Normally, these systems don't talk to each other. If a storm hits Chicago, chaos cascades through the network. Delays pile up. Crews time out. Passengers miss connections. Aircraft sit idle at the wrong airports. But connect everything through Foundry. And suddenly the airline can answer complex questions proactively. Storm incoming at Chicago. Which aircraft should we reroute to which alternate routes? Which planes can we send for scheduled maintenance during the delay? Which crews will hit their time limits? Which passengers need rebooking? Which alternative airports have available gates? Okay, here's our game plan. Questions spanning weather, logistics, HR, and customer service get near instant answers. Disruptions that used to cascade into multi-day chaos now resolve in hours with better service for customers and massive savings for the airline.
In 2023, Palunteer added AIP, their artificial intelligence platform. Now AI can query across all that integrated data and find connections no human would spot. Patterns emerge. Predictions improve. The whole system gets smarter. The market loves this. Palunteer is valued at over $180 billion because bringing order to chaos exponentially compounds. Companies with unified data platforms run circles around competitors stuck in silos.
Now here's the kicker. Tesla also built something like this, but for something far harder. While Palunteer unifies software and data, Tesla unified physical manufacturing alongside the data. Millions of cars, Gigafactories across continents, thousands of robots, energy grids, fleet telemetry, AI training clusters, all connected, all learning from each other, all part of a giant optimizing machine. The fleet informs the factories, the factories inform the fleet, and the AI learns from both. And they built it years before Palunteer even added AI. It's called Tesla 1. Now, let's see what that system actually does.
One nervous system. When Elon Musk hired Jay Vagiian from VMware to develop Tesla's warp drive, Tesla 1's granddaddy, he told him, >> the way he described is um Jay, I want someone like you to build the central nervous system of Tesla. >> Uh this is exactly his words. And then he said I it has to be um seamless vertical integration with a closed feedback loop to um our customers. So basically the feedback loop of um the customers has to be the fastest in any industry. That way we can deliver the product in the fastest way possible at the same time deliver the follow-on services uh anything we deliver in the fastest way possible.
That's not hype. That is what Elon ordered and literally how Tesla 1 works. Think about the scale of the vision behind this request. In 2013, GM and VW each sold 9.7 million cars, had millions of customers, dozens of models, and countless suppliers. Yet, they made do with using SAP's off-the-shelf ERP system for enterprise resource planning. In that same year, Tesla sold only 22,000 cars and had less than 6,000 employees. Yet Elon insisted they build their ERP themselves. And that's just one part of what he wanted Jay Vagiian to create. Why? Because Elon knew that to win Tesla had to become a well-oiled optimizing machine. And when optimizing, data is king.
Traditional automakers operate with data trapped in silos. SAP handles their ERP. Siemens Teamcenter manages their PLM. Different manufacturing systems run the factories. Each robot is controlled by a vendor-specific controller. Third-party platforms handle fleet management. And when these systems need to talk to each other, they use fragile batch-processed integrations that constantly break. Tesla took a different path. They built a unified data fabric with real-time event streaming. Think of a Kafka-style message bus pumping data through the entire company. All this flows into centralized data lakes storing petabytes of structured and unstructured data. At the core sits a canonical ID system where every part, every vehicle, every station, every firmware version gets a unique identifier that follows it through its entire life cycle. This enables something remarkable. A design change in CAD can propagate to manufacturing and deploy to the fleet in days or weeks, not months or years. A supply chain hiccup detected in real time triggers automatic factory adjustments. A fleet issue spotted in the morning becomes a design review by afternoon. The architecture connects everything. Design systems talk to manufacturing. Manufacturing talks to the fleet. The fleet talks back to service. Service informs engineering. Energy systems share learnings with vehicles. And AI training pulls from all of it simultaneously.
If this sounds familiar, that's because it mirrors what Palunteer Foundry does for information systems, except Tesla's version extends into the physical world. Where Palunteer connects databases and dashboards, Tesla connects factories, robots, vehicles, and even supply chains in real time. Think of it as Foundry with actuators. The moment data changes, machines, and workflows across continents adjust automatically. Here's what this looks like in practice. When an engineer at the Fremont factory spots an issue, they can query the system. Show me every Model Y built in Austin during Q3 with bolt torque X from robot Y that later experienced sensor fault Z in the field. The system answers in seconds because the digital twin has tracked every vehicle through its entire life as designed in CAD, as built in the factory, and as deployed in the field. Legacy automakers can't do this. Their as-designed data lives in Siemens. Their as-built data lives in their MES, and their as-deployed data, if they even collect it, lives somewhere else entirely. And these systems don't talk to each other in any meaningful way. I've worked with enough systems to know how rare this level of integration actually is. Most companies spend years trying to get two systems to exchange data reliably. Tesla built an architecture where dozens of systems communicate in real time through a unified event bus. That's not just impressive engineering. It's a completely different operating philosophy. What Tesla built isn't just faster. It's a completely different operating model.
Imagine that several Model Y suffer from steering wheel shutter around the same time. They arrive for service at different service stations. So, nobody even notices there's a trend. But when you can search for anomalies over all service stations, check each vehicle's mileage and condition, and then trace every vehicle back to the specific supplier batch, the exact robot that assembled it, and the precise torque value applied, all within seconds. You're playing a different game entirely. And this unified system doesn't just observe, it acts. The next chapter shows how this nervous system came to exist. Because Tesla 1's origin story is almost as remarkable as the system itself.
Warp drive, the impossible mission. 2012 was a pivotal year. Tesla needed to scale fast and their existing systems weren't cutting it. Consultants came in with their standard playbook. Implement SAP. It's what every major automaker uses. Proven, mature, enterprise-grade, the safe choice. Elon rejected it outright. SAP was too slow, too expensive. It couldn't iterate fast enough. Moreover, there was no way that Tesla, as a tiny player, could get SAP to change their software meaningfully to fit Tesla's way of doing things, which meant Tesla would have to change some of its own operations to fit SAP.
Let's listen to how Jay Vagiant, an ex-Oracle engineer who at the time led VMware's business applications development group, described what happened next. >> VMware, I was taken care really well. I was doing well, the company was doing well, stock was doing really well. I was sitting on equity of multiple couple of million dollars. So there is really no reason for me to go out. Uh, but definitely Elon kind of had that uh vision very, very um impressive. My role was also very interesting. He made it very clear it was not a very typical CIO role basically not run the internal systems for the company only. [snorts] So it was the way he described is um Jay, I want someone like you to build the central nervous system of Tesla.
Tesla was at a very early stage at the time and couldn't come up with an adequately attractive offer. So Jay politely declined. He had a great job at VMware, had obligations for his family, and could not take the job. Jay remained at VMware, and Tesla hired someone else instead. But the project went nowhere. So a year later, Elon contacted Jay again. By that time, Model S was selling well and the company was stronger. But the growth also meant that they needed that central nervous system more than ever. And with a year lost with that other guy, they were running out of time. Elon was tenacious.
>> They said Elon would want to meet me. And I I was flattered. I felt like, wow, he remembered me. And then I went and had a one-on-one with him. Um, it was it was um definitely again great discussion with him. I was also very impressed that he even remembered a few things I mentioned about my interview before. He meets thousands of people and I was like, wow, he's like such a great memory. Um, and then we were talking about my pet patent, what I did at Oracle, and then he was like, oh, so he remembered a few points I mentioned, so I was like, wow, okay, great. And then this time he said, you know what, we need someone like you yesterday, whatever it takes. How about um, we we want to get you in, uh, let's figure out a way to for you to get started as quickly as possible. So this time again, at that time, I knew Tesla couldn't afford to pay the salary. I had to take a pay cut still, and um, I negotiated everything in stock options, which is great, which worked out exponentially more than I thought it would. Um, but at that time, to be honest with you, now it's a no-brainer decision. Um, I made um 10 times more than what I left at VMware. But at that time, it was not an easy decision because Tesla was not proven. They didn't have a car. It was a concept.
The mission itself was the hook. Build a complete ERP from scratch in 3 or 4 months. The system should cover everything such as supply chain, procurement, manufacturing, the tesla.com website, Tesla showrooms, sales, finance, HR, service, the lot. Combining all systems via a central nervous system. Funding at Tesla was tight. So luckily, or very wisely, Jay agreed to take a pay cut compared to VMware, receiving a whole lot of Tesla options to bridge the gap. He assembled 25 engineers and got to work. Three months later, Warp Drive went live. A custom ERP managing Tesla's entire operations. Vagan described Elon's vision this way. Build a vertically integrated organization where information flow happens seamlessly across departments with a closed feedback loop to customers. By doing this, we can provide the best possible product, service, and overall experience to our customers in the fastest way possible while also operating efficiently as a business. That wasn't marketing speak. This napkin back text was the baseline design requirement, and Warp Drive delivered it. Following that early stage, Warp Drive was renamed Warp. So that's the name we'll use from now on.
The advantages compound quickly. There's zero licensing costs, so no need to annually pay millions to SAP. There's no vendor lock-in, so if something doesn't work, you can change it tomorrow. Infinite customization. Build exactly what Tesla needs. Nothing more, nothing less. And most importantly, I repeat, most importantly, iterate in days instead of quarters. Here's a concrete example. When Tesla designed the unboxed manufacturing process for their next-generation platform, they didn't spend months customizing off-the-shelf software. The Warp team developed the supporting systems in parallel with the factory design. Software evolved as fast as hardware. Try doing that with SAP. I've seen this play out at other companies. A major automaker wants to adjust their production scheduling logic. 18 months later, after countless meetings and consultant fees, they get their modification. By then, the market has moved and they need something different. Meanwhile, Tesla has iterated through dozens of improvements because they control their own destiny. Legacy OEMs can't escape this trap. They're locked into multi-year contracts worth hundreds of millions. Their IT teams spend years just understanding the existing customizations that previous consultants built. Making changes requires approvals, more consultants, and quarters of work. The system owns them, not the other way around.
To get some perspective on the size of this feat, consider this. Companies like GM and VW typically spent 18 months just customizing SAP to fit their processes, while Tesla built an entire system from scratch in just 3 or 4 months. This is huge. That's not to say that the initial version of Warp was fully featured. >> You built the entire ERP system within 4 months. >> Um, yeah, that's a little bit of overstatement, but yes, that's how it started. We didn't build the entire thing. Reality is in the four months, we built the basic necessary version. It was definitely very scrappy. It was buggy, but we made it work. And then from there, we kept building. That doesn't detract one bit from the size of that achievement because Tesla didn't try to make a full SAP alternative in three months, just a core that was good enough to build on. Anyone thinking otherwise is trapped in the world of waterfalls while Tesla works with an agile mindset. Get something working fast, start using it to gain new insights, and continuously make it better. What Jay's team delivered is akin to how Linux Torvalds created the Linux kernel, which was good enough to work on and build operating systems powering most computer devices in the world, and Warp certainly was good enough to enable scaling.
>> was not an easy task in in ERP uh as well, like we replaced SAP 100% and built a homegrown system. Just bigger vision, connect every part together. [snorts] What I'm very proud of is not about building a system. Many times I in my career, I've built systems, uh, built software and delivered in such a short time frame. We were able to build and that software has been scaling uh phenomenally well, and I think big kudos goes to my team. Um, I think we built some of the platform when the company literally had zero revenue, and today there's a quarterly revenue of like, I think 7 or 8 billion. Um, it's just rapidly growing. It's just a quarterly revenue, and the platform we built has scaled um phenomenal. So that's my uh, I would say it's a gratifying moment for me to feel like, wow, okay, we what we built is really something that the company is benefiting from, and even in early days, we saw the benefit of that Elon's vision coming to life, that's uh seamless vertical integration with feedback loop.
We got a peek into how much Warp grew in a 2021 lawsuit where Tesla sued Alex Catalov, a QA employee who allegedly stole Warp trade secrets. The lawsuit revealed Tesla had spent roughly 200 man-years developing just the quality assurance scripts alone. But what many people miss is that Warp didn't just grow. It formed a foundation, a data backbone enabling everything else. The real intelligence sits on top of it. The next chapters, which I called DSM1 and DSM2, are prime examples of this intelligence. Let's start with Tesla 1 is a massive optimization engine pulling in data to make better decisions across the company. But the name of the game isn't just data quantity, but also quality. It's about raising the signal to noise ratio. Just like Tesla trains FSD on the best drivers in the fleet. They want Tesla 1 to use the best data with as little noise as possible.
A couple of years ago, I started applying this to my information diet. News is optimized nowadays for engagement, not accuracy. So bias and narrative tend to drown out the signal. To filter out the noise, I use Ground News, the sponsor of this video. Developed by a former NASA engineer, Ground News pulls articles and videos from over 50,000 sources worldwide and uses AI to strip away the bias each outlet brings. Since a lot of my news comes from X, I initially thought I don't need it. Turns out I was wrong. And Ground News even plays a core part in researching for my videos. Its browser extension integrates directly into your social feeds. So whether you're on X, Facebook, Instagram, or browsing the web. When a post references a real news topic, Ground News inserts a small bar to add more info. Say you're scrolling X and you see this post on a California court ordering Tesla to rename Autopilot. Ground News shows that 43% of over 100 articles are left-leaning with only 13% from the right. A counter article showing Tesla's claims isn't covered by left-leaning sources at all. You can hover to save the topic to your notes or open full coverage to read further and compare bias. That's where it gets interesting. You get access to all these sources, plus an AI summary of coverage by right, left, and centrist sources to catch you up quickly, and a compare bias button that compares the claims of each side. Left-leaning sources show Tesla as misleading with false advertising. Right-leaning ones claim it's regulatory overreach, while centrist ones say it's a judge's ruling and Tesla is allowed to fix it. Same facts, but very different narratives. Ground News raises signal and cuts the noise, giving me the best data to base decisions on. Use the link in my description, ground.news/ctdots to get 40% off the Vantage plan that I use. Go to ground.news/ctdots and find a new way to get your news.
DSM number one, the digital system model. Warp gave Tesla a nervous system, but a nervous system alone doesn't learn. For that, you need memory and more importantly, continuity. The ability to follow something from the moment it's imagined through the moment it's built and into the real world where physics, wear, and human behavior take over. This is where DSM enters the picture. DSM has two meanings at Tesla, and we'll explore both. First up is what I call DSM1, the digital system model. Despite the name, it isn't a single piece of software and it isn't a dashboard. It's a living digital twin that follows every car through its entire life cycle from the first CAD sketch through production and into years of real-world use. Digital twins became the hottest thing in automobile manufacturing by 2025 with every major OEM jumping on board. So, if everyone's doing it, why am I even mentioning this? Two reasons. First, Tesla built this years before the industry caught on. And second, even now at Tesla, digital twins means something fundamentally different from what other automakers are doing.
Let's start at the beginning. When a Tesla is designed, it first exists as an as-designed object. CAD models, simulations, specifications, tolerances, bills of materials, everything engineers define before a single physical part exists. In most companies, that information lives in an engineering silo. It's useful during development, but once production begins, it gradually loses relevance, becoming historical documentation filed away somewhere. At Tesla, that's not where the story ends. It's where it begins. As the car moves into production, DSM doesn't reset. It evolves. The digital twin becomes as-built. Not roughly built or built according to spec, the same as 10,000 other cars in the batch, but precisely built. Which exact parts went into that exact vehicle, which supplier batch they came from, which robot installed them, the torque values applied, the calibration data, the timestamps, everything gets attached to that specific VIN's digital twin as structured data. But it goes beyond that. Tesla tests everything from subassemblies to the entire car at each station of the build. And those test results get logged as well. Each car's file includes even the minutest details like the actual resistance and power draw of the seat motor in each direction and how fast it moves. At this point, the car hasn't driven a single mile. And yet, Tesla already knows more about it than most manufacturers will ever know over its entire life.
Then the car leaves the factory, and this is where DSM1 becomes something fundamentally different from what most companies mean by a digital twin. Once the vehicle deploys, the modeling doesn't stop. It becomes as-deployed. Each car sends data to the mothership. So, every mile driven feeds into its twin's digital representation. Speed, loads, thermal cycles, charging behavior, vibrations, impacts, road conditions. The digital twin now reflects reality, not intent. This enables noticing subtle patterns no human would ever spot. Patterns that only become obvious when aggregating data across millions of vehicles. And because Tesla's vehicles are connected, this feedback isn't delayed. It's continuous. The car doesn't just report failures. It reports stress, degradation, anomalies, early warning signs. Long before something breaks, DSM already knows how likely it is to break, under what conditions, and in which vehicles. And it's not just the current state of the car. DSM runs simulations on each car's digital twin, predicting what will happen under that car's usual load conditions and at extreme loads. This enables Tesla to prevent problems from ever happening, either through over-the-air updates or by notifying the driver to schedule service. That enables things that feel almost uncanny from the outside.
What's true for specific cars is even more powerful in aggregate. Remember how we said that if cars arrive for service with similar problems, Tesla can pinpoint the cause? Digital twins enable this long before a single car malfunctions. If a component starts showing abnormal wear across a small subset of vehicles, DSM can trace that pattern backward, not just to a model or a factory, but to a specific production window, a specific robot, a specific calibration drift. At the same time, it projects forward, identifying which vehicles are likely to experience the same issue weeks or months from now. That's how Tesla can send an update or proactively schedule service before a customer ever notices a problem. No recall, no panic, no press release, just quiet correction.
Now, in the last few years, nearly all automakers from GM to VW have deployed digital twins for design, factories, and production lines. These twins evolve with live data feeds from IoT sensors, manufacturing execution systems, and simulations. They get versioned updates at program milestones, but they remain siloed. The as-designed data lives in PLM systems. The as-built data lives in manufacturing systems, and the as-used data lives in service and connected car platforms. Cross-domain queries rely on manual integration, batch exports, or dashboards that reconcile the differences. Tesla's digital system model isn't siloed. It's a unified timeline. Most legacy systems treat as-designed, as-built, and as-used as loosely connected domains. Engineering's product life cycle management doesn't automatically flow into factory twins or fleet data without deliberate exports and reconciliation. Asking a question that spans these domains is slow, error-prone, and often incomplete. At Tesla, those boundaries don't exist. The same digital object persists across all three states. Which means Tesla can ask questions other companies simply can't. Not because they lack smart engineers, but because their data architecture makes those questions unaskable. And it doesn't stop at the car. The digital system model also models the factory that built it. The production line, the robots, the tooling, the supply chain. And because all of this sits on top of Warp's data backbone, everything is interconnected by design. A change in a factory process doesn't just affect yield. It flows into the digital twin of every car produced afterward. A real-world failure doesn't just generate a service ticket. It becomes a design input, a manufacturing signal, and sometimes a software fix all at once.
And here's where it gets really interesting. One of the hottest topics in automobile manufacturing today, right alongside digital twins, is predictive maintenance. The idea is simple: catch problems before they cause production stoppages. Robot manufacturers like Fanuc and ABB will proudly tell you their best machines can run for 100,000 hours between failures. That's 11 years of continuous operation, which sounds incredible until you do the math. A typical body and white production line for something as small as a Toyota Yaris needs around 600 robots. Even with those impressive numbers, you're looking at a potentially line-stopping failure roughly every week. And that's the best-case scenario. Most industrial robots have much shorter intervals between failures, which means something breaks every day or two on a single production line. Production stoppages cost serious money. So, manufacturers have poured resources into predictive maintenance. The problem is traditional predictive maintenance focuses almost entirely on hardware degradation, bearing wear, motor windings, hydraulic seals. But most robot failures don't come from hardware reaching end of life. They come from human error, misalignment, something bumping into the robot during a shift change. So, current preventive maintenance is a bit like obsessing over preventing deaths from pianos falling on people while ignoring the traffic accidents happening every day.
Tesla's approach is different because digital system model doesn't just track hardware metrics. It tracks everything. Picture this scenario. A robot on Model Y's Fremont production line develops an issue that standard predictive maintenance tests don't cover. Because every robot has its own digital twin in DSM1, the problem gets logged immediately. Not just the failure itself, but the degradation pattern leading up to it. The system then queries the digital twins of all cars recently produced by that robot, checking how well they performed in production test and, where possible, what their current real-world condition is. Tesla's AI processes the data and finds that a specific combination of parameters had degraded before the failure. That pattern becomes a signature. And because Tesla 1 connects the entire company, queries automatically go out to check every similar robot in every factory worldwide. The system finds that one robot in Shanghai and two in Berlin are showing the same early-stage degradation. Those robots get flagged for preventive maintenance before the problem surfaces. The test also gets added to the standard monitoring suite, so future degradation along these parameters triggers alerts the moment it starts. The savings in downtime are massive. Tesla's factories constantly break production records, and their as-deployed digital system models deserve part of the credit.
Now, I can't prove Tesla does exactly this. No one can without internal documentation. But as an engineer, here's how the dots connect. Analyzing a robot's work quality over time can absolutely warn of imminent failure. That's just basic statistical process control. And when failures occur, AI can easily check historical production data to find previously unnoticed trends. Tesla extracts extensive data during production from both cars and robots and stores it in digital system models. That much is confirmed. And as good an engineer as I consider myself, if I could imagine this setup while making a video, I'm pretty certain Tesla's engineers who are paid to think about exactly this kind of optimization imagined it years ago. Once you have the idea, the infrastructure, and the data, there are no real technical roadblocks. So either Tesla already has this running or they'll have it fully deployed within a year or two. Either way, that puts them years ahead of other OEMs and robot manufacturers. And the implications compound rapidly. This is the quiet superpower of the digital system model. It turns the physical world into a continuously instrumented experiment. Every component produced becomes data. Every mile driven brings info. Every component stressed becomes a lesson. Every edge case feeds back into the system. So if Warp made Tesla fast, digital system models make Tesla self-aware. And while other OEMs are years behind Tesla in using digital system models, they're not even in the same ballpark when it comes to digital self-management, where Tesla ditched all management layers and let AI call the shots.
DSM number two, [music] digital self-management. Warp gave Tesla a nervous system and digital models gave it perfect memory. But memory alone doesn't make decisions. In most companies, that's where managers come in. They review data, make calls, approve changes, set priorities, coordinate teams. It's slow, but it works. Or at least it mostly worked until Tesla figured out how to replace almost all management with software. To understand what digital self-management means, we need to understand what managers actually do. Strip away the meetings and the politics, which Elon absolutely hates. And traditional management serves three core functions. First, they provide feedback. They tell people whether their work is good or needs improvement. Second, they decide what to work on, setting priorities and allocating resources. Third, management serves as a career path, giving people a ladder to climb and a reason to stay engaged. But here's the thing about managers. They don't produce anything themselves. They only affect what others produce. And Elon's ideal has always been a flat company where 100% of workers are engineers or other creative roles with no middle management layer consuming resources without adding direct value. Tesla's way of approaching this goal was to automate using digital self-management to handle all three managerial functions.
Let's start with feedback. Think about training a dog. The clicker works because the feedback is instant. Click means that was good. No click means try again. Timing also matters. After doing some trick, a dog remains attentive, waiting for feedback. And if you wait 15 minutes to click, the dog has no idea what it did right. Humans are similar. We can wait longer than dogs for feedback. But faster is still better. Feedback is important, but Tesla looked at this and asked a different question. >> Why would we ever ask a human to do this?
We're about to see some more of Joe going forward. So, in case you wondered, Joe Justice, chair of the Agile Business Institute, link in description, is widely considered the world's top expert in agile hardware development. Everyone usually describes how he developed agile operations for Microsoft, Amazon, Tesla, and other tech companies, and that he led agile operations at Tesla. So, I won't bore you with that. Instead, think of major car companies and spin the wheel. Chances are, he worked [music] with them. Now, think of leading defense companies and spin. Again, chances are he taught them agile, too. Think of a fighter or passenger airplane, or maybe a satellite, and it probably has Joe's fingerprints on it. Joe moved to Japan, where he helped several companies transform into agile operations and is deeply involved with Toyota's seemingly serious effort. Years ago, Joe founded an award-winning automobile manufacturing startup, Wikipede, and now owns Japan's largest and highly successful EV racing team. Back on topic, Tesla wondered why humans are even needed for giving feedback. So, let's check. Often the feedback we need is very simple because just like a clicker, sometimes a nod or a thumbs up is all it takes to assure us we did well or hint that maybe we should retrace our steps and try something different. At other times, the feedback is more detailed with the experienced manager giving their two cents on what you did right or wrong. So, for simple feedback, every factory floor station has a green or red light indicating whether the assembly is ready to go downstream to the next station or is subpar and should be improved. And for detailed feedback, each workstation has monitors, as does the Tesla 1 app on every employee's phone or computer. There is one caveat. I don't think this is the case, but there is a chance that as Tesla grew, this has changed. For example, from this video, it appears as if manufacturing employees in Austin do have supervisors, and these are the ones holding the data. Here's Anthony Lopez, a Tesla employee in the Model Y line in Giga, Austin. >> Leads, leads have them like their computer popped up, and you can go and look at theirs. Um, so it's not restricted or anything. It's just they're not everywhere, you know what I mean? >> Okay. >> Yeah. Yes. And to look at the numbers, uh, just the flat-out numbers that we're doing in that one little area. Um, they Yeah. Yeah. That's what really matters. Uh, it's the, >> you know, the the little computers that kind of they're attached like it's hard for me to >> the ones to control the robots >> like this. >> Control the robots. Yes. And and see what's wrong with them or or what's up. Yep. >> Uh, those you can go to the data and you can see how many we've done our shift. >> Um. >> Okay. Yeah. Yeah. >> So, those are everywhere. Those screens are everywhere. >> But you check there. It's not you you don't glance up to see it from where you're working most of the time. Okay. Interesting. >> Nope. No. Uh, at least in the part in the part of the factory that I work.
While I knew that Tesla had overhead monitors everywhere, hearing these were off was not what I expected. Further checking, however, clarified the picture. It seems like this was implemented by Tesla as a precautionary measure for new employees only. Tesla operates using 12-hour workdays, three or four days a week, and not everyone is cut out for that. Attrition among new hires is high. And to sort quitters out as fast as possible, Tesla requires them to work 12-hour days, 5 days a week. Many remain. These are the hardcore ones, which will take Tesla forward, but many others leave. I believe that in an attempt to reduce the amount of IP that quitters take outside, Tesla limits the data newbies can access and turns monitors off around them. This interview was conducted during Anthony's first 90 days at Tesla, and subsequent discussions revealed that things have changed. Following an initial period, greater empowerment and freedom, and surely also greater access to data were later provided. So you always get data to monitor your progress, but displaying feedback is the easy part. Managers are not judged by the speed or elegance of their thumb raising, but by determining when to raise the thumb. This is where intelligence comes in. As the article "Innovation Engineering at Tesla: Agility as a Cultural Practice" by Dove Larkin at Al presented in the latest in Cozy Symposium in Ottawa shows, Tesla makes extensive use of digital assistance, typically with hundreds of artificial intelligence/machine learning applications trained for testing and performance evaluation. So that's basically the Tesla 1 app or monitors showing results of different tests and performance metrics. Test applications include both unit and integration regression testing for functionality and modular interface conformity. This means that every component or subassembly is tested by itself and then integrated with others to show how well it functions and that it conforms to the modular interfaces defined for it. Evaluation of performance innovation, however, includes more than functional pass/fail. When assembling a tried-and-tested part into a car, all the worker needs to know is whether it passed or failed. But when trying to innovate and make new designs, nuances matter. Let's say you want to design a new reservoir for windshield wiper fluid. Blow molding the shape takes seconds. And creating temporary molds can take minutes if, for example, performed via 3D printing. So, you're free to check one design after the other, creating and testing several designs per day. Tesla's production lines are not traditional lines, but rather an ordered flow between production cells. So if you open an assembly station in parallel to the regular one and get a small number of cars diverted your way, you can fit the reservoirs in these cars. Assuming the reservoirs have the same connection interfaces and fit within the designated space, chances are they will clear the tests, get the green light, and the cars fitted with them will continue to the next station and eventually get sold. But getting the green light doesn't say much when the existing design receives it too. More important are variables such as: Does it cost less? Does it weigh less? Can it be assembled faster? Does it perform its intended function better? Is it more visually appealing? Will it last longer and be more reliable? Does it leave more spare room? Can it take on additional functions? In case of software, does it have fewer lines of code? The ability to get immediate end-product results enables you to determine whether to keep on going down the design route you're exploring or backtrack and try a different one. Eventually, if you reach a design that is 1g lighter, 1 cent cheaper, 1 second faster to assemble, etc., your design will win over the existing one, and all cars will start using it. Note that a manager could never peek over your shoulder and provide this level of know-how. But with DSM, as Joe Justice put it bluntly, >> "When I was working for Elon, we would all very often say, there are no bosses. Your manager is data."
I love the idea, unless it ends up like this. Once I have made a decision, it is your job to carry it out regardless of how you may personally feel.
But feedback is the easy part. And managers don't just tell you whether you did well. They also tell you what to work on. So here's the question most people ask at this point. If there are no managers assigning work, no product owners prioritizing backlogs, no directors setting quarterly goals, then who decides what matters? Who sets direction? The answer is simple. The system does, and so do the people. Joe Justice describes this mechanism as justice boards. These huge monitors are also referred to as dynamic activity boards. But this name undersells what's really happening. This isn't a planning tool. It's a real-time decision engine. Each product is divided into separate independent modules. Unboxed manufacturing enables taking this to extremes where these modules can be as big as the entire front section or rear section of the car. But modularity exists in conventional manufacturing as well. For instance, as long as interfaces remain compatible, motors and battery packs may be developed separately from each other for each module. Key performance indicators are defined. For example, motor KPIs can be things like real-world efficiency across the drive cycle, power and torque density, thermal margin under sustained load, watt-hour per kilometer impact in the fleet, failure rate per million km, how early telemetry detects degradation, manufacturing yield and cycle time, production time, supply chain considerations such as rare earth dependency or number of potential suppliers, and modeling considerations such as how closely the digital twin matches real-world behavior. Giant screens and the Tesla 1 app present each module as a separate line. And for each of these modules, they show the current status of its KPIs and the impact that advancing each one would have. From here on, Tesla engineers are free to manage themselves. They don't belong to a motor group or seat group. They're just engineers free to work on whichever problem they think will bring the greatest impact. Instead of waiting idle for someone to assign them the work, they proactively check their phones and activity displays, learn which problems matter most right now, and self-select into the work that resonates with them, matches their skills, or they think they can solve.
Let's hear it from Joe. Each day, teams don't look at a project schedule. They're not even told what to do. Instead, they look at this board, the justice board, and it says the current KPIs of the battery pack, and it's those people's job to try to improve the battery pack today. So, every day the battery pack gets a gram lighter, takes a second less to manufacture, costs a euro penny less to make, and it doesn't require any changes to the seat or to the center display. The project management system has been replaced by what's called the justice board. Now, this board had no name when I worked at Tesla. When I introduced this at Toyota afterwards, I gave it a name. There's a row for each module, and engineers self-organize on a row. So engineers have to choose a row when a new project starts. They don't get to make a new infotainment system. Leadership chose the rows on the board. Then engineers choose which row they work on and how they work on it. Again, here's the board. This is the real board. If you have many products, each with about 11 modules inside, the board is about this big. This is the actual board at Tesla. This is where mobbing comes in.
In his paper, "Agile Methods on the Shop Floor: Towards a Tesla Production System," Teimo DM discusses how work at Tesla is very similar to mob programming or mobbing. Mob programming is a software development approach where the whole team works on the same thing at the same time in the same space and on the same computer. It's like pair programming but extended to several people, usually around five, where everyone takes part while still using a single computer for writing the code. But with Tesla, it's not just programming because hardware-related tasks are mobbed as well. Engineers look at the dashboards, find a KPI they want to improve, and start working on it or join a group that already has. Impactful people tend to be drawn to the single highest impact problem at that moment. Engineers, manufacturing experts, quality specialists, software developers swarm the issue together. Everyone has the same data, the same goal, the same context. They work it collaboratively until it's solved. Then they disperse and regroup around the next mission.
>> The pace of everything is so fast. Um, and people I'm guessing people work at like uh agile and they kind of mob and stuff like that. And the what I will say is the technician or the repair technicians that come and you know uh fix the robots if the robots are down, they they are very agile, meaning or they mob, I guess, when when something breaks down, they all come over there and it's just like a whole group of them, like even if it's just one little tiny thing, they all come over there, maybe one or two, but then after like a couple minutes, you they all start flying in. Um, so that that's still good, good to see that that's still happening. Um, [laughter] all right. And it's so funny because people that work there that I work with, they don't know that. They don't know that term, the mobbing term, and how you describe it. And so they're like, why they got all these people over here just for one thing? I'm like, it's efficient cuz, you know, you have different people bouncing off ideas on what it could be and it gets solved like this. And sometimes it takes, you know, sometimes the >> no escalation ladders, no waiting for your manager to arrive and assign you a new task or for another team to finish their part so you can start yours. Direction emerges continuously from reality itself, and the highest impact work gets the most attention automatically. Most projects are extremely short, so the focus stays on what's happening now. Tesla was making an average of 60 part changes per day in the 2021 to
2022 time frame. The system handles that velocity because it doesn't wait for human approvals. That's not chaos. It's the opposite. It's perfect alignment between what matters and where effort goes. And there's one more thing in this mobbing in that you have another team member in the form of AI. We'll go through how Tesla 1 helps and how it can be so smart in the next chapter, but the fact is that having this team member accelerates the pace of progress considerably. Joe Justice dubbed this mob AI.
But why does Elon want to eliminate management? Isn't it easier to just hire good managers? And how about management as a career path? Elon's answer would be no. Because the problem isn't bad managers. The problem is management itself and what it does to organizations over time. Start with the Peter principle. In traditional hierarchies, people get promoted based on performance in their current role until they reach a position they're not good at. You lose your best engineers to get a group of okay engineering managers. The company systematically converts its top technical talent into coordination overhead, losing expertise while gaining bureaucracy. Alternatively, you can promote your more replaceable engineers, but then you have your second rate guys telling your best guys what to do. Or you can bring in professional engineering managers and without having the engineering knowhow or in the trenches experience, they make life miserable for the engineers and take the entire company the wrong way.
Management also creates silos. Departments form around reporting structures and each one defends its territory. Data flows vertically within chains of command but barely crosses horizontally between them. Knowledge gets trapped. An engineer in one group can't easily learn what an engineer in another group discovered last week, even when both are working on related problems. Feedback loops that should take minutes stretch into months as information navigates the org chart. And finally, every additional layer of management adds latency. Decisions wait for approvals. Opportunities pass while consensus is being built. By the time everyone in the chain agrees, the problem has often changed or the window has closed.
Years ago, I had this textbook case where my team and I worked endless hours and reached a huge breakthrough ahead of time. But then all the time we made was squandered away with our work frozen for over a month just because some top manager went on vacation without previously signing or delegating to a colleague the form required for continuing our effort. To make things worse, nobody in our project even knew he was out of office because he sat in a different location and considered too high up to call. Classic. The good news is I learned from this. But bottom line, adding management kills communications and introduces latency. And the sad thing is for no reason. It seems that manager knew little about our project or its meaning. He just saw that the project manager wanted it and had the funding and gave us the go.
Digital self-management exists to eliminate all of that structural drag. Let's hear Joe again. >> What I would recommend for every company is put a large budget on building your own DSM software because once you have even some recurring management decisions automated by software and it's at least not worse than humans, it's now instant. You've knocked delay out of your company. Pace of innovation is the only thing that matters and pace of innovation cannot go faster than approvals. The more of your approvals become essentially instant through DSM, the faster your company's throughput of innovation, the fundamental winning formula. Replacing human decision points with apps is the digital backbone of a modern company and fundamentally determines the speed of product development and response. When data authorizes decisions, there's no need for permission. When missions are generated by objective impact, there's no room for politics. When teams self assemble around problems, information flows freely because hoarding it hurts you more than sharing it. And when work is organized around outcomes rather than titles, the entire incentive structure changes. When this happens, the management layer doesn't get replaced. It becomes unnecessary.
But with no management positions to grow to, how can you keep workers incentivized? A few times Elon quoted Charlie Munger saying, "Show me the incentive and I'll show you the outcome." Or as Munger says it here, >> "The basic rule on incentives is you get what you reward for." So if you have a dumb incentive system, you get dumb outcomes. >> While true that managerial positions can serve as incentives, they incentivize the wrong type of behavior. Tesla has a flat structure and even Elon is known to work shoulder by shoulder with others when critical problems arrive. Look at this message that a process technician wrote during the 2018 production hell. I just wanted to express my gratitude for CEO Elon Musk coming down to the front lines at Giga 1 this last week. I cannot speak for everyone, but from where I work, he came in and eliminated 80% of the problems we were having in about 20 minutes. It was amazing. He re-engineered process and final product on the spot and in real time. In completely cool fashion, he actually talked and listened to the workers on the line where the work is being done and the tires hit the road. That same night, we blew away the record for the most production by a long shot. My co-workers and I were all giving high fives at the end of shift. And that's from a CNBC article titled Elon Musk's extreme micromanagement has wasted time and money at Tesla where Laura Coladney complains about Elon's insistence on warp drive over using SAP.
And here's where most people misunderstand what Tesla did. They didn't remove managers because they couldn't find good ones or because Mary Bar was busy. They removed managers because management hierarchies distort motivation. Companies like GM and Ford have layers upon layers of hierarchy. Think about it. In a traditional company, how do you get ahead? You impress your manager. You make sure your work is visible. You take credit. You position yourself for the next promotion. The actual impact of your work matters less than how it's perceived by the person who controls your career path. That's not motivating. It's exhausting. Once career advancement depends on perception and visibility rather than measurable outcomes, incentives shift. People optimize for what looks good to their manager, not for what improves the product. Information becomes currency to be controlled rather than shared. Data gets filtered on its way up the hierarchy. Bad news travels slowly, if it travels at all, because nobody wants to be the bearer of problems that might reflect poorly on them. People are afraid to take risks, so say no a lot. If they do, and the bet fails, their ass is on the line. And if it succeeds, chances are someone else will get all the credit. And new technology can kill your engineering career. And most politically maned engineers, um, they'll run from a new supplier with a good idea. In the OEM, in the big OEM world, you get promoted by saying no a lot. The engineer that does not stick his neck out or does stick his neck out and tries to get a new technique or technology which becomes a successful um the OEM will probably wind up working for a new company. If you are success with a new technology, the guy with a political mind will come and take things away from you. say it's his and get a big promotion. If you hearken back to that uh that little slide that I showed you, stolen valor, that's the uh that's the the way that things are done.
With digital self-management, none of that exists. Engineers don't chase promotions because there's no ladder to climb. What they chase is impact. And impact is immediately visible in the data. Quality metrics improve or they don't. Fleet performance changes or it doesn't. Costs drop or they don't. Customer issues decrease or they don't. There's nowhere to hide and no one to impress. The only way to stand out is to solve hard, high impact problems that actually matter. That turns motivation inward. People choose missions because they want to solve them, not because solving them advances their career. Expertise becomes the currency. Results become reputation. And because DSM constantly surfaces the most important problems, individual effort aligns with company goals automatically without anyone having to enforce it.
In Elon's company's, motivation is easy. They're working on cutting edge technology that's changing the world. They're helping transition humanity to amazing abundance. They're building the future of transportation, robotics, and energy storage. The mission itself is the motivation, and the work is genuinely exciting. You're not shuffling papers or attending status meetings. You decide for yourself which of the most impactful problems facing the company you want to tackle and then you go there mob with like-minded people and a helpful AI assistant and get immediate feedback on whether your solutions work. Every employee gets generous stock options. Yes, and many of the people working the line are millionaires. But that's not why they're there. They're there because the work itself is meaningful. because they believe in what Tesla is building and because removing all the bureaucratic friction means they can spend their time building instead of navigating corporate politics and endless meetings. Digital self-management doesn't manage people. It lets them manage themselves and removes everything that gets in their way.
The bigger picture. This is why digital self-management matters. It's not just about efficiency. It's about creating an organization that operates at machine speed with human creativity. Warp provides a nervous system connecting you to all parts and aspects of the company. Digital system models for factories and products provide perfect memory and awareness. And digital self-management provides instant action. Together, they form something no other manufacturing company can match. A company that learns and adapts faster than its competitors can hold meetings about learning and adapting. But connecting things and showing metrics are easy. Reaching good decisions is harder. So, how can Tesla 1 make good decisions? How does it learn and can it be trusted? Let's enter the next chapter. Yoda AI, the invisible master.
We've seen how Warp gave Tesla a nervous system, how digital system models provided perfect memory, and how digital self-management eliminated traditional hierarchy. But there's one more layer we haven't discussed yet. The intelligence that ties it all together. Think about Yoda from Star Wars for a moment. 900 years old. He's seen every possible scenario play out. Every mistake, every success, every edge case. When someone comes to him with a problem, he doesn't need to guess. He's literally experienced it all before. That accumulated wisdom is what makes him valuable. Now, imagine an AI with that same depth of experience, but learned in just over a decade, the 10,000hour rule on steroids. Malcolm Gladwell popularized the idea that it takes 10,000 hours of deliberate practice to achieve mastery in any field. That's roughly 5 years of full-time work. Most engineers never get close to that level of focused experience with any single component or process. Tesla's AI, it passed 10,000 hours years ago. Here's why. With the exception of Cybertruck and Semi on some, but not all components, all Teslas use the same heat pump, the same motors, the same seat, and so on. So, when a part is improved, all models benefit from it. But there's another benefit, which is that if you spend 50 hours improving the seat for Model Y, 30 doing so for Model 3, and another 20 hours improving it for Model X, you get 100 hours of practice on developing the same part. Since around 2012, Tesla has been training its internal AI on every single part, every single day. Not just the final designs, the entire evolution, every iteration, every production step, every test result.
Let's hear Joe Justice explain what this actually means. Here are actual photos of mobs using AI tools on the heat pump line. In Tesla, design happens in the factory. Most companies have a glass office area in the factory with some management and then a separate office for designers often in another time zone in in another country entirely. In Tesla, these are designers. These are engineers. And the first thing they do when they walk onto the heat pump manufacturing line is they physically make a new heat pump. They don't start by drawing it. They don't open a laptop. They start melting plastic. Can we get plastic to move a different way? They start by bending metal themselves. Me too. I did this too thousands of times. Bending metal, cutting metal, drilling metal, braiding wires. Well, this team is trying to make one heat pump that weighs a gram less, cost a penny less, lasts a minute longer in simulated aging. While they're doing it, another member of the team is programming robots to try to automate building the thing. Now most companies make a design and then after the design is approved and several approval steps then they start programming robots and making manufacturing. Elon Musk's idea is you only learn by building, not by drawing, not by talking, only by building. >> Think about what that represents. When you design a new heat pump version, you're drawing it for the first time. You have theories about what will work, textbook examples, the previous version to base yourself on. The AI, it's seen three billion actual heat pumps get made, tested, installed in vehicles, and perform in the real world under every possible condition from Death Valley heat to Norwegian winter. And it's not just heat pumps. It's every single component. Motors, battery packs, suspension arms, door handles, wiring harnesses. The AI has watched millions of each get designed, built, tested, and deployed.
But here's what makes this different from just having a big database. Standard large language models, Chat, GPT, Gemini, Claude, are trained on the internet. Reddit threads with conflicting advice, blog posts with mistakes, marketing materials that exaggerate, and garbage in means hallucinations out as they're trying to synthesize signal from an ocean of noise. Tesla's AI is fed exclusively on pristine structured data from digital system models. Every data point is verified. Every measurement is calibrated. Every test result is traced back to specific known inputs. When the AI says, "This design will fail at 50,000 m." It's not guessing based on internet forums. It's extrapolating from actual failure data collected from millions of vehicles with digital twins tracking every detail. When it says the part you made will cost half a cent less to produce, it knows better than anyone because it's seen every such part Tesla previously tried and it knows material prices and what discounts Tesla gets. The AI doesn't just have 10,000 hours of experience. It has millions of engineer hours compressed into one continuous learning system. And that system never forgets anything. In Palunteer, their artificial intelligence platform, AIP, gives Foundry its reasoning layer, the part that interprets data and recommends actions. Tesla 1's AI plays the same role, but instead of reading spreadsheets and dashboards, it affects real world production.
Mob AI, teaching the master. Now, here's where it gets really interesting. The AI doesn't just passively observe or answer queries. It actively learns from how humans work. And the humans don't just use the AI, they teach it. The AI is a team member. That's why Joe Justice calls this mob AI. When engineers mob on a problem, training the AI happens automatically. They're not sitting there writing training data manually. They're just working normally, and the system learns from everything they do. Here's Joe explaining how >> another member of the team draws a 3D model not of the part that's being made, the heat pump, but of the manufacturing process that makes it by animating the manufacturing steps and taking in models of blocks of metal and plastic pellets and animating melting them. It results in the 3D model of the part. So the part itself is not drawn. The factory steps are drawn and animated which results in the part. The definition of done for mob AI is the production is fully automated and automated testing. That's when this mob can go do something else. A version of the heat pump that weighs a penny less or in some key number is better is in production with automated test. Now these teams are so good at this now and the AI assistance is so good and the robots are so capable that the teams can often go through this entire cycle more than once in a 12-hour shift. And as a result, all the modules of all Tesla products get a tiny bit better once or more time a day. >> Most companies would draw the CAD model of a heat pump and call it done. Tesla engineers draw the production steps needed to make it. And those steps get fed into the AI along with the results. How long it actually took, how much it cost, what the quality metrics were, whether there were any problems. The AI watches humans solve problems in real time. It sees what works and what doesn't. Failed approaches get recorded with full context about why they failed. Successful innovations get logged with measurable results. Nothing is lost. The system steadfastly learns. Over time, this creates something remarkable. The AI develops genuine engineering intuition. It knows that changing this dimension will affect manufacturing yield. It knows that this material choice will create supply chain problems. It knows that this design pattern tends to fail under thermal stress. And because it's been trained on actual production steps, not just theoretical designs, it can do something no other AI can do.
Here's Joe again. >> Tesla's AI is also trained on the manufacturing steps to make these products. You can't just ask Chat GPT or Googlebard or Microsoft Bing to draw a new electric motor that's more efficient than any motor any human has ever made. Microsoft Bing, ChatGBT, and Google Bard aren't that good yet. Maybe someday, but not yet. Tesla has their own AI for electric motors, and it can. It has a CAD model of every motor Tesla has ever made. And it has real-time feedback from all 5 million Teslas in the road, showing which parts of the motors are getting hot, how much electricity flows through them, what their efficiency is, when they fail, and it will draw for you CAD models of motors. But even more importantly, it will show you how to build them. The AI is trained on the manufacturing steps to make these. The real product is your production design and manufacturing and tooling all at once all validated against billions of real world data points. I'll discuss this in the next chapter. But as an engineer, my head is seriously buzzing right now. >> And if you take a learning approach, then you build a system that if it works, it will explode. Mhm. >> Because once the learning accelerates, you get into a quasi monopoly position.
The wide view. Here's where Tesla 1 becomes something genuinely different from any other system. In Star Wars, Yoda can sense the force connecting all living things. He sees patterns others miss because he perceives the whole web of causality. Tesla's AI does the same thing, but with data. Remember that butterfly effect example from the beginning? If a paint supplier ships from plant A instead of plant B, Tesla 1 knows that historically means the paint will dry faster and it adjusts the factory to use those bonus seconds. That's not science fiction. That's just pattern recognition across millions of data points. The AI sees the connection between a supplier's warehouse location and robot timing in Fremont. Because both feed into the same digital twin, it sees that a specific combination of temperature, humidity, and production speed affects quality because it's tracked all three across billions of parts. But it also handles the mundane alongside the futuristic. The same system that optimizes rocket engine simulations at SpaceX also makes sure people don't violate labor laws by working too many consecutive days. The same intelligence that roots service visits for maximum efficiency also tracks vacation time and cafeteria inventory. That might sound trivial, but it's not. Traditional companies have different systems for important stuff and administrative stuff. Those systems don't talk to each other well. Tesla 1 treats it all as one unified organism. The vacationuler knows about production targets. The cafeteria inventory system knows about shift patterns. Everything connects because it's all part of the same digital nervous system, the embodied intelligence.
Here's the conclusion that ties everything together. As we said, most companies regard some data as important and other data as trivial. And most companies think about AI as a tool. They use a chatbot to answer questions, an analytics platform to generate reports, something separate from the business that you query when you need information. But Tesla obsessively logs data and nothing is trivial. And Tesla 1 feeding on it isn't a tool. It's the consciousness of the company itself. When an engineer in Fremont makes a breakthrough on battery thermal management, the AI learns it. When a vehicle in Norway encounters a new failure mode, the AI learns it. When a robot in Shanghai shows early signs of degradation, the AI learns it. And all that learning is immediately available to everyone everywhere, all at once. The company doesn't just have an AI. The company is an AI, an embodied intelligence that learns and adapts faster than any human-led hierarchy ever could. And that's why competitors can't copy it. Honda is building Honda 1. Toyota is developing their mob AI. But they're starting now in 2024 and 2025 with very minor data acquisition skills. Tesla's AI has 10 years of production data they can never catch up to. By the time Honda's AI reaches 2025 level sophistication, Tesla's will have another decade of advancement. Tesla's moat isn't the factories or the battery technology or the software. Their moat is Yoda. 10 years of accumulated wisdom that compounds every single day. But now you understand the invisible superpower. The thing that makes Tesla's impossible production speeds possible. The intelligence that no one sees but everyone feels. The master behind the machine.
A new perspective. I have a confession to make. I've been covering Tesla for years. I was one of the very first to realize the huge significance of Optimus for Tesla and the world. And four years ago, I already described Tesla as ch. I said there were no showstoppers along the way and that Optimus would arrive. And I extrapolated the pace of FSD progress to match safety levels with Cyber Cab's production start. And except at the edges, I am all in on Tesla and as bullish as ever. But here's the thing. With all the excitement around CyberCab, FSD, Optimus, and Energy, I have never been as excited as I am right now. And for two reasons. First, you have no idea what this means to an all-out engineer like me. Looking back at my engineering achievements to date, there are several things I am proud of. But they are all nothing. nothing compared to what I could have achieved with this AI on my side and I'd be having a ball doing it. So, hats off to Elon and the team at Tesla. You truly are amazing. As for the second reason I'm so excited, not investment advice, but I somehow feel bad being allin on Tesla because if I had other shares to sell, I'd be buying more, a whole lot more. Understanding the significance of what I've seen has my head buzzing and my soul soaring. And that's not like me to say this. If you've watched my videos, you know I usually keep an understated tone, but I am elated because the possibilities this brings, they are insane. Nobody realized this, but we've had a few sneak peeks at this in 2025. We'll probably get bigger ones in 2026, and after that, Tesla's tech will go alien grade, nothing less. The video is already long, and there's so much I haven't touched. I didn't show how Tesla 1 handles supply chain intelligence and energy grid optimization. I haven't shown what it means for Optimus and the robo taxi network. I haven't shown the advances in metallergy and material sciences. I haven't shown how it completely transforms car design and takes it into a new era. Other cars truly will seem horseless carriages as Tesla goes into plaid. But all this will have to wait for part two. So, make sure to subscribe and hit the bell icon to get notified when it's out. One of the chapters is they'll need a telescope. And for the first time, the timeline for this promise is getting near.
Now, here's where I need your help. These deeper topics are fascinating, but admittedly niche. The algorithm won't push this video unless people actually watch and engage with it. So, if you found this valuable and want to see part two, please hit that like button. It genuinely makes a difference. And if you know someone who'd appreciate understanding how Tesla actually works under the hood, an engineer, a Tesla investor, an AI enthusiast, please share it with them. Your engagement is what tells YouTube this is worth promoting and tells me it's worth spending another month researching and producing part two. But regardless, let me know your thoughts in the comments. Would you want to work with a system like Tesla 1, or does it feel dystopian to you? Can other companies realistically build something similar, or is Tesla's lead insurmountable? How much of a competitive advantage does this give Tesla, evolutionary or revolutionary? And what part of Tesla 1 surprised you most? Let me know below. I wish you all an amazing 2026, a year of health and success, accomplishment, curiosity, and wonder. Hopefully, a significant year on our road to abundance. And one thing I promise you is that it won't be boring.
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