This piece is a look at the entire uranium and nuclear stack and the best way to play this theme.

Here’s a TL:DR overview of exactly what’s in this nuclear and SMR piece:

• Nuclear is back from 2011-2020 slump because decarbonization, energy security, and AI need dense, 24/7, carbon free power.

• Uranium prices will likely stay propped up and even rise to +$100 again as the utilities keep under-contracting uranium (as they have for the past 10-12 years). Add this to huge demand vs supply imbalances and I remain very bullish on the uranium market for the next decade.

• The best way to invest in the nuclear theme is by understanding the entire stack (metal → fuel cycle → technology).

• Some of the biggest bottlenecks are in the conversion and enrichment process of the fuel cycle.

The physics behind nuclear has always been there.

It’s just been a numbers game combined with political and public appetite, lower natural gas prices, and a history of Western projects that ran into huge cost overruns that made nuclear not worth it over the last 3 decades. Hence we saw nuclear go from generating ~17.5% of global electricity in 1996 to 9% today.

But now we’re in the middle of decarbonization goals, reliable domestic power needs, and AI’s power demand… the physics of nuclear actually starts to make sense.

Nothing else in the entire grid can pack in so much carbon-free power into so little land, and run that 24/7. Solar and wind can compete on speed and cost per installed kilowatt but with land use, capacity, and the need to be online 24/7… nuclear trumps everything.

Despite this, the numbers behind the installed base of nuclear isn’t there yet at all. The world has 400 GW of operable nuclear today but new Western builds remain very scarce relative to China. The “nuclear renaissance” as we call it seems visible in policy, cheques, and contracting…but isn’t close to being visible in actual completed plants.

But the part of the nuclear market that is showing forward demand perhaps before anything else is the price of uranium.

An update on my Web-App

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Uranium Prices

A core factor to understand if you’re bullish on nuclear stocks is why uranium prices are elevated and why they can keep going higher. We’re currently sitting at ~$89 with $136 being the high in June 2007.

Here’s why I forecast uranium prices to keep rising:

1. We’re burning far more uranium than mines produce.

   1. The buyers who keep reactors running have not locked in enough uranium for the decade ahead. Global reactor requirements sit near 69,000 tonnes of uranium per year. Current mine output is 60,000 tonnes per year.

2. Utilities have compounded the problem by under-contracting for years. Nuclear operators have at most 174 million pounds covered for 2026-2035 against ~360 million pounds of anticipated need. That’s 186 million pounds of no contract and no assigned supplier.

   1. This 186 million pounds isn’t any speculative demand. It’s fuel for reactors that already exist. There will be a time when that book has to be filled and buyers will hit the market together competing for a scarce supply. In that sense, under contracting is just concentrating a future wave of buyers.

3. Demand is massive.

   1. The World Nuclear Association sees reactor requirements (total volume of uranium that all operating reactors worldwide need to purchase each year as fuel) rising from 69,000 tonnes of uranium today to ~150,000 by 2040 as a base case (upper case is +200,000).

   2. On capacity, that’s 400 GW today to 746 GW in 2040 (base case) and 966 GW if governments get more aggressive on nuclear targets.

4. Kazakhstan still accounts for ~2/5ths of world production.

   1. Kazatomprom (historically supplied ~40% of all uranium) has approached peak output with production set to decline over the next 15-20 years.

   2. Not many new mines have been built recently and a project still takes a decade or more from discovery to delivery. Supply simply isn’t keeping up with demand.

The Opportunity

From an investment standpoint, the structural shift that matters is who’s buying nuclear, and how.

The original nuclear renaissance was focused on huge contracts with utilities and governments with 10+ year timeframes. That’s changing now.

Most of the new demand that is emerging doesn’t necessarily need 1,000 MW and most importantly it can’t wait 15+ years. An industrial site swapping out gas heat does not need a gigawatt reactor. It wants a few hundred MW it can add in as the plant grows. Same with a retiring coal plant for example. The demand for 1,000 MW+ units is much less now.

The demand is being fragmented into 50 to 300 MW blocks and this is exactly one of the core catalysts behind the next-gen nuclear and SMR’s.

It’s how nuclear gets sold to hyperscalers and industrials instead of only to state utilities.

Small Modular Reactor TAM

The International Energy Agency counts private SMR plans at ~25 GW with forecasts at 40 GW by 2050 as a base case. With policy support, funding, and good execution it sees these numbers closer to 120 GW.

Here’s where it gets slightly more interesting down the road:

Some reports suggest that if factories learn to develop SMR’s on a budget, and at scale, SMR’s have the potential to address 700 GW of industrial demand in North America and Europe. That would be in the region of $500 billion to $1.5 trillion in investment.

To put that into perspective, the world has about 400 GW of nuclear running today. So 700 GW of just North American and European industrial SMRs is almost 2x the entire global existing fleet. That’s roughly a 17x increase from the IEA’s base case assumptions in 2050.

Realize that is an upper-bound opportunity / an extremely bullish case rather than a forecast.

The Numbers:

The SMR market is currently valued ~$6-7B with the IEA seeing a market ~$25B by 2030.

This makes the valuations of pre-revenue companies (you’ll see below) pretty uninteresting at the moment given most are +$4B.

But here’s a thought experiment on where the market can go a 1-2 decades down the line:

At a mid-cycle build cost of ~$6,000-8,000/kW, and IEA current base case estimate of 40GW by 2050…the total construction bill for that fleet of SMR’s would be ~$240-320 billion. IEA’s more bullish take of 120 GW puts us ~$670B of cumulative SMR investment by 2050.

Note this excludes the extra bullish case of potential 700 GW.

The Timeline

The timeline for the uranium/nuclear trade is the biggest bearish case for me. It’s improving year on year…but nuclear is still a 3-5+ year story with SMR’s being a 5-7+ year story.

Aside from the technology advancements, and uranium supply of course, policy is the main thing that can compress the timeline.

On May 23, 2025, Executive Order 14300 told the NRC to change how it licenses and oversees reactors such as with stricter timelines in months, rather than years…and a change of process rules that tended to be hurdles for new builds.

Washington is backing this regulatory push as well. So far, the DOE has allocated $17.5B in loans to improve supply chain issues. We’ve also seen $2.7B in loans for domestic uranium enrichment (more on this below).

None of this makes nuclear a 2027 or 2028 story necessarily…but it does help alleviate some of the clear bear cases we’ve seen over the last decade…essentially that the US will talk about nuclear for decades without actually building anything material.

How to Invest

Let me take you through the nuclear roadmap quickly because understanding the roadmap is key.

A reactor developer sells a license, some engineering, and potentially some royalty on modules. The dollars are actually the uranium in the ground, the conversion, the enrichment, the heavy forging, the vessels, the constructions, and at the end the power contract.

In other words, the listed developer is the thinnest, and most binary part of a huge industry.

They’ll probably be deserving of the highest multiple just like Agility (CCXI) or Unitree in the robotics space will be deserving of the highest multiple, but whether that translates into the best (and safest) investment returns vs the actuator/bearings plays, or perceptions plays in robotics is the more important question.

It’s exactly the way to think about it in the SMR market too.

The way to think about the investment potential here is via a stack. Not through 1-2 pure play SMR tickers.

The Underlying Metal (Uranium)

Cameco | CCJ: CCJ is the blue chip of the group operating McArthur River and Cigar Lake. It sells significant portions of output on long-term contracts at fixed or base-escalated prices meaning it’s a de-risked way to play the more general uranium theme. You also pay up for the safety of this with a stock trading at 73x NTM PE.

Denison Mines | DNN: This is a pre-production developer making it far riskier than CCJ despite sharing similar geography. Construction began in March making it the first new larger scale Canadian uranium mine in decades. Their target production date is mid-2028 so it’s a slightly higher risk way to play the underlying layer of the theme but one that has a big catalyst coming in the next 2 years.

Uranium Energy Corp | UEC: I think of UEC as a similar story to CCJ just with slightly higher risk in that production hasn’t reached full capacity yet and they do not hedge uranium prices meaning investors have purer exposure to uranium spot prices.

NexGen Energy | NXE: NXE owns the Arrow Deposit that by a lot of metrics is the highest grade uranium deposit in the world. It has a grade of 2.37% U3O8 (most uranium mines operate in the 1% range). The forecast numbers in terms of mine life, capacity, and total production are impressive but first production is realistically not till 2031-2032 so it remains another higher risk way to play the uranium theme.

Energy Fuels | UUUU: UUUU operate out the White Mesa Mill in Utah which is the only conventional uranium mill operating in the US. The big story is that UUUU is pivoting into rare earths to produce dysprosium, terbium, samarium, europium and gadolinium. This makes it a uranium / rare earth hybrid play.

Fuel Cycle

This is where some of the most interesting companies sit…but most of them aren’t US public companies aside from the two mentioned below.

Firstly, understand that there is a key conversion from the underlying metal (mining) → conversion → enrichment → fabrication.

The conversion and enrichment stages of the nuclear fuel supply are the ones with the tightest bottlenecks.

Conversion: Raw uranium (U3O8) is what comes out of a uranium mine. After ore is crushed and chemically processed you have a dry powder that is roughly 80% uranium by weight. It’s useless as a fuel though and it’s in the wrong physical form for enrichment. It therefore gets converted to Uranium Hexafluoride (UF6) which is a gas that centrifuges can use for enrichment.

Enrichment: Natural uranium is ~0.7% U-235. Conventional reactors need 3-5% and advanced SMR’s need 5-20%. The industrial process used to achieve this is a process called gas centrifuge enrichment which spins UF6 at 50,000 rpm so the heavier U-238 is separated from U-235.

There are active mines (~40 globally), active nuclear reactors (+400 globally), but there are only 5 companies that make up ~95% of the entire conversion and enrichment markets. That’s one of the reasons why conversion prices have jumped ~400% and enrichment prices have jumped ~300% over the last 18-24 months.

The only Western conversion providers are:

• Cameco (CCJ) is a conversion provider.

• Orano (French) is a conversion provider.

• Solstice Advanced Materials (SOLS) is a conversion provider.

Enrichment is mainly state dominated by Rosatom (Russia), Urenco (UK), Orano (France), and CNNC (China). The remaining Western capacity falls to Centrus (LEU), Cameco (CCJ) and Silex Systems (SILXF).

Solstice Advanced Materials | SOLS: SOLS owns Metropolis Works in Illinois which is the only conversion plant in the US. CCJ is the other conversion play (I wrote about it above in the underlying metals section).

Centrus Energy | LEU: LEU is the only Western company currently producing enriched HALEU at commercial scale in the Western world. The total backlog now stands at ~$4.5 billion through to 2040 with a $900M DOE task order and new supply agreements with Oklo and X-Energy. Advanced SMR’s physically cannot be built without HALEU.

ASP Isotopes | ASPI: ASPI is a complex name in the enrichment layer. It uses Quantum Enrichment which is a laser based isotope separation technology. The issue and complexity with ASPI is that its main business is actually specialist isotope production. It makes ASPI a slightly more diversified holding company with a high-upside laser enrichment option.

Silex Systems | SILXF: This is a higher risk bet on the enrichment process getting disrupted. It has a SILEX laser enrichment process that exploits the quantum mechanical properties of U-235 molecules by firing a laser to get them to react differently and allow separation in far fewer stages than the centrifuge process. If this process materializes, the energy consumption per SWU will translate directly to lower fuel costs (enrichment is ~30% of total nuclear fuel costs). Silex are working with Cameco on commercializing this technology.

Lightbridge | LTBR: LTBR doesn’t sit cleanly in this layer but it’s closer here than others. It’s trying to replace cylindrical rods by using a spiral-twisted metallic alloy that conducts heat far better than conventional ceramics. This ultimately increases power output and increases margins. It’s still completely pre-commercial but in August 2026 they were selected for DOE’s Launch Pad programme which can provide an accelerated regulatory pathway.

Nuclear Engineering

BWX Technologies | BWXT: BWXT is an underrated play in my opinion. It has quite a defensible niche in the stack in that it is the only large commercial nuclear equipment manufacturer in North America. It manufactures nuclear reactor components for defense (US Navy and aircraft) and for SMR. It has a backlog of $7.3 billion.

GE Vernova | GEV: Another blue chip, and arguably the leading play in the SMR niche as well. It’s building BWRX-300 which is technologically the most advanced Western SMR project today. It also acts as a large, and very diversified energy infrastructure company so you get a defensible energy play with a built in SMR option for the upside. It won’t have the upside potential of some of the pure plays but GEV at 44x PE is historically on the cheaper side.

Fluor | FLR: FLR are the EPC contractor (the firm you hire to take a reactor design and actually build a working power plant behind it).

Pure SMR Plays

This is where the potentially outsized upside is more likely but it’s also where you have to respect the timeline the most. I have a very small position in SMR that I’m in no rush to currently build out. My only aim over the next few months/years is to bring my average cost down on SMR as much as I can and then wait. It’ll likely be a bumpy ride as a stock that won’t hold up in a market downturn, but with the size of the position I have that’s completely fine for me.

NuScale | SMR: Commercializing a light-water small modular reactor of ~77 MWe and it already has US NRC certification. TVA and the Romanian Doicesti project are the two large catalysts right now to watch.

X-Energy | XE: Developing Xe-100 which is ~80 MWe. It also has TRISO-X-Fuel meaning the business can generate revenue from reactors and from fuel. Only recently public and pre-construction.

Oklo | OKLO: Building Aurora Powerhouse which is a sodium-cooled reactor. Commercial operation is targeted for ~2030.

Nano Nuclear Energy | NNE: Developing KRONOS. It’s earlier stage but has been awarded a NRC construction permit which should improve regulatory approval time.

Mini-Deep Dives Into My Favorites Within the Stack

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It continues to be a battle between the state of the macro (bearish high beta stocks & bullish commodities) and the state of the micro which continues to shine positively through all higher beta AI infrastructure names.

Commodities

Last week we saw some slight consolidation amongst the commodities and some slight bullish moves across the AI infra names.

iShares MSCI Brazil ETF (EWZ) is showing some signs of a breakout too which is a positive sign for the commodity markets.

Macro environment

It’s early days right now but my stance sits more on the more bullish side (through to the end of the year and most definitely if you look out to 2027/2028) mainly because yields are more of a distraction relative to everything else going on.

Historically the traditional playbook guides to rising long-term yields as a crisis signal. But aside from the headlines look at what is actually happening:

• Credit markets aren’t moving. Junk bond spreads (the premium risky companies pay to borrow) are near record tights meaning there isn’t much default risk right now.

• Inflation expectations, although slightly high right now, are sitting ~2.5%. Inflation expectations aren’t pricing in anything too worrying right now.

Investors clearly need lower rates at some point soon because the market can’t be fully driven by the earnings growth of AI companies. That is currently the reason why returns are subdued but I currently don’t think the macro environment is a strong enough reason for a major pullback.

It’s key to understand that although the yield chart isn’t that pretty…it’s just one part of the story that retail seem to understand most cleanly. The more downstream environment isn’t too bad right now.

Micro environment

On the micro we have:

• S&P PEG at 30-year low.

• KB Securities (largest investment bank in South Korea) said the memory market will see the “tightest supply conditions in history.”

• Nvidia (NVDA) forecast to grow +70% in FY28 even despite being supply constrained.

• Broadcom (AVGO) forecast 100% growth in both FY27 and FY28 (for AI semiconductor revenue) whilst being supply constrained.

• Broadcom (AVGO) CEO: “Demand for lasers, whether it is EML lasers, CW lasers, is far surpassing supply out there in the industry.”

• Credo Technology (CRDO) growing revenues by 115% with the optical transceiver market growing from 60 million units (2026) to 175 million units (2030).

The above are just new narratives from the last 7 days.

When you understand that the yield story is perhaps not as bad as headlines are suggesting (of course something we need to watch) then it really comes down to the fact that we’re entering a year where hyperscaler CapEx is set to be $1.3T.

This large number tends to create the bubble label but it shouldn’t. The size of the spend is not the test people need to focus on. It’s whether paid demand is still running ahead of what can actually be built and that answer remains an astounding yes.

That creates flows throughout the entire supply chain from optics, to memory, to cooling, to power that are quite hard to comprehend because the numbers are so different to what investors have been used to for decades.

The one part of this system that isn’t debatable therefore is that we’re in an undersupplied market. We’re being held up because of atoms (turbines, grid queues, HBM etc etc). It’s why players like Anthropic are still signing ~$517B worth of capacity deals in the last eleven months.

There is a time when an overbuild becomes a risk. It’s something to monitor but for the time being I don’t think we’re close. Being bearish on AI infrastructure is likely the wrong side of the trade if you have a 2+ year timeframe and are willing to look past headlines about rising yields (which may I make clear again are important to monitor…I just don’t think they’re more important than the above).

Crypto environment

And then we also have the crypto markets which are continuing their breakout.

For the last two years, BTC hasn’t done anything relative to the entire AI market around it. We had ETFs get launched, a more crypto friendly administration, and institutional custody mature…but the underlying prices did nothing.

This is slowly changing.

The reason is key. BTC is no longer a retail, leverage, and risk on trade.

The world is starting to realize quickly that AI agents need a financial system that:

• Settles at machine speed, 24/7, without a bank cut-off.

• Can make programmable payments (pay, stop, split, escrow) without a human in the loop.

• Hold and post on-chain collateral.

And the financial system we have to date (with limited banking hours, with quarterly reconciliations etc) simply isn’t fit for agentic purposes whilst Bitcoin, Ethereum, and the crypto ecosystem does.

What’s Mispriced?

This is a section looking at the potential opportunities I’m seeing at the moment. I’ll split them by how I see the risk profile (high risk → low risk).

I’ll tend to aim for at least 3+ opportunities in every article I publish here…but that depends on what the market is offering.

You’ll also likely see overlap week to week. I am not a trader that changes his mind every few hours, days, or even weeks. If I see an opportunity, that opportunity sometimes stays around for weeks.

Last week I touched on:

• Aeva Technologies | AEVA - Still remains a watchlist position.

• Quantinuum | QNT - Still remains a watchlist position.

• Nvidia | NVDA - Still remains a position I’m happy holding.

This week we’re looking at the below:

Coinbase | COIN (Low Risk)

The wider COIN thesis is in line with my narrative on crypto above.

In terms of valuation, here’s the brief valuation write up I wrote to paid subscribers on August 25th:

Read more

So what does all this mean and what is the market telling us?

Macro Take

Let’s start with the bond market.

Warsh came out hawkish at Jackson Hole. This is the kind of speech that, in theory, should reassure bond investors that the Fed is serious about the inflation fight. Core PCE is still at 3.3% (well above 2% target) and yet 10-Yr yields rose.

The reason is unclear but I think there are two ways to read the reaction.

Either:

1. Traders don’t believe the Fed actually has the stomach to hike enough times to bring inflation sustainably below 2%.

2. Or aggressive rate hikes slow growth and slow growth means lower tax receipts which means bigger deficits. Bigger deficits mean the Treasury has to issue more debt regardless of what the Fed is doing at the short end.

Point number 2 is the clear bear argument here. Interest expenses are escaping control. I think watching the 10-Yr is an important consistent check in over the coming weeks.

What this means for Gold & Silver?

Paid subs will know that I’m very bullish on silver. Part of that reason will be explained below (macro). Part of the reason is because I think the market massively underappreciates the silver demand needed in the AI buildout relative to silvers supply.

Warsh’s hawkish speech initially triggered a selloff in precious metals. That seemed like the obvious move (tighter policy/stronger dollar → sell gold/silver).

But longer term if the Fed hikes aggressively, one of two things will happen:

1. Growth slows, the economy softens, tax revenues fall, and deficits widen. That is bullish gold/silver.

2. Inflation stays sticky, confidence in Fed’s credibility weakens, and foreign investors question whether holding US Treasuries at current yields is worth the risk. That is bullish gold/silver.

That’s why the bond market is moving upwards. Investors are demanding a higher premium to hold US government debt and that has historically been a catalyst for gold and silver.

Hecla Mining (HL) is one of the silver positions I hold. It’s currently sitting nicely above the 200D, 100D, and 8 and 21 daily MA’s and above the key support we have at $16. Structurally, it looks like we have a nice floor in either at $16 or ~$12.50.

Contango Silver & Gold (CTGO) is the slightly higher risk silver play that I own. It’s predominantly a gold miner right now but it’s also a future silver story too through Kitsault Valley. Kitsault is still in exploration/drilling phase making this a gold miner with a silver option attached.

Micro Take

The whole market right now seems to be a battle between the more bearish macro takes, the oil markets, and the more bullish micro takes we’re seeing across the AI/semiconductor market.

Here’s a breakdown of some of the more important narratives we’ve seen over the last week from earnings calls, management commentary, and institutional research:

1. Nvidia (NVDA) had a huge Q2 growth estimate and FY28 forecasts were 70% vs original Street estimates of 44% (despite being capacity constrained still).

2. Nvidia (NVDA) projected $1.3T of hyperscaler spend in 2027. That’s vs the original forecasts of $800B and probably more important than anything else in the market right now. There are so many different angles to focus on right now in the market that it can get confusing if you let it get confusing. It’s incredibly important to zoom out sometimes and understand that upward revisions in total CapEx keep on rising. $1.3T in 2027 is a huge uptick vs previous numbers and making bearish arguments on the supply chain is therefore weak.

3. Chinese optics names like Innolight (300308) put out incredibly strong numbers with last 2 quarters revenue growth figures up 192% and 175% respectively with forecasts for H2 to be even higher.

4. SK Hynix (SKHY) CEO said memory shortage is expected to continue through to at least 2030. We also had Sandisk (SNDK) CEO state that “we see structurally massive demand for NAND until 2030.”

   1. Goldman Sachs forecast memory is set to be ~40% of the semiconductor dollar amount for humanoid robots.

5. Elon Musk said SpaceX (SPCX) could reach ~$3.5T in annual revenue by 2033.

The AI infrastructure has (a while ago) entered a phase that requires a different analytical framework to perhaps what most investors are applying.

The easy instinct is to look at capital spending numbers like the $1.3T figure, the GPU orders stretching years into the future, the memory shortage through to 2030, and then mention the world “bubble”.

The important metrics are:

1. Is earnings growth looking sustainable?

So far there is absolutely no solid data points pointing against this. Earning are continuing to beat across the board and trends are only heading one way.

2. Is supply outrunning profitable demand?

Yes. Anthropic is heading towards $120 billion in ARR whilst losing market share. Innolight is growing optics revenue +180%. These are not the result of price cuts.

The part of this that a lot of investors are still missing is what happens when intelligence inevitably gets cheaper. The intuitive bear argument is that open source models and efficiency improvements will eventually compress margins and reduce compute demand as cheaper alternatives enter the market.

The historical evidence from every prior tech revolutions suggests the opposite.

When the cost of a resource falls dramatically, consumption tends to rise faster than the price decline. It’s known as Jevons Paradox.

Cheaper intelligence doesn’t shrink the compute market. It expands the number of tasks worth computing in the first place.

Frontier models and cheap open source models aren’t competing for the same fixed pool of use cases - they’re together expanding what’s economically worth automating.

What’s Mispriced?

This is a section looking at the potential opportunities I’m seeing at the moment. I’ll split them by how I see the risk profile (high risk → low risk).

I’ll tend to aim for at least 3+ opportunities in every article I publish here…but that depends on what the market is offering.

You’ll also likely see overlap week to week. I am not a trader that changes his mind every few hours, days, or even weeks. If I see an opportunity, that opportunity sometimes stays around for weeks.

Aeva Technologies | AEVA (High Risk)

I wrote the following note to subscribers about AEVA on August 10th.

I since messaged very regularly on AEVA updating on no entry given the technicals showed no signs.

AEVA remains in that basket of a potentially very good opportunity here but until we see some consolidation on the chart or a reclaim of the $16.50 support level, I will hold off buying.

An opportunity nonetheless… and one to watch here at sub $16.

Subscribe now

Quantinuum | QNT (High Risk)

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As Jensen Huang says:

“Biology has the opportunity to be engineering, not science. When something becomes engineering and not science, it becomes less sporadic and exponentially improving. It can compound on the benefit of previous years.”

That idea alone is set to create some of the most important AI companies in the entire world.

I hope that opening got your attention about my view of this theme. I’m going to outline in this article:

1. Why this theme is set to be revolutionary

2. The potential monetary inflow into this theme

3. How to invest in this theme

Why Digital Biology Is Set To Be Revolutionary

The fundamental bottlenecks in medicine today are:

1. It’s slow

2. It’s expensive

3. It’s highly uncertain in the lab

4. It’s even more highly uncertain in living systems

It’s why traditional biology and drug discovery has been constrained by the complexity of biology. Hypotheses, data analysis, and eventually failure rates of +80% after years and huge costs.

The digital biology approach inverts this.

Specialized AI models trained on molecular, preclinical, clinical, and patient data can analyze billions of hypotheses instantaneously in parallel.

And then think about the next layer…agentic systems can then read literature, formulate questions, design experiments, interface with robotic labs, analyze results, and iterate this Design-Make-Test-Analyze loop over and over again. Productive wet lab teams pre-AI may be able to fully explore ~1,000-2,000 molecular hypotheses per year but an AI native setup can explore an order of magnitude more whilst feeding every outcome (success or failure) back into the model.

The nature of biological data is itself is arguably the most valuable data in the world. Previously failed testing programs were just treated as a sunk cost but with AI those failed outcomes turn into a goldmine. The models are able to internalize why something failed under specific conditions which offers the edge in avoiding that same mistake again.

The end patient and the lab still remain the ultimate end judge (and will be the new bottleneck), but the time, cost, and attrition of reaching this stage is set to compress massively.

I think the most important part of this entire feedback loop to understand from an economics point of view is that once the percentage of successful trials starts to rise, the compounding loop from there becomes quite aggressive.

And the faster these trials become successful, the faster this loop starts snowballing.

Trials will become more successful because of better data analysis and speed of iteration but also because aging and disease will change from a metaphysical concept to a set of defined, measurable, targetable engineering problems.

It essentially becomes a computational problem rather than a science and lottery problem.

The loop reinforces itself in the sense that better models yield better medicines which yield more capital and knowledge.

And eventually in a world where the algorithms become a commodity, the scarce assets (the proprietary biological data mainly) become extremely valuable.

I want to make it clear therefore that digital biology is set to do far more than accelerate existing pharmaceutical research and development.

It’s set to completely redefine the model for creating medicine. That market today is worth ~$1.8 trillion today. I suspect the end market will be worth multiples higher than that quite soon.

Just a quick note:

I’ve been working very hard on a web-app as you all know so far. We’re currently in beta testing and have a development team working on the final stages for this version 3 set to be released soon.

It’ll add a whole new dimension to my service over the next couple years. You can access the site here to have a look.

Here’s what it’s in the app:

• Guided Valuation Model (EV/Sales, EV/EBITDA, P/E, or MMMT Valuation Model) for every stock in my universe - see screenshot above.

  • This allows you to input your assumptions (or follow my assumptions) and come out with a target price.

• Stock Baskets: As well as showing my personal portfolio as evidence of my track record, I’ll be showing you concentrated stock baskets of the best themes in the markets.

  • These aren’t ETFs. These are 5-10 stock baskets for all themes in the market with individual stocks and weightings.

  • I think of it as a way of introducing you to other high conviction plays I have that I don’t necessarily personally own due to capital. I also think it’s a great way to increase your exposure to a theme that you may like more than I do.

• This is all on top of daily notes, live portfolio, technical screens, and research notes.

A Thought Experiment On Just How Big This Market Can Get

This is hard to model but I’m going to do my best to make the assumptions as clear as I can.

Note: These are a thought experiment. Do not take these are market forecasts.

Short Term

The first way I think about this is looking at the current biotech market today and the effects of a rotation out of AI hardware and into biotech changes the market size today.

The top 15-20 AI hardware / semi names have a combined market cap in the $13-15 trillion range right now.

The biotech market is currently valued at ~$1.25 trillion.

Now the next step is where assumptions come in. I think it’s highly likely one of these two happen:

1. Investors and institutions see potential slowing of the AI infrastructure play and rotate capital elsewhere…of which some goes to healthcare and biotech. If that’s the case I think a fair assumption is ~2.5% rotation from AI infrastructure directly into biotech (I think this is already happening in the early stages).

2. Investors and institutions start to see biotech as one of the purest AI plays out there over the next decade that may create some of the most important companies worldwide (just like I stated in my opening). If that is the case I think we’re looking more towards a +6% rotation out of AI infrastructure and into biotech.

At 6% rotation out of a $14 trillion market means we’re looking at $840 billion in capital.

At 5x inelasticity (fair for the biotech market) that leads to ~$3.5 trillion of amplified buying power.

If ~40% of that gets concentrated into the top 10 names like LLY, TMO etc there is a $1.4T incremental market cap impact on that group of multi-billion dollar companies.

And then on the mid cap to smaller cap names we’d likely have ~$2.1 trillion impact but the current market cap of this range of stocks is no more than $200 billion so the multiple uplift could be in the range of 8-10x.

That’s purely just a thought experiment to evidence how large potential capital flows may impact a smaller, more inelastic market in biotech today.

When you combine these shorter term capital rotation forecasts with the below assumptions, the numbers get quite staggering.

Longer Term

Forecasts today have the biotech market generating ~$1.7T in annual sales with a market sitting ~$1.25T and 2030 forecasts at $2.2T.

Those assumptions are based on the current environment of ~8-9% of drugs entering Phase 1 actually making it through to approval. It’s also based on ~$2.6 billion in spend per successful medicine, and ~10-15 year total timeline. Most of that capital spend is unfortunately taken up by failures rather than successes.

If the thesis plays out like I outlined in the first section and we see the industrial feedback loop work smoothly by spotting early failures, and feeding back data into better models…then the same R&D spend becomes far more productive.

Conservative Assumptions

A conservative take would be to say Phase 1 to approval success goes from 8-9% to ~12% and then gradually towards 15% and time compresses to ~5-8 years. That alone would increase new annual launches by ~75-100% and cut the cost per drug down to sub $2B.

If the same R&D budget (which actually could be increased substantially if clinical outcomes become less of a lottery) produces ~2x the amount of approved drugs per annum, the drugs reach cash production several years earlier and fewer dollars are burnt in Phase 2 / Phase 3 trials and also fewer dollars are held up in projects lasting many years.

Over time my numbers point towards a 2x in earnings and free cash flow which can re-value the market from ~$1.3T to $2.6T, excluding multiple expansions.

Multiple expansions is the easier argument to make. A sector that runs like a compounding production system on some of the most valuable data in the entire market with rapidly improving ROIC will be deserving of a multiple at least 1.5x higher than today.

The narrative changes from a high risk lottery bet to a engineering bet.

A 1.5x increase in the multiple then takes us to ~$4T.

A reminder that’s a base case.

A reminder also that this is excluding what I said above with capital inflows from other areas of the market.

Bullish Assumptions

A more bullish case (which I suspect will happen but just further down the road) assumes the loop industrializes.

Phase 1 approval rate jumps to ~18-20% with compressed timelines ~4-5 years in bio-marker rich niches of biotech. Because of this cost per approved drug reduces to ~$1B and annual launches can therefore head towards ~2.5-3x the rate today.

At this point, the main bottleneck will become the end judges (the humans) and the scale and speed at which we can run trials and manufacture drugs at scale. Discovery becomes faster but unless clinical execution and manufacturing can ramp up there is still a clear constraint.

Current forecasts suggest $2.2T in annual sales by 2030. A 2.5x in approvals in an ideal world means a 2.5x in sales but that won’t be the case as the more drugs approved likely means a lower % of blockbuster drugs. What is likely does mean though is that there will be a faster flow of well branded assets that reach patients earlier, with more years of protected / patented cash flow ahead of them.

That means the net present value rises before we even consider an increase in sales.

So valuation impact then comes from:

1. More approved assets

2. Cheaper produced assets

3. Longer NPV per assets

If earnings therefore increase ~3x based on above assumptions and the multiple increases ~2x we’re looking all of a sudden at a $7.8T market cap.

Most of this value won’t be spread evenly among names. But I think it’s becoming clearer and clearer those 20-30 names that either are:

1. Pure plays on drug discovery

2. Pure plays on specialized compute

3. Pure plays on manufacturing

4. Pure plays on the platforms

5. Pure plays on commercial reach

I’m going to lay out the investment picture here now. I’ll dive a little deeper into my 4 favorite names and list ~20 more that I like today.

How to Invest

If I were to create an ETF on this theme, here’s what it would look like (click on the link to download the spreadsheet).

Read more

I’m going to highlight in this article how I see the stock moving from a $60B MC at $221 today to much higher whilst also showing you a range of situations that may happen and the associated stock prices so you can best gauge your estimates.

The goal though isn’t to give you a single price target. It’s to give you my personal view… and also a range of likely situations in 2030.

As with all modelling, a lot of this work requires assumptions. Sometimes I may be too aggressive and sometimes I may be too conservative. But based on current trends, management guidance, and industry wide forecasts I think I’ve got pretty close to what will likely happen.

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The facts we can be slightly more sure on:

Contracted Capacity

Management has guided us towards 5 GW of capacity to be contracted by year end 2026, with more than 1 GW of additional capacity planned for deployment each year beginning in FY27.

“We continue to build our future capacity pipeline through our own and colocated sites, and today raise our year-end contracted power target to 5 gigawatts. Our future capacity pipeline effectively makes Nebius one of just a few companies in the world able to build more than 1 gigawatt of new capacity a year. And we plan to do so in 2027.”

Connected Capacity

This path therefore produces ~5.2 GW of connected capacity by the end of FY30 if execution remains in line with management forecasts.

“Almost all of that power will come online over the next 3, maybe 3.5 years out of the contracted power.”

A lot of the numbers used in this work are based on this 5.2 GW of connected capacity so here’s a bit more depth on it.

So starting from YE 26, that points to the bulk of today’s 2026 forecasted 5 GW being connected in mid-2029 to 2030 starting from today’s base rate ~800 MW-1 GW.

5.2 GW is therefore not the most aggressive interpretation of the current commentary.

The risk to this forecast is grid interconnection which is structurally more of a nation wide risk. The grid simply can’t meet new demand quickly which is why we’re being forced into the behind-the-meter power solutions and power generation systems that do not rely on the grid.

Right now, we’re seeing grid-interconnection queues above 2,000 GW with an average wait time of ~5 years (application to operation). Only ~13% of projects that entered application before 2019 actually reached operation.

The more positive take for NBIS is that site selection and strategy is built around the interconnection issue.

75% of contracted capacity is sourced from owned, BTM generation (Bloom Energy fuel cells and Vineland), a natural gas power station in Missouri, and a site in Pennsylvania. None of these are stuck in the 2,000 GW queue.

Site selection and strategy is therefore the moat.

Annual Contract Value

More recent landmark contracts are valued at ~$20-25 million of ACV (annual contract value) per MW. Worth noting that original levels were ~$12 million.

My personal base case that reflects:

• Customer mix

• Ramp timing

• Difference between contracted and fully revenue generating capacity

Gives me a deliberate haircut (I always model conservatively) of a blended ACV per MW of $14M.

5.2 GW * $14M = $72.8B ARR by 2030 (this is my base case we will work from)

That’s roughly a 9.1x increase in ARR from YE26 to YE30 which is entirely capacity driven with a conservative ACV.

I’ll get into the valuation section after this but on the face of it a 9.1x increase in ARR in 4 years whilst trading at 8.8x NTM sales isn’t expensive.

ARR Forecast: Bullish Case

I think my assumptions above are a solid base case.

I do want to include a more bullish take so here it is. It’s quite in line with a model that Wells Fargo put out where we reach 7.5 GW of active infrastructure by 2030.

From here, Wells Fargo also used a more positive revenue per MW than my assumptions.

“These deals were for an average of more than $1 billion each. They represent a yield of $20 million to $25 million per megawatt.”

“You should expect the deals we closed in this quarter with more than $20 million per megawatt deals and less than 2-year payback period.”

Industry benchmarks for a fully utilized GPU data center are closer to $20-22M per MW which could push our 2030 ARR up to +$150 billion.

I understand this is a bull case but this is the far more likely scenario vs the below bearish take.

ARR Forecast: Bearish Case

Wolfe Research have put out numbers suggesting we see $41.2B in ARR by 2030 based on 5 GW (potentially not all fully online or fully utilized on day one of 2030 though).

An exact $ per MW is not clear but based on 5GW of active capacity we’re likely looking in the region of $8.24M which seems incredibly low to me. It’s based on:

• Normal utilization rates, ramp timing lags, and some capacity that is reserved or not yet at peak density.

• Mix of older, lower-priced contracts still in the base.

Seems like a heavy haircut to me, but one we should consider and one I’ll work from for a potential share price in each section as well.

Methodology 1: EBITDA

Note: This is all based on a $72.8B ARR.

Full disclosure here: I’m aware a pure EBITDA look may not be the best representation of valuing a capital intensive business like NBIS but when FCF is likely negative or very slim through 2030…you can’t run a clean looking DCF model.

EBIT / ROIC is a nice way of looking at the economics but it’s not a common valuation method to compare to peers today. In my next method, I do look at ROIC.

EBITDA is the purest metric we can use right now. It’s just important we take into account and understand the downsides of using this and that these share prices are likely a little inflated.

Group margin currently sits ~41% with management indicating we should see margins stabilize ~45% once we reach scale.

I’ll be using 45% as my base case longer term assumption but will model between 35% and 45% through to 2030.

$72.8B * 45% → $32.8B

$72.8B * 40% → $29.1B

$72.8B * 35% → $25.4B

An EBITDA of $32.8B which results in a 124% CAGR over a 4 year period.

124% CAGR (4Y) is arguably one of the highest growth rates we’ll see amongst all stocks I follow and deserving of a high growth associated multiple.

• EQIX trades at 23x NTM EBITDA based on 13% CAGR

• DLR trades at 23x NTM EBITDA based on 13% CAGR

• CRWV trades at 8x NTM EBITDA based on 71% CAGR (CRWV has far higher net debt and is lease heavy vs NBIS owned sites).

Here’s a bull to bear case:

Bull: 25x * 32.8B → $820B EV

Base: 15x * 32.8B → $492B EV

Bear: 10x * $32.8B → $328B EV

Net Debt

Assumptions for net debt are as follows:

Net debt is currently ~$2B. Forecasts and maths based on ~$30-38M per MW assume gross invested capital will be ~$156B - $198B.

Here’s what we know on the further details here:

• Customer prepayments are expected to cover ~50-60% of associated CapEx on large deals.

• Convertible notes (~$15B) will fill residual gaps.

• Operating cash flow should become a growing internal funding source.

So, if ~50% of CapEx is covered by prepayments + growing OCF, external capital needed will be in the $70-90B range.

Before I go into the numbers, I just want to be clear that NBIS’s strategy so far has been stellar in terms of their cost of capital. They’re already starting to look like a hyperscaler.

Most neoclouds still fund growth primarily through equity which is pricey and creates this repeated cycle of dilution → deployment → depreciation.

NBIS is already at a stage where they’re accessing asset-backed debt which is collateralized by investment-grade customer contracts. The recent $775M debt facility is collateralized by contracted cash flows from investment grade customers which means lenders are effectively underwriting the credit of counterparties such as META and MSFT rather than NBIS itself.

NBIS management indicated that this can essentially be repeated across the entire $40B of backlog.

Then as discussed we have prepayments directly funding new capacity as well which should mean this dilution figure isn’t as bad as we see tend to see in the neocloud industry.

I think a conservative range is therefore to assume net debt in 2030 is between $40B (bull case) to $80B (bear case).

So if we take the higher end of this range off our EV’s we get the following equity values:

$820B - $80B → $740B

$492B - $80B → $412B

$328B - $80B → $248B

These are equity values far above today’s $60B range.

Dilution

The next stage of assumptions is dilution. I’ll model my dilution out here and then re-use in the below methods too.

It’s also really important to note that the economics of this business sway between being extremely good and not great on quite slim changes.

I.e. if NBIS do manage to work towards +$100B in ARR then operating cash flow starts to cover far more of the CapEx than I am assuming here which in turn means less debt and less dilution.

This is entirely a story based on how quickly NBIS can convert to connected capacity, and from there how quickly they can get that blended $ per MW up close to +$20M per MW and beyond.

Modelling share count is obviously a challenge but today’s starting point is 274 million. We have:

• $5B convertible note that will:

  • 2030 notes convert at $313 → 7.98M shares

  • 2034 notes convert at $325 → 7.7M shares

• Pre-funded warrants at $2B at implied stock price issuance adding ~8.9M shares.

• Ongoing SBC which could add ~5M shares (conservative take).

That’s an additional 22M shares of known dilution added. I’m going to apply another 55M share dilution to account for the additional equity capital raises I expect will be required for the 4.2 GW buildout from here assuming average issuance prices at above today’s levels.

This gives me 351M shares as a base case to work from for the 2030 outstanding number of shares. 351M shares in 2030 is a 6.3% dilution per annum from here which again I think is on the more conservative side of where we actually end up.

Share Price

Therefore we have:

$740B / 351M → $2,108

$412B / 351M → $1,174

$248B / 351M → $707

EBITDA methods will likely give NBIS an inflated share price so I think taking these with a pinch of salt is the best bet.

More bullish case (based on connected capacity and $ per MW)

Assumptions:

• ARR: $150B (see above)

• EBITDA margin: 45% (see above)

• Share count: 351M shares (see above)

($150B * 45%) * 25x / 351M → $4,808

($150B * 40%) * 15x / 351M → $2,564

($150B * 35%) * 10x / 351M → $1,496

More bearish case (based on connected capacity and $ per MW)

Assumptions:

• ARR: $41.2B (see above)

• EBITDA margin: 45% (see above)

• Share count: 351M shares (see above)

($41.2B * 45%) * 25x / 351M → $1,321

($41.2B * 40%) * 15x / 351M → $704

($41.2B * 35%) * 10x / 351M → $411

Note: The business is valued a lot like this today on a pure 17x NTM EBITDA multiple for the most part. That won’t be the case forever.

There will likely be a case soon where margins, where FCF conversion, where CapEx comes under a lot more scrutiny and that will come if/when we get more of an prolonged drawdown.

It’s why I don’t love this above approach when looking out for 2030 onwards.

Methodology 2: EBIT

A longer term DCF approach is ideally the best way to value NBIS at today’s inflection stage because of the multi-year capacity buildout ahead. The issue is based on current forecasts we’re likely looking FCF negative until ~2030-2032.

The main goal with this approach is to quantify the capital required to get to this capacity peak with a realistic funding mix and depreciation.

Here’s a clean walkthrough for you (based on the same assumptions of 5.2 GW and a blended $14M / MW:

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AAOI has become a popular stock amongst the retail community, and for good reason.

There’s not a single quality stock I know about in the market that has the potential revenue ramp up that is possible with AAOI over the next 18-36 months.

It reminds me of NBIS back when it traded at ~$75. It was trading at 2.4x ARR when ARR was projected to 8x over the following 12 months. NBIS then went from $75 →$298 before pulling back slightly now to $186.

AAOI is at a very similar revenue inflection point now…just on an even more bullish scale.

My personal AAOI position:

I’ve now held AAOI for a while with an average cost at $133. It used to be in the $150s but I took advantage of the recent dip we saw down to ~$80 to slowly lower my average cost basis.

It’s now my fifth largest position behind LMND, OUST, NU, and AMZN - you can see the entire portfolio (and much more) as a paid subscriber.

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I’m inclined to make AAOI an even larger part of the portfolio but I’m just being a bit cautious as we approach new highs on the indexes again.

Contents

• AAOI Background

• Optics/AAOI TAM

• AAOI Earnings Report

• AAOI Valuation

Company: Applied Optoelectronics

Ticker: AAOI

Headquarters: Sugar Land, Texas

CEO: Dr. Chih Hsiang (Thompson) Lin

Market Cap: $10.72B

NTM Fwd Sales Multiple: 5.44x

AAOI Background

This will be a very brief AAOI overview.

AAOI fabricates its own Indium Phosphide laser chips in-house and also manufactures the optical transceiver modules those lasers power. Because AAOI grows its own laser material (InP), it controls the entire supply chain whereas almost every peer is constrained.

Lasers are, in the words of the CEO: “The biggest bottleneck right now for the transceiver business.”

AAOI currently has two segments:

1. Data centre transceiver business (100% of my investment thesis)

2. Cable television (CATV) amplifier business - a smaller, profitable business but one I have little interest in looking out for the next 3 years.

The company is in the middle of the most aggressive manufacturing expansion and volume ramp up in its history (and arguably one of the most aggressive expansions in the market) looking to 25x their manufacturing space. The product roadmap is currently 100G and 400G, with 800G as the near term inflection and 1.6T as the next standard as well.

The wave after that is co-packaged optics (CPO) where the laser source moves from a pluggable module directly onto the switch package. AAOI’s ELSFP laser module (the external light source that CPO needs) is targeting ~400,000 units per month by 2028.

This ELSFP would be an earlier story but as per the CEO:

“We just can’t make enough of them to be involved in their current first-generation deployments because there’s just not enough capacity. We have to prioritize our ability to make lasers for our own transceivers first.”

I’ll try to make it abundantly clear throughout this article, but I think you can gauge from what I’ve said so far… AAOI is a company not short on demand.

Customer demand even exceeds the planned capacity targets by another 20%.

This makes AAOI currently purely an execution bet - an execution bet that the manufacturing ramp up in Houston delivers on schedule. If it does, and forecasts around gross margins, ELSFP volumes, and 800G/1.6T volumes are accurate…then AAOI is, in my opinion, one of the better bets in the entire market today at sub $11B MC.

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The TAM

The optics market in general is massive and we’re in the very early innings.

Let’s start big picture.

Goldman have put a forecast out for the total addressable optical networking market to be worth ~$154 billion which is 9x from today. Whether this holds true or not, it’s likely a directionally correct signal for what connectivity infrastructure is worth in a world going through the largest tech revolution of all time.

TrendForce have put out numbers too on the combined value of the CPO and NPO market to expand from ~$100 million in 2025 to $39 billion by 2030 with the majority of that growth being in 2028 and 2029. They also put out forecasts for the pluggable transceiver market to generate ~$26 billion in revenue by 2030. This means their estimates total to ~$65 billion (40% of Goldman’s and also two years further into the future).

For the sake of being conservative, most of the analysis below will work from TrendForce.

Now AAOI does not compete across the full $65 billion / $154 billion market. They compete in pluggable transceivers today (mainly 2027-2028) and are building into CPO laser supply for 2028 onwards.

On the transceiver side, AAOI is guiding towards 930,000 units per month of 800G and 1.6T by the end of 2027. With that equating to ~$471M/month ($5.6B annualized) that’s ~20% of the $26 billion forecasted pluggable transceiver market by 2030.

On the CPO side, AAOI is targeting 400,000 ELSFP laser units per month by 2028. Estimated ASPs are ~$500 per module which equates to ~$200M per month from CPO lasers alone ($2.4B annualized) against a $39 billion forecasted CPO market.

This gives AAOI ~6% of the potential CPO market which gives them some room for growth.

Put this into perspective though in ~2030. At a current market cap ~$11B, a business generating $6-8 billion in annual revenue across transceivers and CPO laser supply (against a total TAM of between $65 and $154 billion) is trading ~1.4-1.8x that potential revenue run rate.

Q2 2026 Report

I will dive into the numbers in this report because that’s what we do in earnings reports, but I think it’s important to caveat with the fact that the numbers in H1 2026 are not central, nor that important, to my investment thesis in any way.

The reason for this is because AAOI is a H1/H2 2027 onwards play in terms of volume ramp up.

That’s when we should start to see the $471M / month numbers which is a 12x ramp up from FY25 revenue.

Most of what I care about today is:

1. Manufacturing: The story now is predominantly about manufacturing execution. Demand is through the roof and the cap for AAOI right now is whether or not they can hit that 930,000 modules per month goal by the end of 2027.

2. Updates of 800G ramp up: Management have made it abundantly clear their timelines for 800G/1.6T ramp up. It was never going to be a Q2 story. It was always Q3 and Q4 and then ramping up nicely throughout 2027 so numbers in Q2 were never going to be backing up this thesis.

Anyway, here’s a look at Q2 numbers:

Demand:

• Q2 was the fifth consecutive record revenue quarter.

• Revenue grew 86% YoY and 27% QoQ.

• 56% of revenue came from data centre products and 42% from CATV.

  • Most of the data centre revenue came from 400G and 100G with 11.9% of data centre revenue coming from 800G (still immaterial but this should start to change in Q3).

Profitability:

• EPS beat estimates and management guidance.

• Returned to non-GAAP profitability for the first time in years with non-GAAP net income of $5.5M vs a guidance of -$2.5M - +$2.8M.

Guidance:

• $1.1B guided for FY26 meaning we’re looking at ~$485M guided for in Q4 (2x what is guided for in Q3). This is where execution risk becomes quite material for AAOI. Management wouldn’t be guiding here if this wasn’t feasible but Q3/Q4 will be almost a make or break for investors in terms of their trust in AAOI management. AAOI management do have a slight reputation over overpromising and underdelivering in the past. I sense this time is different, but it’d be naive not to be aware of this as a risk.

• Long-term gross margin target remains at ~40% with ELSFP laser contribution being ~50-65% gross margins.

Important notes from earnings call:

“We continue to expect by the end of this year that we will be capable of producing over 650,000 pieces of 800G and 1.6 terabit products per month. By the end of next year, 2027, we continue to expect to grow our production capacity to be able to produce over 930,000 pieces of 800G and 1.6 terabit products per month, with over half of that output coming from Texas.”

As I touched on above, the most important part for me for Q2 earnings was the manufacturing capacity trajectory.

One year ago, AAOI had 65,000 square feet of manufacturing space. Today they have 1.6 million square feet in the Greater Houston area (that’s 25x in footprint).

Here’s the ramp up projections management have given us:

Q2 2026: 200,000 monthly capacity for 800G/1.6T units

End of 2026: 650,000 monthly capacity for 800G/1.6T units

End of 2027: 930,000 monthly capacity for 800G/1.6T units

An 800G transceiver at current ASPs generates ~$400-500 per unit. A 1.6T transceiver is much higher.

So 930,000 units per month by the end of FY27 (predominantly 800G and 1.6T though unsure on the split) is where we start to see this potential for $5-7 billion in revenue.

It’s where management’s $471M/month figure comes from.

“We also discussed our plans to increase our manufacturing capacity for our external light source or ELSFP. That's for co-packaged optics or CPO". we anticipate ramping production later this year and into 2027, ultimately culminating in about 400,000 pieces per month in 2028.”

Modelling this into a revenue figure takes guesswork. Above, I estimated the ASPs to be ~$500 a unit based on:

• ELSFP lasers being 6x the die size of a transceiver laser.

• Requiring 300mW output vs 70-100mW for 800G/1.6T.

• DWDM variants are much more complex.

To me this implies material premium over a standard transceiver component which has an ASP ~$400-500 per unit. I think $500 is therefore a base/conservative number to work from.

$500 ASP at 400,000 units per month is $200M per month (or $2.4B annualized) on top of the potential $5.6B we have annualized in FY28 for the transceivers.

What’s more, the ELSFP business carries materially higher margins than the transceiver business meaning on approximately half the revenue, we’ll likely see a similar gross profit figure.

“For laser, the gross margins is about 55% to 65%. For ELSFP, the gross margin should be more than 50%.”

“Our ability to deliver revenue in general, and specifically when it comes to 800G products, is limited by our production capacity right now…if we could produce more, we could ship more right now.”

Just a final reminder that AAOI is a supply constrained company and not a demand constrained company.

Valuation

The stock is expensive if you look at today’s multiples relative to today’s revenue (~17x sales).

But the valuation compresses to sub 2x NTM sales on the mid-2027 run-rate management is guiding towards with a 12x ramp in revenue over 24 months from FY25.

Read more

Part 1 and 2 have already been released:

Part 3 focuses on:

• Uranium

• Brazil

• Application Layer

Uranium

The MMMT Uranium index is currently down 19% over the last year.

That makes it a slightly worse performing basket to hold than the likes of Global X Uranium ETF (URA) which is down 6% over the last year at the time of writing it.

As I try and make clear, the point of these baskets is to find a slightly higher beta way of holding the underlying theme rather than just buying the standard ETFs.

In Part 1 and Part 2, my baskets far outperformed the respective ETF’s so it’s no surprise that we have at least one or two baskets underperforming, especially in sectors that have been struggling over the last year.

Also, it’s worth nothing that my uranium basket is much higher beta than other ETFs because I have included SMR (a small modular reactor play) as a 15% holding.

This is also a play I own in my personal portfolio that I am down on a lot, but currently sized reasonably as to not materially affect the portfolio at all. I still have a lot of conviction in the SMR thesis…the time frame to start producing revenue is likely just a long time away which makes it a far riskier hold during market downturns like today.

Maybe the timing on this thesis has been slightly wrong, but the thesis I believe in.

Uranium Thesis

The supply-demand thesis for uranium is as clear as any commodity thesis I know.

Annual reactor demand globally runs ~200 million pounds of uranium.

Primary mine supply is 130 million pounds.

That’s a structural deficit ~50 million pounds per year that is currently getting filled from finite buffers like secondary supplies, enrichment underfeeding, and inventory drawdowns.

But here’s where it gets more clear cut:

Only ~65% of the uranium that reactors need annually is currently covered by long-term contracts. Utilities have been deliberately undercontracting for years…running down existing supply rather than committing to new supply at higher prices.

That ultimately worked when inventory was comfortable but it no longer works now with supply at record low and time between signing a contract and receiving delivery at record high of 2-3 years. That means it seems inevitable that there will be a buying wave upcoming and with that the price will move in unison.

The Basket

This basket in particular is quite concentrated amongst higher beta names, hence a reason why it’s underperforming vs the index.

We’ve got:

• Centrus Energy | LEU

• Cameco Corporation | CCJ

• Constellation Energy | CEG

• NuScale Power | SMR

• Denison Mines | DNN

Before we move on, a quick word about TrendSpider - the charting platform I personally use.

If you manage your own portfolio the way I do, here’s how TrendSpider fits into my process:

• Scanners for technical and fundamental setups

• Sidekick AI to dig through market data, filings, news, and the names I own or watch without bouncing between tabs

• Charting and automated technical analysis to track support, breakouts, and moving averages across my portfolio and watchlist

• Real-time options, sector rotation, market breadth, news, and sentiment to understand the wider context behind each position

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Brazil

The MMMT Brazil index is currently up 31% over the last year (in line with EWZ).

There a lot of valid lenses to look at the Brazil investment case through (Selic rate, politics, Ibovespa valuation etc) which can all lead to different conclusions on whether Brazil is investable today or not.

I’ll dive into them very briefly. But my main focus is on the fact that Brazil is becoming / has already become one of the most strategically important countries in the world.

The US has a critical mineral national security issue that is much bigger than news and headlines suggest. As we’ve known, China dominates the critical mineral supply chain. That doesn’t sound too damaging right now to most, but when you understand the critical minerals needed for technology for the next 10-20 years it becomes a national security issue:

• Humanoids: Rare earth permanent magnets (neodymium, terbium, dysprosium) power every joint actuator and motor.

• Quantum: High purity germanium and indium for qubit substrates, niobium for dilution refrigerator components.

• AI: Copper and fibre-optic rare earths (erbium and thulium) for data center interconnect, cobalt and lithium for power infrastructure.

The aim is therefore to “nearshore”. I.e. move dependence away from China by building supply chains domestically and building supply chains with allies closer to US borders. Outside of the US, Brazil and Venezuela are the key opportunities.

Venezuela has potential but it’s far less investable because of the infrastructure gap and the governance risk and whether this can be monetized at scale yet is still a big question mark.

Brazil on the other hand has:

1. Proximity

2. Scale

3. Relative political investability (italicised for a reason).

It’s rare earths are second only to China’s in terms of % of global reserves held. It holds the the largest niobium reserves and has huge deposits of lithium, graphite, nickel, copper, and phosphate.

Ultimately, this is all why the Brazilian Mining Institute expects the country to attract ~$21.3 billion in investment through to 2030.

However, there’s an obvious complication or two. But the main one is President Lula and it’s a key reason the Brazil trade isn’t working out just yet.

Lula is a big believer in not allowing foreign powers to exploit its countries mineral resources. The workaround so far has essentially been to bypass the federal government by making agreements with Goias and Minas Gerais (home to large rare earth and lithium deposits) but the federal Industry Minister keeps pushing back on these deals. Repeatedly it’s a one step forward one step backwards situation.

But it’s not all bad news amongst a big, unexploitable opportunity.

• The US Development Finance Corporation has backed Aclara Resources rare earths project in Brazil with a $5 million loan.

• The US Development Finance Corporation has backed Serra Verde with a $565 million grant.

  • After this grant, the US government took a 10% stake in USA Rare Earths (USAR), which is in the basket, which then acquired Serra Verde’s Pela Ema mine in a $2.8 billion deal.

  • It has begun letting its existing offtake contracts with Chinese buyers expire.

  • A 15 year set-price supply agreement has been signed with a consortium of private investors and the US government (mainly terbium and dysprosium).

The next significant catalyst is in October 2026 when Brazil’s elections happen. It’s probably the most important catalyst for this entire Brazil and rare earth trade in 2026.

Currently it’s quite even between Lula and Bolsonaro. For reference, Bolsonaro is positioning himself as the candidate that will turn Brazil into the solution for the West’s US mineral dependency on China and therefore likely open up those forecasted $21.3 billion investment opportunities through to 2030 that I mentioned above.

Risks are clear. Upside seems to me even clearer.

It’s one of the reasons NU is quite a substantial position in my portfolio. I personally think a lot of the bad news has been priced into Brazil (given all the metrics like Ibovespa valuation) so the downside just seems far less than than the potential upside.

Application Layer

The MMMT Application Layer index is currently down 2.5% over the last year.

I wrote about the application layer in a lot more detail here in June.

I’ve always been optimistic about the application layer because history has evidenced it time and time again as the place where capital accrues over the long-term.

Read more

1. All of investable biotech today is no more than $1.5 trillion relative to the semiconductor trade which is above $13.5 trillion. That means that even a small rotation out of the the semiconductor basket and into biotech could be very material. This last weeks price action is potentially early evidence of a slight rotation. Whether that is long term or not is a challenging prediction to make.

2. The potential for new drug discoveries using artificial intelligence is vastly under estimated by the market. Biology is becoming computational and that changes everything.

The wider healthcare industry has fought its way through some storms over the last few years. But we believe the above two catalysts are finally starting to converge at the right time today.

For the last 10+ years or so, healthcare has been innovating. It’s been chugging along with aging demographics, technological advances, and biological sciences advancements.

It may not seem like it in the stock market where everyone’s focus has been on the momentum trades of optics, robotics, and alike (until this week)…but if we zoom out a bit it’s clear the advancements here have been extremely material.

• Cancer mortality rate is now ~145 per 100,000 vs 215 per 100,000 in 1980.

• Reversal of prediabetes is now 51%.

• Cardiovascular plaque reversal is now entirely feasible when once considered a “one-way progression.”

• GLP-1s are increasingly viewed as the first longevity drugs.

AI is now permeating every stage of the healthcare cycle from diagnosis, to personalized care, to actual treatment. The gap between what is “biologically feasible” and what can realistically be done with capital, doctor skillsets, and the labour force is narrowing by the year.

The best part of this is that it’s a virtuous cycle that will keep accelerating.

New treatments unlock more new treatments.

Longer life expectancies lead to finding more diseases.

More diseases leads to more medical devices and more drug discovery.

Enormous value creation is set to take over the healthcare sector and I think we’re in the very early innings.

The AI Angle

Medicine is now moving from broader chemical intervention towards very precise biological programming.

The old drug discovery model was about designing a molecule, running a trial, and hoping it works.

It’s changing to something fundamentally different - closed loop systems where AI designs experiments, robots run them, data feeds back into the model, and the cycle repeats itself at a speed no human can match.

This means the bottleneck is changing from biology itself, to whoever controls the data, the infrastructure, and the discovery engine.

This shift creates three clear parts of the chain that are getting disrupted that make up the premise of this write-up.

All in MMMT’s personal portfolio (sizing differs):

1. Discovery: ABCL

2. Data & Infrastructure Layer: TEM

3. Blend of the Above & Commercialisation: LLY

I’ll be diving into them all below this as very mini deep dives but the wider thematic is the most important part I want to convey in this article and I think I’ve covered it above.

To sum it up quite nicely here I’ll say this:

The healthcare market over the next few years wins by:

1. Capital finally rotating to an undervalued theme and away from anything pure AI infrastructure.

2. Capital continuing to rotate into pure AI but the market starting to realize healthcare (especially ABCL, TEM, and LLY) is an AI trade now.

AbCellera Biologics | ABCL

ABCL operates the abLighter platform - an AI driven system that finds therapeutic candidates in 45 days. It’s attempting to change the industry from essentially a numbers game (screen enough candidates and hope something works) to a precision game.

Here’s the core premise for ABCL: Your immune system already knows how to fight disease.

When it encounters a threat, it produces antibodies. Some of these antibodies lock onto and neutralize the threat and therefore have powerful properties within them that can make powerful drugs.

ABCL’s finds these antibodies rather than building them from scratch in a lab.

Partners (LLY, ABBV, BIIB, NVS etc) bring the end target - the protein involved in a disease that they want to treat. ABCL then runs the discovery using AbLighter. From there, if successful, the partner takes it through trials and ABCL earns as those trials progress.

That’s business model #1.

Business model #2 is the more exciting model with more upside (though riskier). It’s the proprietary pipeline and it’s where ABCL owns the asset itself and the economics flow entirely back to ABCL rather than through royalty payments.

ABCL635 is targeting NK3 for menopausal symptoms. It has Phase 1/2 topline data expected in Q3 which, if positive, will reframe the entire narrative on ABCL to a TechBio firm with its own drug pipeline.

The downside risk with ABCL is more cushioned that perhaps it may feel for a small-cap biotech company. It’s currently sitting at $1.8 billion MC with $504 million in cash and committed government funding. So that’s ~30% of the entire MC sitting in cash with over three years runway at the current burn rate with a management team that has delivered so far on all timelines they have publicly projected.

Poor data on Phase II would most definitely threaten the stock in the near term, but the company would not be threatened too materially in the short term. The royalty stream from the 40+ partners continues and as long as these programs keep advancing, the $1.8B MC seems closer to a floor. It feels like a free call option on ABCL365 to an extent.

Tempus AI | TEM

In a sentence, TEM is the data infrastructure that makes AI driven drug development possible.

The whole premise of AI in drug discovery is pretty straightforward: train a model on enough high-quality data and it can identify patterns that would take humans decades to find, right?

The main issue is that the data required to do this well (real patient records, treatment histories, pathology slides etc) is very difficult to assemble and very expensive to get a hold off.

TEM has this data because they invested huge amounts in their foundational years before anyone knew how valuable it would be.

And this data essentially is changing what is possible for pharma. TEM uses this data to actively tell drug companies things they couldn’t otherwise know before committing hundreds of millions of dollars to a trial. CEO, Lefkofsky, explained this pretty neatly:

He said that a pharma company came forward with 25-30 clinical trials to work through TEM’s network. TEM only took 13 of them because they estimated the other 17 would likely fail before generating any meaningful results.

It’s ultimately all about the data they own.

Here’s how the flywheel actually works in practice for TEM (I’ll make this section fairly concise compared to the complexity of it…feel free to skim over it. I’ll go into a bit more detail on the valuation section).

TEM’s different business segments are ultimately built on the data foundation. They have access to the data and the clinical context behind all of that data.

1. TEM sequences patients. I.e. they run genetic tests on cancer patients, reading the DNA, RNA of their tumours.

2. TEM pulls in connected clinical data. I.e. TEM pulls in patients medical history (treatment records, doctors notes, lab results, scans etc).

3. TEM generates molecular and diagnostic insights. I.e. TEM combines the genetic results with clinical history to produce data with background evidence.

4. TEM pushes those insights back to physicians. I.e. The treating doctor gets a smarter report, not just a list of mutations…actual guidance.

5. TEM gets more data. I.e. Every new test adds another patients genetic and clinical data to the database.

6. TEM’s models improve. I.e. More data makes the AI predictions more accurate.

7. Pharma pays to access the data. I.e. Drug companies then pay TEM to interrogate this dataset to understand why drugs work in some patients and not in other patients.

8. This revenue funds more patient sequencing. I.e. The pharma licensing money subsidises the cost of running more genetic tests.

9. Repeat endlessly.

This is the foundation that Tempus is built on. They’ve now done this so much to the extent they have 500 petabytes of data across 45 million patients.

Take a second to truly appreciate this foundation. I know the path from here isn’t straightforward but just understand how far ahead they are from any companies now that are trying to replicate this business model.

It’s years/decades.

Data will be the moat for years to come. The companies that invested heavily at the right time (i.e. 10 years ago) will be the ones that truly reap the rewards over the next 10 years.

Valuation:

Of course it all comes back to valuation at the end of the day.

Aim #1 is always to own a quality business. Aim #2 is about owning a quality business at the right price.

If one of those isn’t investment worthy, there is no trade.

Here’s why I think TEM is so undervalued:

At ~6.5x sales, the market still prices TEM like a diagnostics lab for the most part… i.e. Guardant Health (GH) trades at 15.9x NTM sales today.

The way to think about TEM’s valuation is to split it out into two separate businesses.

Diagnostics: I think $2.4 billion in revenue by 2028 for diagnostics is feasible if volume continues to steadily compound and ASP moves closer to $2,000. That assumes ~26% CAGR from 2025.

At a 22% operating margin (already operating at 65% gross margins), that’s ~$530 billion in operating profit which means operating profit for that business would be growing at ~25% CAGR. Put it at 1x PEG (25x multiple) and we have a $13.25 billion business there.

Data & Applications: This is where it gets slightly more interesting for me here. My estimates put us at $820 million in FY28 revenue. This is a SaaS like business so +30% margins are very likely (and perhaps still conservative).

As a SaaS like business with +30% margins, I think a 30x NTM PE isn’t overly bullish at all. 30x PE on that gets us a $7.4 billion business which I think is an absolute base case.

Combined that’s an EV of +$20 billion vs $10.3 billion today.

Eli Lilly | LLY

LLY was once (and still is) a GLP-1 story.

Read more

Part 1 was released on Sunday:

Part 2 focuses on:

• Robotics

• Optics

• Solar

Three themes that have been hit hard by the recent market sell off.

Robotics

I think many people appreciate that robotics is a huge megatrend coming up.

But few people realize how much of a national security issue for the US right now it is…which is central to the investment thesis today.

China is currently way ahead of the US. Their 2026 humanoid output is +100,000 vs the US which is ~14,000.

That issue stems because of a few different reasons, but ultimately gaps this visible do attract policy, urgency, and capital into the theme.

The most obvious issue today is the rare earth supply chain. The US isn’t doing anywhere near enough to prioritize this issue right now but that can’t remain which is why USAR to me is a clear thematic opportunity.

But zooming out, the robotics megatrend is so vast.

I wrote about it in depth in the article attached above but a simple summary would go along these lines:

• Backbone

  • Actuation/Bearings: Without actuators robots are just an intelligent statue. There’s only a few key players here.

  • Sensory & Perception: LiDAR, IMU, tactile and force sensing, analog and semiconductor sensor components. There’s many angles to play this.

  • Software/AI Integration: This is where the US leads now. The AI foundations are strong but the physical components that make up the robots is where the US is behind quite significantly.

  • Motion Control: The sophisticated chips that orchestrates a robots every move calculating how much each joint must rotate and at what speed.

  • Communication: The invisible layer that keeps everything connected.

• Frontier:

  • Medical Robotics: More than just a few niche surgical aids now. It’s set to transform healthcare from surgical systems to diagnostics, to rehabilitation exoskeletons, to pharmacy automation and logistics bots.

  • Defense Robotics: Low cost robotic systems are now destroying high value manned assets forcing this shift towards unmanned drones.

  • Logistics Robotics: This will likely be the first large industry to be fully automated. AMZN alone already has over 1 million humanoids deployed.

  • Humanoids: Once quite an un-investable segment of robotics until CCXI went public.

  • Professional Robotics: The smaller, specialized, task-specific machines optimized for professional applications.

The current basket includes 13 names with 40% of the basket allocated purely to OUST and NVDA.

OUST is in my personal core portfolio as my 2nd largest position so I’m very bullish there.

You can see my prediction with OUST here:

The rest of the basket is made up of some frontier and some backbone plays like OII, DGII, AIT, MBLY, PRCT and FLS.

I’ve weighted all those plays quite small but the two that have my most attention at the moment to increase weighting for are:

1. PROCEPT BioRobotics | PRCT: PRCT is a surgical robotics company with one product that treats enlarged prostates. The numbers are very interesting on this one if they execute and if they can scale. Most MedTechs like this really struggle for the initial years because operating and gross margins are so thin. It’s only when scale hits that numbers start to look quite interesting. PRCT appears to be at that inflection point now.

   1. PRCT trades at 2.3x NTM sales whilst set to grow revenue at 29% in FY26 and 24% in FY27.

   2. If PRCT reach EBITDA profitability next year (which is expected), then we should expect ~$170M in EBITDA in FY29 based on $680M in revenue and a 24% EBITDA margin.

   3. At $170M in EBITDA and a 20x EBITDA multiple (TMDX today trades very cheaply at 14x), we could expect an EV ~$3.4B which is 3x from today.

2. MobilEye Global | MBLY: MBLY is building the perception and decision making systems for autonomous vehicles and humanoid robots. It funds its frontier R&D through a highly profitable ADAS chip business. It recently acquired Mentee which is targeting commercialisation in 2027.

   1. Revenue growth has been slow in FY23, FY24, FY25 and likely FY26. But FY27 is when the company is likely to ramp up again to +22% estimated growth which would mean a 3.3x sales multiple would be cheap.

   2. If MBLY meets estimates of $3.8B in revenues in FY29, then they’d be growing revenues at 25% CAGR.

   3. Compare that to a partial competitor like NXPI which is growing ~7.9% CAGR for 5.2x sales, and we start to see the re-rating potential for MBLY down the road.

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If you manage your own portfolio the way I do, here’s what TrendSpider provides:

- Scanners that surface technical and fundamental setups before they’re obvious - the kind of early entries that turned OUST and NBIS into two of my biggest winners this year.

- Sidekick AI to dig into market data and portfolio holdings without jumping between five different tabs.

- Charting and automated technical analysis to track support levels, breakouts, and moving averages on every name you hold or are watching.

- Real-time options, sector rotation, market breadth, news, and sentiment to understand the broader market context behind any single trade.

This is one of TrendSpider’s biggest sales of the year. If you’ve been thinking about trying it, this is a good opportunity to sign up with a discount.

Optics

Copper can’t handle AI-scale bandwidth at 800G and 1.6T speeds which is why photonics is the solution.

The optics theme was obviously one of the biggest winners in the entire market over the last year with the MMMT index of just 5 stocks now returning 303.1% vs the SPY at 20.2%.

This recent pullback with the likes of AAOI down ~42% in the last month is nothing more than a correction in what I deem to be a long term buildout.

The major inflection point we’ll likely see as per most earnings calls is mid 2027 as 1.6T scales and as CPO broadens past NVDA and AVGO.

Markets will likely price this ramp up before it actually arrives, which is what has happened to an extent so far, but looking at individual valuations I am of the opinion we are far from being priced in.

Here’s my optics piece from December 2025…nicely before all optics plays surged in January and February.

My core photonics play is AAOI. It’s a stock I am down ~21% on in my core portfolio, but it’s one of my highest conviction plays right now that I’ll personally keep building out sub $130.

A few reasons for this:

1. If AAOI hits projections, it’s ~12x in revenue growth from $455M in 2025 to $5.6B in 2028 (that’s being conservative on management assumptions because of the timeline). There’s no other company in the market forecast to grow that quickly especially at 6.5x NTM sales today.

2. All hyperscaler customers want to buy all transceivers AAOI can possibly supply. It’s a supply constraint and not a demand constraint.

3. If you have partial trust in management, then looking at current financials for AAOI actually makes no sense. This is a 2027/2028 ramp up story likely like no other in the market. We saw it with NBIS earlier in the year and I personally believe AAOI is in the same boat, if not even more bullish than NBIS was before the recent run from $92 to $300.

Solar

Solar is an interesting, and very underappreciated trend. The MMMT Solar index is up 69.9% in the last year vs SPY at 20.2% but it’s been a very volatile ride.

Read more

Part 1 is looking at the following baskets:

• Critical Minerals

• Grid Infrastructure

• Miners

Here’s a snippet from my web-app:

Critical Minerals

YTD the MMMT Critical Minerals Index has returned 54.6% vs SPY at 9.0%.

It’s been a volatile basket to hold with stocks like MP surging to $100 last summer before falling back to the mid $50s today. USAR has been another volatile play (which we hold in the core portfolio) jumping from $14 to $44 and back to $19 in the space of 8 months.

We’re now ~12 months on from China’s restrictions on rare-earths and permanent magnets which sent the likes of USAR and MP soaring. However, the market so far in 2026 has seemingly forgotten about the technological revolutions ahead in AI, physical AI, and even quantum and the rare earth requirements for this.

Valuing these plays at the moment is a challenge. It’s likely why USAR and MP and LYC have struggled recently.

But despite what the stocks are doing the underlying thesis remains clear:

Brazil remains the big opportunity which is one of the main reasons I am building my USAR position. It’s by far the most “nearshore” ally to the US and depending on the presidency ballot in October this year, the outlook for Brazil as an ally could be extremely advantageous.

Flavio Bolsonaro has already attempted to position Brazil as this ally to the US and the US’s solution for mineral dependency.

This is one reason why I hold USAR in my core portfolio.

USAR acquired Serra Verde which owns the Pela Ema mine. Pela Ema is the first large scale producer or rare earths outside China. It’s also signed a 15 year supply agreement with the US government making it quite a clear bet on this rare earth thesis.

I also have Sunrise Energy Metals (SREMF) in the critical minerals basket. It currently trades at $12.49. Here’s what I sent to paid subscribers back in March when SREMF traded at $5.50.

TLDR Summary for critical minerals:

Markets are not pricing in the physical bottlenecks of the AI buildout. Huge amounts of copper, gallium, silver, gold, platinum, scandium, and niobium are needed for the continued data centre buildout, and the physical AI world we are entering into.

It appears today that the US is technologically superior to China in models but China owns over 70% of the worlds critical minerals supply chain. That’s not a strong position for the US to be in.

Last chance to grab TrendSpider at a steep discount.

Get 38% off + $98 in free upgrades if you sign up by July 8th at midnight — once it’s gone, it’s gone.

TrendSpider is the charting platform I personally use, which has helped me spot the most in my investing.

If you manage your own portfolio the way I do, here’s what TrendSpider provides:

- Scanners that surface technical and fundamental setups before they’re obvious - the kind of early entries that turned OUST and NBIS into two of my biggest winners this year.

- Sidekick AI to dig into market data and portfolio holdings without jumping between five different tabs.

- Charting and automated technical analysis to track support levels, breakouts, and moving averages on every name you hold or are watching.

- Real-time options, sector rotation, market breadth, news, and sentiment to understand the broader market context behind any single trade.

This is one of TrendSpider’s biggest sales of the year. If you’ve been thinking about trying it, this is a good opportunity to sign up with a discount.

Grid Infrastructure

Grid resilience is one of the most underappreciated levers for technological dominance over the next decade.

Forget just AI - think technology in general (robotics, quantum, everything). The most developed economies today are massively constrained by a very outdated grid that is in need of a complete rebuild.

So much so, that the White House invoked the Defense Production Act (Section 303) in April 2026 which specifically focuses on transformers, substations, high voltage breakers. It’s evidence that the domestic capacity is now not sufficient to meet the baseline demands moving forward.

IEA (International Energy Agency) projects are spending ~$550 billion in 2026 alone (that’s a 20% increase YoY).

Total (add in all end-use electrification like EVs, electrified industrial processes etc) electricity investment is now nearing $2 trillion per year.

Now let’s get into the specifics of what this means from an investment angle.

The wider theme is obvious, but how quickly this is actually achievable is a completely different story.

With how fast technology is moving, and how large the demands are for electricity, the grid buildout appears structurally far too slow to keep pace with the demand. In the US alone, data center buildout is projected to go from 21GW in 2026 to 84GW annually by 2030.

Given that grid interconnect is the constraint here, perhaps the bigger opportunity therefore lies in the companies enabling buyers to generate or access power on their own timeline, and not to do with the companies selling generation to the utilities.

Capacity simply isn’t growing fast enough to cover the 21GW to 84GW of US data center demand so there needs to be an alternative.

The biggest winners will be those that allows a buyer to generate power without relying on the utility. The answer today is Behind-The-Meter fuel cells…i.e. Bloom Energy (BE) or gas engine names like Innio (INIO).

The other angle is SMRs but the target for them is likely a 2028-2030 story…perhaps even later. I put SMRs in the Uranium basket and also as a long duration hedge on the grid problem. I haven’t included it in the power grid upgrade basket yet.

Miners

As you’ll know, I’m fairly aggressively building my positions in silver, copper, and scandium miners and I continue to believe this is:

1. One of the most important divergences in the market today.

2. One of the safer ways to expose yourself to the technological revolution without investing in the tech directly.

Read more

I wanted to do a H1 recap, and a look through to H2. It’ll be a short punchy article with the following contents:

Contents

1. What my service is going to look like in H2 26.

2. H1 Results

   1. Overall Performance

   2. Biggest Winners & Biggest Losers

   3. Portfolio at H1 Close

3. What I am thinking into H2 and where I see my portfolio ending the year.

4. Opportunities I see at the moment not in the current portfolio.

What my service is looking like for H2 2026

Just a quick note here:

I want to share a few plans I have for the rest of the year.

I’ve been working very hard on a web-app as you all know so far. You can see the link to the app here.

This is only a beta version that is going through rigorous testing and upgrades. Here’s a couple of the upgrades I’m working hard on right now:

• Guided Valuation Model (EV/Sales, EV/EBITDA, P/E, or MMMT Valuation Model) for every stock in my universe - see screenshot above.

  • This allows you to input your assumptions (or follow my assumptions) and come out with a target price.

• Stock Baskets: As well as showing my personal portfolio as evidence of my track record, I’ll be showing you concentrated stock baskets of the best themes in the markets.

  • These aren’t ETFs. These are 5-10 stock baskets for all themes in the market with individual stocks and weightings.

  • I think of it as a way of introducing you to other high conviction plays I have that I don’t necessarily personally own due to capital. I also think it’s a great way to increase your exposure to a theme that you may like more than I do.

• This is all on top of daily notes, live portfolio, technical screens, and research notes.

Subscribe now

H1 Results

H1 results were really good at 25.4% YTD.

Considering we were down YTD in the March dip, we’ve come back nicely with some quite material wins to bring me up to 25.4% YTD so far meaning if these kinds of returns continue I’ll come out somewhere near my 2024 and 2025 returns of 47% and 48% respectively.

I will be adding a live tracker of my YTD on the next version of my beta web-app release. I’m fully aware it’s not there yet but the calculation to track it live isn’t that easy with the way I invest.

Biggest Winners & Biggest Losers

My biggest winners have been:

1. Ouster | OUST: We’re now +115% at the time of writing this with a $23 average price and a first buy in January ~$24. Early to the game on this one.

2. Nebius | NBIS: Still up +135% even despite this weeks dip post META announcement.

3. Solaris Energy Infrastructure | SEI: Currently +80% on SEI with an average cost of $37.

4. Credo Technology | CRDO: Frustratingly my average cost on CRDO wasn’t as good as it could’ve been, but I still am up 71% in a few months on this.

5. AbCellera Biologics | ABCL: This has been a long term position for me over the last year that has been range bound until the recent biotech breakout. I’m now up 76% on the position.

Those winners are all unrealized / i.e. stocks I still hold.

Some of my realized gains are here (see screenshot):

You’ll note there’s some losers in there which I’ll touch on in the section below, but notice how the winners far outweigh the losers in terms of size (and win rate):

• Ondas Holdings | ONDS: A very quick move for a swing trade that nearly got 200%. I sold in the end for 171%.

• Albermarle | ALB: ALB turned out to be a great trade for me. I managed to get 119% on the investment and perhaps sold too late as it broke the $150 support level. I was up nearly 200% on ALB at one point. A clear thematic trade.

• Silvercorp Metals | SVM: This was my original higher risk play on the silver market with an average cost ~$3.50 and nearly a 3x move.

• AXT Industries | AXTI: I rode the initial hype around this name but only for a 42% gain. In hindsight, one I regret selling so early.

• SES AI | SES: You’ll see more on this below.

• Redwire | RDW: RDW is also there but didn’t fit into the screenshot. That was a 112% winner though.

• Sanmina | SANM: I bought SANM at $125 when it traded at ~0.5x sales and 7x EBITDA which for one of the few PCB players in the market seemed too cheap.

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My biggest losers have been:

I’m only going to include stocks here with a weighting above 1% in my portfolio.

• Upstart | UPST: UPST has been a poor investment for me, but I still see the opportunity in the lending market for disruption. If I’m right about capital rotation into the AI appliers, I do believe UPST will re-rate above a 14x NTM EBITDA.

• Transmedics | TMDX: TMDX has always been a volatile stock but the underlying company is still performing well. Management continue to reiterate their longer term targets of 20k transplants by 2030. If that is the case, I don’t see how TMDX can continue to trade at 3.5x NTM sales or 13x NTM EBITDA based on those growth assumptions. It’s one of the stocks I continue to DCA into.

• USA Rare Earths | USAR: This is my higher risk bet on the rare earth supply chain that will challenge US for the next few years. It’s currently a small position that I am holding happily as long as this $19 support level is held. If that support level is broken and we fall below the 200D MA, I’ll rethink the thesis.

• UiPath | PATH: If there’s one thing to say about PATH, it’s that this $10 level has held extremely well for the time being and the last week has looked strong. I am currently down 20% on PATH with a 2.05% position.

Here’s my portfolio at H1 close:

I can’t screenshot the entire portfolio as it won’t fit…but I will screenshot as much as I can of the top half of the portfolio.

You’ll see my 7 largest positions there making up 52.6% of my portfolio as of today.

NU and DLO are both up less than 20%, OUST and NBIS are up more than 100%, and AMZN and ZETA are up more than 20%.

Here’s the rest of the portfolio:

Read more

I’ve tracked quantum for ~4 years now. I wrote my quantum ecosystem deep dive here where I first introduced LAES.

I’ve been very back and forth on the topic over that time jumping from it being a distant reality to what I now deem it to be on the verge of potential, and material, breakthroughs.

Over the last few months, we’ve seen more and more news come out on quantum and all of that news has labelled quantum to me as now being a serious technology that is being battled out between US and China.

Governments are now doubling down their quantum investments by committing huge sums of money into labs and foundries. 2026 alone has seen:

1. May 21 Commerce CHIPS package

2. China’s accelerated funding

3. France’s accelerated funding

4. Trump’s June 22nd Executive Orders

5. Migration to post-quantum cryptography

If we take a step back, I think it’s important to understand the following:

Peter Lynch: “In stocks, the upside is always greater than the downside. A stock can only go down 100% but it can go up 1000%.”

I don’t always have this exact mindset with stocks. But it’s the mindset I have taken with LAES.

Big picture here’s where quantum is right now:

1. McKinsey is estimating quantum could create between $1.3 to $2.7 trillion in economic value over the next 10 years.

2. Quantum is rapidly becoming a national security priority. We’ve seen $2 billion investments into quantum companies in 2026 across a range of different quantum architectures.

3. Now China and US are arguably on a level playing field technologically now…the next big win could come from quantum?

   1. China are currently investing ~$17 billion into quantum (more than the US) and so far it’s paying off. They’ve announced the Hanyuan-2 (world's first dual-core quantum computer) and Jiuzhang 4.0 (most powerful photonic quantum computer) in 2026.

4. Investment in Europe is also growing with France committing $1 billion in 2026 alone, the UK investing $500 million through to 2030, and Germany investing ~$3.5 billion through to 2032.

5. The economic value created by quantum will be seen most across chemistry, materials science, and drug discovery. I wrote about the drug discovery angle in my recent article.

In short, it’s one of those very early stage technologies, but out of everything, I think the upside is the most substantial relative to today’s levels (maybe biotech is the other sector with similar upside).

A huge number of challenges remain though. The technology is extremely complex, investment is still very early, supply chains are already struggling, and there’s a clear shortage of critical materials.

But if you’re a high growth investor looking for a higher risk bet, then the quantum market should be on your radar.

Company Name: SealSq

Ticker: LAES

Market Cap: $692M

Headquarters: Geneva, Switzerland

CEO: Carlos Moreira

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LAES Introduction

LAES spun out from WISeKey (WKEY) which is a Swiss cybersecurity firm. Today, it’s doing something that very very companies in the world can do:

It has built a chip with post-quantum encryption and it’s further along the path to getting certified than any of its competitors have publicly disclosed.

The timing of what LAES is building is key to everything. On June 22nd 2026, Trump signed two executive orders (EOs) mandating that all federal civilian agencies and their contractors migrate post-quantum cryptography by 2030. The previous deadline was 2035 so the whole narrative around post-quantum cryptography changed in an instance and stocks like LAES became far more important overnight.

Post Quantum Cryptography Market

What does “Q-Day” even mean?

Nearly all encryption securing everything on the internet relies on maths. It relies on a math problem that is ultimately too hard to solve in a reasonable timeframe.

Quantum computers break this bet entirely because they can use Shor’s algorithm to solve these math problems in enough time. That makes the encryption secure against classical computers but not secure against a quantum computer running Shor’s algorithm.

“Q-Day” is the term for the date at which a quantum computer that is powerful enough to execute Shor’s algorithm in sufficient time.

Previously this date was ~15+ years away, but today post Google’s March 2026 whitepaper signalling Shor’s algorithm can run with fewer than 10,000 qubits, we’re far close than 15 years.

It’s looking more likely to be the end of this decade.

The main issue however is that full PQC migration (enterprises’s timelines from nothing to protecting themselves against this quantum) is ~12 years. So if “Q-Day” arrives in the next 3-7 years, organizations that haven’t started yet are structurally very late.

This means the threat timeline is now regulatory driven and deadline driven rather than technologically driven. Customers will begin buying PQC hardware not because quantum computers exist today but because:

1. Governments will mandate this.

2. The migration process may take years regardless.

3. Harvest-now-decrypt-later ultimately means waiting is not safe even if “Q-Day” is 10+ years away anyway.

Market Size

This is a difficult one to gauge because research varies and there’s a lot of companies operating in sub-niches different to what LAES does.

Here’s my best estimate:

The embedded security chip market: This is where LAES’s legacy products ultimately operate and this market is forecast to be ~$10 billion by 2028.

The PQC hardware market: This includes secure semiconductors, TPMs, and HSMs (Hardware Security Module) implementing PQC algorithms in certified hardware. There is the most variety in this estimate because the market is very nascent and because its currently gated by certifications.

Estimates put this market ~$2.5 billion (but ranges up to $20 billion).

Take this with a pinch of salt because customers (governments, defense, financial services etc) legally cannot deploy uncertified PQC hardware in production systems yet so gauging the market size is almost an impossible job today.

The market will not grow linearly at all. And this is another reason why LAES is a riskier investment today. But as regulatory deadlines approach and certified products become available, expect this $2.5 billion figure to jump considerably.

The Certification Issue

As I just touched on, regulated sector customers cannot legally deploy PQC hardware in production systems until that hardware holds specific certifications, regardless of how technically capable the product is today.

There are two key certifications:

1. FIPS 140-3: This validates that a cryptographic module meets specific security requirements. LAES is currently pursuing Level 3 (out of 4). Just like getting FDA approval, this process costs millions and takes between 12-24 months from submission.

2. EAL5+: Level 5+ is an international security standard requirement for hardware targeting defense, intelligence, and critical infrastructure applications. It is one of the most demanding certification processes in existence, typically taking 2-4 years and costing ~$5 million pre product. LAES currently has four variants in the pipeline that each require their own independent evaluation. LAES has already passed two of the most demanding parts of the process (fault injection and side-channel attack testing).

I’ll dive into this more later, but this matters a lot. It’s evidence that the path from "$200M pipeline”, or “interested customer”, or “115 customers actively testing” to customers actually paying in volume is extremely challenging.

This is the main point I want to get across in this LAES deep dive.

The upside is high. The downside is also very clear and right now it’s definitely considered a bet on things going right.

The other important note is that LAES currently has a certification target of Q4 2026. If they miss this target because of delayed or failed certification, then the risks of missing CNSA 2.0 January 2027 new-acquisition deadline become a real risk.

Recent News:

On March 30th, Google Quantum AI released a document co-authored with Stanford and the Ethereum Foundation establishing that breaking cryptography (the security behind Bitcoin and Ethereum) requires fewer than 500,000 physical qubits.

That’s a 20x reduction from prior estimates.

And the CEO of LAES, Carlos Moreira referenced this in their earnings call:

“Remember last year, in January last year, we were still thinking that quantum computers will be only able to break RSA triple desk. In 30 years’ time, this was reduced to 10. And now Google announced yesterday they are actually dividing that by 10.”

“Now quantum companies are also expanding faster than their qubits generation. The company we have invested and the ones that we are in the process of investing in, they are already able to generate between 10 and 100 qubits. And some of them, they are predicting to be able to reach the 500 qubits, which is what Google says that will actually be enough to break cryptocurrencies.”

The implication is that the algorithmic side of the threat is compressing fast. Faster than the hardware side of quantum anyway.

And then on June 22, 2026, Trump signed:

• EO 14411: An effort to produce a scientifically relevant quantum computer within five years, quantum sensors, and networks.

• EO 14409: The PQC mandate. Four binding deadlines:

  • NIST pilot project complete by December 31, 2027.

  • Federal civilian agencies transition high-value assets for key establishment to PQC by December 31, 2030.

  • Federal contractors under covered procurement rules meet the same standards by the end of 2030.

  • Digital signature systems migrate to PQC by December 31, 2031.

Here’s what it actually means:

I think this news gives us a few ideas:

1. The timelines are still quite a way down the road so expecting these stocks to move considerably in the short term is arguably naive…but it’s a great time now to learn and start to slowly build positions. NIST pilot project by 2027 creates a near-term procurement and testing cycle meaning vendors who can demonstrate compliant products by then will have a structural advantage.

2. The orders do not name, reference, or contract with LAES in any way. There is no plans to support specific quantum companies over the likes of IBM, GlobalFoundries, or KeysSight for example. That’s something to be aware of and to be careful about.

3. Despite point 2 above, direct funding recipients are not LAES competitors in the PQC hardware market. IBM and GFS are quantum computing hardware builders, but not PQC hardware builders. That’s an important distinction and not a bear case for LAES.

4. Despite point 3 above, there is still a lot of competition in the PQC market. American Binary is a pure-play PCQ software company with partnerships with players like Oracle. It’s operating in the exact regulatory environment LAES is targeting but just on the software side. Although they compete in slightly different areas of the market, it’s an illustration that there are a lot of well-credentialed players in the PCQ market.

5. The “Made in USA” angle got more valuable. These executive orders prioritize domestic supply chains so LAES’s plan to operate mainly on US soil aligns. Those companies that can credibly say “designed, personalized, and certified in the United States” will likely be very big beneficiaries as we head towards the 2030 deadline.

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Product Portfolio

LAES’s products sit across two categories:

1. Secure elements

2. Trusted platform modules

QS7001 - The Secure Element

This is LAES’s flagship post-quantum secure element.

A secure element is a tamper-resistant chip that sits inside a device (a smart meter, an IoT sensor, a medical device, an EV charging station).

It handles all cryptographic operations such as storing keys, authenticating the device, encrypting communications. The simplest way to explain it like a locked vault inside a device.

The differentiation for QS7001 is that it implements ML-KEM and ML-DSA - these are two NIST-standardised post-quantum encryption algorithms. This isn’t a software add-on. It’s directly into the silicon.

QVault TPM

This is a different product for a different type of customer.

A TPM is a security chip that sits inside a computer and acts as the hardware identity card. Every time the device boots up, the TPM checks that nothing has been tampered with, stores the encryption keys that protects the devices data, and provides a verifiable proof to any network it connects to that the device is what it claims to be.

The simplest way I think about it:

QS7001 goes inside devices. TPM’s go inside computers. This means the QS7001 is designed for manufacturers building smart meters, drones, medical devices, sensors etc… products that need a secure identity chip embedded inside them.

The TPM is designed for the computing infrastructure that runs the world - servers in data centres, PCs etc.

The addressable market is therefore slightly different but the underlying mission is the same: post-quantum security baked directly into the hardware.

PKI & Trust Services

PKI and Trust services currently represents just 2% of LAES’s total revenue - approximately $0.4 million in FY25. Most people skip past that but there’s big potential here.

Here’s what PKI actually is and how it connects to everything LAES is building.

Every chip LAES sells needs a digital certificate. That certificate doesn’t just get issued and then forgotten. It needs to be renewed, managed, and verified throughout the chips life span.

It is unlikely to be the main revenue driver for LAES, but even a $0.10 per chip at a scale of 100 million chips per year (the Kaynes JV in India is targeting 300 million chips at scale) is ~$10 million per year in potential revenue. If you look at my model below, you’ll see the potential of this segment.

Competition

This is arguably the most important part of this article I think because it sets the picture for the small cap, high growth stock vs the established well-resourced semiconductor companies.

I’ll break it down via tiers:

Large Cap Players

Infineon (IFNNY): This is the most direct competitor to LAES core TPM market. IFNNY’s TPM is already designed into hundreds of millions of devices across PCs, servers, industrial systems, and network infrastructure - and it’s next gen chip will include post quantum encryption as an upgrade path for existing customers.

That’s the key danger for LAES. IFNNY customers won’t need to switch vendors or go through a new design process, or change their hardware supplier to access post-quantum security. That’s a huge competitive advantage.

But IFNNY currently has no post-quantum chip that is actually available so they’re far behind LAES in that regard. We have no public knowledge of timeline or launch data.

NXP Semiconductors (NXPI): NXPI product is EdgeLock which will compete with LAES’s QS7001 but just like IFNNY the timeline is behind LAES with no publicly disclosed timeline.

It’s a very similar story to IFNNY in that NXPI has an enormous customer base with existing relationships meaning its post-quantum version is just an evolution of a product rather than an entirely new product.

Microchip Technology (MCHP): MCHP announced its entry into post-quantum hardware security on April 28th 2026 with a direct competitor product to LAES’s TPM. It’s probably the furthest ahead but again has no publicly disclosed certification timeline at all so gauging that is much harder to tell.

Ultimately, every company here is trying to build a chip with post-quantum encryption standards baked in and get it certified by federal regulators.

Right now, no company has achieved this.

To put it as simply as I can - LAES appears the furthest ahead at the moment in terms of product and certification (at least from what has been publicly disclosed) so they have a first mover advantage. But MCHP, IFNNY, and NXPI all have the capital and manpower to get jobs far quicker.

They also have the existing customer relationships so first mover advantage actually means a lot less for them.

An interesting battle of the safer compounders vs the small cap that needs to meet the strict deadlines to stand any chance in the market.

Smaller Cap Players

BTQ Technologies (BTQ): BTQ is the most direct pre-revenue hardware competitor to LAES in the small cap space. From my understanding, certifications are ~12-18 months behind where LAES is today in terms of actually delivering chips to customers.

This makes the timeline risk of meeting certification deadlines far trickier for BTQ.

Valuation

Given everything I’ve told you above, valuing a company like this is extremely challenging.

It’s similar to valuing an early stage biotech seeking FDA approval.

But I think the best thing I can do here is model out a bull, base, and bear case over the next 6-8 years.

You can see my valuation models here below. It’s a Google Doc that I am giving viewing access to with 3 different tabs… bull case, base case, and bear case.

Read more

Growth is still very strong, forecasts are still very strong, the economy is in good stead.

But the market front-runs what is actually happening, and I suspect that by 2027/2028 the rate of growth in the hardware buildout will start to cool. When that happens, the risk/reward profiles across some segments of this AI trade will look a lot less attractive than they do today.

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Let me be clear before I go further: this is not a bearish take.

I’m probably one of the more optimistic bulls out there. I think capital will continue flowing into this once-in-a-lifetime technological revolution for years to come.

But at some point we have to start asking which niches of that revolution will outperform from here?

The first thing to understand is that the growth slowdown and the valuation re-rating are not on the same clock.

The hardware buildout has years left in it. Nvidia’s Vera Rubin platform only began shipping in volume this quarter, with Rubin Ultra’s 600-kilowatt racks not arriving until 2027.

In absolute terms, that part of the story is still early.

But the price you pay for exposure to it is a different question entirely. Big Tech CapEx is tracking toward roughly $725 billion this year, capital spending as a share of revenue has climbed past 23%… more than double what it was pre-ChatGPT. And credit spreads tied to the buildout are already widening as bondholders start asking who is actually paying for all of this.

The second thing to understand is that the infrastructure trade has matured from a basket into a stockpicker’s market. For two years, owning the buildout meant owning everything attached to it (chips, power, data centre landlords) and watching them move together. That is largely over.

The bottleneck has migrated from raw compute toward cooling, power, optics and memory, and each time the bottleneck moves it the baton to a different set of winners. Of those, I remain most bullish on optics and memory.

But the broader point is this: the names that carried the basket higher are not necessarily the names that carry it next.

Which brings me to the bigger question.

If these models are this good (and they are) and if agents can now act rather than merely suggest, where does the return on all this CapEx actually show up?

The first phase funded the obvious things: chips, data centres, power. That was the easiest trade to see and the easiest to underwrite.

The next phase is harder to see, which is exactly why it hasn’t been priced yet.

For me, the answer is the application layer - the companies turning extraordinary intelligence into something a customer genuinely needs and depends on, and finds painful to remove.

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The Application Layer

The bull case for the application layer is simple. Once the underlying models are this capable, the constraint stops being “Is this intelligence good enough?” and becomes “Who turns it into something a customer will pay for and depend on?”.

The trigger that turns this from theory into an actual capital flow is enterprise adoption moving from pilot to production.

For the better part of two years, "AI productivity gains" lived all in narrative.

But what's changing now is that that narrative is actually starting to show up in the numbers… in margins, in headcount that stops scaling with revenue.

Agents now can take an open-ended, difficult problem, and work autonomously on their own. Systems now don’t just answer questions but generate hypotheses, run experiments, execute transactions, and do all of this with zero human approval.

The companies that win here the most are within:

1. The largest industries

2. The most inefficiently ran industries

Those are:

1. Insurance

2. Drug design

3. Some areas of finance

4. Materials science

The prize is enormous. But gauging how much some of these sub $10B companies can actually disrupt the market is something I could argue both ways.

However, positioning yourself on the long side of some of these companies I’ll talk about here is right now, in my opinion, probably the best risk to reward set up in the market.

If these companies do end up disrupting these markets…the upside in some cases is enormous.

How to Invest:

Read more

But the more time I’ve spent listening to those who know where quantum truly is gave me a pretty good push to get this article and quantum basket out. Here’s the basket performance (and constituents):

Sundar Pichai for example thinks quantum is where AI was 5 years ago.

Until more recently quantum has been a theoretical promise and nothing more but it’s now slowly entering a period of commercial utility. Most notably in materials science, chemistry, drug discovery, logistics, and in national security too.

A quick intro on quantum computing. I’ll keep this brief because there’s thousands of YouTube videos and articles out there explaining what quantum does and they’ll probably explain it better than I will.

My edge is in painting out the investing roadmap for you:

Quantum Computing

Quantum computers behave very differently to classical computers. Classical computers process information in “bits”. That’s a 1 or a 0. That’s it.

Quantum computers operate using quantum bits (qubits). This allows them to be in multiple states at once which leads to problem solving on a whole different level to classical computers.

My Focus

My focus is more on the technologies and bottlenecks involved in the quantum computing revolution. Just like with the AI revolution, we’ve seen breakthroughs in optics, lasers, 3D packaging etc…we’re going to see that same process play out for quantum. And this means a new wave of industrial development focused on cryogenic control chips, scalable quantum chip fabrication, precision lithography, and the interconnects needed to link it all together.

Looking at the TAM, McKinsey is currently estimating quantum computing could create $1.3 to $2.7 trillion in economic value by 2035. I take these estimates with a pinch of salt because the range of estimates I’m seeing is so broad and the timelines I’m seeing on when quantum becomes economically active is wide too.

But the big picture is there. Quantum has a big future. And it’s one to start learning about.

The US Department of Commerce is telling us exactly this as well. Just last week, nine letters of intent were signed to direct $2.0 billion through the CHIPS and Science Act to quantum companies for non-controlling equity in each company. These equity stakes were split nicely across a range of quantum architectures such as photonic, silicon-spin, trapped-ion, and superconducting.

Whether it’s worthy of an investment today is another question. Most quantum plays aren’t really breaking out technically just yet so dependent on how much those technicals mean to you could depend on when you consider initiating a position.

Here’s how I’d breakdown quantum (without looking at the pure plays like IONQ, RGTI, QUBT, and QBTS):

• Critical Minerals

• Cybersecurity

• Lasers & Photonics

• Superconducting Circuits

• Cryogenic Test Equipment

• Quantum Hardware

• Quantum Chip Fabrication

• Quantum Applications

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Critical Minerals: The Foundation

It depends on the quantum architecture used, but a range of critical minerals and rare earths are needed in quantum too. I’m already very bullish on rare earths over the next 5 year technological revolution, but add quantum on top of this which could be another tech revolution on its own, and the demand for rare earths over a 10+ year period is likely substantial relative to what it is today.

The key plays are niobium and nickel which are critical for superconducting quantum computers (more on this below). It’s one of the reasons we’ve seen Chinese firms take huge stakes in Brazilian niobium and nickel miners over the last decade.

Examples:

1. CMOC Group acquired full ownership of Anglo American’s niobium business.

2. CNMC (Chinese state owned) purchased Mineracao Taboca.

3. CITIC Group formed a consortium called China Niobium Investment Holdings.

The U.S. currently is 100% import dependent on niobium with the vast majority of that coming from Brazil.

Niobium is used in superconducting qubits, capacitors, and resonators. It’s in the core architecture of IBM and Google style quantum computers. Importantly, the supply risk is quite severe with 85% of global production coming from a single mine in Brazil.

And then there’s nickel. Nickel is part of the mu-metal shielding process. Mu-metal magnetic shielding is the material used to stop any stray magnetic field. Nickel is by far the best metal for this job and probably makes it one of the most underappreciated metal going forward assuming quantum computing turns out like we expect it to.

Here’s the niobium playbook:

NioCorp Developments (NB): I wouldn’t personally overcomplicate it if you’re looking at pure niobium investments aside from NB. It’s developing the only niobium and scandium project in the US making it the most advanced niobium resource outside the US.

Personally, I’m invested in scandium through Sunrise Energy Metals (SRL.AX/SREMF) but I have no exposure to niobium right now. I suspect that’ll change at some point with a potential addition of NB to the portfolio. More than just being nice thematically, NB has a great balance sheet with 8+ years runway before $800M bank financing comes through.

I’ve been using TrendSpider for all of my charting needs for a while now and honestly haven’t looked back - if you’re not already on it… worth checking out. You can sign up through my link above.

Forward P/S is only ~1.5x on implied $577 million in annual revenue confirmed from materials alone. I think this one is cheap and a great trade thematically for quantum and for other reasons too.

Here’s the nickel playbook:

Read more

The dominant model for AI infrastructure for the last decade or so has been massive data centers, hyperscaler compute, and models too large and power-hungry to run anywhere else but the cloud.

That model is being extended now by a new reality where inference happens at the point of need (the device, the vehicle, the robot etc).

That’s all old news though.

But what’s more important is how to invest in this transition now.

We’ve had lots of names move a lot already.

• KLAC has moved 150% in the last year.

• AMKR has moved 259% in the last year.

• MU has moved 704% in the last year.

The question now is where is the value? Are some of these names still valued fairly or is there new names to seek out?

The important thing to understand is that there is always another bottleneck. The bottlenecks just shift around when one bottleneck is solved. I.e. when silicon performance scale, the constraint shifts to memory bandwidth. When that improves, it shifts to advanced packaging…then power conversion efficiency…then thermal dissipation…then materials etc.

Understanding where those constraints are is the challenge and in this brief paper I’m going to outline it for you.

The Edge market is currently valued at $31 billion and set to grow to ~$70 billion in 2031. That’s a CAGR of 17.5% for 5 years. That’s a market we want to be a part of.

Here’s how I have broken down the layers:

• Layer 1: The Bedrock (Materials, Mining) - 0 to10 years

• Layer 2: The Foundation (Equipment, Packaging, Memory) - 0 to 4 years

• Layer 3: The Enablers (Power, Cooling, Connectivity) - 0 to 4 years

• Layer 4: The Builders (Systems, Design Infrastructure) - 1 to 5 years

• Layer 5: The Perceivers (Sensors) - 1 to 5 years

• Layer 6: The Brains (Edge Silicon, Software) - 0 to 4 years

• Layer 7: The Frontier (Humanoids) - 3 to 8 years

You’ll note I added a timeframe next to each year. That’s my best estimate on when I think this layer is going to generate the most returns and for how long. You’ll note I start a few of them at 0 years…indicating we’re already in the big move for that layer.

You’ll also note based on the timeframes that Layer 1, Layer 4, and Layer 5 are to me the best places to be invested right now to be ahead of the moves.

Layer 1: The Bedrock (Materials & Mining)

Every technology revolution eventually runs into the physical world. Edge AI is no different. Beneath the silicon, the software, and the systems lies a layer of materials that no amount of engineering ingenuity can replace. Copper for power. Silicon for conductivity. Rare earths for magnets. Silver for connectivity.

The bedrock layer is the most upstream layer and often the most overlooked by tech investors.

But it’s also the most structurally compelling part of the entire investment universe which I am well positioned for.

Core (Copper & Silver)

Rare Earths

Layer 2: The Foundation (Equipment, Packaging, Memory)

This is one of the more concentrated layers in the Edge AI universe. ASML is the sole manufacturer of the extreme UV lithography machines where literally no advanced chip can be made without it.

TSMC’s CoWoS advanced packaging process which is the critical tech for stacking memory onto compute.

And then you have memory production dominated by 3-4 companies.

It makes this layer a key chokepoint controlled by a handful of companies. Every Edge AI device for the next decade passes through the following companies.

Layer 3: The Enablers (Power, Cooling, Connectivity)

This is the unglamourous, but necessary layer of Edge AI. It’s where incredible technology is met efficiency priorities.

Every additional watt of AI compute that has been generated has to be removed. Power has to be converted. Data has to be transmitted. And it all has to be done as efficiently as possible.

Air cooling is no longer feasibly possible, so liquid cooling is the new norm. Power conversion efficiency becomes a first order cost driver. Optical interconnects are becoming first order cost in deployments.

Power

Thermal Management & Cooling

Networking & Interconnect

Layer 4: The Builders (Systems, Design Infrastructure)

This is the layer where the physical infrastructure of the Foundation and Enablers is translated into deployable intelligence. It’s the layer of design tools, intellectual property, and system integration that determines how chips get designed, assembled into systems, and how enterprises deploy them.

AI chips are becoming far more complex and harder to design. ARM’s set architecture sits inside virtually every edge device on earth collecting a royalty on each one.

And then the electronic design automation (EDA) software is basically a duopoly with Cadence and Synopsys. EDA market is growing +11% through to 2030. Nothing too exciting but structurally also key to the next few years.

Layer 5: The Perceivers (Sensors)

What makes this layer structurally interesting now is that it’s all about the migration of AI inference onto the sensor. Processing is moving from centralised compute to the point of capture, reducing latency, bandwidth consumption, and power draw.

Historically, sensors were a commodity component but today they’re becoming a compute platform in their own right. That should lead to software like margins.

This layer will lag the silicon buildout. Sensor platforms mature after the compute infrastructure that will process their output is in place. This makes the next 12 months a perfect time to start considering some of the plays in this basket.

Layer 6: The Brains (Edge Silicon, Software)

This is the layer we all know about…the NVDA’s, QCOM’s, AMD’s. NVDA has Jetson and DRIVE, QCMON has Snapdragon X Elite.

The hardware is almost the old story here. The more interesting part of these plays today is the software. This software determines which silicon gets designed in at the platform level. It’s ultimately the level that will prove far more durable over time.

Layer 7: The Frontier (Humanoids)

This is a market projected to be worth ~$40B by 2035. We already have Tesla Optimus entering low scale production with goals to price Optimus at $20k. Figure AI has been valued at $39 billion as of September 2025. Boston Dynamics is in commercial production.

This is where the value will ultimately accrue at the end but we’re still years away and there’s a lot of money to be made in all other layers before we get there.

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Not Investment Advice

The opportunity I want to talk about today is beneath this. It’s the chemicals layer that makes almost all of this physical buildout possible.

The pipes, the cooling systems, the thermal infrastructure, the chemicals, the adhesives that make all of this possible.

• How does advanced packaging work without the chemical systems? It doesn’t…Underfill adhesives mechanically bond the die to the substrate. Die-attach films then manage the heat path between these dies with tolerances measured in microns.

• Photoresists and etching are a huge part of the modern fab process and these are predominantly chemical processes.

• Where does the capital in the $6B deal between META and GLW go? Yes, optical figure is the core of this deal but many don’t realize that every inch of fibre is wrapped in a polymer system.

• Every 800G transceiver is held together with adhesives. Every fab is plumbed with fluorpolymer pipes.

Thinking of the AI infrastructure buildout without the chemicals layer is an incomplete look.

No token can be created without a huge amount of optical fibre, polymer coatings, buffer tubes, fab piping, optical adhesives, thermal interface materials, and ultra-pure fluid etc.

But the most exciting part for an investor is that a lot of these stocks haven’t had their big move yet. Some of them have had a nice move from their lows in 2025 but relative to most “AI driven” stocks, chemical stocks haven’t had their time yet.

• Entegris (ENTG) is trading only 30% above where it was 5 years ago.

• Chemours (CC) is down compared to five years ago.

Although some of them feel a bit more expensive today, a lot of the target entry prices you’ll see below are fairly close to todays prices.

Before we get into the article…a quick note on my web-app. I am released a web-app that I have been developing for the last 2 months. It’s a good upgrade on my current spreadsheet. It contains:

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A Concise Look into the Chemicals Thematic

I’ve edited this section a lot.

My first draft was detailed but long. I realized there’s probably not many of you out there who actually want to learn about the chemicals so I made this section about 50% shorter than it originally was.

I’ll go into a bit of detail… but my aim here is to give you an idea on the chemicals thematic without you having to read a 10-minute chemistry paper.

The money right now is in the industrial materials. It’s why miners has been such an important thematic for me to build positions in. We’ve had SREMF jump +50% this month and TMQ, COPX showing some strength. CTGO is also a new purchase for me too which is up nicely.

And it’s why chemicals is next on my radar. If you need interconnect, cooling, efficiency etc, you need the miners and you need the chemicals players.

For me, here’s the four key shifts happening at once which will lead to a chemicals bull market:

1. Fiber Optic Glass

Light moves far quicker than electricity through copper wires which is why optical fibre has quickly become the backbone of the long-distance interconnect. Copper simply can’t move data fast enough or efficiently enough within data centres or between data centres.

Here’s the chemical angle of this:

A fibre-optic is not just “glass”. It is a glass-polymer composite.

The core (the glass) is useless on its own so to make it deployable you coat the strand for protection, bundle those coated fibres into protective inner rubes, and sheathe the whole bundle in a flame-retardant outer jacket with gels and resins and alike.

For every dollar of glass, there is a dollar of specialty polymer pulled in beside it. And this is where names like Celanese, Chemours, Shin-Etsu, and Arkema operate.

It’s where the narrative around these names shifts from “diversified industrial” to an “AI infrastructure derivative”.

2. Fluoropolymer Pipes

A semiconductor fab is a factory where chips are made. Inside these fabs are miles of pipes that carry the chemistries needed to etch, clean, and pattern silicon wafers. These chemicals are highly corrosive acids and bases, aggressive solvents, abrasive slurries, and ultra-pure water that is cleaner than anything used even in the pharmaceutical industry.

Demand is now driving the largest semiconductor buildout in history. TSMC, Samsung, Intel, SK Hynix etc are racing to add 3nm nodes (3 billionths of a metre) onto a silicon wafer.

The smaller the node the more chemically demanding the process becomes. The content-per-wafer increases at smaller node sizes. This means that chemistry benefits from:

1. Smaller nodes

2. More wafers

Every chemistry operation that improves yield by even a fraction directly ties to revenue. Every new fab that comes online requires its own chemical supply infrastructure. Simple as that.

That’s why demand for companies like Chemours, Entegris, and Arkema is set to rise with AI.

3. Liquid Cooling

This is the simplest one.

Today’s AI chips draw ~700-1,200 watts each. And advanced packaging them into a single rack creates power densities that air conditioning cannot physically cool down enough.

This is why liquid cooling is the only way to manage it. Data centres now have coolants flowing through pipes attached directly to the chips (“direct to chip”) or in some cases data centres have entire servers submerged in non-conductive fluid (“immersion cooling”).

This creates a materials problem though. The coolant, the seals, and the gaskets all have to be chemically compatible with each other. There’s only a select few materials that qualify for this which are fluoropolymers, EPDM, silicone, and certain thermoplastics… exactly what Chemours, Arkema, Rogers, and Henkel produce.

4. Optical Transceivers

Back to optical fibre…A fibre on its own does nothing. It’s nothing without an optical transceiver at each end.

A transceiver is a small USB sized device that converts electrical signals from a switch or GPU into pulses of light heading down the fibre. On the other side it does the opposite (i.e. translates incoming light back into electricity).

Every fibre link in a data centre has one of these at each end which means transceiver volumes scale roughly at twice the rate of optical fibre. Here’s where it gets important though.

The speeds that these transceivers have to handle is climbing and will continue to climb. But the device itself isn’t getting any bigger to compensate. At those speeds the issue becomes:

1. Keeping the glass fibre and the silicon chip lined up perfectly because at that speed even slight misalignment scatters the light and the signal degrades.

2. Getting rid of the heat the chip throws off without room to add a larger heatsink.

Guess what solves both of these issues? Materials.

Optical adhesives are glues that bond glass to silicon holding the alignment.

Thermal interfact materials are compounds that move heat from optical chip to a heatsink, keeping the module from overheating itself at 1.6T speeds.

Henkel are one of the key players here.

Here’s Your Basket of Stocks:

How I’ve scored them:

1 = Revenue most directly tied to the AI buildout.

3 = Revenue least directly tied to the AI buildout.

Mini Deep Dives into My Favorites:

Celanese (CE) - AI Purity: 2

CE is one of the largest engineered materials and acetyl chain producers. The most relevant AI part of the company is Crastin PBT. It’s a thermoplastic the company explicitly markets as a top choice resin for high performance optical fibre cables. The rest of the business is in the traditional cyclical chemistry industry (engineered plastics for cars and industrial uses for example).

As hyperscalers continue to commit to data canter build out, demand for optical fibre accelerates and CE should remain one of the larger beneficiaries of this.

Valuation:

CE currently trades at $57 ($6.3B market cap) which means we’re trading ~8x 2026 expected EBITDA of $2.15B. CE currently trades in the mid range of its cycle ~8x EBITDA. Historically, this at the top of cycles this has been ~12.5x so if my assumption is correct and we’re heading into a chemicals cycle, then CE on FY27 EBITDA of $2.3B at 12.5x EBITDA gives you $28.75B EV.

If we subtract net debt of $9B you’re looking at a market cap of ~$20B or $181 share price on current shares outstanding.

That valuation target sits around this key resistance level. Probably a worthy trade to consider if you’re long chemicals and if you can wait for a nice entry around these moving average cluster, though not one in the basket I’d look for if I was looking for one that is going to be a lot more tied to AI demand.

Chemours (CC) - AI Purity: 2

CC is a fluorpolymer company that handles:

• Fab plumbing (pipes, valves, and tank linings to move corrosive chemicals and acids around fabs).

• Cooling (heat transfer fluids sit directly within the direct-to-chip and immersion cooling systems). This is still in the very early days though with capacity coming online in late 2026 making this more of an emerging opportunity for CC.

The broader immersion cooling fluids market is growing at 24% CAGR to ~$1.6B in 2032. CC has potential here to build a meaningful share which, if they execute, should definitely re-rate the company further towards the more AI pure players in this basket and further away from CE trading in the 8-12x EBITDA range.

These two angles make CC a solid play on the physical AI infrastructure buildout. I see more upside here than a company like CE, but the AI demand story is not yet material enough.

Valuation:

I value CC very similarly to CE… on an EBITDA multiple basis. CC is also in a mid-cycle trading at $23 and slightly further extended away from MA’s relative to CE which has run less.

CC trades at 8x EBITDA and 14x EPS whilst set to grow EBITDA at 14% and grow EPS at 37%. These EPS growth rates are historically quite lumpy though.

EBITDA is expected to be between $800M and $900M in FY26 and $1B in FY27 so despite not being as pure as I’d like on the AI story yet, the growth rates are actually very reasonable for the multiples it trades at which to me gives a nice floor in the valuation.

The upside currently is a bit less certain, but the downside risk here is likely limited.

Element Solutions (ESI) - AI Purity: 1

ESI is a pure advanced packaging play… far more “AI Pure” than CC and CE above. It’s arguably as close as we get to a pure play infrastructure materials company that exists in the chemicals space.

It’s involved in the plating, solders, and metallization processes that hold modern AI accelerators and HBM memory stacks together.

Here’s the AI angle:

• Semiconductor Solutions: Advanced packaging materials for leading-edge chips where AI GPU demand is the key driver.

• Semiconductor Solutions: Thermal interface materials for high power consumption AI GPUs and CPUs.

• Kuprion: This is a next-gen die-attach material that uses copper rather than silver sintering. It is currently a supply constrained segment of the business but ESI are investing into capacity to make this a material revenue generator in 2027-2028.

• Circuitry Solutions: This is the chemistry involved to plate and etch the dense multilayer PCBs inside AI servers and networking gear that just generated a record quarterly revenue growing organically 17% YoY.

AI pure but pricey…see below:

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This won’t be an in-depth article…but it will be a 5-minute read where I hopefully give you a very good insight into where I think the market opportunities currently sit.

I’m writing this post the hyperscaler reports where only GOOGL is up after hours. META, AMZN, and MSFT are all down between 2.5-6%.

Oil is trending a bit higher again, airline stocks look like they’re heading down in the near term, and SPY looks like it needs a little cooling off.

This is not a bearish argument or anything saying we are near the top of the cycle. I don’t think we are. It’s just an honest assessment that we moved a lot in quite a short amount of time, and that often is a sign that a cooling off period is needed.

Look at my plays today for example. LMND reported very strongly…and closed down 14%. I think a large part of this move was algo driven but the story remains.

Before we get into the ideas for the week, I just wanted to highlight the database I’m currently building which is currently in beta testing and will be scheduled for full release in May.

This is a fully functional web-app / database / research website all in one go. You also get:

• Daily market commentary

• My personal portfolio and watchlist

• Weekly thematic commentary

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I am nearing the end of my beta testing now. I do not have a specific release date but I’m working as quickly as I can to make sure this product is one of the best on the market. I hope for the release to be in early May.

Here’s where I think the opportunities lie right now:

Paid subs should know that I am getting quite bullish on certain critical metals. I have been buying scandium regularly for weeks now. I’m up ~22% on that. I’ve been buying copper now for months. I’m still flat on that position.

And more recently I started initiating buys into silver. You can see why I’ve been buying silver here (it also tells you the 3 silver stocks I've been buying).

If you’re not reading that article…let me do a very brief silver summary here:

Silver is no longer just a monetary metal and inflation hedge. That framing misses the bigger picture entirely.

The bull case for silver is that it sits at the heart of the AI trade and I think the metals/materials/chemicals are next in line for a bull market.

Silver has the highest electrical conductivity of any element on earth, making it a critical metal inside the connectors, switches, and advanced semiconductor packaging that power both data centers and edge AI devices like robotics and drones.

As the AI buildout then shifts from brute force training to inference at scale, efficiency and energy loss becomes the advantage. And that’s precisely where silvers properties become indispensable.

Critically, silvers cost as a percentage of the total AI buildout is completely immaterial, meaning even a significant percentage increase won’t slow demand.

On the supply side, the picture was already broken even before AI entered the equation. The market has been in a deficit for 4 consecutive years because silver essentially is only mined as a byproduct of other metals making it impossible for miners to simply ramp output.

Yet despite this backup, silver miners remain historically undervalued - PAAS for example has seen cash from operations frow 196% and margins expand from 2.2% to 31.7% yet trades at just 12x forward earnings as if the market expects silver prices to collapse. The gap between miner valuations and the underlying commodity price represents what looks like a significant opportunity to me.

Metals and mining are a big opportunity today I think.

The Other Big Opportunity is With The Chemicals:

Paid subs will know that I have not bought into a chemicals play yet, but I’ve been researching them heavily to understand exactly where each stock lies in the supply chain.

I’ll have a separate piece out on the chemicals layer soon (within the next week) so make sure you’re subscribed here so you get that for free.

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The core argument behind chemicals is that the chemicals and purification systems are extremely in demand for the ongoing AI trade. It’s the “hidden layer” as I like to think about it.

As AI drives demand for more complex chip architecture (HBM memory, 3D packaging, 2nm logic etc), manufacturing tolerances tighten, and the risk of yield destroying contamination grows.

This is where companies like Entegris (ENTG) come in.

Ideas For the Rest of the Week:

Here’s some charts I’m monitoring closely outside of the thematics above.

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AI started as a computing power problem. It then became a memory and packaging problem. It then became an electricity problem. It’s now a physical materials problem.

Jensen Huang is telling us exactly this as well. He estimated an $85 trillion physical world infrastructure upgrade over the next 15 years and I bet silver plays a bigger role in all of that than many people are expecting right now.

My focus recently has been less on the stocks/thematics people are agreeing with (optics, thermal management, power systems etc) and more with those thematics people aren’t talking about as much (silver, metals, chemicals).

I’m not saying there’s no more money to be made in optics, thermal management, and power systems… I think there is. But I look for outsized risk to reward opportunities and when every man and their dog is on board an investment theme…it tends to be a signal that the risk to reward is diminishing more than many think.

I ultimately wouldn’t be doing my job right if I was discussing the next theme to invest in as being packaging or optics.

That’s why I have started to research silver on a daily basis over the last month or two… all of which I’m going to concisely unpack into this article.

Introduction

Paid subscribers will know that I have been bullish on silver for some time now. I invested in Silvercorp Metals (SVM) last year at ~$3 and sold it on the 30th of January ~$12.15.

Above is the timestamped alert of that from my Google Spreadsheet (soon to be updated to my web-app).

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I invested in SVM more on the basis of a catch-up trade that I thought miners were undervalued compared to the underlying commodity (which I still deem to be very true) but my conviction on the underlying commodity (silver) was nowhere near as high as it is now.

So now I have:

1. Huge conviction in silver and its potential to head to +$150-$250 in the next 24 months.

2. A remaining conviction in silver miners being undervalued relative to the underlying commodity.

That creates a strong investment thesis.

Silver has always been thought of a precious metal or a monetary metal. One that acts as an inflation hedge or moves against the US Dollar. Whilst that may be true, it’s only a very small part of the silver thesis today.

Silver is an industrial metal with properties that no other metal has. And unsurprisingly… these properties are core to the entire AI trade.

When you combine the entire demand story which I’m going to explain with a supply picture that was already broken before AI entered the equation properly… and then you overlay that with equity valuations that remain historically depressed… you get a setup where the risk to reward seems very unique.

Contents

1. Silver Demand

2. Silver Supply

3. How high can silver go?

4. Why silver miners are still undervalued

5. The best silver miners to invest in

Silver Demand

Silver has the highest electrical conductivity of any element on earth… higher than copper, higher than gold. For most of the AI buildout early phase, that fact wasn’t too important because the near term bottlenecks were so important.

You needed more GPUs, more memory, faster networking, more power etc. It’s why stocks like:

• AMKR is up 330% in the last year

• BE is up 1,140% in the last year

But the phase we are entering next is different. It’s gradually becoming less and less about brute-force training and more becoming inference at scale and high efficiency. The economics begin to change quite a lot based on this.

The difference with inference is that the demand for continuous compute power is much more than in training. Tasks require thousands of loop-backs, multi-tasks working as part of a larger task, and above all continuous work.

The question becomes more about who can produce AI output at the lowest cost per unit with the least amount of energy wasted. At least, that’s what it is becoming. The first 3 years or so was brute force training and a race to build out AI. At some point efficiency had to become the more important part of the equation.

And that’s exactly why silver is becoming very important.

Silver sits inside all of the current bottlenecks:

• You have connectors for AI data centers and high-voltage grid infrastructure.

• You have switches for power electronics, circuit breakers, and grid distribution.

• You have packaging for advanced semiconductors and microelectronics.

All of these components govern electric loss and thermal performance so as hardware scales, demand for these components scales with it. And subsequently when these components become more important, the makeup of these components (i.e. the materials…i.e. silver) becomes more scrutinized.

But there’s much more to the AI trade than just the data centers. AI is heading to the edge with robotics, drones, medical devices, smartphones etc. And the same logic that applies in the data centers applies to the edge…but its’s amplified at the edge.

Edge devices have to have smaller batteries meaning power efficiency becomes even more important. There’s no room in edge devices just to add another cooling devices if thermal management is the issue. In data centers, there’s always room and capital available to spend your way out of the problem. That is not the case at the edge. You can’t just add more components because weight matters so much.

Here’s where we can take it an extra step.

Silvers cost as a percentage of the total AI buildout is negligible. By that I mean, when you’re building $1 billion data centres, the line item for silver is barely noticed. The material items are the land, the power, the GPUs, the memory, the networking gear. It’s not the cost of the silver within those components.

That means if silver costs rise by 50% over the next 2-3 years, it will still remain an immaterial part of the AI buildout cost. And people will keep buying it because:

1. It’s so necessary.

2. It’s so cheap relative to everything else.

So, from the demand side, I put it simply like this:

Bullish on the AI data center buildout? You should be bullish on silver.

Bullish on the Edge AI thematic? You should be bullish on silver.

I haven’t discussed solar, EVs, and other electrification here in this article either. But those longstanding themes remain and are still bullish for silver.

Supply

Before AI entered the equation, the silver market was already in a structural deficit. In 2024, the silver market was pressured because of solar demand, a grid upgrade, and general electrification. Today, those same pressures still apply but we also have the AI hardware demand as well.

There is currently ~148.9 million ouches of silver shortage which is the 4th consecutive years we have had a deficit. The main issue in supply is two fold:

Firstly, the vast majority of silver is mined as a byproduct of other extractions like lead, copper, and zinc. This simply means miners can’t just produce more silver. The silver output is fundamentally constricted by the output of copper, lead, and zinc.

Secondly, Mexico. Mexico supplies ~25% of global silver. For reference, that’s a much more concentrated source share than Saudi Arabia holds in oil.

Mexican output has been in a sustained mulit-year decline with no replacement capacity on the horizon. When your largest supplier is structurally shrinking and there is no pipeline of new projects ready to offset it, you do not have a cyclical supply problem. You have a long-term structural issue.

How high can silver go?

This is where the trade starts to get really interesting for me.

At the time of writing this, silver sits at $75.36. Most investors look at this and think that’s a 56% increase on 1980s and 2010 levels which may seem like a fair jump based on CPI and a modest increase in demand.

But it’s way off.

Silver is a monetary asset. It should not be measured against how consumer prices move. Instead, it should be measured against how much money exists to chase a fixed supply of metal…i.e. M2 money supply.

M2 money supply was $1.6 trillion in 1980. It stands at approximately $22.6 trillion today.

That’s a 14x increase.

So, if you apply that multiplier to the 1980 peak of $47, the money-supply adjusted equivalent is $658/oz today. You can look at different money supply aggregators but you’ll end up above $500/oz no matter how you look at it…compared to $75 today.

Let me be clear. This is not a price target. It’s a way of calibrating a bullish outcome and it’s a way of doing it fairly logically as well.

Adjusted for the dollars in existence, silver at $75 represents ~ a ninth of what $47 represented in 1980. Hopefully that gives you a gauge of the potential upside for the underlying commodity.

A reminder as well:

This article is actually going to get all tied up in the section below talking about the silver miners. The silver miners currently have a clear valuation gap between themselves and the underlying commodity…and I just put a logical way (though fairly bullish way) of the underlying commodity prices increasing over 8x from here.

Why Silver Miners Are Undervalued

With silver prices above $75/oz the earnings profile of silver miners has changed fundamentally from 2-3 years ago.

Here’s a couple examples of cash from operations for Hecla Mining (HL) and Pan American Silver Corp (PAAS).

PAAS’s cash from operations relative to FY23 has increased 196%.

Margins have increased from ~2.2% to 31.7% (PAAS) and 6.0% to 37.3% (HL).

It’s a completely different outlook for well operating mines.

Yet, despite this equity valuations have actually depressed since 2023. In 2023, PAAS traded above 22x NTM PE. Today it trades at 12.1x NTM PE. Its the market pricing silver miners as if the current earnings stories are temporary… or as if a reversion in silver prices is likely and therefore cash flows should be heavily discounted.

I personally disagree with all of that as I’ve made evident in this article.

For what it’s worth…if you adjust PAAS for example to measure how the stock is valued relative to the underlying commodity… you see PAAS near the very lows.

So, here’s how the situation can get resolved:

1. Unlikely: Silver prices break down sharply which would agree with the current multiples miners are trading at but disagree with my silver thesis entirely.

2. Likely: Equities re-rate upward to reflect the earnings power that these businesses have now silver is at $76/oz. This should only get stronger and stronger over time.

Given my thesis on the structural supply constraints and the expanding demand base, I think my likely scenario has ~80% probability of happening.

Which Silver Miners Are My Favorite?

The natural next question is of course how we play this theme. Here’s my plan:

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1. We’ve had our scandium play (SRL / SREMF) jump 50% in the last month.

2. NuScale Power (SMR) just 42% in the last week.

3. ClearPoint Neuro (CLPT) jump 37% in the last week.

4. And most importantly, Lemonade (LMND), my largest position jump 29% in the last week.

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This is a very challenging, but great time for stock pickers. I think it’s very important here, as always at new highs, to act with a bit of caution. Futures are currently down which is of course more than welcomed and expected after the recent surge. Whether this is a technical pullback (good), or the start of a more long-term structural pullback because of re-escalation again (bad), I’m not sure.

But as ever, I’ll let the index charts dictate what I do rather than getting lost in the political and macro madness of today’s world.

On a less macro level, I still see the biggest opportunities in hardware, mining, and LatAm as well as my AI application plays like ZETA, LMND, and UPST (of which my returns are currently quite mixed although they all had great weeks last week).

Before we get into the ideas for the week, I just wanted to highlight the database I’m currently building which is currently in beta testing and will be scheduled for full release in May.

This is a fully functional web-app / database / research website all in one go. You also get:

• Daily market commentary

• My personal portfolio and watchlist

• Weekly thematic commentary

• New Ideas

• Weekly Videos

All for just $24 a month (or $240 a year).

Subscribe now

Lowest Risk: Chemicals

This is the trade people aren’t thinking about enough. For the last few years, the AI investment conversation has been centered around the likes of NVDA, TSMC. Now it’s moved rightly on to the bottlenecks (packaging, optics etc). The more upstream layers is where I think the biggest alpha gap currently is though.

That’s critical metals (silver, copper) like I talked about last week:

And then the molecular foundation that makes all of this physically possible: Chemicals. Of which most are trading around 2019 levels.

Let me be more specific. Many investors now know about the optics trade (AAOI, CRDO, LITE etc). Fibre optical cables are not just glass though. It’s much more complex.

Every part of this fibre requires UV-cured polymer coatings, adhesives, protective jacketing made from advanced compounds etc. So as hyperscalers continue to deploy billions of metres of fibre optic cables, they are also relying on the demand for the specialty chemicals that make this possible.

The market has yet to price this in.

I’ll dedicate a separate post to this theme soon. But here’s the best chemicals stocks:

1. Element Solutions (ESI)

2. Entegris (ENTG)

3. Chemours (CC)

4. Cabot Corp (CBT)

5. Rogers Corp (ROG)

Dedicated post incoming on the chemicals industry.

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Medium Risk: Silver

I spoke about silver last week quite comprehensively. I always get torn here between discussing new themes or just doubling down on themes I have already spoken about. I have recently decided to do the latter.

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The war in Iran continues to reshape global energy markets at a pace that is almost impossible to keep up with but markets are slowly starting to look a lot more bullish again.

This environment remains very favourable to the stock pickers rather than index holders. YTD there’s been some incredible moves in many thematics (stablecoins, optics, semiconductor testing etc) whilst indexes have struggled to stay afloat.

Personally, I’m seeing the biggest opportunities in hardware, mining, and LatAm world right now whilst still remaining very long term bullish on a few AI application plays like LMND and ZETA, though the market currently isn’t rewarding those plays in 2026.

That’s why I’m starting to slowly increase my exposure to plays like copper, solar, and scandium which have some very nice momentum at the moment. If you want to see my exact stock portfolio, click this link:

Before we get into the ideas for the week, I just wanted to highlight the database I’m currently building which will be released to paid subs for beta testing in the next 48 hours with full release scheduled for May.

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Lowest Risk: Silver

Most investors still think of silver as just another precious metal but that’s a massive underappreciation.

Silver has the highest electrical and thermal conductivity of any metal…by far. That makes it a critical one in the AI infrastructure buildout, especially as we shift from training to inference. The aim over the next few years will not just be to build the biggest models, but more importantly to run them efficiently and continuously.

This challenges the economics of the entire hardware cycle that we are in.

The objective now ultimately becomes a focus on lower cost per token and electrical and thermal efficiency. Silver sits within all of these hardware stacks like connectors, relays, packaging, thermal interface materials, and switching systems.

This makes silver heavily in demand in the agentic and physical AI world. More hardware equals more silver in systems where electrical loss and heat management matter enormously.

And then you need to add in the angles of:

1. Solar: The world just installed a record amount of solar capacity in 2025. Silver is the critical input in a photovoltaic cell.

2. Defence: Modern warfare is becoming more and more an electronics problem. Drones, sensors, ruggedized connectors etc. Silver is a critical metal in all of this.

3. Supply: Silver runs as a byproduct of lead, zinc, and copper so miners cannot just increase the supply of silver. We’ll likely run at a supply deficit for a while.

Considering all that…it’s hard not to be uber bullish on silver.

A higher risk way to play the silver trade is through SVM. Paid subs will know that I sold this last month for a +200% gain. I have not currently entered this one again, but it’s high on the watchlist.

Other potentials are Hecla Mining (HL), Pan American Silver Corp (PAAS), and Contango Silver & Gold (CTGO).

Medium Risk: Solar

I’m becoming very bullish on solar as nice way to play the next 2-3 years. Paid subs will know that I have recently started buying into TAN but it’s quite likely I’ll also add an individual stock quite soon to heighten the risk on this theme a bit.

Solar panel costs have now dropped 50% since 2022 and annual installations have nearly tripped. A solar panel built today generates electricity for 30 years with no fuel costs, no prices increases, and no supply risk.

The events of the last 6 weeks show you why this is so important.

From an individual stock pick point of view, my current options are:

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