The jump in performance per watt was incredible. M chips did the same tasks Intel chips did at roughly a quarter of the power draw. The result: the M1 Air managed 15 to 18 hours of real use versus barely 8 hours on the Intel Air with the same battery capacity.

Apple achieved this with custom silicon. Intel could never build this chip for Apple because Intel had to serve everyone. Its processors carried decades of baggage and had to work in every Windows machine from a $300 Dell to a gaming rig, so they could not be optimized for one laptop or one operating system.

Apple’s decade long investment into custom silicon paid off — Mac revenue which was stagnated at ~$25B from 2017 to 2019 jumped 60% and touched ~$40B by 2022 before normalizing to $33B for the latest year.

This is not about laptops; It is that owning your silicon converts directly into performance per watt, and performance per watt converts directly into a product nobody else can build.

Nowhere is this now more important than AI. Our estimates show that for every $100 of inference cost, hyperscalers currently hand roughly half to Nvidia. This eye-watering 85% gross margin Nvidia charges for its AI chips is currently the single largest tax on the AI buildout, and it is a tax you can escape by designing your own silicon.

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What is an ASIC?

ASIC stands for Application Specific Integrated Circuit. It is a chip designed by one company for its own specific use case, rather than sold on the open market. Examples include:

• The Apple M series chips we just discussed

• Google’s TPU AI processor

• Amazon’s Trainium 3 AI processor

• Nokia’s ReefShark base station processor

If you are wondering how these differ from Nvidia or AMD GPUs: a GPU is a Swiss Army knife whereas an ASIC is a scalpel.

Nvidia builds its GPUs to run anything. Training, inference, any model architecture for text, vision, protein or physics, in any precision format. That flexibility comes at a price. A meaningful share of every GPU die is spent on circuitry your specific workload will never touch.

An ASIC flips the trade. You strip out everything except what your workload needs and pour the entire silicon budget into exactly that. Bitcoin mining chips are the purest example. They compute one hashing function billions of times a second and can do literally nothing else. Not even a calculator app would run on one.

The AI versions apply the same logic to neural networks. The result is 2 to 3x better performance per watt and real savings on silicon cost: Google's TPU7x matches NVIDIA's B200 on raw compute at roughly a third of the price: ~$13,000 per TPU vs $35,000.

Artificial Analysis benchmarked open source Llama models across hardware. The results below are clear: Google's TPUs squeeze roughly 2x more tokens out of each TFLOP than any Nvidia chip.

Your Margin Is My Opportunity

With AI going mainstream, the nature of AI compute is changing. Inference is starting to take a larger component of overall compute. Inference workloads are predictable and repetitive: exactly what custom chips are built for. Projections show that just the inference demand is the fastest growing segment at a 35% CAGR and will need 90GW of data center by 2030.

The catch with ASICs is that they are expensive to build. A leading-edge custom chip costs $500M+ in design, IP licensing, and tape-outs before a single unit ships. The math only works at scale. You need enough deployment volume that the per-chip savings outrun the upfront design cost.

Every hyperscaler has now crossed that threshold and is building their own chips.

Google has been at it the longest, and its TPUs are the most mature ASICs in production. Amazon is not far behind with Trainium and Inferentia, which we believe will boost AWS returns in the coming years. Meta is the newest entrant: its MTIA chips already run recommendation inference internally, and in February it signed a multibillion-dollar deal to lease Google’s TPUs, with talks underway to install them in Meta’s own data centers from 2027.

The race to serve models at the lowest cost

In our view, companies are currently in the token-maxxing era. Every employee, whether they need it or not, has access to frontier models with a nearly unlimited AI budget.

This will not last, for two reasons.

First, the C-suite is starting to demand ROI for the spend. Uber burned through its entire 2026 AI budget in four months on coding tools, and its COO now admits it is very hard to draw a line between token consumption and useful product output. Microsoft reportedly canceled most of its direct Claude Code licenses this month and moved engineers to GitHub Copilot.

Second, not every task needs a frontier model. Writing an email response or updating a deck does not require Claude Fable 5. An open source model can do the same work at a tenth of the cost or less, and as open models get smarter, the share of workloads running on them will keep expanding.

Note that this does not mean AI spend shrinks. Usage will keep exploding while the price per token collapses, so the pie grows even as every workload gets cost-optimized. What it means is that serving intelligence becomes a race to the lowest cost per token, the same way Japanese carmakers won the US market by delivering most of the performance at a much better price.

If that is the race, hyperscalers have no option but ASICs.

Investing in Accelerating ASIC Adoption

The custom ASIC market is projected to grow from 8% of AI accelerators today to 19% by 2033, a $118 billion segment compounding at 27%. Given how fast the open source models are improving, we believe this projection might be understated.

We have identified 5 companies that sit directly in this flow, and we are backing them with ~20% of the MS portfolio.

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Quite a few of you have joined us over the past week (we were #5 in the Rising Finance Bestsellers) and here’s a guide on what we do at Market Sentiment and how to use the subscription effectively.

Investing in asymmetric opportunities

There are only two ways to make consistent alpha in the market:

1. Be early

2. Be contrarian

It’s futile to look at a company that’s covered by 20 analysts and expect to find something that all of them have missed. Our focus at Market Sentiment is to be early to trends that are going to define the next few years.

Whether you like it or not, that trend right now is AI. We are pumping a mind-bending amount of capital into compute. 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.

AI is the consensus trade now. But you can still be early to what it depends on. Capital at this scale never flows evenly. The railroad mania of the 1800s bankrupted most railroads but made fortunes for the companies selling steel and rights of way. Every infrastructure boom in history worked this way.

The AI buildout will be no different. The hyperscalers spending hundreds of billions will not be where the returns concentrate — that trade is long over. The returns will concentrate at the bottlenecks that these companies can’t sidestep. Our focus is to find them before the market prices them in.

Thematic Coverage

We have built five thematic baskets to date. Three of the five have already re-rated. Energy and Agentic AI have not, which is why they are worth a closer look right now.

1. Energy — AI needs energy. A lot of it. And more importantly, it needs it now. The power demand for data centers is expected to grow 10x the historical generation capacity growth. This is the one basket that’s underperformed (4% vs 15% for the S&P 500), and the reason was a simple mistake: we underestimated how fast the price of power would turn political, and over-allocated to companies exposed to electricity prices. The fix is just as simple: strip those out, and the rest of the basket handily beats the market (more on this below).

2. Optics — We were one of the first to cover the Optics trade last year. While the tech behind is complex, our thesis was simple: As models become more powerful and AI pushes data transfer speeds higher, the industry has no choice but to adopt optical interconnects. The basket has now rerated and is up 75% vs. 16% for QQQ.

3. Advanced Packaging — Once transistor shrinks stopped being cheap, the industry stopped chasing smaller nodes and started gluing chips together. Advanced packaging became the way to keep Moore's law alive, and the bottleneck moved from the wafer to the package. The basket is now up 102% vs 15% for QQQ.

4. Memory — Everyone knows memory stocks are cyclical and have burned investors before. But that’s the wrong question. The right question is whether we’re in a structural shift — and the data says yes. We made memory our highest allocation (34%) and were handily rewarded with a 134% weighted return (we bought the dip during the Iran war).

5. Agentic AI — We are at the inflection point for Agentic AI. Most AI conversations right now are about coding assistants, but that’s really only useful for the ~25 million developers out there. With Claude now generally available in Microsoft Office, AI can meaningfully support the 1.4 billion knowledge workers who spend their days buried in email, spreadsheets, and jumping between a dozen tools.

MS Portfolio

Our portfolio is where we put capital behind these emerging trends. Below you'll find every position, its current buy/sell/hold rating, and all the changes we've made along the way. If you're just starting out, begin by gradually allocating to our buy-rated names — these are the companies we still consider undervalued.

Market Sentiment Portfolio

Two important notes:

1. Many of you have DM’d us asking how you should allocate. As much as we’d like to help, we’re not financial advisors and can’t give customized allocation advice. What we can share is exactly what’s in the sheet: our positions, allocations, and buy/sell/hold ratings, plus how we think about each of them in our deep dives.

2. Starting Monday, this sheet moves behind the paywall. Our full portfolio, including positions, allocations, and ratings, will be available exclusively to paid subscribers. If you’ve been on the fence, this week is a good time to upgrade.

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What’s coming?

Which brings us to the good stuff. There are two big themes that we have been tracking for a while and will be sharing with you over the next few weeks.

1. ASICs

ASICs (Application Specific Integrated Circuits) are projected to overtake GPU unit shipments by next year.

ASICs are chips custom designed for one specific workload. Because these chips are tailored at the transistor level, they are highly efficient at the task its designed to do.

Google’s TPU7x matches NVIDIA’s B200 on raw compute at roughly a third of the price: ~$13,000 per TPU vs $35,000.

The catch here is that ASIC development is a multi-year, multi-billion-dollar project that only hyperscalers like Google and Amazon can afford. But that’s exactly who is building them. As model winners emerge and applications standardize, ASICs lead the next wave of compute growth.

Here’s how we look at the ASIC supply chain: design tools (Cadence, Synopsys), design services (AVGO, Marvell), and licensed IP blocks (Arm, Aspeed).

The real prize sits with the designers. Complex AI ASIC design is effectively an AVGO–Marvell duopoly. This lets them charge fixed development fees plus a variable price per chip.

Given ~95% unit growth and per-chip pricing power, AVGO and Marvell earnings should see superior earnings growth over the next 2–3 years. AVGO expects its AI semiconductor revenue to grow from approximately $20 billion in FY25 to over $100 billion in FY27 primarily driven by these ASICs.

When your data-center investments run into hundreds of billions, spending a few billion on a custom ASIC is a no-brainer.

2. Energy

One mistake we made with the energy basket: we expected power prices to keep rising as demand scaled. Demand did scale. But power prices became a political problem rather than a supply-demand equation. In January, the governors of all 13 states in the PJM grid region, along with the Trump administration, jointly declared that power prices needed to be reined in and that data centers should pay for the grid costs they create. The price ceiling on capacity auctions was then extended to 2030, taking the best-case scenario we had modeled for these companies off the table.

The fix is to separate the demand thesis from the price thesis. The demand thesis was right and remains right. The price thesis is now capped by regulators until 2030.

So we are restructuring the basket around companies that get paid on volume, not price. That means the companies selling the physical building blocks of the grid: gas turbines, transformers, switchgear, high-voltage cables, backup power, and cooling systems, along with the contractors who install all of it. A transformer sold to an Amazon data center faces no price ceiling, because voters don’t pay for it.

That’s it for now. Leave a comment if you have an idea/theme that you think we all should be looking at.

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Disclaimer: Market Sentiment work is provided for informational purposes only, is intended solely for readers in the United States, and should not be construed as legal, business, investment, or tax advice. You should always do your own research.

What’s more interesting is the shift in mainstream sentiment. It has moved from “AI investment is setting cash on fire” to “maybe these trillion-dollar companies aren’t spending for nothing.”

Take the chart below from the FT:

While estimates should always be taken with a bucket load of salt, it’s worth digging into what they are projecting:

1. The CapEx is expected to plateau (not drop) — Once you get past 2027, the CapEx is expected to stop growing at 70 to 80% a year that we are seeing now and settle into a maintenance plus refresh level.

2. Revenue will catch up quickly — If inference, cloud, and ad monetization compound while CapEx flattens, operating cash flow keeps climbing.

3. Finally, CapEx is paid upfront but counts against profits gradually in the form of depreciation over the life cycle of the asset. As the accumulated asset base grows, the depreciation add-back in the FCF bridge gets very large.

If this plays out and free cash flow explodes, the multiples will re-rate immediately. These names now carry a discount due to CapEx uncertainty. When quarterly prints start confirming that capex is flattening and cash is converting, the uncertainty discount will lift and the multiple will expand.

While all 3 are projections, we have some signs of revenue catching up. Bloomberg reported last week that AI revenue has now caught up with the quarterly CapEx depreciation. If the revenue trend holds while capex plateaus, it confirms that the trillions hyperscalers poured into AI capex were worth it.

Global AI sales, excluding China, reached $25 billion in the first quarter of 2026, exceeding the industry’s estimated $21 billion in depreciation costs tied to investments in data centers and chips for the second consecutive quarter. — Bloomberg

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That brings us to the MS portfolio. We are more or less in line with where we were at the beginning of last month, with the MS portfolio up 75% YTD. Memory continues to be our strongest performer, with SK Hynix, Samsung, and Micron all performing exceptionally well.

MU -6.25%↓ trades around $1,050, roughly 8–9x forward earnings — versus Nvidia’s ~30x trailing multiple, for a company that’s just as levered to AI capex. The gap exists because investors still expect memory prices to crash as they did before.

The Strategic Capacity Agreements (SCAs) are Micron’s direct answer to that skepticism: floor pricing through 2030 for a significant share of its supply, signed by the same hyper-scalers and automakers. We believe the market is still not pricing this long-term structural shift in memory demand and profitability for the next 3-5 years.

As we highlighted last week, we will continue to hold these names and expect them to rise further as the market digests the impact of long-term contracts and becomes less concerned about the cyclicality of memory stocks.

Update on Optics

Our Optics basket was a bet on AI data centers hitting the “Copper Wall”, a physical limit beyond which copper cables cannot transmit data fast enough over a given distance.

Existing high-speed copper cables can transmit data at 112 Gbps over a distance of 2 meters. The next generation of servers is projected to run at twice that speed, and at that point traditional passive copper will no longer reliably carry a signal across a server rack.

The basket was built intentionally to get exposure to both approaches being developed to overcome this problem:

1. Add signal boosters to existing copper data cables (Active Electrical Cables)

2. Convert the electrical signal into an optical signal, achieving near-perfect transition (Co-Packaged Optics – CPO)

Since its launch (Dec’25), our Optics basket is now up 72% vs 14% of QQQ. This outperformance was driven by AEC volumes scaling faster than planned: in the most recent quarter, Credo Technology Group reported ~150% YoY revenue growth and Astera Labs ~90%.

While AEC volume scaled, our optical bets also got several validating events:

• Nvidia announced a multibillion‑dollar purchase commitment for Co-Packaged Optics along with a $2B investment in Coherent.

• Arista Networks also launched XPO-MSA, a switch that uses removable optical connectors. It’s a stepping stone toward denser optical connections while the industry works toward fully integrated optics (CPO).

AEC companies benefit from mature products and are expected to grow by ~60%-80% through 2027, with a strong outlook through 2030. CPO companies are expected to grow ~30%-40% in 2027 as their initial product scales, with potential for better growth over the long run.

Post this strong rally, most portfolio companies in our basket are trading above 100 TTM P/E. Given this, we are updating the optics basket from Buy to Hold.

MS Portfolio Holdings Update

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

It’s always either peak optimism or peak pessimism in the AI world. Last month was relatively tame for AI, as all the attention was captured by the SpaceX IPO. Memory trade is still going strong, with Micron coming out with a monster quarter and SK Hynix filing the largest ADR listing in history.

What’s more interesting is the shift in mainstream sentiment. It has moved from “AI investment is setting cash on fire” to “maybe these trillion-dollar companies aren’t spending for nothing.”

Take the chart below from the FT:

While estimates should always be taken with a bucket load of salt, it’s worth digging into what they are projecting:

1. The CapEx is expected to plateau (not drop) — Once you get past 2027, the CapEx is expected to stop growing at 70 to 80% a year that we are seeing now and settle into a maintenance plus refresh level.

2. Revenue will catch up quickly — If inference, cloud, and ad monetization compound while CapEx flattens, operating cash flow keeps climbing.

3. Finally, CapEx is paid upfront but counts against profits gradually in the form of depreciation over the life cycle of the asset. As the accumulated asset base grows, the depreciation add-back in the FCF bridge gets very large.

If this plays out and free cash flow explodes, the multiples will re-rate immediately. These names now carry a discount due to CapEx uncertainty. When quarterly prints start confirming that capex is flattening and cash is converting, the uncertainty discount will lift and the multiple will expand.

While all 3 are projections, we have some signs of revenue catching up. Bloomberg reported last week that AI revenue has now caught up with the quarterly CapEx depreciation. If the revenue trend holds while capex plateaus, it confirms that the trillions hyperscalers poured into AI capex were worth it.

Global AI sales, excluding China, reached $25 billion in the first quarter of 2026, exceeding the industry’s estimated $21 billion in depreciation costs tied to investments in data centers and chips for the second consecutive quarter. — Bloomberg

That brings us to the MS portfolio. We are more or less in line with where we were at the beginning of last month, with the MS portfolio up 75% YTD. Memory continues to be our strongest performer, with SK Hynix, Samsung, and Micron all performing exceptionally well.

MU -6.25%↓ trades around $1,050, roughly 8–9x forward earnings — versus Nvidia’s ~30x trailing multiple, for a company that’s just as levered to AI capex. The gap exists because investors still expect memory prices to crash as they did before.

The Strategic Capacity Agreements (SCAs) are Micron’s direct answer to that skepticism: floor pricing through 2030 for a significant share of its supply, signed by the same hyper-scalers and automakers. We believe the market is still not pricing this long-term structural shift in memory demand and profitability for the next 3-5 years.

As we highlighted last week, we will continue to hold these names and expect them to rise further as the market digests the impact of long-term contracts and becomes less concerned about the cyclicality of memory stocks.

Update on Optics

Our Optics basket was a bet on AI data centers hitting the “Copper Wall”, a physical limit beyond which copper cables cannot transmit data fast enough over a given distance.

Existing high-speed copper cables can transmit data at 112 Gbps over a distance of 2 meters. The next generation of servers is projected to run at twice that speed, and at that point traditional passive copper will no longer reliably carry a signal across a server rack.

The basket was built intentionally to get exposure to both approaches being developed to overcome this problem:

1. Add signal boosters to existing copper data cables (Active Electrical Cables)

2. Convert the electrical signal into an optical signal, achieving near-perfect transition (Co-Packaged Optics – CPO)

Since its launch (Dec’25), our Optics basket is now up 72% vs 14% of QQQ. This outperformance was driven by AEC volumes scaling faster than planned: in the most recent quarter, Credo Technology Group reported ~150% YoY revenue growth and Astera Labs ~90%.

While AEC volume scaled, our optical bets also got several validating events:

• Nvidia announced a multibillion‑dollar purchase commitment for Co-Packaged Optics along with a $2B investment in Coherent.

• Arista Networks also launched XPO-MSA, a switch that uses removable optical connectors. It’s a stepping stone toward denser optical connections while the industry works toward fully integrated optics (CPO).

AEC companies benefit from mature products and are expected to grow by ~60%-80% through 2027, with a strong outlook through 2030. CPO companies are expected to grow ~30%-40% in 2027 as their initial product scales, with potential for better growth over the long run.

Post this strong rally, most portfolio companies in our basket are trading above 100 TTM P/E. Given this, we are updating the optics basket from Buy to Hold.

MS Portfolio Holdings Update

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No paywall on this one. Share it with a friend :)

This one’s a bit different from our usual reports. I just saw an incredible stat from the Nomura research team, and it felt like a good time to revisit our research on Leveraged ETFs from two years ago.

Leveraged ETFs have grown nearly tenfold in the past decade. AUM now sits at an all-time high — this surge is a key driver of the rising volatility we are seeing in markets. The more concerning part is what sits underneath it. Most of this money is retail chasing performance in semiconductors. Over $50B in leveraged ETF AUM is now concentrated in semiconductor funds.

Investors buy 2X and 3X leveraged ETFs expecting 2X and 3X returns. It rarely plays out that way. The risk of capital loss runs far higher than intuition suggests. Here is what happened to the 2X-leveraged MicroStrategy ETF, a fund with over $1B in AUM.

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To understand what’s going on, we have to first understand how leverage works in investing:

Let’s say that you had $10K in your brokerage account. You have a high-conviction stock you think is going up and want to invest more than $10K in it. You can take another $10K as a loan from your brokerage, staking your entire position as collateral.

Assume the stock went up 10% over the next month, and you exit the position.

This is the magic of leverage. You used a 2:1 leverage on your investment and doubled your return1

What’s important to realize is that this works the other way, too — If the stock went down 10% and you had to exit the position, the 2:1 leverage would literally double your losses.

Having a large amount of leverage is like driving a car with a dagger on the steering wheel pointed at your heart. If you do that, you will be a better driver. There will be fewer accidents but when they happen, they will be fatal - Warren Buffett

Understanding Leveraged ETFs

It’s vital to understand how leveraged ETFs work — class action lawsuits were filed (and later thrown out) because investors did not understand how they are structured.

For example, let’s take a quick look at the TQQQ fact sheet:

The fund is only trying to 3x the daily return of QQQ and not the long-term return.

To understand how the index gains and index volatility affect the total return, consider this — Let’s say you invested $100 into QQQ and TQQQ. On day 1, the index went up 10%. On day 2, the index went down 10%.

Here’s how both funds would have performed2

• Day 1:

  • Index (QQQ): 100 x (1 + 10%) = $110

  • 3x Leveraged Index (TQQQ): 100 x (1 + 3 x 10%) = $130

• Day 2:

  • Index (QQQ): 110 x (1 - 10%) = $99

  • 3x Leveraged Index (TQQQ): 130 x (1 - 3 x 10%) = $91

• Overall Return (after 2 days):

  • Index (QQQ): (99 - 100)/100 = -1%

  • 3x Leveraged Index (TQQQ): (91 - 100)/100 = -9%

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Leverage vs. Concentration

Investors prefer concentration over leverage when maximizing their expected return.

This means most of us will allocate larger and larger portions of our portfolios to stocks, expecting higher returns. In contrast, you would have gotten a better risk-adjusted return if you had a well-diversified portfolio and then leveraged that.

One key reason for increasing concentration rather than leverage was aversion to leverage. Investors believed that increasing concentration was the “natural or conventional” path to taking leverage, which felt speculative.

What’s the optimal leverage?

While there is no one right answer, leveraging 10x on the market rarely works out. That’s because if you are levered 10x and the market drops 10% (relatively common), you will be wiped out.

In this thought experiment by Elm Wealth, the impact of using leverage in the S&P 500 from 1927 to 2020 was evaluated.

Notice that 1.5x leverage leaves you with the most money at the end, but at some point, you’d have experienced a drawdown of 95%.

Above 1.5x leverage, return goes down and risk goes up, driven by the volatility drag we’ve been discussing.

At 4x leverage, after suffering more than a 99.99% drawdown, you’d eventually only be left with the $1 you started with (just $0.09 inflation-adjusted), and at 5x leverage you’d have been fully wiped out on October 19th, 1987.

Most studies recommend up to 2x leverage, and there are few good reasons to go above 3x.

TQQQ during the Dot-com bubble and the Global Financial Crisis

The biggest concern regarding leveraged ETFs is that most of them were not present during the dot-com bubble and the Global Financial Crisis.

For example, TQQQ was launched in 2010.

So what happens if we create a simulation to evaluate how a hypothetical TQQQ portfolio would have performed?

The 3x-leveraged QQQ would have been wiped out twice — once during the dot-com bubble crash and again during the global financial crisis.

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$100 invested in TQQQ at the end of 1999 would have grown ~10x by the top of the dot-com bubble in 2001. But in the crash that followed, the portfolio would have dropped to <$1. While in our simulation, the portfolio does make a comeback, we would argue that, given the transaction and borrowing costs (which we ignored here), a real fund would have been shut down.

This also corroborates our earlier research, which recommends that optimal leverage be <2. Let’s take another example of a real leveraged ETF — SSO (2x leveraged S&P 500), which was launched in 2006 (before the Global Financial Crisis).

$100 invested in SSO at its launch would drop to ~$20 during the Global Financial Crisis. While this drawdown was brutal, it was not a scenario so extreme that the fund had to shut down. During the bull market that followed, the SSO portfolio overtook the S&P 500 by 2014, and by 2025, your investment in SSO would be 2x that of the S&P 500.

Researchers at Vanderbilt University had similar findings.

When they ran 10,000 Monte Carlo Simulations on leveraged ETFs based on the S&P 500, they found that over a 20-year period, a 2x-levered ETF had only a 0.5% probability of shutting down, and a 3x-levered ETF had a 7.6% probability of shutting down.

It’s also interesting to note that if you were 2x short the S&P 500, you had a 94% chance of the fund shutting down over a 20-year investment horizon.

Should you be investing in leveraged ETFs now?

Not if you are looking at individual stocks and especially semiconductors. Not to be “that guy”, but our MS portfolio has nearly doubled YTD without touching leverage.

There is no reason to add additional risk by using 2x or 3x leverage on an already volatile name. That’s how you end up in situations like this:

Finally, you have to be aware that for leveraged ETFs to make money, it’s not enough for the market to go up; it should do so with relatively low volatility.

Volatility Drag

While most investors are worried about the fund shutting down, the real risks with leveraged ETFs lie in the volatility drag. Take our earlier example of the fund's +10% and -10% moves over 2 days. The 3x levered ETF declined 9% compared to the underlying index's 1% drop.

In extreme cases, even if the index is moving up, you will lose money due to volatility. From February 2020 to January 2023, QQQ was up 30.76%. But, TQQQ declined 11.37%. This was due to

1. ~60% drop due to the COVID-19 crash

2. +200% run-up during the everything bubble in 2021

3. Another 50% drawdown during the 2022 rate hikes.

This incredible amount of volatility caused the 3x levered index to move very differently compared to the underlying index.

Leverage — The secret behind Buffett’s returns

Once again, it all comes down to your skill as an investor. While leveraging and investing in speculative stocks rarely works out, you can make leverage work for you if you know what you are doing.

While Buffett eschews debt publicly, a large chunk of his returns can be attributed to the smart use of leverage.

Berkshire Hathaway has realized a Sharpe ratio of 0.76, higher than any other stock or mutual fund with a history of more than 30 years.

In the famous paper Buffett’s Alpha, researchers analyzed Buffett’s portfolio to understand how he was able to generate such a high risk-adjusted return. They found that Buffett used the cheap leverage available to him from his insurance float and invested it in high-quality value stocks to generate market-beating returns.

We estimate that Buffett’s leverage is about 1.6-to-1 on average.

Buffett’s returns appear to be neither luck nor magic, but, rather, reward for the use of leverage combined with a focus on cheap, safe, quality stocks.

The real alpha might be in realizing that very few of us have the patience and the skill of Buffett.

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Footnotes

2

Ignoring borrowing costs and transaction fees. Here is the exact mechanics of return compounding: (source)

• Quarterly revenue: $41.5B (up 340% yoy)

• Guidance for next Quarter: $50B (Estimate was $43B)

• 16 long-term contracts worth $100B+

Since the beginning of the year, we have consistently highlighted that memory has shifted from a cyclical product to a structural need for hyper-scalers, and Micron’s latest earnings prove it.

Why AI needs memory?

Reiterating our demand drivers from our memory bottleneck post,

• 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.

Nvidia’s own bill of materials tells the same story. The 2025 GB300 NVL72 rack carries about $374K of memory — roughly 9% of its ~$4M total cost. Just a year later, next-gen VR200 packs roughly 3x the memory capacity at roughly 2x the price per GB, pushing memory cost to ~$2M — a 435% jump that brings memory to ~26% of AI server cost.

New demand vectors

In the earnings call, Micron CEO Sanjay indicated memory demand for a robot is 10x that of an autonomous car. And these robots are expected to grow exponentially as the price per unit falls and they can tackle general-purpose tasks.

Powered by these tailwinds, we believe Memory will stay in a supercycle for years. That conviction is shared. Sixteen companies, including two hyperscalers, signed long-term agreements with Micron to lock in supply for three to five years. Traditional memory deals run two to three quarters at most — the shift reflects a long-term supply crunch.

These contracts cover advanced memory products and account for roughly 25% of Micron’s supply volume and 40% of its revenue at current prices. This is bullish for Micron as it secures prices and profit margins for the next three to five years.

The biggest issue with memory stocks was their cyclicality. Just 4 years ago, Micron’s gross margin went from the high 40s to near -31%, with Micron selling memory below cost due to oversupply. These long-term contracts prevent a similar downturn — The largest agreements carry a ceiling at the current CQ2 market price and a floor that holds through the term.

Even at the floor, Micron expects a gross margin well above its prior peak quarterly margin of roughly 61%, which puts the floor north of 65% on our estimate.

How are we positioning?

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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.

Market Sentiment is now fully reader-supported. A lot of work goes into these articles, and if you enjoyed this piece, PLEASE HIT THE LIKE BUTTON. It’s the only way new readers can discover our reports :)

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We are publishing this without a paywall.

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.

Market Sentiment dives deep into the bottlenecks of the AI world. Join 44,000 other investors to make sure you don’t miss our next briefing.

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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!)

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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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:

Market Sentiment dives deep into the bottlenecks of the AI world. Join 65,000 other investors to make sure you don’t miss our next briefing.

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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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