Aug 10, 2026 · 9:56 PM UTC
This is a big moment for computing and @NVIDIA. We’ve made the leap from building chips to creating a new investable asset class: AI factory infrastructure. Every company will be powered by it. Every country will build it.
Thanks to Larry Fink of @BlackRock, Jon Gray of @blackstone, Bruce Flatt of @Brookfield, David Solomon of @GoldmanSachs, Jim Zelter of @apolloglobal, and Waldemar Szlezak of @KKR_Co for joining me in this historic partnership to build the infrastructure of the AI industrial revolution.
Compute and verifiable knowledge are an asset class
xitter.twitterapi.xyz/DrevZiga/status/208694…
One small step for a couple of agents on @origin_trail DKG…
✅ SPARQL query: sold and settled in $TRAC
✅ Inference: sold and settled in $TRAC
The Neurosymbolic Marketplace prototype is a two-sided market for verifiable knowledge and AI inference. Both buyer and seller agents run their own nodes. Every charge is locally recomputable. Settlement is real $TRAC on @Base mainnet.
Next up: an RFC to take it from prototype to protocol — a keystone of the refreshed roadmap.
NVIDIA offers unmatched ROI. That’s why builders want to build with NVIDIA in the first place. Financing should be viewed as an accelerator of that existing ROI advantage, not the reason the demand exists.
patreon.com/The_AI_Investor/…
How will NVIDIA’s residual-value support mechanism (capped at up to 25% of each opportunity) be structured in practice within these independent financing platforms, and how will this support fully eliminate circular-financing concerns without compromising the investors’ completely independent underwriting process?
INVEST IN THE FUTURE
NVIDIA PARTNERS WITH 6 WALL ST. FIRMS ON $500B AI FUNDING
“I want to thank all my partners for joining me here today. I think this is the first time this has ever happened before, and I can't imagine a more important time to do it. We're announcing six partnerships today.
These partnerships are gonna pull together independent long term capital to fund and support AI infrastructure buildout. This is an extraordinary time, as you know, because this is the first time in some 60 years that the computing industry is going through a fundamental platform shift. From the way that software was done before, to the way that it's gonna be done in the future, called artificial intelligence.
Fundamentally, what's different about this industry, and this way of doing computing is that the computer is now part of the infrastructure, like electricity, like the internet. And so you have to think about it like its infrastructure, and build it out accordingly. Every company will powered by it, every country will build it.
And so we're talking about an extraordinarily significant infrastructure build. It's a hefty price tag. Each gigawatt is something like $50-60 billion. And so there's energy involved, there's land power and shell involved, and, of course, there's a computing part of it.
This is, of course, also a milestone for our company. We used to build chips that we sell, and these are technology components that people buy and use. But now, MVIDIA's AI factory platform is really an investable asset, an infrastructure asset.
And the reason for that is because it's productive as revenue generating, it is fungible. It's used by just about every cloud service provider. It runs every AI model.
It runs algorithms of all different types. And so it has really broad, deep reach, and offtakers. This is a really great opportunity for us to build out the infrastructure, take advantage of an asset that is investable, long-lived, and productive. And with the partnerships that we have here, we can support a really broad ecosystem build out.”
@JensenHuang @nvidia
via @BeckyQuick @SquawkCNBC
ht @apolloglobal @BlackRock @blackstone @Brookfield @GoldmanSachs @KKR_Co
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I just explained the numbers here:
NVIDIA just convinced the biggest credit firms on earth to bet $500 billion that Michael Burry is wrong
The bet is over ONE number: how long does a GPU live?
Jensen thinks it's longer than most and a new asset class w/ a secondary market
The hyperscalers write off GPUs over 5 to 6 years (Amazon uses 5, Meta 5.5, Google 6)
On the other hand, Burry's famous bear thesis says the real life is 2 to 3 years becuase the newer models make the older models effectively worthless. If true, this would mean the industry is overstating profits by roughly $176 billion from 2026 to 2028
Here's why this one number matters so much. On every $100B of GPU capex:
1. 3 year life = $33B a year of depreciation expense
2. 6 year life = $17B a year
3. 8 year life with resale value = $10B a year
The gap between Burry's world and Jensen's world is $20B+ of profit per year on every $100B spent, and the industry now spends well over $500B a year!
So who's right and is this new $500B AI infra fund a risk on the market?
Let's look at what the market is paying for it right now: A100s are 6 year old chips, fully depreciated, worth roughly zero on the books, and their rental price is UP about 25% this year (see the chart I reposted below) A supposedly dead asset from Burry has been climbing all year
Azure ran V100s for 7.5 years before retiring them, Google still runs 7 to 8 year old TPUs in production. Old GPUs don't seem to die, they get demoted from training to inference and then continue earning cash
That's what Jensen showed the credit desks, and it changes everything, because an asset with real resale value can be financed more similar to something like an aircraft
Airlines lease planes instead of buying them because lenders trust the residual value. If GPUs work the same way, customers don't need equity to buy chips anymore, credit does it, and the pool of NVIDIA buyers expands massively
It looks like Jensen is going to be right about this (which makes sense, he is the King of GPUs) and $500 billion of the smartest credit money on earth too his side too
This is extremely bullish for NVIDIA of course, but also the hyperscalers and even neoclouds who are the main buyers/sellers of GPUs. The longer their assets retain value, the more they will buy and resell
If you want to see how I'm investing in this market you can track my entire portfolio and research in real-time, alongside 4 other analysts, inside Milk Road PRO. It's currently just $1 to try it out (insane price just to check it out) and you can learn more and join here: link.milkroad.com/Kyle
Good luck out there!
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The interesting shift is that AI compute is starting to get financed like infrastructure instead of purchased like hardware. Once institutional capital underwrites utilization and residual value, the market can scale far beyond the balance sheets of individual AI companies and I think this is a pretty meaningful transition.
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The most annoying aspect of buying used items is not the difficulty in finding them; rather, it’s the large price differences for the same lens on different platforms, requiring one to check each one individually. This time I sent an iMessage to @airtap_ai, who then went through all 20 listings on eBay and Mercari right within the cloud phone – checking the prices, the descriptions, and assessing the condition of the lenses as well as any accessories they came with. What he sent me back wasn’t a list of links, but rather a statement indicating what a reasonable price should be for that Sigma 35 lens, as well as which listings were fraudulent.xitter.twitterapi.xyz/RealYDT/status/2086638…
兄弟们，你们平时买二手东西是不是最怕买贵？
我现在直接发条iMessage就能让@airtap_ai替我刷eBay和Mercari。
最近我在看 Sigma 35mm F1.4。
买二手最烦的其实不是找不到，而是同一款东西在不同平台上价格能差一大截。
有的便宜是因为成色一般，有的贵是因为配件齐，还有的看着像捡漏，点进去才发现有划痕，缺配件，或者版本根本不一样。
真要自己比，就得在 eBay 和 Mercari 里面一个个点开商品看描述。
所以这次我直接给 Airtap 发了一条 iMessage。
让它先打开 eBay，搜索 Sigma 35mm F1.4，找到最新发布的 10 个商品，记下价格和商品描述。
然后再打开 Mercari，也搜同一款最新发布的 10 个商品。
最后把两边放在一起比，看看正常成色现在大概卖多少钱，哪些明显偏贵，哪些虽然便宜但有坑，哪些值得我继续留意。
然后它就在云端手机里自己打开两个 App，把 20 个 listing 一个个翻完，再把结果整理回来。
我觉得这里最好用的地方不是它能够收集商品信息。
而是它一边真的在 App 里翻商品，看价格，看描述，一边又能用 AI 去判断成色，配件，瑕疵这些东西值不值这个价。
最后我拿到的也不是一堆链接。
而是这款镜头现在大概什么价格算正常，哪些贵了，哪些低价但实际是因为有明显问题，还有哪几个值得我再点进去仔细看。
这种事情我自己当然也能做。
问题是太烦了。
尤其是你还要同时翻几个海外二手平台的时候，真正浪费时间的不是搜索，而是那几十次重复点击和比较。
现在我可以直接发一条 iMessage，把这部分丢给一台云端手机去跑。
以后再想买海外二手东西的时候，我都会先让它替我摸一遍行情。
至少先别当冤种。
感兴趣可以去 airtap.ai 获取号码，直接用 iMessage 开始体验。
@airtap_ai
#justtextit #textyourapps #airtap
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Thoughts on Jesen’s write up
- big Wall St. firms are working together instead of competing. Tells you size of the pie
- $NVDA carving an edge with CUDA. Competitors don’t have an equivalent. Etched is baking in transformer architecture into the chips and TPUs are not general purpose. NVDA remains jack of all trade and even master of some
- AI factories as nomenclature is good. It’s broader than just data centres which is old world
- 25% financing on case by case basis makes it fluid
Ultimately — this is TBTF — any corrections will be short lived — the cavalry will find ad infinitum ways of making this work
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