u/Final-Letterhead-367

Everyone knows NVIDIA. The stock went from $10 to $1,000. DNVIDIA is, by every measure, the most dominant company in the AI revolution.

Cerebras is IPOing this week (13MAY2026) with a valuation around $50 billion.

Let me tell you about Cerebras.

They Didn't Try to Beat NVIDIA at Their Own Game

That's the first thing you need to understand. Every other chip company AMD, Intel, a dozen startups, tried to out-NVIDIA NVIDIA. Better GPU, faster memory, cheaper price. They all got crushed.

Cerebras looked at the problem differently. They asked one question: what is actually slowing AI down?

The answer isn't compute power. It's communication. Every time data has to travel between chips, across cables, across boards, across racks, you lose time. NVIDIA's approach is hundreds of tiny chips talking to each other constantly. That conversation has latency. And latency kills performance.

They took an entire silicon wafer, the thing factories normally cut into hundreds of separate chips, and kept it whole. One chip. The size of a dinner plate. By keeping far more compute and memory on one wafer-scale engine, Cerebras reduces a major source of latency: data movement across many separate chips. Just the fastest AI inference on the planet.

The Numbers Are Genuinely Insane

Cerebras's chip is 58x larger than NVIDIA's B200. It has 2,625x more memory bandwidth. On typical inference workloads it runs 15x faster. On certain specialized workloads it has hit 1,000x faster.

Revenue went from $24.6 million in 2022 to $510 million in 2025, roughly a 175% CAGR. The growth is real, but still concentrated and execution-heavy. And they have $24.6 billion in contracted backlog already sitting on the books before they've sold a single public share.

But NVIDIA Isn't Going Anywhere

NVIDIA did $215.9 billion in FY2026 revenue. Cerebras did $510 million in 2025. That scale gap is enormous. NVIDIA's CUDA software ecosystem has 20 years of developer loyalty baked in. Thousands of AI researchers have written their entire careers in CUDA. You don't switch that overnight. You don't switch that ever, for a lot of people.

NVIDIA still dominates training and the broader AI infrastructure stack. CUDA, NVLink, networking, software, and developer inertia are massive advantages. Cerebras’ cleanest wedge is not replacing that entire stack. It is winning the narrow but rapidly growing market where inference speed, latency, and memory movement matter more than general-purpose GPU flexibility.

And Cerebras lost $146 million at the operating level in 2025. The GAAP profit headline is real but it got a boost from non-operating items. The core business is still burning cash. At a $23 billion valuation they need flawless execution for years to justify the price.

So What Is Cerebras Actually Competing For?

Inference. Running AI in real time. Every time you use ChatGPT, Claude, or any AI product, that's inference. And inference is growing faster than training. Bloomberg Intelligence projects the inference market grows at twice the speed of training infrastructure through 2029.

This is the shift that matters. The AI industry spent five years obsessing over who could build the biggest model. Now the obsession is who can run it fastest and cheapest at scale. That's a completely different competition. And Cerebras built their entire company for exactly this moment.

The Real Comparison

NVIDIA is the highway. Cerebras is the sports car built specifically for that highway.

NVIDIA built the infrastructure that made AI possible. That story is mostly told. The next decade isn't about who can build the biggest training cluster. It's about who can make AI fast enough, cheap enough, and responsive enough to run inside every product, every app, every workflow on earth.

That's an inference problem. And right now Cerebras is the best answer to that problem on the market.

Is it a NVIDIA killer? Not today. Maybe not ever in training.

But in inference? In the market that's actually growing fastest? Cerebras isn't chasing NVIDIA. They're running a completely different race.

IPO is May 14th. Ticker is $CBRS.

Do your own research. But pay attention to this one.

Cerebras is a credible public-market pure play on the inference bottleneck: memory bandwidth, latency, and data movement. NVIDIA remains the AI platform king, but Cerebras may own a valuable lane inside the next phase of AI infrastructure.

Next week is not just about companies announcing their earnings. It is a time when we find out if some of the market trends are real or just hype.

Nuclear, AI infrastructure, quantum, space, batteries, fintech, crypto and IPOs have all been stories. Now we get to see which ones have results behind them.

Space / Satellite / eVTOL / Drones

$ASTS Mon AH
$SRFM Mon AH
$ACHR Mon AH
$LUNR Tue AH
$DPRO Thu AH

If $ASTS shows progress on direct to cell technology, investors will jump on to the opportunity as we saw with $RKLB. If not, the whole sector might lose momentum.

LUNR is more focused on government contracts. The market wants to see cash flow visibility and repeatable mission revenue.

ACHR is still working on certification. The market wants to see progress on FAA certification, aircraft timelines, manufacturing readiness and cash runway.

SRFM and DPRO are more speculative. Any credible commentary on defense, drones or cash flow can move them. The big question is whether space, drones and eVTOL are becoming real businesses.

Nuclear / Power / Critical Minerals

$OKLO Tue AH
$DNN Tue AH
$USAR Wed AH
$UAMY Thu AH
$NNE Thu AH

These companies are all part of one story: can the US fix its energy supply chain?

OKLO and NNE are working on the reactor side of things. Investors want to know if they are making progress on licenses, buildout timelines and access to fuel. DNN is important because it shows us what is happening with uranium, which is a key part of the nuclear chain. If DNN does well, it helps the nuclear story. If it does poorly, it hurts the story.

USAR and UAMY are working on minerals like rare earths and strategic minerals. These minerals are important for the US energy supply chain. If these companies make progress on production, processing or government support, it can help the whole minerals sector.

Semis / Quantum / AI Data Center Hardware

$RGTI Mon AH
$QUBT Mon AH
$CLSK Mon AH
$MARA Mon AH
$QBTS Tue PM
$TSEM Wed PM
$NBIS Wed PM
$POET Wed AH
$BTDR Thu PM
$AMAT Thu AH
$BTBT Thu AH

Quantum companies like RGTI, QBTS and QUBT need to show real results, like bookings and customer progress. If they do not, the market might decide the recent excitement was hype.

POET, TSEM, AMAT are AI hardware names. They show us if AI demand is spreading beyond the biggest winners into other areas like photonics and specialty hardware.

CLSK, MARA, BTDR and BTBT are still connected to crypto. Investors are watching to see if they can pivot into AI data centers. The market wants to see contracts, utilization and credible spending plans.

NBIS is another AI infrastructure company. After a run, it needs to show strong results to keep the AI compute sector moving.

Battery / Storage

$MVST Mon AH
$PLUG Mon AH
$EOSE Tue AH
$ENVX Wed AH

Everybody agrees that storage demand is real. Data centers, renewables, grid stability, electric aviation, robotics and defense all need batteries and storage systems.

The question is whether these companies can make money supplying that demand.

EOSE is a company in long duration grid storage. ENVX is working on silicon anode adoption. MVST is a high risk battery turnaround story. PLUG is still focused on cash burn, policy support, funding visibility and whether hydrogen can become financially stable.

This sector has demand, but investors want to see execution.

Blockchain / Fintech / Crypto Platforms

$CRCL Mon PM
$ETOR Tue PM
$PAGS Tue AH
$KLAR Thu PM
$BLSH Thu PM
$NU Thu AH
$DLO Thu AH
$STNE Thu AH

This is a test for fintech and crypto infrastructure.

CRCL is a company in stablecoins. A strong report will show that stablecoins are becoming financial infrastructure. A weak report will cool down the crypto payments story.

ETOR shows us retail trading appetite and crypto participation. BLSH adds the digital asset platform angle.

NU is a quality leader in LATAM fintech. PAGS, DLO and STNE show whether digital payments, merchant acquiring and cross border fintech are still growing. KLAR is an earlier stage company, so credit quality and loss ratios matter most.

The important thing to watch is whether fintech and crypto infrastructure are still growing.

AI / Cloud / Enterprise Software

$MNDY Mon PM
$WIX Wed PM
$DOCS Wed AH

These companies are being judged on whether their AI features are actually helping their businesses.

The market wants to see evidence that AI is improving retention, pricing and customer usage. Just talking about AI is not enough anymore. If these companies can show results from AI, the software sector can regain momentum.

IPOs

$GMRS
$FRVO
$CBRS

CBRS is Cerebras, the AI chip IPO. It matters because it tests investor appetite for custom AI silicon outside the Nvidia ecosystem.

FRVO is Fervo Energy, the geothermal company. It fits into the power bottleneck story because data centers need energy.

GMRS is less tied to AI. It still gives us a read on IPO risk appetite.

If these deals do well, it shows that capital is still willing to chase growth stories.

Next week is a big test for some market sectors.

I looked at the space sector through a bottleneck lens.

I organized the sector into five categories:

Space Infrastructure: RDW, SIDU, MNTS, VOYG

Space Manufacturers: ATRO, TDY, DCO, APH, PH, HEI

Orbital Launch: RKLB, FLY

Satellite Communications: ASTS, SATS, VSAT, TSAT

Earth Observation: PL, BKSY, SPIR, SATL

Then I compared each category against QQQ using Relative Strength Gap across 1M, YTD, and 1YR.

If a category is negative, it has lagged QQQ. If it is positive, it has already run.

Here is what I found:

Space Infrastructure: -15.0%; Lagging

Space Manufacturers: +6.8%; Mild outperform

Orbital Launch: +34.9%; Running

Satellite Communications: +139.9%; Already ran

Earth Observation: +180.4%; Already ran

At first, I thought infrastructure was the clear laggard. It is the only category still below QQQ, and it covers everything after launch: ground operations, data, logistics, and all the behind-the-scenes activities.

Manufacturers also caught my attention since they are only slightly ahead of QQQ, and this kind of hardware is not easy to replace. It takes time, trust, and a solid track record.

As I thought more about it, and once I got more feed back, I was keep coming to orbital launch.

Nothing happens without launch. If you can’t get something into orbit, the rest does not matter.

That is why I keep looking at RKLB, FLY, and Blue Origin (pvt), especially if the reported SpaceX IPO draws more attention to the space theme.

RKLB and FLY are not SpaceX; they are smaller and riskier, but they offer some of the few public ways to invest in that part of the market.

So, my conclusion is straightforward. Infrastructure is lagging, manufacturers feel overlooked, but orbital launch still appears to be the choke point.

It feels less about which stock is cheapest and more about which ones are closest to the real constraint.

m curious how others see it and what is the bottleneck in your opinion?

Happy May 4th.

I’ve been trying to map where the real bottlenecks sit beneath major sectors, not just what’s running.

This week I used the space sector as a case study.

Most investors focus on rockets, launch companies, and the visible narrative.

But from a bottleneck perspective, those are just the top layer.

The more interesting question is:
what parts of the system are hardest to replace once everything is in motion?

So I broke the space sector into 6 sub-themes:

Sub-theme
Space Infrastructure — RDW, SIDU, MNTS, VOYG
Aerospace and Defense — LMT, NOC, RTX, BA, LHX
Space Manufacturers — ATRO, TDY, DCO, APH, PH, HEI
Orbital Launch — RKLB, FLY
Satellite Communications — ASTS, SATS, VSAT, TSAT
Earth Observation — PL, BKSY, SPIR, SATL

Then I applied two scoring layers.

First: Relative Strength Gap
This compares each sub-theme vs QQQ across multiple timeframes (1M, YTD, 1YR).

• Negative → lagging
• Positive → outperforming

This is not a valuation signal. It just tells me where capital has and hasn’t flowed.

Second: Bottleneck Score (B(i))

This is my attempt to quantify how hard each node is to replace.

I score based on:

• Dependency
• Substitutability
• Supplier concentration
• Qualification difficulty
• Time-to-scale

Higher score = more structural constraint.

The setups I care about are where lagging performance meets high bottleneck intensity.

Relative Strength Gap vs QQQ

Space Infrastructure — -15.0% (Lagging)
Aerospace and Defense — -12.6% (Lagging)
Space Manufacturers — +6.8% (Mild outperform)
Orbital Launch — +34.9% (Running)
Satellite Communications — +139.9% (Already ran)
Earth Observation — +180.4% (Already ran)

Bottleneck Scores (B(i))

Space Manufacturers — 88
Qualified space hardware is hard to replace. Reliability, testing, and mission heritage matter.

Space Infrastructure — 82
Support layer after launch: ground segment, mission ops, data movement, orbital logistics, in-space services, commercial platforms.

Aerospace and Defense — 78
Clearances, procurement, qualification standards, and long-term customer relationships create real barriers.

Satellite Communications — 76
Spectrum, orbital coordination, telecom integration, and regulatory friction.

Earth Observation — 71
Data history, analytics pipelines, AI processing, and customer integration.

Orbital Launch — 58
Still critical but becoming more competitive and less scarce relative to other layers.

What stood out to me was not just that launch scored lower.

It’s that launch scored lower while infrastructure and manufacturing scored higher.

That suggests the constraint may already be shifting.

Rockets get attention, but once assets are in orbit, the harder problem becomes:

Who builds the qualified hardware?
Who operates the systems?
Who moves and processes the data?
Who keeps everything functional at scale?

From that lens:

Space Infrastructure looks like the most interesting lagging node.
Underperformance + high bottleneck score is exactly the combination I look for.

Space Manufacturers may be the cleanest structural bottleneck.
You can’t shortcut qualification, reliability, and mission heritage.

Orbital Launch is still important, but it’s the most visible layer, and likely not the deepest constraint anymore.

This is not a stock call. It’s a framework.

The model is still evolving, so I’m more interested in stress-testing the thinking than defending the outputs.

For those who think in systems / supply chains:

• Are these the right inputs for measuring bottlenecks?
• Where would you adjust the scoring?
• Do the theme definitions make sense, or would you redraw the map?

And more broadly:

Where do you think the real bottleneck in the space economy sits today?

Happy May 4th.

Recently, I was interested in finding out the bottlenecks that are lagging behind main sectors.

I picked Space Sector this week paying homage to Star Wars.

Most investors are watching rockets, launch companies, and the flashy space narrative.

But the real opportunity may be in the less obvious layers that make the whole space economy work.

I divided the Space Sector into 6 different sub sectors.

Sub-theme	Tickers

Space Infrastructure	RDW, SIDU, MNTS, VOYG

Aerospace and Defense	LMT, NOC, RTX, BA, LHX

Space Manufacturers	ATRO, TDY, DCO, APH, PH, HEI

Orbital Launch	RKLB, FLY

Satellite Communications	ASTS, SATS, VSAT, TSAT

Earth Observation	PL, BKSY, SPIR, SATL

Then I used two scoring models.

The first is Relative Strength Gap. This compares each space sub-sector against QQQ across multiple timeframes (1M, YTD, and 1YR). If the number is negative, that basket has lagged QQQ. If it is positive, it has outperformed.

That alone does not mean that market missed it. Plenty of sectors lag for good reasons.

The second model is my own Bottleneck Score, which I call B(i).

I score each theme based on dependency, substitutability, supplier concentration, qualification difficulty, and time required for new entrants to scale.

Higher score means the node is harder to replace.

The most interesting setups are where both signals overlap: a theme has lagged the market, but still sits in a critical, hard-to-replace position in the space supply chain.

These are the results.

Relative Strength Gap vs QQQ

Sub-theme	RS Gap vs QQQ	Signal
Space Infrastructure	-15.0%	Lagging
Aerospace and Defense	-12.6%	Lagging
Space Manufacturers	+6.8%	Mild outperform
Orbital Launch	+34.9%	Running
Satellite Communications	+139.9%	Already ran
Earth Observation	+180.4%	Already ran

Bottleneck scores B(i)

Sub-theme	B(i) Score	Why it matters

Space Manufacturers	88	Qualified space hardware is hard to replace. Components need reliability, testing, and customer trust.

Space Infrastructure	82	This is the support layer after launch: ground segment, mission operations, data movement, orbital logistics, in-space servicing, and commercial space platforms.

Aerospace and Defense	78	Defense procurement, clearances, qualification standards, and customer relationships create very high barriers.

Satellite Communications	76	Spectrum, orbital coordination, telecom partnerships, and regulatory approvals are real chokepoints.

Earth Observation	71	The moat is less about cameras and more about data history, analytics, AI pipelines, and customer integration.

Orbital Launch	58	Launch is important, but it is becoming more competitive.

The result that stood out to me because the Orbital Launch scored low. That was a sector I was following and RKLB had a generational run. Rockets get the attention, but once more assets are in orbit, the constraint shifts.

Space Infrastructure is the most interesting. It has underperformed QQQ in my model but scores high as a bottleneck. That combination is what I like to look for. If the space economy keeps scaling, the support layer matters more, not less.

Space Manufacturers may be the cleanest bottleneck.

Orbital Launch is where I am excited and at the same time looking at it cautiously. RKLB is a real company doing real business, but that space is getting dominated by SpaceX, and other private companies. It just means that Orbital Launch may not be the deepest bottleneck anymore.

This is my framework and the broader thesis feels right to me

I’m still refining the model, so I’d appreciate feedback on the scoring system.

Does the combination of Relative Strength Gap + Bottleneck Score make sense as a way to find underpriced supply-chain nodes?

And if you disagree with the rankings, where do you think the model is wrong: the relative-strength side, the bottleneck inputs, or the theme definitions?

More importantly, would you rank any of these space themes differently?

Everyone's buying rocket stocks.

The Death Star had a power reactor, a targeting system, and two million personnel. It had one exhaust port nobody thought mattered. That's where it died. Your portfolio might be making the same mistake.

Happy May 4th. I ran the numbers on the space sector last night while rewatching A New Hope, and the parallel is too good to ignore.

Retail is piling into the visible stuff. Rocket companies. Earth observation darlings. The names that show up when you Google "space stocks." Meanwhile the actual bottlenecks; launch infrastructure, ground stations, space-qualified components; are sitting there lagging QQQ by 12 to 47 points, largely ignored.

The Rebel Alliance didn't win by buying the most popular ship. They found the one component the Empire forgot to defend. That's the whole game here.

I scored every publicly listed space name across five variables; dependency, irreplaceability, concentration risk, qualification difficulty, and time-to-scale, and combined it with the lag model across all seven sub-themes. The results are uncomfortable if you're holding the names everyone's talking about.

"Earth observation is up 320% above QQQ. Ground infrastructure is still down 47%. Higher bottleneck score. Lower price. That inversion is the trade."

The Lag Score

Six sub-themes.
Lagging first. QQQ benchmarks: 1M +14.6% · 3M +9.4% · 6M +6.7% · 1Y +38.7%.

Sub-theme	1Y Gap	Lag Score	Signal
Space Infrastructure RDW SIDU MNTS VOYG	−47.1%	−15.0%	⚡ WATCH
Aerospace & Defense LMT NOC RTX BA	−15.2%	−12.6%	LAGGING
Space Manufacturers ATRO TDY DCO APH PH	+19.1%	+6.8%	MILD OUTPERFORM
Orbital Launch RKLB FLY	+64.6%	+34.9%	RUNNING
Satellite Communications ASTS SATS VSAT TSAT	+314.7%	+139.9%	ALREADY RAN
Earth Observation PL BKSY SPIR SATL	+320.4%	+180.4%	ALREADY RAN

PL is up 963% versus QQQ over one year. BKSY up 312%. These are not undiscovered ideas. The market found them. The ground infrastructure that every one of those satellites depends on is still negative on a 1Y basis; and just started inflecting in the last six months.

Luke didn't fire at the visible hull of the Death Star. He followed the exhaust port nobody was watching. Same idea.

The B(i) bottleneck scores

Five variables. Scored 0–100. Higher means harder to replace.

1 - B(i) SCORE 94

Launch Site Infrastructure

Active orbital launch pads in the U.S. can be counted on one hand. FAA licensing for a new commercial spaceport takes years. Cape Canaveral is effectively a shared national asset. Geographic positioning cannot be substituted.

RKLB FLY

2 - B(i) Score 88

Space-Qualified Components

Radiation-hardened electronics must be individually certified for space. That process takes 18–36 months. No shortcut. The supplier base is thin and ATRO holds certifications a new entrant cannot replicate quickly.

ATRO TDY DCO

3 - B(i) Score 82

Ground Station Networks

Every satellite needs ground infrastructure to receive data and upload commands. As constellation density increases, ground coverage becomes the binding constraint. RDW sits here. The market hasn't priced it.

RDW SIDU MNTS

4 - B(i) Score 76

Spectrum and Orbital Slots

ITU spectrum coordination takes years. Orbital slots in key bands are finite and contested. Any constellation without allocated spectrum cannot legally operate. Already partially priced in SATS and ASTS.

SATS ASTS IRDM

5 - B(i) Score 58 

Launch Vehicle Manufacturing

RKLB is real. Electron works, Neutron is coming. But the launch market is getting more competitive. SpaceX's dominance means RKLB is a second-source story, not a monopolist. Already run 65% above QQQ.

RKLB

Ground Infrastructure 

RDW

B(i) Score (82)· down 47% vs QQQ (1Y)

vs

Earth Observation (already ran)

PL

B(i) Score (71) · up 963% vs QQQ (1Y)

Lower bottleneck score. Already 963% above QQQ. Versus higher bottleneck score, still negative. The exhaust port is always the thing nobody's watching.

Three names. One bottleneck.

RDW — Redwire

Owns the ground-side constraint

Space infrastructure and in-space manufacturing. Ground segment hardware. The physical link between satellites and their operators. Lagging QQQ by 18% over 1Y. As constellation density compounds, owning the ground compounds too. The B(i) model puts the ground station node at 82. The price hasn't agreed yet.

B(i) NODE: 82 · CRITICAL · LAGGING

ATRO

Space-qualified components

Power systems, lighting, avionics. Already certified. 18–36 months for a new entrant to reach the same position. As launch cadence accelerates, qualified component suppliers become the real constraint.

B(i) NODE: 88 · CRITICAL

SIDU

Ground network · high risk

Small, thin, early stage. I'm not pretending otherwise. But sitting directly at the ground station node that the model flags. One government contract in this environment reprices it fast. Eyes open on the risk.

B(i) NODE: 82 · HIGH RISK

The closing argument

The Photonics call worked because optical transceivers were non-discretionary demand before the narrative arrived. Nuclear worked because LEU owned an irreplaceable supply position while NNE got all the attention. Space is the same pattern.

PL and BKSY are great companies. Earth observation is a real and growing business. But at +320% above QQQ, you're not finding alpha there. You're arriving after the party. The ground infrastructure those satellites depend on — and that nobody is writing about — is where the B(i) model and the lag score agree simultaneously.

The Death Star's engineers spent everything on the laser. Nobody filed a work order for the exhaust port cover.

May the 4th be with you. The force is on the ground.

Note: Bottleneck scores, lag model, and investment thesis are independently researched. Article formatting and visualization built with Claude (Anthropic). Data sourced from live price feeds, google finance.

Every retail investor is buying reactor stocks. NuScale. Oklo. Nano Nuclear. X-Energy. The thesis is simple: AI needs power, power needs nuclear fuel. The narrative is correct. But the trade is pointed at the wrong part of the supply chain.

Advanced reactors need fuel. The fuel needs enrichment. That enrichment capacity does not exist at commercial scale in the United States.

The real constraint in the nuclear buildout is not the reactor design. It is not the NRC licensing timeline. It is the fuel chain sitting three steps upstream of every reactor on every drawing board.

I scored twenty-three publicly listed companies across the nuclear and uranium supply chain on five variables: dependency, irreplaceability, concentration risk, qualification difficulty, and time-to-scale. The fuel chain won by a wide margin.

Here is what I found.

https://preview.redd.it/pkmnw4kw0yyg1.png?width=501&format=png&auto=webp&s=5b0101048af6200bc5be31c04d2bd4d43cc1aa82

Everyone is buying reactor stocks. Nobody is buying the fuel.

Been deep in the nuclear supply chain for the past few weeks. Not the reactor names. The stuff upstream that nobody talks about.

Here is what I found.

| Node | Key Tickers

| HALEU Enrichment | LEU, ASPI, LTBR

| TRISO Fuel Manufacturing | BWXT, XE

| Domestic Uranium Processing | UUUU

| Uranium Supply at Scale | CCJ, UEC, URG, EU, NXE

| Advanced Reactor Platform | XE, OKLO, SMR, IMSR

HALEU Enrichment

Every advanced reactor on the drawing board needs HALEU. High-Assay Low-Enriched Uranium. Enriched to between 5% and 20% U-235. Standard light water reactors run on fuel enriched to around 5%. Every SMR, every high-temperature gas reactor, every advanced design currently being funded, licensed, or hyped on this platform needs HALEU to run.

The DOE has said publicly that HALEU is not currently available from domestic suppliers at commercial scale.

Let that sit for a second.

The reactors everyone is buying cannot run without fuel that does not commercially exist yet in the United States.

The U.S. response has been the HALEU Availability Program. The DOE contracted with Centrus Energy to run a demonstration cascade at Piketon, Ohio. As of mid-2025, that facility has produced around 920 kg of HALEU total. The contract supports up to 900 kg per year.

A single commercial advanced reactor at full operation will need multiples of that annually.

The TRISO problem

HALEU is only half of it. Most advanced reactor designs do not just need HALEU in standard form. They need TRISO fuel. Each particle is a uranium kernel wrapped in ceramic coating layers that contain fission products under extreme heat. It is what X-Energy's Xe-100 runs on. It is what most high-temperature gas-cooled reactors are designed around.

BWX Technologies is the only U.S. company that has manufactured irradiation-tested TRISO fuel using production-scale equipment.

In February 2026, X-Energy's TRISO-X subsidiary became the first company in about 50 years to receive a new NRC fuel fabrication license. The application was filed in 2022. It took four years to approve. Their facility in Oak Ridge is still under construction. Fuel production is not expected until early 2028.

So we have a situation where the fuel the reactors need does not exist at commercial scale, the only facility that can make it will not be producing until 2028 at the earliest, and the reactor names are trading like the buildout is already happening.

The domestic uranium situation

U.S. reactor operators bought 55.9 million pounds of uranium in 2024. Domestic origin was 8% of that. The other 92% came from Canada, Kazakhstan, Australia, Uzbekistan, and others.

Energy Fuels runs White Mesa Mill in Utah, the only fully licensed and operating conventional uranium mill in the United States. It has processed roughly two thirds of all domestically produced uranium since 2017. Not because it is special. Because there is nothing else.

The three names I think matter most

$LEU — Centrus Energy. The only licensed HALEU producer operating right now. If the advanced reactor buildout happens, every reactor needs what Centrus can supply. The DOE just handed them $900 million to scale up. This is not a speculative bet on a technology. It is ownership of a physical constraint.

$ASPI — ASP Isotopes. Developing alternative enrichment technology that could, if the science validates, break the enrichment monopoly entirely. The company itself says its uranium enrichment technologies cannot be tested on uranium until regulatory approvals are obtained. High upside, low proof. You are betting on the disruption of the bottleneck from the supply side.

$LTBR — Lightbridge. Designing a metallic alloy fuel rod for existing light water reactors. No HALEU required. If it works at scale, existing reactors produce more power without touching the advanced fuel supply chain at all. Irradiation testing started at Idaho National Lab in November 2025. Very long path to commercialization. You are betting on the bottleneck becoming less relevant from the demand side.

Summary

I am not saying $OKLO, $NNE, and $SMR are wrong. I am saying they are downstream of a constraint that has not been solved yet. And the companies sitting on that constraint are getting a fraction of the attention.

Not financial advice. DYOR.

Most investors watch markets move and wonder why. We watch supply chains and see it coming.

I've spent a lot of time watching markets react to supply chain events that were completely predictable in hindsight. Drone component shortages. Grid transformer backlogs. Rare earth concentration in single countries. The information was always there; the market just wasn't pricing it.

I started mapping it out. Which nodes in global supply chains are so central, so hard to replace, and so geographically concentrated that a disruption becomes a market event almost by default. I called it the B(i) bottleneck scoring model; centrality, substitutability, geographic concentration.

This is the community for people who think that way.

What belongs here:

• Bottleneck and chokepoint analysis on any sector
• Supply shock signals the market hasn't priced yet
• Second-order beneficiary plays — who wins when a node breaks
• New issue drops and framework updates

What doesn't:

• Ticker pumping without analysis
• Generic macro takes with no supply chain angle
• Low effort "what should I buy" posts

To see the framework in action, here's the first published case study on the Uranium and Nuclear Reactor supply chain bottleneck:
The Hidden Choke Point in the Nuclear Boom | The Bottleneck Investor

Drop a comment below; what sector chokepoint do you think the market is most underpricing right now?

I’ve been trying to look at the AI trade from a slightly different angle.

A lot of the obvious AI infrastructure winners have already had huge moves. Optical networking, data center construction, cooling, and power equipment have all been bid up hard. So instead of asking “what is the next AI stock,” I wanted to ask a more boring question:

Which AI-linked sector has lagged the broader AI/growth trade over the last two years?

This is not meant to be a “best stocks to buy” list. It is just a relative-performance screen to find areas worth doing more research on.

How I built the baskets

I grouped public AI-linked companies into sector baskets: AI power supply, power distribution, compute, semiconductors, cooling, networking, data center construction, materials, critical minerals, defense AI, healthcare AI, etc.

The ticker selection was thesis-first. I tried to include companies where revenue could plausibly benefit from AI infrastructure capex or AI adoption, not just companies that mention “AI” in a press release.

For example, the AI Power Supply basket included names like:

SMR, NNE, FCEL, PLUG, NPWR, ORA, FLNC, EOSE, GWH

I excluded the obvious mega-cap AI winners because I was specifically trying to find parts of the value chain that may not have fully run yet.

How I calculated it

I pulled adjusted price data from 2024-04-29 to 2026-04-28 and calculated each ticker’s 2-year return. Then I compared every ticker against QQQ, since QQQ is a reasonable proxy for the AI/growth trade.

Over that same period, QQQ returned about +53.6%.

For each sector, I used the median return, not the average. I did that because one monster stock running 500–1000% can make an entire sector look hot, even if most of the basket did not participate.

Then I calculated:

Sector normalized return = sector median 2-year return minus QQQ 2-year return

Main results

| Rank | Sector | Vs QQQ |

|---:|---|---:|

| 1 | Health AI | -103.5% |

| 2 | Defense AI | -86.1% |

| 3 | AI Power | -27.4% |

| 4 | Industrial AI | +5.2% |

| 5 | Semi/HW | +14.8% |

| 6 | Compute | +18.7% |

| 7 | Power Dist. | +40.3% |

| 8 | Telecom | +48.9% |

| 9 | Minerals | +62.7% |

| 10 | Components | +84.7% |

| 11 | Materials. | +96.7% |

| 12 | Auto AI | +127.4% |

| 13 | Cooling | +139.0% |

| 14 | DC Buildout. | +273.6% |

| 15 | Networking | +568.0% |

The part that stood out to me:

Healthcare AI and Defense AI were the most underperforming baskets overall, but they are more AI application sectors than core AI infrastructure sectors.

For core AI infrastructure, the cleanest laggard was:

AI Power Supply

That basket had a median 2-year return of about +26%, while QQQ was up +54%. More than half the names in that basket were still below QQQ.

Meanwhile, networking, data center construction, and cooling have already had massive moves. That does not mean they cannot keep going, but the market has clearly already found those bottlenecks.

My takeaway

The AI trade has already aggressively repriced the obvious infrastructure bottlenecks: networking, cooling, and data center buildout.

But the power side still looks relatively under-ran on a 2-year normalized basis.

Curious if anyone else is looking at the AI power bottleneck the same way, or if there are better ways to build the basket.