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

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

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

2. Optics +0.7% vs -6.6% QQQ

3. Energy +1.5% vs 0.6% SPY

MS Portfolio Holdings Update

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

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

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We are publishing this without a paywall.

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

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

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

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

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

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

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

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

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

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

Who did better?

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

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

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

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

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

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

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

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

• Micron: +6%

• Samsung Electronics: +30%

• SK Hynix: +24%

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

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!)

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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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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:

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

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

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

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

Enter SK group.

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

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

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

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

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

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

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Business Overview

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

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

The company operates in two major segments:

DRAM (Dynamic Random Access Memory)

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

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

NAND Memory

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

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

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

Capitalizing on AI-manufactured scarcity

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

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

Let’s dig in:

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

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

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

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

How much more?

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

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Advanced Packaging

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

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

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

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

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

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

Why now?

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

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

Building an Advanced Packaging Portfolio

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

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

Let’s dig in:

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

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

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

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

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

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

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

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

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

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

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

  

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

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

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

Advanced Packaging

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

Copper & Other Commodities

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

The U.S. Grid

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

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

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

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

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

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

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

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

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

Now contrast this against the exchanges:

New York Stock Exchange - 230 years old

London Stock Exchange - 220 years old

Toronto Stock Exchange - 170 years old

Bombay Stock Exchange - 150 years old

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

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

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

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

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

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

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

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

This leads to the more interesting question.

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

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

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

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

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

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

Green paper is not evergreen. Gold is.

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

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

Energy and optics portfolio

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

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

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

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

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

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

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

Buy the dip

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

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

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

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

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

Betting on betting

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

This brought us to the betting industry.

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

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

The magic of drawdowns

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

Then the market turned.

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

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

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

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

Simple Portfolio Series

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

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

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

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

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

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

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

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

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

Get 20% off forever

It wasn’t gold; it was canals.

At the time, Pennsylvania was the Silicon Valley of the world.

The state had embarked on a feverish building spree, constructing a massive network of canals and railways to compete with New York’s Erie Canal.

To fund this “bridge to the future,” the state borrowed aggressively, convinced that the tolls and future growth would easily cover the debt.

It was the ultimate “Buy Now, Build Later” scheme.

The state was borrowing to sustain an image of progress while the underlying math turned toxic.

The economy grew until it stuttered.

On February 1, 1842, Pennsylvania defaulted on its interest payments. The “wealthy” state was effectively broke.

The fallout was swift and brutal: credit markets slammed shut, the state was forced to halt all infrastructure projects mid-construction, and the reputation of American credit was so thoroughly trashed that even the U.S. Federal Government was later rejected for loans by European bankers.

Today, BNPL has made a return.

The only difference is that people are financing burrito bowls and the weekly grocery haul.

But the psychological trap is identical -

What is Buy Now Pay Later?

“Buy Now, Pay Later” (BNPL) was originally sold as a savvy tool for the tech-forward shopper, a way to snag a new pair of Nikes without the credit card interest.

The model is simple: split any purchase into four interest-free payments over six weeks.

However, in late 2025, the mask of “convenience” has slipped.

BNPL has morphed into a literal bridge to the next paycheck.

Almost 15% of Americans have used these services, with the highest usage among those who have the most to lose.

64% of Gen Z are now “split-paying” their lives, and the frequency is increasing: 11% of respondents report using these services at least six times!

Moreover, over 1 in 4 Americans report regretting using buy now, pay later (BNPL) after realizing the actual amount they owed (the debt trap).

Nearly 60% admit they’ve used BNPL to finance a purchase they couldn’t otherwise afford, and around 50% of young consumers prefer buy now, pay later over credit cards.

Additionally, according to Federal Reserve data, the share of BNPL users making late payments is rising. Nearly one-quarter of BNPL users (24%) have made a late payment.

The next logical question is, how much are Americans really spending on BNPL?

According to a January 2025 Consumer Financial Protection Bureau report, the average transaction amount for a BNPL loan was $142 in 2022, the most recent year for which data was available.

This may sound less on the surface. But when you look deeper, you realize that most BNPL users don’t just stop at one loan.

What’s more, is that 19% of BNPL users have lost track of their payments.

Millennials were the most likely to do so (25%), while Gen Z, despite having the highest late-payment rate, were least likely (13%).

This is very concerning, since it’s the equivalent of taking one loan to pay off another loan, without any lenders checking your eligibility prior.

It doesn’t take a lot to estimate where this sort of “convenience” practiced by the majority leads.

Why the Spigot is Opening

Why the surge? It isn’t a sudden love for apps.

It is a desperate response to an economy where the “bottom line” is hurting.

1. The Paycheck Bridge: 33% of users now explicitly state they use BNPL as a bridge to their next paycheck, up from 27% two years ago.

2. The Utility Crunch: As the costs of heating, electricity, and water continue to climb, households are redirecting cash to the “must-pays,” leaving a hole in the “daily-pays.”

3. The Grocery Shift: Perhaps most alarming, 25% of borrowers now use BNPL for groceries (up from 14% last year).

We are seeing an institutionalization of this “debt-for-food” lifestyle.

The recent partnership between DoorDash and Klarna allows consumers to finance a single meal over four installments.

When you are paying for a pizza over the course of six weeks, you aren’t “shopping” as much as you’re subsidizing a deficit.

The economy looks healthy at a 3-4% GDP growth rate, but that growth is being fueled by a ghost in the machine.

We are building a financial house of cards, and the wind is starting to pick up.

The “Shadow Debt” Trap

The real danger isn’t just the debt, but more the invisibility.

BNPL is what economists call Shadow Debt.

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We are publishing this without a paywall.

Here’s the most counterintuitive investing insight we found this year:

We created a Lindy portfolio in 2023, a basket of companies that were more than 100 years old as of 2000. The list included famous names such as General Electric (founded 1892), Pfizer (founded 1849), Johnson & Johnson (founded 1886), and many other household names.

ChatGPT was launched on 30th November 2022, which kick-started the AI wave and catalyzed the staggering performance of the “Magnificent 7”. How do you think a portfolio that was created 25 years ago, which only included companies that were older than 100 years, would perform in the age of AI?

Better than you expect!

From December 2022 to December 2025, the Lindy portfolio returned 83% compared to 81% of the S&P 500 — while having lower volatility!

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Nassim Taleb, in his 2012 book Antifragile, expanded on an interesting idea known as the Lindy Effect — If something has been around for a long time, then the probability that it will stick around for longer is higher.

This partially explains why people still read Shakespeare and Socrates but forget the bestsellers from the last decade.

If a book has been in print for forty years, I can expect it to be in print for another forty years. But, and that is the main difference, if it survives another decade, then it will be expected to be in print another fifty years.

“This, simply, as a rule, tells you why things that have been around for a long time are not “aging” like persons, but “aging” in reverse. Every year that passes without extinction doubles the additional life expectancy. This is an indicator of some robustness. The robustness of an item is proportional to its life!

For the perishable, every additional day in its life translates into a shorter additional life expectancy. For the nonperishable, every additional day may imply a longer life expectancy. — Nassim Taleb

Half of all companies fail within five years, and 80% in the first 20 years. However, some companies become outliers and survive for hundreds of years. So, in theory, the Lindy Effect applies to companies.

Take Coca-Cola, for example — The company was founded in 1892 and survived the Great Depression, two World Wars, and the 1980s Cola wars, just to name a few. Not only did the company survive all this, but it has also thrived and increased its dividends for the past 61 consecutive years. Based on the Lindy Effect, Coca-Cola has a greater likelihood of surviving to the next century than Google.

If you are a value investor, this is an important but often overlooked factor. Ultimately, the company valuation is based on the present value of future cash flow. The problem with the current valuation technique is that we always project that the company will survive in perpetuity.

This is why it becomes so hard to value companies in their growth stage. A classic example is that during the dot-com bubble, economist Burton Malkiel pointed out that at Cisco’s implied growth rate (Cisco was only 15 years old at that time), it would become larger than the entire U.S. economy within 20 years.

But once a company has matured, it will have an established brand, a loyal customer base, and a strong market position, making it easier to assign a fair value to the company. Buffett understands this intuitively, and it’s one of the key reasons why he only invests in established companies — The average age of the top 10 holdings of Berkshire is 117 years, with eight companies older than 100 years!

Performance of Lindy stocks

Survival is only half the story.

What we are particularly interested in is how legacy companies have performed over the long run. On the one hand, older firms will have better structures, more efficient processes, higher productivity, and an easier time attracting new talent. But, on the downside, older firms will have a negative base effect (they tend to be larger), lower sales growth, and tend to move slowly.

To test this, we are using an excellent dataset compiled by Boris Marhanovic, which consolidates all publicly traded companies in the U.S. that are more than 100 years old. There were 485 companies on the list, and after some data cleaning, we are left with 73 target companies that we can consider as Lindy stocks.

To give you a flavor of the type of companies that we are investing in, below are the top 10 companies from our list of Lindy stocks.

How did these companies perform over the long term?

$100 invested in Lindy stocks1 would have grown into $1,054 (954% return) compared to only $740 (640% return) if you had invested in the S&P 500. The cherry on top was that this outperformance was achieved with comparable portfolio volatility (20.5%) and max drawdown (61%) to that of the S&P 500.

As we highlighted at the outset, the portfolio created 25 years ago continues to outperform in the age of AI. This is incredible considering that none of the Mag-7 companies are in our portfolio.

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Endurance of Lindy stocks

Ultimately, we would argue that more than outperformance, the endurance and downside protection of these companies are incredible. Of the 73 companies we invested in 25 years ago, 72 are still trading in the market, and the only company that was acquired (Marathon Oil) delivered a 19x return to its investors.

Regarding downside protection, only six companies lost investors’ money over the long run, of which only one we will consider “bankrupt.”

Anyways, here are a few limitations that you should be aware of before trying to replicate this [Portfolio raw data and holdings are shared at the end of the sheet]

• Our portfolio is not rebalanced annually, nor is it updated every year (with the new companies that make it into the 100-year list)

• The analysis is prone to survivorship bias: because we started with companies that were surviving as of 2017, there may be other 100-year-old companies that died between 2000 and 2017.

• We believe a few, less objective quality checks, like the industry tailwind, management competence, etc., could have improved our returns considerably more.

Time is the friend of the wonderful company, the enemy of the mediocre. — Warren Buffett

It is often said that time is the best filter for quality, and the only way a company can survive more than a century is to be a wonderful company. Jeff Bezos was once asked how Amazon became so successful, and his response gives us an insight into what it takes to build companies that last for generations.

I very frequently get the question: ‘What’s going to change in the next 10 years?’ And that is a very interesting question; it’s a very common one.

I almost never get the question: ‘What’s not going to change in the next 10 years?

‘ And I submit to you that that second question is actually the more important of the two -- because you can build a business strategy around the things that are stable in time. In our retail business, we know that customers want low prices, and I know that’s going to be true 10 years from now. They want fast delivery; they want vast selection.

It’s impossible to imagine a future 10 years from now where a customer comes up and says, ‘Jeff I love Amazon; I just wish the prices were a little higher,’ [or] ‘I love Amazon; I just wish you’d deliver a little more slowly.’

Impossible. And so the effort we put into those things, spinning those things up, we know the energy we put into it today will still be paying off dividends for our customers 10 years from now.

When you have something that you know is true, even over the long term, you can afford to put a lot of energy into it.

Finally, if you consider that our lives and the world around us are changing fast, we will leave you with this remarkable passage by Nassim Taleb.

“Tonight I will be meeting friends in a restaurant (tavernas have existed for at least twenty-five centuries). I will be walking there wearing shoes hardly different from those worn fifty-three hundred years ago by the mummified man discovered in a glacier in the Austrian Alps.

At the restaurant, I will be using silverware, a Mesopotamian technology, which qualifies as a “killer application” given what it allows me to do to the leg of lamb, such as tear it apart while sparing my fingers from burns. I will be drinking wine, a liquid that has been in use for at least six millennia.

The wine will be poured into glasses, an innovation claimed by my Lebanese compatriots to come from their Phoenician ancestors, and if you disagree about the source, we can say that glass objects have been sold by them as trinkets for at least twenty-nine hundred years. After the main course, I will have a somewhat younger technology, artisanal cheese, paying higher prices for those that have not changed in their preparation for several centuries.”

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Data

1. List of public companies that are more than 100 years old — Here

2. Lindy portfolio with stock data — Here

That’s it for now. If you are here, please “♡ Like” this piece. It helps us massively!

This work is provided for informational purposes only and should not be construed as legal, business, investment, or tax advice. You should always do your own research

1

Lindy portfolio is market-cap weighted as of 2000 and then never rebalanced. For those curious, an equal weighted portfolio had even better performance.

The nascent internet was growing, and a statistic began circulating in boardrooms and investor pitches. A statistic that became gospel: “Internet traffic was doubling every three months.”

If you believed that curve, no amount of infrastructure you could build would ever be enough. The future belonged to whoever owned the pipes.

Triggered by this collective belief, a massive capital-intensive race began.

Companies like Global Crossing, WorldCom, and Qwest raised billions of dollars (much of it in debt) to lay down fiber optic cables around the planet.

They were laying hundreds of strands across the floor of the Atlantic and Pacific, drilling through mountains, and trenching alongside every major highway.

The spending was astronomical for the physical right-of-way to the future.

Investors cheered the CapEx.

Every dollar spent digging a trench was seen as a dollar creating future monopolistic value.

The stock prices soared based not on current profits, but on the sheer scale of their construction projects.

Then, around 2000, the music stopped.

The reality dawned that while internet traffic was growing fast, it wasn’t growing that fast. The “doubling every three months” metric was a myth.

Suddenly, the world realized it was awash in bandwidth.

There was so much redundant fiber in the ground that “dark fiber” (infrastructure sitting unused) became the industry standard.

Prices for data transmission collapsed by nearly 90%.

Global Crossing filed for what was then one of the largest bankruptcies in history.

Investors lost billions, leaving behind a technologically impressive, but financially disastrous, web connecting a demand that hadn’t arrived yet.

The Silicon Trap

Today, in late 2025, we are seeing a new, equally expensive race to lay the foundation of a different future.

But instead of glass cables under the ocean, we are pouring billions into silicon.

And the numbers make the telecom bubble look insignificant.

To understand the magnitude of what is happening right now, we first need to understand the reality of Capital Expenditure (CapEx).

CapEx is not just “spending money.”

In the tech world, it is the process of turning liquid cash into illiquid concrete, steel, and silicon.

When a company buys Nvidia’s H100 or Blackwell series GPU, that money is effectively gone.

It is locked into an asset that starts losing value the moment it is plugged in.

Historically, software companies hated CapEx.

The beauty of software was that you could write code once and sell it a million times with zero marginal cost.

But AI has broken that model.

AI is not software; it is industrial manufacturing. It requires factories (data centers) and raw materials (electricity and compute).

This shift has introduced a terrifying new variable to Big Tech balance sheets: The Depreciation Cliff.

When Google spends $10 billion on chips, it doesn’t expense it all at once.

They spread that cost over the “useful life” of the asset, typically 4 to 6 years for accounting purposes.

But here is the catch: in the AI arms race, the technology is advancing so rapidly that a chip purchased in 2023 is effectively obsolete by 2025.

Companies are depreciating these assets over six years on paper, but the assets are becoming useless in two years.

This creates a “profit air pocket” waiting in the future: a massive write-down of hundreds of billions of dollars in infrastructure that is still being paid for but is no longer competitive.

Oracle, for example, was a reliable cash-generating machine for the past decade, reliably generating $12-14 billion in free cash flow every year.

Then the mania hit.

Oracle decided to build the infrastructure that OpenAI couldn’t afford to build itself.

To achieve this, Oracle raised its CapEx forecast by $15 billion (to a total of $50 billion!) in a single year to build massive superclusters.

This is the price of admission in 2025.

The State of the Mania

As we close out 2025, the spending has detached from reality.

In 2024, the “Hyperscalers” (Amazon, Google, Microsoft, Meta) spent roughly $250 billion on CapEx. This year, that number has ballooned to nearly $400 billion.

To put that in perspective:

• Amazon alone is projecting ~$120 billion in spending.

• Google is nearing $93 billion.

• Microsoft and Meta are close to $88 billion and $72 billion, respectively.

The cumulative CapEx of these four companies since 2000 has just passed $1 trillion, with nearly half of that spent in the last 24 months.

Why? The revenue isn’t there yet. The entire AI software market generates roughly $50-60 billion in annual revenue.

The industry is effectively spending $400 billion a year to chase a $50 billion prize. To understand this better, read:

This is the Prisoner’s Dilemma of CapEx.

None of these CEOs is stupid.

They know the math doesn’t work in the short term. But they are locked in a game where the only rational move is irrational spending.

If Google spends $100 billion and AI is a bust, they lose money.

But if they don’t spend and AI becomes the future operating system of the world, they cease to exist. They are not spending to make a profit now, but to stay alive tomorrow.

Why This “Bubble” is Different

If the SaaS bubble bursts, it wipes out venture capitalists.

If the AI bubble bursts, something similar to the dot-com bubble happens, where only a handful of companies survive.

But if the AI CapEx bubble bursts, it poses a systemic risk to the pillars of the modern economy.

The S&P 500’s three-year, $30 trillion bull run has largely been driven by the world’s biggest tech companies like Google, Meta, and Microsoft.

Additionally, firms benefiting from spending on AI infrastructure, Nvidia and Broadcom Inc., and electricity providers such as Constellation Energy Corp., also drew huge gains from the AI momentum.

If they stop rising, the equity indexes will follow.

The danger lies in the concentration of risk, specifically surrounding OpenAI.

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Ferrari was the most dominant team in F1 from 2000-2004, having won 5 drivers’ and constructors’ championships in 5 consecutive years.

Under the technical leadership of Ross Brawn, the design genius of Rory Byrne, and the team management of Jean Todt, the team operated with a cold, terrifying efficiency.

At the center of it all was Michael Schumacher, a driver so dominant he made the sport boring.

So much so that 20 years later, the record for the most championships still belongs to Schumacher (tied by Lewis Hamilton in 2020)

Those years were the most glorious years for Ferrari in F1.

But in the boardroom in Maranello (Ferrari’s HQ), Chairman Luca Di Montezemolo was unhappy.

He didn’t necessarily hate all the winning; he was just unhappy with who was doing it.

By the end of 2006, he had successfully forced Schumacher into retirement.

The politics within the team had finally begun the institutional rot.

What followed was an exodus of the very top management that gave Ferrari its best years.

Ross Brawn retired with Schumacher, Byrne moved on to a consultant role with significantly less involvement, and Todt slowly exited his role from 2007-09.

The results seemed fine at first.

Ferrari won the championship in 2007 with Kimi Räikkönen. The car was still fast; the momentum of the “Dream Team” era was still pushing them forward.

But the institutional rot finally caught up with the performance.

Ferrari started making critical strategic errors while getting outdeveloped by rivals like Red Bull and Mercedes.

And even on days when everything else clicked, they simply couldn’t sustain that form for a full season.

It was like a massive game of whac-a-mole. Fix one thing, and something else would go wrong.

It has been 18 years since Ferrari’s last championship.

The most prestigious, well-funded brand in the world has spent nearly two decades watching more efficient, meritocratic teams drive past it.

All of this, because the leadership couldn’t do what’s best for the team.

Then and Now

As we analyze the economic roadmap of the second Trump administration, it is imperative that we first focus on the state of the economy.

While Trump has declared that the economy is the healthiest it’s ever been, it’s worth taking a look at what’s actually happening.

The Whac-a-mole Economy

Tariffs

In the first eight months of the second Trump administration, there have been so many tariff policy announcements that it is difficult to even keep track.

A good resource to track every tariff movement can be found here.

At present, average tariff rates are far higher than they were at the beginning of the second Trump administration, standing at about 17%, the highest since 1935.

For reference, the average tariff rates for the last 50 years have mostly remained under 4%!

The immediate rise of tariffs from the period of ‘mellow’ in the last 50 years has had significant effects on the U.S. middle class.

As of September, estimates of the cost for a typical household due to tariffs and the One Big Beautiful Bill Act (OBBBA) are in the thousands of dollars.

Only the top 10% of households are expected to walk away with a net positive.

The tariffs are established to be a net negative for a typical household in the U.S., but how does it affect the GDP of the country?

The tariffs aren’t a net positive in any way, shape, or form. In fact, tariffs are estimated to continue losing revenue as the years go by.

Here’s the estimated revenue effects of all tariffs this year, and a projection for the future:

The ‘Conventional’ accounting (assuming businesses and consumers will continue buying normally even after tariffs) promises a windfall.

Now look at the ‘Dynamic Effect.’

This measures the macroeconomic damage.

When taxes/tariffs go up, businesses invest less, consumers spend less, and the overall economy slows down.

This row quantifies how much revenue is lost due to that slowdown, and it is consistently negative.

Inflation and other economics

The erosion of the U.S. economy comes at a time of sticky inflation, uncertainty about growth, and an AI-fuelled capital spending boom.

Inflation remains a key uncertainty after the pandemic, constantly at ~3%.

Add to that the tariffs that inherently push the prices to the consumers, and you’ve got a crisis -

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Just two weeks after we published our deep dive into energy being the best AI trade, the portfolio is already up 5.2% outperforming the S&P 500, with Fluence Energy up 55%, Oklo up 20%, and NuScale Power up 14%.

While energy is a second-order impact of AI, we found a third-order impact while building our energy portfolio. As far as we can tell, we are the first to cover this trade in depth. This portfolio is taking a bet on one of the most profound hardware transformations occurring in computing right now.

But before we jump in, we have to understand how an AI data center rack works.

To put it simply, the storage server stores massive amounts of data that feed the AI compute nodes, where all calculations are performed. The power and cooling units are self-explanatory.

Our focus here is on the Top-of-Rack (ToR) Switch.

The ToR switch manages data traffic between the storage server and the AI compute nodes, as well as between server racks in a data center. With AI companies planning 1 GW data centers, even with the best AI training GPUs, each data center will have more than 10,000 of these server racks.

That’s why communication between and within these racks is crucial to maintaining efficiency and scaling effectively, which brings us to the Copper Wall.

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Copper Wall

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.

In the early 2010s, a standard passive copper cable could reliably transmit 10 Gbps up to 7 meters. But with exploding data density and AI workflows, we have now increased the speed to 112 Gbps, where traditional copper cables can only transmit up to 2 meters.

The upcoming generation of servers will operate at 224 Gbps, at which point the reach of passive copper cables drops to less than 1 meter. This 1-meter limit is the “Copper Wall.” A standard data center rack is roughly 2 meters tall (as highlighted in the pic). If a switch at the top of the rack cannot reliably send a signal to a server at the bottom of the rack using passive copper, the traditional architecture fails.

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.

Why now?

This once again comes back to AI.

The massive training and inference load requirements mean that GPUs are improving at a breakneck pace. For example, to properly utilize Nvidia’s latest Blackwell cluster, the switches must support 800 Gbps ports, which requires the underlying silicon to support 224 Gbps speed. The next generation of Nvidia’s chips is expected to double this speed!

But as with any transition, it will not be a step change but a gradual replacement of existing components. Here’s how we think it will play out over the next 3-4 years:

• 2025 - 2026 | At current speeds of 224 Gbps, passive copper cables are limited to <1 meter. The simple solution here is Active Electrical Cables (AECs), which use a chip to boost the signal halfway along the cable, extending the reach back to 2-3 meters.

• 2027 - 2028 | As we move to 1.6 Terabit speeds, even the AECs become too thick and inefficient. The only solution is to use Linear Drive Optics (LPO), which uses the GPU itself to handle the signal processing.

• 2029 onwards | The final step would be Co-Packaged Optics (CPO), in which the signal is converted to an optical signal directly on the chip. It achieves near-perfect transmission with almost zero electrical resistance losses, but it requires a complete redesign of server manufacturing.

The bigger players have already started making moves to prepare for this shift. Just five days back, Marvell Technology announced the acquisition of Celestial AI for $3.25 billion. Celestial AI is at the forefront of developing optical interconnect platforms. Even Nvidia is there, trying to fuse compute with NVLink, NVSwitch, and networking silicon to keep traffic inside its walled garden.

Companies like Corning Inc. , best known for its Gorilla Glass used in iPhones, are gaining momentum in optical communications as AI computing is exploding. Analysts expect a 39% YoY growth for Corning’s Optical Communication segment, which provides high-speed fiber connectivity in AI data centers.

Building an Optics Portfolio

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

The portfolio we have built is based on a balance of near-term revenue reliability and long-term moonshot potential. We have bucketed it into four segments, with no single position exceeding 20% of the portfolio allocation.

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

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We will start by focusing on Active Electrical Cables companies, which are experiencing a massive demand surge, followed by taking informed bets on companies with exposure to Linear Drive Optics (LPO) and Co-Packaged Optics (CPO).

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Upon his inauguration in 1969, Nixon inherited a recession from Lyndon Johnson who had spent generously on the social programs of the Great Society and the Vietnam War.

Despite some protests, Congress went along with Nixon and continued to fund the war and increase social welfare spending.

But President Nixon’s primary concern was not the U.S. dollar, or deficits, or even inflation. He feared another recession.

He and others who were running for re-election wanted the economy to boom. The way to do that, Nixon reasoned, was to pressure the Fed to lower interest rates.

Nixon fired Fed chair William McChesney Martin and installed presidential counselor Arthur Burns as his successor in early 1970.

In the winter of 1971, Fed Chair Arthur Burns made a decision that would haunt the American economy for a decade.

Under intense pressure from the White House to “juice” a slowing economy before the election, Burns cut interest rates.

He looked at a softening labor market and decided the war on inflation was effectively won. However, as Burns would come to realize, the cut was premature.

It felt like a victory at the time for the “soft landing” camp.

It wasn’t.

Inflation roared back aggressively, forcing the country into a punishing cycle of “stop-go” policy that eventually required a decade-long painful recession to fix.

This was also the period where the American economy entered ‘stagflation’.

Burns went down in history not as the man who saved the economy, but as the man who let the genie back out of the bottle.

History doesn’t repeat itself…

Fifty-four years later, Jerome Powell is staring down the exact same barrel.

As the Federal Reserve meets next week for the final time in 2025, the parallels are terrifyingly clear.

Inflation is sticky at 3%, stubbornly above the target, just as it was in the early 70s.

Unemployment is ticking up, just as it was then. And once again, a newly elected President is making his preferences for lower rates loudly known.

But Powell faces one danger that Burns did not: data, or the lack thereof.

Due to the recent government shutdown and the resulting data blackout, the Fed is about to make its most consequential decision of the post-pandemic era without a working instrument panel.

They are attempting to stick a soft landing while the runway lights are out.

Market Expectations

After lowering rates by 25 basis points in both September and October, Chair Jerome Powell has made it clear that another rate cut before the end of the year isn’t necessarily a given.

Yet, the market is clear on the direction it wants the Fed to take.

Traders are now pricing in an ~89% probability of a 25-basis-point cut next week.

However, this wouldn’t be a celebration cut; but a concern cut. The narrative has decisively shifted from “mission accomplished” to “prevention.”

The Fed essentially has two options:

The Fed’s Dilemma

The case for a cut

• The Labor Slide: The unemployment rate has drifted up to 4.4% (from 4.3%), crossing a threshold that often signals a non-linear deterioration in jobs.

• Anemic Growth: The Fed’s own projections (from the September SEP) forecast real GDP growth slowing to just 1.6% in 2025.

With growth barely above what’s normal, a rate cut acts as “recession insurance.”

Powell would likely rather cut early and risk slightly higher inflation than cut late and risk a full-blown employment crisis.

The Case for a Pause

Sticky Inflation: Core inflation remains stubbornly parked at 3%, a full percentage point above the Fed’s mandated target.

Easing financial conditions now could reignite the very fire they spent three years fighting.

Reducing inflation was the main goal of the quantitative tightening implemented in 2022, which has now ended as of December 1, 2025.

The Data Blackout: The strongest argument for a pause is the “fog.” Without the delayed October government data, easing policy is a gamble.

A pause would buy the Fed six weeks to see if the inflation is truly tamed or not.

The Fog of War: Navigating the Data Blackout

The recent government shutdown effectively cut off the access to information for the US economy.

For 43 days, the federal agencies responsible for measuring the country’s economic health, specifically the Bureau of Labor Statistics (BLS) and the Bureau of Economic Analysis (BEA), were largely empty.

Apart from several other implications, this also meant that essential data for federal policies was never collected.

The “Blackout” Mechanism Economic reports like the Consumer Price Index (CPI) or the monthly jobs report rely on thousands of human interactions.

Field agents physically visit stores to check prices, and call businesses to verify payrolls.

When the government shut down in October, those surveys never happened.

The Fed crucially operates on a principle of “data dependence.”

They adjust interest rates based on whether inflation is cooling or the job market is cracking.

Typically, they would have a clear picture of October and November to guide them. Instead, they are missing critical pieces of the puzzle:

• Inflation (CPI/PCE): Without the October data, the Fed can’t be certain if the recent trend of cooling inflation has stalled or continued.

• Employment: The October jobs report was cancelled. The Fed is effectively missing a month of insight into whether high interest rates are causing layoffs.

They are currently relying on “proxy” data from private payroll companies (like ADP) and anecdotal evidence, but these lack the precision of the official government numbers.

For instance, payroll processor ADP reported 42,000 new jobs added in October, exceeding both economists’ and Dow Jones’ expectations.

Yet data from Revelio Labs showed a loss of 9,100 jobs and a 37% surge in layoffs.

Challenger, Gray & Christmas revealed an even bleaker picture with job cuts spiking 183% in October to the highest monthly level in 22 years.

The risk of inconsistent data is policy error. Cutting rates too late because they didn’t see the job market crashing, or cutting too early because they missed a hidden spike in inflation.

The Search for the “Magic Number”

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This report’s a bit different than our usual ones. We are starting to cover macroeconomic news, given its increasing influence on stock performance. Let us know what you think at the end, either by voting on the poll or leaving a comment!

The Genius and the madness of people

In 1711, one of the earliest shell companies was formed - The South Sea Company.

It was marketed as a trading company with a monopoly on trade with Spanish South America.

But in reality, the trade routes were largely blocked by war. The company sent very few ships and made almost no profit from trade.

Behind the scenes, its real purpose was to take on British government debt and pay it off with the proceeds of a stock offering.

The strategy worked.

The demand for the company’s stock soared on expectations of strong profits, although none ever materialized.

In 1720, the bubble was at its peak.

Sir Isaac Newton, who was the Master of the Mint (chief officer responsible for coining money) at the time, had also invested £7,000 in the South Sea Co.

That’s $1.4M in today’s money.

The stock rose from £128 in January to £1,050 in June.

Wisely, he realized the exuberance of the market and sold his shares during the spring of 1720.

It turned out, however, that like many around him, even Newton couldn’t sit idle while everyone around him made vast profits.

He had reinvested in the stock near its high, and towards the end, lost upwards of £20,000! ($4.3 million today)

By September, the bubble had burst, and the stock was down to £200, off 80% of its high.

When asked about the direction of the market, he replied:

Then and Now

That brings us to today.

Today, we’re surrounded by AI - a technology built on the very calculus pioneered by Newton.

But, just as in 1711, reality is being overshadowed by mania.

The S&P 500 has hit 37 All-Time Highs this year alone, with AI stocks as the primary drivers.

Moreover, if you remove AI from the equation, the US GDP grew only 0.1% in H1 2025.

The final nail in the coffin is the valuation of AI companies.

Arguably, the biggest name in AI right now is ChatGPT, which is developed by OpenAI.

OpenAI was valued at $500B in its most recent funding round, despite generating just $4.3Bn in revenue during the first half of 2025. Their goal is to generate $13.5Bn in revenue in 2025.

For comparison, Google is currently valued at $3.8T - with $400Bn in annualized revenue.

Google generates OpenAI’s annual revenue goal roughly every 12 days.

Yet Google is valued at roughly 10x its sales, while OpenAI is valued at ~50x!

The market has already written a trillion-dollar check. Can AI actually cash it?

A View Behind the Curtain

Costs of running AI

The economic reality of AI is structurally different from the software businesses we are used to.

In the SaaS (Software as a Service) boom of the 2010s, companies enjoyed 85%+ gross margins because software is “write once, sell infinitely.”

AI breaks this rule. It introduces a physical cost to every digital interaction.

The most important one today is inference.

A single ChatGPT query consumes 10x -15x more energy than a traditional Google search:

While a Google search costs the company roughly $0.00003, a Generative AI response can cost upwards of $0.01 - $0.02. That’s a 500x jump in operating costs.

Then comes the training cost -

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To everyone who reads this newsletter, has subscribed, or has simply shared a post with a friend or colleague, thank you.

Given that we have been doing this for a while now and have many new readers, here are 6 of the best reports we have published over the last 5 years!

We went long gold in February 2025.

The thesis was simple — Central banks were scooping up gold at record levels, and escalating geopolitical tensions should be the perfect tailwind for gold to act as a safe-haven asset.

Gold went on a tear, up 40% since we published our report.

We went long GLP-1 in July 2025.

Once again, the thesis was simple. GLP-1 drugs were set to generate nearly double the U.S. revenue of the iPhone’s first 10 years, and two companies had a monopoly on the market. After just 5 months, here is the performance of our top 4 holdings from the GLP-1 basket.

• Eli Lilly is up 44%

• Novo Nordisk is down 24%

• Amgen is up 16% and McKesson is up 25%

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.

Do you know how he achieved it? By using leverage!

Even though Buffett eschews leverage, when researchers analyzed Buffett’s portfolio, they found that he used the cheap leverage available to him from his insurance float and invested it in high-quality value stocks to generate market-beating returns.

Many investors are now very comfortable holding a 100% equity portfolio.

But consider a Japanese worker who started working in 1980.

Assuming a 30-year career & $1K annual investment into Nikkei 225, by the time he retired in 2010, the $30K invested would have turned into $20K.

A -31% return over 30 years.

An often underrated strategy is buying individual companies during periods of market distress. While buying the dip is almost a reflex for us, we often shy away from buying the underlying companies that are in distress.

However, doing this meticulously can yield exceptional returns.

Nassim Taleb, in his 2012 book Antifragile, expanded on an interesting idea known as the Lindy Effect — If something has been around for a long time, then the probability that it will stick around for longer is higher.

What if you apply the same principle to companies?

$100 invested in Lindy stocks would have grown into $776 (676% return) compared to only $486 (386% return) if you had invested in the S&P 500

p.s. We will be doing an update to this portfolio by the end of the year :)

Finally, most, if not all, of our revenue comes from reader subscriptions like yours. It helps us be objective and maintain an unbiased opinion.

Please consider upgrading your subscription to support our work.

It’s often said that you get the customers you deserve. If that’s true, we consider the quality of our readers to be the best compliment we could hope for!

Thanks for all the support. Which one was your favorite?

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The greatest shortcoming of the human race is our inability to understand the exponential function — Albert Bartlett

In the late 1980s and 90s, Japan offered 100-year ‘multi-generational’ housing mortgages. The idea was to pass on homes like ancestral property from grandparents to grandchildren - along with the mortgage, of course. By stretching loan payments over a century, this financial innovation was intended to increase affordability for homebuyers.

Did it?

Well, no. A 1995 study found that 100-year mortgages did little to increase home affordability or reduce monthly payments. Instead, they became an ‘estate planning tool’ deployed by the affluent Japanese to reduce inheritance taxes. In 1990, the Japanese asset market bubble burst, marking the onset of the Lost Decade - a prolonged period of economic stagnation. Once asset prices collapsed, people suddenly owed far more on their mortgages than their homes were worth. Borrowers were left drowning in negative equity.

The New York Times reported about a Tokyo-based bureaucrat who bought a four-room apartment in the suburb for $400,000 during the bubble. By 2005 (nearly two decades later), the apartment’s value had fallen to $200,000 while he still owed the bank $300,000.

Fast forward to today, policymakers in the United States are weighing on a 50-year mortgage.

Last week, President Trump floated this idea, in contrast to the 30-year mortgages championed by Franklin D Roosevelt after the Great Depression of 1929. Even 30-year mortgages are a unique feature of the US economy, with their secondary market (for mortgage-backed securities) a major reason for their existence.

So does a 50-year mortgage really make a house more affordable?

It’s quite intuitive that the longer you stretch the loan payment, the smaller the monthly payments you make. But you also increase your interest burden — the lender is taking on duration risk by lending to you for a longer period. And at some point, the interest burden outweighs the payment reduction due to a longer horizon - at which point it stops being ‘more affordable’ than the smaller tenure loan.

Let’s put things in perspective. Suppose you take out a mortgage for a $500,000 mortgage with 20% down payment. Assume the interest rate per annum on the 15-year tenure is 6%, and that on the 30-year tenure is 6.25%.1

You can stretch your loan payments across -

• 15 years — in which case you will make monthly payments of $3,375 (with 34% of the total amount you pay being the interest component)

  

• Or 30 years, in which case you will make monthly payments of $2,462 (with 55% of the total amount you pay being the interest component)

  

When you go from a 15-year mortgage to a 30-year mortgage, affordability increases by 34%.

Extending the same argument to a 50-year loan (assuming it carries a 6.75% p.a. interest2) — it should make the house even more affordable to buyers who were previously priced out of the market, right?

If you go with the 30-year option instead of the 15-year option, you save $912 per year in monthly installments (for 15 years). And if you choose a 50-year mortgage over a 30-year mortgage, you save about $132 every month.

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While the $912 savings switching from 15 to 30 may compound and grow large enough to cover the total interest difference between these tenures, the $132 savings compounded when you go from 30 to 50 don’t grow enough to cover the $517K total interest differential between these tenures.

In fact, beyond a threshold (which appears to be around 40 years), there is only a marginal reduction in the monthly installments. After that, you may stretch the loan over a century if you like, but it won’t make it any more affordable.

While going from a 15-year mortgage to a 30-year mortgage substantially improves affordability, going from a 30-year mortgage to a 50-year mortgage does not.

Why does the 50-year tenure stop being any more affordable than the 30-year tenure? And can we invest the difference in the monthly payments into the market to make up for the interest differential?

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