Conor Moore retweeted
spiritually, it feels inevitable that AGI is born in California as a kind of useless and largely uneconomic toy, and then dragged to New York, caged and put to work by men in grey tall towers that poke and prod it until it spills trillions out filtering through PDFs
Truly believe this is an education gap btwn credit markets and ai. Working to close it. Spoke with about 25 asset managers this week collectively representing couple hundred bn. They want to deploy
Conor Moore retweeted
We’re co-hosting a conversation on the future of compute with @USDai_Official × @Alliance in NYC on 10/28.
We’re bringing together founders, traders, GPU buyers and sellers, investors, and operators to discuss the most pressing questions in compute, energy, and frontier tech.
Have a hot take? Building in the space? Join us.
luma.com/theopenfloor
With all of the focus on capex heavy businesses, VCs need to establish credit sleeves to stay relevant in competitive equity rounds
Natural evolution of things. Owning servers in rented Colo space is not differentiated. The end state is that every company will own their own gpus just like every company has a website. Data privacy is the only defense legacy businesses have against hungry ai labs
it's going to get harder to stay a neocloud. if you can't raise enough $ to keep up with buying chips and power, you'll get bought
probably not by other neoclouds though. we're seeing more neocloud acquisitions, but never by other neoclouds because there's no point
a neocloud gets almost nothing from buying another neocloud:
- for gpus, big neoclouds can buy new ones directly from nvidia. (though in a shortage, buying a struggling small cloud just for its gpus could maybe make sense depending on rising prices)
- no power. most neoclouds rent their data centers
- acquiring a neocloud means taking on the debt that they have from borrowing $ to buy gpus
that's why we see neocloud acquisitions by non-neocloud companies:
- @VoltagePark merged with @LightningAI
- @MistralAI bought @gokoyeb
- @Brookfield bought ori
- @rumblevideo bought @NorthernDataGrp
buying a neocloud makes more sense for a video site or an AI lab
when neoclouds buy, they buy software and power
The outcome of this constraint is many more small clusters. To turn non-IG credit into IG, you need diversification. That’s the alchemy that unlocks large swaths of capital. Not 1bn to one borrower, 1bn to 100 borrowers. Then you can forget the Nvidia backstops and ig wrappers, and turn GPUs into a standalone financable asset class. Standardization is what makes ai legible to Wall Street.
The parallel constraint in the power market is already pushing us towards 2-5mw deployments instead of 250mw. Both of these forces will be positive for the sustainability of the industry, as small deployments with a couple of month lead time can better match supply to actual, current demand, reducing the risk of overbuilding.
Ironically, over time we might even see these smaller deals pricing similarly to larger ig deployments, despite worse credit, as the project complexity becomes a major tradeoff if there is an abs market to sell into.
The AI buildout needs more capital than backstops can cover.
“There’s bank lenders, insurance lenders, they don’t want to take a lot of risk. You’ve got private equity, they want to take a lot of risk. You’ve got private credit, they’re in the middle.”
“What you do is you tranche risk. If there are losses, the equity will take the loss first, then the B piece, and then part of the A2. The loss has to be so severe to affect the senior tranche, which is how you can create different risk profiles.”
“We think NVIDIA has the capacity to support up to 46 gigawatts. That’s still not enough. There’s 240 gigawatts that’s gonna come in.”
“The lending market has to evolve to take risks outside of backstops. It’s really the biggest question of our time.”
As someone who is definitely on “the edge” of ai infra financing, I can tell you the days of cowboying back-to-back contracts and calling yourself a neocloud are over. If you aren’t actually differentiated, you are toast.
Conor Moore retweeted
Crucible Capital founder @Melt_Dem reveals how she financed her own GPU cluster with stablecoin debt and personal credit risk because she refused to sit out the AI boom:
"We're a no-name operator. They're like, who are these two women building compute? Their perception's like, who's this crypto lady?"
"We got debt from USDAI, which is a protocol that crowdsources capital through stablecoins, and the GPUs back the loans. We also took on some personal credit risk. I think being levered to the tits is a great way to live."
"In the greatest build-out of infrastructure in human history, are you really just gonna sit on the sidelines and tweet? I'm gonna ship some bangers, and I'm gonna take some risks, and if I die, I die."
"I'm not gonna sit there and tell my children, during the AI boom I sat back and I tweeted."
@CrucibleCap
Construction is hard and speculative. Operating an installed cluster is by comparison easy and predictable.
Waiting for installation before funding is an under appreciated risk mitigant to financing GPUs
Sources: ~$18B of debt tied to an Oracle New Mexico data center slid into stressed territory, as investors grow wary of construction delays amid local pushback (Financial Times)
(Visit Techmeme dot com for the link and full context!)
compute twitter has finally come to the realization that "everything is neocloud" as the industry converges on owning gpus as the optimal use of funds
the next coreweave is no coreweave.
the new american dream is to own your own compute.
i spend one half of my day listening to lenders say GPUs will be worthless in 24 months
and the other half listening to neo labs say GPUs will cost 10x more in 24 months
a truly incredible opportunity to be in the middle
Grateful for the partnership and support of @ThomasFarley , @BonannoDavid and the rest of the @Bullish team.
Bullish is uniquely positioned in its depth of expertise across both DeFi and TradFi, allowing them to understand and underwrite all of the components of the USDAI products holistically.
This is just the beginning.
Compute is rapidly becoming one of the largest credit markets.
@Bullish (NYSE: BLSH) is entering with a $100M stablecoin liquidity facility for USDAI.
The facility will support USDAI loans backed by the AI buildout.
Made possible by @USDai_Official
VCs (should) want their own compute. Do they actually want to be a neocloud?
I've chatted with a few different VCs owning/operating their own clusters, but they don't have the infra team or the operations team to deal with issues from cluster uptime to pricing. Some funds, especially those with neocloud connections, effectively outsource the whole thing to a neocloud. @ycombinator's dedicated cluster, for example, is operated by @togethercompute. Others are closer to owning the underlying capacity themselves, finance + built it, and need an operations layer on top.
Several startups are already looking into operations as a service for VC compute. Separating ownership from operation allows for capital exposure to compute + for higher liquidity and margins (VC clusters often don't have long term locked in contracts). Splitting financialization from logistics is just the start.
The optimistic view of these insane progressions in ai and robotics is that they will usher in an era of unprecedented artistic human creation as we collectively operate exclusively in the “self actualization” tip of the pyramid
There are fundamentally two businesses in the ai value chain: hardware and software.
The hardware business starts at power/land and ends at token production. The datacenter, neocloud and token factory will all converge upon eachother and compete directly. I think the token factories (ie baseten) are doing the hardest thing in this group and will vertically integrate and eat the other two.
The software business starts at token consumption and ends at consumer use.
The labs and ai applications will compete directly. I think the labs will ultimately develop massive data advantages over the specialized consumer applications and win here. Importantly, the advantage to the labs is NOT in their model weights. It is in the control of broad swathes of consumer data.
$NVDA SIGNS $6B POOLSIDE DEAL FOR U.S. OPEN-WEIGHT AI
Nvidia will pay $6B to license Poolside’s AI technology and invest another $1B in the startup at a $12B pre-money valuation.
More than 100 Poolside employees are expected to join NVIDIA and work on Nemotron, its open-weight AI model family.
The goal is to build a U.S.-based open AI ecosystem capable of competing with Chinese models such as DeepSeek and Kimi, while also offering a lower-cost, customizable alternative to closed models from OpenAI and Anthropic.
NVIDIA is betting Nemotron can rival leading frontier models within the next year.
Source: WSJ
I was skeptical of claims that ai would replace junior investor roles at first but seeing the progress made on the USDAI agent over the past few months leaves no doubt in my mind that the role will not exist in 5 years.
If you’re 20 and want to be an investor better work on your handshake and eye contact. Relationships & deal flow only edge left.