A. @Brainmetry

View of Starship in space from a Starlink V3 satellite on Flight 13. This composite is made of imagery from four separate cameras on a single satellite. Six of the satellites were equipped with cameras to scan Starship’s heat shield and transmit imagery down to operators to continue testing methods of analyzing Starship’s heat shield readiness for return to launch site on future missions

Holy sh*t This is newly released footage of Starship’s landing burn and splashdown during Flight 13. Absolutely insane.

@ZubyMusic Yes.

Market is overreacting to hyperscale credit spreads widening from my perspective. TL;DR Spot pricing for renting GPU compute materially above contracted rates implies hyperscalers are underearning while operating cash flow acceleration is an underestimated source of funds for AI capex. The fact that spot prices for GPU rentals are at least 2x higher than contracted rates is the missing piece from the discussion about hyperscaler credit, which is the only fundamental factor behind this selloff. Multiple private companies are planning on spending at least 2x more per GPU for compute as contracts roll-off and some have spoken about this publicly.   As contracts roll-off, hyperscale growth rates are going to continue to accelerate as their installed bases of compute reprice higher. Hyperscale operating cash flow growth using a mix of estimates and actuals is modeled to accelerate from 31% in the first quarter of 2026 to 50% in the second quarter. This acceleration should continue for the rest of the year and this is not in estimates which incorrectly model a deceleration in the third quarter from my perspective.   Some math. Consensus estimates are probably for 25-35 gigawatts added by hyperscale and neoclouds in CY28 (using a range as standing up datacenters is hard and a lot of the neos plus labs are still private).  At 60b per gigawatt, that is 1.5 to 2.2 trillion in capex. Consensus estimates for hyperscale/neo operating cash flow is 1.3 to 1.4 trillion. I think this gets revised up materially as contracts reprice and growth accelerates so the 100b to 700b that would hypothetically need to be plugged by debt goes away. And their credit profiles materially improve. Not to mention the said 100b to 700b would be less than 1 turn of incremental leverage on consensus EBITDA estimates. And obviously the Nvidia and Broadcom “credit wrappers” help improve creditworthiness as well given their FCF profiles.   OpenAI, Cursor/Grok and the various Open Source inference clouds have accelerated materially over the last two months per public data and Anthropic continues to grow insanely fast while likely generating FCF. This - along with the fact that spot prices for GPU rentals are so far ahead of contract - are the missing pieces from the BofA chart on hyperscale FCF vs. semiconductor FCF.   Hyperscalers are underearning and anyone who signed a contract for GPU compute in 2024 and 2025 is overearning. Operating cash flow will be enough to fund capex but as contracts reprice and cloud growth continues to accelerate then spreads likely come in as well.  
Would also note that CDS markets are easy to manipulate - was a huge feature of the GFC - short the stock and then buy the CDS. So I would not put attach much signal to CDS. 
Net, net I’m not that concerned about the widening spreads in hyperscale credit. The real risk is that bringing power online and energizing all these GPUs is really hard but we are getting better at this every day.

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Give it a read: wsj.com/opinion/the-ai…

My increasingly strong opinion is that compute is the biggest moat in AI. Scientific breakthroughs don’t stay secret for long. Papers get published, researchers move, ideas leak, open source catches up. Give it enough time and almost everyone has access to roughly the same algorithms. What doesn’t diffuse nearly as fast is the ability to run 10x bigger training runs, collect 10x more RL experience, serve billions of inference tokens, and iterate faster because you can afford thousands of experiments in parallel. Scaling compute is brutally hard. It’s GPUs, power, substations, networking, cooling, land, permits, supply chains… and hundreds of billions of dollars. You can’t just decide to build that overnight. The more I look at the industry, the more it feels like the frontier is less about having one genius idea and more about who can compound compute the fastest. Scaling laws reward whoever can keep scaling. That’s a very hard moat to replicate imo!

The Kobeissi Letter @KobeissiLetter

5 days ago

BREAKING: Nvidia is in talks with OpenAI to guarantee $250 billion in financing for a data center in Ohio. Details include: 1. The guarantees from Nvidia would help OpenAI lease a 10GW project that SoftBank is developing in Ohio 2. In total, the project could cost more than

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Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That’s why we created the Open Secure AI Alliance.

NVIDIA @nvidia

5 days ago

AI security advances when the industry builds in the open, together. We're introducing the Open Secure AI Alliance with industry leaders to develop new techniques and tools to safeguard software and agents. By sharing models, tooling and research in the open, we can broaden the

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@Scobleizer Optimus will be running AI5 not Nvidia

For my first post, I’m sharing a letter @NVIDIA signed on why open models matter. AI will transform every industry, power every company, and be built by every country. Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty. The world needs both frontier closed models and frontier open models. images.nvidia.com/pdf/Open-Weigh…

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HyperFrames -> Video Agent Our HTML motion graphics framework, now inside HeyGen Prompt what you want, our agent builds the entire video Avatar, graphics, captions, music Infinitely customizable, just ask Comment + RT "prompt" for a free month of HeyGen (must follow)

@HeyGen Prompt

Just like with FSD, it all comes down to intelligence—the brain. That’s what truly matters. Tesla’s moats are their ability to manufacture complex machines at scale. I believe they’re trying to fine-tune the intelligence to navigate the real world and be truly useful, not just for lab demos.

The mega bull case for AI infrastructure would be *if* market share shifted away from certain frontier labs with 90%+ inference margins toward cheaper models, whether open-source or closed. It would increase the ROI on AI spend for end customers by increasing intelligence per dollar, which would drive incremental token demand. Margin dollars would effectively get redistributed from the frontier labs to AI infrastructure providers. The infra winners would be those with the lowest per token cost and the winners at the model layer would be those with the highest token efficiency. There are many reasons Jensen is so focused on open source, but this is likely the most important one as I think he is probably less worried about a monopsony these days. Lower margin % at the model layer = more margin $ at the infra layer all else equal. With SpaceX and Meta being vertically integrated and possessing the #3 and #4 models respectively it is more possible than ever. Note that Grok 4.5 is ahead of Fable for some useful tasks at a much lower cost, so ranking them #3 is conservative. This is not happening yet. Cheap, mostly open source tokens are likely the majority of volume today but the majority of economic value is still accruing to the most intelligent models. Might change though. We will see.

Cassandra Unchained @michaeljburry

3 weeks ago

This is true as I have heard this from contacts in the Valley. Goes with my pinned post. The AI race is shifting from bigger models to cheaper, smarter systems cnbc.com/2026/07/10/the…

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@SpaceX Let’s go 🚀

The path to launch is filled with obstacles and success is only possible through the tireless efforts of many working together towards a common goal. “Critical Path” continues the ongoing Starship series, following SpaceX engineers through the final days before launch of the first Starship V3 and the challenges that come with development of the world’s most powerful and fully reusable rocket.

@IndustrialSaint @TonySeruga The new Tesla Megapack, also known as the Megablock, features an integrated transformer that eliminates the need for grid connection. Users simply purchase and connect the unit.

Earth from space 🌍 Captured LIVE by Sen's 4K cameras on the ISS x.com/i/broadcasts/1…

“How does it know!?”

23 years ago, we set out to prove that electric cars could be great – not just great electric cars, but the best cars overall. We’ve gone from one electric sports car to  – Over 9 million vehicles on the road – Model Y becoming the world’s best-selling car of any kind only 3 years after first deliveries   – 5 Gigafactories & other manufacturing sites across 3 continents – The largest & most reliable fast charging network w/ over 80,000 Superchargers globally  – Energy generation & storage systems helping power homes & grids (over 1 million Powerwalls installed, 70+ GWh of industrial energy storage operating globally across 2,200+ projects) Today, we’re bringing AI into the real world with autonomy @Tesla_AI and robotics @Tesla_Optimus.  Tesla is only getting started – a world of amazing abundance awaits

@wholemars Energy