or in the “there is no rational reason for you to pay ~100x revenue multiple for a loss-making company.”

But what if there was a middle ground? What if you could trade this IPO without ever buying the trillion-dollar story? The setup is in the mechanics, not the fundamentals, and that's what we are going to find out today. But before we get there, let’s take a closer look at the company and how they make money.

Business Overview

While you can read the full 400-page prospectus here, SpaceX has 3 main business segments:

• Space — sells launch services. Revenue comes from two streams: (i) Launch Services, deploying payloads to orbit for commercial and government customers on the reusable Falcon 9 and Falcon Heavy, and (ii) Launch and Development, building spacecraft and providing launch/mission services for government space programs (Falcon 9, Falcon Heavy, Starship, and Dragon).

• Connectivity — is the Starlink business; revenue comes from broadband and Starlink Mobile service across 156 markets, split into Consumer Broadband, Enterprise Solutions, Government Solutions, and Mobile.

• AI — is the newly acquired xAI/X business. Revenue comes from advertising on the X platform plus AI solutions and infrastructure: subscriptions, data licensing arrangements (access to X's real-time and historical data), and API access to the Grok models.

Looking at the numbers,

• Starlink is the strongest business where revenue grew ~50% ($11B now), operating income more than doubled, and adjusted EBITDA rose 86%.

• Even though revenue improved for launch services, operating profit flipped from $21M profit to $657M loss — the driver here is Starship development, which increased R&D costs from $1.7B to $3B.

• AI revenue grew 22% (arguably small base of $2.6B), but now the operating loss is $6.3B. Once again, R&D is driving losses, as the company spent nearly $5B on training the Grok model and building out AI compute capacity.

This also allows us to build a rough valuation model to assign a value to each division.

SpaceX Model

Here’s how the numbers look:

• Starlink — We modeled subscribers to grow at a 25% CAGR from 9 million in 2025 to 84 million by 2035, a number that only holds if direct-to-cell scales alongside fixed broadband. Blended ARPU falls from $81 to $48 a month, because cheap direct-to-cell plans dilute the premium home base. At a blended 15x multiple, it carries $167 billion in intrinsic value.

• Launch Services — External Falcon 9 is anchored to third-party demand, near the 58 launches seen in 2025, rising into the late 2020s then declining as Starship absorbs the heavier external missions. Assuming a successful transition to Starship, modeled revenue reaches $25B by 2035, bringing the valuation for launch services close to $60B.

• xAI / AI — is the big gamble. As of now, the numbers are based on real contracts, but we are assuming a growth rate of nearly 50% for 10 years. 80% of the 2035 revenue comes from contracts that do not exist yet. Even with this optimistic projection, AI will add only $343B in value.

What’s interesting is that even with the most optimistic projections for each business segment, the company's total value comes to only $568B ($43 per share), a far cry from the asking price of $1.75 trillion ($135/share).

The kicker? If you exclude the AI division, the company's total value is around $220 billion — which is what it nearly traded at in private markets before the IPO hype and the xAI merger.

How to trade the world’s largest IPO?

While by valuation, SpaceX will be listed among the top-10 companies, it will trade like a mid-cap due to its small float. Broadcom, which is similar in size, has $1.7T in tradable float compared to only $75B for SpaceX.

Now that we know that the stock is going to be volatile and priced way above what it should be priced at by any reasonable estimates, here’s how you can trade it (based on whether you got the IPO allocation or not)

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This is a guest post from . We launched Rebound Capital last June as our equity research division after more than 4 years of research at Market Sentiment. Here’s the one year update (and a special offer for Market Sentiment readers who make it till the end)

On June 5, 2025, we launched Rebound Capital. It was the culmination of more than 4 years of research at Market Sentiment, where we evaluated everything from investing in companies more than 100 years old, to economic moats, to tracking insider trades by members of Congress.

There’s this one idea we always came back to: investing in quality companies in a drawdown.

Here’s what we wrote about Meta in 2022:

No matter how you look at the company, the financial metrics are incredible. 80% gross margin and 30% ROE with growing revenue. But the stock is down 55% in the last 1 year.

I am not pitching it as a great business. They certainly have some incredible challenges ahead of them in terms of privacy and attracting a young user base. No one knows directly the solutions for their hardest problems—that’s why they’re the hardest ones.

But writing off a company based on two bad quarterly reports is just too short-sighted.

The same theme kept recurring:

• Netflix lost 3/4th of its value because it did not grow subscribers one quarter.

• ASML lost ~1/2 its market cap on tariffs and export restrictions.

• Amazon was down 56% after its first quarterly loss since 2015.

So we finally pulled the trigger and launched Rebound Capital, putting our own capital on the line. We followed Charlie Munger’s advice of “take a simple idea and take it seriously” and spent the past year going through hundreds of stocks to find the optimal rebound setups.

Fast forward a year, and we are outperforming the S&P 500 while holding top 3 positions in relative value names such as Amazon, Constellation Software, and MercadoLibre. To put this in context, 79% of all active large-cap U.S. equity funds underperformed the S&P 500 last year.

While it’s easy to talk a good game when your portfolio is outperforming, sticking to the strategy when it’s underperforming is the hardest thing to do as a public facing equity research platform. Q1 was hard on the Rebound Portfolio, where we underperformed the market by 9%. We continued to share our portfolio performance publicly throughout the period of underperformance (here are our Feb, Mar, & April updates). But sticking to our strategy helped us double down on quality names that rebounded strongly after the March dip.

Not to name names, but there are a lot of “gurus” who have Excel spreadsheets where the portfolio never goes down, and they magically exit at the right time. For us, we have maintained the policy of informing you of all our trades 24 hours in advance, and honestly, our biggest success till now is that nobody has ever said anything bad about us.

Overall, we are now in a good position with our portfolio compounding faster than the index while trading at essentially the same valuation. The headline numbers (weighted average of our portfolio holdings):

• Revenue growth, next 3 years: ~18%

• EBIT growth, next 3 years: ~19%

• EV/EBIT: ~27x, in line with the S&P 500

Here is the unusual part. We own a basket whose EBIT is set to grow ~19% per year over the next three years, compared with <13% for the S&P 500, and we pay no premium on the multiple to do so.

Divide valuation by growth, and the portfolio sits at ~27/19 = ~1.4, versus 27/11 = ~2.5 for the index (lower is better). In short, our portfolio compounds faster than the market while costing about the same, which on a growth-adjusted basis makes it materially cheaper than the index it is built to beat.

Your support has been incredible, and it’s what makes this work feel meaningful. While most creators are hush-hush about how much they make, we are going to show you our real numbers. After all, this is the easy bit after sharing our portfolio performance :)

As of today, we have more than 21,000 investors who subscribe to us, of which 969 investors pay to read our research, which translates to revenue of $358K (ARR). The spike you see in December is when Michael Burry became a paid subscriber to Rebound Capital and endorsed our note on Birkenstock.

Consider this your nudge to put your own work out in public. You never know who’s reading.

Finally, of all the metrics at Rebound Capital, our retention rate is the one that we are most proud of. ~90% retention at 6 months is unheard of in the newsletter space and is the best testament to the quality of the research we produce and the value you derive from it.

Right now, the market is in love with AI. Money is pouring into a narrow group of names at prices that already reflect the next 5 years of growth. To be clear, we are bullish on what AI can do over the long run. But when an equipment vendor that has always been cyclical gets priced as if the cycle has ended, the reward for chasing it is small, and the cost of being wrong is high. With many indexes changing their decade-old rules to accommodate hyped-up IPOs like SpaceX, indexing is no longer the safe, neutral choice. The case for doing your own research only gets stronger from here.

And this is where being independent matters. A fund manager has to answer to a committee, hug the benchmark, and own the hot name so they don’t look wrong at the next quarterly review. We answer to no one but you. We never have to chase the story of the moment and can sit in an out-of-favor name and wait for the market to catch up, because no one is going to pull our capital for being early.

While investors flock to one part of the market, high-quality companies are being ignored. Some of the best compounders we know are at their lowest multiples in years. We will continue to focus here. The crowd overpays for what it loves and ignores the good businesses on sale. But the market eventually catches on.

So we will keep researching the names nobody wants to talk about. The rotation back to high-quality names will come. Our job is to be patient enough to hold the right names when it arrives.

Anniversary Offer from Rebound Capital

To celebrate one year of Rebound Capital, we are offering 33% off to all current subscribers of Market Sentiment. This offer is valid only for the next 24 hours and if you are an existing reader of Market Sentiment.

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With how the markets are right now, we are right in the middle of the good old days. In case you missed the everything bubble of 2021, this is right about how it felt back then.

Every thesis was proven in days, and random companies were doubling up every week. Hell, we were even trading pictures of rocks at the same value as that of an actual waterfront property in Florida.

While we are not that crazy yet (as the price rise is somewhat supported by earnings improvement), when we did our last portfolio update a month back, the MS portfolio was up 33% YTD.

Today, we are at ~78%!

The problem is that even though demand is real, there's a lot of crowding into semiconductor stocks. "Long Semis" is now the most crowded trade, and investors are already discounting multiple years of strong growth.

As Rebound Capital highlighted, there is literally panic buying in semiconductor stocks.

AMD is up roughly 330% over the trailing 12 months and now trades at 50x forward earnings, compared with Nvidia at 24x. The semicap names have rerated even harder: ASML at roughly 52x trailing earnings versus 38x at the end of 2024, Lam Research at 60x against a ten-year average of 20x, and Applied Materials at 42x against a ten-year average of 18x. These are cyclical equipment vendors being valued as if the cycle no longer exists.

The memory and optics complex is where the melt-up is cleanest. SK Hynix is up about 248% year to date and Micron 226%, with both joining Samsung in the trillion-dollar club. In optics, Applied Optoelectronics is up roughly 335% YTD, Lumentum 120%, and Coherent close to 80%. Some of the other Tier 2/3 suppliers are up similarly.

With the bad news out of the way, here’s what’s happening with the fundamentals:

Demand for semiconductors is nothing like what we’ve seen before. Until now, investors expected that we were at peak demand, with hyperscalers reinvesting almost all of their cash flow into AI data centers. With Google raising $80B from stock sales to fund its AI buildout, there is now theoretically no upper limit on the money that can be pumped into the ecosystem.

Still, even with the parabolic increase in revenue and profits, we're slightly jittery as the stock price tends to drop well before the slowdown data comes through in earnings reports. Based on the previous cycles, by the time the estimate cuts arrived, IT stocks had already fallen an average of nearly 30% — and individual names like Intel, Nvidia, and Micron fell considerably more.

With this level of crowding and hype, any hiccup, however temporary, will lead to a sharp sell-off. The optimal strategy here would be to take a step back, dig into names and sectors outside the current hype cycle, and invest at more palatable valuations.

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Bonds might just be that.

So, with the AI trade in full swing and investors more than willing to pay, check notes, 100x revenue multiple on a loss-making company, it’s important to take a step back and evaluate what will happen if (when?) the Fed starts raising interest rates.

Interest rates are important once more

What most investors conveniently forget in a hype cycle is that a stock’s value is ultimately what the company will earn in the future, discounted back to today. So, when you are buying a company with a P/E of 50, it means that, based on current earnings, it will take 50 years for the company to “pay back” your investment.

You could argue that the 50 P/E company is growing rapidly, and your payout will be much faster. But the fact remains that most of your return is coming from projected cash flows that are 10+ years out.

Coming back to the interest rate bit, the moment rates rise, future earnings are worth less in today’s dollars. A company whose value is predominantly based on future profits will immediately get re-rated.

That’s what happened in 2022 once the Fed started raising rates:

In many cases, the company fundamentals actually improved — Shopify nearly doubled its revenue from $2.9B in 2020 to $5.6B in 2022, yet the stock fell ~70% because of multiple compression. In 2020, the company traded at a 50x revenue multiple, compared with an 8x multiple in 2022.

Same company, different interest rate environment.

Now look at where Semiconductor stocks are trading now:

We are in the best market for semiconductor stocks in the last few decades, and that has driven valuations too far for some companies. So even if the growth projections actually come through, if the Fed starts raising rates, all these companies will get re-rated.

What are the odds of a rate hike?

According to the CME FedWatch Tool, there’s a 70% chance of the Fed raising the federal funds rate by the end of the year. The heaviest odds (more than 40%) are on the Fed doing one quarter-point rate hike from the current target of 3.50%-3.75%. The good part is that there is only a 2% chance of a rate hike in the next FOMC meeting (Mid June).

The real worry is not the 25bps hike: it’s a combination of a new hawkish Fed chair, sticky inflation (due to tariffs and Iran war), and rising national debt — all hitting simultaneously when the semiconductor names are priced for perfection.

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1. We are still early, and there is a long way to go.

2. We are near the top, on the cusp of a dot-com style collapse.

While no one can be certain, it’s probably somewhere in the middle. But if you are in the it’s a bubble bucket, let me start by highlighting the mistake I made at the end of 2023 when I published “The Problem with the Magnificent 7”.

Nvidia had ended the year with a 245% return and became one of the top 5 most valuable companies in the world. Nvidia’s Price/Sales had doubled from ~11x in 2022 to 22x in 2023. We were naturally skeptical of how much more the company could grow from these rich valuations.

Turns out, we underestimated just how massive the AI buildout would be. While we were busy looking at Nvidia's P/S and thinking it was expensive, the company's actual sales were about to explode. Revenue jumped from $61 billion in FY2024 to $130 billion in FY2025 and $216 billion in FY2026. Every big tech company was suddenly in an arms race to buy as many GPUs as they could get their hands on, and Nvidia was the only one selling. So even though the stock looked expensive at the end of 2023, earnings grew so fast that we missed the rally.

It took me quite a while to change my mind about AI and to understand that it’s a structural, not a cyclical, change. It’s often said that the mark of a good investor is the ability to change his mind when presented with new information.

So, before we get into the Agentic stack, here are 3 bits of information on how much AI is going to change the world in the next few years:

Market Sentiment dives deep into the bottlenecks of the AI world. Join 45,500 other investors to make sure you don’t miss our next briefing.

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1. Outside of the AI hype, we are quite a bit early

As per the latest estimates, ~78% of humanity has never used any AI tool. So while it feels like everyone is running an agent on tech Twitter, reality is far different.

2. Even with this, foundational company revenues are exploding

3. We haven’t even considered physical agents

Figure just had a 24-hour live stream featuring an autonomous humanoid robot picking and sorting packages.

Agents are going mainstream

Most AI conversations right now are about coding assistants (and that is what is driving the revenue surge for OpenAI and Anthropic), but that’s really only useful for the ~25 million developers out there.

Agents are going after something much bigger. The 1.4 billion knowledge workers: who spend their days buried in email, spreadsheets, and jumping between a dozen different tools. And once agents start handling even a slice of that work, AI usage goes through the roof, which means a lot more compute, a lot more GPUs, and a lot more demand flowing right back to the semiconductor stack.

On May 7, Anthropic announced that Claude is now generally available inside Excel, PowerPoint, and Word, with Outlook in public beta.

Up until a few months ago, using AI in your day job meant writing prompts, hitting an API, or running tools like Cursor. With this release, AI will now operate the 3 apps that 1.4 billion knowledge workers use the most. Microsoft has its own version of this, called Copilot. Copilot has ~20 million paying users, and they rolled out their latest Copilot and Office 365 E7 bundle on May 1st.

Watch the demo to appreciate what this looks like in practice. This is an inflection point for AI usage in everyday tasks.

Agentic AI: the inflection point

A developer running a focused 30-minute coding session on Cursor uses around 50K to 200K tokens (unit of compute). Compare that to a financial analyst asking Claude to reconcile a 200-row file against a pivot table, draft the email summary, and prep three slides for Monday’s review.

That single workflow uses 800K to 2M tokens.

Now multiply that by 1.4 billion office workers. Microsoft has already said that over ~80% of Fortune 500 companies have rolled out Copilot to part of their workforce. The load on enterprise systems caused by Agentic AI will make chatbots' token consumption seem trivial.

And the infrastructure to handle all this simply isn’t there yet. A chatbot session is quick, GPU-heavy, and low-context. An agentic session is the opposite. It runs for hours, constantly does things, and pulls on CPUs (to orchestrate actions), memory (for more context), and GPUs more evenly. As we wrote in ‘Notes on Compute’ last week, the server CPU to GPU ratio has already shifted from 1:12 in old training setups to 1:2 in agentic inference builds.

An explosion in token consumption

Goldman Sachs Global Investment Research now models that consumer and enterprise agents will push monthly token consumption to roughly 24× current global capacity by 2030. The crucial line on their chart is the dashed marker labeled ‘Token Economics Turn Positive in 1H26’, the moment any operator can deploy an agent whose output value exceeds its compute cost. That is when every company in the world will need to implement AI agents to compete.

Right now, agentic use cases are still more hobbyist tinkering than full-fledged commercial deployment.

Once an enterprise agent can operate continuously, 24/7, handling complex tasks and recoup its costs, adoption accelerates rapidly, probably within months rather than years.

Mapping the stack

Most readers already have meaningful exposure to popular names such as Nvidia, AMD, Intel, TSMC, Samsung, and SK Hynix, either directly or through our Advanced Packaging basket.

The Agentic Basket below is deliberately tilted toward the under-covered stocks, enabling agentic AI. These are the stocks with a higher chance of re-rating, as the broader street discovers them.

Morgan Stanley’s exposure map is the best reference we’ve seen for the full agentic stack. We’ll walk through each segment, then assemble the basket.

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We are publishing this without a paywall.

It’s impossible to miss what’s happening in the AI compute market now. While QQQ is up 13% YTD, AMD is up 96%, Arm Holdings is up 117%, and Intel, a company that most investors wrote off, is up 206%!

The performance has been so mind-bending that even the President is posting stock gains.

Jokes apart, there is a fundamental reason these companies are getting re-rated — AI, specifically AI agents.

What is an Agent?

An agent is simply a clever bit of software that acts autonomously to achieve a certain goal. For example, if you want to find a house to rent in New York, you can spin up an agent and share your requirements (rooms, space, neighborhood, budget, etc.). — The agent will then browse the web, filter the houses based on your criteria, and give you a shortlist.

Before AI, automating this would have taken a few days of coding, and you would most likely just do it yourself. But with AI, you can just give the task, and it will write the underlying code and handle it for you.

Now imagine the potential if it could automate almost anything you ask it to. That’s what Manus promised to be (it’s worth watching the 4-minute video to get an idea of what an agent is and what’s possible). The demand for the product was so much that they had a 2M waitlist a few days after launch.

So why are compute stocks rallying?

Nvidia has more than 10’xd since the launch of ChatGPT. This is expected as we were using massive arrays of Nvidia GPUs to train and serve these Large Language Models. But AI has advanced from a chatbot that answers questions to an operator that takes action.

Coming back to the above example of a house search, the agent has to open up a browser, navigate to a particular page, scrape the data, compare the listings, and repeat this hundreds of times.

Most of these tasks load the CPU more than the GPU. So the ratio of CPU work to GPU work is much higher in an agentic system than in a simple chatbot.

According to Morgan Stanley Research, earlier server builds used 1 CPU per 12 GPUs. With rising agentic workloads, the ratio is now closer to 1 CPU per 2 GPUs, which is driving the CPU bottleneck.

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Where are we in the cycle now?

This is where it gets interesting. While the current cycle is definitely looking stretched (and we took some profits this week), the long-term outlook is promising.

Right now, agentic use cases are more hobbyist tinkering with an idea than full-fledged commercial deployments. The moment when (if?) an enterprise agent, which is on 24x7 and can do complex operations, we see an inflection point in token usage. When an agent can run profitably (i.e., the value of the work it does exceeds its token usage), adoption will explode.

Putting this together, we have a good outcome for hyperscalers. With the cost to process each token falling, the price per token stabilizing, and demand exploding, hyperscalers will be able to dramatically improve their margins.

The biggest overhang on the hyperscaler (GOOG, AMZN, META) stocks is whether the hundreds of billions spent on AI data centers will provide adequate ROI. The moment they show margin improvement, the companies will get re-rated (also the reason you see analysts increasing their price targets after the stock pumps. New information is passed to the valuation model, resulting in a new target price. There is no alpha in following price targets without understanding the underlying reason for the rerating.)

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The CPU Trade

If we are purely looking at exposure to CPU, there are only 4 companies in the field who is having meaningful exposure:

• AMD — is the market leader in data center CPUs, and the gap is expected to widen further with the launch of 2nm Venice chips. AMD should get a better share of the incremental revenue driven by agentic demand for CPUs.

• Intel — is staging a comeback with Xeon 6 processors being adopted by Nvidia and Google as the CPU "head node" in AI infrastructure. Plus, being strategically critical to the US does not hurt.

• Nvidia — has launched its AI CPU, Vera, designed exclusively for AI agents.

• ARM — launched its first-ever data center processor — AGI CPU focused on handling agentic tasks. Rather than going the raw-power route, ARM is developing a more efficient processor to reduce token costs.

But we would argue that much of the upside here is already priced in. AMD, Intel, and ARM all have more than doubled YTD.

A better trade is to build an agentic basket with:

1. CPU Compute

2. High Bandwidth Memory & Storage

3. Foundry & Chip design

4. PCB, Substrate, and Controllers

5. Security & Payments

This way, rather than crowding into one overstretched sector, it gives you clean, diversified exposure across the full agentic stack. This is exactly what we are working on, and we will be publishing the full report next week.

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Disclaimer: This is not financial advice or a recommendation for any investment. The Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice.

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Why?

Big-Tech earnings are crushing it

Last week's earnings from Amazon, Microsoft, Meta, and Google once again validated the strength of the AI super-cycle. All of these companies stated that they are supply-constrained.

• Amazon — Revenue of $181.5B (+17% YoY), beat by 2.4% | AWS Revenue of $37.6B (+28% YoY), fastest growth in 15 quarters.

• Google — Revenue of $109.9B (+22% YoY), beat by 2.7% | Cloud Revenue of $20.0B (+63% YoY), beat by 8.8%.

• Microsoft — Revenue of $82.9B (+18% YoY) | Azure revenue growth of +40%, beat guidance of 37–38%.

• Meta — Revenue of $56.3B (+33% YoY), beat by 1.4% | Ad revenue +33% YoY.

It’s worth taking a step back and reading those numbers again! We already had sky-high expectations on incredibly high base numbers, and yet these companies delivered. For example, Google Cloud's expectation was ~50% YoY growth on a $12B base, and it delivered a 63% YoY growth.

The strong growth has led hyperscalers to double down on capex, which bodes well for our bottleneck stocks. MS Portfolio (launched Feb 1st) is now up 33% compared to 7% for QQQ and 4% for SPY.

Also, Intel handed us our first 2x after it reported arguably its best earnings in 2 decades.

While there are some supply constraints, Intel is an asymmetric bet on the US government’s influence in the chip race. — Market Sentiment Jan’26

With that, let’s dig into why compute is becoming the new AI bottleneck, how we are positioning for the next few months, and what the bear case is if oil stays up.

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CPUs are the new GPUs

There’s a reason AMD is up 61%, and Intel is up 153% YTD — AI Agents.

Agents can now plan, reason, and execute multi-step tasks without user input. Planning and coordination tasks rely heavily on CPUs and can account for up to 90% of the processing time in an agentic system.

According to Morgan Stanley Research, earlier server builds used 1 CPU per 12 GPUs. With rising agentic workloads, the ratio is now closer to 1 CPU per 2 GPUs, which is driving the CPU bottleneck.

But, it’s not just AMD and Intel that stand to benefit here:

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But one downside is that you will miss on relatively smaller companies whose deals won’t be big enough to show up in either 10K or news. To overcome this, you must do bottom up research where you go one by one to identify the common supplier.

That’s exactly what we did to identify MKS Inc. MKS products are present in 85% of all semiconductor production tools worldwide. Nearly every step in the wafer fabrication process has an MKS component inside it and as far as we can tell, we are the first to cover this company in depth.

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What does MKS do?

When TSMC builds a chip at its 3-nanometre node, the wafer passes through hundreds of process steps. Etching, deposition, lithography, inspection, cleaning. Each step requires a tool, and those tools come from equipment companies you may have heard of: Applied Materials, Lam Research, KLA, Tokyo Electron, ASML. These are the companies that collectively dominate wafer fab equipment spending, an industry worth roughly $133B in 2025.

But here is the thing. Those tools don’t work without subsystems. An etching chamber needs to maintain exact vacuum pressure, or the plasma that carves nanometre-scale features into silicon will behave unpredictably. That vacuum pressure is measured by an MKS gauge. The reactive gases that feed the plasma need to be delivered at precisely controlled flow rates, measured in nanolitres per minute. Those flows are controlled by an MKS mass flow controller. The RF energy that ignites the plasma in the first place is delivered by an MKS power supply.

If any of these are off by fractions of a percent, the wafer is ruined. At 3 nanometres, the margin for error is effectively zero.

MKS sells these subsystems to the equipment makers, who integrate them into finished tools and sell those tools to fabs like TSMC, Samsung, and Intel. MKS doesn’t sell to the fabs directly (for the semiconductor business). It sells to the companies that sell to the fabs.

It is, quite literally, the picks and shovels supplier to the picks and shovels makers.

MKS’s moat is exceptionally strong

The qualification cycles for semiconductor subsystems are measured in years. An equipment maker like Applied Materials doesn’t switch vacuum gauge suppliers because someone quotes a lower price. The gauge has been validated through months of process qualification at the end customer’s fab. The data, the documentation, the process recipes all reference that specific component. Changing it means requalifying the entire process. Millions of dollars. Months of engineering time. And most importantly, potential yield risk.

This creates an extraordinary moat. Not because MKS has patents (though it does have many), but because switching costs are so high that once you’re designed in, you stay designed in for the life of the tool platform. Often decades!

And yet, MKS trades at roughly 26x forward earnings, the cheapest forward P/E in the entire semiconductor equipment peer group (median forward multiple of ~34x).

One of the reason for the discount is the $4.4 billion debt load from an acquisition that, as we’ll argue, may turn out to be the smartest deal by any player in the sector. The market is mispricing MKS due to the debt-load, and the asset that it bought is structurally undervalued.

A far less visible layer of the Semiconductor world

There are really only five semiconductor equipment companies that matter. Applied Materials, ASML, Lam Research, Tokyo Electron, and KLA. Between them, they account for the vast majority of global wafer fab equipment spending.

But none of them build all their tools from scratch.

Below them sits a layer that is far more fragmented and far less visible. MKS, Advanced Energy, VAT Group, Inficon, Edwards, Brooks Automation. Each one commands a narrow, deeply technical niche. Vacuum. Power delivery. Gas flow. Measurement. Motion control.

The reason no single company spans all of these is that the physics is genuinely different in each domain. Building a mass flow controller that can meter corrosive gases at nanolitre-per-minute rates is a completely different engineering problem from building an RF power supply that delivers megahertz-frequency energy to a plasma chamber. The metallurgy is different. The sensor physics is different. The failure modes are different.

The equipment OEMs figured this out decades ago. If Applied Materials tried to build its own vacuum gauges, its own RF generators, its own laser systems, and its own gas delivery modules, it would need to maintain world-class R&D programs in half a dozen unrelated physics domains simultaneously. That would slow it down and dilute its focus on what it actually does best, which is integrating all of these subsystems into a finished tool and qualifying that tool at the customer’s fab.

So the OEMs outsource. And the subsystem suppliers specialize. And the result is an industry structure where five companies sit on top, visible to everyone, and a web of specialists sits underneath, visible to almost nobody, each one indispensable.

MKS, in our view is the broadest of those specialists. And that breadth is what makes it different from every other name in the layer below.

The Atotech Deal

In 2022, MKS acquired Atotech, a German company, for $4.4 billion. This was not a small deal for a company of MKS’s size. MKS’s EV was around $10 billion at the time of announcement of this deal. It loaded the balance sheet with debt, about $4.5 billion of it as of early 2026, which is why the stock traded at a persistent discount to its semiconductor equipment peers for years after the deal closed.

But the strategic logic was, in our opinion, genuinely brilliant. To understand that, we’ll need to understand what Atotech really does and how it integrates seamlessly with MKS.

Atotech is the global leader in advanced electroplating chemistry for PCBs and semiconductor packaging. Making a PCB requires plating copper onto its layers. That plating uses specialized chemistry, specific chemical formulations that have been tested and qualified for each manufacturer’s production process. Atotech makes that chemistry.

The business model works like this: MKS (through Atotech) sells specialized plating equipment to PCB manufacturers. That equipment runs on MKS’s proprietary chemistry. Once a PCB maker buys the equipment and qualifies the process, they are essentially locked into buying MKS chemicals for years. Management calls this a “high attach rate.” The industry term is more descriptive: it’s a razor-and-blade model.

Once the equipment is installed and qualified, the customer has no practical alternative but to keep buying MKS’s chemistry for years. The equipment sale is a one-time event. The chemistry revenue recurs for as long as the production line operates.

This is a fundamentally different business from MKS’s legacy semiconductor subsystem operation. Semiconductor subsystems are cyclical. When chipmakers cut capex, subsystem orders drop. But chemistry gets used up during production and needs to be constantly replenished. As long as the PCB factory is running, it needs chemistry. Management estimates that roughly 40% of MKS’s revenue now comes from consumables and services, chemistry that gets consumed in production and ongoing service contracts. This provides a layer of recurring revenue stability that the old MKS never had, which has also started to reflect in how markets are viewing the company.

Why the company is mispriced?

Now let’s dig into what has kept MKS’s stock at a persistent discount to its equipment peers (and why MKS is a strong buy at current prices):

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This is a classic example of anchoring bias. Since you saw the stock trade at $100 and it’s now at $150, you mentally anchor yourself to the first price, which makes the stock feel expensive. Conversely, a stock down 50% feels like a bargain because it's cheap vs. the anchor. What’s interesting is that none of these decisions is made by evaluating fundamentals, but rather by going purely by vibes.

Most of the companies we invest in at Market Sentiment trade near their all-time highs — and that's by design. We focus on businesses with bottleneck-like characteristics, where the market has yet to fully price in the long-term scope of their dominance.

And all this is not just theory: Here is our full portfolio holdings as of today. Instead of betting on which model is going to come out on top, it’s much more profitable to bet on the underlying companies that the AI boom runs on. The other thing worth noting: 15 out of 16 positions are in the green (while QQQ is down YTD), and there's no single name carrying the portfolio.

With that, let’s get into a few names that are instrumental for the AI buildout. We already have one of these in our portfolio, and we will add a few more in the coming weeks.

4. Corning Inc ($GLW)

Corning is a 175-year-old materials science company best known for Gorilla Glass — the scratch-resistant glass on virtually every smartphone screen. But recently, the company has quietly become one of the most essential infrastructure companies in the AI buildout. The reason is fiber. AI data centers require up to 10 times more fiber connectivity than traditional cloud computing, and Corning makes the glass it runs through.

The big break for the company came in January, when it announced a $6B deal with Meta, under which Corning will supply optical fiber, cable, and connectivity solutions for Meta’s AI data centers. Meta's 5-gigawatt Hyperion data center under construction in Richland Parish, Louisiana, alone will require 8 million miles of fiber.

While the Meta deal is already priced in, and the stock is definitely not cheap (PE of 92!), we expect a few other players, like Google and Amazon, to make similar deals. Another tailwind for the company is that newer, more powerful chips require more fiber-optic connections. For example, Nvidia's 72-GPU Blackwell nodes require 16 times as much fiber as traditional cloud switch racks. Finally, the geopolitical angle is also there, with Corning manufacturing optical fiber domestically, including two of the world's largest fiber facilities in North Carolina that employ over 5,000 people.

3. SK Hynix ($HY9H)

We already did a deep dive into SK Hynix 2 months ago, and it’s our largest portfolio position (up 22.4%).

The company primarily makes DRAM (Dynamic Random Access Memory) and NAND memory chips (long-term storage). Traditionally, most clients for these products were smartphone and PC manufacturers, and the market was chugging along just fine.

Then AI changed everything — models required enormous amounts of memory to operate, and AI-generated images and videos required significant storage. With companies spending hundreds of billions on capex, demand for DRAM and NAND skyrocketed, far outstripping supply.

As a result, SK Hynix’s revenue tripled from $22.8B in 2023 to $66.3B in 2025.

The thesis of our investment was based on SK Hynix's domination in high-bandwidth memory. With over 50% market share, SK Hynix has significant pricing power, enabling it to set terms and expand profit margins while maintaining its market share.

The memory bottleneck is only expected to worsen as:

• Agentic reasoning generates a long sequence of steps before giving the final answer. This means that the AI model should now retain all prior steps in memory so it has context for what it’s expected to do.

• Images, audio, and video generation consume far more memory than mere text generation.

• Finally, since model providers are pushing universal assistants that know everything about you, they now simultaneously have to keep the current conversation, general information, and old conversations.

FYI — SK Hynix is primarily listed on the KOSPI (Korea Composite Stock Price Index) under the ticker 000660. For international investors, the most accessible entry point is through Global Depository Receipts (GDRs). These are listed on the Frankfurt Stock Exchange under the ticker HY9H.F. They have filed for ADR listing in the U.S. market (Expected by mid 2026).

Micron is a decent alternative if you can’t access SK Hynix (for the Schwab investors). We have a small position there.

2. Hammond Power Solutions ($HMDPF)

Here’s a not-so-fun fact: Nearly half of planned US data centers are now being delayed or canceled due to shortages in critical electrical components like transformers, switchgear, and batteries.

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Q1 2026 has not been kind to equities. Amid geopolitical uncertainties and rising oil prices, the S&P 500 is down ~4% YTD, and the Nasdaq is down 5%. We had briefly entered a correction territory for QQQ before the bounce back in the last two days.

Our bottleneck thesis continues to shine, as even with the drawdown, all three of our baskets have outperformed the market.

1. Advanced Packaging +4.5% vs -6.7% QQQ

2. Optics +0.7% vs -6.6% QQQ

3. Energy +1.5% vs 0.6% SPY

MS Portfolio Holdings Update

March was tough for the portfolio compared to Feb as geopolitical crisis muted AI investments and Google launched its TurboQuant AI-compression algorithm, which is expected to reduce the LLM memory usage — a headwind for our memory thesis. Even with these headwinds, our portfolio is in the green for the year while QQQ is down 5%.

With Oil price rising a whopping 78% this year and the crisis far from over, here’s how we are positioning our portfolio:

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We are publishing this without a paywall.

This has now become an yearly ritual — exactly one year back, we had issued our buy the dip report when the S&P 500 was down 10% from it’s ATH as Trump had just started yet another trade war. Barring the global financial crisis, it was the worst start to a presidency in the last 50 years. Yet, one year later, the S&P 500 is up 13% (and 27% up if you had bought the liberation day dip).

We are once again looking at a similar playbook with Trump doing something irrational causing the market to drop. YTD, S&P 500 is down ~9% and QQQ is now down 12%.

One of our favorite thought experiments during times of extreme market uncertainty and fear is:

What trades do you expect to regret not making this week… a year from now?

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The key question in a market correction is whether to wait out the storm before investing again or double down and buy more?

To find out, let’s consider three investors: Cautious Charlie, Average Andy, and Daring Dave.

Each invests $100 in the S&P 500 at the beginning of every month. When the market falls below 10% of its previous all-time high (correction territory), each investor reacts differently to the dip.

• Cautious Charlie “holds” - He stops investing, holds the cash, and waits till the market crosses the threshold again to deploy the capital.

• Average Andy “stays” - He continues investing as usual.

• Daring Dave “doubles” - He invests double the usual amount till the market crosses the threshold again.

Who did better?

Here’s how they would have performed if they started investing in 2000:

At the end of 25 years, Dave, who doubled down during all the drawdowns, would have had the highest return, at 523%, compared to Andy’s 453%. Finally, cautious Charlie, who waited for the market to “stabilize” before deploying his capital, would have had the lowest return, at 295%.

This stark difference in performance is due to the underlying behavior of the stock market. According to JP Morgan, just missing the 10 best days in the stock market over the past 20 years would have dropped your return from 548% to 197%.

The kicker? Seven of the 10 best days took place in bear markets. Cautious Charlie, by trying to wait out the worst unknowingly missed out on some of the best days as well. Daring Dave, who bet hard when the markets were down, came out on top over the long run.

Baron Rothschild, an 18th-century British nobleman, is credited with saying, “Buy when there’s blood in the streets, even if the blood is your own.” He should know — He made a fortune buying during the panic that followed the Battle of Waterloo.

The worse things are in the market, the better your chances of finding a great deal. And things sure do look bleak now…

Disclaimer: This is not financial advice or a recommendation for any investment. The Content is for informational purposes only, you should not construe any such information or other material as legal, tax, investment, financial, or other advice.

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While QQQ is down 4% YTD, the Advanced Packaging portfolio we put together is now up 8%! What’s more interesting is how strongly the memory basket within the advanced packaging has performed. Our focus was solely on High Bandwidth Memory, as these three companies own the entire market:

• Micron: +6%

• Samsung Electronics: +30%

• SK Hynix: +24%

Even with our 35% allocation to these 3 companies, we might have underestimated how much bottleneck memory (both DRAM and NAND) is now becoming to AI. With memory inventory still going down and pricing increases accelerating, let’s dig into why AI is using so much memory and how we are positioning ourselves with a memory basket.

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Why memory is becoming an AI bottleneck

Have you ever had a long chat with ChatGPT or Claude where, at the end, the bot behaves like it has no clue what the conversation was about?

It’s because the model ran out of context, which it keeps in memory. Context is what the model retrieves before answering so that it can make sense of what’s going on. The longer the context window, the more useful the conversation. But on the other hand, when you double the amount of context, the underlying model needs 4x the memory.

Adding fuel to the fire are the recent breakthroughs and usage patterns in AI:

• Agentic reasoning generates a long sequence of steps before giving the final answer. This means that the AI model should now retain all prior steps in memory so it has context for what it’s expected to do.

• Images, audio, and video generation consume far more memory than mere text generation. For example, to ensure characters are consistent throughout an AI-generated clip, the model must retain the entire frame in memory rather than just a few words.

• Finally, since model providers are pushing universal assistants that know everything about you, they now simultaneously have to keep the current conversation, general information, and old conversations to make sure the experience is seamless, which pushes the memory usage even further.

This is the first half of the problem. Keeping billions of users' full conversation histories, generated images, and agentic workflow records means that demand for long-term memory has also exploded. All this meant that the prices for both long-term (NAND) and short-term (DRAM) memory have gone parabolic.

Is this time different?

None of the above is new information to investors who are closely following the AI market. Yet, while the market (S&P 500) is trading at a forward P/E of 21x,

• Micron is at 10x

• SK Hynix is at 5x

• Samsung is at 6x

This gap is because investors have been burned by the cyclical nature of these companies. We had a similar playbook during the Windows PC super-cycle in 1996, the cloud and smartphone boom in 2016-2019, and the COVID cycle a few years back. In all three cases, the playbook was the same.

A catalyst will cause a huge spike in demand for memory (for example, during COVID, everyone was sitting at home, and there was a rapid demand increase for gaming PCs and workstations, driving up demand for DRAM). Companies will race to build new manufacturing plants, end up overshooting demand, and crater prices.

The phrase "this time is different" is considered one of the four most dangerous in investing, for good reason. But as my friend argues in his latest report, this time it might actually be different. A snippet from his report:

In every previous memory cycle, the demand driver was the same: humans buying devices. PCs in the 1990s. Smartphones in the 2010s. Laptops during COVID. The demand function was ultimately capped by the number of humans and the number of devices each human needs. One person buys one phone. Maybe one laptop. Perhaps a tablet. The DRAM content per device grows, but the number of endpoints is bounded.

This meant that once the initial adoption or upgrade wave passed—once everyone who needed a new PC had bought one, or every smartphone had been upgraded to the latest generation—demand would flatten. Supply, which was ramped during the boom, would overshoot. Prices would crash.

In the AI era, the demand function for memory has fundamentally changed. Memory is no longer predominantly serving a fixed number of »human endpoints«. Memory, especially HBM, is now a critical input for generating intelligence.

In the PC cycle, once every household had a PC, demand plateaued. In the smartphone cycle, once penetration hit saturation, annual unit growth went to zero. But the number of AI inference calls per day is growing exponentially and is nowhere near saturation. Every enterprise, every consumer app, every autonomous vehicle, every AI agent is an incremental consumer of memory bandwidth. — UncoverAlpha

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Where are we now in the cycle?

Given that all the memory stocks look like the following chart, the all-important question becomes: where are we in the cycle?

From our perspective, all the demand vectors are still largely ahead of us and not behind us. Outside of the Twitter & Substack bubble, you can rarely see someone using an “Agent” to get anything done or using an AI as anything other than a simple replacement for Google search.

Given the capex and time required for the new facility to come online, we can safely bet that the supply side is locked in for years, while the demand curve continues to grow exponentially. Another factor that supports this trend is that the companies buying these chips have strong cash flows and good margins, which means that the memory companies can get away with incredible price hikes: Just as Samsung did last month.

How are we positioning?

As discussed at the start, we already have significant positions in SK Hynix, Samsung, and Micron as part of our Advanced Packaging portfolio (Opened on Jan 30th).

Given how severe the memory bottleneck is, it makes sense to gain exposure to a few more companies specializing in both DRAM and NAND. We are adding 10% more allocation to the memory bottleneck with the following additions:

ASML Holding ($ASML) | 5%

You can read our deep dive into ASML here (up 68% since our coverage). According to Morgan Stanley research, even though semiconductor companies experience an upturn in revenue and profitability one or two quarters ahead of the memory makers (as equipment orders are placed well in advance), there is a significant lag before their share prices catch up to those of the memory makers (we are looking to capitalize on this). If you are a value investor, you can check our valuation model for here.

Western Digital Corp | 2.5% || SanDisk Corp | 2.5%

More of a momentum play than anything else. Both these companies have risen considerably over the last year (SanDisk 1088% & Western Digital 508%). These companies trade at a significant premium to their HBM counterparts (primarily because they are listed in the U.S.) and are proportionally riskier bets (hence the low allocation). The bet here is that even with the price hikes, NAND inventory levels have kept falling, so we might not be at the top of the cycle.

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Before you go

Unlike our Energy and Optics portfolio, this is not a long-term trade you can buy and forget, and comes with a visible expiration date. The bull case for memory will only hold as long as these 3 conditions remain intact:

1. Declining inventory: Hyper-scalers are still stockpiling, and we see no line of sight on inventory bottoming. Until we do, demand is structurally outpacing supply.

2. Accelerating YoY pricing: Pricing for DRAM and NAND is expected to rise into Q2 2026, with potential for a 20% hike in Q3, which shows us that the pricing cycle has not peaked.

3. Stocks still rewarding good news: All strong results are met with the stocks pumping and not sell-the-news behavior.

Any one of the above conditions breaking would be a good signal to exit the trade. As always, we will continue to monitor these conditions and update our subscribers. Consider upgrading your subscription to get these updates!

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More interesting reads

• Advanced Packaging

• The Great AI Trade: Energy

• 10 AI-proof stocks in a drawdown

​Disclaimer: This is for informational and educational purposes only and is intended solely for readers in the United States. This is not an offer, solicitation, or investment recommendation. Please consult an advisor and do your own diligence. Past performance may not repeat itself.

Given that we have no insight into how oil and gas pricing will behave over the next few years, and with the potential for TPL to pivot strategically into AI data centers, we will issue a Hold rating for now.

But given that both of these situations have materially changed in the past few months, it’s worth taking another look at the company. But before we jump in, settle an argument for us:

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You probably have that one friend who owns a couple of rental properties and never stops raving about how effortless the passive income is.

Now, imagine owning almost a million acres of land. That’s exactly what Texas Pacific Land Corp (TPL) has — They have over 880,000 acres of land in Texas.

The company has a fascinating history. In 1871, the Texas and Pacific Railway Company began building a transcontinental railroad from Texas to California, and the states agreed to grant land for every mile of rail built. While the company was never able to complete the railroad and went bankrupt, it had built 972 miles of track, entitling it to 3.5 million acres of land in Texas. In 1888, this land was placed in a trust (Texas Pacific Land Trust) for the benefit of bondholders who had invested in the railroad.

The trust sold the land bit by bit to repay the bondholders and began leasing land to oil and gas producers in the 1920s. This was smart as there is no operational or capex risk for the company, and they just take a small cut from the oil produced on their land.

The stock basically went nowhere for 35 years after IPO(from 1985 to 2010), as the company’s main source of revenue was selling small parcels of land and royalties from leasing land to oil and gas companies.

Digging into the 1995 10-K, we find that the company made a total of only $6.5 million, of which 32% came from selling the land. Obviously, this was never a sustainable source of revenue.

Everything changed around 2010 when oil and gas operators developed an advanced drilling technique that enabled them to drill sideways instead of just drilling down. By pumping water and sand through these long horizontal wells, they could crack the rock open and release far more oil than before which made it profitable to drill in TPL’s land.

TPL’s revenue exploded. From only $2.5M in oil and gas royalties in 1995, it grew to $373M by 2024. TPL also diversified its revenue stream by selling & recycling water to oil and gas companies, as the new technique was water-intensive.

This is precisely why owning a perpetual asset is beneficial.

TPL had to wait almost 90 years since the first well was drilled for the technology to develop enough to make drilling on their land economical.

But once it did, the returns were incredible. TPL has now generated a 100x return over the last 15 years. (That’s 4x return of Apple!)

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The cyclical nature of TPL

TPL is directly exposed to the price of commodities as it gets royalty on each barrel of oil produced on its land. This means that producers have less incentive to drill as oil prices fall. For example, in its Q2 2025 results, TPL reported a 21% drop in its royalty payouts per barrel , which led to an earnings miss in both revenue and EPS.

Since the company is trading a premium valuation, any earnings miss cause brutal drawdowns (as we can see below — 2015 Crude dropped from $100 to $30, 2020 Crude briefly went negative, 2023 post Ukraine spike).

Yet, the latest drawdown in 2025 (down ~50%) was predominantly the AI hype cooling off. The stock tripled in 2024 (up 233%) due to the expectation that TPL would get into AI data centers. Investors expected it to be a shoe-in, as the company already hosts some Bitcoin miners and has access to natural gas for energy and fresh water for cooling. Surprisingly no deal materialized for the company till end of 2025!

But two developments over the past few months have materially strengthened the bull case for TPL.

Let’s dig in:

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Since quite a few of you have joined us over the past few weeks, MS Portfolio is where we invest in our highest-conviction trends. We strive for absolute transparency, and we will be meticulous in sharing our portfolio performance at the beginning of every month, providing a detailed view of our transactions, current allocation, and returns for each position.

To date, we have published reports on 3 AI bottlenecks and have invested in one (MS portfolio was launched on Jan 30th 2026). As of today, all three of our baskets are now outperforming the market. Let’s dig in:

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1. Energy

AI may look like software, but under the hood, it behaves not much unlike heavy industry. Every model, from GPT to Gemini, runs on compute clusters that consume power at the scale of small cities.

Our thesis is simple — while U.S. power generation capacity is growing at 2% per year, data center energy demand is now growing at 20% YoY. AI workloads are estimated to consume nearly 12% of the total U.S. power supply by 2030.

We put together a portfolio of 4 segments, each uniquely positioned to capture AI’s accelerating demand for power. To date, the portfolio has returned 8%, compared to 5% for the S&P 500.

You can read our full report and justification for each segment and company below [Free report]

2. Optics

The Copper Wall is the physical limit beyond which copper cables cannot transmit data fast enough over a given distance. As the signal frequency increases (to push more data), the current density tends to concentrate near the surface rather than flow uniformly across the cross-section. This reduces the effective conductive area of the wire, leading to higher resistance and signal loss.

To scale AI clusters from thousands to hundreds of thousands of GPUs, the industry has no choice but to adopt optical interconnects for short-reach applications that were previously the exclusive domain of cheap, passive copper.

Instead of using a copper wire to send data, an optical interconnect converts electrical signals into pulses of light and sends them through a fiber-optic cable, allowing it to carry massive amounts of data without loss or distance limitations.

We put together a portfolio that will drive and benefit from this change over the next 4 years. After 3 months, while QQQ is down 3%, the optics portfolio is up 1.2%.

3. Advanced Packaging

Finally, we come to our highest performing basket. In just one month, the Advanced Packaging portfolio is up 14% while Nasdaq is down 4%. Our two biggest allocations, SK Hynix is now up 25%, and Samsung is up 37%!

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FYI — Live tracker is only available to paid subscribers and can be accessed at the end of this report.

In the series finale of The Office, Andy Bernard hits us with this devastating quote: “I wish there was a way to know you’re in the good old days before you’ve actually left them.”

With how the markets are right now, we are right in the middle of the good old days. In case you missed the everything bubble of 2021, this is right about how it felt back then.

Every thesis was proven in days, and random companies were doubling up every week. Hell, we were even trading pictures of rocks at the same value as that of an actual waterfront property in Florida.

While we are not that crazy yet (as the price rise is somewhat supported by earnings improvement), when we did our last portfolio update a month back, the MS portfolio was up 33% YTD.

Today, we are at ~78%!

The problem is that even though demand is real, there’s a lot of crowding into semiconductor stocks. “Long Semis” is now the most crowded trade, and investors are already discounting multiple years of strong growth.

As Rebound Capital highlighted, there is literally panic buying in semiconductor stocks.

AMD is up roughly 330% over the trailing 12 months and now trades at 50x forward earnings, compared with Nvidia at 24x. The semicap names have rerated even harder: ASML at roughly 52x trailing earnings versus 38x at the end of 2024, Lam Research at 60x against a ten-year average of 20x, and Applied Materials at 42x against a ten-year average of 18x. These are cyclical equipment vendors being valued as if the cycle no longer exists.

The memory and optics complex is where the melt-up is cleanest. SK Hynix is up about 248% year to date and Micron 226%, with both joining Samsung in the trillion-dollar club. In optics, Applied Optoelectronics is up roughly 335% YTD, Lumentum 120%, and Coherent close to 80%. Some of the other Tier 2/3 suppliers are up similarly.

With the bad news out of the way, here’s what’s happening with the fundamentals:

Demand for semiconductors is nothing like what we’ve seen before. Until now, investors expected that we were at peak demand, with hyperscalers reinvesting almost all of their cash flow into AI data centers. With Google raising $80B from stock sales to fund its AI buildout, there is now theoretically no upper limit on the money that can be pumped into the ecosystem.

Still, even with the parabolic increase in revenue and profits, we’re slightly jittery as the stock price tends to drop well before the slowdown data comes through in earnings reports. Based on the previous cycles, by the time the estimate cuts arrived, IT stocks had already fallen an average of nearly 30% — and individual names like Intel, Nvidia, and Micron fell considerably more.

With this level of crowding and hype, any hiccup, however temporary, will lead to a sharp sell-off. The optimal strategy here would be to take a step back, dig into names and sectors outside the current hype cycle, and invest at more palatable valuations.

MS Portfolio Holdings Update

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In 1999, when the Asian financial crisis hit South Korea hard, the government was desperate to streamline the economy and ordered a series of mergers. One of these was the shotgun wedding between Hyundai Electronics and LG Semicon. LG didn’t want to sell, and Hyundai didn’t want to buy, but the government forced the issue. This chipmaking business was unprofitable and debt-laden.

For what it’s worth, Hyundai had good reason not to buy. The company nearly went under due to debt during the dot-com bubble crash, and to prevent an overall collapse, the chipmaking business was spun off and renamed Hynix (a blend of Hyundai and Electronics).

By 2011, Hynix had been under the control of its creditors for over a decade. No company wanted to own Hynix. To keep a chip company alive, you don’t just buy it once; you have to pour billions into capital expenditure every single year just to keep the factories from becoming obsolete. Hynix was essentially a zombie company with seemingly no light at the end of the tunnel.

Enter SK group.

SK Group is South Korea’s second-largest conglomerate, specializing in semiconductors, energy, telecommunications, and pharmaceuticals. The Group’s chairman, Chey Tae-Won, said that the group needed a “new growth engine” because the domestic telecom and energy markets were saturated. Although this decision drew backlash from investors and analysts, SK bought a 21% stake in February 2012 for approximately 3.4 trillion won ($3.1 billion).

What happened next was a complete reversal of how Hynix had been run for the previous 10 years:

SK Group had massive trust in its newly acquired child. They readily opened their purse for a massive R&D expansion. The banks had previously starved Hynix of cash, and the company could only spend what it earned. SK Group immediately invested $3.5B in the first year, despite the chip market remaining depressed. This gave the company a head start on the next generation of technologies, while its competitors cut costs in a weak market.

With this investment, SK Hynix now had the best tools in the world. With the deep pockets of the SK group, the scrappy, survivalist culture of Hynix was transformed into that of innovation and tech leadership. This investment surge funded early research into HBMs, which is now helping the firm post record-breaking results.

Luck also played a part — just weeks after SK finalized the deal, Elpida Memory (a major Japanese competitor) declared bankruptcy. This cleared the field, leaving only three major players in DRAM: Samsung, Micron, and the newly reborn SK Hynix.

By the memory market upswing of 2017-2018, the company was making more profit in a quarter than the entire $3 billion purchase price SK had paid in 2012.

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Business Overview

The company primarily makes DRAM (Dynamic Random Access Memory) and NAND memory chips (long-term storage). Traditionally, most clients for these products were smartphone and PC manufacturers, and the market was chugging along just fine.

Then AI changed everything — models required enormous amounts of memory to operate, and AI-generated images and videos required significant storage. With companies spending hundreds of billions in capex, the demand for DRAM and NAND skyrocketed, far outstripping the available supply. As a result, SKY Hynix’s revenue tripled from $22.8B in 2023 to $66.3B in 2025.

The company operates in two major segments:

DRAM (Dynamic Random Access Memory)

DRAM refers to the volatile, temporary memory used in servers, PCs, smartphones, and GPUs. In simple terms, DRAM is the memory where your device stores applications you are currently working on. Without it, the computer must search every file in permanent memory to retrieve it. If you've ever experienced lag when too many applications are open, it’s likely because DRAM was full and the computer was using slower, permanent storage. The segment accounts for 76% of the company's total revenue, and SK Hynix has a ~35% market share in the DRAM market.

The DRAM segment also contains the highly sought-after high-bandwidth memory (HBM). HBMs are created using advanced pacdeliver significant performance gains in memory speed, without which AI processors would sit idle most of the time waiting for data transmission. Given that HBM has emerged as a critical bottleneck for AI chips, the segment is projected to grow at a robust 40% CAGR over the next three years.

NAND Memory

NAND refers to the permanent, non-volatile storage that retains data without any power. This segment accounts for 23%s revenue.

Only five years ago, SK Hynix's NAND segment was widely viewed as a loss-making “secondary” business. The acquisition of Solidigm (a semiconductor subsidiary) for $9B from Intel in 2021 has helped turn around this segment. The company has strategically moved away from the volatile consumer SSD market to prioritize high-margin enterprise SSDs (eSSDs) for AI data centers. This pivot towards specialized, high-capacity storage has further boosted the company’s profitability and overall margins.

Sandisk ($SNDK), which has delivered 1500%+ returns to shareholders over the past 6 months, operates exclusively in this segment. SK Hynix has substantial exposure to the segment and is an alternative to Sandisk at a much more attractive valuation.

Capitalizing on AI-manufactured scarcity

Being positioned perfectly during the perfect storm of supply constraints and surging demand meant the stock has quadrupled over the last year, after being flat for the previous 5 years.

Based on our valuation model and industry projections, we believe there is significant upside still left in the stock.

Let’s dig in:

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When Nvidia launched the Hopper H100 back in 2022, it was the most powerful chip at the time, with 80 billion transistors. What’s more interesting was the size of the chip — 814 square mm. This number is important as it was very close to the Reticle Limit of 858 square mm.

The Reticle Limit is simply the area within which the current chip-printing machines can focus without causing errors. It has been the industry standard for roughly 35 years because a larger lens would be significantly heavier and more prone to blurring during chip printing.

With the Nvidia H100 chips already using 4nm transistors and an 814 square mm chip size, the only way to double the number of transistors for the next model was to reduce the transistor size to 2nm. Instead of waiting around for a reliable 2nm process1, Nvidia used Advanced Packaging to glue together two of the largest possible chips to make the Nvidia Blackwell (B200). Blackwell had a mind-bending 208 billion transistors, more than doubling the H100 transistor count.

While making chips faster by shrinking them has been our go-to solution for the past 50 years, we might be approaching the limit of how small an individual transistor can be. Without getting into the nuances, as you approach 2nm and beyond, you have to spend exponentially more in manufacturing costs and cooling solutions. That’s why companies are betting more on Advanced Packaging.

How much more?

Approximately $100 billion in investment has flowed into the field over the last 3 years.

Market Sentiment dives deep into the bottlenecks of the AI world. Join 64,000 other investors to make sure you don’t miss our next briefing.

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Advanced Packaging

The following section is highly simplified, but it should provide clarity on how Advanced Packaging is addressing a critical bottleneck. Anyone who has ever assembled a PC knows that all components are mounted on a motherboard and connected via PCBs (as shown below).

The problem here is two-fold. One is that CPUs are considerably faster and spend 90% of their time waiting for data to arrive from RAM. The second is that moving data over long distances requires high voltage, which increases overall power consumption. Both issues are resolved using Advanced Packaging. Instead of assembling components in 2D, we now stack them on top of each other.

Since the memory now sits on top of the processor, there is no delay in accessing data from memory, and the operation becomes much more efficient [as seen in the video below].

With Advanced Packaging, we are solving two of the biggest problems in chip design:

• First, we can now join multiple chips and make them behave like one, keeping Moore’s law intact for a little longer.

• Secondly, since we are able to mount memory right on top of the processor, we make the data transfer significantly faster. A traditional DDR5 RAM reaches speeds of up to 70 GB/s, whereas with Advanced Packaging, you can hit 1,200 GB/s.

Why now?

As we highlighted in the beginning, the go-to solution for building faster chips was to cram more transistors into the same chip. This has worked out well, and over the past decade, we have reduced transistor size from 22nm in 2015 to 2nm by 2025. But this reduction has been achieved at exponentially rising costs, with 2nm chips 10x more expensive in both design and manufacturing than in 2015.

The costs are so high that only 3 companies in the world are currently developing 2nm technology: Intel, TSMC, and Samsung. The optimal solution to rising costs (as shown by Nvidia) is to use older 4nm technology and leverage Advanced Packaging to connect multiple chips to act like one.

Building an Advanced Packaging Portfolio

As promised last week, we will be launching the Market Sentiment portfolio by investing in a basket of stocks positioned to benefit from the rise of Advanced Packaging. This sector is expected to grow between 30 to 40% CAGR for the next 5 years, and the total market is expected to cross $100B by 2030.

To put this in perspective, only 3 players use Advanced Packaging to create High Bandwidth Memory and own 100% of the market. The demand for this technology is so high that these $100B+ companies have tripled in the last 6 months.

Let’s dig in:

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We have been thinking long and hard about how the economy and markets are shaping up. With AI, every industry is being pushed to its limit, and decades of investment are being made every few weeks. The current geopolitical situation is just adding fuel to the fire.

We believe there is significant alpha at these bottlenecks — to capture this, Market Sentiment is transforming into a full investment platform. (more on this later).

While everyone is aware of the increasing capital allocation by companies to AI infrastructure, almost everyone misses the true scale at which we are doing this. Here are some numbers to put things in perspective (all figures adjusted for inflation):

• Every month, we are spending more on AI infrastructure than the total cost of the Manhattan Project.

• Every quarter, we now allocate more to AI than the entire 14-year Apollo program.

• Just last year, we spent more than what it took to build our entire Interstate highway system over three decades.

While no one can be certain whether AI has the potential to generate returns on this scale of investment, the massive amount of capital being pumped in is pushing companies, industries, and technology to their limit.

The problem we have noticed here is that most media outlets just attribute AI to the Magnificent-7 stocks and do the bare minimum due diligence on other companies. The “research” is so bad that, just a few years ago, the BBC News called ASML "a relatively obscure Dutch company."

We believe the real impact (and alpha) lies with the second- and third-order companies and industries. A $100B deal now barely moves the needle for Nvidia, but even a $1B deal for a supplier to Nvidia will double its stock price overnight.

We have been watching this play out over and over again in the past year.

• If you’ve ever saved an AI-generated image, you’ve likely noticed the massive footprint: A simple Gemini-generated image can easily reach 10 MB, nearly ten times the size of a standard screenshot. Now imagine how much storage a Gen AI video would take. We are watching an entirely new market for memory chips being created, and unsurprisingly, storage company stocks are going parabolic!

  

• You must have noticed your electricity bill creeping up lately. It’s because data centers already consume 5% of total US electricity demand, and that demand is rapidly rising. Energy remains a bottleneck, with hyperscalers scrambling to lock in long-term energy deals to power their data centers. We have already covered in depth 2 months ago with our energy portfolio, which is now outperforming the index!

• The massive scale of investments is pushing the boundaries of what’s possible — we have already hit the limit on how fast data can be transmitted through traditional Copper cables, and companies are rushing to develop better technologies to overcome this Copper Wall. The portfolio we put together last month now has now 4.3% return compared to -2.8% for QQQ.

This is just scratching the surface. There are dozens of other industries and companies at the limit due to this massive capital inflow and demand. Here are a few more that are on our watchlist:

Advanced Packaging

The need for an exponential increase in computational performance for AI means that we have already reached the limits of Moore’s law of adding more transistors to a single chip. The proposed solution is to go beyond single-chip packaging by integrating multiple dies, memory, and functions into a single system using advanced packaging. The demand for a solution is so strong that more than $100B in capital has been allocated to advanced packaging alone.

Copper & Other Commodities

The data center boom (Copper is required everywhere as the primary conductor) and supply disruptions at major mines in Chile, Peru, and Indonesia have driven Copper prices up by ~40% over the last year. Lithium has also doubled in price in the last year.

The U.S. Grid

The U.S. Department of Energy had highlighted the shortcomings of the current grid a year before ChatGPT was released. Most of the transmission lines currently in use were laid in the 20th century and were never designed to carry so much load. With the U.S. home to 45% of the world’s data centers and modular nuclear reactors at least half a decade away, the U.S. grid will need massive improvements to sustain the current pace of progress.

As anyone who has worked in a manufacturing plant will agree, bottlenecks get all the attention and capital. It’s natural, since that’s the section holding up the entire plant. We have been watching this play out over and over again in the past year. The alpha is in the bottlenecks, not the hype.

Starting today, we are no longer researching these trends but will build portfolios and invest in these bottlenecks.

Just as with Rebound Capital, we like putting money where our mouth is. So if we are recommending any industries or companies, we will back our recommendations with real capital and share regular portfolio updates with you.

Nothing changes for you if you are an existing paid subscriber to Market Sentiment. While the research required to find these bottlenecks is intensive (as you probably can tell from our Energy and Copper Wall Reports), you can enjoy these reports and deep dives at the same price.

For everyone else, we will increase our pricing from $247/year to $497/year 24 hours after this report goes live — So, this is your last chance to upgrade the subscription at the current price :)

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p.s. — If you are an existing paid subscriber and not enthusiastic about this new direction, we understand and are happy to give you a pro-rated refund. Just reply to this email :)

Stock exchanges serve as venues where transactions occur, security prices are determined, and capital is allocated. Here’s an interesting aspect about exchanges — they are basically Lindy companies.

Consider the S&P 500 — since its inception in 1957, only half of the originally listed companies have survived, while others were displaced by bankruptcy, mergers, or replacement.

Now contrast this against the exchanges:

New York Stock Exchange - 230 years old

London Stock Exchange - 220 years old

Toronto Stock Exchange - 170 years old

Bombay Stock Exchange - 150 years old

The New York Stock Exchange was born in 1792 under a buttonwood tree in New York City. What emerged from the informal gathering, the Buttonwood Agreement, was a pact to impose order and transparency on financial exchange. These merchants laid the groundwork for a controlled marketplace, where prices could be discovered fairly.

Over time, this pact evolved into an exchange worth over $30 trillion.

Today, there are approximately 60 stock exchanges worldwide, listing more than 50,000 public companies. While most companies struggle to survive a business cycle, exchanges have endured across centuries.

But what is it about the exchange that makes it immune to the tempests of the market?

• Think of the exchange as a ‘contra-index equity class’. If the index goes up, the constituent stocks thrive. If the index sinks, there’s market carnage. But the exchange is agnostic to these movements. It’s monetizing ‘activity’ instead of market optimism. Whether participants want to buy or sell stocks, the exchange pockets spreads and fees and also benefits from rising volatility and transaction volume during periods of market chaos.

• Another advantage of exchanges is their inherent adaptability. Despite market carnage or disruptors in an industry, the exchange still stands to win because it’s not betting on any outcome. Indices continue to rotate, and markets continue to go through bear-and-bull cycles. But whatever happens, everyone is paying rent at the exchange — short sellers, hedgers, speculators, passive investors.

• Exchanges can scale unencumbered once the infrastructure is in place. They do not require proportionally more capital to support larger trading volume.

• In addition, exchanges are positioned to capture new assets, financial instruments, or vehicles as markets evolve. Whether it’s obscure asset classes like Bitcoin or orange juice futures, the exchange has everything to offer as long as you’re willing to trade on the asset. If a new financial product comes into existence tomorrow, the exchange will list it and retain the fees, irrespective of whether the product succeeds or becomes a credit default swap case study for the books.

This leads to the more interesting question.

If exchanges profit from activity rather than market direction, should we expect them to outperform the very markets they host?

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To test this hypothesis, we construct a global portfolio of exchanges and compare its performance to a portfolio of the indices they host.

NASDAQ and Intercontinental Exchange operate the two largest stock exchanges in the US: the NASDAQ and the NYSE, respectively. Euronext operates the major stock exchanges in 8 European countries. Japan Exchange Group owns the Tokyo Stock Exchange. Likewise, each exchange in the portfolio operates the primary exchange in its respective country.

For comparison, we consider a global portfolio of equity indices, mirroring the geographic allocations used in the exchange portfolio.

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This year, as we navigated the complexities of the shifting macro winds, your support remained our most valuable asset.

This year was big. We crossed 60K readers, launched Rebound Capital, and published 90 articles, ranging from deep dives to thematic equity baskets. Below, we’ve curated the highlights - the pieces that resonated the most with you, and made you some money along the way ;)

Green paper is not evergreen. Gold is.

We wrote about how gold remains an important asset class a few months ago. The underlying logic was simple: no one in power seems interested in managing the ever-increasing U.S. debt.

Since our report was published, Gold has outperformed the S&P 500 by 3.8x! Everyone from retail investors to central banks is buying gold at an unprecedented rate.

Energy and optics portfolio

Not long ago, the choke point in building AI compute capacity was GPU production. However, that bottleneck has recently receded, making way for another bottleneck - energy.

OpenAI wants to build 250 GW of compute capacity by 2033. This is more than India’s entire peak power demand in 2025.

Computing capacity lies at the bedrock of the AI economy. AI may look like software, but under the hood, it behaves not much unlike heavy industry. Every model, from GPT to Gemini, runs on compute clusters that consume power at the scale of small cities.

While energy is a second-order impact of AI, we found a third-order impact while building our energy portfolio.

As far as we can tell, we are the first to cover this trade in depth. This portfolio is taking a bet on one of the most profound hardware transformations occurring in computing right now.

While traditional mega caps, such as Nvidia, Broadcom, Intel, etc., have some exposure here, the best way to capture upside will be to build a basket of optics companies that will drive this change over the next 5 years.

A simple backtest validates our thesis: YTD, the Optics portfolio would have returned 3x that of the QQQ, with only 2 of the 9 picks dropping in value.

Buy the dip

“Buy when there’s blood in the streets, even if the blood is your own.”

While it may feel like a few years ago, just 8 months back, the stock market was in extreme fear, per the CNN Fear and Greed Index.

The S&P 500 index was down 10% from its ATH, Trump had started another trade war, and it was the worst start to a presidency in 50 years.

If you have a long investment horizon, the best thing to do is buy the dip.

The market is now up 23% since we published the report.

Betting on betting

After the stellar response to our GLP-1 basket, we were left in a unique position to identify similar industries that are largely flying under the radar, but are growing rapidly.

This brought us to the betting industry.

If you are like us, you must have at least wondered once why everyone’s betting on everything. Gambling ads are everywhere, and with the legalization of event contracts and the overall lax enforcement, the market is only expected to grow.

And fascinatingly, people almost always underestimate the amount of money these companies generate.

The magic of drawdowns

Netflix, at the end of 2021, was a pandemic darling and doubled from March 2020 to October 2021.

Then the market turned.

Over the next 7 months, the stock lost 73% of its value. What’s incredible is that before the downturn, Netflix was worth $300B, employees were dying to work for the company (FAANG), and it was one of the strongest brands of the S&P 500.

The company is now up 540% from its 2022 lows.

This is not only the story of Netflix. Every major company goes through a period of crisis where investors write it off, only for it to eventually make a big comeback.

Now imagine a portfolio full of companies in a drawdown, on their way to a rebound.

Simple Portfolio Series

In this 5-part series, we focused on finding and evaluating the performance of some of the best portfolios on the planet:

Simple Portfolio #1 - The 3 Fund Portfolio: This simple portfolio outperformed 83% of active fund managers and 90% of endowment funds over the last two decades while having an expense ratio of less than 0.1%.

Simple Portfolio #2 - All Weather Portfolio: During the Covid-19 crash, when the S&P 500 was down 20%, the All Weather portfolio was down only 1%. This is the definitive guide to building an All-Weather portfolio.

Simple Portfolio #3 - The 60/40 Portfolio: “You don't have to be brilliant, only a little bit wiser than the other guys, on average, for a long, long time.” – Charlie Munger.

Ben Trotsky became one of the most successful fund managers in the 80s simply because he devised a brilliant strategy for investing in junk bonds that he called “Strategic Mediocrity.” The closest idea to strategic mediocrity in the stock market would be the 60/40 portfolio.

Simple Portfolio #4 - Cockroach Portfolio: Most portfolios are designed to work well only in periods of growth. But what if we experience another stagflation like in the 70s, when stocks were down ~50%, and the bonds 30%? It’s situations like these where the Cockroach Portfolio shines through.

Simple Portfolio #5 - Coffee Can Portfolio: The problem in modern investing is that there is too much information readily available, and most investors tend to give undue weight to news, and most of the time, it pays to ignore the sensationalized headlines. One way to defend against this folly is to use the coffee can portfolio.

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As you can see (and our paid subscribers will vouch for this), implementing any of these strategies on even a $10K portfolio should easily cover the cost of your subscription — many times over.

If you made it till the end and are still on the fence, here is a limited-time 20% discount on the paid subscription. This offer is only valid for 24 hours, and the price is locked in as long as you remain a subscriber.

Get 20% off forever