I wasn’t going to write this today. I was saving Celestica [] for the Weekly. Then Tuesday and Wednesday happened, and I decided the point was worth making as the entire AI space is melting.

Here is the point. The fundamentals of this business are strong, and they are getting stronger. What moved prices this week was sentiment. Feelings about China. Feelings about who is funding the AI build. Feelings about a memory-chip listing in Shanghai. None of it touched a single line of Celestica’s income statement, and yet it took 10.8% out of the Kospi and 15% out of SK hynix in a session. When feeling starts doing the work that thinking is supposed to do, Mr. Market becomes irrational.

Celestica’s Q2 continued the exact trend:

It beat on the top line (3rd quarter in a row)…

… beat on the bottom line (has beaten each consensus estimate since 2022)…

… and raised the year.

What it did not do was follow the script the market wrote in January and again in April. After Q4 2025 the shares fell 15%.

After Q1 2026 they fell 13.5%.

I wrote both of those up and called both reactions wrong. This time the shares went up. Up 8% on Tuesday…

…while almost everything else with an AI label attached to it was being taken apart on that day.

That is the first time in three quarters that the market has agreed with the print on the day. It happened on one of the ugliest days the memory space has seen this year. And as I am writing this on Wednesday, the market is continuing to melt.

I think that is worth twenty minutes of your time.

Table of Contents

• Position and disclosure

• TLDR

• How CLS ranks

• What the market was actually afraid of on Tuesday

• Q2 2026: what the print actually said

• Guidance: what changed in 90 days

• Thesis scorecard after Q2 2026

• Valuation: why the target stays at $575

• Risk register

• Verdict

Position and disclosure

I have held CLS since 2023.

And when I started this newsletter, it was the first position I added. And in April 2025, I published the deep dive calling it the most important AI stock most people had never heard of.

It is one of the largest positions in the portfolio at 8.3% weight, and one of the largest contributors to portfolio performance since inception.

The tracker screenshots show the TSX-listed line in Canadian dollars, which is where I hold it. All of the analysis below is in US dollars, where the stock trades at $342 against my $575 target. The difference between the two readings is the exchange rate.

TLDR

• Q2 revenue of $4.70B (+62% y/y) and adjusted EPS of $2.54 (+83% y/y) both came in above the high end of guidance. Adjusted operating margin printed 8.2%, another record.

• Management raised the 2026 outlook for the third time this year: revenue $19.0B to $20.5B, adjusted EPS $10.15 to $11.30, operating margin 8.1% to 8.4%, and FCF $500M to $600M. The FCF guide had been stuck at $500M since October and finally moved.

• The 2027 statement is the important one. Revenue growth in 2027 is now expected to accelerate past the 65% guided for 2026, with adjusted EPS growing faster than revenue. That translates to a 2027 revenue base north of $33B and adjusted EPS north of $18.65.

• The mystery digital-native customer is OpenAI. Celestica is building custom racks alongside Broadcom [] for OpenAI’s accelerator roadmap, with mass production in 2027. Add the AMD [] Helios scale-up switch, where Celestica sits on every rack programme, and there are two multi-billion-dollar 2027 opportunities that did not exist in the original thesis.

• Customer concentration improved for the first time in a year. The top three went from 35% / 15% / 15% to 32% / 17% / 14%. The largest customer came down 3 points.

• Five of six thesis pillars confirmed and strengthening. The sixth, capital returns, is still evolving as capex absorbs the cash.

• Keeping BUY. Target stays at $575. I explain below why holding the target flat after a guidance raise of this size is the conservative choice.

How CLS ranks

Before I get into the details, here is the quantitative read from RankedStocks.com.

CLS carries an RS score of 72, which puts it in the Strong band and in the 75th percentile globally (the universe is still small, the US, Canada and the UK which I just included), the 76th in the US, and the 74th within its sector. The factor breakdown tells you almost the entire story of this article in five bars.

Growth 90. No argument. Revenue +62%, EPS +83%, and a company guiding to 65% growth for the full year. This is as clean a 90 as the system produces.

Outlook 85. A raised guide, a 2027 that accelerates, and a demand pipeline the company describes as record.

Profitability 75. Record operating margin, adjusted ROIC of 55.5%, and 0.5x leverage. The score is held below the top band by the trailing history, because as recently as 2023 this was a 6% EBITDA-margin contract manufacturer and the trailing window still carries that. Give it four more quarters.

Sentiment 53. This is the score that matters this week. Sentiment sits in the neutral band while growth sits at 90. That gap is the opportunity. When a business scoring 90 on growth and 85 on outlook carries a 53 on sentiment, you are being handed something the market has not yet decided to love.

Valuation 20. CLS is not cheap on trailing numbers. At $342 the stock trades on 30x this year’s guided adjusted EPS. The valuation case rests entirely on 2027 and 2028 earnings power, which I walk through below. If you need a low trailing multiple to sleep at night, this is not your stock.

What the market was actually afraid of on Tuesday

Tuesday was a rout, and it had four distinct fears stacked on top of each other. Japan’s Nikkei 225 fell 3.95%. South Korea’s Kospi fell 10.84%. SK hynix dropped 15%, Samsung Electronics 13%, Kioxia 18.3%, Tokyo Electron 11%. In the US, , , , , , , , and fell more than 10%.

Celestica rose.

Let me take the four fears one at a time, because three of them do not transmit to this business the way Mr. Market assumes, and one of them genuinely does.

Fear #1: China is building its own DUV lithography

Reports that Chinese companies are developing domestic deep ultraviolet lithography systems reignited the worry that China is closing the equipment gap. That is why the equipment names were dragged down alongside the memory makers. The logic runs: domestic DUV means Chinese memory makers can expand capacity without Western tools, which means more DRAM supply, which means lower memory prices.

Counterpoint. The premise is weaker than the reaction to it. China has had access to DUV lithography for years. Building the machines domestically changes who makes the tool, not what a Chinese fab can print with it. The constraint that actually separates Micron [], Samsung and SK hynix from CXMT is EUV.

Lithography prints a chip’s pattern onto silicon using light, and the wavelength of that light sets how small the features can be. Deep ultraviolet works down to a point. Below that point you need extreme ultraviolet, a much shorter wavelength that resolves finer features. ASML is the only company on earth that builds EUV machines, and export controls keep them out of China. A Chinese fab can still make advanced memory on DUV, but it has to expose the same wafer several times to get there, and every extra pass costs money and yield. Domestic DUV does not close that gap. It means China buys the same class of machine at home instead of importing it.

Now bring it back to Celestica. CLS procures substantially all of the materials and components used in its products on behalf of its customers, which makes memory a cost line for this business rather than a revenue line. Cheaper DRAM shrinks the bill of materials Celestica has to fund and frees the working capital tied up in it. More than that, memory is one of the specific components the company has had to sign supply agreements to secure, and component availability has been the binding constraint on its own guidance all year. Management said in plain terms that demand exceeds the revenue figures being guided, and that the limiter is material. A world with more memory capacity is a world where Celestica converts more of its order book into revenue.

The tape sold the whole neighbourhood on news that relieves this company’s supply chain.

Fear #2: CXMT’s debut and DRAM oversupply

ChangXin Memory Technologies listed on Shanghai’s STAR Market on Monday, raised $8.6B, and the shares surged 466% on debut to a $489B valuation. The read-across was immediate: a well-capitalised Chinese DRAM champion means oversupply and weaker pricing across the memory industry.

Counterpoint. Two layers here. First, even the bearish desks conceded in the same breath that CXMT is a rising competitor in commodity DRAM and is likely years behind the Korean players in high-bandwidth memory, which is the part of memory that AI infrastructure actually consumes. Second, and more to the point for this thesis, Celestica’s AI exposure sits in 800G and 1.6T Ethernet switching, in co-packaged optics, and in rack-scale compute integration. Communications was 56% of Q2 revenue and Enterprise 25%. The obvious objection is that Enterprise is servers and storage, which is where the DRAM goes. The answer is that the vast majority of Celestica’s compute programmes are consigned, meaning the customer supplies the silicon and it never enters Celestica’s revenue or cost lines. On the networking side, the silicon is included, and it is switching ASICs and optics rather than memory.

There is a real second-order concern. On turnkey programmes, where Celestica buys the silicon and it flows through revenue, falling component prices mechanically reduce revenue dollars per unit shipped. It compresses the revenue line without touching the profit dollars, because Celestica earns a spread on value added rather than a margin on the bill of materials. If anything it improves the reported margin percentage.

On timing, which matters more than the headline. CXMT is running toward 350,000 DRAM wafer starts a month by the end of 2026, close to Micron’s roughly 375,000, then 420,000 in 2027 and 500,000 by the end of 2028. That would take it to something like 17% of global DRAM supply from 11% in 2025. Its Shanghai equipment installs run through the second half of 2026 with production starting in 2027. On HBM, the part of memory AI infrastructure actually consumes, its capacity is a fraction of Samsung’s and SK hynix’s and does not scale meaningfully until 2027 and 2028. There is also a distribution problem: CXMT’s output goes mostly into Chinese PCs and handsets, and selling it into Western data centres would invite protectionism and questions about how independent the intellectual property is.

That timeline cuts two ways for this portfolio. Cheaper memory helps Celestica. It does not help Micron, which I also own. But the pressure is in commodity DRAM and it is not what is happening today. Third-quarter DRAM contracts settled 20% to 30% higher this month. There is not enough memory in the world right now: Intel has said it cannot meet demand, and Apple has raised consumer prices because of the squeeze. None of that describes a market about to be flooded.

Fear #3: AI infrastructure financing and circular deals

Hyperscaler AI capex is approaching $700B and an increasing share is debt-funded. The top five hyperscalers are expected to move from $175B of annual bond issuance in 2026 toward $300B. Investor appetite is visibly thinning: cover ratios on those deals have fallen from nearly five times in February to below two times in July…

… and Amazon had to pay up to clear a $25B issue. The Bank for International Settlements named circular financing, where a chipmaker takes equity in an AI lab which then commits to buying that chipmaker’s silicon, as one of the three biggest risks to global financial stability. On Tuesday, AMD fell 10% on a data-centre capacity deal and Nvidia [] fell 2% on a $19.6B lease commitment. The market is repricing the financing.

Counterpoint. The question that matters for a Celestica holder is where it sits in the capital stack when the financing stretches too far. On the evidence of this quarter, the answer is unusually well protected.

• Celestica extends no vendor financing. Management was explicit that it will not fund customers’ growth, and the working capital arrangements run the other way: $481M of customer cash deposits sit on the balance sheet to absorb the cash impact of inventory purchases and the risk of obsolescence.

• The order book is contractually anchored. Customers have placed non-cancellable, non-refundable orders for long-lead silicon. In some cases, binding forecasts run 12 to 18 months out. Customers cover specialised equipment and certain non-recurring expenses, with Celestica covering the majority of the rest.

• The balance sheet carries little of the financing risk. Net debt of $204M against $536M of cash and $740M of term loans, leverage of 0.5x, and $2.3B of available liquidity. This is a company growing revenue 62% with almost no financial leverage, though the trade credit discussed below is a real dependency.

• The working capital build is funded by suppliers and customers rather than by shareholders. Days in payables extended from 54 a year ago to 73. Cash cycle days improved from 66 to 47.

The 19-day improvement is almost entirely payables extension. Days in receivables improved 7 and days in inventory were flat year on year, and six days worse sequentially. So the cash cycle is not proof that product is moving. It is proof that Celestica has the standing to make its suppliers wait, which is its own kind of evidence, because a company the supply chain is worried about does not get better terms. The case that the inventory is real rests on the awarded-programme disclosure and the customer deposits.

The honest version of this risk is a 2028 problem rather than a 2026 one. If hyperscaler financing genuinely seizes, orders eventually slow and Celestica’s revenue follows with a lag equal to the length of its book. The book currently runs into 2028.

Fear #4: Chinese competition in AI hardware

Chinese companies are climbing the technology stack and will compete away Western hardware margins.

Counterpoint. Celestica does not compete with Chinese firms and it does not sell to them, so a Chinese company climbing the technology stack only hurts Celestica if it can take Celestica’s customers. Its competitive set is Taiwanese and American: Wiwynn, Quanta, Foxconn, Jabil [] and Flex []. Its customers are US hyperscalers and OpenAI, plus AMD as a design partner whose racks are bought by third parties.

The reason a Chinese ODM cannot take those customers is the silicon. Every programme in this order book is built on export-controlled US parts: Broadcom Tomahawk switching, AMD accelerators, custom hyperscaler ASICs. A Chinese manufacturer cannot buy that silicon. And if the rules changed tomorrow, a US hyperscaler qualifying a new partner for a rack-scale AI programme is a multi-year exercise measured in design cycles rather than quarters. Chinese competition is a genuine threat to companies selling commodity hardware into open markets. Celestica sells engineering into closed ones.

What actually happened to Celestica’s competitive position this quarter is the opposite of the fear. Programmes that had been dual-sourced are being consolidated back to Celestica, because in a constrained supply environment customers value continuity and surety of supply. The company has the material already pipelined, which means it does not have to rejoin the queue. Its design engineering organisation reaches close to 2,000 engineers by the end of this year, from 1,350 in April. Design plus manufacturing under one roof is the reason share is moving toward them.

Q2 2026: what the print actually said

Revenue grew 62% and landed above the top of the guide. Adjusted EPS grew 83% and cleared the top of the guide as well. Adjusted operating margin of 8.2% set another record, up 80bps. GAAP EPS was $3.17 against $1.82. Adjusted ROIC reached 55.5%, twenty percentage points better than a year ago.

That is four consecutive quarters of clearing the high end of the company’s own EPS guidance. As I mentioned in a previous article, management is good at providing conservative estimates and then beating them.

The one soft number is adjusted gross margin at 11.5%, down 20bps y/y on CCS mix, with management expecting to hold the mid-11s through 2026. I am relaxed about this because the operating margin is where the leverage shows up, and it went the right way by 80bps. Celestica earns a spread.

Segments

CCS did the work and the mix kept improving. Revenue grew 84% and now represents 81% of the company, with segment margin up 40bps to 8.7% and segment income up 93%.

Inside CCS, both end markets beat their own outlook by wide margins. Communications grew 62% against a guide of 50%, driven by 800G switching with 400G proving more durable than expected. Enterprise grew 167% against a guide of approximately 130%, on an accelerating AI/ML compute ramp with a hyperscaler and better storage demand. Recall that in Q1 the Enterprise line was capped by a component shortage that management said was a supply issue rather than a demand issue. It has now caught up, exactly as they said it would.

HPS revenue was up 58%, and 41% of the company. This is the design-led business at the centre of the original thesis.

ATS is finally pulling its weight. Revenue grew 8% against a mid-single-digit guide, with every ATS business contributing, and segment margin of 6.3% is the highest in the segment’s history, up 100bps on operating leverage and richer engineering-driven mix. Capital equipment is the driver of the second-half acceleration, riding a wafer fab equipment upturn that management expects to run into 2027.

Cash, capex and the balance sheet

FCF was $147M in the quarter and $285M year to date. Capex stepped up to $264M, or 5.6% of revenue, against 1.1% a year ago. The full-year capex guide holds at approximately $1B, with $1.5B still the placeholder for 2027 and a firm number coming in October.

The capacity is going into Thailand, Japan, and two Texas sites at Richardson and Fort Worth. Customers are asking for an equal amount of growth in Asia and the US, which is a useful detail. This is demand-led capex tied to specific awarded programmes rather than a speculative build.

Inventory rose 27% sequentially and 77% y/y, which will trigger alarms for anyone reading it in isolation. Read it with the rest of the table instead. Days in payables went from 54 to 73. Customer cash deposits for inventory sit at $481M. Cash cycle days came down from 66 to 47, and from 55 last quarter. The inventory is pre-positioned for programmes that are already awarded, and it is being financed by the supply chain and the customers rather than by Celestica.

Net debt of $204M. Leverage of 0.5x, improved from 0.9x a year ago. Liquidity of $2.3B with nothing drawn on the revolver. No shares were repurchased in the quarter, which is the trade-off I flagged in April: capital is going into capacity rather than into the share count while the order book justifies it.

One thing belongs in the same paragraph as that 0.5x. Accounts payable went from $1.87B at the end of December to $3.82B at the end of June, and days in payables extended from 54 a year ago to 73. That is trade credit rather than debt. It carries no coupon and no covenant, and it is also a funding dependency that reverses quickly if supplier terms tighten.

Guidance: what changed in 90 days

Three raises in six months, against a plan first published in October. The 2026 revenue guide is 28% higher than the $16.0B it started at and the EPS guide is 38% higher. EPS is raised faster than revenue at every step, which is the operating leverage thesis.

In April I flagged FCF as the single pillar where the analytical picture had shifted, because the guide sat at $500M while the revenue plan kept moving up, which took FCF conversion down to 2.6% of revenue. Management has now raised FCF to $600M while holding capex at $1B and raising revenue again. Conversion of 2.9% is still thin, and it moved in the right direction for the first time this cycle.

Q3 guidance is revenue of $5.25B to $5.55B, up 69% at the midpoint, and adjusted EPS of $2.88 to $3.08, up 89%. Communications is guided to approximately 60% growth with the first 1.6T mass production ramps beginning, Enterprise to approximately 190%, and ATS to the mid-teens.

The 2027 statement

In April, management put a 2027 revenue floor of approximately $25.5B on the table against a $19.0B base for 2026. That was 34% growth, and I treated it as the single most important disclosure of that quarter.

This quarter they replaced it with something stronger and less specific. 2027 revenue growth is now expected to accelerate relative to the 65% growth guided for 2026, and adjusted EPS is expected to grow faster than revenue.

Do the arithmetic. 65% on a $20.5B base is $33.8B, and the company is telling you 2027 comes in above that. On EPS, faster than 65% growth on $11.30 puts 2027 adjusted EPS above $18.65. The full 2027 outlook lands in October.

Three drivers were named for the acceleration. 800G volumes keep scaling with 400G proving unexpectedly resilient. The 1.6T ramps surge, with 10 active programmes and more awards expected. And two rack-scale programmes that did not exist in the thesis a year ago start contributing.

The customer reveal

The digital-native customer Celestica has been building custom racks for is OpenAI. Celestica will work with OpenAI and Broadcom on their multi-generational custom accelerator roadmap, starting with an accelerator entering mass production in 2027 and extending into multi-gigawatt deployments. Sample deliveries begin this year.

On the AMD side, Celestica is the R&D design and manufacturing partner for the Helios scale-up networking switch, which is the interconnect backbone of the rack. Samples ship at the end of 2026 and the ramp begins in the first half of 2027. Several customers have placed orders, and Celestica is involved in every one of the Helios rack programmes.

Both are described as multi-billion-dollar 2027 opportunities. One accounting nuance to hold on to: the OpenAI programme will be consigned, meaning the material cost does not flow through Celestica’s revenue. That understates the revenue optics and flatters the margin percentage. It changes nothing about the profit dollars.

Thesis scorecard after Q2 2026

Pillar #1. From commodity EMS to AI-infrastructure design partner: confirmed and strengthening

HPS at 41% of revenue, growing 58%. The design engineering organisation reaches 2,000 people by year end, from 1,350 in April. Celestica is now the design and manufacturing partner on OpenAI’s rack and on AMD’s Helios switch, which puts it at the architecture table on two of the most consequential AI hardware roadmaps outside Nvidia. The transformation has stopped being a question and is now simply the business.

Pillar #2. Hyperscaler AI infrastructure spend is structural: confirmed and strengthening

Capacity conversations now run through 2028 and in some cases 2029. Lead times on some components run well beyond 52 weeks, which has forced customers to commit orders far earlier, and that is where the widening visibility comes from. Demand exceeds what the company is willing to guide.

Pillar #3. The 400G / 800G / 1.6T networking ramp: confirmed and strengthening

Communications grew 62% against a 50% guide. 1.6T mass production begins with two hyperscalers in Q3, with 10 active programmes and more awards expected in coming quarters. The co-packaged optics programme won in April has samples in the first half of 2027 and mass production in the second half, and work is already under way with that customer on the generation after. 400G, which everyone had modelled as declining, is described as remarkably resilient.

A fair question is whether 400G lasting longer means less 800G and 1.6T. It does not, because they are not competing for the same slot. The 800G and 1.6T ports go into the AI back-end fabric, the network that connects accelerators to one another, where bandwidth per port is the binding constraint. 400G lives in the front-end and general-purpose fabric, and a large share of it is now served by splitting an 800G port into two 400G links down to servers. So 400G volume rides on 800G deployment rather than competing with it. The market data says the same thing: 800G modules pass 40 million units in 2026 while 1.6T enters its first volume ramp year, and the AI transceiver market moves from $16.5B in 2025 to $26B in 2026. For Celestica, 400G durability means the older programmes keep paying while the new ones ramp.

Pillar #4. Inference-driven durability beyond 2026: confirmed and strengthening

This was the pillar where I said in April my model was most conservative. It got worse in the best way. The 2027 revenue implication moved from $25.5B to above $33B in a single quarter, and the two new rack programmes ramp into 2028.

Pillar #5. FCF machine and capital returns: confirmed, still evolving

YTD FCF of $285M, with the full-year guide raised to $600M. Capex holds at $1B for 2026 and $1.5B is still the 2027 placeholder. No buybacks in the quarter. Net debt of $204M and leverage of 0.5x, better than the 0.9x of a year ago and better than last quarter, so the balance sheet is strengthening while the capex runs.

This remains the honest soft spot. FCF conversion at 2.9% of revenue is thin, and shareholders are funding growth rather than receiving cash. My view is unchanged from April: at a 55.5% adjusted ROIC, capital reinvested into awarded programmes with contracted demand beats capital returned.

Where we were versus where we are: in January I set the $575 target with the stock at $280, which was 105% upside. Today the stock is $342 and the same target is 68% upside. The gap narrowed because the price rose through the spring, and it has widened again because the price fell 28% from the June high of $474 while the 2026 EPS guide rose 11%.

Pillar #6. Defensive moat via design complexity: confirmed and strengthening

The clearest evidence this quarter is competitive rather than technical. Customers are pulling programmes back from second sources and handing them to Celestica, because Celestica already has the material pipelined and can ramp reliably at scale. Design plus manufacturing under one roof is the stated reason. Moats show themselves under stress, and the industry is under stress now.

Five of six pillars strengthened this quarter. The sixth is unchanged in direction and evolving in shape.

Valuation: why the target stays at $575

I am keeping the target at $575. BUY, unchanged.

Holding a target flat after a guidance raise this size looks like inertia.

My DCF carries a 2026 revenue forecast of $17.0B and a 2027 forecast of $21.9B. Those numbers were set in January. Here is how they compare with what the company has since told the market.

The $575 target is produced by a model whose 2027 revenue line sits 35% below what management has effectively pre-announced. That is the conservatism. I am not raising the target today because I would rather see two more quarters delivered against the new 2027 framework. $575 is a conservative anchor.

Management credibility

Four consecutive quarters clearing the high end of their own adjusted EPS guide. Three full-year raises in six months, each one raising EPS faster than revenue. When this team calls something high confidence, the record says read it as a floor.

Risk register

Customer concentration: materialised, improving sequentially, still worse year on year. Three customers each accounted for 10% or more of revenue at 32%, 17% and 14%, for 63% in total. Last quarter it was 35% / 15% / 15% for 65%. On the reasonable assumption the ordering held, the largest customer came down 3 points. That sequential improvement is the first in over a year and it is genuinely good news.

Top 10 customers were 83% of revenue in Q2 2026 against 78% a year ago. A year ago only two customers cleared the 10% threshold; now there are three. So the quarter improved and the year got worse. This remains the single biggest structural vulnerability, because a pause at the 32% customer is still a very large event.

Component supply: constant, and now a moat. Supply remains tight and demand exceeds what the company will guide. The framing has changed though. Extended lead times are pulling customer commitments forward, in some cases 12 to 18 months, which is where the improved visibility comes from. Celestica performed better than it expected on supply this quarter and describes 2026 and 2027 as appropriately hedged. Tightness is now helping more than hurting.

Capex absorption: evolving. $1B this year, $1.5B placeholder for 2027, no buybacks. FCF conversion of 2.9% is thin for a company of this profitability. The offset is a 55.5% adjusted ROIC and a balance sheet that got stronger through the build. Watch the October capex number. If it goes well above $1.5B, that tells you 2028 demand is bigger than anyone is modelling, and it also tells you FCF stays compressed for another year.

AI capex financing: rising, and the one that could actually break the thesis. Hyperscaler bond issuance is heading toward $300B annually with visibly weakening investor appetite, and the BIS has flagged circular financing as a systemic risk. Celestica is well protected contractually and on the balance sheet, and no amount of contractual protection survives customers who cannot fund their build. This is a 2028 risk transmitted through the length of the order book, and it is now the top item on my watch list.

Valuation: elevated on trailing numbers. 30x guided 2026 EPS with a RankedStocks valuation score of 20. The case depends on 2027 and 2028 earnings power arriving. If the growth rate steps down before the earnings arrive, the multiple compresses and the stock falls hard.

Volatility: The stock has fallen 28% from its June high of $474 while the 2026 EPS guide rose 11%. In the last twelve months it has traded between $169 and $474. Anyone who cannot hold through that will sell at the wrong moment regardless of how the business performs.

Verdict

Keeping BUY, target unchanged at $575.

The thing I keep coming back to is what happened on Tuesday. Every proxy for the AI trade was taken apart on news about lithography tools, memory pricing and bond cover ratios. Celestica went up. It is as exposed to sentiment as anything else on that screen. It is simply very hard to sell a company that had just printed 62% revenue growth, cleared its own guide for the fourth straight quarter, raised the year for the third time, and told you 2027 gets faster, on a story about DRAM oversupply.

My model still thinks this company does $21.9B of revenue in 2027. Management thinks it does more than $33B. One of us is going to be wrong, and I have deliberately chosen to be the one whose error costs nothing.

Hello fellow Sharks,

While the S&P 500 retracted 0.6% last week, the portfolio advanced +2.9%. If you want to skip straight to the numbers, jump to the Portfolio Update.

Last week 2 companies reported earnings and 4 will be reporting next week. As per the Thought Of The Week, I am removing the paywall on the May 2025 pick and refreshing the thesis. The stock is already +110% up since it was picked. In the thesis I go over why I think there is still upside.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• Earnings Results

• RYAM: Up 111% Since It was Picked, Now What?

• Portfolio Update

Earnings Results

I have added a calendar function on RankedStocks.com. Now you can see the earnings calendar for your own portfolio.

Last week we had a stock split, a dividend from Dell, and two earnings results. Both beat EPS consensus and one came in light on revenue.

Next week we have four portfolio companies reporting every day except Tuesday, starting with CLS on Monday.

Thought Of The Week

RYAM: Up 111% Since It was Picked, Now What?

Back in May 2025 I sent paid subscribers a trade alert to buy Rayonier Advanced Materials [] at $3.96. I made the full deep dive open to free subscribers.

Since then the stock is up 111%.

I have been reviewing the thesis ever since the bid for the business was made, especially lately as the shares have come down from $12 to a bottom of $7 and now partially recovered to the high $8s.

I am keeping the position as an acquirer has already put $11 to $12 a share on the table for the entire company, my own sum‑of‑the‑parts work says the pieces are worth $13.28 in a base case, my DCF says $14.90, and the single most cash‑generative business RYAM owns just got a fresh boost from Washington. The easy money has been made. I think there is a second leg.

TLDR

• Rating: Strong Buy, maintained. Fair value $14.90. That is my FCFF DCF base case. My sum‑of‑the‑parts base case is $13.28, and the bull cases run to $22 to $41.

• The floor is real. American Industrial Partners (AIP), a $17B industrial PE, made an unsolicited $11 to $12 all‑cash bid in November 2025 (rejected), then bought about 5% of the stock and, on 15 July 2026, signed a confidentiality agreement and standstill to enter RYAM’s formal strategic review. A capitalized buyer has already marked the company well above today’s price.

• The crown jewel keeps getting better. Cellulose Specialties pricing rose to $2,040 per metric ton in Q1 2026, up 17% y/y, at 26% to 28% EBITDA margins. RYAM is the sole US producer of high‑purity dissolving pulp, and a new 25% tariff on Brazilian imports (on top of RYAM’s own antidumping case) tilts US pricing power its way.

• The mess is being cleaned up. The loss‑making Temiscaming dissolving‑pulp line is now permanently closed, the company is in a Morgan Stanley‑led strategic review, and an activist, Mill Pond Capital, is pushing for a sale.

• The risk. Leverage is still high at 4.3x net secured, Q1 2026 was a weak transition quarter, and the review could end with no deal. My bear case runs from break‑even to negative on the equity.

How RYAM ranks

Before I go any deeper, a quick word on where RYAM screens. I run every name through RankedStocks.com, my own scoring system that grades a company from 0 to 100 across five factors plus an overall score. RYAM lands at an overall 73, with a country rank of 51% and a sector rank of 52%.

A pure score would not have surfaced this gem on its own. The value here is buried, in a sum-of-the-parts gap, a rejected takeover bid, and a crown-jewel business masked by trough consolidated numbers, and only a detailed dig brings it out. What RankedStocks.com did do was power the research and the valuation work behind this thesis, the DCF, the reverse DCF, and the Monte Carlo you will see further down.

Look at the tension in the factors. Valuation scores 75 because the trailing numbers still carry the trough, so on a backward-looking screen RYAM looks expensive.

But Profitability scores 36 and Growth (10) and Sentiment (73) are firming as the strategic review plays out. That split is the entire setup. The screen is scoring what already happened. I am underwriting what happens next.

What’s in this update

• The story so far, in 60 seconds

• What has changed since I first wrote this up

• Thesis scorecard after Q1 2026

• The Brazil catalyst: a 25% gift to the crown jewel

• American Industrial Partners: who put a real bid on the table

• The DCF: $14.90, and what the market is actually pricing

• Sum‑of‑the‑parts: the jewel is worth more than the whole

• Biomaterials and BioNova: the free option

• Steel‑manning the bear case

• Verdict

The story so far, in 60 seconds

If you are new to the name, here is the original thesis.

RYAM is a specialty materials company built around one gem of a business: High Purity Cellulose (HPC). HPC is a super‑refined wood pulp that goes into things you touch every day such as LCD screens, pharmaceuticals, food thickeners, filters, paints, tire cords, and impact‑resistant plastics. It is sold on annual contracts, it is hard to substitute, and the top specialty grades earn EBITDA margins in the high‑twenties. HPC is roughly 80% of RYAM’s sales. Wrapped around it are two non‑core commodity businesses at the Temiscaming site in Quebec, a multi‑ply Paperboard mill and a High‑Yield Pulp mill, that the company has been trying to sell or shut.

My argument was simple. The market was pricing RYAM like a struggling commodity pulp maker at $4, yet the HPC business alone was worth more than the entire enterprise value. New management was doing the right things, selling the junk, shutting cash‑burning lines, and focusing on the jewel. On a sum‑of‑the‑parts basis, I saw $16 in a base case and $35 in a bull case, with a bear around $3. It reminded me of my Tenneco trade in 2022: a beaten‑up, over‑levered industrial that nobody wanted, until Apollo bought it for double the price a month after I wrote it up. RYAM had that same shape.

Fourteen months later, the stock has done the first part of the job. Now the question is whether the second leg, the one that gets you to the intrinsic value, is still there. I think it is, and the reasons are more concrete than they were in May 2025.

What has changed since I first wrote this up

A lot, and most of it moves the thesis forward.

Management churned, then landed a dealmaker. De Lyle Bloomquist, the CEO who started the clean‑up, retired on schedule in a transition announced in December 2025. His replacement, former Olin chief Scott Sutton, lasted about three and a half months and resigned on 20 April 2026, the same day the board announced a formal strategic review. On 22 June 2026 the board named Daniel Krawczyk as permanent CEO. He ran Huber Engineered Materials, a $1.3B specialty‑chemicals portfolio, and executed more than $2B of M&A and divestitures over the past decade. Three CEOs in seven months is messy, and I will not pretend otherwise, but the person now in the chair is an M&A operator, which is exactly the profile you want if the endgame is a sale or a break‑up.

The strategic review became formal. In April 2026 RYAM hired Morgan Stanley and Wachtell Lipton and put every option on the table: a standalone plan, a strategic investment, a merger, or a sale of part or all of the company. This is no longer me speculating about hidden value. The board has hired bankers to go find it.

A real buyer showed up. American Industrial Partners bid $11-$12 a share in cash last November, got rejected, bought about 5% of the stock, and has now signed into the process. I devote a whole section to AIP below because it is the single biggest change to the risk‑reward.

An activist is pushing. Mill Pond Capital, run by Daniel Farb, owns about 3% and went public on 17 June 2026 urging the board to complete the review and sell. His argument is blunt: a loss from continuing operations every year since 2019, guidance rarely met, and three CEOs cycled through. He is not wrong, and he is aligned with me on the outcome.

The junk is finally, permanently gone. In Q1 2026 RYAM decided to permanently cease dissolving‑pulp production at the idled Temiscaming HPC line, taking a $41M non‑cash charge. That closes the book on the highest‑cost, lowest‑margin part of the footprint.

Q1 2026 itself was weak. More on that in the scorecard, but the print was a transition quarter, not a turnaround quarter.

Thesis scorecard after Q1 2026

Pillar #1. HPC is a hidden specialty business worth more than the whole company: Confirmed and strengthening

Original thesis: HPC is a quasi‑duopoly specialty franchise with pricing power, mispriced as commodity pulp.

What happened since: Cellulose Specialties pricing climbed from about $1,750 per MT through 2024 to $1,875 by Q4 2025 and $2,040 in Q1 2026, up 17% y/y, negotiated on new value‑based contracts. Segment EBITDA margins held at 26% to 28%. The company deliberately traded some volume for price, and volumes fell as acetate and ethers demand stayed soft, but the pricing power is exactly what the thesis said it was.

Assessment: This is the pillar that matters most, and it got stronger. When a business can push price up 17% into a soft‑volume year and still earn high‑twenties margins, it is not a commodity.

Pillar #2. New management sells the junk and focuses on the jewel: Confirmed

Original thesis: management would monetize or shut Temiscaming and run RYAM as a pure‑play HPC business.

What happened since: the idled Temiscaming HPC line is now permanently closed, and RYAM has collapsed its reporting into two segments, HPC and a combined Paperboard & High‑Yield Pulp. The strategic review formalizes the intent to separate or sell the rest.

Assessment: direction confirmed, even through the CEO churn. The permanent closure is the cleanest signal yet that the company will stop feeding cash to the commodity lines.

Pillar #3. The catalyst path is a sale, a break‑up, or a buyout: Confirmed and accelerating

Original thesis: value would be unlocked by asset sales or, in the Tenneco template, an outright buyout.

What happened since: a formal Morgan Stanley review, a rejected $11-$12 cash bid from a disciplined PE firm now inside the process, and an activist publicly demanding a sale. Every one of those is a catalyst I flagged in theory. They are now real.

Assessment: this is the pillar that has changed the most, and entirely in my favor.

Pillar #4. Downside is limited by asset value: Evolving

Original thesis: even a bear case around $3 sat close to the then‑price, so downside was protected by HPC’s value.

What happened since: the stock has doubled from the alert, so the cushion is thinner. Leverage is still heavy at 4.3x net secured, and my bear sum‑of‑the‑parts is now negative because the debt sits ahead of a much smaller equity stub. The floor today is less about book value and more about the fact that a buyer has already offered $11 to $12.

Assessment: the nature of the downside protection has shifted from asset value to takeout support.

Pillar #5. FCF normalizes as the drag comes off: Evolving

Original thesis: strip out the loss‑making segments and the corporate overhead, and FCF normalizes to $45M or more.

What happened since: Q1 2026 adjusted EBITDA was just $8M on $319M of sales, with adjusted FCF of $12M. Management calls 2026 a transition year and guides to full‑year EBITDA above 2025 and positive FCF, with sequential improvement. The normalization is real but slower than I would like.

Assessment: the FCF story is intact but back‑end loaded. This is the pillar the bears can still poke at, and fairly.

The Brazil catalyst: a 25% gift to the crown jewel

The first of the two live catalysts is trade policy, and it lands right on the business that matters.

On 15 July 2026 the US Trade Representative announced a 25% Section 301 tariff on certain imports from Brazil, effective 22 July, after a year‑long investigation into unfair trade practices. Crucially for RYAM, high‑purity dissolving pulp was taken off the exemption list after testimony tied Brazilian production to illegal deforestation. RYAM is effectively the sole US producer of high‑purity dissolving pulp, and its main import competitor is Brazil’s Bracell. When you raise the landed cost of the only real import alternative by 25%, the domestic producer gets pricing power and volume it did not have to fight for.

That tariff sits on top of a second, separate action RYAM started itself. In August 2025 RYAM and the United Steelworkers filed antidumping and countervailing‑duty petitions against dissolving pulp from Brazil and Norway. On 27 May 2026 Commerce issued preliminary affirmative dumping determinations: Brazil at 7.20% (Bracell, a 6.09% cash‑deposit rate after the subsidy offset) and Norway at 6.54% (Borregaard). Final determinations are due in Q4 2026, with the injury hearing set for October.

So the picture is two tariff walls going up around RYAM’s biggest market at the same time. This is the mirror image of the tariff fear that hammered the stock in 2025, when China’s retaliation on US fluff pulp was the story. Back then trade policy was a headwind. Now it is a tailwind, and it flows straight to Cellulose Specialties pricing, the highest‑margin line RYAM has. That is why the $2,040 per MT Q1 print matters so much: the pricing runway just got longer, and it is being protected by law.

American Industrial Partners: who put a real bid on the table

The second catalyst is a buyer, and it deserves a proper section because a lot of the risk‑reward now rests on understanding who AIP is and how it operates.

Who they are

American Industrial Partners is a New York PE founded in 1988, with $17B in AUM and a current fund of $5.1B. It is not a financial‑engineering shop. AIP describes itself as an operationally‑oriented firm run mostly by engineers and operators, and about 70% of what it does are corporate carve‑outs and public‑to‑private deals in industrial businesses. It has a reputation for conservative leverage, and in its own words it does not rely on excessive debt. In its bid letter it noted it had already been, during 2020 and 2021, likely RYAM’s largest bondholder, owning nearly $200M of face value of the notes. This is a buyer that has read RYAM’s balance sheet from the inside.

The track record: what they have actually done

AIP’s pattern with public companies is to take them private, keep management, and rebuild them operationally, often by combining them with an adjacent platform. A few examples that show the range:

• SEACOR Holdings. A negotiated all‑cash take‑private at $41.50 a share, roughly $1B, closed in 2021. AIP walked the bid up from $40 to $41.50, called it best and final, and closed it. Disciplined on price. (source)

• Molycop. Bought the mining grinding‑media business out of a distressed seller in 2017 and sold it in June 2026 at about a $1.5B enterprise value, a clean multi‑year win. (source)

• Attindas Hygiene Partners. Carved Domtar’s personal‑care business out for $920M in 2021. Attindas makes infant diapers and adult incontinence products, which are end uses for fluff pulp. Hold that thought. (source)

• Global Cellulose Fibers. In January 2026 AIP closed its carve‑out of International Paper’s Global Cellulose Fibers business, the leading global producer of absorbent fluff pulp, $2.3B in revenue across nine mills. (source)

• V2X, Avanos, REV Group. AIP merged a private carve‑out (Vertex) with a listed company (Vectrus) to build V2X in 2022, agreed in April 2026 to take Avanos Medical private at $25 a share (a 72% premium), and rolled up and IPO’d REV Group. (V2X, Avanos, REV)

It is not a spotless record. Rand Logistics went through a 2018 Chapter 11, though AIP was the loan‑to‑own creditor that emerged owning it rather than an equity wipeout. And RYAM itself is the rare case where AIP went unsolicited and got rejected, which is out of character for a firm that usually transacts on friendly terms. That matters for how you read the standstill, which I come back to below.

The strategic angle that makes AIP the natural owner

With Global Cellulose Fibers, AIP already owns the largest global fluff‑pulp producer. With Attindas, it owns a downstream diaper and incontinence maker that consumes fluff pulp. AIP now sits on both ends of the fluff‑pulp chain. RYAM’s cellulose specialties plus GCF’s fluff pulp is exactly the combination AIP pitched to RYAM’s board: in its own words, the deal would create the leading global participant in the cellulose specialties and fluff pulp industries, with downstream upside in renewable power, bioethanol, and crude tall oil. It is the one buyer uniquely positioned to run and integrate these assets.

The sequence, and what it means

The timeline: on 18 November 2025 AIP submitted a fully committed, no‑financing‑condition proposal at $11 to $12 a share, a 99% to 117% premium to the prior close and above RYAM’s five‑year high. The board rejected it in December. AIP then bought 3.4 million shares for about $25M ($7.48/sh) and disclosed a 5% stake in February 2026. On 15 July 2026 it filed an amended 13D disclosing a confidentiality agreement and customary standstill, and said it is waiting to be invited to participate further in the process.

The standstill is the key. AIP is not going to run a hostile proxy fight; that is not how it operates. It has parked 5%, agreed to behave, and positioned itself to bid inside a board‑run process. For me the read is straightforward: a capitalized, operationally credible buyer who once owned RYAM’s debt has already told the world it values the equity at $11 to $12, in cash, with no financing risk, and it is still at the table. That is the most durable floor this stock has had in years, and it sits well above the current market price.

The DCF: $14.90, and what the market is actually pricing

I run a DCF on RYAM on the new DCF tool I developed on RankedStocks.com. I keep adding to it; the newest feature lets you share a model with anyone using the share button. Here you can review the DCF model for RYAM.

Below are my WACC assumptions for the three scenarios.

That produces an enterprise value of about $1.69B, an equity value of $995M, and, across 66.8 million diluted shares, $14.90 a share.

The scenarios matter as much as the point estimate:

The bear case printing $0 is not a typo. It is the model telling you what heavy leverage does: if HPC margins collapse to 7% and stay there with no growth, the $779M of debt in the capital structure absorbs the entire enterprise value and the equity is a zero. The flip side is the asymmetry: the base is +82% and the bull is +168%, against a bear that requires a permanent structural collapse in a business that just raised prices 17%.

If you want the full assumptions, follow the link above and click Print PDF on that page. You get everything below.

The most useful lens is the reverse DCF. At today’s price the market is pricing RYAM for revenue that shrinks 1.3% a year, forever.

My base case does not assume heroics. It assumes 2.6% revenue growth.

It assumes HPC margins normalize toward 18% and the top line grows at low‑single digits as the commodity drag comes off. You are not paying for a turnaround that has to go perfectly. You are being paid to wait for one that only has to be mediocre.

I also run this through a 10,000‑trial Monte Carlo with the variables below:

The distribution is the interesting part.

82.8% of the 10,000 runs land above $8.15 (that was the market price when I run the simulation so maybe today is closer to 78%-80%). 52% land above my target, and 25% land above even my bull case.

Sum‑of‑the‑parts: the jewel is worth more than the whole

The DCF values RYAM as one going concern. The sum‑of‑the‑parts values it the way a buyer like AIP would, piece by piece. This is the framework I have used on RYAM from day one, and it is the cleaner way to see the mispricing. Here is the base case straight from my model.

From that $1,937M of operating value (in the base scenario), you subtract the corporate cost drag (capitalized at $354M), add back the equity investments ($20M), add cash ($68M), subtract debt ($763M) and minority interest ($12M). That leaves $896M of equity, or, across 67.4 million shares, $13.28 a share in the base case. Run the same build with trough prices and multiples and the bear is negative $4.53, because the debt sits ahead of a shrunken stub. Run it with a full specialty re‑rate and the bull is $41.16.

The single most important line in that first table is HPC’s $1,732M enterprise value. That is the crown‑jewel business alone, and it is larger than RYAM’s entire current enterprise value. In other words, at today’s price the market is handing you Paperboard, High‑Yield Pulp, the equity investments, and the biomaterials platform for free, and then some. My 7.5x on HPC is deliberately cautious: the specialty tons alone would merit something closer to Borregaard’s roughly 10x median, and even AIP’s rejected $11 to $12 bid implied about 6.5x to 7x on mid‑cycle EBITDA. Three independent methods, my DCF, my sum‑of‑the‑parts, and a real acquirer’s cash bid, all point to the same place: RYAM is worth meaningfully more than $8ish.

Biomaterials and BioNova: the free option

I carry the biomaterials platform in my model at $20M, which means almost none of the upside is in my $13.28 base case.

RYAM runs a second‑generation bioethanol plant at Tartas in France, using the residual sugars from the pulp process, and it has housed the growth projects in a subsidiary called BioNova. In late 2024 it raised €67M of green capital for the effort, including €30M of preferred equity from SWEN Capital Partners for a 20% stake in BioNova, which implies a valuation north of $160M for the subsidiary alone. Management targets more than $40M of biomaterials EBITDA by 2027. (source)

If BioNova delivers even part of that, it is a chunky addition to a company with a current equity value under $600M, and it is the kind of asset a strategic acquirer could value far above my placeholder. I am not underwriting it. I am noting that it is there, and that you are getting it close to free.

Share Beating The Tide: Stock Picks That Outperform

Steel‑manning the bear case

The leverage is genuinely dangerous. RYAM carries $763M of total debt and $705M of net secured debt, at 4.3x net secured leverage, against a business that earned just $8M of adjusted EBITDA last quarter.

At FY2025, net debt to EBITDA stood at 4.9x.

Interest runs near $95M a year. In a prolonged trough, that debt is what turns a cheap stock into a zero, and my bear DCF says exactly that. This is the first thing that has to go right, or at least not get worse.

Q1 2026 was ugly under the hood. Cellulose Specialties volume fell 35% y/y. Some of that was a deliberate trade of volume for price, and some was soft acetate and ethers demand. If the volume weakness is more structural than management thinks, the pricing story alone will not carry the earnings.

The review could end with nothing. RYAM has explicitly said there is no timetable and no assurance of a transaction. AIP has a standstill and has told the market it will not chase. If the board decides to stay standalone and the macro stays soft, the catalyst I am leaning on evaporates and you are left holding a levered cyclical.

Environmental liabilities are real. RYAM carries about $184M of accrued environmental liabilities, with disclosure that up to $84M more is possible. I have not added those to the DCF or the SOTP but that’s a discount a buyer will apply.

It is a small, illiquid, underfollowed name. That cuts both ways, but it means the stock can stay cheap longer than you would like, and it can move hard on light volume.

None of this breaks the thesis for me, because the takeout floor and the specialty pricing power are both concrete and current.

Verdict

Maintaining the Strong Buy, fair value $14.90.

Plenty of cheap cyclicals can claim upside. What makes RYAM unusual is who is doing the buying. A disciplined industrial firm that was once RYAM’s largest bondholder, that already owns the world’s largest fluff‑pulp business, looked at these exact assets and offered up to $12 a share in cash. When the people who understand the balance sheet best are the ones bidding above the market, the value trap label gets hard to defend. I got subscribers in at $4. I still think there is a second leg to $14.90, and I am staying long.

Portfolio Update

The week was better than the closing number suggests. The S&P 500 gave back 0.6% while the portfolio added 2.9%, but look at the path. We were up 6.3% by Wednesday’s close and handed back more than half of it in the last two sessions. DELL did the heavy lifting with 90bps of contribution, helped by the dividend, and MU added 56bps.

Portfolio Return

• Month-to-date: -5.9% vs. the S&P 500’s +1.2%.

• Year-to-date: +35.7% vs. the S&P 500’s +8.3%. That is a gap of 2,741 basis points.

• Since inception: +91.5% vs. the S&P 500’s +28.9%. That’s 3.2x the market.

Contribution by Sector

Tech and industrials led the gains.

Contribution by Position

How to read the heat map? Click here.

• +90 bps DELL 3.90%↑ (Thesis)

• +56 bps MU 2.83%↑ (Thesis)

• +19 bps CLS 3.09%↑ (TSX: CLS) (Thesis)

• +18 bps STRL 4.60%↑ (Thesis)

• +17 bps CDE 0.74%↑ (Thesis)

• +9 bps TSM 1.99%↑ (Thesis)

• +6 bps (Thesis)

• +3 bps DXPE 2.42%↑ (Thesis)

• flat POWL 1.10%↑ (Thesis)

• -1 bps LRN 0.14%↑ (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Hello fellow Sharks,

Last week, the market declined 1.6%, and the portfolio lost 5.7%. If you want to skip straight to the numbers, jump to the Portfolio Update.

Mr. Market did not want to be in semis last week. The sector sold off hard, the semiconductor ETF fell 3.8% last week.

Luckily for me, I got new funds last week and used them to buy MU and CLS. Note that I did not add to MU or CLS in the portfolio as:

1. We already hold large positions in those names, and

2. We deployed the cash in the account to buy the July Stock Pick (more on that below in In Case You Missed It).

I noticed participants getting nervous, both in the comments on my articles and others’, and in the Fear & Greed Index.

We have been here before (check Weekly #27, the Fear & Greed Index was 35 then), so if you need a refresher on the psychological game in these periods, please read those old Weeklies.

Besides releasing the July Pick and closing GAP, I have been refreshing last year’s May Pick and adding more features to RankedStocks. Right now, the tool replaces thousands of dollars of annual subscription fees!

A couple of weeks ago, I listened to this episode of with Don Lincoln, a Fermilab physicist.

I found the conversation about gravity so interesting that I am making it the next addition to the Physics of Investing series and today’s Thought Of The Week.

As a refresher, here are the previous articles in this series:

• The Pendulum Effect

• Friction

• Higher-Order Investing

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• In Case You Missed It

• Earnings Results

• The Physics of Investing, Part 4: Gravity Always Wins

• Portfolio Update

In Case You Missed It

On July 14, I closed the position in GAP.

The stock is cheap; my base case says $30 against a price under $20, and even my bear case barely loses money. That was never the problem. The problem is that cheap and going up are two different things, and after a full portfolio review, I could not find a single catalyst to close that gap within the next year or two. The turnaround has mostly already worked, margins are stable rather than climbing, and the market has seen all the good news and still will not pay up. The danger here was dead money: capital sitting still in a cheap stock while better ideas compound. So I took the small loss and redeployed the cash. Nothing is broken. There is just nothing happening.

And on July 15, I sent the trade alert for the July Stock Pick, and on July 16, I sent the deep dive to paid subscribers.

This company just printed a record year, and the market is pricing it like the accident-prone version of itself from a decade ago. Strip out every windfall flattering current results, price only management’s own conservative multi-year plan, and the business is still worth more than what I paid. In other words, the record year, and everything after it, comes free. The setup offers $3 of upside for every $1 of downside.

Earnings Results

Two portfolio companies reported earnings last week. Both beat EPS consensus estimates, and one missed on revenue.

TSM — Q2 2026

TSMC [] delivered a blowout. Revenue came in at $40.2B, up 33.7% y/y, net income rose 77%, and EPS printed NT$27.25 (US$4.31 per ADR). Gross margin hit 67.7%.

Read that again: a manufacturer running software margins. HPC, the platform that carries the AI accelerators, is now 66% of revenue…

… and 2nm has started contributing at 3% of wafer sales.

The guidance is the part Mr. Market should have read twice before “correcting” last week. Management raised full-year growth to slightly above 40%, lifted capex to $60B-64B, committed another $100B to Arizona ($265B in the US in total), and said demand visibility extends “probably to 2029, 2030.” The one soft spot is self-inflicted in the best way: the steep 2nm ramp will dilute gross margin by 3 to 4 points in the second half. That is the cost of building the next leg of the thesis rather than a crack in it.

My target stays at $565. And yet the stock finished the week down with the rest of the semis. We saw this movie in Weekly #78.

The position is up +135% from the cost basis.

Thought Of The Week

The Physics of Investing, Part 4: Gravity Always Wins

Gravity is the force we notice least and owe the most. Lincoln puts the everyday version simply: “when you trip, you fall. That is the nature of gravity that we all experience day to day.” The cosmic version is grander. Gravity collected clouds of hydrogen into stars and squeezed them until fusion ignited. It swept the leftovers into planets, holds our atmosphere against the vacuum of space, and keeps Earth on its rails at exactly the distance where water stays liquid. Remove gravity and there are no galaxies, no stars, no planets, and nobody to read this newsletter. Everything interesting in the universe exists because gravity kept pulling.

The weakest force wins

Here is the paradox. Of the four fundamental forces, gravity is by far the weakest. A toy magnet lifts a paperclip against the gravitational pull of the entire planet. So why does gravity, and not electromagnetism, run the universe?

Two properties.

It works over any distance, and it only ever adds. Electric charges come in positive and negative and cancel each other out at scale. Mass has no negative version. Every atom pulls on every other atom, forever, in the same direction. Weak but relentless beats strong but self-cancelling, given enough mass and enough time.

Valuation is the gravity of markets. On any given day, it is the weakest force on your screen. Momentum, narrative, index flows, options positioning, a stray Fed comment: each can shove a stock around this afternoon while the fundamentals do nothing at all. Last week was a great example, semis lost ground as the AI narrative shifted. But fundamentally, the results and outlooks for those semis are stronger than ever as shown in the TSM Q2 2026 summary above.

Traders live on those forces the way lightning lives on electromagnetism. But the daily forces cancel. Buyers offset sellers, narratives flip, flows reverse. Cash flow only adds. Every quarter a business earns money, or burns it, a little more mass lands on one side of the scale, and the pull gets stronger. Ben Graham said the market is a voting machine in the short run and a weighing machine in the long run.

He never told us what the weighing machine runs on.

It runs on gravity.

Warren Buffett reached for the same physics when he explained interest rates in 1999: they “act on financial valuations the way gravity acts on matter: the higher the rate, the greater the downward pull.” Rates are the gravitational constant of investing. When they change, nothing inside your companies changes, yet the weight of every future dollar changes, everywhere, at once. That is why 2022 repriced everything with a duration, from Treasuries to profitless tech, in the same direction at the same time.

Here is the secret to surviving flat or red markets: gravity is completely independent of price. While traders are obsessing over the temporary forces pulling a stock up or down, a great business is quietly accumulating mass behind the scenes.

Every time a company retains a dollar of FCF and reinvests it at high rates of return, its fundamental mass increases. Every time management buys back shares, the gravitational pull of the remaining shares gets mathematically heavier. Even if the stock price trades sideways for three years the mass of the business is growing. When the narrative finally shifts and the temporary forces align with reality, the “snap-back” to fair valuation is violent, precisely because the business has so much more fundamental mass than it did three years ago.

Why price follows the curve

Newton’s leap was not discovering that things fall; everyone knew that. It was realizing, as Lincoln tells it, that the force pulling an apple down and the force steering the Moon are “one and the same.”

One law, from your backyard to the edge of the solar system. Investing has its own universal law: an asset is worth the cash it will return over its life, discounted back to today. It applies identically to a community bank and a $1T foundry in Taiwan.

Einstein went further. Gravity, he said, is not a force reaching across space. Mass curves spacetime, and objects simply follow the curve. Lincoln describes the mental leap as taking your flat map of space and having to “crinkle it and bend it.” I find that the most useful picture of how value works. A durable competitive advantage does not yank a stock price up day to day. It bends the space the business operates in: customers, suppliers, capital, and talent all start orbiting it, and the price eventually follows a geometry it cannot see. You do not need to predict every path. You need to know where the mass is.

Share Beating The Tide: Stock Picks That Outperform

Escape velocity, and what it costs

Rockets do escape gravity. It costs them 85% to 90% of their launch weight in fuel, and the moment the thrust stops, gravity resumes, unimpressed. Stocks reach escape velocity too. A story, a short squeeze, or a mania can push any quote out of valuation’s reach for months, sometimes years. The 2021 cohort of profitless tech proved that, and then proved the second half of the physics: when the cash flow is not there, thrust is borrowed, and gravity does not negotiate. The names that stayed at altitude kept adding mass underneath themselves; earnings grew into the price. That is the only escape that lasts, and it works by raising the ground beneath the price.

Speaking of rockets, escape velocity and gravity, SpaceX [] came back from orbit, and now it is trading below its IPO level.

The moment to be most careful is the top of the arc. Throw a ball straight up and at the apex it is briefly weightless: velocity zero, everything calm. Euphoria feels identical. Nothing in the price is moving against you yet, and gravity is already fully in charge.

Using the pull instead of fighting it

NASA does not fight gravity either; it uses it. Voyager reached the outer planets by slingshotting around Jupiter, borrowing a little of the giant’s momentum. Engineers call it a gravity assist. The investing equivalent is letting Mr. Market’s panics accelerate you. Every drawdown in a business whose mass is intact is a slingshot: CLS after three post-earnings drops, MU during this month’s AI capex scare. Last week’s semi sell-off is the current exam.

This is also where the series comes together. The pendulum (Weekly #70) says sentiment always swings back; gravity is the reason why, because the level it swings back to is valuation. Friction (Weekly #72) is what costs and taxes do to your trajectory on the way. And higher-order thinking (Weekly #77) is the discipline of asking not “is there a sell-off?” but “did the mass change?” In physics terms, first-order investors watch velocity. Second-order investors watch mass.

Lincoln spends part of the conversation on the one thing gravity refuses to do: merge with the other three forces. Physicists unified electromagnetism with the weak force decades ago, but gravity “is seemingly fundamentally significantly different,” and testing a unification would take energies a quadrillion times beyond today’s accelerators. I have stopped expecting unification in markets too. Valuation will never merge with narrative or momentum into one tidy model; they are different kinds of forces answering different questions. The practical playbook is the physicist’s: respect the fast forces, use them for your entries, but build the portfolio on the one force that never turns off.

Gravity has never lost a fight. Neither has valuation. You just have to give both enough mass and enough time.

Portfolio Update

Last week’s sell-off in tech, and especially in semis, took the S&P 500 down 1.55% and our portfolio down 5.7%. Concentration cuts both ways, and this was a week it cut against us.

Portfolio Return

• Month-to-date: -8.57% vs. the S&P 500’s -0.6%.

• Year-to-date: +31.8% vs. the S&P 500’s +8.9%. That is a gap of 2,288 basis points.

• Since inception: +86.0% vs. the S&P 500’s +29.7%. That’s 2.9x the market.

Contribution by Sector

Tech led the losses, partially offset by energy.

Contribution by Position

How to read the heat map? Click here.

• +12 bps DXPE 2.42%↑ (Thesis)

• flat POWL 1.10%↑ (Thesis)

• -3 bps LRN 0.14%↑ (Thesis)

• -31 bps STRL 4.60%↑ (Thesis)

• -35 bps CDE 0.74%↑ (Thesis)

• -52 bps TSM 1.99%↑ (Thesis)

• -82 bps DELL 3.90%↑ (Thesis)

• -93 bps MU 2.83%↑ (Thesis)

• -184 bps CLS 3.09%↑ (TSX: CLS) (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Ten years ago, the company I’m writing about today was a corpse. Book equity was negative by more than half a billion. Leverage sat above 5x. The stock had fallen more than 70% from its $15 IPO price, and the board had just handed the keys to a new CEO whose stated ambition was survival.

That company just reported the best quarter in its history. It raised full-year guidance by 15% only ten weeks after setting it. It’s buying back stock worth 7% of its market cap, has no debt due for two years, and management has published cash flow targets that put the shares on a near 7% FCF yield at the current price.

The market’s response has been to keep pricing it like the accident-prone cyclical it used to be. The shares trade 8.7x forward earnings and at half of what my DCF says the business is worth. Even if you throw out every temporary tailwind the company is enjoying right now and price management’s own deliberately conservative targets instead, you still get a value above today’s price. You’re paying for the old company and getting the new one free.

Table of Contents

• How does this stock rank?

• From near-death to record year

• How the business actually makes money

• The record quarter, decomposed

• The crown jewel

• The problem child grows up

• The broken corner

• Management and capital allocation

• Valuation: what the model says

• Catalysts: what closes the gap

• Steel-manning the bear

• Verdict

Before the name, this is my live pick track record: every position, its return since I flagged it, and where it closed.

Those trades went to paid subscribers in real time; DELL is up 226%, MU up 206%. The rows marked “Upgrade To Paid” are the ones still behind the paywall, and this deep dive is one of them. If you’re a free subscriber and want the name and the full thesis, upgrade to paid and get every deep dive plus the full portfolio with real-time updates.

Read more

A decade ago this business was, for all practical purposes, a corpse. Negative equity. Leverage that would have made a lender wince. A stock that had lost most of its value and a new boss whose stated ambition was simply to survive. The market watched it nearly blow itself up, filed that memory away, and never quite updated it.

Ten years of quiet repair later, that same company just reported the best quarter in its history. Management raised full-year guidance twice inside a single quarter. It is buying back a meaningful slug of its own shares, the balance sheet is finally boring in the best way, and yet the stock still trades as if the old accident-prone version is the real one.

That is the whole opportunity in one line: the market is pricing the company this was, and management is running the company it has become. My model says the business is worth double the price I paid. Even if I strip out every temporary tailwind it is enjoying right now and use management’s own deliberately cautious targets instead, I still get a number above today’s price. You are paying for the old company and getting the new one free.

That is as far as I will go out here in the open. The name, the exact price I paid, the full thesis and the downside case are all behind the paywall, and the complete deep dive follows in a few days.

If you want the name and the number, upgrade to paid. Paid subscribers get every deep dive, every trade alert the moment I act, and the full portfolio with real-time updates.

Read more

I just closed my position in GAP.

I’m down 12% on GAP [].

That small loss in not the real problem and if I hold the position…eventually…it will show a +50% gain. But the reality is that it has gone nowhere while the rest of the book has worked.

Yes, GAP is cheap, not just optically cheap. My base case says it is worth about $30 against a price under $20, and even my bear case barely loses money.

So why am I selling it?

Because cheap and going up are two different things. GAP is a good business with nothing to close the gap between what it is worth and what it trades for. The risk here isn’t losing money. It is sitting in dead money for two years while better ideas compound. In other words, the gap between price and valuation in the top image is the lack of catalysts.

This came out of a portfolio review. I’m hunting for cash to fund new positions, so I’m asking what to let go. GAP made the list, not because it is broken, but because I just don’t see anything happening here any time soon.

TLDR

• The trigger: a portfolio review. I’m freeing up cash, and GAP is the one holding where I went looking for a catalyst and found nothing.

• The scorecard: of the five reasons I owned it, two still hold, one is fading, two are stuck. The value is real. The growth and the re-rating aren’t.

• The one change that matters: they raised EPS and cut the sales guide in the same breath. The raise came from tax, interest income and buybacks, not the business.

• Valuation: base case $29.95 (+51%), bear $13.94 (-32%), bull $47.74 (+144%). Risk/reward is a healthy 1 to 1.6.

• Verdict: sell and redeploy. Nothing is broken. There is just no catalyst to turn a cheap stock into a rising one, and the cash works harder elsewhere.

What this update covers

• Ranked Stocks score

• Thesis scorecard after Q1 FY2026

• Q1 FY2026 in one read

• The guidance tells the real story

• What Gap is worth: base, bear and bull

• So why sell? The missing catalyst

• Risks to the redeploy call

• Performance versus the sector

• Verdict

Ranked Stocks

GAP scores 63 out of 100 on RankedStocks, top third of the market and 37th percentile of its sector. The shape tells the story. Valuation 89 and Outlook 83 are near the top, because it is cheap with a decent setup. Profitability is 70, Growth a soft 53. And Sentiment? 13. Basically the floor.

Cheap, fine on the numbers, and totally unloved.

Thesis scorecard after Q1 FY2026

Pillar #1. Deep value with a floor under it: Confirmed

Original thesis: GAP was priced for a decline it wasn’t actually having. Net cash, real FCF, a single-digit multiple, four brands almost everyone knows.

What happened since: FY2025 did $15.4B in sales (+2%), a 40.8% gross margin, 7.3% operating margin, $816M net income, $2.13 EPS. As of Q1 2026, it holds $2.6B of cash (30% of its market cap). Even my bear case, margins gutted and no growth, values the stock near $14. The base is $30.

Assessment: Confirmed. The floor is real and it is made of cash. It is also why GAP is so hard to lose money in.

Pillar #2. The Dickson turnaround is real: Confirmed, normalizing

Original thesis: Richard Dickson, CEO since 2023, would make four tired brands relevant again with better product, sharper marketing and tighter costs.

Evolution: through 2025 this was the best leg of the thesis. Comps turned positive across Old Navy, Gap and Banana Republic, brand by brand.

What happened since: Q1 was the ninth straight quarter of positive comps (+2%). The Gap brand did +10%, one of its best prints in twenty years. Banana Republic put up its fourth positive quarter in a row.

Assessment: Confirmed, but normalizing. The turnaround worked. The catch is it has mostly already worked, and nobody paid up for it. A turnaround that is 80% done isn’t a catalyst any more.

Pillar #3. Margins climb toward the 10% goal: Challenged

Original thesis: cost cuts and more full-price selling would push operating margin from mid-single digits toward management’s 10% target.

What happened since: Q1 adjusted operating margin dropped 230bps to 5.2% on tariffs and investment, and gross margin slipped to 40.5%. Management held the full-year margin guide at 7.3% to 7.5%, right where FY2025 landed. My model caps it at 7.5% to 8%, nowhere near 10%.

Assessment: Challenged. Margins are stable, not climbing. The 10% goal is a stretch I won’t underwrite, and flat margins on flat sales don’t re-rate anything.

Pillar #4. Old Navy carries the company: Evolving

Original thesis: Old Navy, 56% of sales and the biggest apparel brand in the US, is both the profit engine and the growth story.

What happened since: it comped just +1% in Q1, dragged by weak women’s dresses even as denim, active and kids sold. I see about 2% comps long term and maybe 200 net new stores in a whole decade.

Assessment: Evolving toward challenged. Old Navy is fine. Fine doesn’t move a $7B company. When your biggest brand grows 1% to 2%, the whole thing grows 1% to 2%.

Pillar #5. Cheap enough to re-rate: Challenged

Original thesis: at well under 10x earnings and a fraction of intrinsic value, the discount would close.

What happened since: it hasn’t, and it hasn’t for years. GAP trades at roughly 7x trailing and 9x forward earnings, and 5.7x EV/EBITDA, the lowest in its group.

Looks like a screaming mispricing. It isn’t. GAP’s EBITDA margin has bled from the high teens in the 1990s and 2000s to about 7% today. The reasons are structural: gross margin fell from the mid-40s to around 40% as the brands lost pricing power (check out my Weekly on Brand Investing), and costs climbed from the mid-20s to 33.5% of sales as competition, online fulfilment and weaker stores piled on. A 7% margin is worth fewer turns of EBITDA than a 14% one.

Line GAP up against its peers and it is almost mechanical. Ross and TJX run 14% to 16% margins and trade near 19x to 20x. Urban Outfitters and American Eagle sit in the middle on both. GAP is stuck in the bottom-left corner, 7% margin and 5x, right on the same line as everyone else (R-squared 0.87). The market isn’t mispricing GAP. It is pricing it exactly where a low-margin retailer belongs.

The cheap multiple is an honest read on the margin, not a gift.

Assessment: Challenged, and this is the crux. My base case says $30, so the upside is real on paper. But stocks re-rate when the margin or the growth turns, and GAP is flat on both. Cheap is the right price for a business the market has decided can’t grow into a better margin.

Q1 FY2026 in one read

The quarter was a small operational step back dressed up as an EPS raise. Sales of $3.5B rose 1%, comps were +2%, the ninth straight positive quarter. Adjusted EPS of $0.38 met consensus. GAAP EPS was $0.90, but $313M of that was a one-off legal settlement, so the clean number is $0.38. I read it as in line, not a beat.

The brand split is the story under the headline. The Gap brand comped +10% on denim, fleece and kids. Banana Republic did +2%, its fourth positive quarter. Old Navy, 56% of sales, managed only +1% as weak women’s dresses cancelled out wins elsewhere. Athleta comped -11% and is still being rebuilt, with the new assortment landing in the back half. So the engine idled while the brand that is meant to be the kicker kept shrinking.

Margins went backwards, as management flagged they would. Gross margin of 40.5% fell 130bps on about 200bps of tariff drag, and still beat guidance. Adjusted operating margin slid 230bps to 5.2% on tariffs and investment. Not alarming. Just not a business breaking out.

Capital return did the heavy lifting. GAP handed back $464M, $401M in buybacks and $63M in dividends, with $599M left on the authorization. That same $313M settlement that flattered EPS also helped pay for the buybacks. The dividend is $0.175 a quarter and well covered.

The guidance tells the real story

The EPS went up while the business guide went down. They lifted adjusted EPS to $2.30 to $2.40 and, in the same release, cut sales growth to 1% to 2% from 2% to 3%. The margin guide didn’t move. So where did the extra EPS come from? A lower tax rate (about 25% from 27%), more interest income on the cash (about $25M), and fewer shares from buybacks. Every dollar of it sits below the operating line. Meanwhile capex jumps to about $650M from $470M, which keeps FCF in check this year.

On credibility, the team has earned some trust. Dickson and the CFO have hit their numbers at least for the last 12 quarters, so I take the guide at face value. That is the problem. When you trust the guide and the guide is flat, you are underwriting flat.

What Gap is worth: base, bear and bull

I ran all three on my new DCF calculator on RankedStocks.com, free for you to use. The assumptions come straight from GAP’s own history and guidance: low-single-digit growth, a gross margin near 40.5%, an operating margin that tops out around 8%. Every input has a note in the model; the summary is below.

The implied-expectations read is where it gets interesting. At about $20 the market sits between my bear and my base, closer to bear. It is pricing a business that never grows and never expands margins, basically permanent decline, which the last nine quarters argue against. That gap is the bull case.

The risk/reward is okay. About $10 of upside to base against $6 of downside to bear, roughly 1 to 1.6, and the probability-weighted average is around $30. Fine, but short of the 1 to 2 I want before I open a position. So even as a fresh idea, GAP wouldn’t clear my bar.

Odds are that a few years out, GAP will be trading near $30, about 50% above today's price. Get there in a year and that is a 50% return. Three years, it is 14.5% a year. Five years, 8.5%, which is index-fund territory for single-stock risk. The value is real. The timing is everything, and nothing I can see points to the fast end.

Which brings us to the only question that matters.

So why sell? The missing catalyst

Everything above says cheap. None of it says what makes the stock move in the next year or two. A discount closes when something forces the market to re-underwrite the earnings. I went looking for that something. I can’t find it.

Growth is capped by design. My model caps sales growth near 2% and operating margin at 7.5% to 8%, and the EBITDA-margin chart above shows why: it has been grinding down for twenty years. No moat, four brands in the most brutal corner of retail, an Old Navy store count that barely moves. Nothing here inflects.

The obvious re-rating events are dead or distant. The Old Navy spin-off that might have unlocked value was scrapped years ago. Management won’t do deals after a run of bad ones. Athleta, the one brand with real upside, is a 2027 story at best, and its new range doesn’t even hit shelves until the back half. Tariffs are a swing factor, but a resolved tariff is just relief, not growth.

The buyback supports the price but doesn’t re-rate it. GAP can buy back billions over the next decade out of FCF, which puts a floor under the shares. It is not a spark, and the company has a habit of buying higher than today.

The market has already seen the good news. Nine positive comps, a +10% Gap quarter, stable margins, all public, and it still trades at 7x earnings. When a business does everything right for two years and the multiple won’t budge, the multiple is telling you something. GAP has been stuck in single digits for years.

Add it up: a cheap, safe, well-run stock with no engine. The downside is protected, but so is the upside, by a lack of a catalyst. I can be right on value and still make nothing for two years.

Risks to the redeploy call

Selling something cheap has its own risks. Here is the honest register, including the case against me.

Materialized: the sales slowdown I worried about showed up. The full-year guide got cut to 1% to 2%, and Q2 is guided flat to down 1%.

Faded: balance-sheet risk. Net cash, nothing due until 2029, a covered dividend, rising interest income. Not a stressed company, which is exactly why the downside is shallow.

Unchanged: tariffs. Still a swing factor on gross margin, now softened by a new 10% Section 122 assumption worth about $80M, or 50bps, this year.

New risk, and it’s mine: I am selling at about two-thirds of what I think it is worth. If a catalyst I can’t see shows up, an activist, a buyer, an Athleta reset that actually works, or the market just deciding to close the gap, I hand back +51% to base. That is the real cost, and I am making the call with eyes open. The bet is that clearer stories deliver that +51% with a lot less waiting.

Performance versus the sector

Since I bought it, GAP has drifted down. It is off 23% year to date, worse than TJX [] at -2% and Urban Outfitters [] at -9%, though not as bad as American Eagle [] at -36%. Same margin story: TJX, the highest-margin name, holds up best; the low-margin turnarounds take the beating. The market is paying for durable margins and punishing everything else, and GAP is on the wrong side of it.

Verdict

I’m closing GAP. It is worth about $30 and trades under $20, and I still can’t tell you what moves it there inside two years.

Hello fellow Sharks,

The portfolio beat Mr. Market last week and sits at +39.8% YTD, 3.1x the market since inception. If you want to skip straight to the numbers, jump to the Portfolio Update.

It has been a busy week. The July stock pick is almost done. I have also been refreshing the thesis on the May 2025 pick:

At one point we were up 188% on this position; it has since retracted to a gain of 80%.

A year has passed since my last refresher, so the position is due a second look. That refresher will most likely be available to paid subscribers only.

Some RankedStocks news. I just shipped a great feature: alerts. Any financial site can ping you when a price moves. RankedStocks also alerts you when the RS score or any factor changes: valuation, growth, profitability, sentiment, or outlook. A price alert tells you what the market did; a factor alert tells you the business changed. Only one of those is worth interrupting your day for, and this week’s Thought explains why. And in case you missed it: the whole site is now free.

As for the Thought of the Week, these past couple of weeks I have been listening to audiobooks while walking Nagnush. A book I read years ago struck a different chord this time and turned into the best description of Mr. Market I’ve found anywhere, from an author who wasn’t writing about markets at all. It also explains what the Mustang above is doing at the top of this article.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• The Voice in Your Head Is Mr. Market

• Portfolio Update

Thought Of The Week

The Voice in Your Head Is Mr. Market

You may remember that we all went to Chile in December 2025. In April, my wife, my daughter and I flew back; my mother stayed behind for a bit, and her dog came home with us. Since then I’ve walked him twice a day, every day.

With her, he was used to three or four walks a day. With me, he gets two, because two is what my calendar can give him. I pay down the guilt with distance. We go long, and I fill the hour with audiobooks.

I have a rule for audiobooks. They get two kinds of books: ones I’ve already read and want a refresher on, and ones that seem worth hearing but don’t earn a slot in my few real reading hours. The Untethered Soul by Michael Singer sat in the first bucket. I read it once years ago, filed it under interesting, and moved on.

This time was different. Heraclitus said you never step into the same river twice, because it’s never the same river and you’re never the same man. Books are rivers. While the words hadn’t changed since my first listen, I had.

Maybe it was the state of mind I carried onto the walk: the AI names were correcting, and I had meditated before clipping on the leash, a practice I reinstated a month ago. Somewhere in the second chapter, on a loop around the neighborhood while Nagnush audited every mailbox, it clicked: Singer keeps describing a character I’ve known my whole career.

He was describing Mr. Market.

Every time a passage mapped to markets, I typed a quick note on my phone mid-walk. Back home, I searched each one in my Kindle copy and highlighted it. The indented passages in this article are the highlights.

The timing sharpened the whole thing. The portfolio has had a loud few weeks, and the AI-related names have been the loudest: Powell [], Sterling [], Micron [], Dell [], and Celestica [] all swung hard while their own businesses reported nothing that justified the ride.

The trigger sat in Seoul: Samsung estimated a 19-fold rise in Q2 operating profit, beating expectations, yet the market traded on the worries about what comes next, and the whole complex sold off. I covered why I think the AI cycle itself is intact in Weekly #87.

Weeks like these are when the voice in your head talks the most. Singer’s book turned out to be the best manual I’ve found for understanding what that voice is.

Ben Graham taught us how to handle the madman outside.

Singer explains the one inside.

So this week: what the book says, the places where it describes Mr. Market almost line for line, and how I used it during the swings in our AI names.

The maniac Graham hired

In chapter 8 of The Intelligent Investor, Ben Graham asks you to imagine owning a stake in a private business alongside a partner called Mr. Market. Every day, without fail, he shows up and names a price at which he’ll buy your share or sell you his. Some days the number is sensible. Many days it’s absurd in one direction or the other, because your partner has incurable emotional problems. You’re free to ignore him. He’ll be back tomorrow with a new quote and no memory of yesterday’s.

Most people read that as a lesson about the stock market. Read it again after Singer and you notice what Graham did: he took an emotion that lives inside everyone and moved it outside, into a character, so his students could finally see it.

Now listen to Singer explaining how to get to know the voice in your head:

The way to catch on to what your inner roommate is really like is to personify it externally... Just imagine that another person is now saying everything that your inner voice would say. Now spend a day with that person.

That’s Mr. Market. Graham ran the exact exercise Singer prescribes, six decades earlier. He took the crowd’s inner monologue, gave it a body, and sat it across the desk so we could watch it rave from a safe distance.

Which raises the uncomfortable part. The daily quote on your screen is the sane half of the arrangement. A price is a number that clears buyers and sellers. The mania Graham warned about needs a mind to run on, and the only mind at your desk is yours.

Five places the book describes Mr. Market line for line

1. He never stops quoting. Singer’s opening observation:

In case you haven’t noticed, you have a mental dialogue going on inside your head that never stops. It just keeps going and going.

Swap “inside your head” for “on your terminal,” and you have Graham’s partner. The exchange quotes MU every second of the trading day, whether or not anything happened at the company. The voice quotes your feelings about MU on the same schedule. You’re running two tickers: one for the stock, one for your emotions about the stock.

2. He argues both sides of the same trade.

Notice that the voice takes both sides of the conversation. It doesn’t care which side it takes, just as long as it gets to keep on talking... It will change sides in a moment if that seems to help.

That’s a perfect description of any stock’s price action across a volatile month. At the highs, my voice tells me the memory supercycle is generational and trimming would be a crime. Three red sessions later, on the same facts, the same voice explains that cycles always end and the smart money is clearly leaving. Graham’s Mr. Market flips from euphoria to despair on nothing; Singer shows you the flip happening in-house.

3. He’s wrong constantly and never gets fired. Singer imagines hiring an advisor who gives you the advice your inner voice gives you:

Had you listened to the advisor, you never would have picked up the phone. Wouldn’t you fire them on the spot? ... No, you never hold it responsible for the trouble it causes. In fact, the next time it gives advice, you’re all ears.

One reason I started Beating The Tide was to keep a public record of my picks, with the thesis and the post-mortems attached. A side benefit: the record keeps me honest, and it lets us check the voice’s track record on our own names.

CLS is my favorite example.

The market sold the stock down after earnings beats, three quarters running. I wrote up the pattern in the Q1 déjà vu review…

… and Weekly #66 covered the 12% drop that came with no valid reason attached.

Each drop was a pitch from the voice: something must be wrong, get out. Anyone who took the pitch sold a record-margin business to whoever was doing the arithmetic. The cost of listening is measurable: Barber and Odean studied 66,000 brokerage households and found the most active traders underperformed the market by about 6.5 points a year.

4. He’s loudest exactly when he should be quietest. Singer explains when the voice talks most:

For example, in some cases the mental voice talks for the same reason that a teakettle whistles. That is, there’s a buildup of energy inside that needs to be released.

Volatility is the flame under the kettle. Notice that the voice’s volume tracks your P&L swings, and carries no relationship to the amount of new information. On the days our AI names moved 5% or more, the businesses published nothing. The voice still filed hourly reports, followed by reports from Bloomberg and the rest of the talking heads. I wrote about what the tape looks like at maximum kettle-whistle in Weekly #24, when the VIX hit 45.

5. He narrates instead of informs. This one deserves its own section.

A price is a fact. The story about the price is a choice

My favorite passage in the book is about weather:

You’re walking outside in the winter, you start to shiver, and the voice says, “It’s cold!” Now how did that help you? You already knew it was cold. You’re the one who’s experiencing the cold. Why is it telling you this?

Singer’s answer is the most useful sentence in the book for anyone who owns stocks:

You re-create the world within your mind because you can control your mind whereas you can’t control the world.

MU down 6% is a fact. “The AI trade is unwinding” is narration. The fact arrives from outside and is beyond your control. The narration is manufactured inside precisely because it’s the only part you can control. That’s why narrating feels productive during a drawdown: it converts an uncontrollable price into a controllable story. It also explains the existence of an entire industry. Financial media is the inner voice. The red numbers already announced the turmoil; CNBC airs “Markets in Turmoil” to sell the narration, and demand for narration peaks exactly when the kettle whistles. Markets in turmoil is a broadcast. Minds in turmoil are the audience. I made the broader case for tuning this out in Weekly #23.

The test I now apply to any sentence the voice produces on a red day: does this sentence contain information that the price itself didn’t already give me? “STRL is down 9%” fails the test; I can see the screen. “STRL’s largest end market just cut capex guidance” passes it, and notice that sentences which pass the test come from filings and calls, never from the voice. The voice’s only input is the price.

There is always another problem

Early in the book, Singer makes an observation about the mind that doubles as the most accurate description of market sentiment I’ve read:

Honestly, when was the last time you really had nothing bothering you? Before you had your current problem, there was a different problem. And if you’re wise, you will realize that after this one’s gone, there will be another one.

Run that against our own archive. In the 21 months this newsletter has been tracking them, the market’s problem of the day has been tariffs, then a VIX at 45, then tariffs again, then a Fed nominee, then a war, then another war and now the durability of AI capex. Each one was presented as the reason to wait. Each one passed, and a new one checked in before the old one checked out. The voice runs the same inventory system: it has never once reported that everything is fine, because a mind with nothing to solve is out of a job.

Singer traces this to fear, and to what fear does to the world it looks at:

If you have a lot of fear, you won’t like change. You’ll try to create a world around you that is predictable, controllable, and definable.

In markets, the demand for a predictable, controllable, definable world has a name: waiting for clarity. It sounds prudent. It’s the most expensive habit in investing because clarity is the one product the market prices instantly and fully. By the time the AI capex question is settled, the settlement will be in the quotes. The uncertainty premium is the payment for showing up while the voice still has a problem with inventory. That’s the mechanism behind Weekly #77’s higher-order thinking: the crowd’s first-order fear is the second-order buyer’s margin of safety.

Why a 9% drop can feel like 2008

The book’s deepest idea is the samskara, and it explains something about drawdowns that took me years to learn on the Moneda credit desk. Singer defines it like this:

A Samskara is a blockage, an impression from the past. It’s an unfinished energy pattern that ends up running your life.

His example: years ago, you thought you saw your girlfriend’s light blue Mustang with someone else in it. You never dealt with it, just pushed it down. Five years later, married, happy, driving with your family, a light blue Mustang passes:

Five years ago, for just a few moments, an event took place. You never discussed it with anybody, and now five years later, a light blue Mustang drives by and it changes the energy flow through your heart and mind.

Every market participant is driving around with a garage full of light blue Mustangs. 2008 if you’re old enough. March 2020. The 2022 bear. The position you rode to zero early in your career and never wrote the post-mortem for. Mine includes a Brazilian sugar producer from my junk-bond years that taught me what leverage does to a cyclical, a lesson I paid tuition for in the 2014-15 downcycle.

When a portfolio name gaps down 9%, the pain you feel is a compound object: today’s price plus every unprocessed drawdown in the archive. This is why the emotional intensity of a red open is data about your history rather than data about the company’s future. Once I understood that, the intensity itself became useful. The stronger the urge to act immediately, the more likely it is that a Mustang just drove by, and the less likely it is that anything happened to the business.

The archive stores euphoria too. Singer is careful to point out that good experiences get stuck the same way:

Yes, you store positive impressions too. When a wonderful experience happens to you, it doesn’t make it through because you cling to it.

The positive samskara has its own trading desk. Everyone who watched a friend ride Nvidia [] through 2023, or watched our own POWL run, carries a stored flash of that feeling, and the voice replays it every time a hot name gaps up: this one is the next one, get in before it runs. The same discipline applies on both sides: the feeling is archived footage, and the witness asks for today’s documents.

I’ve been victim to this

The recent stretch in MU gave me a live exercise. At its peak the stock was up close to 26% for the month; it finished flat. The rest of the cohort split, as the chart at the top of the article shows: DELL ended up about 10%, while POWL, STRL, and CLS ended down.

I bet your inner voice said at least one of these phrases, if not all of them. Mine did:

• “You’re sitting on a 4x in MU. Nobody ever went broke taking a profit.”

• “Lock in MU here, buy it back 10% lower, that’s free money.”

• “This is how tops start. Remember 2022? It started exactly like this.”

Then I noticed the tell, and it’s the single most useful thing this book did for my process. Every pitch cited the price. None cited the thesis. Nothing about HBM supply agreements, data center backlogs, electrical gear lead times, or a single line from a 10Q. The voice had no new facts about our companies. The week’s one real datapoint was Samsung’s beat, and the voice read a competitor’s record quarter as a warning about the cycle’s future. It was arguing from the tape, and from my archive of Mustangs.

So I did what I always do when the voice gets loud, which is also what I did when the disposition effect nearly cost me my best compounders: I asked the two questions from Weekly #81.

Would I open this position at today’s price? Has anything changed in the thesis? Those questions have a useful property: the voice can’t answer them. They require documents, and the voice doesn’t read.

The thorn, or why protection becomes the problem

Singer’s best parable is about a thorn. Imagine one lodged in your arm, touching a nerve. You have two options: take it out, or make sure nothing ever touches it. He walks through the second option: you thin out the woods so branches can’t brush it, you build a sleeping apparatus, then a full collision-sensing device with wheels and hydraulics. You declare victory. His verdict:

It turns out that the life of protecting yourself from your problem becomes a perfect reflection of the problem itself. You didn’t solve anything.

Portfolios are full of thorn-protection devices. The thorn is the pain of watching a position fall. The apparatus is everything we bolt on to avoid ever feeling it: stop-losses that convert temporary declines into permanent exits (I stopped using them 14 years ago), trimming winners to a size that no longer matters so we can sleep, sitting in cash waiting for clarity that’s never on the schedule, checking the app a dozen times a day so the drop can’t surprise us. Each device is sold as risk management. Each one is the thorn running the portfolio from inside the arm.

The alternative is to pull it out: accept, in writing, that drawdowns are the toll the market charges for multibagger returns. Weekly #64 made this case: no investment is risk-free, and that’s the point.

POWL and STRL only became 1,000% positions because they were allowed to fall double digits along the way without being sold. Every one of those drawdowns touched a nerve.

Let go now, or fall

Chapter 8 contains the closest thing the book has to a trading rule:

When your stuff gets hit, let go right then because it will be harder later.

Singer then describes, step by step, what happens when you don’t, and he might as well be narrating a capitulation. First the disturbance pulls your consciousness in. Then perception distorts:

Now, as you look out through your disturbed energy, everything is distorted by the haze of your disturbance. Things that looked beautiful now look ugly.

Any long-term holder knows that haze. It’s the state where a business you researched for months suddenly looks broken because the stock is down, where record backlog reads as a peak instead of a foundation. And then Singer names the point of no return:

It’s one thing if the disturbance is going on inside of you. But the moment you allow it to express itself, the moment you let that energy move your body, you have descended to another level. Now it’s almost impossible to let go.

In a portfolio, letting the energy move your body has a specific name: a market order at 9:31 on a red morning. The feeling becomes a trade, the trade becomes a fact, and the fact has commissions, taxes, and a hole where a compounder used to be. My standing rule follows directly: no sell decisions while the market is falling. Decisions happen on quiet days.

Companies and investment theses do break.

I closed Heritage [] at +20.8% this year and AXIA [] on the ADR delisting, and both were sells I stand behind. The difference is the sourcing. A broken thesis cites facts: a delisting, reserve deterioration, a customer walking. The voice cites price. My test: if I can write the sell case without mentioning where the stock is trading, it might be a thesis break, and it gets the full work-up. If the sell case collapses without the price in it, it’s the roommate talking, and the answer is the same one I give him every time.

Unconditional process

Late in the book, Singer asks the reader a single question: do you want to be happy, or do you not want to be happy? The trick is in what comes next. Most people answer yes with conditions attached, and he lists the conditions life will test you with. Read his list closely:

Now, if you say yes, it might happen that your wife leaves you, or your husband dies, or the stock market crashes, or your car breaks down on an open highway at night.

The stock market crash is on the list. A meditation teacher reached for a market crash as one of the four archetypal tests of whether a person meant what they committed to. He understood something most people haven't accepted: the crash is a scheduled examination. The date is unknown; the event is certain.

Singer’s point about happiness converts directly into a point about process. A commitment with conditions attached is a mood. “I’m a long-term investor unless the drawdown passes 20%” describes a person who will sell at the bottom, with extra steps. “I hold quality compounders unless the thesis breaks” only counts if it survives the week the voice screams, which is the one week the commitment exists for. The whole value of a process is delivered in the moments when following it feels wrong. On calm days a process and a mood are indistinguishable; the selloff in our AI names is precisely where they separate.

So the question Singer would ask any of us is the portfolio version of his happiness question:

Do you want to be a long-term owner, or do you want to be one unless it hurts?

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The witness has a desk

The book’s core move is a question: if you can hear the voice, who’s listening? Singer:

There is nothing more important to true growth than realizing that you are not the voice of the mind—you are the one who hears it.

He calls that listener the witness. In meditation, you reach it by stepping back. In investing, the written thesis is the witness’s memory. The DCF is its sense of value that doesn’t reprice at 9:30. The 5% position cap from Weekly #79 is its guarantee that no single kettle-whistle can force an action.

The quarterly audit is its calendar, deliberately out of sync with the voice's schedule, which is always now. The journal is where the voice's pitches go to be graded later instead of being executed immediately. Even my RankedStocks scorecard belongs to the witness: a first opinion from a machine with no amygdala attached. The new alerts I just shipped extend the same idea: set them on the RS score or any factor rather than on price, and the machine interrupts you when the business changes, staying quiet when only the quote does.

Note what’s missing from that list: any attempt to silence the voice. Singer is explicit that you’ll lose that fight:

Do not fight it. Do not ever fight your mind. You will never win.

The goal is cheaper than silence. The voice can talk all it wants; it just doesn’t get order-routing privileges. Graham said the same thing about his Mr. Market: you’re free to ignore the quote. Singer adds the part Graham left out, which is that ignoring the quote outside is easy compared to ignoring the analyst inside who keeps repeating it.

Live workout: the voice vs. the witness, name by name

Here’s the exercise applied to the AI cohort, ordered by size of gain. For each: what the voice pitched during the volatility, and what the witness asks instead. The witness’s question is always the same two from Weekly #81: would I buy today, and did the thesis change. I’ll keep the numbers out of this section on purpose; the deep dives and updates linked carry them.

STRL, +1,000% name that has recently retracted to +790%.

The voice’s pitch during the drawdown: “data center construction is the bubble’s ground zero.” The witness’s answer is that we already acted, calmly, months ago: I trimmed STRL when the asymmetry was spent and documented why in the Q1 update. Trimming on valuation during calm is what letting go early looks like in practice. Selling the rest into a red tape would be the fall.

POWL, the second +1,000% name that has recently retracted to +767%.

The voice’s pitch: “a gain like this doesn’t survive a capex pause, take it.” The witness’s read: demand for electrical equipment stretches across data centers, grid hardening, and energy, lead times remain long, and the thesis question is demand durability rather than the tape. I’m holding the unicorn at fair value, a decision made on a quiet day, in writing.

MU, my top pick for 2026.

The inner voice: “memory is cyclical, this is the top, lock the 4x.” The witness’s file says the supply side of this cycle is behaving unlike any prior one and the customers keep pre-paying; I raised the target after Q3 results in Weekly #88, off filings rather than feelings. Volatility in the stock has run far ahead of volatility in the thesis.

DELL, the operator thesis.

The voice hates round-trips: “you’ve watched a 220% gain wobble, protect it.” The witness re-reads the Q1 FY2027 update: the AI server backlog is the largest in the company’s history, and the original mispricing (the market confusing commodity with uninvestable) hasn’t fully closed.

CLS, Mr. Market’s favorite gift shop.

Three post-earnings drops after three beats, and Weekly #54 already showed what happens when we ignore Mr. Market. The voice reads each drop as a verdict. The record reads each drop as an entry that worked.

The middle way

The book closes with the Tao, and regular readers will recognize the shape: it’s the pendulum from Weekly #70.

Singer:

The extremes create their opposites; the wise avoid them.

All-in and all-out are the two extremes the voice loves, because both are dramatic and drama is its product. The middle way in a portfolio is position sizing: sails held neither so loose that the wind is wasted nor so tight that one gust tips the boat. Singer’s sailing image is the best description of sizing I’ve read outside a finance book. Hold the rope just right and the same wind that capsizes the over-levered moves you forward. That’s the whole argument of Weekly #79 in one image.

And one more line from the Tao chapter, which I’m considering framing and hanging in my office 😊:

Remember, whoever remains present with fixity of purpose comes out on top in the end.

Portfolio Update

Last week, the portfolio outperformed Mr. Market, closing some of the ground we lost two weeks ago.

Portfolio Return

• Month-to-date: -3.1% vs. the S&P 500’s +1.0%.

• Year-to-date: +39.8% vs. the S&P 500’s +10.7%. That is a gap of 2,910 basis points.

• Since inception: +97.3% vs. the S&P 500’s +31.7%. That’s 3.1x the market.

Share

Contribution by Sector

Tech and energy led the gains, partially offset by gold.

Contribution by Position

(For the full breakdown plus commentary on earnings results and the big movers, see Weekly Stock Performance Tracker)

• +89 bps (Thesis)

• +60 bps (TSX: CLS) (Thesis)

• +10 bps (Thesis)

• +3 bps (Thesis)

• +1 bps (Thesis)

• +1 bps (Thesis)

• -13 bps (Thesis)

• -13 bps (Thesis)

• -28 bps (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Further Sunday reading to help your investment process:

• Never Stop Asking Why, Using the “5 Whys” to Solve Problems and Pick Stocks

• The Rise and Fall of Moats: When Walls Built to Protect Become Traps

• Design Your Life Around Your Nature

• How I Earn $3,000-$7,000 a Month While Waiting to Buy Great Stocks Cheaper

• Cheat Codes for Life Monopoly

Hello fellow Sharks,

It was a down week for the portfolio while the S&P 500 was slightly up. The portfolio is still up +36.4% YTD and 3.1x the market since inception. If you want to skip straight to the numbers, jump to the Portfolio Update.

Last week was quiet on the surface. By Tuesday I realized the stock I was studying wasn’t going to be the July pick, so the hunt for a new candidate continues. The writing was anything but quiet, though: I finally publish my thoughts on the SpaceX IPO and what it means for our portfolio. One number to whet your appetite: for SPCX to repeat Tesla’s post-IPO run, it would need to be worth about $437 trillion by 2042. The entire planet produces $126 trillion a year. That math, and a lock-up calendar that starts biting in August, is this Thought of the Week.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• The $437 Trillion Rocket

• Portfolio Update

Thought Of The Week

The $437 Trillion Rocket

Everyone is talking about SpaceX []. That alone is usually my cue to write about something else. I’m covering it anyway, for a selfish reason: I think this stock will shape the entire market in 2027 and spill over into positions we actually own. The float mechanics, the index inclusions, and the sheer size of the thing will move our AI names whether we hold SPCX or never touch it. So consider this less an opinion on a rocket company and more a map of where the turbulence will come from, so we can position the portfolio around it.

I have been in this exact situation before. Every few years, a company comes along that pushes the technological frontier, and I know society will eventually be better off because of its efforts.

I love that as a citizen!

Then I put on the investor hat, and the fun stops. I know I’m not a typical investor; I stick to my process, whether that serves me well or badly. The person who bought Starbucks [] because he loved his Frappuccino and the one who bought Tesla [] because of its push for EVs are millionaires now thanks to that.

Good for them, but in my book, they speculated rather than invested. SpaceX puts me in that seat again: I like what the company is doing on the space front (more than what Grok is doing on the AI front 🙂), and for the rest of this piece, I will look at the shares the only way I know how, as an investor.

What the Market Bought

SpaceX priced its IPO at $135 per share, selling 555.6M Class A shares and raising $75B at a $1.77 trillion valuation, the largest equity raise in history. Only 4.2% of the 13.1B shares outstanding went to the public. Elon Musk kept 82.4% of the voting power through 10:1 super-voting Class B shares, making SPCX a “controlled company” that’s exempt from a chunk of Nasdaq’s governance rules. Public buyers never saw $135: the stock opened at $150.05 ($1.98T market cap) on its first day, hit an all-time high of $225.61 the next session ($2.96T market cap), then to the $147 area, erasing a trillion dollar in market cap on the way down. As I write, it has stabilized near $162, a $2.1 trillion market cap.

The business the market bought has three segments.

Space, the launch business, did $4.1B of revenue in 2025 and lifted more than 80% of all mass sent to orbit worldwide.

Connectivity, meaning Starlink, did $11.4B, up 50% y/y, passed 10 million active customers in February, and is the only segment that makes real money: $4.4B of operating income in 2025. AI, the old xAI folded in this February, did $3.2B of revenue and lost $6.4B at the operating line. Group totals for 2025: $18.7B of revenue, and $6.6B of adjusted EBITDA…

… and $20.7B of capex.

Q1 2026 alone added another $4.3B net loss. Management’s targets on the roadshow deck are the pitch in miniature: gross margin from 49% to ~70%, GAAP net margin from -26% to ~45%.

The first month as a public company was busy even by Musk standards. Four days after listing, SpaceX agreed to buy Cursor (Anysphere) for $60B in stock, a coding-assistant startup at roughly 15x its reported ARR. A week later it priced a $25B inaugural bond across five tranches (5.35% to 6.65%, 2031 to 2056) to repay the bridge loan, with $100.8B of cash on hand as of June 19. Sequoia’s Roelof Botha joined the board. The company also disclosed the Terafab chip-fab project with Tesla and Intel [], cloud compute agreements with Anthropic and Google, and Starship V3’s first test flight.

I have been reading the bull and bear cases from other analysts. Bulls note those two compute deals alone reportedly amount to a $26B annualized run-rate, more than all of 2025 revenue. That’s the bull case in one line: the only company that can build the rocket, the constellation, and the datacenter, with demand landing faster than it can build. The launch cost curve is the part I find hardest to argue with: Falcon 9 cut the cost per kilogram to orbit from a $18,500 historical average to $2,700, Falcon Heavy to $1,400, and Starship targets a +99% reduction.

The bears have numbers too. The sharpest critique argues Starlink’s claimed $1.6T connectivity TAM overstates the realistically addressable market by an order of magnitude, closer to $129B once you strip out populations that terrestrial broadband already serves cheaper. And Grok, the model the AI segment’s $27T opportunity rests on, still trails OpenAI, Anthropic, and DeepSeek in enterprise adoption while losing twice its revenue.

Why There’s No DCF This Time

Long-time readers know I build a DCF for everything I cover. I didn’t build one for SPCX. A DCF prices a stream of cash flows you can reason about. At $1.77 trillion, 95x trailing sales, almost all of the value sits in ideas rather than in the businesses that exist today: orbital AI datacenters, point-to-point rocket travel, asteroid mining, lunar manufacturing, Mars cargo. You can’t discount a cash flow that has no product, no price, and no customer yet. Any spreadsheet I built would be false precision. I see an investment in SPCX stock as a speculative bet on the future, closer to a venture capital position than an equity you can underwrite.

, who is braver than I am, did run the numbers and valued SpaceX at $1.22 trillion 31% below the IPO valuation.

went further, arguing the structure only works while each new story raises capital for the last one.

Paul Krugman

Elon Musk, Human Ponzi Scheme

Yesterday I took a short trip. I began with a ride on the local Hyperloop, which ran through a tunnel dug by Boring Company. Then I used my neural implant to summon a fully self-driving Tesla robotaxi. While enroute I read the latest news from the Mars colony…

Read more

2 months ago · 5348 likes · 653 comments · Paul Krugman

The bulls answer both with Starlink’s compounding and the $26B of compute contracts. All three can be right about the facts and still disagree about the price.

The Tesla Mirror

Now, take my skepticism with a grain of salt, and I’ll hand you the salt myself: I never invested in Tesla. My reasons then rhyme with my reasons now. The valuation priced flawless execution of products that didn’t exist yet, everything depended on one man, and the promises (Model 3 volumes, full self-driving, robotaxis) sat years ahead of the income statement.

Here’s what that caution earned me:

Up 24,600% since the shares became public. I was right about the reasons and wrong about the stock, which is the most expensive kind of wrong. So when I tell you SPCX looks uninvestable to me, weigh that against the scoreboard.

But before you dismiss me and buy more SPCX, look at the two differences between these launches.

Difference #1. The starting line.

When Tesla went public in 2010, it raised $226M at a $1.7B market cap, about $2.2B after the 41% first-day pop. Had Tesla been in the S&P 500 that day, it would have ranked among the smallest 20 or so companies in the index, roughly the bottom 4%. There was a continent of room in front of it.

SPCX launched at $1.77 trillion. That would rank sixth in the S&P 500 today, behind only Nvidia [], Apple [], Alphabet [], Microsoft [], and Amazon [], larger than roughly 99% of the index, including the company its own founder already runs.

Run the replication math. For SPCX to repeat Tesla’s 247x over the next 16 years, its market cap would have to reach about $437 trillion by 2042. The IMF puts world GDP at roughly $126 trillion this year.

Grow the entire global economy at 4% nominal for 16 years, and you get to about $236 trillion in 2042. One company would be worth 1.9x the yearly output of the entire planet. And suppose Starship really does find something extraordinary up there, gold in an asteroid, rare earths on the Moon. It would still take a decade and hundreds of billions of dollars to commercialize; discovery and cash flow live years apart. More fundamentally, a $437 trillion valuation can’t be funded out of thin air: the money would have to drain out of every other stock, bond, and bank account on Earth. The S&P 499 would become a funding source for the one, hollowing out the rest of the market along the way. SpaceX shares would stop being part of the market and become the market itself. The constraint on repeating Tesla’s return is arithmetic, before we ever get to execution.

Now, the realistic bull case is nowhere near 247x. If revenue doubles to about $37B in 2026, as forward estimates suggest, today’s price works out to roughly 57x forward sales, and a 60-70% growth runway held for four or five years brings the multiple down to normal without the price falling at all. From there, bulls argue for 2-3x over a decade.

Fine.

But look at what you’re being paid to underwrite. To earn that 2-3x, you accept the downside of a 113x trailing multiple meeting gravity: at Nvidia’s 12x forward sales on that same $37B, SPCX is worth about $444B, a 79% drawdown; at Tesla’s 14x it’s worth about $528B, a 75% drawdown.

A bet that offers 2-3x if everything goes right and a 75% loss if the multiple normalizes early has a poor risk/return profile even by venture capital standards, and VCs at least get paid for that risk with 20-100x winners. SPCX offers venture-grade downside with large-cap-sized upside. The asymmetry runs the wrong way.

Difference #2. The multiple.

Tesla’s price-to-sales at IPO was about 20x on its first-day close. Over 16 years, the multiple peaked just above 30x twice and sits at 14x today.

Tesla’s stock went up 247x because revenue grew 874x, fast enough to outrun a multiple that ultimately compressed.

SPCX starts at 95x trailing sales at the IPO price, 113x at today’s $162. There’s no headroom in the multiple; the only direction over time is down, likely a long way down. So the entire return has to come from revenue growth outrunning that compression. To simply hold today’s price while the multiple compresses to Tesla’s IPO-day 20x, revenue has to grow more than 5x, from $18.7B to over $105B, with the market awarding no growth premium along the way. Guidance-based estimates for 2026 sit between $23B and $35B. Growth that explosive for that long has no precedent at this scale.

Ask yourself the question that matters:

Nvidia is the counterexample bulls will reach for: revenue ran from $27B in FY2023 to $131B in FY2025 and then $216B in FY2026 at megacap scale…

… and the stock still re-rated.

The difference is what a dollar of growth costs. Nvidia sells chips at 75% gross margin with almost no incremental capex per dollar of revenue; SpaceX has to launch hardware into orbit at 49% gross margin, and it spent $20.7B of capex last year to add $4.7B of revenue.

Gravity Has a Calendar: The SpaceX Lock-Up Schedule

Seven years ago I published a strong sell on Beyond Meat []. The thesis aged well.

I argued that BYND's price implied 56% annual revenue growth for eight years, and that the nearest catalyst for reality was the lock-up expiration on October 29, 2019.

On that day, 48 million shares, about 80% of the company, became sellable, and the stock fell 22% in a single session despite reporting its first profitable quarter the night before. Good news lost to supply. It never traded at those levels again.

I’ll draw the limits of the analogy myself. BYND was a single-product consumer fad with collapsing gross margins and competitors flooding in; SpaceX is a launch monopoly with a profitable Starlink growing 50% attached. The businesses are worlds apart but the lock-up mechanics are identical, and mechanics are what this section is about.

SPCX is the same movie with a bigger budget. I pulled the lock-up schedule from the underwriting agreement. Only 4.2% of the company floats today; the other 95.8% is locked behind a staggered timetable:

• August 2026 (Q2 earnings): insiders on the standard lock-up can sell 20% of their shares two trading days after the print, plus another 10% if the stock closes 30% above the IPO price ($175.50+) in five of the ten sessions into the release. At $162 that trigger is out of the money, for now.

• August 20 to October 24, 2026: an additional 7% unlocks at each of days 70, 90, 105, 120, and 135, another 35% in five slices.

• November 2026 (Q3 earnings): a further 28% unlocks.

• December 8, 2026 (day 180): the standard lock-up ends entirely. In parallel, holders who signed the “extended” form had 25% of their stakes on the schedule above, with the remaining 75% dripping out through 2027: 20% after Q4 earnings (February 2027), 10% on March 18, 20% after Q1 earnings, 10% on May 17, and the final 20% on June 12, 2027.

• June 12, 2027 (day 366): Musk’s own founder lock-up expires.

Follow the arithmetic: by next summer, nearly all of the 13.1B shares can trade against a stock that IPO’d on a 4.2% float. The scarcity that let more than $100B of retail orders chase 555.6M shares in June works in reverse when twelve billion shares become sellable into that same order book. The BYND lesson had nothing to do with insiders being villains; early investors sitting on 10x to 100x gains at a +100x sales valuation behave exactly as you’d expect. The lesson was that the catalyst for a story stock’s return to reality is usually the supply arriving rather than the story breaking. The calendar above is where I expect SPCX to meet its fundamentals, tranche by tranche, from August 2026 through the summer of 2027.

To be precise about the mechanism: an unlock creates the option to sell, and an option only becomes pressure if it gets used. I think it gets used, because the setup is a prisoner’s dilemma. Every early investor, including the ones who want to hold, knows that hundreds of other insiders sit on the same 10x to 100x gains and watch the same expiry dates, and that whoever sells first sells at the best price. When everyone reasons that way, the individually rational move is to sell early, and the collectively rational restraint never happens. Strong Q2 or Q3 prints could soften the first tranches; they don’t change the game theory of the later ones.

The Index Problem Nobody Is Pricing

Here’s where SPCX stops being entertainment and starts being everyone’s problem, including yours if you own any index fund. Damodaran’s video on the indexing question is the best 46 minutes on this, and his conclusion cuts through the noise: “S&P needs these companies in its index more than they need to be in the index.”

The index providers have split into two camps. Nasdaq changed its methodology to fast-track mega IPOs into the Nasdaq-100 after just seven days of trading rather than waiting for December reconstitution, and the rules weight SPCX at roughly 3x its float, about $270B of index weight, or 0.68% of the Nasdaq-100. FTSE Russell modified its fast-entry rule ahead of the mega IPOs. S&P alone held its ground: a company must trade for 12 months and show positive GAAP earnings before S&P 500 eligibility. So, SPCX enters S&P consideration around June 2027 at the earliest, and with a $4.3B net loss in Q1 alone, the profitability test could push it further out.

Look at what that timeline collides with. The forced passive bid (QQQ buyers taking $680 of SPCX per $100k invested, and every fund tracking Russell and MSCI benchmarks behind them) arrives through late 2026 and 2027. The lock-up supply above peaks over exactly the same window. Passive vehicles will be buying, on autopilot, at whatever price prevails, precisely when insiders receive their first permission slips to sell. If S&P adds SPCX in mid-2027, index funds buy their full weight within days of Musk’s own lock-up expiring. I couldn’t design a cleaner wealth transfer from index savers to early investors if I tried.

One assumption I should state openly: the wealth-transfer framing presumes the derating happens. The same collision can be read the other way, with passive inflows acting as price-insensitive buyers arriving exactly when insiders need liquidity, the way index demand absorbed every megacap secondary of the past decade. My base case is that the cushion is too small for the wave: the forced index bid is measured in tens of billions, while the calendar above frees up hundreds of billions. But if SPCX holds its price through the December and June unlocks, this thesis is wrong and I’ll say so.

History says index inclusion won’t save the price either. S&P’s own research on 715 additions from 1995 to 2021 found the inclusion pop has shrunk to roughly nothing.

Research Affiliates found that additions underperformed discretionary deletions by about 2,300 basis points over the following 12 months between 1989 and 2017, because committees add stocks when they’re hot. And the cleanest case study is Tesla itself: after joining the S&P 500 in December 2020, it underperformed both the index and AIV, the apartment REIT it replaced.

How much does any of this cost index investors? At the index level, honestly, little. SPCX is 0.68% of the Nasdaq-100, so even a 50% derate costs QQQ roughly 34 basis points, about $340 per $100,000 invested. The index survives; nobody’s retirement is dented by one weighting. The real cost lands on the people who bought SPCX directly in the first weeks. And the setup still matters for the pattern it establishes: index rules that absorb supercap IPOs at peak float scarcity, then keep buying on autopilot while the insider supply calendar runs.

What It Means for Our AI Positions

I laid out in Why AI Isn’t a Bubble, in 5 Dominoes why I think the AI buildout rests on real demand: the cost of a fixed level of AI capability falls roughly 10x per year, and every price drop has unlocked more than 10x the token demand. SPCX now sits directly on that chain, in both directions.

If SpaceX succeeds, orbital compute stops being science fiction and becomes a capacity story. The roadshow’s case is that solar-powered, radiatively cooled satellite clusters sidestep the two constraints choking terrestrial datacenters: power and permits. On the June teach-in call, management framed orbital datacenters as “literally another constellation,” an extension of what Starlink already does. SpaceX could do for space-based datacenters what Tesla did for EVs: force the industry to treat it as a market rather than a demo. That accelerates domino #4, the falling cost per token, which is long-term bullish for everything downstream of cheap intelligence, including Micron [], TSMC [], and Dell [] in our book. Chips get consumed wherever the datacenter sits, on land or in orbit.

The nuance is our grid names: Powell [] and Sterling [] earn their multiples on terrestrial datacenter buildout, and a credible orbital alternative would cap that story sometime in the 2030s. The timeline math protects them for now; even SpaceX needs Starship V3 flying at industrial cadence before a single gigawatt goes up.

Before treating orbital compute as inevitable, let’s talk physics. In orbit every watt has to leave by radiation: scaling ISS-class hardware implies roughly 100 tons of radiators per megawatt of compute, which is why cooling, rather than launch cost, is the real obstacle to space datacenters. Today’s GPUs aren’t radiation-hardened, and satellites refresh on five-to-seven-year cycles while GPU generations turn over every 12 to 18 months, so an orbital cluster risks being obsolete before it amortizes. If orbital compute proves permanently uneconomic, the AI segment falls back to Grok subscriptions and X advertising, and the $27T TAM collapses with it.

This is the part I’ll be watching most closely. Real traction in orbital compute, meaning a working cluster at scale rather than a slide, won’t touch Powell’s or Sterling’s earnings for years, but markets discount early, and it could start compressing their multiples long before it shows up in a backlog. If I see that traction, I’ll act on the multiple risks rather than wait for the earnings evidence.

If SpaceX fails, or simply derates 50% under lock-up supply, the damage arrives through sentiment rather than earnings. A top-six market cap falling that far dents every AI-adjacent multiple and hands the “AI bubble” chorus its loudest data point since 2022, even though a stock priced at 113x sales finding a lower multiple says nothing about whether Micron’s HBM sells out. Our companies’ cash flows wouldn’t change; their prices would. That’s the distinction that matters for us: an SPCX crash would be a repricing event for our names, and a buying opportunity, because the token demand underneath (domino #1) doesn’t care where SPCX trades.

Tactically, that points to three things. Expect SPCX-sourced volatility clustering around the unlock dates above, starting with the August earnings release. Keep dry powder for the AI infrastructure names we already own and understand, because we may get 2022 prices for 2027 fundamentals. And treat any panic headline that says “SpaceX crash proves AI is a bubble” as noise until the dominoes in the demand chain actually wobble.

Verdict

I’m passing on SPCX at 113x sales, and the calendar, rather than the narrative, is the catalyst I’d trade around: first unlock in August 2026, full gravity by June 2027. The most useful thing I can do with the most talked-about stock on Earth is use its turbulence to buy more of what we already own at better prices.

Portfolio Update

The market was slightly up while the portfolio closed in negative territory.

For some reason IBKR is not updated for July 2nd and 3rd but the S&P 500 closed +0.51%.

Portfolio Return

• Month-to-date: -5.4% vs. the S&P 500’s +0.2%.

• Year-to-date: +36.4% vs. the S&P 500’s +9.3%. That is a gap of 2,710 basis points.

• Since inception: +92.5% vs. the S&P 500’s +30.1%. That’s 3.1x the market.

Share

Contribution by Sector

Industrials and tech led the losses, partially offset by consumer non-cyclicals and energy.

Contribution by Position

(For the full breakdown plus commentary on earnings results and the big movers, see Weekly Stock Performance Tracker)

• +26 bps (Thesis)

• +13 bps (TSX: CLS) (Thesis)

• +4 bps (Thesis)

• +3 bps (Thesis)

• -10 bps (Thesis)

• -11 bps (Thesis)

• -31 bps (Thesis)

• -73 bps (Thesis)

• -111 bps (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Further Sunday reading to help your investment process:

• Never Stop Asking Why, Using the “5 Whys” to Solve Problems and Pick Stocks

• The Rise and Fall of Moats: When Walls Built to Protect Become Traps

• Design Your Life Around Your Nature

• How I Earn $3,000-$7,000 a Month While Waiting to Buy Great Stocks Cheaper

• Cheat Codes for Life Monopoly

Hello fellow Sharks,

It was a down week for both the market and the portfolio. The portfolio is still up +39% YTD and 3.5x the market since inception. If you want to skip straight to the numbers, jump to the Portfolio Update.

It occurred to me it would be fun for you to see the results of prior polls, and it might encourage more of you to participate. We are a community of almost 2,000 people. Imagine the insights we could get if even half of you voted.

Last week I asked whether AI is a bubble. Most of you (41%) think we are, only 14% more than those who think we are not (36%). But only 22 of you voted. Come on, people, vote in future polls!

This week I was going to talk about SpaceX, but Micron got in the way again: it reported great results and blew past the target price.

In this Thought of the Week, I'll go over my new valuation ($1,630), what changed, and what justifies it.

Another note, I watched a Bloomberg piece this week on the Korean AI bubble, and it has stayed with me.

Two things worried me. The first, an article for another day, is TSMC [] and how its competitive position holds up against the ambitions of SK Hynix and Samsung. The second is related to this Thought of the Week: the euphoria behind the AI trade, and nowhere is it louder than in Korea, where the memory names have been bid up with the kind of leverage and conviction that usually marks a top.

Korea feels far away, but I have watched enough cycles to know that a bubble in one corner of an industry rarely stays in that corner. Memory is a small enough club that if the FOMO money crowding SK Hynix and Samsung decides to leave, it will impact MU as well.

So why am I still comfortable holding my AI names? Because euphoria and fundamentals are two different things. Some owners of these stocks are there for the momentum and will get burned when it turns, because they never understood why they held them. Those of us who can name the drivers and the risks, who own the business rather than the ticker, should be fine. That is the test I put Micron [] through below, and the latest quarter only made me more confident it passes.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• Earnings Release

• Micron Blew Past My $1,120 Target. I’m Raising It to $1,630.

• Portfolio Update

Earnings Release

Two portfolio companies reported earnings this week. Both beat EPS consensus estimates and one missed revenue consensus estimates. Paid subscribers can read my view on the earnings here. Below, I discuss MU earnings and the refreshed valuation.

Thought Of The Week

Micron Blew Past My $1,120 Target. I’m Raising It to $1,630.

Since I picked Micron as my Top 2026 pick, it has run from $295 to $1,250, before it settled at $1,132.

That is up nearly 284%...

… almost a four-bagger, and comfortably one of the best performers in the portfolio.

Three weeks ago I raised my target to $1,120 and said I would trim Powell [] and Sterling [] long before I touched MU.

On 24 June, Micron reported a quarter that makes $1,120 look timid. The stock jumped almost 16% the next day to an intraday high of $1,213, blew straight through my target…again.

I re-underwrote it with a higher target price. The short version is that the quarter did not just beat the outlook, it revealed a different business underneath it. Micron signed 16 multi-year, fixed-price supply contracts that run to the end of 2030, booked more FCF in three months than it took in revenue in some full years of the last decade, and held capital spending below what my model assumed even as revenue exploded. So I updated the model. The target moves from $1,120 to $1,630, and I am keeping the full position.

But first a poll 😊

You can check out the DCF model here.

TLDR

• The trigger: Q3 2026 blew past my $1,120 target. Revenue of $41.46B rose 346% y/y and beat consensus by $5.6B; non-GAAP EPS of $25.11 beat by 21%; GAAP gross margin hit 84.6%. The stock hit $1,213.

• The Q4 guide is bigger than the beat: management guided Q4 to $50.0B of revenue at roughly 86% gross margin and about $31 of non-GAAP EPS, a further +21% step up in a single quarter.

• The real reveal, beyond the outlook: 16 multi-year fixed-price Strategic Customer Agreements through 2030 are converting a spot-priced commodity into something close to recurring revenue. Cash generation went vertical ($25.4B operating cash flow, $18.3B FCF, with Q4 FCF guided above $30B, and a record $24.4B net-cash position). Capex was held to about $27B.

• Scorecard: of my five original pillars, four are confirmed and one (supply discipline) is still evolving, now with the best evidence yet on its side. Nothing is challenged.

• Verdict: I am raising the DCF target from $1,120 to $1,630 and holding the full position. I still trim POWL and STRL well before MU.

What the quarter actually said

Start with the scale, because the year-on-year comparison is the only way to grasp it. Revenue of $41.46B compares to $9.30B in the same quarter last year. Micron added $32B of quarterly revenue in twelve months, and that single incremental figure is larger than the company's total revenue for some full years as recently as FY2023. Gross margin went from 37.7% to 84.6%, nearly 47 points of expansion. That is a wholesale repricing of the product rather than operational tinkering.

The mix tells you where it came from. DRAM is the engine, and it has been the constrained, high-value part of the market all year; this quarter DRAM was 76% of revenue at $31.3B, up 343% y/y, while NAND was up 361% y/y.

DRAM remains far harder to supply than NAND, and HBM is the tightest node of all. HBM revenue cleared $1B for a second consecutive quarter, and the entire 2026 HBM output is sold out under long-term agreements.

Operating cash flow was $25.39B and adjusted FCF was $18.3B, both quarterly records…

… and management guided Q4 FCF above $30B. Micron exited the quarter with a record net-cash position. A business that spent the 2023 downturn burning cash and defending its balance sheet just generated more FCF in ninety days than it earned in some full years of the last cycle.

Then the guide. Fiscal Q4 revenue of $50.0B at 86% gross margin and about $31 of non-GAAP EPS.

So MU guided about 16% above consensus on the top line and roughly 22% above on the bottom line. The guide alone, $50B in a quarter, is more than MU’s entire revenue in FY2025.

The “so what” is simple: the demand wall is now a backlog rather than a forecast.

The real reveal: a commodity business signed contracts

Micron signed 16 multi-year Strategic Customer Agreements with four large customers, several of which lock both volume and price, with terms running to the end of calendar 2030. Some include HBM and some include the hyperscalers directly. That is the mechanism that turns a spot-priced commodity into contracted, quasi-recurring revenue. It is the single most important sentence in the release, because the entire bear case on memory rests on the idea that today’s pricing is a spike that the next downturn erases. You cannot erase a price you have already signed for five years.

The floor pricing in the new contracts sits ahead of prior-cycle peak gross margins, and the agreements already cover roughly half of MU’s revenue across four customers. This could prove transformational for the industry’s structure, much as the wafer-fab-equipment names re-rated once their business models improved. I still believe that DRAM is the single most important bottleneck in AI, because memory capacity and bandwidth set the ceiling on model performance, and only three companies can make the AI-grade product.

This is the difference between this cycle and 2018 or 2022. In prior peaks, memory makers rode spot pricing up and then watched it collapse when supply caught demand. This time the largest buyers are voluntarily locking multi-year price floors because they fear missing allocation more than they fear paying up. Much of the negotiation now goes into helping customers accept that the constrained volume is all Micron can deliver in the timeframe. When your customers are signing five-year contracts to guarantee supply, you have pricing power of a kind the memory industry has never had.

Capital allocation moved the same way, and the balance-sheet swing is just as striking: MU went from carrying net debt at the 2023 trough to a record $24.4B net-cash position today…

… with all three rating agencies upgrading its credit this year. The principal capital return will be share repurchase, and Micron intends to step up returns after 9 December (the second anniversary of its CHIPS Act agreement), while keeping a cash cushion for investment. At this level of cash flow, retiring roughly 10% of the shares a year is within reach; management would not commit to that pace but confirmed buybacks are the main tool. With a record net-cash balance sheet and Q4 FCF guided above $30B, the capacity to shrink the share count meaningfully is now real, and every share retired below intrinsic value compounds the per-share math underneath my target.

What Micron's numbers are doing now echoes Nvidia [] between 2022 and 2025, a business the market kept treating as cyclical while its earnings power stepped permanently higher. Micron is investing at record levels in technology, products and supply to meet that demand… records in revenue, in margin, in cash, and in the contracted visibility behind all three.

Why the cycle keeps extending

I laid out the structural supply-and-demand case in my May update, so I will only mark what changed, because every change pushed the same way.

The tightness window got longer. In May, the guidance pointed to tightness continuing well beyond calendar 2026. Now it runs well beyond 2027, with HBM3E and HBM4 fully booked through calendar 2027 and demand extending into 2028. The HBM total available market, previously expected to cross $100B in 2028, is now seen crossing it in 2027, a full year earlier. Goldman Sachs pegs the 2026 DRAM supply-demand shortfall at 4.9%, the tightest imbalance in fifteen years. If anything, the shortage is widening.

The supply side is structurally pinned. HBM remains a wafer-capacity sink: it takes more than three wafers to deliver the bits one wafer of commodity DRAM used to, and that trade ratio worsens with each generation. Standard DRAM is being pulled offline to make room for HBM, which tightens the entire memory market, not just the AI node. The binding constraint is clean-room space measured in years against demand measured in quarters. Micron is responding the only way it can, by building greenfield (Idaho, New York, Singapore and China), but that new capacity, including Idaho and China, only contributes meaningfully from calendar 2028. There is no fast supply answer.

The business is broader than the AI headline. One underappreciated disclosure: the non-data-center units (mobile, embedded and the consumer-facing business) are still close to 40% of company revenue. That breadth matters, because it means the franchise is more than a single-product bet on one HBM customer; it is a full-stack memory supplier steering wafers to wherever the value is highest, with a long tail of demand underneath the AI spike.

The one genuinely two-sided factor is helium. The Iran-driven squeeze I flagged in May is still raising input costs across the industry, but it is also constraining everyone’s ability to add output, which on net tightens the market and hands pricing power to whoever can still ship.

Thesis scorecard, June 2026

Pillar #1. Memory is the underappreciated AI play: Confirmed.

Original thesis: the market priced MU as a commodity cyclical while AI was quietly turning memory into a strategic, capacity-constrained asset across HBM, server DRAM, LPDDR and graphics memory.

What happened since: the print and guide put it beyond argument. Revenue +346% y/y, 84.6% gross margins, and a Q4 guide that is 16% above consensus. The only thing that changed is that almost everyone now agrees with me.

Assessment: confirmed, and now the consensus view rather than the contrarian one, which is itself a reason to keep watching valuation rather than narrative.

Pillar #2. Full-stack memory position: Confirmed.

Original thesis: MU is positioned across the entire memory hierarchy, so it wins whether demand lands in HBM, high-capacity DDR5, LPDDR or NAND.

What happened since: the quarter confirmed strength across all of it. DRAM and NAND are both constrained, HBM is sold out, data-centre SSD continues to gain share, and roughly 40% of revenue still comes from outside the data centre.

Assessment: confirmed. The breadth is now a planning advantage, letting Micron point wafers at the highest-value product while keeping a diversified demand base behind it.

Pillar #3. HBM leadership is real and improving: Confirmed and strengthening.

Original thesis: MU had closed the HBM gap and could take share with a better-yielding, lower-power product.

What happened since: HBM4 is in high-volume production for the lead customer on the 1-beta node, with qualification samples out to multiple additional customers, and HBM4E is in development on 1-gamma for calendar 2027. Micron’s lower-power HBM is winning designs even though it carries less manufacturing scale than Samsung or SK Hynix.

Assessment: confirmed and strengthening. Execution risk on the highest-value product keeps falling, not rising.

Pillar #4. Supply discipline keeps margins structurally higher than the old cycle: Evolving.

Original thesis: a three-player DRAM oligopoly plus the HBM capacity sink would keep through-cycle margins above MU’s historical average.

What happened since: the 16 multi-year, price-locked SCAs are exactly the evidence I wanted, and they are stronger than I expected, because they convert spot pricing into contracted revenue through 2030. But 84.6% gross margins, and an 86% guide, are also the most powerful magnet for new capacity imaginable, and China’s CXMT is the wildcard that has not shown up in HBM yet.

Assessment: evolving, decisively in my favour, but still the pillar I watch hardest because it is the one embedded in the terminal value.

Pillar #5. Undervalued on normalized earnings: Confirmed.

Original thesis: even on conservative through-cycle assumptions, the cash flows were worth more than the price.

What happened since: the stock has nearly quadrupled, yet on 2027 estimates it trades below 16x forward earnings, because earnings power keeps rising faster than the share price.

Assessment: confirmed, and the heart of the valuation.

Valuation: what moved the target to $1,630

The model now outputs $1,630. Almost all of the increase comes from three assumption changes, every one of them anchored to hard guidance rather than hope.

Two of the three changes (higher revenue, higher margin) are obvious and pull the target up. The third, capex, also pulls it up, and it is counter-intuitive. Management is spending more dollars than ever, about $27B this year and above the mid-$40B range next year, yet as a share of a revenue base that is exploding, capital intensity falls from the 33.5% my model carried toward the low-20s. That is the financial signature of a cycle where growth comes from price, not from buying ever more wafers. Lower capital intensity on higher revenue is a FCF machine, and FCF is what a DCF capitalizes.

Crucially, I did not touch the terminal. The fade to a 40% terminal gross margin by FY2037 still embeds a full cyclical-reversion view: it treats today’s 80%-plus as peak and assumes the industry eventually normalizes toward something only modestly above its roughly 30% historical average. I also pulled the out-year revenue deceleration forward, so the higher near-term base does not compound into an implausible terminal number. In other words, the target moved because the next two years are now demonstrably bigger and more profitable, not because I got more optimistic about the 2030s. If anything, the terminal stays deliberately sober.

Now the part that should reassure anyone nervous about a $1,630 target on a stock that was $295 in December. At about $1,130 today, MU trades below 16x forward earnings, against a semiconductor sector north of 30x.

An 86% gross margin looks like software, yet the stock is priced like a commodity that is about to roll over. To believe today’s price is fair, you have to believe the contracted, sold-out, supply-pinned setup reverses inside eighteen months. My model does not assume the cycle is dead; it assumes the next two years look like the contracts the company just signed, and then it normalizes. On those terms, fair value is near $1,630.

At today’s multiples, the market is pricing in a swift reversion to mid-cycle memory economics, essentially treating FY2027 as a peak to be discounted. My model, anchored to the $50B Q4 run-rate and five-year price-locked contracts, says the back half of this decade is being underwritten by the market at a discount to what management has already contracted.

Risk register update

Cyclicality (unchanged, still the headline risk). Memory has never stopped being cyclical at the cell level. My defence is the structural-plateau argument and a deliberately conservative 40% terminal margin, not a claim that the cycle is repealed. The SCAs dampen the amplitude of the next downturn; they do not abolish it.

Margin sustainability (sharpened by the print). An 86% gross-margin guide is extraordinary, and history says memory margins peaked nearer 60% in past up-cycles. The risk is asymmetric: because so much of the incremental margin is pricing rather than cost, even a modest decline in contract pricing hits gross margin almost immediately.

China / CXMT (rising, slow-burn). CXMT keeps adding subsidized commodity DRAM capacity and is now a clear number four globally by wafer share. It is not competing for the high-margin HBM dollar yet, and today’s shortage absorbs everything it can make, but later in the decade it is the single most credible threat to the supply discipline that underpins my terminal margin. Micron's answer, focusing on complex, high-performance products where it is differentiated, is the right one, but it is a watch item, not a closed one.

Hyperscaler capex digestion (live). The whole edifice rests on the roughly $2 trillion hyperscaler build continuing. The buyers are forward-purchasing, which is a hedge against an air-pocket, but any pause in that spend is the fastest route from tightness to glut. The SCAs running to 2030 are precisely the structure designed to blunt that, which is reassuring, but a sharp AI-capex slowdown remains the cleanest way to break the thesis.

Helium and input costs (two-sided). The Iran-driven squeeze supports pricing and tightness today, but a sustained input-cost shock, or a forced output cut, could pressure margins and disrupt Micron’s own ramp. If the Strait reopens, normalization still takes months. Watch it both ways.

Share Beating The Tide: Stock Picks That Outperform

Verdict

I am keeping my BUY and raising the target to $1,630. Here is the single thought I would leave you with. The entire debate on MU comes down to one word: cyclical. For thirty years, that word has been a life sentence, and it is why a business now earning software-like margins still trades as if it is about to break. But you do not sign five-year price floors with your four largest customers at the top of a cycle you believe is about to roll over, and neither do they. Someone is wrong about whether memory is still a commodity, and for the first time in this industry’s history, the contracts say it is the bears. I might be the one who is wrong, which is what the risk register is for.

Ranked Stocks

A quick word on the tool behind much of this work. In February I launched RankedStocks.com alongside this newsletter, and I have been pouring time into making it better. The newest additions include a chart for every stock that plots its historical RankedStocks score, full historical financial statements, smarter screening filters, and more on the way.

So how does MU screen on my own system? It carries an overall RankedStocks score of 91, in the 96th percentile of its country and the 97th of its sector, and it has now spent 138 days in the Elite band. Here is the score-by-score read, and notice how each factor maps onto what this article just argued.

Valuation scores 76. At 16x forward against a sector north of 30x, MU is the cheapest way to own the memory shortage. Growth scores 68, which looks modest next to revenue up 346%. Profitability scores 55, the weakest factor, trailing profitability still carries the 2023 trough when gross margin was 2.7%, and the jump to 84.6% has not yet aged into the metric, so this is the score most likely to re-rate. Sentiment scores a perfect 100, no surprise after a near-quadruple and a 16% post-earnings pop, and it is also the flashing light from my opening, because a maxed sentiment score is when you most need to know why you own the stock. Outlook scores 91, the forward-looking factor, fed directly by the contracts, the $50B guide and the tightness that now runs into 2028.

Add it up and MU sits in the Elite band, the top few percent of its market, with the only soft spots being a backward-looking profitability score and a growth screen still catching up to the print. The price history below, shaded by score, shows it has held Elite for 138 straight days.

The site already draws decent traffic, though if I am honest I expected more by now, and I have a long list of features still to build. Each one means more API calls and a higher service tier, so the plan is to let paid subscriptions fund the next stage rather than bolt on costs I cannot justify. If any of this speaks to you, I am looking for help on three fronts, the content, the look and feel, and the sales and marketing. If you have an idea, or you would like to be part of building it, reach out and let’s see if there is something we can do together.

Portfolio Update

The market and the portfolio both fell during the week. The S&P slipped about 2%, and the portfolio gave back about 3.1%, trimming a little of the YTD lead after a strong multi-week run.

Portfolio Return

• Month-to-date: flat vs. the S&P 500’s -3.0%.

• Year-to-date: +39.4% vs. the S&P 500’s +7.4%. That is a gap of 3,193 basis points.

• Since inception: +96.7% vs. the S&P 500’s +27.9%. That’s 3.5x the market.

Share

Contribution by Sector

Energy, education and consumer cyclicals were the only positive contributors; technology and industrials led the decline.

Contribution by Position

(For the full breakdown plus commentary on earnings results and the big movers, see Weekly Stock Performance Tracker)

• +4 bps (Thesis)

• +1 bps (Thesis)

• -16 bps (Thesis)

• -21 bps (Thesis)

• -22 bps (Thesis)

• -30 bps (Thesis)

• -39 bps (Thesis)

• -40 bps (Thesis)

• -100 bps (TSX: CLS) (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Further Sunday reading to help your investment process:

• Never Stop Asking Why, Using the “5 Whys” to Solve Problems and Pick Stocks

• The Rise and Fall of Moats: When Walls Built to Protect Become Traps

• Design Your Life Around Your Nature

• How I Earn $3,000-$7,000 a Month While Waiting to Buy Great Stocks Cheaper

• Cheat Codes for Life Monopoly

Hello fellow Sharks,

Another strong week. The portfolio crossed the 100%-mark, 3.4x the S&P 500 performance. If you want to skip straight to the numbers, jump to the Portfolio Update.

A good analyst spends as much time reading the other side as their own. The newsletter I read to argue against myself is Where’s Your Ed At, written by Ed Zitron, who is about as anti-AI as it gets, and good at it. On June 15, he published a piece titled the leaked OpenAI financials, with numbers the Financial Times independently verified. He presents them as proof that OpenAI is a money furnace with no way out. In this Thought of The Week, I take the other side of his argument and explain why I think we are not in an AI-bubble. It is a long very long post but I think it is well worth it, especially if you are investing in the AI-theme stocks.

I want to be honest about my own risk and reward here, because it’s the opposite of how I invest. In stocks, I only take positions where the reward is at least twice the risk. This post is the reverse: high risk, because I’m trying to predict the future, which is impossible, and low reward, because the market isn’t pricing an AI collapse anyway, so I’m not protecting anyone from a trade. I’m writing it because I believe the argument needs to exist, and I’ve never hedged my opinions, in this newsletter or in life. I won’t start now.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• In Case You Missed It

• The Economics of Intelligence

• Portfolio Update

In Case You Missed It

On June 18, I closed the last insurance company in the portfolio for a +20.8% gain.

I took the gain and closed out my last insurance position as I see better use for that capital into my July Stock Pick.

On June 16, I released the deep dive on the June Stock Pick

It's a boring, cash-rich middleman the market has left for dead, roughly $600M of net cash against a $9B opportunity, the kind of unglamorous setup I like best. It's already up 11% since the June 10 trade alert.

Thought Of The Week

The Economics of Intelligence

Last year I went in for my regular checkup. Before we started, my doctor asked if she could let an AI tool record and transcribe our conversation. I said yes, and it turned out to be the best consultation I’ve ever had.

Think about what a checkup used to be. She would sit half-turned toward a screen, typing notes while I talked, breaking off to take my vitals, then turning back to the keyboard to print my lab requisitions. The visit was over before it began. This time she sat facing me, no desk wedged between us, and asked how I was doing. We actually talked. We discussed my fatty liver, what my options were, and what she would recommend. Then she took my vitals and handed me my requisitions. She looked relaxed in a way I’d never seen. I suspect she enjoyed it more than I did, because for twenty minutes she got to be a physician again rather than a note-taker.

That small scene is the whole thesis in miniature. AI is good at the parts of our work we never wanted to do, which frees us to spend more time on the parts we did. I don’t buy the story that AI is coming for our jobs, at least not on any horizon I can underwrite. The version of that story that scares people requires machines that think for a fraction of today’s cost, and getting there runs straight into the two hardest walls in physics and engineering, energy and compute. I’ll come back to both.

The bear case, stated at its strongest

Before I argue against a thesis, I build the strongest version of it I can, the one a smart person would actually hold. So here is the bear case with no padding.

OpenAI booked $13.07B of revenue in 2025 and spent $34B to do it. Cost of revenue alone was $7.5B, R&D was $19.18B, sales and marketing $5.73B, and G&A $1.57B. That’s an operating loss of roughly $20.9B, more than three times what the company lost in 2024. The headline net loss attributable to OpenAI was $38.5B. Worst of all, OpenAI paid Microsoft [] $17.2B in 2025, more than its entire revenue for the year. A company that pays one supplier more than it earns from every customer combined has a problem deeper than margins. It has a broken model. And the spending only grows from here: OpenAI has committed to something on the order of $1.4 trillion of compute over eight years, and doesn’t expect to generate positive cash flow until 2030. HSBC thinks it faces a funding gap north of $200B to get there.

That is a serious argument, and anyone who waves it away with talk of exponential curves is not being straight with you. The figures are real. Sources for each: the loss and cost lines come from the leaked audited statements; the $1.4T compute commitment and the 2030 cash-flow target were confirmed by OpenAI in February (and walked back to roughly $600B by 2030); the funding-gap estimate is HSBC’s.

Before I take that case apart, sit with three pictures it leaves out of frame. The first is demand, and it is still accelerating.

Demand on that scale drags revenue up behind it, and the same analysts keep revising OpenAI’s revenue forecast higher rather than lower.

And the capital is voting with conviction. The strongest balance sheets in the world are taking on serious debt to fund the build-out.

My job for the rest of this piece is to test that bear case point by point against the evidence, and to show you where it holds and where it breaks. It breaks in more places than it holds.

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What the leaked numbers actually say

Start with the financials, because they are the foundation of Ed’s case, and two things in them are widely misread.

The first is the $38.5B net loss. It sounds catastrophic until you read the footnotes. Of that figure, $41.55B is a single non-cash charge tied to OpenAI converting from a nonprofit to a for-profit structure, a fair-value remeasurement of convertible and warrant liabilities. No money left the building. Strip it out and the company’s loss is driven by its operating loss of about $20.9B. The headline number is real accounting, but quoting it as a run-rate is like quoting a homeowner’s net worth on the day they mark their mortgage to market. The number that matters for the business is the $20.9B, and even that, as you’ll see, is mostly a choice.

The second is the line that does the most rhetorical work: OpenAI paid Microsoft $17.2B, more than its revenue. True, and it sounds damning. But that $17.2B is not the cost of serving customers. It bundles two different things. About $6.05B of it is cost of revenue, the compute that actually runs ChatGPT and the API for paying users. The other roughly $10.6B sits in R&D, which is the compute used to train the next generation of models. One is the cost of running the business you have. The other is a discretionary bet on the business you want. Measure the part that matters, the $6.05B of serving cost against $13.07B of revenue, and the core product runs at about a 54% gross margin. Those are healthy unit economics: a software company funding an enormous science project out of a profitable core.

You can see the same story in the trend. Here are the leaked figures, with the operating loss corrected to tie to the cost lines.

Read that table like an analyst. Revenue grew 253%. Gross margin went from 28% to 43%, a 15-point jump in twelve months, which tells you the cost of serving a dollar of revenue is falling fast. The loss comes from R&D at 147% of revenue and a sales line that tripled as the company grabbed land. Those are the two most discretionary lines on the whole statement, and the product underneath them earns a positive margin. A company chooses to spend 1.5x its revenue training the next model. Negative unit economics would have to be forced on it, and OpenAI shows no sign of them.

One honest caveat, since I care more about being right than being convincing. The original table that’s been circulating, including the one I first built from, showed a smaller operating loss of around $6B in 2024 and $16B in 2025. Those figures don’t tie to the sum of the cost lines, most likely because they added back stock-based compensation. The audited math gives $8.8B and $20.9B, and I’ve used the audited math throughout. If anything, that makes the loss look worse, and the argument still holds. I’d rather make the case against the harder number.

Why a young company losing money proves nothing

Here is what surprises me most about Ed’s take. I can’t think of a single transformative technology that was profitable when it started. The pattern is so consistent it’s almost a law: the bigger the eventual payoff, the longer and deeper the early losses.

Amazon [] is the obvious case, and it's worth being precise about it because the myth is fuzzy. Amazon went public in 1997 and lost money for years, and every year the losses got bigger.

Then in 2001 it started shrinking the losses.

It didn’t post a full year of profit until 2003, six years after listing.

People who sold in 2001 because Amazon “had no path to profitability” were looking at the same kind of income statement Ed is looking at now.

The ones who held compounded their money more than 2,424x or 30.73% per year.

Amazon isn’t alone. Uber [] burned more than $30B and didn’t turn an operating profit until 2023, fourteen years after it was founded.

Tesla [] didn’t post a profitable year until 2020, roughly seventeen years in, and was weeks from bankruptcy more than once along the way.

Salesforce [] ran GAAP losses for years as a public company while it built the model that now mints cash. Go back further, and the story repeats with electricity, with the railroads, with the telephone. Every one of them required enormous upfront capital that looked insane right up until it looked inevitable.

None of this proves OpenAI will win. Plenty of cash-burning companies deserve their skeptics, and I’ll grant that OpenAI’s burn is an order of magnitude larger than anything on that list. But the mere fact of large early losses tells you almost nothing about whether a business model works. It tells you the company is young and the opportunity is big. To know whether the model works, you have to look at the demand, the supply, and the direction of unit costs. That’s what the five dominoes do.

Domino #1: Demand is strong, growing, and going nowhere

Every week I find a new use for AI, and I’m sure I’m neither the first nor the last. My doctor found one. Millions of people have found their own. For me the list keeps growing. I’ve used AI to sharpen my research process and to automate my second brain in Notion. I finished a project in two months that had sat unfinished for three years, RankedStocks.com. I built my own Monte Carlo simulator for Google Sheets, which you can download and use for free here. I even built an exam simulator to study for a test I had to sit. My only real bottleneck now is the number of hours in a day, and we are still in the first innings.

That’s the anecdote. Here’s the data.

ChatGPT reached around 400 million weekly users in February 2025, roughly 800 million by the end of the year, and passed 900 million by the spring of 2026, with about a billion people using it monthly. No consumer product in history has scaled to that size that quickly. The usage shows up in revenue. OpenAI crossed a $25B annualized run-rate by February 2026, roughly $2B a month, up from $13B for all of 2025.

Anthropic went from about a $1B run-rate at the end of 2024 to roughly $30B by April 2026, an 80-fold jump in sixteen months.

The hyperscalers tell the same story in their own books. Microsoft’s AI business hit a $37B annual run-rate growing 123% y/y, with more than 20 million paid Copilot seats. Google [] Cloud’s order backlog ballooned to roughly $460B. The cleanest proxy for real usage is tokens processed, the raw units of AI computation, and there the growth is almost comic: Google went from processing 480 trillion tokens a month in May 2025 to 1.3 quadrillion by October.

Microsoft’s Azure processed more than 100 trillion tokens in a single quarter, five times the prior year. Enterprises are along for the ride: McKinsey’s 2025 survey found 88% of firms now use AI in at least one function.

I want to be careful here, because demand statistics are where bulls get lazy. So let me steel-man the doubt. First, token counts are inflated. Google’s quadrillion includes search summaries and reasoning models talking to themselves, so treat it as a direction, not a literal measure of paid demand.

Second, and more important, adoption is not the same as value captured. That same McKinsey survey found only about 6% of companies are getting a real earnings impact from AI, and only a few percent of Microsoft’s Office base has converted to paid Copilot. The productivity is real but uneven. The clearest win I can point to is a study of 5,000 customer-support agents that found a 14% lift in issues resolved, rising to 34% for the least experienced workers. Medical scribing, the thing my doctor used, has a real clinical literature behind it showing sharp drops in burnout and more eye contact with patients. But coding, the use case everyone assumes is settled, is contested. One controlled study found AI made experienced developers 19% slower even as they believed they were faster.

The consulting world ran the same experiment with the same result. A Harvard and BCG study put 758 consultants to work and found that those using AI completed 12% more tasks, 25% faster, at 40% higher quality, and the weakest performers gained the most, a 43% jump against 17% for the strongest. The same study flagged the catch I keep in mind: on tasks that sat just outside the model’s competence, what the authors called the jagged frontier, AI users did meaningfully worse, because they trusted a confident wrong answer. So the productivity is real, it’s largest for the least experienced, and it rewards people who know where the tool’s edge is. That last part is a skill in itself, and it’s one more reason the demand for these systems compounds rather than fades.

So what do I take from all this? Demand for AI is large, growing fast, and increasingly attached to real money rather than hype. The open question isn't whether people want it, because they plainly do. It's how much of that want converts into durable enterprise spending, and the early read, $25B here, $30B there, 20 million paid seats, says a lot of it already has. A technology that a billion people reach for every month and that businesses are wiring into their workflows does not have a demand problem. Hold that thought, because it’s the hinge of the next domino.

Domino #2: Supply isn’t even meeting today’s demand

When people call AI a bubble, they’re reaching for a specific historical pattern, so let’s actually examine the pattern. Bubbles, the real ones, are stories of supply running miles ahead of demand, usually on the back of demand that turned out to be imaginary.

Start with the cleanest example, the telecom and fiber build-out of 1998 to 2002. Carriers poured more than $500B, most of it borrowed, into laying over 80 million miles of fiber across the US, convinced by WorldCom’s claim that internet traffic was doubling every hundred days. The traffic was actually doubling about once a year. When the imaginary demand failed to show, the result was carnage. By the trough, only a single-digit percentage of the installed fiber was lit and carrying traffic. WorldCom and Global Crossing filed two of the largest bankruptcies in history, and the broader dot-com index fell 78% and erased more than $5 trillion of market value.

It had happened before, almost to the script. In Britain’s Railway Mania of the 1840s, Parliament authorized thousands of miles of new track on the promise of endless traffic, and a large share of it was never built or was torn up as duplicate lines went bust. People who had bid railway shares to the sky were wiped out when the real traffic arrived at a fraction of the forecast. The American railroad booms of the 1870s and 1890s ended the same way, in receiverships and bank panics. Different century but same behaviour. Capital races ahead of demand, builds capacity for a future that shows up late or not at all, and the overbuild drags the financiers down with it.

The common thread across the genuine technology bubbles is the same: capacity got built for demand that wasn’t there.

Now look at AI in 2026, and you’ll see the opposite of that picture. The constraint here is the reverse of a glut. The industry cannot build fast enough to serve the demand it already has. This is not my inference. It’s what the people running these companies say out loud. In the space of one earnings season, captured well by The Transcript:

• Google: demand for our AI solutions is “at levels that are meaningfully exceeding our available supply.”

• Microsoft: “we are supply-constrained.”

• Nvidia: “we don’t have enough supply.”

• OpenAI: “in ‘26 we still won’t have enough compute... in ‘27, it’s pretty limited as well.”

• Cerebras: “the builders are so far behind the demand, it’s absurd... that’s, in a lot of ways, the opposite of a bubble.”

The hard data backs the quotes. Nvidia’s newest chips are sold out into the second half of 2026, with a backlog measured in millions of units. The advanced packaging that assembles them is booked through the middle of 2027. Power has become the binding constraint, to the point that a year ago Sam Altman had to throttle ChatGPT because, in his words, the GPUs were melting. You don’t ration a product that nobody wants.

So where does this leave the bubble question? A bubble is too much supply chasing too little demand. We have the opposite: a shortage, where demand is straining against every physical limit the supply chain can throw at it. That distinction matters for pricing, which is the next domino. When supply is short and demand is deep, the producer holds the power over price. That takes us to the question Ed treats as fatal and I treat as backwards: what happens to the price of a token.

Domino #3: The price of frontier intelligence will rise, and that’s fine

The leaked numbers already show a positive and rising gross margin, so the product makes money on the margin today. But I think the price of the best AI is going up from here, and I want to explain why that’s a feature rather than a flaw.

Two forces push the price of the frontier up. The first is that building each new frontier model costs more than the last. Training-run costs have been climbing roughly two-and-a-half to three times a year.

GPT-4 cost somewhere around $78M to train; the field expects the first $1B training run by 2027.

The second is capex. The chips, buildings, and power behind a frontier model run into the tens of billions, and someone has to earn a return on that. When the input costs of the best product keep rising, the price of the best product follows.

Here’s the part the bears miss. People will pay it, because the value dwarfs the price. I’m exhibit A. I started where almost everyone starts, on the free version, the first hit handed over at no charge so you can feel what it does. Within two weeks it had saved me hours a month, so I upgraded to the $20 plan without thinking twice. Then I discovered I could pair it with my code editor, Visual Studio Code, and build software I’d wanted for years, so I vibe-coded my own ERP, customized to my own messy reality of multiple currencies, asset classes, and tax jurisdictions. Then I found that Claude could drive my computer and connect to my Notion, email, and calendar, which retired the idea of hiring an assistant and gave me back two hours of every working day. I moved up to the $100 plan and I haven’t upgraded to the $200 tier as I haven’t hit my limit …yet.

I’ll say something I probably shouldn’t, so don’t tell Dario… if the price went to $1,000 a month, I would still pay it, because the hours it saves and the leverage it gives me are worth far more than that, and I haven’t even started using it to grow my revenue rather than just my productivity.

I’m not unusual, I’m just early. The market is already pricing this way. OpenAI launched a $200-a-month Pro tier in December 2024, and the industry followed. Box’s CEO [] put it well: companies used to pay $10 to $50 per software seat per month, and now they’ll pay hundreds or thousands per employee in tokens. Cisco [] described a single AI-heavy worker burning $200 a week in tokens, which across 90,000 employees would be a $900M line item that simply didn’t exist before.

Step back, and the whole strategy looks like the way a drug dealer builds a customer. The first hits are free, so you get hooked. The paid tiers that follow are still cheap, priced below what each token costs to produce, because the labs are buying adoption rather than margin with every cheap token. The aim is to wire the habit into your daily work. OpenAI and Anthropic are not pricing to recover their capex and R&D today, and the $20 and $100 plans come nowhere near it. They are subsidizing your dependence on purpose. Once the workflows are built and the value is plain, the leverage flips, and they can lift the price into demand that has turned price-insensitive. That is the whole plan, and as a piece of strategy it is a sound one.

There’s a darker edge to this that I won’t pretend away. As the best models command real money, the people and firms who can afford them will pull further ahead of those who can’t, and that widens an already wide gap.

So the price of frontier intelligence rises, and that’s a sign of pricing power. But notice I keep saying frontier. The price of the best available model rises, while the price of any given level of capability falls off a cliff. Both things are true at once, and holding them together is the key to the whole argument. That second curve, the falling one, is the next and most important domino.

Domino #4: The cost per token will fall

If there is one number to remember from this whole piece, it’s this: the cost of a fixed level of AI capability has been falling ten times a year.

Not ten percent. Ten times.

This is the single most underappreciated fact in the entire debate, and it’s been confirmed from three independent directions. Andreessen Horowitz measured it across the market and named it LLMflation. Sam Altman states it plainly, that the cost to use a given level of AI falls about 10x every twelve months. Epoch AI, looking across benchmarks, found the decline is sometimes even faster than that.

A model with the quality of GPT-3 cost about $60 per million tokens to run in late 2021. By late 2024 an equivalent-quality model cost about $0.06 per million tokens, a thousand-fold collapse in three years. Stanford’s AI Index clocked a 280-fold drop for a GPT-3.5 level of quality in just eighteen months. You can watch it happen in OpenAI’s own price list: GPT-4 launched in early 2023 at $30 and $60 per million input and output tokens, and by mid-2024 GPT-4o mini delivered better quality at $0.15 and $0.60.

For comparison, Moore’s Law doubled performance roughly every eighteen months. This curve is dramatically steeper, and most of it comes from smarter software rather than faster chips, which means it doesn’t depend on any one breakthrough in silicon to continue.

None of this should surprise anyone who has watched a real technology scale, because falling unit costs are the signature of every one of them. The pattern even has a name, the experience curve, and it’s remarkably consistent: for many technologies, every doubling of cumulative production knocks a fixed percentage off the unit cost. Look at how reliably it has held across completely unrelated fields.

So what drives the AI version of this curve, and will it keep going? The answer comes down to two things, the cost of the datacenter and the efficiency of the model, and both are moving in our favor.

The dominant cost of running a token is not electricity, it’s the amortized cost of the GPU, which means the price of the chip and how busy you keep it. That’s why this is fundamentally a manufacturing and scale problem. The macro picture looks frightening at first: the hyperscalers are spending on the order of $400B in capex in 2025.

It is heading toward $600B or more in 2026, and the IEA expects datacenter electricity to roughly double to around 945 terawatt-hours by 2030, near 3% of global demand. But spend and cost-per-unit are different things. That capex is buying staggering volume, and volume is exactly what pushes a technology down its experience curve. The cost of building a datacenter falls as construction industrializes, and the cost of powering one falls as operators stop buying retail electricity and generate their own.

That second shift is already underway, and I’ll come to the nuclear deals in a moment.

The build-out itself is industrializing, which is what bends a cost curve. The first datacenters of this era were hand-assembled megaprojects. The next ones are converging on standard, repeatable designs, prefabricated power and cooling modules, and gigawatt campuses built like production lines. Every doubling of installed capacity teaches the builders something, and the capex figures that look terrifying in aggregate are buying exactly the volume that drives the per-unit cost down. The same dollar of spend that the bears count as evidence of a money pit is the fuel for the experience curve that makes the next unit cheaper.

The model side is moving even faster, because software improves with no factory required. Three techniques, mixture-of-experts, distillation, and quantization, let a small modern model match or beat a giant from two years earlier. To put a number on it, a one-billion-parameter model today can outperform the 175-billion-parameter GPT-3 that stunned the world in 2020, while costing a tiny fraction to run.

Newer models also use fewer tokens to finish the same task, so the work itself gets cheaper even when the per-token price holds. The most discussed example, China’s DeepSeek, claimed to train a frontier-class model for about $5.6M, and I’ll add the caveat that this counted only the final compute run; the true all-in figure was more likely several hundred million to over a billion dollars. DeepSeek also made plain that the frontier has become a contest between nations as much as between companies, a thread I pulled on in an earlier piece on AI and the race for global power.

The same intelligence keeps getting cheaper to produce, and more places can now produce it.

Here’s the twist. When the price of something useful collapses, total spending on it usually goes up, not down, because the lower price unlocks uses that were never worth it before. Economists call this the Jevons paradox, after the observation that more efficient steam engines burned more coal, not less, because cheap power created whole new industries that wanted it. AI is following the same path. Each tenfold drop in the cost of a token has been met with more than a tenfold rise in tokens consumed, which is why the hyperscalers keep spending even as unit costs fall. For OpenAI this is the best kind of math: the cost curve that supposedly dooms the company is the same curve that keeps expanding its market faster than it deflates its prices. Ed reads the falling cost of intelligence as a margin trap. I read it as the mechanism that turns a few million power users into a few billion.

That’s the load-bearing version of this domino, and it stands on its own: a ten-times-a-year cost decline, driven by experience curves and software efficiency, that has already shown up in real price lists.

Everything that follows, the next three sections, sits on top of that as upside rather than underneath it as support. They are options on the 2030s. I find them the most exciting part of the story, and I want to be disciplined about labeling them as options, because the thesis doesn’t need them to work. It just gets better if any of them land.

Step-change #1: Datacenters in space

I was going to write about the SpaceX IPO this week, but I moved it to next week because I really wanted to write this article now 😊. The one thing I agree with Elon about here is that putting datacenters in orbit is a serious answer to the bottlenecks we face on the ground.

A startup called Starcloud launched the first Nvidia H100 into orbit in November and trained a small AI model in space. Days earlier, Google unveiled Project Suncatcher, a plan to fly clusters of its own AI chips on satellites, with a two-satellite demo targeted for early 2027. Jeff Bezos has gone on record predicting gigawatt-scale datacenters in space within ten to twenty years.

The physics is attractive. A solar array in the right orbit sees the sun more than 95% of the time, against roughly 24% for a panel on the ground, and it needs no land and no water.

So why isn’t it done already?

Two reasons.

First, launch is still too expensive; the economics only work if SpaceX’s Starship drives the cost of putting a kilogram into orbit below about $200, from somewhere over $1,000 today. Second, and harder, you can’t cool a computer in a vacuum the way you do on Earth, because there’s no air or water to carry heat away, only radiation, and a one-megawatt datacenter would need something like a hundred tons of radiators. The hardware flying today is a single chip, not a datacenter. This is a story for the 2030s, and the wall is heat, not power.

Step-change #2: Quantum computing

Almost no one connects quantum computing to AI economics, and I think they should, with heavy caveats. My starting point is that I think intelligence can’t be sustainable consuming this much energy (think about the energy our brains consume), and we aren’t close to the real thing yet. Either we find far cheaper energy, which is the next step-change, or we find a way to do the computation with far less effort. Quantum is one candidate for the second path, because for certain classes of problems it promises answers that would take a classical machine effectively forever.

Google’s Willow chip crossed a key error-correction threshold in late 2024, and IBM has a credible roadmap to a fault-tolerant machine by 2029. Governments are pouring in money: the US reauthorized its national program with another $1.8B, the EU has a billion-euro flagship, and China is spending on a scale that may dwarf both.

Quantum computers do not run today’s AI models, and they won’t any time soon. Training and inference for a model like GPT or Fable map beautifully onto classical chips, with no proven quantum speedup. Quantum is a specialized accelerator for specific problems, and a possible piece of a far-future answer to the energy question. It belongs in this primer as a clearly labeled long-dated option, and nothing more.

Step-change #3: Cheap, abundant energy

The deepest bottleneck of all is energy, and the near-term answer is fission, already being signed. Faced with the power wall, the hyperscalers have contracted roughly 9.8 gigawatts of nuclear power. Microsoft is paying to restart Three Mile Island. Google has ordered small modular reactors from Kairos. Amazon and Meta [] have struck their own nuclear deals. This is the self-reliant, behind-the-meter power I mentioned earlier, generated next to the datacenter rather than bought at retail off a strained grid. Unlike the moonshots, it needs no new physics. The reactors exist, the supply chains exist, and the contracts are signed.

This is the mechanism by which the energy cost of a token comes down over the coming decade, because in the long run the cost of intelligence is mostly the cost of energy. Sam Altman talks about a future where intelligence is a utility you buy on a meter, like electricity, and Dario Amodei frames the whole race in gigawatts. Cheap, abundant, clean power is the input that drags the entire cost curve down, and fission is how the industry reaches for it this decade.

Fusion is the option that sits further out. Helion, chaired and funded by Sam Altman, has raised about $1.5B, signed the world’s first fusion power-purchase agreement with Microsoft for 2028, and reached 150 million degrees in early 2026. Commonwealth Fusion aims to hit net energy gain in 2027 and connect a 400-megawatt plant to the grid in the early 2030s. These are real machines with real money and real deadlines, and real delivery risk too, as the giant public ITER project shows after slipping its timeline to 2039. I should also correct a claim I used to make. Even with fusion fuel that is effectively free, the plant is so capital-intensive that the power would still cost something like $30 to $80 per megawatt-hour, in the range of wind and today’s nuclear. The right word is abundant and clean, and abundant is not the same as free. Fusion would be a gift if it arrives on time, and fission is what powers the build-out until it does.

Domino #5: The labor market finds a new balance

The right lens here is to optimize tasks rather than people, and I learned that long before AI, on a right-sizing project early in my corporate career. We mapped every task a team performed, then asked three questions of each one: is it actually needed, is it being done well, and could it be automated or streamlined. Only once we had the full map and a priority order did we allocate tasks to people. The jobs fell out of the tasks, never the other way around. That is exactly how I read AI’s effect on work. AI automates tasks rather than whole jobs, and economists have framed work this way for years, with the AI usage data now confirming it.

Anthropic studied how people actually use its models and found the split was about 57% augmentation and 43% automation, with few occupations handing over most of their tasks. A job is a bundle of tasks, and AI picks them off one at a time, starting with the ones that are cheapest to do with tokens and leaving the ones that need judgment, accountability, or a human in the room. That’s why the right way to think about the future of any job is to break it into its tasks and ask, for each one, whether a human or a machine is the cheaper way to get it done.

Take a lawyer, and split a year of work into tasks. Suppose a competent lawyer costs you $200,000 a year. Here’s what those tasks might consume in tokens, and what they’d cost at two different token prices.

Watch what happens as the price of a token falls, which is exactly what Domino #4 guarantees. At $10,000 per million tokens, you’d hand the first four tasks to an AI agent, because together they cost about $125,000 against a $200,000 salary, but you’d still pay a human to argue in court, because that one task alone would cost $240,000 in tokens.

At $1,000 per million, the entire bundle costs $36,500, far below the lawyer’s salary, and automating all of it starts to make sense. At $100,000 per million, even the simplest task is too expensive and you keep the human for everything. The cost per token sets the height of the water line, and as it drops, the line rises through the job one task at a time. The same exercise runs for every job in the economy, which is why this re-prices labor rather than simply deleting it.

The aggregate studies fit this shape. Goldman Sachs estimated AI could expose the equivalent of 300 million jobs globally to some automation, and McKinsey put the annual value created at $2.6 to $4.4 trillion.

The aggregate numbers tell the same task-by-task story from several angles. Start with exposure, meaning how much of the work is even in range of being automated.

Exposure is only half the picture, because the same build-out is creating work as fast as it removes it.

History says the same thing over a longer lens.

When you add up the value rather than the threat, two things stand out, where it comes from and how concentrated it is.

And it is lopsided in where it lands.

The World Economic Forum, netting it out, expects AI and related forces to create about 170 million jobs and displace about 92 million by 2030, a net gain of 78 million. Exposure is not the same as replacement, and I’ll give the bears their best counter here too: the Stanford team found a real 13% relative decline in employment for 22-to-25-year-olds in the most exposed jobs, so the bottom rung of the career ladder is already feeling it. And the economist Daron Acemoglu, no cheerleader, thinks the whole productivity boost may be under 1% over a decade.

There’s an wrinkle worth naming. Today’s most capable agents burn enormous quantities of tokens to complete a task, so for many complex jobs the AI is currently more expensive than the human, not less. Klarna learned this in public, replacing the work of 700 support agents with a bot and then quietly rehiring humans for the cases where quality slipped. That’s the messy reality. The crossover is real, but it arrives task by task and year by year as the cost per token falls, and it has already arrived for high-volume, low-judgment work. The legal world shows where it’s heading: Harvey, an AI built for law firms, went from roughly $100M to $190M of annual recurring revenue in months.

So what does the labor domino tell us? It tells us the economy adjusts the way it always has, by re-pricing tasks and reshuffling people, and that the speed of the adjustment is governed by the falling cost of a token. Every task that crosses the line becomes a new stream of token demand, which feeds straight back into Domino #1. The dominoes form a loop: demand pulls supply, scarcity supports price, scale and energy push cost down, and falling cost pulls more tasks, and therefore more demand, into the machine.

Now I can do what Ed wouldn’t, and put real numbers on where that loop settles.

Rebuilding the steady state: what the math supports

The version of OpenAI’s economics that’s been circulating ends with a steady-state column: about 70% gross margin and a 49% operating margin. I built one of those myself. The trouble is that a 49% operating margin is what Microsoft earns, and assuming a young company lands exactly there is a forecast of the best case dressed up as a base case. So I rebuilt it as three scenarios, each anchored to what real mature businesses actually earn.

The anchors come from companies that have already crossed to the other side. Microsoft runs a 46% operating margin, Meta 41%, Alphabet 32%, Amazon’s AWS around 35%, and Oracle about 25% on a reported basis. On gross margin, traditional cloud earns close to 77%, while AI and inference services today earn somewhere around 50% to 60%, because compute is a heavier ongoing cost than hosting software.

Those two ranges bracket where OpenAI can plausibly land. Here’s what that looks like, applied to a common $100B revenue base so the scenarios compare on margins rather than on size.

Even in the bear case, where AI stays expensive to run and the research arms race never lets up, OpenAI earns a 17% operating margin. That’s a profitable software business. The base case, a 33% margin, puts it right alongside Alphabet and AWS. The bull case reaches Microsoft’s neighbourhood. There is no scenario here, including the deliberately pessimistic one, in which the business fails to make money at scale. The word unprofitable does a lot of work in the bear case, and the model simply doesn’t support it once the company stops choosing to spend 1.5x its revenue on the next model.

The other thing the circulating table got wrong was scale, and scale is where the real money is. That steady-state column assumed about $78B of revenue, 6x the 2025 figure. But OpenAI is already at a $25B run-rate and targets around $280B by 2030. I won’t underwrite that target, but I can show you what each margin scenario throws off across a range of revenue, so you can pick your own number.

Sit with the bottom of that table. If OpenAI hits the revenue it’s guiding to, even my bear case spins off close to $50B of operating profit a year, and the base case nearly doubles that. Those are the economics the leaked statements are supposedly disproving. The loss today is the cost of buying that future at maximum speed, and a company growing revenue 253% a year while gross margin climbs 15 points has earned the benefit of the doubt on whether the future arrives.

Bringing it home

Let me come back to my doctor, because she’s the answer to Ed’s whole argument. She didn’t adopt AI because a chart told her to, or because she was caught in a bubble. She adopted it because it gave her back the thing she went to school for, the chance to look a patient in the eye. That’s the demand in Domino #1, the kind that doesn’t reverse. Multiply her by a billion people and several million companies, run it through a supply chain that can’t keep up, a price the best users gladly pay, and a cost that falls tenfold a year, and you don’t get a bubble. You get one of the largest businesses ever built, working through the same losses-first arc as electricity, the railroads, and Amazon.

I’ll hold myself to the same standard I hold everyone else, so here’s what would prove me wrong. If the cost per token stops falling, and demand saturates, and OpenAI turns out to have no pricing power after all, then the loop breaks and Ed wins. That’s three things going wrong at once, against the grain of everything in the data today. I’m not betting on it.

The first domino has already tipped.

The rest is physics, scale, and time.

Portfolio Update

While the market gained +0.9% in the week, the portfolio gained almost double that at +1.8%, expanding the outperformance.

Portfolio Return

• Month-to-date: +3.3% vs. the S&P 500’s -1.1%.

• Year-to-date: +43.9% vs. the S&P 500’s +9.6%. That is a gap of 3,428 basis points.

• Since inception: +103.0% vs. the S&P 500’s +30.4%. That’s 3.4x the market.

Share

Contribution by Sector

Tech and financials led the gains, partially offset by energy.

Contribution by Position

(For the full breakdown plus commentary on earnings results and the big movers, see Weekly Stock Performance Tracker)

• +108 bps (Thesis)

• +54 bps (Thesis)

• +29 bps (Thesis)

• +10 bps (Thesis)

• +6 bps (Thesis)

• +2 bps (Thesis)

• +2 bps (Thesis)

• -14 bps (Thesis)

• -66 bps (TSX: CLS) (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Further Sunday reading to help your investment process:

• Never Stop Asking Why, Using the “5 Whys” to Solve Problems and Pick Stocks

• The Rise and Fall of Moats: When Walls Built to Protect Become Traps

• Design Your Life Around Your Nature

• How I Earn $3,000-$7,000 a Month While Waiting to Buy Great Stocks Cheaper

• Cheat Codes for Life Monopoly

On 8 April 2026 I closed Kingstone [] for a +35% gain and redeployed the capital into the April stock pick.

Now I’m closing the second insurer in the portfolio, Heritage, for +20%.

I added Heritage Insurance [] to the portfolio on 6 August 2025 at $19.30.

On 18 June 2026 I closed the position at $23.31.

That’s a +20.8% capital gain, and because HRTG never reinstated its dividend while I held it, the capital gain is the total return too. In a touch over ten months, that works out to roughly +24% annualized.

The original thesis was simple. HRTG was a battle-tested regional property insurer trading at roughly 6.4x forward earnings while generating close to 28% ROE, run by a CEO and CFO who together owned 6% of the company and were buying more in the open market. I argued the discount would close as the market learned to trust the turnaround, and that an 8.5x multiple would put the stock around $32. The business has executed almost exactly as I underwrote it, but rather than closing the position when it hit my target price, I revised the target price to $36. When the stock first tagged $32 around the Q4 2025 print, the fundamentals had moved up alongside the price. That was a record quarter: net income rose 228% y/y on a 62% combined ratio with zero catastrophe losses, and book value per share kept compounding. Higher sustained earning power on a bigger book meant the same kind of multiple now sat on bigger numbers, so I revised my model and lifted the target to $36 rather than ring the register.

Table of Contents

• TLDR

• What I got right

• What I underestimated

• Why close a winner now?

• The valuation paradox

• Two lessons I’m taking forward

• Verdict

TLDR

• I closed HRTG for a +20.8% total return in about ten and a half months.

• The thesis was mostly right. Underwriting discipline held, the combined ratio fell to 81%, book value per share compounded +61.5% y/y to $17.15, and the balance sheet is the strongest it’s ever been. The business did its job.

• I’m closing for three reasons: the top-line growth inflection I underwrote still hasn’t shown up in the headline numbers, the eye-catching ROE that justified a re-rating is normalizing as equity rebuilds, and I don’t want to hold a Florida-exposed insurer at peak-ish earnings through hurricane season with no offsetting catalyst.

• This isn’t a “sell, everyone” call. HRTG still looks cheap in a clean weather year. I’m stepping aside on risk/reward and opportunity cost.

• The proceeds go into July’s pick, which I think offers better asymmetry from here.

A quick scorecard before the post-mortem: of the things I bet on, the business quality bets won and the re-rating bet stalled. Here’s the honest breakdown.

What I got right

Belief #1. The turnaround was real and durable. This was the core of the thesis, and it held up beautifully. In Q1 2026 the net combined ratio improved to 81.0% from 84.5% a year earlier, and the net loss ratio of 45.9% was the lowest first quarter since 2015, even after $36.7M of weather losses from Northeast winter storms. Strip out weather and prior-year development and the attritional loss ratio sat at 31.6%, essentially flat with the prior year and stable for two years running. Management also did the hard, unglamorous thing: it let Florida commercial residential premium fall 7.8% rather than match competitors it considered underpriced. That is exactly the disciplined behaviour I underwrote, and it’s why the margins are real rather than borrowed from future losses.

Belief #2. Management’s alignment showed up in the capital account. The team kept prioritizing balance-sheet repair and opportunistic buybacks over chasing premium. Debt-to-capital fell to 13%, statutory surplus rose to $407.6M, and the company carried $517M of cash at quarter end. The Board retired the old $25M repurchase plan and authorized a fresh $50M one in May, having already bought back $12M of stock when the shares looked cheap to them. Owners ran it like owners.

Belief #3. The downside was protected the whole way. I bought a single-digit-multiple insurer with insider buying and a fortress balance sheet, and the position behaved like one. Book value per share went from under $10 in 2024 to $16.51, a +73.75% jump, so the floor under the stock kept rising even when the price didn’t.

I never had a night where I worried about a permanent loss of capital here. For a name in a catastrophe-exposed business, that’s the whole point.

What I underestimated

Miss #1. The growth inflection kept slipping into “next quarter.” My thesis leaned on HRTG moving from “shrink to get healthy” to “grow profitably,” and that the return to premium and policy growth would help force a re-rating. It still hasn’t landed in the headline numbers. Gross premiums written fell 2.6% y/y in Q1 2026, premiums-in-force were essentially flat at $1.427B, and the policy count was down 6.3% y/y. Management says new business written rose 62.7% y/y and that written premium should turn positive for FY2026, and I believe the inflection is coming. But I’ve now watched several quarters of “in the coming quarters,” and the market wants to see it in the totals before it pays for it. So do I.

Miss #2. I anchored on a peak ROE that was always going to come down. The headline that sold the thesis was an insurer earning roughly 28% on equity against an industry around 10%. Q1 2026 ROE was still a strong 28.5%, but it fell from 39.3% a year earlier, and it fell for a telling reason: average equity grew 65.5% y/y. That’s the catch with a turnaround off a depressed equity base. As the company rebuilds book value, the same dollars of profit earn a lower percentage return, so the very success of the thesis (compounding book value) erodes the metric that made the stock look special. I treated a rebuilt-equity ROE as a durable advantage when a chunk of it was always going to normalize toward the high teens or low twenties.

Why close a winner now?

If the business is executing and the stock still looks cheap, why sell? Three things line up, and together they tip the risk/reward.

First, the two misses above are not just history, they’re the present. Without a visible growth inflection and with ROE drifting down rather than up, the two engines that would force the market to pay a higher multiple are both idling. The re-rating case needs at least one of them firing.

Second, the calendar. We’re at the doorstep of the 2026 Atlantic hurricane season. The forecasters actually call for a quiet one: NOAA puts the odds of a below-normal season at 55% and only 10% on above-normal, and Colorado State’s June update trimmed its call to 11 named storms, five hurricanes and two majors, all below the long-run average. So this isn’t a “the big one is coming” call. It’s simpler. A Florida-and-Gulf-exposed insurer carries a binary tail from June to November that no forecast erases, because it takes only one landfall in the wrong metro to wipe out a year of earnings. The market knows it, which is why these names rarely re-rate going into the season. With no growth inflection in hand and ROE on the way down, there’s nothing to pay me for sitting through that window. The next six months look like flat upside against a fat tail.

It’s worth being fair about that Q1 print. On the surface it was a “miss,” with EPS $0.34 below consensus. But the gap came from weather rather than the franchise. The Piper Sandler analyst on the call noted the miss was effectively all catastrophe load, and the first quarter is seasonally Heritage’s weakest because of Northeast winter storms. The operating engine is fine. The issue is that a fine operating engine in a cat-exposed insurer still can’t outrun a single bad storm, and that’s the risk I’m choosing not to underwrite into peak season.

Third, opportunity cost. I’m not closing HRTG because I’m afraid of it. I’m closing it because the capital has a better job to do. July’s pick offers what Heritage no longer does from here: an idea where the catalyst is in front of me rather than perpetually one quarter away, and where the risk/reward isn’t gated by a weather map.

Recycling capital from a winner (I know +20% is not such a big win compared to the rest of the portfolio, but a win is a win 🙂) whose easy money has been made into a fresher asymmetry is how the portfolio compounds

I’ll be straight about the near-term setup. The shares gapped down after the 7 May earnings print, and they will probably close that gap as the weather-driven miss fades. I could wait for that bounce, but that is speculating on a re-trace rather than underwriting a thesis. I would rather book the win now and keep the capital ready for July’s pick than play for the last few points.

The valuation paradox

Here’s the part that makes this a judgment call rather than an obvious one. HRTG isn’t expensive. At my $23.31 exit it traded at about 1.4x book value and 5.5x what it could earn this year if the weather cooperates.

Note that both metrics are cheaper now (1.4x book and 5.5x forward earnings) than when I entered in August 2025 (1.9x and 6.4x, respectively).

So why walk away from a cheap stock?

Weight the storm tail properly and the multiple stops looking like a bargain and starts looking like fair compensation for the risk. That’s the trap with catastrophe-exposed insurers: the low multiple is often a permanent feature rather than a mispricing waiting to close, the market’s standing discount for the chance that one season erases a year. I spent ten months waiting for that discount to lift, and the round trip from a near-$32 high back to the low $20s is the market telling me it isn’t ready to.

There’s a subtler drag too. The thing that protects my downside, compounding book value, is the same thing capping the upside. Every dollar of retained earnings lifts book value, so the price has to climb just to hold the same price-to-book. Without multiple expansion, the stock can only grow about as fast as book value does, and book-value growth is slowing as the equity base balloons.

Two lessons I’m taking forward

Lesson #1. On catastrophe-exposed insurers, a low multiple is often structural rather than temporary. A high ROE alone won’t force a re-rating when the market believes a single storm can erase a year of profit. I treated HRTG’s discount as a mispricing on a clock. It behaved more like a standing feature of the sub-sector. Next time I underwrite a cat-exposed insurer expecting multiple expansion, I’ll demand a concrete, durable catalyst for the re-rate (sustained multi-year premium growth, a dividend reinstatement, a genuine de-risking of the book) rather than betting that strong returns will speak for themselves. They didn’t speak loudly enough here.

Lesson #2. Normalize a turnaround’s ROE off rebuilt equity, don’t extrapolate the trough. The spectacular returns that make a recovering insurer look mispriced are partly an artefact of a depressed equity base. As book value heals, ROE mean-reverts, and the headline that drew you in fades on its own. I should have modelled the glide path of ROE as equity rebuilt rather than anchoring on a near-peak number. The valuation case looks very different at a normalized high-teens ROE than at 28%.

Verdict

I’m closing a winner rather than cutting a loser. Heritage did almost everything I asked of the business, and +20.8% with no sleepless nights is a result I’ll take. But the re-rating I was really paid to wait for is stuck behind a growth inflection that keeps slipping, an ROE that’s normalizing down, and a hurricane season that caps the upside for the next two quarters. Cheap isn’t a catalyst, and I have a better use for the capital. If Heritage strings together two or three quarters of clear premium growth and gets through the season clean, I’d happily look again, but from a higher base of proof. For now, the cash goes to work in July’s pick.

As I mentioned a couple of weeks ago, while less than 20% of the positions are in the AI-theme, it represents 40% of the portfolio value due to the gains in those positions. And also I am getting a bit burnt out of reading, analysing, talking AI, so I am switching to another sector that I love and that would help the portfolio diversification and keep me sane…financials.

Most people who glance at this company see a +$100B revenue business that earns a wafer-thin margin and assume it’s a low-quality commodity reseller. They stop reading there. That is the mispricing. Almost all of that +$100B is the pass-through cost of physical commodities it moves for clients. The number that actually matters, net operating revenue, is compounding at 20% a year. On that real top line it earns a 20%-plus pre-tax margin and, in the last two quarters, a ROE north of 22%. The market is paying about $9B for the whole thing.

Here is the part that made me sit up. In the first half of this fiscal year alone, the business earned $313M of net income, more than it booked in the whole of the prior year. Run that pace for a full year and you have a company throwing off well over $600M of cash against a $9B price tag. For a capital-light intermediary where accounting earnings and free cash flow sit close together, that is a free-cash-flow yield few people would ever pin on a name that screens as a thin-margin commodity trader.

So the question is simple. Is this a volatility-juiced one-off that just had two lucky quarters, or a genuine compounder that the market still misreads because of how its revenue is presented? I think it’s the second, and the cash flow against the price is the cleanest way to see it.

I sent this pick to paid subscribers as a real-time trade alert on 10 June…

… and it is already up 8.3%.

Table of Contents

• TLDR

• How the company actually makes money

• Industry structure: the middle-market gap

• The volatility engine, and why it’s a feature

• The four segments

• Acquisitions and the consolidation tailwind

• What the market is paying: free cash flow against a $9B price

• Scenario analysis: discount rate and growth

• Financials and the DCF

• The bear case

• Variant perception

• Verdict

Read more

Hello fellow Sharks,

Another strong week. The portfolio gained +4.9% while the S&P 500 added +0.7%, so the lead just got wider. If you want to skip straight to the numbers, jump to the Portfolio Update.

A quick personal win first. As I mentioned last week, I had to sit a test to renew my Chilean license. Rather than wade through 500-plus pages of material, I built a simulator from it and drilled only my weak points. Ten hours of prep, tops, and I passed. 🥳

On another note, I got this note from a subscriber.

I DO have a fund, and I think I've mentioned it here before. If being part of it is something you'd want, let me know and I'll send you the details.

I also almost scrapped the Thought of the Week to write about SpaceX's IPO, but I'm saving that for next week. I think SpaceX will be the single biggest source of volatility in 2027, and I'm still working out how to play it without blowing up the AI-theme positions we already hold. More on that soon.

For the record, I didn't buy the SpaceX IPO. I never do IPOs, only spin-offs, split-offs and carve-outs (you can read here why I invest in those). We still caught the updraft through a name we already owned: the April Stock Pick, a defense company, is up +20% this month, with a clear pop the week SpaceX listed as the hype spilled into the defense space.

A couple of weeks ago, a subscriber sent me a short one: is Nike a buy?

Here’s what I sent back.

The short version: I don't see how Nike fixes its China problem, and brands no longer mean what they once did. The dividend survives because management knows reinvesting the cash won't repair the business, so paying it out is the least bad option. Yes, the stock is beaten down enough that any good news pops it, but that's speculation, not a thesis.

That one question pulled a much bigger thread, and that thread is this week's piece.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• In Case You Missed It

• The Slow Death of Brand Investing

• Portfolio Update

In Case You Missed It

This week I closed the AXIA position almost flat, down 0.6% on my cost basis but up +2.6% from where I recommended it on January 8, 2026.

The exit has nothing to do with the thesis. In April the board approved delisting the US ADRs to consolidate trading into the ordinary shares on Brazil's B3 exchange, part of its move to the Novo Mercado governance tier. The ADRs are only about 2.5% of the shareholder base, so management is pulling them to concentrate liquidity at home. Holders with Interactive Brokers or a Brazilian broker can convert the ADRs into ordinary shares and stay in, but most subscribers can't trade in São Paulo, so I closed the position to keep the model portfolio clean and trackable. A disappointing way to leave a name that hit my target inside three months, but it's a governance call, not a business one.

Also, a day before the start of the World Cup, I shared the June Stock Pick.

I will be sharing the deep dive with paid subscribers next week.

The scorecard below is every call I've published, open and closed, scored in real time. I don't hide the losers.

Thought Of The Week

The Slow Death of Brand Investing

Investing in brands used to be one of the great strategies in markets. Buffett built part of his record on it: See’s Candies in 1972, Coca-Cola in 1988, American Express, Heinz in 2013. The logic was almost mechanical. Find a name people trust, confirm they’ll pay extra for it, and let pricing power compound for decades.

That machine is breaking, at least for brands that sell to the masses. Below is why I think it broke, what the K economy has to do with it, and the test I now run before paying a single dollar for a brand.

What a brand is actually worth

Strip away the marketing language and a brand is worth exactly one thing: the incremental price people willingly pay over a comparable product without the name.

That’s the entire asset. Take the swoosh off a $130 Nike running shoe and ask what the identical shoe sells for as a no-name product. Maybe $70. The swoosh is worth the $60 gap, multiplied by every pair sold, for as long as buyers keep paying it. Marketing academics formalized this years ago: Ailawadi, Lehmann and Neslin measured brand equity as the revenue premium a branded product earns over an equivalent private-label product.

This definition explains why brands made such good investments for so long. The premium is close to pure profit. It costs Coca-Cola nothing extra to sell a bottle of Coke at twice the price of store-brand cola; the syrup costs the same. A strong brand lets a company raise prices a little every year without losing customers, and that pricing power required no new factories and no new capital, nothing but the name. Buffett’s See’s Candies is the cleanest example: he bought it for $25M in 1972 and raised prices nearly every year after.

But notice what the definition implies. The brand isn’t the logo, the history, or how many people recognize the name. It’s the premium. And the premium exists only inside the customer’s willingness to pay it. When that willingness fades, the asset evaporates.

Why mass brands are losing the premium

Four forces are squeezing the premium out of mass-market brands, and none of them look temporary.

Force #1. The information gap closed. A brand’s original job was to be a shortcut for trust. Fifty years ago you couldn’t test a shoe’s quality before buying, so you paid extra for the name that had never burned you. Today every product carries thousands of reviews, comparison videos, and teardown threads. Quality became observable. When you can verify, you don’t need to trust. And when you don’t need to trust, the trust premium shrinks.

Force #2. The dupe flipped from shame to status. My generation hid its knockoffs. The current one posts them. On TikTok, finding a $30 copy of a $128 Lululemon legging isn’t embarrassing, it’s content, and the word dupe has billions of views behind it.

@arianavitalecan promise you that these are the best & most identical Lululemon dupes on amazon EVER 🤍 All linked on my amazon in bio! 🫶🏽 #fyp #lululemondupe #lululemonhack #lululemoneducator #amazonfinds #luludupes #amazonluludupe #gymoutfits #foryoupage #lululemon #greenscreen

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Paying full price for the logo is what gets mocked now. That’s a cultural reversal of the exact mechanism that made brands valuable: the logo used to signal status, and today overpaying for the logo signals you got played.

Force #3. Private label got good. Store brands used to be the sad row at the bottom of the shelf. In 2025, US private-label sales hit a record $282.8B, growing 3.3% against 1.2% for national brands, and store brands now move a record 23.5% of all units sold.

Costco’s Kirkland Signature alone does about $90B a year in sales, roughly double Nike’s entire revenue.

Read that again. The single biggest beneficiary of brand erosion is itself a brand, except its promise is the opposite one: Kirkland’s pitch is that you’re smart for not paying the premium. The premium didn’t just shrink, it changed owners. Retailers captured it.

And some national brands handed it over themselves: to fill idle factory capacity, they took on contracts to manufacture the very store brands now eating their share, a short-term margin fix that trained their own replacement and traded away long-term pricing power.

Force #4. Brand heat became a fashion cycle, not an asset. VF Corp’s Vans was one of the hottest names in footwear in 2018.

Sales have now declined three years in a row.

The dividend was cut from 21 cents a quarter to 9 cents…

…and the stock trades more than 80% below its high.

Nothing structural protected Vans on the way down because nothing structural put it on top.

The dividend tell

Which brings me back to Nike, because Nike shows you how management teams behave when they know the premium is fading.

In FY2025, Nike’s revenue fell 10% and net income fell 44%.

The same year, Nike raised its dividend 6% to $2.3B, the 23rd consecutive annual increase, and cash on the balance sheet dropped $1.4B because operations no longer cover dividends plus buybacks. A company with a defensible brand and a broken stock price would pour that cash into product and the comeback.

Nike is choosing not to.

That’s the most honest signal Nike gives us: management is telling you, with $2.3B a year, that reinvesting in the brand won’t earn an acceptable return.

When a management team tells you that, believe them!

The K economy, explained

None of this happens in a vacuum. The reason the premium is collapsing for mass brands while Hermès runs a 47% EBITDA margin is the shape of who has money in 2026.

The K economy describes an economy splitting into two arms moving in opposite directions. The upper arm owns assets: stocks, homes, and businesses. Their net worth inflated through the 2020s, and their spending followed.

The lower arm lives on wages. Cumulative inflation since 2021 ate into their paychecks, and their spending shifted from wants to needs. Moody’s Analytics estimates the top 10% of earners now account for about 46% of all US consumer spending…

… and that spending by this group grew 62% between late 2020 and late 2025, more than any other income group. Fair warning: some Fed researchers argue official surveys put the top-10% share much lower. The level is debated. The direction isn’t.

Here’s the part most commentary misses: the K didn’t start in 2020. Median real wages stagnated for decades while asset prices climbed. The pandemic stimulus, the 2021-2023 inflation wave, and the asset boom that followed pulled the two arms apart fast enough that everyone finally noticed and gave the thing a name. Mass-brand erosion and the K are the same story told from two directions. Kraft’s write-down came in 2019, before anyone said K economy. The customers were already leaving.

The spending data tells the same story from four different angles.

Moody’s shows the top 10% drove the bulk of spending growth since 2020 and now command a rising share of every dollar spent:

Bank of America’s card data shows the same divide in real time, the top tier accelerating while the bottom stalls:

The New York Fed’s cumulative read since 2020 has high earners steadily pulling away:

And the BLS survey shows how lopsided the base already is, the top deciles taking the biggest slices:

So what does the K mean for brands?

A mass-market premium brand is, by construction, a bet on the middle: customers with enough money to pay extra but not enough to ignore prices. That middle is exactly what the K hollows out. The lower arm trades down to Kirkland and private label. The upper arm trades up to brands the lower arm can’t follow them into. The mass-premium brand loses customers in both directions at once. And because the consumer space at that level is a volume game, not a margin one, losing volume breaks the whole model. Nike can’t take Hermès pricing, and it can no longer beat Kirkland economics. That’s the kill zone.

The K cuts through luxury too

The upper arm explains the winners. Hermès grew revenue 9% in 2025, with double-digit growth in leather goods and that 47% EBITDA margin. Ferrari grew revenue 7% and hit its 2026 targets a year early; the stock has multiplied by 8x over the last ten years.

Neither customer base noticed grocery inflation. A Birkin waitlist doesn’t shorten because eggs got expensive.

But before you buy a luxury basket and call it a day, look closer, because the K cuts through luxury itself. Bain estimated that around 50M customers left the luxury market between 2022 and 2024, with more following in 2025.

The ones who left weren’t the rich. They were the aspirational buyers, the middle-class customer stretching for an entry-level logo bag or a 500-euro sneaker. Brands that depend on that stretch are mass brands in disguise, and the market is repricing them accordingly. Shares of Burberry, Kering and LVMH have lost 27% to 66% in the past 5 years.

Burberry’s own CEO called the market “very bifurcated” on the Q2 2026 call. His word for the K.

The dividing line isn’t luxury versus mass. It’s simpler and harsher: which arm of the K writes the checks. Hermès sells to the arm going up. Burberry’s growth depended on the arm going down.

The three questions I ask before paying for a brand

Now the steel-man, because brands aren’t uninvestable, and famous names that crash can produce violent rebounds. Nike will jump on any real good news, exactly because expectations sit this low. New management could land the turnaround. If that happens, holders get paid. My objection is the label. Buying Nike today isn’t brand investing. It’s turnaround speculation on a company whose core asset is eroding, and speculation should be sized and priced as what it is.

When I evaluate a company whose value rests on a brand, I ask three questions, in this order.

Question #1. Where is the premium, and which way is it moving? Find the closest comparable non-brand product and measure the price gap. Then watch how the gap gets defended. A company raising prices while holding volume has a live brand. A company hitting volume targets through promotions, outlet channels, and discounts is liquidating its premium one markdown at a time. Hermès raises prices and keeps the waitlist. Nike funds sneaker discounts.

Question #2. Which arm of the K is the customer on? If the median buyer owns assets, pricing power is structural, and the brand can compound through whatever the wage economy does. If the median buyer lives paycheck to paycheck, the brand is fighting gravity, and every grocery bill is a competitor. The uncomfortable version of this question: is this brand’s premium the first thing its customer cuts in a hard month?

Question #3. Is management defending the moat or harvesting it? Follow the cash. Reinvestment in product, experience, and distribution says management believes the premium can grow. A fat dividend held steady while free cash flow shrinks says management is monetizing decline. 3G ran Kraft Heinz that way, and the write-down followed. Nike’s 23-year dividend streak, paid out of a shrinking pool, reads the same to me.

Kraft Heinz failed all three questions by 2019. Nike fails all three today. Hermès passes all three. This test won’t hand you cheap stocks. It will keep you out of expensive mistakes dressed up as blue chips.

Portfolio Update

While the market gained +0.7% in the week, the portfolio gained +4.9% in the week, expanding the outperformance.

Portfolio Return

• Month-to-date: +1.5% vs. the S&P 500’s -2.0%.

• Year-to-date: +41.4% vs. the S&P 500’s +8.6%. That is a gap of 3,282 basis points.

• Since inception: +99.5% vs. the S&P 500’s +29.2%. That’s 3.4x the market.

Share

Contribution by Sector

Tech led the gains, followed by industrials.

Contribution by Position

(For the full breakdown plus commentary on earnings results and the big movers, see Weekly Stock Performance Tracker)

• +88 bps (Thesis)

• +69 bps (TSX: CLS) (Thesis)

• +25 bps (Thesis)

• +18 bps (Thesis)

• +14 bps (Thesis)

• +10 bps (Thesis)

• +4 bps (Thesis)

• -2 bps (Thesis)

• -16 bps (Thesis)

That’s it for this week.

Stay calm. Stay focused. And remember to stay sharp, fellow Sharks!

Further Sunday reading to help your investment process:

• Never Stop Asking Why, Using the “5 Whys” to Solve Problems and Pick Stocks

• The Rise and Fall of Moats: When Walls Built to Protect Become Traps

• Design Your Life Around Your Nature

• How I Earn $3,000-$7,000 a Month While Waiting to Buy Great Stocks Cheaper

• Cheat Codes for Life Monopoly

This morning I bought a new position. It’s a misunderstood middle-market financial firm that most people misread as a thin-margin commodity trader. It books around +$100B of pass-through revenue, so it screens ugly, but the number that actually matters, its real top line, has compounded close to 20% a year for five years.

It earns more when markets get volatile, which makes it a rare thing: a hedge that also compounds. It just posted back-to-back record quarters with a return on equity north of 20%. It recently became the largest player of its kind in the US after a major acquisition. And it throws off FCF worth a mid-single-digit yield against, mostly retained and reinvested at a 20%-plus return. My DCF base case sits +50% above where I bought it this morning.

That’s the shape of it. A boring-looking business, a cash machine underneath, and a market that still files it under the wrong category.

The ticker, my exact entry, the trade ticket, the full thesis and my DCF target are below the line for paid subscribers.

Paid subscribers have ridden calls like DELL (+217%), Micron / MU (+213%), NGD (+86%, taken out in a buyout) and ODC (+57%). Not every pick works, and the full record with the misses is right here, but the winners have carried the book.

Read more

The reason is simple. In April the board approved delisting the ADRs to concentrate trading in a single listing, the ordinary shares on Brazil's B3 exchange in São Paulo. Management's logic is that the ADRs make up just 2.5% of the shareholder base, so pulling them consolidates liquidity at home and lines up with the company's move to the Novo Mercado governance segment.

It's a disappointing way to exit. I picked AXIA in January…

… and the shares hit my target within three months.

Holders with Interactive Brokers or a Brazilian broker can ask for the ADRs to be cancelled and take delivery of the ordinary shares instead. I looked at it, but most of my subscribers can't trade on the Brazilian exchange, so I'm closing the position in the model portfolio to keep the tracking clean.

Hello fellow Sharks,

What a week. By Tuesday, the portfolio was up 4.3% against 0.4% for the S&P 500, and then Friday's jobs report turned the tide. We closed the week down 3.3% versus 2.6% for the index. If you want to skip straight to the numbers, jump to the Portfolio Update.

The S&P fell 2.7% on Friday alone. The jobs print came in strong, and the market is now pricing a rate hike by year-end. Tech took the brunt of it, which makes sense: most of the intrinsic value in a tech company sits in its terminal value, and that is the part a higher discount rate punishes most. I think the selloff is overblown. Mr. Market likes us and decided to hand us a discount, so I put some of the cash in a few client accounts to work in tech. I left this portfolio’s cash untouched, since I am saving it for the June stock pick.

That June pick has been slow going, and this week had plenty competing for my attention. We had a run of doctor appointments, blood work, and an ultrasound, because my wife is pregnant again 😊. I have also been refreshing my Chilean regulation knowledge for my license renewal. I think I know about 90% of the material; the trouble is the other 10% is new since I last sat the exam. So I vibe coded an exam simulator off the published material. It mimics the real structure and time limit, tracks every right and wrong answer, then tells me what to study next. I can even weight each new test toward the questions I have already missed. That felt smarter than rereading 500-plus pages to hunt down the missing 10%.

The passing score is 70%. I am borderline right now, so I will run more tests today to clear it.

The exam is Tuesday, so wish me luck.

On the June pick itself, I have been digging into a couple of financials. The setup is turning constructive for some of them, and I needed the change of scenery too. I have been reading about AI and data centers nonstop, and I want to keep this fun rather than burn out on the same story.

Speaking of that story, this week I walk through Dell’s Q1 FY2027 results and why I raised my target to $639 while the stock stays a buy in my book.

Enjoy the read, and have a great Sunday.

~George

Table of Contents:

• Earnings Results

• Dell: The $51 Billion Backlog, and Why I’m Raising My Target to $639

• Portfolio Update

Earnings Results

One company reported this week. It beat both top and bottom line. Paid subscribers can see my thoughts on the results here.

Thought Of The Week

Dell: The $51 Billion Backlog, and Why I’m Raising My Target to $639

Since adding Dell to the portfolio in February, it is up 220%, the largest contributor to the YTD performance.

I bought at $123 the week before its Q4 print, watched that report carry the stock to $295, and then watched Q1 FY2027 push it past $460. It has retreated to $394 along with other tech names due to the job report on Friday.

This update asks one question: with the easy double already banked, is the second one still in front of us?

On 28 May, Dell reported a quarter that broke its own records and then raised the bar again. EPS up 282% y/y, and revenue up 88%.

The AI server backlog hit a record $51.3B. Management lifted full-year revenue guidance by about $27B to a $167B midpoint.

I first laid out the Dell thesis in my deep dive in February, built on five pillars: an operator edge, an AI demand wave, an attach-later margin story, a cash machine, and a shareholder flywheel.

I updated it on 24 May after the Q4 record and walked the published target from $206 to a Monte Carlo base case of $486.

Q1 is the cleanest read yet on whether those pillars hold. They do, and one of them just grew a new leg. I also raised my bear case from $200 to $325, which changes the risk and reward math more than the higher target does.

TLDR