What is true on Fleet?

BREAKING: Starlink was all over United’s Q3 earnings, with the airline calling it a “meaningful differentiator.” CEO Scott Kirby: “We are winning new long-term, brand-loyal United customers because they are trying us for Starlink…” • More daily Starlink flights than the rest of the U.S. airline industry combined. • Over 5.4 million customers experienced Starlink in September. • 640+ aircraft equipped, adding around 3 per day. • Available on about half of all United flights, including regional flights. • Expected to reach 61% of flights by end-2026, 87% by June 1, 2027, and the full fleet by end-2027. • Customer service scores on Starlink-equipped planes nearly double those with other Wi-Fi. • United’s best-ever third-quarter scores for inflight entertainment and Wi-Fi. • Live NFL and college football on Starlink-enabled seatback screens. • Free for MileagePlus members in every cabin. • Powered an ESPN fantasy football draft livestream from a flight. • Tom’s Guide praised the rollout as a “masterclass in passenger experience.”

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.

ARK just released its new Tesla model, it sees $TSLA worth $17.5 TRILLION by 2031 ($4,400 per share!) Here's how they get there: 1. Units in 2031: 8.1M robotaxis, 10.2M cars sold, 1M Optimus robots and 2,278 GWh of storage 2. Revenue by product: robotaxi $753B (52%), cars $320B (22%), Optimus $212B (15%) and storage $163B (11%) 3. 2031 financials: $1.449T in revenue, $902B EBITDA at a 61% margin 4. Robotaxi makes up ~80% of Tesla's enterprise value and Optimus is 14% 5. ARK sizes the humanoid robot market at ~$26T, bigger than the $10T-$15T it sees for robotaxi 6. Cybercab's cost per mile is ~40% below Waymo's next-gen robotaxi 7. Tesla's AI infrastructure spend reaches ~$225B cumulative by 2031, including Terafab with SpaceX Also, NOT in the model but an interesting take, a possible SpaceX merger, which would mean $3.8T in combined 2031 revenue and $2.74T in EBITDA At those numbers, the combination of these two companies would be larger than $50 TRILLION in market cap Personally, I have not modelled this far out for the two companies, but I do believe that SpaceX will become the largest company in the world and the first to reach $100 Trillion The combination of Tesla and SpaceX is ultimately how they get there, which I explained in detail in the post below This is a big reason I gave Milk Road PRO members the signal to buy $SPCX when it hit $115 a few months back. For more signals like this and to see my entire real-time portfolio (and 4 other analysts), check out Milk Road PRO here: https://t.co/0kheLn0QRB

Tesla just introduced a new Robotaxi and safety report website with lots of new info, including a live Robotaxi mile counter. Here’s everything new: • People Injured by Tesla Robotaxis: 0 • Tesla Robotaxi Airbag Deployments: 0 • Pedestrians or Cyclists Hit: 0 Tesla Robotaxi miles by city: • Austin, TX: 2,118,015 • Dallas, TX: 608,130 • Houston, TX: 225,964 • Miami, FL: 46,262 • Orlando, FL: 61,053 • Tampa, FL: 53,117 Other new Robotaxi info: • Miles driven after dark: 862,000 • Miles in rain: 113,000 • Miles in rush hour: 575,000 https://t.co/mSLDlBGqaf

This is honestly insane. Marc Boroditsky, CRO of $NBIS, just revealed that the company has 1 GW of Vera Rubin GPU capacity coming next year, with significant customer demand already lined up. That alone could translate into tens of billions of dollars in ARR. 🤯 https://t.co/X2CNpaJaSH

Sparks of abundance by 2029 https://t.co/uKjmoLpc0p

ELON MUSK TARGETS 13 GW OF AI COMPUTE BY 2027, WITH 440,000 NVIDIA VERA RUBIN GPUS IN A SINGLE TRAINING CLUSTER Musk says SpaceX is on track to bring roughly 200,000 $NVDA GB300 GPUs online per month for the remainder of 2026, with deployments continuing at a similar pace next year, primarily using Vera Rubin. The company expects to have 2.5-3 GW of compute online by the end of 2026, with another 10 GW potentially coming online in 2027. Musk also plans to have a 440,000-GPU Vera Rubin training cluster operational by the end of Q2 2027. To support the expansion, SpaceX is targeting an additional 20 GW of power and cooling infrastructure next year. Musk said there is a “non-zero possibility” of exceeding 20 GW of operational AI compute in 2027, although he acknowledged the probability remains small.

"Ghost Shark has teeth now." - @PatConroy1, Australia's Minister for Defence Industry. The @ausgov and @Australian_Navy confirmed the payloads that our Ghost Shark extra-large autonomous submarine carries: sea mines, logistics caches, and air-launched effects for ISR and strike. Ghost Sharks are rolling off the production line. Payload testing is active.

$SPCX just reported ABSOLUTELY INSANE earnings, and I don't think people realize how quickly Elon Musk is scaling this company. SpaceX generated a staggering $14.6 BILLION in quarterly revenue, representing 161% year over year growth, while earnings per share surged 197% to $0.35. That's a 12% revenue beat and a 119% EPS beat against the estimates shown. And the numbers get even CRAZIER. SpaceX reportedly generated $4.4B in net income and $8.8B in adjusted EBITDA, with EBITDA growing an incredible 306% year over year. We're talking about a company that's not only more than doubling its revenue but potentially expanding its profitability at an even faster pace.

Q3 2026 highlights: - Delivered revenue of $14.6 billion, up 161% year-over-year - Successfully launched our first revenue-generating Starship mission, delivering 26 Starlink V3 satellites to our broadband constellation, representing approximately 10x the downlink capacity added by a Falcon 9 Starlink launch - Added $6.7 billion of incremental Super Intelligence solutions and infrastructure revenue year-over-year, driven by cloud services agreements, the acquisition of Cursor, and our growing SI models and software applications, including @Grok @Bot - Delivered year-over-year revenue growth of 69% and income from operations growth of 99% for the Connectivity segment, supported by strong momentum in enterprise and government adoption, and continued @Starlink consumer subscriber growth - Continued to scale nameplate compute, reaching 1.7 GW as of September 30, 2026

$SPCX: "I would be surprised if we're brought on less than 13 gigawatts of compute by the end of next year."

Former $GOOGL employee on why data center capacity and electricity are the real bottlenecks, not GPU production itself, and how unreliable some of the neoclouds are ($AMZN, $GOOGL, $MSFT, $NVDA): 1. According to the expert, open-source models are closing the gap with frontier models faster than expected, with customers increasingly replacing Anthropic and OpenAI models in multi-agent architectures with open-source alternatives starting from Q2 of this year. The shift started with simple tasks like tool calling and MCP, but has moved up the stack to low-level orchestration. 2. The expert identifies capacity as the biggest constraint at the moment, with certain companies' traffic growing 5x while GPU availability, particularly in the U.S. where latency requirements are tightest, simply cannot keep up. Sourcing capacity overseas in Asia or Australia is technically possible but adds latency that most customers will not accept. 3. Hyperscalers offer reliability but are expensive, require multi-year commitments of three or more years, and have limited availability of the latest hardware, with customers already chasing B300s. AI-native companies tend to gravitate toward neoclouds due to hyperscaler pricing and contract rigidity, but neoclouds come with their own reliability issues, with GPUs dropping off unexpectedly and requiring engineering resources to manage fleet stability around the clock. 4. The third tier consists of smaller independent operators who have invested in GPU racks, seeing the opportunity to lease out capacity and recoup costs within a year or two, with the remainder being pure profit. The expert describes these as essentially unusable in practice, with operators who do nothing more than find land, install racks, connect electricity, and set up cooling, with no real cloud management, maintenance, or operational expertise behind them. 5. The expert expects overall GPU availability to increase meaningfully over the next year or two, with $NVDA continuing to ramp production. The real bottleneck is not GPUs themselves but data center capacity and electricity in the U.S. On the supply side, the expert also sees players $SPCX beginning to lease out spare capacity to the market, with more likely to follow. 6. The expert notes that U.S.-based neoclouds typically require 20-30% prepayment upfront, with the remainder paid monthly or quarterly over the contract term, and rebates negotiated at the end for any reliability incidents like traffic loss. APAC-based providers operate differently, with no prepayment required at all. found on @AlphaSenseInc

How could you POSSIBLY be bearish on compute right now when GPU rental prices are doing THIS? According to data from FastGPU, GPU rental prices increased as many as 81 times across providers over the past 60 days, with some of the most important chips used for AI training and inference seeing MASSIVE price increases. NVIDIA B300 rental prices increased 52.8%, going from $8.50 to $12.99 per GPU hour. H200 prices jumped 33.3%, B200 prices increased 27.8% and H100 prices rose 30% and the RTX PRO 6000? Rental prices surged an absolutely INSANE 307%. We're watching AI infrastructure companies get absolutely DESTROYED in the stock market, with investors freaking out about debt, financing costs, oversupply, and whether these companies will ever generate attractive returns on their investments. Meanwhile, the actual cost of renting the underlying compute is going UP. If we were genuinely heading toward a massive compute oversupply, you would expect rental prices to start collapsing as providers competed to fill their available capacity. Instead, we're seeing pricing strength across some of the most important GPUs powering the AI industry. And remember, we're still in the EARLY stages of AI adoption. Frontier labs are scaling their training clusters, inference demand is exploding as more people and businesses adopt AI, and the amount of compute required to serve increasingly sophisticated models continues to grow. This is EXACTLY why I've been so bullish on companies like $NBIS , $MU , $CRDO , $IREN etc. I genuinely believe compute will remain one of the most valuable resources in the AI economy for years to come. And while Wall Street continues panicking over the short term volatility, I'm BUYING THE DIP. If you want to know exactly what I’m buying on this dip and how I’m positioning across the compute buildout, check out portfolio below. https://t.co/Hkywss4mqJ

We trained our Muse foundational models, including Muse Wonder, at Prometheus, our gigawatt-scale supercluster in New Albany, Ohio. Across a supercluster with hundreds of thousands of GPUs, even one underperforming host can slow an entire training run. We engineered Prometheus for resilience at scale, achieving 85%+ goodput on pretraining, 90%+ on reinforcement learning and ~47K GPU hours mean time between interruptions during Muse Wonder training. Take a look at the tech powering Prometheus: https://t.co/WSFODnGRST

Hard lighting cut our expert-only π0.5 checkpoint's success roughly in half. The same lights cost the full fine-tune nothing. New from @castorhat: the fine-tuning ablations behind our IROS demo. https://t.co/JciqcCBMYy

Yesterday, @OrnnExchange's B200 and B300 indexes reached a new all-time high prices, with the B200 hitting $8.54/GPU-hour. It is clear that today's market is lacking GPU capacity, and that the companies building deploying capacity are looking to improve their credit. In the past trading week as the B200 rental price increased by 12.9%, $CRWV fell by 25.2%. Why does $CRWV move inversely to rising compute prices as a neocloud? 98% of Coreweave's Q2 revenue came from committed contracts, 72% from three customers, and most of its $104B backlog is recognized after mid-2028. CoreWeave's backlog is locked at fixed prices. These higher market rates matter for new contracts they sign, so the question is where rental rates end up when their contracts roll off. Ornn's index tracks transacted prices in order to capture what buyers are actually willing to pay for on-demand GPU rentals, real signal for tracking the value of compute.

The AI infrastructure buildout is about to enter an ENTIRELY different phase in 2027. SemiAnalysis just released a new report forecasting 51.2 GW of new data center capacity in 2027, compared to 25 GW in 2026 and just 15.6 GW in 2025. We're talking about more than DOUBLING annual capacity additions in a single year. To put that into perspective, SemiAnalysis estimates that the industry could add roughly as much data center capacity in 2027 alone as it had built cumulatively through 2024, excluding China. And here's where things get REALLY interesting. While some investors, including Brad Gerstner, have questioned whether the industry can actually deliver this much capacity, SemiAnalysis points to satellite imagery showing more than 24 GW of data centers already in advanced stages of construction, with those facilities expected to be delivered in 2027. On top of that, the firm reports roughly $1 TRILLION in binding, non cancellable data center leases signed in 2026 so far. Now, think about what this means for the ENTIRE AI ecosystem. Every additional gigawatt of data center capacity creates enormous demand for GPUs, HBM memory, networking equipment, cooling systems, electrical infrastructure, and POWER. And remember, adding data center capacity doesn't automatically mean every facility will be fully equipped with GPUs on day one. In fact, SemiAnalysis is already raising the possibility that as power and construction constraints ease, the next major bottleneck could once again shift towards semiconductors and memory. And here's what I think a LOT of investors are missing. More compute capacity doesn't necessarily mean demand suddenly gets saturated. As inference becomes cheaper, AI adoption can accelerate across autonomous agents, enterprise applications, robotics, and eventually physical AI. Lower costs can unlock entirely new use cases that simply weren't economical before, creating another wave of demand for compute. If SemiAnalysis is even remotely close to being right, we're about to witness one of the largest infrastructure expansion cycles in history. And I believe the companies supplying the compute, memory, networking, and power behind this expansion could be some of the biggest beneficiaries over the next several years. If you want to see how I’m investing around this next phase of the AI infrastructure buildout, check out my full portfolio below: https://t.co/Hkywss4Ugh

China is imposing export controls on more drones and machinery, as the world’s second-biggest economy flexes its industrial muscles. https://t.co/rV2sxNCoHP

Impressive… NORINCO showcases its explosion-proof humanoid robot, called Fuxi, at the WRC. It is designed for reconnaissance and patrol missions in high-intensity environments, assisting in emergency response. Each humanoid robot can replicate the actions of its assigned operator in real time. The goal is to create a fleet of humanoid robots to replace human personnel in dangerous missions.

This flight shows the power of crewed-uncrewed teaming and the advantage it provides our warfighters. We must test often, learn fast and give commanders what they need, when they need it. https://t.co/R7drAByBS5

One of the biggest mistakes VCs make is confusing a founder who’s great at raising money with a founder who’s great at running a company. Some founders are incredible storytellers. They know exactly what investors want to hear. They have the perfect deck, the right connections, and can make a $50M valuation sound like a bargain. Every meeting ends with investors fighting to get into the round. Then you look at the actual business. Customer churn is terrible. Revenue growth is slowing. Nobody knows what the company spends every month. But hey, the founder just landed another podcast interview. I’ve invested in founders who weren’t particularly polished. Some were awkward in meetings. Some couldn’t explain their market opportunity in 30 seconds. But they knew their customers, understood their numbers, and could actually execute. Those are the founders I want to back. Being charismatic is an advantage. Being able to raise capital is absolutely a skill. But neither automatically makes you a great CEO. The best pitch doesn’t always win the market. And sometimes the founder who struggles to raise $2M is building a much better business than the one who just raised $50M. VCs love pattern recognition. Maybe we should spend a little less time recognizing great pitch decks and a little more time recognizing great operators.