Nvidia & Circular Financing, AI Outlook, and Physical AI

Goldman recently went on an AI field trip to Silicon Valley and came away with the following conclusions:

“We hosted our 3rd annual Silicon Valley AI Field Trip on 8/18-8/19, featuring AI companies, VCs and researchers from Stanford and UC Berkeley/UCSF. Model capabilities are continuing to improve, agents are progressing from assistance to workflow execution and monetization is expanding beyond seats toward consumption, transactions and outcomes. The strongest businesses combine proprietary data, domain expertise, customer context, workflow ownership, distribution, verification and permission to act. Agentic adoption should advance fastest where outputs are verifiable and responsibility for errors is clear. Open models should gain token volume across routine tasks, and frontier models should retain value where reliability justifies higher costs, supporting continued token and compute growth. AI should also expand cybersecurity demand, and physical AI will require distinct architectures and commercialization strategies. For Business & Information Services, AI should increase the value of differentiated information assets and help platforms capture more customer spending through embedded workflows. For Software, select vendors are well positioned to help customers navigate an evolving intelligence curve that segments workflows into frontier vs. non-frontier tokens and enriches models with business-specific context.”

We have a similar view and see multiple winners in this space—frontier models for the most advanced tasks, and open models for routine tasks. Companies with proprietary data will have a strong advantage—the better the quality of your data is, the better the AI’s output will be. This is relevant in both training and inference. We also continue to think that there will be obvious winners in the software space from AI, and that cybersecurity companies and data management systems such as Snowflake and MongoDB are particularly well-positioned.

CLSA tracks a wider database of AI startups and shows that the number of startups hitting key ARR milestones continues to grow rapidly:

We suspect that we’re still only at the very early start of the S-curve when it comes to AI demand. Evercore’s recent channel checks found that:

“Hyperscalers turning away customers because they don’t have enough GPUs.”

And thus AI capex continues to move higher—Nvidia sees 70% revenue growth in calendar 2027 (fiscal 2028) while the semi supply chain continues to be constrained. Nvidia’s CFO estimates that real customer demand is growing 100% year over year:

“We expect to grow revenue by approximately 70% in fiscal 2028. This is a supply-constrained outlook. With cloud industry backlog now greater than $2 trillion, CapEx by the top 5 hyperscalers is expected to reach nearly $800 billion in 2026 and $1.3 trillion in 2027. Incredibly, we are seeing demand acceleration even at our scale. Customers forecasts point to our growth, doubling next year. However, as I mentioned earlier, we expect to grow approximately 70% as we are supply constrained.”

While overall AI capex growth rates are now slowing down—36% expected for ‘27 compared to ‘26 and ‘25 which both grew around 90%—Nvidia’s comments suggest that growth rates for next year would have been almost 1.5x higher if it had not been for constraints in the supply chain.

Over the past two years, we’ve seen Nvidia’s top line growth rates decelerating while AI capex growth rates kept accelerating (chart below). However, in 2027, we see again a reversal—Nvidia will now be accelerating its growth rate again while we see a strong deceleration in AI capex growth:

One possible reason is that AI capex estimates for next year are simply too low. Another reason is that Nvidia is seeing tremendous neocloud demand from around the world—which the data above from Evercore likely isn’t properly capturing. This is Nvidia’s CFO again:

“Hyperscalers will remain a major growth driver, but non-hyperscaler growth, our AICE segment—spanning sovereign, regional NeoClouds, enterprise edge and air-gap data centers—will represent roughly half of our data center business. We don’t own a cloud ourselves, we are a neutral partner to every sovereign and NeoCloud. NeoClouds are emerging everywhere—Firebird in Armenia, Cassava Technologies across Africa, GMI Cloud in Taiwan, Yotta and Neysa in India, Firmus in Australia, YTL AI Cloud in Malaysia—pairing local land, power and operating expertise with our platform. NeoClouds are seeing strong demand pipelines for many diverse offtakers. Rather than allocating their entire capacity to a single long-term offtake guarantee that lenders typically require to finance a data center independently, we have introduced a revenue-sharing structure. NVIDIA provides a take-or-pay commitment on a portion of the facility’s capacity, a minimum revenue guarantee that gives lenders the confidence to underwrite the project, and in exchange, we share in a portion of the NeoCloud’s revenue earned above that floor.”

We fully understand why Nvidia is doing this. The problem for Nvidia is that they don’t want a too concentrated customer base. If they would be purely selling silicon to the top four hyperscalers and the top two frontier labs, it’s likely that these big 6 will increasingly shift share to their custom ASICs over time. All are already doing this. This would make Nvidia a melting ice cube at some stage.

So, Nvidia’s answer is to set up a competing network of AI data centers comprised of neoclouds around the world, which supply AI compute to a wide number of AI startups and also innovative enterprises— like Shopify, Figma, etc. As these neoclouds often don’t have access to capital—they basically don’t have the money to build these data centers—Nvidia now provides a minimum revenue guarantee on this capacity so that neoclouds can get the necessary loans in capital markets.

We definitely think this is a rational strategy by Jensen and that he has to roll the dice here. The additional benefit is that Nvidia gets bigger upside—once from the hardware sales and a second time from recurring revenues on its infrastructure via the revenue sharing agreement. However, this works both ways. If in ‘29 or so there would be an overcapacity of AI compute, then Nvidia would see its hardware sales go into a cyclical downturn while it could also be on the hook for neoclouds that aren’t generating substantial revenues (for example, in the event that certain startups which purchased neocloud capacity go bust).

So, it’s clear that Nvidia will become an even higher beta play on AI demand, with its risk profile moving up, closer to that of a neocloud. This is great as long as there is an AI shortage, but not so great if there’s large overcapacity at some stage.

Nvidia is similarly providing guarantees for frontier AI labs to build out capacity, and from the 10Q we can see that these are substantial:

“In August 2026, we entered into guarantees with SB Energy Corp. to provide credit support on the land, power, and shell buildout at SB Energy’s PORTS Technology Campus in Pike County, Ohio, covering leases for approximately 4.25 gigawatts of IT load. The campus will exclusively host our compute under 20-year leases to OpenAI, subject to limited exceptions, with our obligation capped at $105 billion in the aggregate, subject to certain conditions including SB Energy, the lessor, satisfying applicable ready-for-service conditions. Each guarantee generally becomes effective upon commencement of the applicable lease, with corresponding guarantee amounts increasing as each of nine data centers is placed in service, which is expected to begin in fiscal year 2029. Our exposure declines as OpenAI fulfills its lease payments. Our guarantees are limited to defined portions of lease and power payments and not the full cost of the site or all of the tenant’s obligations. The guarantees terminate upon certain events, including OpenAI achieving a satisfactory credit rating or after each respective lease term has completed. We also hold an option, exercisable in our sole discretion, to provide additional credit support in phases for approximately 3.8 additional gigawatts as the site scales.”

Nvidia is guaranteeing the bulk of the cost of this site. Jensen mentioned that 1GW of Vera Rubin generates $40 billion in revenue for Nvidia, and thus the 4.25GW campus is worth about $170 billion in Nvidia hardware. With Nvidia guaranteeing $105 billion of this investment, this is about 62% of the cost.

Again, we don’t have a huge problem with this—it’s clear that both OpenAI and Anthropic are generating stellar revenue growth, with both companies combined adding $75 billion in ARR since the start of the year. And as we’re still in the early phases of token consumption, it’s obvious that AI tokens are going to become a multi-trillion industry over time. Note that OpenAI also reports revenues on a net basis, i.e. stripping out the revenue share that hyperscalers take in return for them hosting the models, while Anthropic reports revenues on a gross basis. So, OpenAI will see a huge boost to revenues by hosting their models in their own data centers.

These companies need cash to scale AI compute, and so Nvidia steps in as the guarantor so that AI labs can get the necessary loans from the banks. Again, we think this is the right move by Jensen. Nvidia makes sure that the AI labs who need to scale compute can do so, and by doing this, Nvidia cements its central position in the supply chain and continues to drive stellar revenue growth. This strong position then allows Nvidia to secure a large share of available wafer supply and advanced packaging at TSMC, memory capacity at SK Hynix and Micron, and optics at Lumentum and Coherent. In a supply constrained world, this makes it physically impossible for competition, such as AMD, to grow large amounts of share.

But again, everything hinges on AI compute not going into large oversupply. If in ‘29 or ‘30, we have a large oversupply of capacity, that massive site in Ohio isn’t worth that much. Available GPUs for which there is no demand aren’t really worth anything actually. And if OpenAI isn’t generating the revenues they expected, Nvidia might have to start paying out to cover the loans of that data center.

However, we continue to be extremely bullish on AI demand. And the key reason is that we’re using the most advanced models to write large code bases. So, being a programmer gives us a competitive advantage versus the rest of the market to really understand the progress and capabilities of the latest, most advanced models. And they truly are beyond the wildest imagination. Benchmarks don’t properly capture what’s going on. We were watching the Lex Fridman podcast yesterday with well-known programmer DHH, and his conclusions were exactly the same as what we’re seeing:

“We have AI in the pre-agentic era. I was excited about that, but it was not fundamentally rewriting the rules of the game for me. It was not completely changing how I worked. I was still chiseling code, I just had a little helper, a little sidekick- who could bounce ideas off, and I could look up this information online and so forth in a more efficient way. It was just a more efficient way to do what I was already doing, and it didn’t change the emotional connection I had to the computer. Then we get to November 24th, 2025. Opus 4.5, to me, was the dividing line, where suddenly I gave it a couple of tasks, and I realize that the quality of the output is uncannily close to what I would’ve written. And I remember just leaning back and thinking, “What just happened?” And again, Opus 4.5 now looks like a retarded model. I had to be in the driver’s seat. I had to tell it what I wanted, and I had to be the reviewer, the auditor of what was coming out. And then finally now, this summer, with Opus 5, Fable, and Sol, GPT Sol, and to a lesser extent, some of the open weight models, we’ve arrived at a new era where I’m not telling it where we’re going. I’m telling it the problem I have. I’m telling it the fuzzy, vague idea I have. It tells me where we’re going. It tells me which path to take, and I will still look at it because I’m a curious person and I like computers and I like the outcome of it, but I really kinda don’t have to. I’ve become optional in the part that produces the code that picks the route.You could really get to the 100% of code written by AI and really almost– if you’re a good programmer and you have a good intuition about what’s happening behind the scenes, you can legitimately not look at the code, at least that’s been my experience. I don’t know how much prior experience with programming you need to have to kinda know that things are working correctly behind the scenes, like intuitively by observing the ripple effects, the symptoms of the system.I’ve been working on Omarchy for the last three months, this version that just dropped a few days ago called Quattro. And almost right from the beginning I’m working on that version, the agent acceleration neared 100%, and in the last two months it has been 100%. I have not written any of the code that’s shipped in Quattro by hand. I’ve reviewed the shape of all of it. I’ve reviewed the individual lines of anything that’s critical in the model layer of the system, and I’ve not looked at a bunch of the UI code. I have not looked at a bunch of the auxiliary code, and I’ve not written any of the new functionality entirely by hand.”

It’s clear that models are now matching the capabilities of the best engineers in the world, and across more and more fields. These models are not only becoming state-of-the-art software engineers and at 100,000x the speed of the best human programmer, but also top notch mathematicians, physicists, biologists, etc. What looks like a certainty here is that there is going to be scientific progress in the coming decades at a pace we’ve never seen before. And thus, the big conclusion for us—we’re going to need a huge amount of data centers. Therefore, we continue to be very bullish on AI demand and that Nvidia is making all the right moves here.

Long term, another key attraction in the Nvidia story is that revenue per GW deployed keeps moving up. Initially, the company was making like $3-5 billion per GW. That grew to $18 billion per GW with Hopper. $25 billion with Blackwell, and now $40 billion with Vera Rubin. And after that, it’s going higher again. Note that this is positive for the entire semi supply chain—from TSMC all the way down to ASML, as this rise in the revenue per GW is driven by more advanced manufacturing processes.

So, if you install 1GW of Rubin, it gives you 30x more tokens versus Blackwell, and at a 35x lower tokens cost. All AI capacity that’s coming online in the coming years is going to be massively more productive than in the previous years, and provide much cheaper tokens.

One big concern in the market were Nvidia’s gross margins, given the explosion in memory pricing, however, the impact of this looks to be very manageable:

“As you are already aware, we are experiencing extreme pricing conditions in memory. The magnitude of the price increase has exceeded our prior expectations and are headed even higher into next year. As a result, we are resetting expectations today. For Q3, we expect GAAP and non-GAAP gross margins to be 74% plus or minus 50 basis points. We expect margins to bottom in Q4 in the 71% to 72% range before settling at 72% to 73% in fiscal year ‘28, as executed price increases take effect in Q1.”

The three key takeaways for us from Nvidia’s call were:

• Revenue growth is accelerating again and AI capex estimates for next year are likely too low.
• Nvidia is becoming a higher beta, and higher risk play, on AI demand as the company is becoming a large guarantor in the global neocloud and frontier AI buildouts.
• Memory prices will continue to move higher next year.

We continue to stay long Nvidia here. We like the acceleration story in revenues, the progress in model capabilities—which are far ahead of what many in the market are thinking—and the stellar revenue growth at the frontier labs. All while the valuation of Nvidia shares remains highly attractive at a 19x forward PE:

Next, we’ll review two more names we’ve purchased this week—one semi name and one physical AI name.