Mr. Orange

Casually disguised. Fascinated by people

[Clark Tang @_clarktang]

The reality is what we are seeing unfold is Nvidia speedrunning the creation of a synthetic hyperscaler.

Apologies in advance to all the investors who are stuck in their priors that this will trigger.

But what is a hyperscaler? Strip it down and it’s a scaled infrastructure collective of CPUs, networking, storage with a development platform on top. It fulfills two purposes. Financial: it pools and smooths the financial obligations of its users, renting infrastructure as opex instead of capex. And Operational: it builds software that makes consumption the underlying primitives simple by abstracting them away.

The hyperscaler makes a healthy 35-40% operating margin by buying hardware at bulk pricing, pooling scale to get a lower cost of capital, and driving utilization of that hardware with software that shares and shards workloads across many customers.

But in the age of AI, the atomic units of compute changed. Training (massive coherent clusters) and inference (agentic workloads) - require a fundamentally different configuration of resources. These new workloads require dramatically more accelerated compute, shifting the design target from multi-tenant utilization (the cloud era) to absolute workload performance (the AI era). The economics of the data center inverted. A giant, redundant fleet of Amazon Basics CPUs and storage doesn’t work when the job is synchronous training and one straggling node stalls the entire cluster. For inference, tokens per watt and time to first token dominate the economics, not how many VMs you can pack in a box. And none of it works in a world of limited power (at least in the West. Maybe in China).

As Nvidia built more compute and sold it to the hyperscalers, it faced a fundamental problem. The hyperscalers had classic innovator’s dilemma - expecting 35-40%+ op margin, along with an underlying desire to commoditize Nvidia's 75% GMs with their Amazon Basics equivalent. Pay an ASIC vendor a 25% margin instead of Jensen’s 75%, then stack your own 40% on top! They owned the customer relationships too, enterprises developed on AWS, Azure, GCP and their data was captive there too. But most important of all, these companies moved at their own pace. They were not scrappy or hungry to operate at the pace Nvidia or the AI labs felt was necessary to build out compute to fulfill the demand in front of them. They would never look at retrofitting a 35MW site outside of Ashburn, Virginia!

Meanwhile, a group of hungry entrepreneurs noticed the fat margins the hyperscalers earned renting what was basically stock Nvidia hardware with limited software on top, and started building businesses around it. Nvidia - skeptically at first - recognized that working with these partners would lead to faster development cycles and competitive fires and pressures for the ecosystem. Thus the neoclouds were born.

The software these neoclouds co-developed with Nvidia were purpose built for the new workloads. They solved the new problems and requirements operating the new infrastructure needed. They were ready with hotswaps, they did predictive maintenance, they built new storage software that was built for training with cheaper ingress and egress fees, because their competitive drive was to win workloads, not to lock in enterprise data on their platform. And it was working - AI labs started preferring to work with them over the hyperscalers. Common complaints on the incumbents: too slow, too particular with how their clusters were built, virtualization and networking overlays that made GPU clusters underperform stock Nvidia reference designs. Neocloud bare metal was cheaper too as their teams built AI software, not a cloud data warehouse business. And they were happy to run at half the margin (~20%) that the big guys would never accept.

But the hyperscalers still had one structural advantage: their balance sheets. Investment grade. Able to fund speculative capacity ahead of demand and rent it out at much higher spot rates. The neoclouds couldn’t play that game as lenders would only finance hardware that was already contracted with offtake. And more expensive if that offtake were the labs which at an earlier point were much more speculative. If only they could build ahead of demand, they could maybe earn the kind of returns Elon is achieving on Colossus.
But the twist is that balance sheet edge is eroding in real time. Google just printed its first negative-FCF quarter and raised $50B equity. Microsoft is carrying $329B of leases signed but not yet commenced. Even the IG balance sheets hit the wall - more capital had to come from somewhere else.

And that's how we got to where we are today. Look at what Nvidia has actually built. The operational half of a hyperscaler: DSX OS and Mission Control to run and operate GPU fleets, DSX reference designs and Omniverse digital twins as hardened playbooks for building a data center itself. Dynamo for inference serving. All the old secret sauces of the hyperscalers built specifically for new age data centers that they have led the way in architecting. Offered to any hungry, technically competent team with a serviceable site.

And then the financing half: the revenue share and credit support model that smooths utilization across a distributed fleet the way multi-tenancy used to. Support the operator, release capacity to demand, share in upside, and now bring $500B of third-party capital to the table. Nvidia standardized the asset with reference designs, proved the compute was “fungible and transferable across customers and operators” and showed infrastructure investors DD unlevered yields across 7-8% hurdles. Those investors wet their beaks on early special situation financings, saw the paybacks, and understood the demand was global. That’s why Jensen spent 2025 flying around Europe, the Middle East, and Southeast Asia - these are the ground zero for new compute sites. The reality is that this didn’t happen just over the last 3 months. CoreWeave master agreement in 2023, the $6B spot reserve backstop in 2025 (to sponsor capacity for the inference clouds), the Blackrock AI Infrastructure Partnership in 2024, Brookfield’s $100B fund with Nvidia in 2025, KKR Helix with Nvidia in 2026. And now six independent financing platforms. Chess!

So now the three fears by name. Circularity? Monday was the opposite with Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs, and KKR bringing third party capital, independently underwritten apart from one another, replacing Nvidia’s balance sheet rather than just extending it. Useful lives / underwritability of these assets? CoreWeave just disclussed A100s, 6 year old silicon contracted through 2029 and pushed 25% price increase on its fleet in July. The collateral is aging more like an aircraft than a smartphone as feared. Market share? If you don’t see that the platform of Nvidia and the fungibility of this compute is the reason why this is even possible - the skeptics themselves are making the bull argument. The complaint that these platforms keep capital tethered to Nvidia and away from other ASICs / accelerators… $500B that can only buy Nvidia reference architecture is a moat dressed up as a risk.

So what were you doing when the first synthetic hyperscaler was built under your nose? :)

All views expressed are my personal views. Does not reflect the views of Altimeter or Nvidia or anyone else. Full disclosure I/we may hold positions in companies mentioned. Purely for discourse and thinking - no financial advice.

[@jason @Jason]

I have great respect for individuals who leave their dream job to start a company.

It's scary to take that risk, but it's worth it.

[Mr. Orange @MaestroOrange]

How long until @travisk becomes CEO of @Uber again?

[Mr. Orange @MaestroOrange]

@Ronanchamberss An Atoms competitor. Apparently Demis was spotted on a jetski on the Thames

[Mr. Orange @MaestroOrange]

Goes exceptionally hard

[etn. @etnshow]

they have woken the beast

[DAN KOE @thedankoe]

If you even slightly entertain the thought of failure, you give your mind a chance to start noticing reasons to fail. But if the only option on your mind is to win, then the world seems to conspire in your favor, because your mind will notice opportunities it wouldn't have before. Become delusionally optimistic, not because it's realistic, but because it's effective. It drastically increases your chances of success.

[Mr. Orange @MaestroOrange]

@JJEnglert GTM GUIDE

[Mr. Orange @MaestroOrange]

@BenyaminHolley Salesforce

[Mr. Orange @MaestroOrange]

One of the clearest and resonating career and life advice.

[Emmett Shear @eshear]

When I was CEOing at Twitch one of the thing I’d do every batch of interns was a very short presentation on the origins of the company and then a Q&A. One of the questions was always, “Where should I work and what job should I get, or should I start a company?”

[signüll @signulll]

working at a big company is basically an accelerated course in how power actually works. once you see the sheer level of inefficiency, rent-seeking, & arbitrary decision-making, it kind of breaks all illusions about big companies.

the best part is realizing that half the people in charge have no clue what they’re doing but just sound confident. once you internalize that, you stop overestimating the competition & start realizing that most barriers to entry are just psychological.

[Mr. Orange @MaestroOrange]

@signulll Wise lesson. Don’t confuse confidence with competence

[Mr. Orange @MaestroOrange]

Imagine if Zuck and Elon finally fought at the end of the inauguration.

He did say any place, any time.

[Aaron Levie @levie]

Coming off of meeting a couple dozen enterprises around the future of their AI strategies, here are a few notes on the state of AI in the enterprise right now.

1. The AI-first enterprise is emerging. Given AI increasingly is starting to be used across coding, customer support, marketing content creation, risk management, client onboarding, contract management, and more, it’s clear AI will touch almost every department in some way. Companies are starting to think through how entire functions get reimagined in a world of AI.

2. Enterprises want choice in their AI stack. The past couple of years have proven out that there are going to be models that perform different tasks in different ways, and enterprises increasingly want to flexibility in what they use. Further, the rate of innovation coming from the frontier model labs is so incredible that companies want to be in a position to leverage the latest breakthroughs from these players and not be stuck on a single architecture.

3. We will need more interoperability in AI. Especially as AI Agents emerge, and your software has to complete entire tasks for you just like a person would, increasingly there’s going to be a need for AI Agents from disparate systems to talk to each other. As an AI industry, we’re only in the earliest of stages of figuring out standards around this, but it’s going to have major implications on enterprise adoption.

4. Your AI stack will define who you can hire. Employees of the future are going to simply expect that the company they work for is going to enable them to be as productive as possible, and AI is going to be a core part of that. This is going to become more acute as the next generation enters the workforce. Having used AI in high school or college for years, the way they research, collaborate, and generate work product is going to be totally different. You won’t join a company that makes you work 40 hours to get 20 hours done when there’s another company that lets you get 80 hours worth of work done. This will define employee choices in the future.

5. The role of IT is continuing to change tremendously. Jensen called this out in his CES keynote, but we’re seeing a reshaping of what the IT organization will do in the future. In the past, IT has been responsible for deploying and maintaining software that enables the workforce; in a world of AI Agents, IT will increasingly be responsible for actually getting the work itself done. This has massive implications around how strategic IT becomes, and how this org more tightly coordinates with the company.

6. We’re still insanely early. What’s remarkable is that while we’ve seen a tremendous amount of growth in consumer AI, datacenter growth, GPU sales, and many initial breakthrough AI use-cases, we’re still very early. This feels eerily similar to the the first few years of cloud, where adoption is starting with the first movers inside an organization (IT teams, creative employees, etc.) and then expanding from there. Unlike the cloud, however, it’s perceived to be inevitable that every enterprise will be transformed by AI. The main hurdles to getting there are generally ones of AI quality, change management, privacy and compliance work — but not fundamental philosophy challenges, like we saw in cloud.

Overall, this is the most energized I’ve seen enterprises in nearly two decades of being in enterprise software. There’s a palpable sense that we’re on the cusp of major changes to how business and work happens in the future, and it’s unbelievably exciting.

[Mr. Orange @MaestroOrange]

@AsgerSR SWIFT

[Mr. Orange @MaestroOrange]

@grok recommendation from @elonmusk on personal productivity. Seems sensible

https://t.co/z11kgo5Yy6

[Mr. Orange @MaestroOrange]

@TheGregYang @xai AGI 😉

[Mr. Orange @MaestroOrange]

@johncoogan @techbrospod Dig it. Logan Roy would be proud

[Mr. Orange @MaestroOrange]

Can confirm, very soothing

[Trung Phan @TrungTPhan]

Everytime a friend asks me to recommend a “relaxing and low-stress film that keeps the blood pressure low”, I tell them to watch Uncut Gems.

[Mr. Orange @MaestroOrange]

@elonmusk @elonmusk should buy Oracle given his oracle like abilities also and for the memes

[Mr. Orange @MaestroOrange]

@levelsio But do they have 6 fingers? That is the decider