On March 3rd, 2026, Chamath Palihapitiya posted a number on X that most investors scrolled past without understanding what they had just read.

He was describing a data center project in Arizona. When the project started, one gigawatt of compute cost four to five billion dollars. Today, Chamath said, that same gigawatt costs one hundred billion dollars. The capital intensity went up twenty times in a single investment cycle.

Most people read that as a warning that AI infrastructure is expensive. They are reading it backwards.

The correct interpretation is this: any company that built its neocloud before that inflection happened is now sitting behind a capital barrier that essentially no new competitor can cross. Not because building a data center is technically impossible. Because gigawatt-scale AI infrastructure now requires simultaneously locking in four things that are all scarce at the same moment: secured grid power, Nvidia GPU allocations, anchor customer contracts, and the capital to carry it. The companies that crossed that threshold before the cost exploded did not just get lucky. They have a structural advantage that compounds with every passing GPU generation.

This is the neocloud trade. But it is not a blanket trade on everything that calls itself a GPU cloud. It is a contracted utilization trade — and the distinction matters enormously.

 THE MARKET NOBODY BUILT FOR EVERYTHING

Hyperscalers — AWS, Azure, Google Cloud — are extraordinary businesses built to serve everything: storage, databases, enterprise software, networking, video delivery. Their GPU pricing reflects that full-stack overhead. Every H100 they rent has to amortize the fixed cost of maintaining infrastructure their customers use for non-AI workloads. It has to satisfy the margin expectations of a public company with a ten-figure cloud franchise to defend.

Neoclouds have none of that overhead. They build for one workload: AI compute. The result is a meaningful and durable cost advantage on identical Nvidia silicon. Public list price data shows the spread is real across every measured configuration — and the advantage is not simply cheaper list pricing. It is cluster design, power efficiency, utilization economics, and the anchor-customer financing that lets a neocloud amortize its infrastructure over committed revenue rather than spot demand.

The market sizing reflects this. Industry estimates put the neocloud sector at $35 billion in 2026, growing to $236 billion by 2031 at a 46% compound annual rate — faster than cloud computing grew in its own first decade. Gartner projects neoclouds capturing 20% of the $267 billion AI cloud market by 2030.

 THE REBUILDING OF A SEARCH ENGINE EMPIRE

 Of all the publicly traded proxies for the contracted neocloud trade, Nebius Group is the most interesting — and arguably the most mispriced.

Nebius is the infrastructure arm of the former Yandex, Russia's Google equivalent. When Russia invaded Ukraine in 2022, founder Arkady Volozh separated the international infrastructure assets from the Russian operation and relisted the rebuilt company on Nasdaq in October 2024. The team that built Yandex already knew how to run internet-scale compute at the lowest possible cost — exactly the operational DNA a neocloud requires.

In Q1 2026, Nebius reported revenue of $399 million, up 684% year-over-year. Then, in March 2026, the company announced the most significant enterprise infrastructure contract of the year.

The $12 billion committed tranche is what matters structurally. It is not a letter of intent or a capacity option. It is contracted infrastructure spend with a defined start date. The additional $15 billion is tied to Meta's actual consumption of third-party capacity — real optionality, not a phantom number. The take-or-pay structure is exactly the contract quality that separates Tier 1 neoclouds from the rest of the market.

 WHY GPU ALLOCATION IS A STRATEGIC RELATIONSHIP, NOT A TRANSACTION

 Nvidia does not sell to the highest bidder.

GPU allocation is a relationship asset earned through proof of operational capability. Nvidia allocates its most advanced silicon to operators who have demonstrated they can deploy at scale, on time, with the power and organizational infrastructure to make the hardware productive. The operators who delivered Blackwell deployments on schedule receive Vera Rubin allocations. The operators still ramping their first H100 cluster go to the back of a queue that stretches years.

This is why the Nvidia equity stake in Nebius matters more than it might appear. It is not a financial investment. It is a signal of roadmap alignment — a statement that Nebius is inside the allocation relationship in a way that smaller operators are not.

The moat compounds with each generation for the operators inside that relationship. It does not compound permanently. The AMD wildcards — Meta's 6GW Instinct commitment and OpenAI's separate 6GW AMD agreement — are early evidence that hyperscalers are beginning to build Nvidia alternatives. That does not threaten the 2026 neocloud thesis. It is the 2028 question that every holder of Tier 1 neocloud equity should be tracking.

 WINNERS, LOSERS & THE TIERED LANDSCAPE

 PRESSURE POINTS

 CREDIBILITY FIREWALL

What is sourced and confirmed versus where we are drawing conclusions.

 BEAR CASE SPOTLIGHT

The most important risk in the neocloud thesis is not AMD, not hyperscaler retaliation, and not GPU pricing compression. It is contract quality — and the investor's inability to distinguish it from the outside.

The neocloud trade only works at the Tier 1 end of the spectrum: contracted utilization, take-or-pay capacity agreements, customer prepayments, secured power, and Nvidia roadmap relationships. Nebius and CoreWeave have varying degrees of all of those. But a significant number of companies marketing themselves as neoclouds have none of them. They are renting uncontracted H100s into a spot market that has already started to soften as Blackwell supply improves. Same hardware. Completely different equity story. The Lucent parallel from our Cheap Tokens issue is precise: in 2000, Lucent's revenue looked extraordinary right up until the moment its customers stopped buying. Uncontracted GPU clouds are financing depreciation today and calling it growth.

The second risk is hardware cycle timing. Neoclouds are financing gigawatt-scale infrastructure against long-term contracts that may or may not survive a silicon transition. Operators who locked in ten-year power agreements for Blackwell-era deployments could find themselves holding partially stranded assets when Vera Rubin's successor arrives and customers demand the new architecture. The contracted neocloud landlord weathers that transition because their customers re-sign. The uncontracted spot GPU operator does not.

The third risk is the AMD wildcard. Neoclouds are structurally Nvidia-first today. But Meta's 6GW AMD Instinct commitment and OpenAI's separate 6GW AMD agreement are not small bets. If AMD's software ecosystem reaches parity with CUDA for the dominant inference workloads — a 2027–2028 question, not a 2026 one — Nvidia's allocation leverage over neoclouds weakens. That does not break the thesis. But it means the moat is not permanent by default. It has to be renewed every hardware generation.

FIVE TAKEAWAYS

 1. The neocloud moat is not GPUs. It is contracted utilization, secured power, and Nvidia roadmap alignment — and it has to be renewed every generation. The companies that have all four — committed capacity agreements, take-or-pay customers, grid power, and confirmed next-generation silicon access — are AI infrastructure landlords. The companies that have only the hardware are depreciation vehicles.

2. Nebius is the most interesting public proxy for this trade, and it is not priced like one. Q1 2026 revenue up 684% year-over-year. A $12B committed Meta capacity agreement, with up to $15B in additional options. One of the first large-scale Nvidia Vera Rubin deployments, backed by a $2B Nvidia equity stake. The discount exists because the company was incorporated out of Russian internet infrastructure. That headline risk is real. So is the operational DNA.

3. CoreWeave is the better-known name, but $30 to $35 billion in projected 2026 capex makes it a leveraged bet on backlog execution. The $66.8B contracted backlog is real. So is the financing structure required to deploy it. CoreWeave is not wrong. It is rate-sensitive. The spread between its cost of capital and its contract yields is the whole equity story.

4. The bottleneck has already migrated from silicon to power and capital — and the next migration is to grid interconnection. In 2023, the constraint was H100 availability. In 2026, the constraint is financing a gigawatt of infrastructure that starts at tens of billions before the first GPU is racked. By 2027, the constraint will be high-voltage grid capacity and transmission infrastructure. Follow the bottleneck: ETN, VRT, PWR, and GEV are not AI plays in the traditional sense, but they are AI infrastructure plays in the correct sense.

5. Not all neoclouds are the same trade. Know which tier you own. Contracted AI factory with Nvidia roadmap alignment: structurally sound. Uncontracted spot GPU rental capacity: avoid. The market has not yet fully priced the distinction. It will.

Many of today’s data centers are still using copper wiring. The same metal we’ve been using for a hundred years.

At the speeds AI demands with data moving between thousands of GPUs, billions of times a second, copper doesn’t just slow down.

It turns that data into heat. The more you push through it, the worse it gets. There’s no software for fix for that.

So what’s the answer?

Explore the Photonics Layer…..

This is going to be interesting. We know Trump wants rates cut, we know how he treated Powell when he didn’t play ball, and we know that Trump is telling everyone higher inflation is transitory…….

Higher rates is one thing that can mess with this rally.

Tech sold off yesterday, could have been profit taking after a huge up day, or could have been this….

Reports are now coming out that this may not be accurate, and this morning the NASDAQ 100 has retraced almost half yesterday’s losses.

$SPCX ( ▼ 3.56% ) is up again pre market. Yesterday it took all the oxygen away from the other space stocks. So far today those are looking green. As I talked about in yesterday’s webinar, the space trade is still one of my favorites as we aren’t even in the early innings of what will end up in space.

When capital chases a tech theme, it tends to pile into the most obvious
layer and miss the one underneath. AI spending is now bumping hard
against memory. Hyperscalers — the big cloud builders like Amazon,
Google, and Microsoft — have shifted memory from 8% of their build
budgets to an estimated 30% in a single cycle. That capital has to go
somewhere. If the constraint is memory, and the build can't move without
it, shouldn't an investor own the layer AI runs on?

View HBMX fund holdings →

Distributor: Foreside Fund Services | Investing involves risk including
possible loss of principal.

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$SATS ( ▼ 2.27% )

From Oppenheimer…..

Forced selling is done, not fundamental. SATS sold off as arbitrageurs who had built synthetic SPCX longs (using SATS as the primary SpaceX proxy) unwound en masse post-IPO. That exit was purely mechanical not a reassessment of SATS's intrinsic value leaving the stock trading at an estimated ~50% discount to its residual SpaceX NAV exposure.

The H.E.A.T. (Hedge, Edge, Asymmetry and Theme) Formula is designed to empower investors to spot opportunities, think independently, make smarter (often contrarian) moves, and build real wealth.

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