u/Smorgasbord3

With Nebius and CoreWeave announcing good results and their stocks going up, I think it's worth pointing out that SpaceX's rental deals with Anthropic and Google mean it has to be considered as a NeoCloud provider itself.

At SpaceX's company meeting, Musk claimed that by as early as next month (Sept), the revenue from AI will be more than from the rest of the company combined.

https://www.benzinga.com/markets/prediction-markets/26/08/61145623/spacex-ai-100-billion-revenue-run-rate

Grok isn't what Musk wants it to be, but renting out GPUs is a way for SpaceX to bring in some revenue and profits while it tries to make Grok more than a second or third class LLM. Remember, Space-X is charging more per GPU-rental-hour than anyone else, yet Anthropic and Google have signed on. Why? Because the compute capacity is available now. This is a big advantage over the other 3 NeoClouds.

SemiAnalysis published a recent article on GPU rental and SpaceX's role:

https://newsletter.semianalysis.com/p/spacex-10gw-in-2027-why-its-real

"Elon needs to build datacenters faster than anyone else. We believe that he can. What gives us this confidence? We’ve written a few times about Elon’s speed, with 122 days to build Colossus 1’s 300MW, six months to build 200MW at Colossus 2, the decision to build an onsite generation plant 1km across the border to avoid permitting, and much more.

There’s been even more displays of speed since then. The power plant in Southaven has expanded from 27 turbines (~495MW) in February 2026, to 69 turbines (1.7GW) in July 2026.

As well as the arrival of “MiniHard,” which upon vertical construction in March 2026, will likely reach 450-500MW in just ~5 months! That doesn't mean Elon builds better than everyone else. He's just applying a different playbook.

Switchgear and large power transformers are sold out for 2 years? Just buy power modules from China, and skip LPTs by delivering medium voltage power from power gen to low voltage transfos, which are much more widely available. Elon’s companies have the world's most talented electrical engineers - no one knows better than them how to manage speed vs efficiency tradeoffs. Most datacenter operators in the world optimize for efficiency and quality - it's the only way to land a 15-20 year take-or-pay hyperscaler datacenter contract. SpaceX will focus on entirely different tradeoffs: it's speed above all. In times of compute constraints and extremely high AI token margins, a 500mw cluster available in three months, with 90-day cancellation policy, is one of the most scarce and valuable asset in the world. All typical “quality” metrics don't matter. Proof? Google, the most vertically integrated infra company in the world, somehow ended up signing with SpaceX.

Gas turbines are 5yr+ backlogged? GEV’s are, but there are plenty of other options - our Energy Model 30+ manufacturers of gas gen equipment that have secured large scale orders to serve datacenters. There is plenty of available capacity if you look hard enough and you're open to working with new suppliers. Secondary market volumes for turbines is surging - for example, all the turbines initially scheduled to go to Oracle’s New Mexico site are on the market. Secondary market prices are very high, but Elon can pay.

Labor is the ultimate constraint? Just parallelize as much as possible, reduce the commissioning process, and preassemble as much as possible. Reports out Colossus 2’s peak daily labor at ~3k construction workers, which as about ex lower than other gigawatt-scale datacenters under construction. Elon has a long history of accomplishments with less staff than industry standard - just look at Tesla and SpaceX’s history."

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What does this mean for the legacy NeoCloud providers? The good news for them is that demand for GPU hours is still through the roof, so everyone will sell whatever they can put together. But, SpaceX is going to get a lot of attention and business due to timing.

This also reinforces for me that long-term the NeoCloud business will suck. Despite what Nebius and CoreWeave are saying, the big deals don't include software infrastructure; the big model providers and hyperscalers want raw GPU access. Sure, there will be smaller startups, including some AI application companies, that may want to have access to some software infrastructure so they don't have to hire that expertise internally, but that's a tiny portion of the business today, and I think unlikely to grow to be significant.

I'll be taking advantage of this pop in the NeoClouds to unload my positions. There's probably still some money to be made, but I think there are greener fields, especially in the medium/long term, to plough.

I think Mr. Market doesn't understand Nvidia's latest news. He thinks it's yet another form of "circular financing," which it's not. It's Nvidia establishing its GPUs as actual assets that have value and so can be used as collateral for loans, much like United Airlines takes loans to buy 747s.

ICYMI: Nvidia has brokered deals with 6 of the top financial companies: Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR for them to provide a total of $0.5Trillion of financing for Nvidia GPUs and servers.

https://nvidianews.nvidia.com/news/nvidia-partners-with-apollo-blackrock-blackstone-brookfield-goldman-sachs-and-kkr-to-establish-ai-compute-infrastructure-financing-platforms-to-mobilize-over-500-billion-of-third-party-capital

This is NOT NVidia backstopping the financing, this is these companies agreeing that Nvidia's server racks have value and should some AI startup or high-flying neocloud want to get a loan to buy them, these companies will loan them the money, using the Nvidia hardware as collateral in case those companies fail. They're willing to do this because the market for Nvidia servers is so strong that they're confident they'll be able to sell them to get their money back. Like an airplane loan or a car loan.

For those who doubt the long-term viability of GPUs, note the recent CoreWeave earnings had them revealing they just did a contract for Nvidia A100 rental through 2029. That chip is 6 years old right now, and will be 9 years old by the time the contract ends. So much for the old 3-4 year depreciation schedule and all the worry over companies extending those to 5-6 years. If A100s are good for 90 years, what do you think the usable life of Blackwells and Rubins will be?

And there's another layer - this is ONLY for Nvidia GPUs and servers. Part of what these big financial companies are agreeing to is that Nvidia hardware and software are "investable assets." There are two drivers enabling this:

1. The general purpose nature of Nvidia's offerings. Unlike ASICs (TPUs, Trainiums, Maias, WSEs, etc), Nvidia's GPUs are general purpose and are suited for both training and inference, and a wide body of software runs on them.

2. CUDA. Part of what enables the general-purpose applicability is CUDA, the software layer that runs only on Nvidia GPUs. But CUDA also enables older chips to perform better than they did when they were new. Benchmarks of Nvidia need to include what version of CUDA they're running since those 6 year old A100s are more than 15% faster today than when they were first sold. OTOH, Google is giving away TPU v4 (circa 2021) cloud access for free.

I don't know if Jensen Huang is going to be able to make all this clear to Wall Street in the upcoming days. I've seen much misunderstanding of it on social media. This is Nvidia showing that their products are far better than custom ASICs many investors seem to be worried about.

You've probably seen the headlines where 4 top AI researchers have left Google. They include Jeff Dean , Google's chief scientist and one of its most consequential technical leaders, who is leaving the company to become chief executive of Discovery Loop. He is joined by longtime collaborator Sanjay Ghemawat , as well as Quoc Le and Oriol Vinyals. Inventors of Transformers and other technology that led to the AI world we know today.

All sorts of comments are being made around how this affects Google, what it means for its Gemini product, its AP Capex, etc. But, what I found interesting as a tidbit is that despite being co-creators of Google's TPU silicon, Dean made the announcement that the new company won't be using TPUs.
https://radical.vc/our-investment-in-discovery-loop

https://dealroom.co/news/143272-googles-top-ai-minds-exit-to-build-discovery-loop-with-alphabets-backing/#:\~:text=Dean%20also%20noted%20that%20Google's,on%20Google's%20TPU%2Dbased%20systems.

It'll be interesting see where they land, compute-wise. Musk has already thrown his support behind Nvidia, and I'm laying odds Discovery Loop will do the same. ASICs like TPUs are good for specific things, but this is a new frontier and they'll want the best performing flexible silicon, which is Nvidia's GPUs.

https://youtu.be/9dyJleicVOM?si=ozl8vYDrEI4AtBAW

Drew gives great background on the history of their acquisitions and internal development, company culture, and how the company makes money today, covering the different products and how they intertwine. If you struggled to completely understand the business, this video will lay it out well.

As usual, since he's a registered investment advisor, he won't give a summary recommendation. I'm continuing to hold today.

Watch this:
https://www.youtube.com/watch?v=u0KGuSq-UEM

Read this:

https://blogs.nvidia.com/blog/vera-rubin/

If Nvidia truly stays ahead of ASICs, not just on performance but performance per watt, and adds in "a Vera Rubin NVL72 system with no cables, fans or hoses in the tray, cutting compute tray assembly time from hours to one minute. A 45-degree Celsius liquid cooling inlet temperature design enables chiller-free dry-cooler operation. For new AI factories, this higher-temperature dry cooling along with the closed-loop liquid cooling system saves millions of gallons of water per megawatt annually."

Then perhaps ASICs continue to do well only because companies can't get enough VeraRubin allocation? And, then, what does that mean for ASIC adjacent companies like Astera Labs, which I see is down over 10% today for no news on the feed?

In a new article yesterday, SemiAnalysis goes after Meta hard:
https://newsletter.semianalysis.com/p/metas-infrastructure-team-needs-a

"Meta Infrastructure has become bloated, with middle managers expending resources on over-engineered technology solutions that lose sight of broader organizational needs. The company appears burdened by far too many disparate groups that are over-optimizing for certain metrics as opposed to delivering usable technology for the company as a whole. "

They give examples from having a 6-month performance review cycle that has a resulting bottom 10%-15% layoff (encouraging short-term thinking instead of long-term execution), to engineering design and supply chain teams being completely separate, frequent pivots and canceled projects, lack of financial discipline, bad handling of acquisitions, side-effects of company-wide decrees from Zuck, even that engineers can't get assigned desk stations - they have to grab what they can when they get in the office and often times equipment isn't working.

They then detail a few specific AI server hardware choices that were optimized for things the software team didn't actually want/need, and so were non-optimal in other ways, sometimes (like "Grand Teton") ending up worse than if they had just taken the standard configuration from Nvidia or, most recently, AMD.

It's quite the read, and if Meta's engineering culture and execution are half as bad as stated, it's a company I'll continue to stay far away from. Zuck screwed up big time with the "Metaverse," and it appears they're on track to mess up with AI as well, although maybe not quite as badly.