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Performance is holding up surprisingly well.

It’s remarkable how much a couple of bad weeks can distort your perception of just how good the year has been.

In this article, I’ll answer some of the more complex questions submitted by my paid subscribers.

Let’s get into it.

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Introduction

In this article, I’ll examine a micro-cap semiconductor equipment company selling into the PCB, panel-level packaging, and glass-substrate supply chains, with companies such as TSMC, Samsung, and Intel as end customers.

This company is exposed to both glass substrates and panel-level packaging, the two revolutions coming to semiconductor manufacturing that every investor should be aware of.

I’ve been mapping these supply chains across several articles, as I believe they will produce multiple multibaggers as the technologies move from trials, where they are today, to mass-scale production over the coming years.

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Time for an update on memory, optics, and what institutions are saying.

In this article, I’ll also cover exactly where I’m adding and the stocks currently on my watchlist.

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I hold one position with exposure to drones, laser-based C-UAS systems, robotics and cheap counter-drone missiles.

However, this $10B market cap company also has a less flashy business segment that could prove to be much more significant than the low value the market is assigning to it today.

Concretely, this company has space-based orbital laser communication technology, which so far is being used for defense satellite communications, as well as in missions like the most recent Artemis II.

In 2026, safe and fast defense communication is a much larger business than the nascent orbital data center industry. However, on a 10-year timeframe, this company may have a rough diamond in the form of lasers.

In this article, I’ll explain the business, the great potential I see, and why I’m buying more.

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In this article, I’ll explain the current situation in the AI trade and what I’m doing.

First, we need to understand what is happening.

The Sell-Off

On Wednesday, the S&P 500 fell 0.2% and the Nasdaq dropped 0.7%.

The semiconductor index fell 6.3% on the first trading day after the sector’s best quarter on record. Micron dropped 11%, wiping out $140 billion of market value in a single session. SanDisk lost 11%, Applied Materials fell 10%, Intel dropped 9%, and AMD fell 7%.

The sell-off was heavily concentrated in semiconductors, which requires some context.

Micron, Intel, and AMD added $2 trillion of combined market value in Q2 alone. The VanEck Semiconductor ETF gained 82% in the first half, its best first-half performance since the fund launched in 2000. Micron was up 260% year to date going into Wednesday.

Bernstein had already published a cautious note on Monday comparing neocloud economics to colocation, flagging GPU depreciation, financing costs, and operating complexity as the key risks to profitability.

The AI buildout trade was priced for zero doubt, then doubt arrived.

Meta: From AI Lab to Neocloud

Meta is reportedly drawing up plans for a cloud business that would sell access to AI computing power and models under an internal initiative called Meta Compute.

Two structures are reportedly under discussion:

• A Bedrock-style service, where developers access models hosted on Meta’s infrastructure, including Muse Spark, the closed-weight model Meta shipped in April.

• A raw compute business, which is effectively the neocloud model.

The plans are still in development and could change. But this comes after Zuckerberg had already said in May that selling excess compute was “definitely on the table.”

The market was so stretched that what moved stocks was not an actual product launch, but a reported intention.

Meta itself closed up 9% on Wednesday. Investors liked the idea of monetizing a capex bill guided as high as $145 billion this year, instead of relying only on LLMs that appear to be going nowhere and are becoming less competitive with Anthropic and OpenAI frontier models.

The companies hit hardest were Meta’s compute suppliers.

CoreWeave fell 14% to $86, while Nebius dropped 17% to $229.

Both count Meta as an anchor customer, with a disclosed $21 billion commitment at CoreWeave and an agreement worth up to $27 billion at Nebius. A Meta that builds enough capacity to sell the excess is a Meta that may eventually rent less capacity from others, while also competing for external workloads.

Beyond the hit to its most direct suppliers, this creates a dangerous narrative:

If Meta has excess compute, maybe the AI buildout is overbuilt.

Once that narrative exists, every long AI hardware trade gets sold at the same time, especially the highest-beta names in data centers, photonics, and memory.

Macro: Not Enough

Thursday’s jobs report, released a day early because of the July 4 holiday, should have been fuel for growth assets.

• Nonfarm payrolls rose by just 57,000 in June, below expectations of 110,000 to 115,000. April and May were revised down by a combined 74,000 jobs.

• The unemployment rate fell to 4.2%, but for the wrong reason: labor force participation dropped 0.3 percentage points to 61.5%, the lowest level since March 2021, while household employment fell by 507,000.

• Leisure and hospitality shed 61,000 jobs, its worst month since 2020.

This was a very soft report.

Consequently, September hike odds fell from the mid-60% range to a coin flip, with the remaining hike bets pushed toward Q4. The two-year yield slipped to 4.12%, the dollar index fell about 0.5%, its biggest one-day drop in two months, and gold moved above $4,100.

With Warsh’s Fed holding rates at 3.50% to 3.75% behind a hawkish June dot plot, the weak print should have been a pressure relief valve for long-duration assets.

Not for the AI trade, though.

The Dow added 595 points, rising 1.1% to a record close of 52,900. The S&P 500 finished flat, even though most of its members advanced.

In contrast, the Nasdaq fell 1%, and the semiconductor ETF dropped another 5%. Teradyne fell 14%, KLA dropped 12%, and Micron lost another 6%. Even Meta gave back 4%.

Across the two sessions, SOXX lost 13%, its sharpest two-day drawdown since March 2025.

Memory: The Most Leveraged Bet

For better or worse, memory is treated by the market as the most cyclical part of the AI trade.

So when the market hears “overcapacity,” memory gets sold first and questions are asked later.

Korea is where that reflex did the most damage.

• The KOSPI closed down 8% at 7,648, falling back below 8,000 only weeks after crossing 9,000 for the first time.

• SK Hynix fell 15% to ₩2,187,000, while Samsung Electronics dropped 9% to ₩286,000. SK Square, Hynix’s largest shareholder, lost 13%. Samsung Electro-Mechanics fell 13%, and the KOSDAQ dropped 7%.

The open was violent enough, down 5.4%, that the exchange halted trading for five minutes.

This was the third Korean trading halt in under three weeks, all triggered by the same two stocks, which now represent half of the KOSPI’s weight, compared with about a quarter at the end of last year.

The difference in sentiment by investor class was clear:

• Foreign investors net sold ₩5.2 trillion of KOSPI stock on the day.

• Domestic retail investors absorbed ₩5.4 trillion.

The drawdown spread through the Asian supply chain. SMIC fell 11% and Hua Hong dropped 14% in Hong Kong, while Kioxia fell 10% in Tokyo after a 600% year-to-date run.

Korea’s own macro backdrop did not help either. June CPI printed 3.2%, the highest level since December 2023, strengthening the case for a Bank of Korea hike ahead of the next monetary policy meeting on July 16.

The Other Side of the Coin

On the same day, SK Hynix CEO Kwak Noh-jung detailed a ₩100 trillion, or $64 billion, domestic investment plan:

• ₩80 trillion for the M17 NAND fab, with construction starting next year and operations targeted for H1 2029.

• ₩20 trillion for the P&T7 advanced packaging facility.

This sits inside the previously announced ₩1.1 quadrillion expansion program, and comes just days after Seoul unveiled a combined ₩800 trillion national semiconductor plan with Samsung.

Does a semiconductor sell-off make sense given the acceleration in fab capacity expansion?

At the micro level, probably not.

But at the macro level, maybe. These capex plans become more fragile if hyperscaler AI capex starts to slow.

Management teams are telling investors they see enormous multi-year demand for HBM, DRAM, and enterprise NAND.

The market is interpreting something else:

Meta excess compute + rapid future supply growth = lower prices.

In sharp contrast with live sentiment, IBK and NH raised their Hynix targets to ₩4.0 million and ₩4.1 million on the same day the stock fell 15%. Morningstar had also lifted fair values on both Korean names on Wednesday, calling the memory cycle “tracking substantially stronger than expected,” while warning that the capacity now being added could set up a correction later in the decade.

In Micron’s case, the median analyst target sits near $1,550, with Cantor and Barclays both at $2,000.

There is also a secondary, less relevant headwind.

A class action was filed on June 25 in California federal court alleging that Samsung, SK Hynix, and Micron coordinated to restrict DRAM supply, citing 700% price increases over four years.

I wouldn’t worry about this, but it’s not ideal for an already noisy industry.

In summary, memory stocks are being treated as an instrument that the market holds when times are good and sells without hesitation at the slightest sign of problems in the AI buildout.

The “Excess Compute” Fear

Meta selling compute can mean several very different things. It does not automatically mean there is a market-wide structural flood of compute.

It could mean:

• Meta overbought or misallocated capacity.

• Meta wants to monetize idle capacity between internal training and inference cycles, which hyperscalers already do routinely.

• Meta’s model and product execution is lagging its infrastructure spend, which is bearish for Meta’s AI ROI, but not automatically bearish for the supply chain.

• Meta is turning infrastructure into a commercial cloud product, which arguably validates that compute is valuable enough to sell.

It would be one thing if Meta were selling compute at liquidation prices, but that is probably not what is happening.

SpaceX/Google Deal

Meta is likely trying to capture rates similar to the deal SpaceX signed with Google, which were very generous. If anything, that signals compute is still scarce and valuable, just not necessarily valuable for underperforming AI labs.

SpaceX secured pricing that was 4x the five-year neocloud average, which makes it easy to understand why Zuckerberg would want to pursue a similarly profitable opportunity.

In fact, just four days before this drawdown, we got highly contradictory rumors suggesting that Google had been rationing Meta’s access to Gemini since March because Meta wanted more capacity than Google could supply.

According to those rumors, Meta was forced to tell staff to conserve tokens, while other Google customers were also affected, though to a lesser degree.

Moreover, Pichai has called Google “compute-constrained in the near term.” Google Cloud’s backlog has doubled to more than $460 billion, capex is running above $180 billion this year, and Google is still reportedly renting bridge capacity from SpaceX at around $920 million per month for 110,000 GPUs.

This is another sign that Meta may have excess compute, but the industry does not, and what matters is the latter.

These numbers do not point to excess compute. Compute is extremely valuable for companies that know how to extract value from it.

What we are seeing is the free market allocating compute toward the players that can monetize it most efficiently.

Meta: Still an AI Lab

Moreover, Meta does not seem to have thrown in the towel on frontier models entirely.

In a recent post from Alexander Wang, Meta’s head of AI confirmed that the company is training an Opus-level model for coding.

This suggests that Meta may enter the neocloud business as another way to profit from AI, while its capex remains strong and its commitment to training frontier models stays alive.

Neoclouds: An Unclear Threat

The risk for neoclouds comes down to basic supply and demand dynamics.

If AI labs like xAI and Meta go from being huge compute demanders to huge compute suppliers, that could shift the supply/demand equilibrium.

It would be similar to AMD or Amazon becoming memory resellers instead of simply using memory themselves. Sure, NVIDIA and Broadcom would still demand memory, but if you are a memory supplier, you want a market with the highest number of potential buyers and the lowest number of potential sellers.

The same logic applies here.

If Meta sells raw capacity, it could become less dependent on CoreWeave and Nebius. And if hyperscalers broadly start reselling unused capacity, GPU-hour pricing could compress unless demand from other AI labs grows even faster on a relative basis.

However, in the case of Nebius, for example, most capacity is already tied to highly profitable contracts. Combined with the blueprint of the SpaceX/Google deal and the rest of the compute shortage across the industry, a high-quality neocloud like Nebius is not highly exposed to spot pricing.

More importantly, there is no reason to assume GPUs will suddenly be sold at liquidation prices just because of this move by Meta.

If Zuckerberg decided to pursue a neocloud strategy, it would be absurd to assume it is because he sees a compute flood coming and GPUs being sold for pennies. He clearly sees a shortage, just not inside Meta, and plans to take advantage of it by supplying the successful AI labs creating that shortage.

• I do not understand why anyone would think Zuckerberg would enter the neocloud business if he believed it was low-margin or that Meta’s entrance would crash prices.

• I also fail to understand why the market took the SpaceX/Google deal so positively, while punishing the AI supply chain so aggressively for Meta potentially doing something very similar.

After a record year in returns, the market preferred to lock in gains before properly questioning whether the AI trade is truly under threat.

That shows how immature this industry still is from a public-market perspective.

What Would Prove the Market Right?

The market would be right to punish the AI supply chain if:

• Hyperscalers cut 2027 capex.

• GPU-hour pricing starts falling across contracted deals.

• Neocloud utilization weakens.

• Meta sells capacity at distressed pricing.

• Memory pricing rolls over before new HBM and DRAM supply is absorbed.

For memory, the key risk is a 2028 supply wave arriving before AI demand is large enough to absorb it.

For neoclouds, the key risk is that contracted revenue proves less profitable after accounting for financing, depreciation, and data-center lease costs, and that steady profitability is largely never achieved.

For power developers, the risk is that they own attractive sites but fail to convert them into high-quality, financed hyperscaler contracts.

So far, nothing even close to this has happened.

In fact, everything points in the opposite direction.

However, it is still worth knowing what should make investors’ warning bells start ringing.

What Fixes This Market Pessimism?

The AI trade needs to mature from a narrative trade into a financial trade.

Right now, the market is still treating large parts of the AI supply chain as unstable and cyclical. Demand is enormous, but the path from demand to revenue, earnings, and free cash flow is still uneven.

That is what creates the violent swings.

So far, the AI trade has often been about finding the next bottleneck supplier, whether that is GPUs, HBM, power, networking, liquid cooling, data centers, packaging, or substrates.

That phase can create huge returns, but it also creates extreme narrative volatility. Stocks move violently because investors are constantly trying to price scarcity before the earnings fully arrive.

The next phase should be more mature.

It should be less about finding the next bottleneck that gets bid to stretched valuations, and more about owning the companies that can turn AI demand into revenue and earnings efficiently.

That is how mature supply chains work:

• Demand becomes revenue.

• Revenue becomes margins.

• Margins become cash flow.

• Cash flow supports higher valuations.

Neoclouds Need to Mature

Neoclouds need to prove they can become durable infrastructure businesses, not just high-beta AI capex vehicles.

The best outcome would be for the strongest neoclouds to earn a status closer to AWS, Azure, and Google Cloud.

They do not need to become as large or dominant, but they do need to become businesses the market can underwrite with the same level of financial confidence.

Until then, the market will keep treating them as risky AI infrastructure intermediaries with heavy fixed costs and uncertain long-term pricing power.

Memory Needs to Prove This Cycle Is Different

The market still assumes high memory prices eventually create too much supply, followed by a crash.

For the pessimism to fade, memory prices need to remain resilient through the next few years, even as new supply comes online.

The key proof will be that AI has structurally changed the demand curve.

That is when Samsung, SK Hynix, and Micron can rerate more meaningfully, not just through aggressive upside swings, but through consistently double-digit forward earnings multiples.

AI Needs Productivity Proof

The biggest proof is productivity.

AI will not feel like a safe long-term investment until the productivity gains clearly justify the multi-trillion-dollar capex cycle.

Companies around the world are spending enormous amounts on AI, which now needs to move from impressive demos to measurable business impact.

Once investors see that AI capex produces real productivity gains, the market will stop asking whether the spending is a bubble and start asking which companies capture the most value from it and which companies support it.

My Take and What I’m Buying

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In this article, I’ll cover two portfolio companies, with an updated valuation framework for each.

These two companies are:

• A defense technology company focused on drones, counter-UAS systems, directed-energy laser defense, and space laser communications.

• A data center developer and operator with three signed hyperscaler-grade HPC campus leases.

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Portfolio is up 130% YTD.

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In this article, I’ll cover the latest updates on the companies in my portfolio, including:

• What I think about their current valuations

• The upside I see and why

• My price targets for each holding

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In this article, I’ll cover my new position:

• SpaceX, TSMC, and Intel supplier

• $720M market cap

• 50% gross margins

• More cash than debt

• Heavy exposure to panel-level packaging and glass substrates, the two next revolutions in the semiconductor industry

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In this report, I’ll share, with explanations, what LPKF's CEO told me in our meeting, and how I am looking at glass substrates from an investment perspective, including other stocks in the supply chain.

Introduction

Traditional organic substrates are reaching limits in terms of flatness, warpage, thermal stability, and fine-pitch interconnects. Glass offers a much flatter and more dimensionally stable platform, which could make it a key material for the next generation of HPC packaging.

But glass is also difficult to process. To make it useful in advanced packaging, companies need to create thousands of extremely precise holes through the glass, called TGVs, or through-glass vias. These vias are then filled with copper so electrical signals can pass through the substrate.

This is where LPKF becomes relevant. The company’s core technology, LIDE, uses lasers and chemical etching to create these TGVs with very high precision. That is why LPKF has become one of the most discussed small-cap names in the glass substrate supply chain.

For my full deep dive on LPKF, including the company’s technology, valuation, and investment thesis, you can read my previous article here:

Market Share

The CEO told me they have 80% market share in TGV, and that they see this coming down to 70%. That would still be very impressive.

I continued by asking how the company would finance all that machine manufacturing if they achieved that market share.

I asked this because the balance sheet is not the most resilient.

Funding Expansion

Q1 free cash flow was -€7.6M, they had €5.8M of cash, inventory was already rising ahead of deliveries, and the syndicated loan carries a €25M working capital facility, which exists precisely because a tool ramp consumes working capital.

This is why investor activists pushed for a raise. They think €25M of working capital headroom is not enough to build tools quickly and grab share before SCHMID, TRUMPF, and the domestic players scale.

Fiedler’s answer is that LPKF can scale with little money, meaning the facility, the 1.4 book-to-bill, and North Star cost cuts can get them there without dilution.

The risk he is accepting is clear. If 2027 orders surge, the company could become working capital constrained, unable to build enough machines right when the window opens.

They told me they are capex light and that they would not have a problem scaling.

I responded by asking how machine manufacturing can be capex light, and whether they would use third party manufacturing. They told me they could, but that they do not plan to do so, and that most of the expenses are electricity.

While counterintuitive, it can make sense.

LPKF does not manufacture the expensive parts of the machine, it buys them. The laser source comes from a laser maker, the precision optics from an optics house, and the motion stages, control electronics, handling systems, and vacuum systems from their respective vendors.

The capital-heavy work of actually producing a femtosecond laser or precision optics sits with those suppliers. LPKF’s job is to integrate all of it into a finished NEXAR tool, layer on the LIDE process IP and software, align it, test it, and ship it.

So its own plant is basically assembly bays, a cleanroom, alignment and test rigs, and engineers, not a billion-dollar production line.

Beyond what they have in cash, LPKF has €25M of drawable working capital headroom and €7.5M of guarantee capacity, locked until the end of 2028. That gives the company flexibility, but it is still modest.

First, the facility is small if production orders scale quickly. Second, it expires before the potential 2029–2030 high-volume phase, creating a refinancing event right before the biggest expected revenue years.

I mentioned this to him, and he told me he has talked with banks about possible financing if demand exceeds their capacity.

I do not know if he was referring to this facility. He mentioned it in relation to extra capital, and I would assume that if they actually get a large backlog, there would be banks willing to provide order-backed loans.

The Bottleneck

Fiedler told me TGV and LIDE are no longer bottlenecks for the glass substrate ramp. This process is qualified, and yields seem to be good enough, at least for low-volume production.

I asked him what the biggest problem to solve is in order to ramp glass substrates.

He told me that the biggest yield loss is not caused by TGV, but by metallization, which occurs when the holes etched through TGV are filled with copper.

The hardest parts are filling those holes with copper completely, with no voids, getting copper to adhere to glass, and most importantly, surviving thermal cycling.

Copper and glass expand at different rates. This is called CTE mismatch, or coefficient of thermal expansion. Every time the package heats and cools, that mismatch stresses the glass around each via and can crack it.

The leader in copper-to-glass adhesion is Atotech, part of MKS Instruments.

When I asked about it, he told me this is now fixed. In other words, if companies like SEMCO or Absolics are saying they will ramp through 2027 and 2028, it is because this is already working well enough.

But if glass substrates are still not at high enough yields, it is because of the copper fill step, not TGV.

Competition

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My portfolio is almost at ATHs and has reached a YTD performance of 135%.

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There are a lot of things happening in the markets, and my watchlist has expanded considerably.

Today, I’ll cover what I’m doing, what stocks I’m looking at, and my action plan for the next few weeks.

I’ll also disclose a SpaceX supplier trading at 13x 2026 earnings that is completely under the radar.

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Portfolio is up 124% YTD, slightly below last week’s all-time highs

In this article, I’ll explain the portfolio moves I’m making: what I’m buying, what I’m selling, and why.

What I’m Buying

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In this article, I’ll cover my new position, a near monopoly in a key high-end semiconductor manufacturing process that will gain much more importance over the next few years

The company trades at an attractive valuation and has promising management targets through the end of the decade

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In today’s article, I’ll go over a current AI buildout bottleneck, as well as a future bottleneck, covering the companies benefiting the most across the supply chain and my most interesting picks.

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My portfolio is now up 129% YTD, compared to 10.5% for the SPY.

My New Buy: An AI Giant

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Portfolio performance stands at 101% YTD, carried by the data center-related holdings.

In today’s article, I’ll cover my new data center position, a stock I know very well but never owned, until today.

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In this article, I’ll go over Nebius to analyze whether there is still upside left. I’ll also comment on other companies in the portfolio, including two semi plays, share what opportunities I’m seeing lately, and explain what the next articles will be about.

For all the new subscribers, I’ll explain how I value data center companies.

Instead of using vague valuation approaches based on what EBITDA a company will generate in a few years, then applying a multiple and a discount rate, I analyze these companies much deeper than that.

What I do is go site by site, valuing each project based on its specific characteristics, and then I add everything together. For the growth potential upside, I use the same valuation approach for data centers that have not been signed or constructed yet, and I also value the rest of the power pipeline using a data-based approach.

After I’ve valued every asset from these companies, used and unused, approved and unapproved, I add everything together and discount it to the present to reach what would be my fair value for the company.

If the fair value comes considerably above the current price, I invest in them.

I take this same approach with Nebius to analyze its true upside beyond hype, momentum, or surface-level analysis.

Time to get into it.

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Portfolio performance crossed 100% YTD on Thursday. After Friday’s close, it was down to 94%.

In this article, I’ll give an update on some of the companies in the portfolio.

While most of my theses have worked well and performance has been strong, there are still many things to improve. There are companies where my conviction is split, and more importantly, I’m assessing potential portfolio rebalances to shift exposure again and be better positioned for the coming months.

Let’s get into it.

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In this article, I’ll cover what I’m doing with my holdings.

The portfolio is up almost 100% YTD, so there are some assessments that need to be made at these valuations, and probably stocks that should be considered as new targets.

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In this article, I’ll cover the earnings of two holdings.

Earnings season has been going great overall. The portfolio has crossed 90% YTD returns, with the three largest positions delivering triple-digit YTD returns on average.

Let’s get into it.

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YTD performance stands at 63.8%, compared to 5.5% for SPY

6-year total return now stands at 864%, compared to +154% for SPY

The Portfolio

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