Investing on Philipp D. Dubach | Quantitative Finance & AI Strategy

Midyear Portfolio Review: The Rotation Worked. Europe Didn't.

me@philippdubach.com (Philipp D. Dubach) — Fri, 15 May 2026 00:00:00 +0000

In December I rebalanced my portfolio around five theses for 2026. Five months in, this (early; well just too much happened) midyear review puts four of them ahead of where I expected and one well behind. The one I had the most conviction in, an overweight in European equities by five percentage points, has trailed every other rotation it was meant to beat.

Portfolio performance so far

Through May, the portfolio is up +3.7% in CHF on a time-weighted basis. A fairly-constructed 60/40 benchmark (60% MSCI ACWI in CHF, 40% global aggregate bonds CHF-hedged) is up +3.8%. The S&P 500 in CHF, total return: +5.8%.

The 2025 outperformance, when this same approach beat the S&P 500 by a wide margin in CHF terms, was driven by the dollar’s collapse against the franc. USDCHF fell 11.5% last year. So far in 2026 it has fallen 1.5%. The FX tailwind that made the CHF-hedged strategy look brilliant is gone, and the portfolio is doing what it should in a year when the diversifier isn’t needed yet: matching the global benchmark and trailing pure US exposure.

What worked, what didn’t

Emerging Markets returned +19.0%, the single biggest contributor relative to weight. US Small Cap +12.5%, Japan +11.7%. The rotation thesis, that valuation differentials between US large-cap and almost everything else were too wide to ignore, paid off in three of the four sleeves where I increased exposure.

The fourth was Europe. Up +3.3% in CHF, trailing US large-cap by 3.4 points and trailing every other rotation alternative by 8 to 16 points. Germany’s fiscal pivot is real (€500bn infrastructure fund, €400bn defense, €600bn private commitments). Citi’s European equity strategy team kept its overweight call and projects German EPS growth at a 13% CAGR through 2029. BofA’s February investor survey showed 89% of investors expecting European upside. None of that has shown up in equity prices yet.

Bitcoin and Listed PE were the worst calls, both down roughly 11 to 12 points in CHF. Crypto was always sized as a 4.5% allocation precisely because of this kind of move. Listed PE I’m watching more carefully, since the discount-to-NAV widening that drove the drawdown isn’t a thesis call gone wrong so much as the trade getting more crowded going into a year of higher dispersion.

Valuations got more extreme

The CAPE was the spine of the December argument. The S&P 500 traded at 40.5x cyclically-adjusted earnings, twice the long-run mean of 17.3, and the gap had to compress one way or another, through multiple contraction or earnings growth.

It compressed in neither direction. The CAPE is now 42.0, up from 39.8 in December. That puts US large-cap closer to the dot-com peak (44.2 in December 1999) than at any point since. The compression I expected didn’t happen, and the multiple expanded modestly while the rest of the world kept its mouth open.

Concentration is messier. The December article said “approximately 45%” for the top 10. The actual figure today, from iShares’ IVV holdings file, is 39.1%. Either December’s number was overstated or it has drifted down. NVIDIA, however, grew from 7.2% to 8.17%. Microsoft slipped from 5.9% to 5.0%. Apple is flat. The concentration risk is now a single-name risk, not a Mag-7 risk.

The Europe trade is alive but not paying

The European valuation discount the December piece flagged was 22% on a forward-earnings basis (US 23x, Europe 14x, per UBS’s Year Ahead 2026). On a trailing basis today, US trades at 27.7x and Europe at 18.1x, a 34% discount.

The cushion has mattered less to performance than I expected. Two quarters of strong US earnings (Q1 2026 net margin hit 13.4%, a record per FactSet) made the multiple expensive without making the index look fragile. JPM’s Lakos-Bujas raised the S&P 500 year-end target to 7,600 in April, lifting 2026 EPS to $330 (+22% YoY). Morgan Stanley’s Mike Wilson is at 7,800 with explicit “early cycle” framing. The bear is BofA’s Subramanian at 7,100; Hartnett’s Bull/Bear indicator hit 9.6 (extreme sell) in February before falling to 6.6 in April as positioning unwound. Consensus has converged at the higher end of S&P 500 targets, not the lower.

For the Europe overweight, if discounts that wide can persist for years (and the 25-year history of the gap says they can), then the cheap-versus-expensive case isn’t enough on its own. You need a catalyst. Germany’s fiscal pivot was supposed to be it. The pivot is happening; the multiple isn’t expanding.

Dollar, AI, bonds

Dollar. December’s call was for 4% to 10% USD depreciation through 2026. So far it’s down 1.5% against CHF, a stall rather than a fail. Goldman, UBS, Pictet, ABN AMRO, and MUFG all kept their dollar-weakness calls but pushed the bulk of the move into H2. The fundamentals (twin deficits, declining rate differential, fiscal sustainability concerns flagged by the IMF in April) haven’t gone anywhere.

AI capex. Hyperscaler capex was forecast at $571bn for 2026 in December’s piece. Q1 confirmed actuals plus guidance now point to $660-725bn at the conservative end, with Morgan Stanley as high as $805bn. No major house has trimmed. The Anthropic Mythos release on April 7, which JPM credits as the catalyst for the late-April rebound, made the model-improvement curve look steeper rather than flatter. My own view that AI may end up as a competitive commodity rather than a winner-take-all hyperscaler windfall is unchanged, but the capex cycle has clearly not topped.

Fixed income. The “best fixed-income setup since GFC” thesis has held up unevenly. CHF Corporate Bonds returned −0.0%, Euro Govt CHF-Hedged −0.4%, US Treasuries 7-10Y −1.4% in CHF (the FX drag wiping out a flat USD return). Carry has been fine. The duration call was whipsawed by April’s oil shock (Brent traded in a $118-126 range after the Iran flare-up) and a futures-priced “no Fed cuts” episode that has only partially mean-reverted. Two cuts is still the consensus baseline, but conviction is lower.

H2 Outlook

Three themes that weren’t on my radar in December.

Sovereign debt sustainability. The IMF April 2026 Fiscal Monitor warned that the US Treasury “safety premium” is being compressed. CBO projects a $1.9 trillion FY26 deficit and $2.4T average for FY27-36. Goldman now publishes notes on fiscal contagion to bonds, currencies, and stocks. This was barely mentioned in the December outlook cycle and is now treated as a primary risk.

Tariffs as a live variable. The February SCOTUS IEEPA ruling and the Section 122 substitute (10% global escalating to 15%) make tariffs the largest US tax increase as a share of GDP since 1993, per the Tax Foundation, and roughly $1,300-$1,500 per household. The IMF’s April WEO cut its 2026 US and global growth forecasts citing both tariffs and the Middle East shock.

Gold’s continued ascent. UBS now models gold at $5,900/oz by late 2026, JPM at $6,300 by Q4 2026, Deutsche Bank at $6,000. Q1 2026 central bank gold buying hit 244 tonnes per the World Gold Council. December consensus was $3,200 to $3,800. My CHF-hedged gold position is up +4.3% in CHF, decent but well below what unhedged USD gold did. I held the hedge expecting the dollar to fall fast enough to offset; the dollar has not moved, and the hedge has cost me roughly 4 points of relative performance.

Je ne regrette rien

Modest tilts, not regime change. A small rotation from CHF Corporate Bonds (which yielded zero in CHF) into IG credit, where UBS is recommending “lock in rates” at the May House View. Marginally less Europe, not because the case is wrong but because at 13% I’m taking position-sizing risk on a multi-quarter call without enough catalyst proximity. A smaller US Treasury allocation, since the FX drag is doing all the work and I would rather take duration in CHF Govt or hedged USD. The crypto sleeve takes a 12-point drawdown in CHF and I keep it where it is. It was sized at 4.5% precisely because of this kind of move.

The one I am undecided on is whether to thin gold. The hedge has been a cost. The unhedged version has done what gold should do in a year of fiscal stress. If I unwind the hedge mid-year, I am giving up the protection I bought it for; if I keep it and the dollar falls in H2 as the consensus still expects, the hedge starts paying again. I am leaving it as-is and adding the next gold dollar to the unhedged side.

Two Anthropics

me@philippdubach.com (Philipp D. Dubach) — Sat, 09 May 2026 00:00:00 +0000

Anthropic was founded to be the safety lab that would pull rivals upward. Five years later it is the most aggressive frontier scaler at $380 billion, the company most likely to build the dangerous thing it warns about.

A personal note first. This post is an outtake from a 14,000-word profile of Dario Amodei I just published. I don’t like Anthropic noticeably more than I dislike the other hyperscalers and AI model providers. Amodei is probably the AI CEO whose language and thinking land closest to mine, so over the last few weeks I worked through a dozen of his interviews, a stack of his essays, and a lot of hours of him on YouTube. The longform is the portrait. This post is the structural argument that fell out of it. If you want the character work, the family backstory, and the scenes a paradox piece can’t carry, read the full thing.

Dario Amodei founded Anthropic in 2021 with six other ex-OpenAI researchers and a thesis with two halves: (1) powerful AI is coming whether or not safety-aligned labs are at the frontier, so safety-aligned labs have to be at the frontier. (2) the load-bearing claim, was that competing on safety would pull rivals upward, a “race to the top.” Both halves are still in the company’s public materials. The first half is doing fine. The second is colliding with what the company has actually become. The Series A raised $124 million in May 2021. The Lightspeed round added $3.5 billion in early 2025. By September 2025 the valuation was $183 billion; by February 2026 it had reached $380 billion. Revenue went from zero to roughly $10 billion annualized in three years, with 10x year-on-year growth in each of them. The character work behind that claim is in a separate longform, Inside the Mind of Dario Amodei. This post is the strategic argument that emerges from it.

Race to the top, on paper

Anthropic registered as a Public Benefit Corporation. Its self-description is that it is “an AI safety lab that is also an AI lab,”. The argument goes: a lab that genuinely cares about safety has to be commercially competitive at the frontier, because otherwise the frontier is set by labs that care less. Being at the frontier lets you publish safety practices, hire the best alignment researchers, and shape policy with credibility. Rivals see your practices working and copy them. The whole industry shifts. Race to the top.

In January 2026, Amodei published The Adolescence of Technology, a roughly 22,000-word essay laying out a five-category risk taxonomy: misalignment, individual misuse, state misuse, economic disruption, and indirect or unknown effects. The essay’s anchor sentence on the geopolitical category reads:

autocracy is simply not a form of government that people can accept in the post-powerful AI age.

This is the language of someone who believes the company’s mission is civilizational, and who is willing to say so under his own name. It is also the language of a company that has positioned itself as the democratic-world frontier lab, which has consequences for who its customers and adversaries become.

The strongest external signal that Anthropic’s safety thesis is not pure positioning came in November 2023. After Sam Altman’s brief firing and reinstatement at OpenAI, the OpenAI board approached Amodei with two offers: take the CEO job, or merge Anthropic into OpenAI. He declined both. Walking away from the CEO chair at the most-valuable AI company in the world, less than three years after leaving it, was the most expensive credibility signal Amodei could send that the safety thesis was the actual thesis and not a brand exercise. Roughly fourteen OpenAI researchers had followed him out two years earlier.

There is a softer counterweight worth keeping in mind. Asked by Nicolai Tangen in 2024 about scaling timelines, Amodei said:

frankly, although I invented AI scaling, I don’t know that much about that either. I can’t predict it.

What scaling produced

Five years after that $124 million Series A in May 2021, Anthropic is valued at $380 billion and runs at roughly $10 billion in annualized revenue. Revenue went from zero to $100 million in 2023, $100 million to $1 billion in 2024, then to a $10 billion run-rate by the end of 2025, three consecutive years of 10x growth. Investor decks show projections of $26 billion for 2026 and $70 billion for 2028. The forward trajectory is the steepest in the history of enterprise software.

The capital base behind that revenue is more telling than the revenue itself. Amazon has committed roughly $8 billion cumulatively. Lightspeed wired the first $1 billion of the $3.5 billion early-2025 round on the same Monday Nvidia dropped 17% on the DeepSeek shock, a piece of timing that, deliberately or not, communicated conviction at the moment everyone else flinched. A year later, the $30 billion Series G in February 2026, led by GIC and Coatue, brought the post-money valuation to $380 billion in the second-largest tech funding round on record. The Fluidstack data-center deal was $50 billion. Project Rainier, the Amazon-anchored compute build, was $11 billion. Compute commitments through 2028 total roughly $78 billion. The headcount is ~2,500 employees as of late 2025, up from a few hundred two years earlier.

The customer list reads like a sales-led enterprise software company, because that is what it now is. Pfizer. United Airlines. Novo Nordisk. AIG, which reports an 8x to 10x speed-up on insurance underwriting workflows over an 18-month pilot. These are not safety partnerships. These are revenue contracts in regulated industries that came down to a procurement bake-off. Claude Code, the developer-tier productisation that shipped in February 2025, gave Anthropic a per-seat developer footprint inside enterprises that previously bought through API alone. Anthropic claims internally that it generates 2.1x more revenue per dollar of compute than its largest rival, a figure that should be treated as a claim rather than a verifiable number, but which is consistent with a company optimizing seriously for unit economics.

Anthropic today is a frontier lab competing for every contract that crosses procurement. The structural pivot is that this is no longer a research lab with a corporate appendage. It is a frontier company with a research function, and the research function inherits the constraints of the company, not the other way around. None of this is bad on its own. The question is whether the founding thesis still describes what the organism does.

Why both cannot fully be true

To pull rivals upward you have to stay competitive at the frontier of the thing you call dangerous. The faster you scale, the more your safety claim depends on rivals copying your practices rather than on you slowing down. But rivals’ incentive to copy weakens precisely as you become a credible competitor on revenue and contracts. One empirical hedge worth flagging: OpenAI’s Preparedness Framework and DeepMind’s Frontier Safety Framework share structural features with Anthropic’s Responsible Scaling Policy, and some of the convergence may be parallel development inside large labs facing similar regulatory pressures, not Anthropic-pulled diffusion. The race-to-the-top claim is harder to falsify than it looks.

Amodei himself has publicly acknowledged the tension, telling Fortune that Anthropic struggles to balance its safety mission with commercial pressure. Three forcing functions are the operational shape of that struggle.

(1)) the Department of Defense. On March 26, 2026, a federal judge issued a temporary injunction against the DoD in a dispute that started when Pete Hegseth’s department asked Anthropic to drop the contractual ban on Claude being used for mass domestic surveillance or fully autonomous weapons in democratic countries. Anthropic refused. The DoD then labeled the company a “supply-chain risk.” The judge’s written opinion described the DoD’s actions as “classic First Amendment retaliation.” The point is not that Anthropic was wrong to refuse, it almost certainly was right to refuse, but that at frontier scale a safety constraint becomes a federal court fight, not a research-policy choice.

(2)) the Pottinger op-ed. In January 2025, Amodei co-authored a Wall Street Journal opinion piece titled Trump Can Keep America’s AI Advantage with Matt Pottinger, the former deputy national security advisor. The piece argued for tighter chip-export controls against China. This is policy advocacy from the perspective of a national-security actor, not a research lab. There is a coherent through-line from the safety thesis to chip controls, the argument is that frontier capability in the wrong hands is a category-five risk, but the public posture is different from “we publish safety research and hope rivals copy us.” Anthropic is now a constituency in great-power competition.

(3) the Huang feud. In August 2025, Amodei and Jensen Huang traded public criticisms over export controls. Amodei accused Huang of an “outrageous lie.” He grew visibly emotional discussing his father’s preventable death, a thread that surfaces in nearly every long-form interview he gives and is the emotional spine of the longform profile. The Nvidia CEO is one of two or three people who can directly affect Anthropic’s compute supply. Picking that fight in public is a choice you make as a political actor, not as a research lab. It is also a choice the founding thesis would not have predicted in 2021.

The point is not that any of these are bad. Each is defensible on the merits. The point is that “race to the top” was a 2021 framing for a 2021 company. The 2026 company is a different organism. It has federal-court fights, named geopolitical adversaries, a Senate that voted 99-1 against the kind of ten-year state-AI moratorium Amodei opposed in his June 2025 New York Times op-ed, and a 60% revenue-growth trajectory on a forward base measured in tens of billions. None of those is what a safety lab looks like. All of them are what a frontier company that takes safety seriously looks like.

Three scenarios

(A) The thesis holds. Frontier labs converge on Anthropic-style safety practices, race-to-the-top works as advertised, and Anthropic earns a durable safety-narrative premium. The conditions for this case are an EU AI Act enforced with teeth and a US transparency framework that gives federal cover to the practices Anthropic already publishes. There is some support: the Senate’s 99-1 vote against the proposed ten-year state-AI moratorium suggests the political ground is not hostile to oversight. The reasons to discount it: the Senate vote was a defensive outcome, not a positive endorsement of federal standards, and a US transparency framework is still nowhere despite Amodei’s June 2025 NYT op-ed (Don’t Let A.I. Companies off the Hook). The regulatory tailwind that scenario (A) needs has not materialized at the speed the thesis requires.

(B) The thesis becomes a constraint, not a moat. Most likely. Anthropic loses ground on raw frontier capability to less-constrained competitors, xAI, a more permissive next-generation OpenAI, leading Chinese labs, and the safety stance becomes a self-imposed handicap rather than a market-shaping lever. The 80% wealth pledge from Anthropic’s seven cofounders, disclosed in the January 2026 essay, is a real governance constraint, not a PR move, but it works as a partial offset to wealth-concentration concerns rather than a reversal of the competitive dynamic. The DoD fight, the Pottinger op-ed, and the Huang feud are early evidence that at frontier scale, your safety stance creates adversaries you cannot route around. The pattern accelerated in early 2026, when Time reported that Anthropic dropped its flagship safety pledge in favor of a non-binding framework the company itself said “can and will change,” the kind of operational adjustment scenario (B) predicts. The implication is that the safety premium investors paid in 2021-2023 should compress, because the thing the premium was paying for, a safety lab that pulled rivals upward, is becoming a frontier company that constrains itself relative to less-constrained rivals.

(C) The paradox dissolves because the scale itself ends. AI capex hits a Jevons-paradox-for-labor wall, model commoditization compresses margins, frontier scale becomes uneconomic, and Anthropic returns to looking like a research lab again because every lab does. Implication: the paradox was about a phase, not a company. The reasons to discount this case are that Anthropic’s $26 billion and $70 billion forward revenue projections make a structural retreat harder for them than for capex-heavier hyperscalers, and that a commoditization scenario hits Anthropic’s gross-margin profile later than it hits the labs running on rented compute.

What this means for pricing AI labs

Three observations for an allocator.

(1) safety narrative is not moat at this scale, it is a constraint. Price it accordingly. The premium investors paid in 2021-2023 was for Anthropic against a counterfactual where there was no safety-aligned frontier lab. That counterfactual no longer exists. Anthropic is the frontier lab. The safety stance now binds the company in ways that show up as legal exposure, slower product cadence in some categories, and a narrower set of customers it can serve. None of these alone breaks the company. In aggregate, they change what an investor is paying for.

(2) the signal to watch is whether the rate of frontier-capability spread is faster than the rate of safety-practice diffusion. That ratio decides whether race-to-the-top is happening at all. If safety practices spread faster than capability, the thesis works. If capability spreads faster than safety practices, the thesis is a story the company tells about itself. The current evidence leans toward the latter, but it is the kind of variable that can move with one EU enforcement action or one US framework.

(3) Anthropic-the-company and Anthropic-the-thesis are now two different things. An investor can be long the company and short the thesis. The company has revenue, named customers, governance constraints that work as a partial offset to wealth-concentration risk, and a forward trajectory that is hard to bet against. The thesis is a 2021 framing under increasing structural strain. The longform makes the case that Dario sees this; here we make the case that the market should price it.

Based on twenty-five hours of Dario Amodei’s on-record interviews and six long-form essays. The full reported profile, Inside the Mind of Dario Amodei, runs 14,000 words at the author’s newsletter.

AI Models Are the New Rebar

me@philippdubach.com (Philipp D. Dubach) — Wed, 11 Mar 2026 00:00:00 +0000

Qwen 3.5-35B-A3B, a model released by Alibaba in February 2026, runs on a single consumer GPU with 24 gigabytes of VRAM. A secondhand RTX 4090, available for around $2,000, generates 60 to 100 tokens per second with it. On select benchmarks per Alibaba’s own evaluations, it matches or beats Claude Sonnet 4.5. The Qwen 3.5 Flash tier costs $0.10 per million input tokens through Alibaba’s API. Claude Sonnet 4.5 costs $3.00.

That’s a 97 percent discount. For comparable performance.

I’m not cherry-picking. Zhipu AI’s GLM-5 scores 1,452 on the Chatbot Arena leaderboard, the highest Elo rating of any open-source model, and its developer’s own figures put it at roughly 95 percent of closed-model performance at around 15 percent of the cost. Moonshot AI’s Kimi K2.5, a trillion-parameter model, scores 99.0 on HumanEval and 96.1 on AIME 2025, with a Chatbot Arena Elo of 1,447, at roughly 88 percent less than Claude Opus 4.5 per token. The Stanford HAI 2025 AI Index found the performance gap between open-source and proprietary AI models on the Chatbot Arena leaderboard shrank from 8 percent to 1.7 percent in a single year.

This is not an IP story. It is not a China story. It is an industrial economics story. And we know how those end.

What the steel mills can tell us

In the mid-1960s, electric arc furnace mini-mills entered the steel market at the lowest-quality segment: rebar. Capital costs ran one-fifth to one-seventh of what an integrated plant required. Nucor, the most aggressive operator, built its first mill for $6 million when a comparable integrated facility cost $500 million or more. The response from companies like U.S. Steel was rational: retreat from low-margin rebar, harvest the better-margin products, improve average profitability in the short term. Sensible but wrong.

Each segment mini-mills conquered had higher margins than the last. From rebar to structural steel, from structural steel to sheet metal, the disruptors climbed the value chain until there was nowhere left to climb. The American steel industry lost money for five consecutive years in the early 1980s, posting aggregate losses of $3.38 billion in 1982 alone. U.S. Steel shed more than half its workforce, pivoted to oil and gas, and by June 2025 accepted a $14.9 billion acquisition by Nippon Steel, a fraction of its inflation-adjusted peak valuation. Nucor, the mini-mill, became the largest American steelmaker.

Clayton Christensen spent a career documenting this pattern of disruptive innovation. The incumbents never failed because they made bad decisions. They failed because they made good decisions for their existing customers while the market shifted beneath them. OpenAI is serving demanding enterprise customers with the most capable models available. Anthropic is building trust with regulated industries. These are the correct moves for their current customers. They may also be exactly the wrong moves for the next five years.

The cost decline eats strategy

Epoch AI’s research, published in 2025, found that AI inference prices are declining at a median rate of 50x per year for equivalent performance levels, with a range spanning 9x to 900x depending on the task. Achieving GPT-4’s original performance on PhD-level science questions cost $30 per million input tokens when GPT-4 launched in early 2023. Through open-source alternatives today, the same performance costs under $0.10. A roughly 300-fold reduction in three years, at a pace that dwarfs Moore’s Law.

David Cahn at Sequoia Capital put the structural problem plainly in his "$600 Billion Question" analysis: “GPU computing is increasingly turning into a commodity, metered per hour. Without a monopoly or oligopoly, high fixed cost plus low marginal cost businesses almost always see prices competed down to marginal cost, like airlines.” The airline analogy is more foreboding than it sounds. The global airline industry generated cumulative net profits of $36 billion between 1945 and 2000, a net margin of 0.8 percent across 55 years. In the 2000s, the industry lost more than it had earned in the prior half-century combined. Even today, IATA projects airlines’ return on invested capital at 6.8 percent, below their weighted average cost of capital of 8.2 percent.

The difference between AI and airlines is that switching a flight carrier requires rebooking. Switching an AI model requires changing two lines of code.

Switching costs that approach zero

The OpenAI API format has become the de facto industry standard, supported by virtually every major model provider and open-source inference engine. LiteLLM, an open-source gateway with approximately 37,000 GitHub stars, provides a unified interface to over 100 providers through a single configuration change. OpenRouter offers managed access to more than 400 models. Setup time: under five minutes.

Enterprise behavior already reflects this. Perplexity’s own data shows 92 percent of Fortune 500 employees use multi-model AI platforms, and their top enterprise accounts access an average of 30 different models. These are Perplexity’s internal figures, not independent market research: treat them as directional. The one meaningful source of lock-in is custom fine-tuned models, which are provider-specific and cannot be directly ported. That affects a small fraction of deployments. For the vast majority of inference calls, the model is interchangeable, and the customer buys on price.

What OpenAI’s numbers actually require

On February 27, 2026, OpenAI closed a $110 billion funding round, the largest private capital raise in history, at a post-money valuation of $840 billion. Amazon committed $50 billion. SoftBank $30 billion. Nvidia $30 billion. The valuation implies extraordinary confidence in OpenAI’s ability to maintain pricing power and grow revenue to somewhere between $200 and $280 billion by 2030. At 42x trailing revenue, it is priced not for today’s market but for a specific version of the future.

OpenAI reported $20 billion in annualized recurring revenue as of January 2026, up 233 percent year over year. Impressive. But the adjusted gross margin fell to 33 percent in 2025, down from 40 percent the prior year, as inference costs quadrupled to $8.4 billion. In the first half of 2025 alone, OpenAI lost $13.5 billion. Compute and technical talent costs consume approximately 75 percent of total revenue, and Microsoft takes another 20 percent through 2032. That leaves very little room for the margin expansion the valuation demands.

Anthropic tells a similar story at a smaller scale. At a $380 billion valuation on $14 billion in run-rate revenue, 27x, the company is also unprofitable, projecting positive cash flow somewhere around 2027 to 2028. Both companies are betting they can simultaneously grow revenue and expand margins. In commoditized markets, that is the bet that fails.

Part of the financing is also circular. Amazon invests $50 billion in OpenAI; a portion flows back to AWS as compute spending. Nvidia invests $30 billion; the same money returns as GPU purchases. This inflates revenue figures while obscuring how much of the demand is genuinely independent.

Who actually wins when the model layer is a commodity

Before writing off the incumbents, two historical cases are worth sitting with.

Amazon Web Services has cut prices 134 times since 2006, yet its operating margins expanded to a record 39.5 percent in Q1 2025. Apple captures roughly 80 to 85 percent of global smartphone operating profits with around 18 to 21 percent of unit shipments, while commodity Android manufacturers earn negligible margins. Both got there the same way: years of accumulated switching costs, vertical integration, ecosystems that cost real money to leave. The question is whether AI model providers can build any of that. I don’t think they can, not at the model layer. An API endpoint returning text is not an iPhone. You change it in a config file on a Tuesday afternoon.

So who does benefit? Nvidia and cloud providers collect rent regardless of which model runs on their hardware. That position is durable. The application layer looks better still: companies embedding AI into domain-specific workflows with proprietary data, where the model is an input rather than the product. As Andrew Lewis at EQT put it, “Over time, the value is likely to accrue to the application layer and the product companies.” And then there are the platforms with distribution so large they can integrate AI at near-zero marginal cost: Meta embedding Llama into Instagram and WhatsApp, Google weaving Gemini into Search and Workspace. When Mark Zuckerberg open-sources Llama, he is deliberately commoditizing the model layer to prevent any single player from owning the stack above his distribution. When a $1.6 trillion company is your most committed price-cutter, that tells you something about where the margins are going.

AI Capex Arms Race: Who Blinks First?

me@philippdubach.com (Philipp D. Dubach) — Sun, 08 Mar 2026 00:00:00 +0000

Alphabet’s free cash flow is projected to fall roughly 90% in 2026. Not because the business is in trouble. Because the company has committed to spending $83–93 billion more on capital expenditure than it did last year.

That is what $660–690 billion in AI capex looks like up close. Amazon guided to $200 billion alone. Meta’s long-term debt more than doubled to $58.7 billion to help finance its share. Goldman Sachs projects cumulative 2025–2027 spending across the Big 4 at $1.15 trillion, more than double the $477 billion spent over the prior three years combined. BofA credit strategists found this will consume 94% of operating cash flow minus dividends and buybacks.

At what revenue growth rate does any of this pay for itself? And what happens if inference costs fall 100-fold before the infrastructure is fully depreciated? We want to think about this the way a credit analyst would. Not as a technology story but as a corporate finance story. Because the numbers, assembled from earnings releases and analyst reports through February 2026, look less like a technology platform buildout and more like a leveraged buyout of the future.

The LBO

An LBO thesis goes like this: we borrow heavily today, acquire an asset, generate enough cash flow to service the debt, and eventually sell or refinance at a profit. The bet works if the returns from the acquired asset exceed the cost of capital. It fails if the asset underperforms, the cost of capital rises, or the timeline extends beyond what the capital structure can absorb.

The hyperscaler capex thesis has the same structure, substituting “equity” and “operating cash flow” for debt. Each company is telling shareholders: we will deploy enormous capital today, accept near-zero or negative free cash flow for 18 to 36 months, and recoup that investment through AI revenue growth. Sundar Pichai put the bull case plainly at Alphabet’s Q2 2024 earnings:

The risk of underinvesting is dramatically greater than the risk of overinvesting for us here.

At five-year straight-line on $175 billion in Alphabet capex, you get $35 billion in annual depreciation. Add a conservative 10% cost of capital on the incremental investment, and the hurdle gets harder still. For the full $690 billion in 2026 hyperscaler capex, the annual depreciation burden alone approaches $115–140 billion at five-year lives. That is before interest, power, operations, or the cost of next year’s upgrade cycle.

The revenue side of this ledger is far smaller than the capex side. Rough estimates place direct AI revenue across the ecosystem at $40–60 billion in 2025, against AI-specific capex of roughly $300 billion. Coverage ratio: approximately 0.15x. Sequoia’s David Cahn calculated that the AI ecosystem needs to generate $600 billion in annual revenue to justify current infrastructure spending, against perhaps $50–100 billion it is actually generating. By 2026, with AI revenue perhaps reaching $80–120 billion and AI capex at $450 billion, the ratio improves to roughly 0.25x. Still not a business.

What would have to be true

The spending is not obviously irrational. The bull case is worth taking seriously: the right moment to build infrastructure for a platform shift is before the platform fully exists. Railroads were overbuilt. Fiber was overbuilt. Both excesses funded genuinely useful infrastructure that later ran at capacity. If AI becomes the general-purpose technology that most proponents claim, the AI infrastructure being deployed today could look like the most prescient investment since Standard Oil.

But that argument requires you to believe some very specific things about revenue growth that have not yet materialized. The 2025–2030 revenue ramp embedded in current capex implies AI revenue growing from roughly $60 billion today to somewhere between $600 billion and $2 trillion by 2030, depending on which bullish scenario you pick. Bain calculates that even under the most aggressive adoption scenario, AI generates $1.2 trillion in revenue, against the $2 trillion the spending requires to break even.

MIT’s Daron Acemoglu, who won the 2024 Nobel Prize in Economics, projects AI will deliver a total GDP increase of just 1.1–1.6% over ten years: roughly a 0.05% annual productivity gain. Only about 5% of economic tasks, he estimates, are cost-effectively automatable at current prices. Goldman Sachs’ Jim Covello made a similar argument in a June 2024 note: “Replacing low-wage jobs with tremendously costly technology is basically the polar opposite of the prior technology transitions I’ve witnessed in my thirty years of closely following the tech industry.” Neither of these is a fringe view. If either is roughly right, the revenue scenarios baked into current capex budgets do not close. And yet the same market is destroying software stocks because AI adoption is supposedly too strong. Both readings cannot be true.

Dario Amodei, who is himself building the infrastructure, put it very bluntly on the Dwarkesh Podcast in February 2026: “If my revenue is not $1 trillion, if it’s even $800 billion, there’s no force on Earth, there’s no hedge on Earth that could stop me from going bankrupt if I buy that much compute.” He was describing his own spending discipline relative to peers. The companies spending three times as much as Anthropic apparently believe they have found the hedge he could not.

Depreciation time bomb

One risk most analysis underweights: AI hardware obsoletes faster than any previous infrastructure cycle.

Hyperscalers have extended server useful lives from four to five and six years, saving billions in annual depreciation. But Amazon reversed course: in Q4 2024 it took a $920 million charge to early-retire certain servers and networking equipment, then effective January 1, 2025 it shortened useful lives for a subset of servers from six to five years, citing “the increased pace of technology development, particularly in the area of artificial intelligence,” a decision expected to reduce 2025 operating income by a further $700 million. Jensen Huang, not a man known for underselling his own products, said of H100 GPUs once Blackwell shipped: “You couldn’t give Hoppers away.” Nvidia now releases new architectures annually, where it previously released them every two years.

Michael Burry, who spent 2005 correctly modeling the mortgage market’s hidden risks, estimates that hyperscalers will understate depreciation by roughly $176 billion in aggregate between 2026 and 2028, causing them to overreport earnings by more than 20%. I have no idea whether Burry is right on the specific number. But the direction is correct. If the useful life of a Blackwell GPU is closer to three years than five because Rubin replaces it in 2027, the depreciation math gets far worse.

Epoch AI measured inference costs falling at a median 50 times per year, accelerating to 200 times per year after January 2024. GPT-3-era processing cost around $20 per million tokens at launch in 2020. By early 2026, models of comparable capability cost roughly $0.07 per million tokens. That is a roughly 280-fold decline over five years, and there is no obvious reason for it to stop.

The hyperscaler response to this is Jevons, an argument I explored in January: cheaper inference will explode demand, and the total compute consumed will far exceed what efficiency gains removed. They may be right. But the timing matters. Infrastructure being deployed today, at today’s GPU prices, needs to generate enough revenue before the next architecture cycle renders it economically obsolete. The payback window is not 36 months. It may be 18.

Arms race logic

Mark Zuckerberg acknowledged the possibility of an AI bubble “definitely” in September 2025, then spent $72 billion anyway. This is not irrationality. It is game theory. If AI really does create winner-take-most outcomes, slowing down is a bet that the platform shift is smaller than your competitors believe. Most boards are not willing to make that bet. So everyone keeps spending, and as I wrote last week, every bulge bracket bank agrees they should.

But the same logic drove WorldCom’s Bernie Ebbers. The same logic drove Global Crossing. The specific claim driving the 1990s telecom bubble was that internet traffic was “doubling every 100 days.” It was false: researcher Andrew Odlyzko traced it to misleading WorldCom/UUNET claims, and actual traffic doubled roughly once per year. By 2001, only 5% of installed fiber capacity was in use. The infrastructure eventually ran at capacity; it just took a decade and several dozen bankruptcies to get there.

Howard Marks published a December 2025 memo asking, with characteristic deliberateness, “Is It a Bubble?” He noted hyperscalers’ capex was outpacing revenue momentum and lenders were sweetening terms to keep deal flow alive. J.P. Morgan projects $300 billion in investment-grade bonds for AI data centers in 2026 alone. That is the same fragility that destroyed the telecom builders: cheap debt financing infrastructure before anyone has proved the revenue exists to service it.

Without AI spending, Pantheon Macroeconomics calculated in February 2026, U.S. corporate capex would currently be negative. The entire infrastructure investment story depends on this cycle continuing: total U.S. GDP grew just 1.4% annualized in H1 2025, and AI-related investment accounted for essentially all of it.

Every Bulge Bracket Bank Agrees on AI

me@philippdubach.com (Philipp D. Dubach) — Sun, 01 Mar 2026 00:00:00 +0000

I spent the last week reading 12 bank AI research reports from nine of the world’s largest financial institutions: Goldman Sachs, JPMorgan, Morgan Stanley (three separate reports), UBS, Barclays, Bank of America, HSBC, Citi, Deutsche Bank, and Santander. I wanted to understand how institutions that collectively manage trillions of dollars and employ thousands of analysts actually see this technology heading into 2026: where they agree, where they diverge, and what they’re being less than forthcoming about.

What I found is useful, sometimes impressive, and (mostly) worth reading.

Concerning consensus

Every single institution frames AI as a general-purpose technology, not a product cycle. The analogies converge almost word-for-word: Goldman Sachs draws the line through railroads, electrification, and telecom. Santander deploys a formal three-stage GPT framework: steam, ICT, AI. Morgan Stanley’s semiconductor team writes that AI is “closer to electricity than consumer gadgets.” Deutsche Bank projects +$7 trillion in global GDP over the decade. UBS puts the AI revenue opportunity at $2.6 trillion by 2030.

Not one of the twelve reports seriously entertains the possibility that AI is more like 3D printing: genuinely useful in pockets, broadly disappointing in aggregate. Santander comes closest, citing Daron Acemoglu’s conservative +0.7% cumulative TFP estimate over ten years, but even Santander frames that as the floor of the range, not the central case. The optimistic end of the same distribution sits at +10–15%. That’s not a rounding error. It’s a fundamental disagreement about whether AI will re-run the productivity miracle of electrification or prove more modest in aggregate, and most banks quietly pick the point on the distribution that best supports their commercial positioning.

The chart below plots each bank by how bullish they are on AI’s economic impact against how grounded their analysis is in current empirical data versus forward projections. Bank of America sits alone in the top-right: data-driven and moderately bullish. Goldman sits at the bottom-right: maximally bullish, maximally projective. Santander is the lone occupant of the top-left: empirical and cautious.

That chart is an editorial interpretation, not a precise measurement. But the shape is right. Bank of America is the only institution that consistently anchors its claims to actual GDP data rather than projections. Goldman Sachs, at the other extreme, produces a report that reads as a pitch to every infrastructure CFO and sovereign wealth fund in the world. Both can be making valid arguments. They’re just not making the same kind.

What’s happening vs. what might happen

BofA and Santander are the two worth pausing on, because they’re doing something different from the rest: they’re reporting what’s happening rather than what might happen.

Bank of America, using Bureau of Labor Statistics and Bureau of Economic Analysis data, finds that AI capex contributed 1.4–1.5 percentage points to US GDP growth in H1 2025. Headline growth rates were running around 2% in that period. So AI infrastructure spending was the single largest driver of US economic expansion. That’s a real number from real data, and it’s the most important figure in any of these reports.

BofA also finds a positive correlation between AI adoption and employment in white-collar sectors: software developers are up +17.9%, while insurance appraisers, a role where AI substitutes directly for human judgment, are down -20%. The disruption is concentrated in specific tasks. It hasn’t shown up in aggregate employment. Yet.

Then there’s Santander, which writes the most academically rigorous report of the twelve and includes numbers the consensus would rather not linger on. The enterprise AI adoption rate data is sobering: only around 10% of US companies are actually using AI to produce goods and services. 42% of companies abandoned GenAI projects in 2024, a figure corroborated by MIT’s 2025 GenAI Divide research, which found 95% of enterprise pilots fail to reach production. Only 1% of companies describe their rollouts as mature. Meanwhile, 78% say they use AI in at least one function. The gap between “we have a pilot” and “this is generating value” is enormous.

Goldman’s $800 million per day in hyperscaler capex and Santander’s 42% abandonment rate aren’t as contradictory as they look. Capex precedes productivity in every infrastructure cycle. That part is historically unambiguous. The question is how long the gap lasts, and whether the eventual productivity gains justify what’s been spent getting there.

Dotcom comparison

Every report that addresses the bubble question reaches the same conclusion: this isn’t the late 1990s.

The primary evidence is valuation. Nvidia trades at 25–30x forward earnings versus Cisco’s ~140x at the March 2000 peak. The Magnificent 6 sit at roughly 35x versus 55x for the TMT index at its apex. Morgan Stanley’s Silicon Backbone report makes this comparison rigorously, and I think they’re right that the earnings quality is categorically different from dot-com era technology stocks.

But the comparison works less cleanly when you look at concentration rather than individual valuations. Deutsche Bank notes that the top 10 S&P 500 companies now represent 40% of total market cap, an extreme not seen at the dot-com peak. A Bank of America fund manager survey from October 2025 found 54% of global managers believe AI equities are in a bubble, and 60% view global equities as overvalued. You can simultaneously hold that Nvidia’s PE is reasonable and that a portfolio with 40% weight in ten companies carries concentration risk that PE comparisons don’t capture. Reassuring on one axis. Alarming on another. Most sell-side AI research cites whichever data point supports its preferred conclusion and leaves the tension sitting there unaddressed.

There’s also a subtler version of the bubble question that none of the twelve reports asks directly. The “infrastructure comes before productivity” argument is historically correct: railroads were overbuilt before they transformed commerce; the internet fibre glut of 1999–2000 eventually became the backbone of the digital economy. But the investors who financed Global Crossing and 360networks still lost everything. The infrastructure thesis being correct in the long run isn’t the same as every current valuation being justified. Goldman’s report is particularly careful to avoid addressing that distinction. The implicit message, “we financed the pipes before and it worked out,” skips past the question of which financiers got paid and which got wiped out in the transition.

Sell side

The following chart maps risk awareness against bullishness of tone, and the clustering is revealing.

Goldman and UBS are in the bottom-right: aggressively bullish, risk-dismissive. Santander and BofA are in the top-left, actually wrestling with the uncertainty. HSBC is the clearest case of motivated reasoning: the report is written explicitly to stop private banking clients from panic-selling their SaaS positions after multiple quarters of multiple compression. (Whether that advice turns out to be right is a separate question.)

I don’t think this makes any of these reports dishonest. But the reader needs to supply the discount rate that each institution’s interests warrant.

Goldman Sachs earns advisory fees on the data centre and energy deals it describes. Barclays lends to energy infrastructure projects. Morgan Stanley is selling both EM equity exposure and second-order stock-picking strategies through its asset management arm. UBS provides a clean three-layer investment framework that maps directly to its wealth management product shelf. Citi frames AI as accelerating the electronification of markets, the very trend that drives Citi’s trading revenue. Deutsche Bank, most self-aware of the ten, used AI to generate its AI report. The meta-commentary is right there in the methodology.

Not a single report concludes “this may be overhyped and you should meaningfully reduce exposure.” Every institution has a commercial interest in the AI narrative staying bullish. That doesn’t mean the narrative is wrong. It does mean unanimous conviction from nine sell-side AI research teams is not the same thing as nine independent analyses reaching the same conclusion.

Second-order AI beneficiaries

The next two charts contain what I think is the most interesting tension across all twelve reports.

Morgan Stanley’s Counterpoint Global team, in the second-order effects report, presents historical data that should make the rest of this collection at least slightly uncomfortable. In the railroad era, Walmart’s equivalent outperformed Ford’s equivalent by 1,622x to 23x. In the internet era, Netflix returned 519x versus Cisco’s 4x. It’s the same pattern every time: the companies that use the infrastructure to serve customers dramatically outperform the companies that build it.

Yet nearly every bank’s actual investment positioning sits in Nvidia, ASML, hyperscalers, data centre REITs, nuclear utilities, overwhelmingly first-order enablers. Either the historical pattern won’t repeat this time (possible, but not argued anywhere in these reports), or there’s a valid timing explanation (first-order wins in the buildout phase, second-order wins in deployment) or most of these recommendations will look dated within five years.

Morgan Stanley’s own three reports collectively make the case for second-order investing over the long run while still recommending first-order plays in the near term. That’s not quite inconsistent. But the tension deserves more acknowledgment than it gets.

Power

If I had to pick one analytical claim that holds up regardless of where the productivity debate lands, it’s this: power is the binding constraint, and the infrastructure required to relieve it is real, expensive, and already being built.

The numbers are consistent across institutions. US data centre power consumption runs at 150–175 TWh today. Barclays projects 560 TWh by 2030, approximately 13% of total US electricity. Goldman Sachs estimates 60% of new data centre power through 2030 will require net-new generation capacity. The US power grid has an average age of 40 years. Token consumption grew 4,274% in a single year. Data centre construction spending has grown roughly 60% year-on-year since ChatGPT launched in late 2022.

Barclays frames this as a Jevons paradox: efficiency improvements in model inference will, counterintuitively, increase total energy consumption because they make AI cheaper and drive higher usage. I think that’s right. It’s exactly how personal computing and the internet played out. Every report that addresses energy lands on nuclear as the preferred long-term solution: four executive orders in early 2025, a 400 GW capacity target by 2050, the Three Mile Island restart. That consensus may prove correct. It may also be the sector where the infrastructure-before-returns gap runs longest.

What the reports don’t say

The quadrant charts map where the banks are looking. They’re less revealing about what’s off the frame entirely.

No report models a structured downside scenario: AI capex producing disappointing returns, hyperscalers pulling back, or a major data centre financing default triggering something worse. The closest is Santander’s 42% abandonment statistic, but even Santander doesn’t ask what happens if that number climbs to 60%.

No report discusses AI safety or alignment risks. UBS notes that AI task completion duration has doubled every seven months and explicitly references the AGI trajectory, then moves directly to investment implications, as if “AGI trajectory” carries no risk premium at all. I find that strange.

The collision between AI energy demand and climate commitments gets almost no treatment. Only Barclays mentions that global CO2 emissions hit a record 37.7 gigatonnes in 2023. The institutions projecting AI consuming 13% of US electricity by 2030 don’t reconcile that with the net-zero commitments in their own sustainability reports.

JPMorgan, which provides the most detailed geopolitical analysis of the twelve, never models a Taiwan Strait disruption scenario. Morgan Stanley identifies Taiwan, Korea, and China as “irreplaceable” nodes in the AI hardware supply chain, while calling emerging market semiconductor exposure “long-term infrastructure participation.” Those two characterisations sit in very uncomfortable proximity, and neither report acknowledges it.

I came away from this with real respect for several of these pieces, particularly BofA’s empirical rigour and Santander’s willingness to cite unflattering numbers. The energy infrastructure thesis seems to me the most durable of the lot: the power bottleneck is real regardless of where you land on the productivity question.

But I also came away convinced that this consensus is shaped as much by institutional incentive as by analytical independence. When nine institutions with combined AI-related revenue exposure in the hundreds of billions all agree you should increase AI exposure, the interesting question isn’t whether they’re right. They may well be.

The Absolute Insider Mess of Prediction Markets

me@philippdubach.com (Philipp D. Dubach) — Sun, 22 Feb 2026 00:00:00 +0000

Someone at Google, or close enough to Google, deposited $3 million into Polymarket on December 3, 2025, bet on 23 separate “Google Year in Search” outcomes, got 22 right, and walked away with $1.15 million in profit in under 24 hours. One of those bets: that d4vd would be the most-searched person of 2025, purchased at roughly 5 cents when the market gave it a 0.2% probability.

The wallet, originally called AlphaRacoon, had previously made over $150,000 correctly predicting the exact launch window of Google’s Gemini 3.0 in November 2025. As blockchain engineer Haeju Jeong, who first flagged the account, put it: this is a Google insider milking Polymarket for quick money. The wallet later changed its username to 0xafEe, which might be the most half-hearted attempt at anonymity since an MIT researcher Googled “how sec detect unusual trade” before insider trading.

I’ve been following prediction markets for a while, mostly for the macro forecasting angle, but also because the regulatory ambiguity is fascinating and there are some market inefficiencies worth watching. But the last three months have produced a concentration of insider trading cases that made me want to work through the problem more carefully. The AlphaRacoon case is the most entertaining. The two that followed are more serious.

Three cases, three months, zero enforcement

On February 12, 2026, Israeli authorities indicted two people for using classified military intelligence to bet on Polymarket during Israel’s 12-day war with Iran in June 2025. A Polymarket account called “ricosuave666” placed seven bets on questions like “Will Israel attack Iran on Friday?” and got every one correct. The most profitable single wager: nearly $129,000 that Israel would strike by a specified date. Total winnings: roughly $150,000-$152,000. The Shin Bet, Israel Police, and Defense Ministry called it a real security risk to IDF operations. This is the first criminal prosecution anywhere in the world tied to prediction market insider trading.

In between, there was Venezuela. On the evening of January 2, 2026, an account called “Burdensome-Mix,” created less than a week earlier, placed over $20,000 in bets that Maduro would be removed from power by January 31. Less than an hour after the final bet, Trump ordered the military strike. By 4:21 AM, Maduro was captured. The account’s $33,934 across 13 bets returned $436,759. Chainalysis found the trader cashed out through mainstream U.S. exchanges with no apparent effort to hide their identity. The trader has never been identified.

Each case escalates. AlphaRacoon is someone profiting from corporate knowledge. Burdensome-Mix had advance knowledge of U.S. foreign policy. The Israeli soldiers were monetizing classified operational intelligence during wartime. The surface area for insider trading on prediction markets is, to use the technical term, enormous.

Now there’s an AI that hunts them

Peter Liu, a former Google DeepMind research scientist now co-founding Twenty Labs, published results from Compound AI’s Polymarket integration that systematically detects suspected insiders. The system built a custom database optimized for AI agent queries rather than relying on Polymarket’s rate-limited API. Liu described the agents as “super-human at making data science queries,” noting that each agent operates like 10 concurrent human analysts.

Compound AI independently rediscovered AlphaRacoon despite the username change. More interestingly, it found that AlphaRacoon has friends: a user called “yicici” who made money in the same Google markets, suggesting a coordinated network rather than a lone wolf. When pointed at OpenAI, the system found accounts “oddly good at predicting OpenAI launch dates for models and products,” with at least one that exclusively traded OpenAI events.

It’s not just Compound AI. Polysights, built by 29-year-old Canadian trader Tre Upshaw, has attracted 24,000 users and is closing a $2 million funding round after receiving a $25,000 Polymarket grant. Roughly 85% of flagged trades turned out to be winners. Individual programmers have built copytrading bots that follow suspected insiders, with one reportedly turning $5,700 into $80,000 by tailing signals during the Maduro event.

The irony is rich. Blockchain’s radical transparency, the thing that was supposed to make financial markets honest, is simultaneously enabling insider detection and insider copytrading. The same data pipeline that lets Compound AI catch cheaters also lets copytraders amplify their profits.

Regulation

On regulated stock markets, insider trading law is well-established. SEC Rule 10b-5, decades of case law, a well-staffed enforcement division, cooperation agreements with every broker-dealer in America. Everyone in the industry knows it’s illegal.

On prediction markets, almost none of that infrastructure exists. SEC Rule 10b-5 doesn’t apply because prediction market contracts are swaps, not securities. That puts them under the CFTC, which has historically focused on commodity manipulation (spoofing, cornering), not information-based trading. The CFTC has brought exactly zero enforcement actions for prediction market insider trading.

The CFTC does have Rule 180.1, modeled on 10b-5, which prohibits trading on material nonpublic information. But with a distinction that matters: it requires proof of a breached “pre-existing duty.” In securities law, nearly any MNPI-based trade violates the law. In commodities law, trading on proprietary information is the entire point: a farmer trading grain futures based on their own crop outlook is how the market is supposed to work. Former CFTC Commissioner Caroline Pham has argued that importing securities-law concepts into derivatives markets is analytically confused.

Daniel Barabander of Variant Fund published an analysis on February 6 that crystallized the problem. Insider trading is fundamentally about breaching a promise: a Tesla employee trading on a “Will TSLA beat Q4 estimates?” prediction market violates their confidentiality obligations. But someone who overhears investment bankers discussing a deal at a restaurant generally commits no crime, because no promise exists to breach. Prediction markets, “by making almost anything tradable,” expand valuable inside information into contexts where the existence of any relevant promise is far less clear.

The strongest enforcement tool may be criminal wire fraud. At the Securities Enforcement Forum on February 5, SDNY U.S. Attorney Jay Clayton was asked whether prediction market participants were beyond the reach of fraud statutes. His answer: “No.” Asked whether to expect enforcement actions: “Yes.” But Polymarket’s terms of service don’t specifically mention insider trading, which complicates the wire fraud theory.

Matt Levine, who has written about this topic at least three times between December 2025 and February 2026, puts it best. His core argument: insider trading is not about fairness. It’s about theft. The problem isn’t that you have information the market doesn’t. You’re supposed to try to get information the market doesn’t; that’s the entire point of financial markets. The problem is that you’re using information that belongs to someone else, your employer or client or country, without their permission. You’ve breached a duty.

This framing matters because prediction market enthusiasts instinctively believe insider trading is good for their markets: it makes prices more accurate. Levine acknowledged this directly. But he also identified the fatal flaw: if prediction markets are full of insider traders, there’d be no one to trade against. He estimated that the first 20 people to get arrested for insider trading on Kalshi “will be very surprised.”

Why regulation matters: the lemons problem

The economic case for regulating insider trading on prediction markets goes beyond fairness or legality: it’s about whether these markets can survive.

George Akerlof’s 1970 “Market for Lemons” paper described a dynamic where information asymmetry between buyers and sellers causes markets to collapse. When sellers know more than buyers about product quality, buyers reduce their willingness to pay. Honest sellers with good products leave the market because they can’t get fair prices. This raises the average “lemon” rate among remaining sellers, causing more buyers to withdraw. The process continues until only lemons remain.

Applied to prediction markets: if insiders consistently win, uninformed participants recognize they’re trading against counterparties with superior information and leave. Market makers widen spreads or exit entirely. Dartmouth economist Eric Zitzewitz, who studies prediction markets, has stated this directly: prediction markets “require loads of uninformed investors to function” for liquidity. If liquidity providers worry about adverse selection, they provide less liquidity, and any accuracy benefit from insider trading is more than offset by the participation loss.

Wall Street firms are entering prediction markets at speed: DRW is building a dedicated desk at $175,000-$200,000 base salary, Susquehanna became Kalshi’s first official market maker, Jump Trading is taking equity stakes in both platforms, and Goldman Sachs CEO David Solomon has met leadership of both Kalshi and Polymarket. These firms are there to make markets, not to bet on whether Israel will strike Iran. Market makers who systematically take the other side of trades bleed money when their counterparties have inside information. If the institutional players conclude the game is rigged, the resulting liquidity withdrawal would hollow out the market.

Combined Polymarket and Kalshi weekly volume exceeded $6 billion by early 2026. Full-year 2025 volume across all platforms reached approximately $44 billion, a roughly 300x increase from early 2024. Bloomberg terminals now carry prediction market data. CNN struck a deal to integrate Kalshi markets into its coverage.

Prediction markets as macroeconomic forecasting tools

On February 12, 2026, the same day Israeli authorities announced the first-ever prediction market insider trading prosecution, Federal Reserve Board economist Anthony Diercks, along with Jared Dean Katz (Northwestern) and Jonathan Wright (Johns Hopkins/NBER), published “Kalshi and the Rise of Macro Markets” through the Fed’s Finance and Economics Discussion Series. It’s the most thorough empirical study yet on whether prediction markets work as macroeconomic forecasting tools.

The headline finding: Kalshi’s macro markets perform as well as, and in some cases better than, traditional forecasting instruments. For federal funds rate decisions, Kalshi’s median and mode forecasts matched the actual policy outcome on the day before every FOMC meeting since 2022. That’s a perfect record. The mean absolute error for rate forecasts 150 days out was comparable to the New York Fed’s Survey of Market Expectations, a survey of professional forecasters. For headline CPI, Kalshi forecasts statistically outperformed the Bloomberg consensus in certain windows.

The paper identifies a specific structural advantage. Fed funds futures force a binomial assumption: two possible outcomes per meeting. Kalshi’s contract structure assigns nonzero probability to seven or more distinct rate outcomes simultaneously. After speeches by Fed Governors Waller and Bowman, Kalshi markets adjusted the implied probability of a July 2025 rate cut to around 25% within hours. That probability dropped after the June employment report beat forecasts. This is what the authors call “rich intraday dynamics”: the market updates continuously as information arrives, unlike surveys that provide snapshots every six weeks.

The Fed paper is preliminary research, not official policy. But the central bank’s own economists are treating prediction markets as credible information infrastructure. The authors intend to make the underlying data publicly available, which would further normalize prediction market data as a standard input to policy analysis.

If prediction markets are valuable enough that the Federal Reserve is studying them as forecasting tools for monetary policy, the insider trading problem becomes a question of whether a tool the central bank wants to rely on can maintain the informational integrity that makes it useful. Insiders trading on classified military intelligence don’t make the Fed’s rate probability distributions more accurate. They make them less trustworthy.

The regulatory picture, fractured

There are two regulatory tracks, and they aren’t converging.

Kalshi is CFTC-regulated, explicitly prohibits insider trading, runs an in-house surveillance system called “Poirot,” has completed over 200 investigations in the past year, and requires KYC/AML verification. Polymarket’s international platform, operated by a Panama-incorporated entity, allows permissionless crypto wallets without identity verification. Its terms of service don’t specifically mention insider trading.

CFTC Chairman Michael Selig, confirmed in December 2025, laid out a four-part plan on January 29: withdraw the Biden-era proposed ban on political event contracts (done February 4), begin drafting new rules, assess ongoing litigation, and support market development. On February 17, he published a Wall Street Journal op-ed asserting exclusive CFTC jurisdiction over prediction markets and filed an amicus brief supporting Crypto.com against Nevada gaming regulators. Selig announced an advisory committee whose planned members include both Polymarket CEO Shayne Coplan and Kalshi CEO Tarek Mansour.

Rep. Ritchie Torres (D-NY) introduced legislation in late January, directly responding to the Maduro trade, that would ban federal officials from trading prediction market contracts related to government activity. The bill targets a real problem, Levine’s point about government officials profiting from events they can influence, but it doesn’t create a general insider trading prohibition. It wouldn’t have stopped AlphaRacoon or the Israeli soldiers.

I’m genuinely unsure where this lands. The libertarian case for prediction market insider trading, that it makes prices more accurate and the market should be a pure information aggregation mechanism, has intellectual appeal. The Akerlof case against it, that unchecked adverse selection destroys the market’s ability to function, has empirical support. The Diercks, Katz, and Wright paper suggests the stakes are higher than either camp acknowledges: these aren’t just gambling venues. They’re becoming part of the plumbing that central banks and institutional investors use to make real decisions.

My instinct, and I want to be honest that it’s more instinct than conclusion at this point, is that the prediction market industry will end up roughly where securities markets were after the Securities Exchange Act of 1934. Some insider trading enforcement is necessary to maintain market integrity, not because trading on private information is inherently wrong, but because without it, the adverse selection spiral will destroy the markets that are otherwise proving genuinely useful. The question is whether that enforcement framework gets built proactively or whether it takes a scandal large enough to force it.

Polymarket’s CEO has called insider trading “super cool.” The Fed is studying his platform’s macro forecasting ability. The Israeli military is prosecuting soldiers who bet on it.

The SaaSpocalypse Paradox

me@philippdubach.com (Philipp D. Dubach) — Fri, 13 Feb 2026 00:00:00 +0000

The market is simultaneously pricing AI capex failure and AI destroying all software. Both cannot be true.

Anthropic released 11 open-source plugins for Claude Cowork on January 30. Apache-2.0 licensed, file-based, running in a macOS-only research preview. Within a week, the IGV software ETF had fallen 32% from its September peak to a 52-week low of $79.65, roughly $2 trillion in market cap had evaporated, and hedge funds had made $24 billion shorting the sector. The RSI hit 18, the most oversold reading since 1990. JP Morgan titled their note “Software Collapse Broadens with Nowhere to Hide.” Jefferies coined the term SaaSpocalypse. It was the worst software stock crash since the dot-com bust.

Bank of America’s Vivek Arya identified the paradox at the center of this: investors are simultaneously punishing hyperscaler stocks because AI capex might generate weak returns, while destroying software stocks because AI adoption will be so pervasive it renders all existing software obsolete. Both cannot hold simultaneously. If AI tools aren’t generating meaningful ROI, they’re not replacing enterprise software at scale. If they are replacing enterprise software at scale, the hyperscalers are earning extraordinary returns on their infrastructure investment.

This paradox can only resolve in one of three ways: AI adoption is real and hyperscaler capex is justified, AI adoption stalls and software incumbents are fine, or the truth is somewhere in between and the market has mispriced both sides. The first two are internally consistent. The market is pricing neither.

The bear case for enterprise software

The structural argument against enterprise software is serious and worth stating on its own terms.

Enterprise software monetizes through per-seat licensing. The SaaS business model depends on a stable correlation between headcount and license count. AI agents break that correlation. If 10 agents do the work of 100 people, the software doesn’t get replaced directly, the headcount that justifies the seats does, and CRM seat revenue drops with it. AlixPartners estimates up to $500 billion in enterprise software revenue could be at risk over time. IDC predicts pure seat-based pricing will be obsolete by 2028.

The moat question is equally uncomfortable. Enterprise software’s traditional defense was the trained-user-interface moat: the years of institutional muscle memory that makes switching costs prohibitive. Databricks CEO Ali Ghodsi told TechCrunch that this moat collapses when the interface becomes natural language. If the value of Salesforce or ServiceNow lived in their UI rather than their data, and the UI can now be replicated by a general-purpose model, then the moat was shallower than anyone thought. VC has fled traditional SaaS entirely; as one investor noted, “an entrepreneur approaching a VC fund today with a SaaS startup won’t even reach the pitch stage.”

The build-versus-buy equation is inverting in real time. Klarna ditched Salesforce and Workday, consolidated onto its own AI-augmented stack, and used an OpenAI-powered bot to handle work that previously required 700 employees. SaaStr’s analysis of Gartner’s $1.43 trillion 2026 software spending forecast reveals that roughly 9 percentage points of the 14.7% headline growth is price increases on existing software, not net new demand. AI is eating SaaS budgets, redirecting IT spend toward infrastructure while reducing the headcount that generates software seats.

This is the case priced into the IGV at $80.

The bull case for software stocks

The structural argument for enterprise software rests on a distinction the current sell-off is ignoring entirely.

The bear case assumes a shrinking TAM. Goldman Sachs Research argues the opposite: the application software market grows to $780 billion by 2030 at a 13% CAGR, with agents accounting for over 60% of the total. The profit pool shifts from SaaS seats to agentic workloads, but the overall market gets larger, not smaller. a16z’s Alex Rampell takes it further: if AI enables software to not just enhance productivity but actually complete work, the addressable market isn’t roughly $350 billion in enterprise software spend (about 1% of GDP). It’s the ~$6 trillion white-collar services market (~20% of GDP), a 20x expansion into work that was never software-addressable before.

David Friedberg made the sharpest version of this argument on the All-In Podcast: software transitions from helping people do work, to completing work, to doing work humans cannot do. At that point, the SaaS pricing model transitions from per-seat to value-based, and “SaaS basically takes over the services economy.” His estimate: the combined market cap of software companies could be 4x to 10x higher in five years, but “not evenly distributed.”

The software vs semiconductor valuation picture strengthens this framing. The sector is delivering 17% aggregate earnings growth in 2026 while trading at November 2022 EV/Sales multiples, back when the Fed was aggressively hiking into recession fears. The Russell 1000 Software subsector now trades at 32.4x forward earnings versus 43.6x for semiconductors. Recurring-revenue businesses with 90%+ gross margins and 95%+ renewal rates trade at a lower multiple than cyclical chipmakers with 40-60% margins and concentrated customer bases. Historically that’s an inversion that has not persisted.

This is the case that BofA called a paradox and JP Morgan called a mispricing.

The hyperscaler AI capex question that connects both sides

There is a number that both cases have to account for, and it’s the one that determines which side of the paradox resolves first.

Combined 2026 capex guidance from Microsoft, Alphabet, Amazon, Meta, and Oracle now approaches $700 billion, more than doubling from $256 billion in 2024. Bank of America calculates this consumes 94% of operating cash flows after capital returns. The Big Five raised $108 billion in bonds in 2025. AI-related services generate roughly $25 billion in direct revenue against $400+ billion in annual infrastructure spending, a coverage ratio of about 4%.

If the bear case is right and AI agents are replacing enterprise software at scale, this capex should already be generating enormous returns. It isn’t. If the bull case is right and AI is expanding the TAM into the services economy, this capex is early-stage infrastructure investment that will compound over a decade. In that reading, $700 billion in annual spend is the foundation of a $6 trillion market, not a write-off. Both interpretations require the same capex figure to mean something fundamentally different. The market hasn’t decided which.

Microsoft is the sharpest illustration of this tension. Quarterly capex went from $1 billion in early 2015 to a record $37.5 billion in Q2 FY2026, with roughly two-thirds going to short-lived GPU/CPU assets. And yet Microsoft is the only hyperscaler that can fund this buildout from operating cash flow. Azure grew 39% in Q2 FY2026, crossing $50 billion in quarterly cloud revenue for the first time. The company is simultaneously the biggest AI capex spender, the one best positioned to generate returns on that spend, and the company whose products (365, Dynamics, Azure) are supposedly being disrupted by Claude plugins. The market is punishing all three at once.

Bifurcation, not extinction: the SaaSpocalypse resolved

A 60% recession probability, a partial government shutdown, elevated tariffs, and a structural pricing transition are being sold as a single story. They aren’t. Separating the macro from the structural requires asking which software categories are genuinely at risk and which are being sold by association.

Janus Henderson makes a useful distinction between “systems of record” and “systems of engagement.” Systems of record are deeply embedded in business processes, require regulatory compliance, and carry enormous switching costs: ERP, core finance, cybersecurity, observability. PitchBook described replacing one as “effectively open-heart surgery for an enterprise.” Systems of engagement are user-facing workflow tools where the interface is the product: content creation, tier-1 support, basic analytics. When the interface becomes natural language, that moat collapses.

The bear case is correct about the second category. The bull case is correct about the first. The market is wrong to price them identically. Selling both at the same multiple compression implies that switching costs, regulatory requirements, data gravity, and enterprise procurement cycles have all vanished simultaneously. Gartner predicts over 40% of agentic AI projects will be cancelled by 2027. Salesforce’s Agentforce reached 18,500 customers in its first year, the fastest-adopted organic product in company history. These are not the behaviors of a category that has been disrupted. They are the behaviors of incumbents absorbing a new paradigm.

Stated precisely: the bear case is a zero-sum repricing where AI agents compress existing software revenue by eliminating seats and commoditizing interfaces. The bull case is a positive-sum expansion where the surviving software companies capture the $6 trillion in white-collar services that was never software-addressable before. The cost of intelligence has fallen 99.7% in two years (Stanford AI Index). Cumulative AI infrastructure investment is expected to exceed $3 trillion by 2030. That kind of capital deployment doesn’t produce a world where software shrinks. It produces a world where the definition of “software” expands to include most of the services economy.

I wrote recently about how passive flows create mechanical, price-insensitive selling that overwhelms fundamental buyers. This software sell-off is a textbook case. JP Morgan’s Murphy described index arbitrage basket selling, programmatic de-grossing, and passive flow liquidity vacuums. The IGV recorded its highest single-day trading volume in 25 years. JP Morgan’s follow-up argued the sell-off has gone far enough. BofA called it a paradox that “doesn’t make any sense.” History suggests these kinds of extremes, the 2016 LinkedIn panic, the 2022 rate-shock drawdown, the January 2025 DeepSeek crash, tend to mark inflection points rather than starting points for further decline.

The hardest trade right now is the one that requires distinguishing between stocks that are cheap because they’re broken and stocks that are cheap because the market is broken. The SaaSpocalypse priced into the IGV at $80, with a 30-year-extreme RSI, pricing in an extinction event that operating results don’t remotely support, looks a lot more like the latter.

Buying the Haystack Might Not Work This Year

me@philippdubach.com (Philipp D. Dubach) — Sat, 31 Jan 2026 00:00:00 +0000

I’ve been reading the January 2026 state of markets reports from Andreessen Horowitz and AQR, and their conclusions on the AI bubble question in 2026 are almost impossible to reconcile.

The a16z view is straightforward: AI fundamentals are real, and current prices reflect that reality. Their evidence is compelling. The top 50 private AI companies now generate $40.6 billion in annual revenue. Companies like ElevenLabs and Cursor are hitting $100 million ARR faster than Slack or Twilio ever did. GPUs are running at 80% utilization, compared to the 7% utilization rate for fiber optic cables during the dotcom bubble. This isn’t speculation, they argue. It’s demand exceeding supply.

AQR looks at the same market and sees something else entirely. Their capital market assumptions put the U.S. CAPE ratio at the 96th percentile since 1980. Expected real returns for U.S. large cap equities over the next 5-10 years? 3.9%. For a global 60/40 portfolio, just 3.4%, well below the long-term average of roughly 5% since 1900. Risk premia, in their framework, are compressed across nearly every asset class. The narrative doesn’t enter their models.

a16z points to earnings growth. The market rally hasn’t been driven by multiple expansion, they note, but by actual EPS growth. Tech P/E multiples sit around 30-35x, elevated but nowhere near the 70-80x of 2000. Tech margins have “lapped the field” at 25%+ compared to 5-8% for the rest of the S&P 500. The fundamentals, they insist, are doing the work.

AQR’s response would be that fundamentals always look good near peaks. Their research shows a 50% probability that realized equity returns will miss estimates by more than 3 percentage points annually over the next decade. Compressed premia don’t announce themselves with blaring headlines. They just quietly erode returns until investors notice they’ve been running in place.

Cumulative hyperscaler capex is projected to reach $4.8 trillion by 2030. To achieve a 10% hurdle rate on that investment, AI revenue needs to hit roughly $1 trillion annually by 2030, about 1% of global GDP excluding China. Goldman Sachs estimates that $9 trillion in revenue could flow from the AI buildout, which at 20% margins and a 22x P/E multiple would create $35 trillion in new market cap. Only about $24 trillion has been pulled forward so far, leaving $11 trillion “on the table.“

Or not. AQR would point out that the expected return for U.S. buyouts, private equity’s bread and butter, is now 4.2%. That’s barely above the 3.9% for public large caps. The illiquidity premium has essentially vanished. If sophisticated PE firms can’t find excess returns, why should AI capex be different?

I find myself uncertain, which feels like the more honest position. Neither source is disinterested. a16z manages billions in venture capital and growth equity; bullish AI narratives support their portfolio valuations and fundraising. AQR runs systematic strategies that benefit when investors diversify away from concentrated U.S. tech exposure toward international equities and alternatives. Both are talking their book, which doesn’t make either wrong, but it’s worth noting.

The a16z data on utilization and revenue growth is hard to dismiss. 80% GPU utilization isn’t vaporware. Harvey users nearly tripled their time on the platform in nine months. Navan’s AI handles half of all customer interactions at satisfaction levels matching human agents. These are real products generating real engagement. But AQR’s valuation work has a longer track record. Their models don’t care about narratives, and historically that discipline has been valuable. When they say U.S. equities offer the lowest expected returns among major markets, that’s not pessimism. It’s arithmetic.

The reconciliation might be this: AI winners could thrive spectacularly while broad market indices disappoint. a16z’s portfolio companies operate in a different universe than the average S&P 500 constituent. Compressed risk premia can coexist with individual companies generating enormous returns. The question is whether you’re buying the index or picking the winners.

Non-U.S. developed markets, by the way, offer expected returns of around 5%, versus 3.9% for U.S. large caps. The valuation gap is real even if the AI story is true.

The Market Can Stay Irrational Longer Than You Can Stay Solvent

me@philippdubach.com (Philipp D. Dubach) — Sun, 11 Jan 2026 00:00:00 +0000

A friend recently recommended Steve Eisman’s podcast to me. Eisman, you might recall, is the hedge fund manager portrayed in The Big Short who famously bet against subprime mortgages before the 2008 crisis. In his most recent episode, Eisman laid out a thesis for something that made me uncomfortable ever since the Covid-19 stock market crash recovery: the U.S. equity market has structurally decoupled from everyday economic reality.

I’ve written about market concentration in my 2026 portfolio allocation. But Eisman’s point isn’t just about concentration. It’s about what this concentration means for everyone else. Consider what happens to consumer-exposed sectors. Combined, healthcare, consumer discretionary, and consumer staples have fallen from 38% of the index in 2015 to just 25% today. This matters because roughly 70% of U.S. GDP is consumer-driven. The traditional logic was simple: consumer spending drives the economy, consumer stocks reflect that spending, and therefore the stock market reflects economic health. That relationship has broken down.

The disconnect shows up in daily American life. Healthcare costs continue rising, housing remains unaffordable for many, and grocery prices have yet to normalize. These are real pressures on real households. Yet the S&P 500 gained 16% in 2025, with the Nasdaq up 21%. The market doesn’t care about rent or insurance premiums because the companies reflecting those costs barely register in the index anymore. As Eisman puts it:

The market has become unmoored from everyday life.

This creates a structural problem for active managers that compounds over time. When NVIDIA alone represents 7.7% of the S&P 500, Apple 6.8%, and Microsoft 6.1%, most institutional mandates physically prevent managers from holding proportional positions. Risk limits cap initial positions at perhaps 5% of assets under management. Sector allocation rules require diversification across all eleven sectors. The result is systematic underweighting of the fastest-growing names. Meanwhile, the bottom five sectors combined represent just 14% of the index. Real estate, with 31 constituents, accounts for barely 2%. Why dedicate research resources to an entire sector that can only marginally move your portfolio?

The rise of passive investing amplifies all of this. Index funds now control roughly 60% of flows versus 40% for active managers. When money enters an index fund, it buys stocks in proportion to their existing market cap. Large positions grow larger. There’s no portfolio manager deciding NVIDIA looks expensive. The buying is mechanical, price-insensitive, and self-reinforcing. This doesn’t eliminate price discovery entirely.

Eisman points to Oracle’s Q3 2025 experience: shares surged after reporting a massive backlog, then corrected below pre-earnings levels once investors realized the backlog concentrated in a single customer with questionable financing. Active managers still matter. They just matter less.

In a normal correction, sellers meet buyers who evaluate whether prices have become attractive. In a passive-dominated market, redemptions trigger mechanical selling. Index funds don’t decide that a 20% drawdown makes stocks compelling. They sell what they own in proportion to what they own. If active managers control only 40% of flows, the stabilizing bid may prove insufficient. The February-April 2025 correction saw the S&P fall 19% peak-to-trough. Eisman’s assessment: if an actual recession materializes, or if AI spending disappoints expectations,

the decline will almost certainly be steeper. It will be fast and very ugly.

There’s also a tax dimension creating behavioral lock-in. Years of technology outperformance have embedded massive unrealized capital gains in both retail and institutional portfolios. Selling NVIDIA means realizing those gains and paying taxes on them. Investors avoid this until forced by margin calls, redemptions, or actual fundamental collapse. This creates asymmetric liquidity: plenty of buyers on the way up, scarce ones on the way down.

What does this mean for portfolio construction? First, understand that traditional cap-weighted benchmarks now represent a concentrated bet on technology and AI capital expenditure. Second, active management faces structural headwinds that have nothing to do with manager skill. Third, liquidity assumptions that held in previous corrections may not hold in the next one. And fourth, consumer welfare can deteriorate materially without meaningfully impacting index returns. The K-shaped economy produces a K-shaped market, where the experience of median households and the experience of median stock index performance have genuinely diverged.

Praise by Name, Criticize by Category: Warren Buffett Retires at 95

me@philippdubach.com (Philipp D. Dubach) — Tue, 06 Jan 2026 00:00:00 +0000

Warren Buffett has stepped down as CEO at 95. Greg Abel inherits a company that paid $26.8 billion in federal income taxes last year, roughly 5% of what all of corporate America paid combined. I do not have much in common with Buffett, but I will miss his shareholder letters. Berkshire’s archive is a rare case of a public company explaining decisions candidly to its owners.

In the 2024 letter Buffett repeats Tom Murphy’s rule: “Praise by name, criticize by category.” Murphy gave him this advice 60 years ago. The letter closes with another line worth keeping: “Kindness is costless but priceless.”

Three behaviors from those letters matter. (1) Communication discipline: Between 2019 and 2023, Buffett used “mistake” or “error” 16 times in his letters. Many Fortune 500 companies never used either word once. Amazon made “brutally candid observations” in its 2021 letter. Elsewhere, it has been happy talk and pictures. (2) Patience as allocation strategy: In the 1999 letter he told shareholders that “truly large superiorities” over the index were past because Berkshire’s size constrains opportunity. That reads today like the core constraint of the Abel era, a point I reflected on when writing about portfolio limits in 2026. (3) The non-theatrical life: Coverage of the retirement keeps returning to the same facts: still living in the Omaha home he bought in 1958, still driving 7 minutes to work every day and stopping at a drive-through for McDonald’s breakfast. The man is a true American hero.

Read the letters chronologically and you see Berkshire become a system rather than a portfolio. The early articulation is there in the 1983 letter: partnership mentality, per-share intrinsic value, and a preference for businesses that generate cash and earn strong returns on tangible equity.

The engine is insurance float. Property-casualty insurers collect premiums upfront and pay claims years or decades later. That gap creates investable capital at zero or negative cost. As Buffett puts it in the 2024 letter: “When writing P/C insurance, we receive payment upfront and much later learn what our product has cost us, sometimes a moment of truth that is delayed as much as 30 or more years.” In 2024, Berkshire’s insurance operations generated $9 billion in underwriting profit and $13.7 billion in investment income. GEICO, “repolished” over five years by Todd Combs, had what the letter calls a “spectacular” year.

On the question of where Buffett was right: if you ban the word “mistake,” risk does not vanish; it goes off balance sheet until it detonates. In the 2024 letter he writes:

I have also been a director of large public companies at which ‘mistake’ or ‘wrong’ were forbidden words at board meetings or analyst calls. That taboo, implying managerial perfection, always made me nervous.

He was also right about scale. In 2024, 53% of Berkshire’s 189 operating businesses reported declining earnings, yet the company posted $47.4 billion in operating profit. That is diversification at scale, but also its constraint. The next decade hinges on a handful of large moves. Markets understood this as the Abel era officially began.

On a more critical note: Berkshire is an insurance-anchored allocator with operating companies plus a concentrated equity book. The 2024 marketable equity portfolio stood at $272 billion, down from $354 billion after significant Apple sales. At its peak, Apple represented roughly 40-50% of Berkshire’s public equity holdings. A single stock, bought mostly between 2016 and 2018, drove a substantial portion of portfolio returns over the past decade. The rest is insurance. This is not a criticism of the Apple thesis (it was correct), but the Buffett track record includes one very large, very right bet on a technology company he famously avoided for most of his career. His letters do not pretend error is rare; they treat delay as the sin. He has been candid about blind spots, discussing lessons from IBM and airlines.

Berkshire’s businesses also face difficult labor dynamics. BNSF has drawn union criticism over attendance policies and OSHA findings in retaliation cases. The railroad earned $5 billion in 2024, flat with 2023.

So what now? Abel inherits roughly $300 billion in cash and Treasury bills. The letter explains this is not a preference for cash: “Berkshire will never prefer ownership of cash-equivalent assets over the ownership of good businesses, whether controlled or only partially owned.” The cash is a byproduct of not finding anything worth buying at current prices. The first large capital move will tell us more than any profile can, which is why coverage keeps circling back to the cash pile and the question of how much “Buffett premium” was embedded in Berkshire shares.

If Buffett truly goes quiet, I hope he gets to experience not working at all, or at least the version that suits a man who prefers thinking to talking. The compounding may continue just fine.

How AI is Shaping My Investment Portfolio for 2026

me@philippdubach.com (Philipp D. Dubach) — Fri, 12 Dec 2025 00:00:00 +0000

I have two portfolios: (a) long-term, diversified, low-cost ETFs, and (b) collecting diamonds in front of bulldozers, short-term option plays, and some individual stocks I find interesting. Here, we will only look at (a). This essay is structured along five themes I believe to be true for 2026:

(1) Market Concentration and High Valuations
(2) US Dollar Depreciation Expected Despite Continued Dominance
(3) AI Investment Remains Central But Requires Scrutiny
(4) European Fiscal Revolution Creates Investment Opportunities
(5) Fixed Income Offers Best Prospects Since Global Financial Crisis

Let’s start with the conclusion. Here’s how I will rebalance my portfolio going into 2026:

What changed and why?: US Equities (-10%): S&P 500 valuations at 23× forward P/E reflect peak optimism; Nasdaq’s 30× trailing P/E is unsustainable. I reduce large-cap exposure from 33% to 23% to avoid dual headwinds: equity mean reversion and USD depreciation. I redeploy 5% into US small-cap stocks, which offer better valuations and risk-adjusted returns. Europe (+5%): European equities trade at a 22% discount to global peers and benefit from Germany’s €1T+ infrastructure commitment and structural reforms. This compounds three tailwinds: improving fundamentals, valuation re-rating, and EUR stability vs CHF. I increase the allocation from 8% to 13%. Fixed Income (+4%): Global yields remain near post-GFC highs with 10-year UST around 4.2% in December 2025. I reallocate from 10% equities-focused bonds to 14% fixed income (CHF Corporates +5%, EUR Govt Bonds +3.5%, US Treasuries +2%), establishing duration exposure and counter-cyclical protection ahead of Fed rate cuts. Japan (Unchanged): Japan’s structural reforms and BOJ stimulus remain supportive, but a 3% Asia Developed exposure is adequate. JPY strength vs USD and CHF acts as a tailwind on existing holdings. Asia EM (+0.5%): Increase from 10% to 10.5% to capture Chinese stimulus and attractive tech valuations. CNY appreciation vs USD provides diversification and a natural hedge against dollar weakness. Alternatives (+0.5%): Increase Listed PE/Alt from 1.5% to 2%, maintaining access to Swiss private markets and uncorrelated returns with currency-matched positioning. Gold (+1%): I increase from 4% to 5%. Gold serves as a hedge against USD weakness while benefiting from record central bank reserve diversification. This measured increase captures structural de-dollarization demand without chasing 2025’s roughly +58% performance. Crypto (+0.5%): I increase from 4% to 4.5% as a diversification component. At the end of this article, I have included a short, more technical insight into how I structured my research process.

Before we dive into the detailed rationale for my 2026 asset allocation, let’s quickly look at how this portfolio performed this year. The portfolio delivered solid returns in 2025, outperforming the 60/40 benchmark, primarily driven by the CHF-hedged S&P 500 allocation and domestic dividend stocks, with CHF-hedged gold providing additional diversification gains. The 11.5% USDCHF depreciation proved to be the defining factor this year, while the S&P 500 returned +18% in USD. Unhedged USD exposure translated to just ~5% in CHF terms, vindicating the currency-hedged core equity strategy. Detractors included the unhedged MSCI World SRI ETF, also hit by FX exposure.

Now if you are still with me, let’s dive into the five themes that I believe to be important going into the next year.

• Market Concentration and High Valuations: The S&P 500 has become dangerously concentrated. As of December 2025, the top 10 companies represent approximately 45% of the index’s value, a historic concentration level not seen since the dot-com bubble. Nvidia alone accounts for over 7%. What was once a diversified investment across 500 companies is now heavily weighted toward a handful of tech giants, most betting heavily on AI.

The top 10 US companies dominate the world equity market: Top 5 US tech firms alone have a collective value ($17.6) that exceeds the combined GDP of the Japan, India, UK, France, and Italy ($17.1).

Even though there might be a consensus view among analysts that elevated valuations are supported by earnings growth rather than multiple expansion alone,

Valuations are especially high in the US. The S&P500 trades at 23 times forward earnings, near the top of its historical range. While the Nasdaq’s 30× trailing P/E is well below the dotcom bubble peak, it still reflects significant optimism. Outside the US, valuations are more moderate: European and Chinese equities are 10% and 7% above their 20-year average valuations, respectively, and Japan’s index trades at a discount to its long-term average. via UBS Year Ahead

The Shiller CAPE ratio sits at 40.5 as of early December 2025, more than double its historical mean of 17.3 and approaching levels last seen, again, during the dot-com peak.

Under these circumstances, I think small-cap and international equities offer more attractive entry points than US large-cap indices. This creates an opportunity to shift part of the US equity holding out of the S&P 500 into a mix of: S&P 500 value index funds (excluding high P/E ratio stocks), mid-cap stocks, international index funds (for geographic diversification), and small-cap stocks (which have more normal valuations and haven’t experienced the same speculative growth). This also aligns with my view that the AI boom might not end with a winner-takes-all situation for the hyperscalers.

• US Dollar Depreciation Expected Despite Continued Dominance: There is growing consensus among analysts for continued dollar weakness, with JP Morgan estimating the currency remains roughly 10% overvalued and Goldman Sachs projecting 4% depreciation over the coming year. But, dollar dominance in global finance will erode only slowly over decades through structural shifts in trade and GDP share, while dollar valuation can decline much faster due to less exceptional US economic performance and difficulty attracting unhedged capital flows. The key driver is the US’s shrinking share of global trade and persistent fiscal deficits, not an imminent collapse of reserve currency status. This aligns with the points we outlined in our previous review of Pozsar’s Bretton Woods III.

This distinction is clearly visible in historical data: according to IMF COFER data, the dollar’s share of global reserves has declined gradually from 71% in Q1 1999 to 56% by Q2 2025, a structural erosion occurring over 25 years. In contrast, the trade-weighted dollar index has experienced far more volatile swings, fluctuating between 95 and 130 over the same period, with particularly sharp movements during crisis periods (2008 financial crisis, 2020 pandemic). The dollar can lose 15-20% of its value in just a few years while maintaining its reserve currency dominance. Recent strength to 130 in 2022-2024 appears unsustainable given widening fiscal deficits and declining US share of global trade, suggesting room for significant near-term depreciation even as the dollar’s reserve status erodes only gradually.

The divergence between structural dominance (slow decline) and cyclical valuation (rapid fluctuations) shows that dollar depreciation can occur independently of reserve currency status changes. Gold and the dollar typically move in opposite directions, and when the dollar weakens, gold becomes more attractive as an alternative store of value, driving its price higher. The outlook for gold in 2026 reflects a convergence of supportive factors beyond simple dollar weakness. Gold has already broken above $4,000/oz for the first time, driven by persistent inflation volatility and increasing demand from both investors and central banks. The structural case strengthens as central banks accelerate reserve diversification, with official sector gold purchases reaching record levels in 2023-2024 as institutions reduce dollar concentration.

I modestly increased my FX-hedged gold position from 4.0% to 5.0%, reflecting upgraded return forecasts but keeping the allocation measured, given gold’s exceptional performance: up roughly 58% year-to-date through December 2025. This increase captures institutional conviction without chasing momentum.

• AI Investment Remains Central But Requires Scrutiny: Almost all investment reports I read over the past weeks position AI as the dominant investment catalyst, with capex projected to reach $571 billion in 2026 (UBS) and potentially $1.3 trillion by 2030. The five largest hyperscalers now account for ~27% of S&P 500 capital expenditure.

AI capital expenditure is projected to reach $1.3 trillion by 2030 (3.8% of US GDP), which would exceed all previous infrastructure booms including broadband (1.15%), electricity (0.98%), and the Apollo program (0.74%). However, as UBS notes,

no investment boom has ever seen capital spending perfectly match future demand.

I personally view the current AI boom as potentially speculative at least in terms of the current valuations, with many top S&P companies having inflated price-to-earnings ratios. I’m also not convinced that AGI is imminent or that AI model providers will capture most of the economic value, believing AI may become a competitive commodity where value flows to companies using AI rather than those providing it. For portfolio allocation purposes, the actions derived are consistent with what we outlined in (1) Market Concentration and Active Management Opportunity.

• European Fiscal Revolution Creates Investment Opportunities: Germany’s historic abandonment of its debt brake policy, committing over €1 trillion to infrastructure, defense, and security spending (with an additional €600 billion in private sector commitments), represents a structural break from decades of fiscal conservatism.

JP Morgan upgrades eurozone growth to 1.5% and Goldman Sachs identifies a structural shift focused on defense independence, energy security, and reindustrialization. This fiscal activism is expected to narrow the US-Europe growth differential from 60bps to 30bps, making European equities, currently trading at a 22% discount to global peers, increasingly attractive despite elevated valuations elsewhere.

• Fixed Income Offers Best Prospects Since Global Financial Crisis: Higher starting yields and steeper curves have dramatically improved bond return potential. As of early December 2025, 10-year US Treasuries yield around 4.2%, with medium-duration quality bonds expected to generate mid-single-digit returns. All major research houses project 2-3 additional Fed rate cuts in 2026, while the ECB is expected to hold steady and the Bank of Japan to continue hiking. As usual it is to be expected that front-end yields are more sensitive to central bank policy and offer strong counter-cyclical properties, while fiscal concerns drive term-risk premia higher at the long end, benefiting strategic curve positioning.

Michael Cembalest, Chairman of Market and Investment Strategy, J.P. Morgan Asset & Wealth Management on the impact of geopolitical events:

It is shocking how little geopolitics actually matters to markets unless it gets truly terrible.

• Other points to consider: (1) Global growth remains resilient, with the US expected around 1.8% and global growth near 2.5%. Consensus points to America’s economic outperformance becoming “less exceptional” relative to other regions. (2) Expect elevated inflation volatility and sticky pricing pressures. Fed easing cycles are underway, but the path remains uncertain with tariffs adding to price pressures. (3) Europe’s fiscal pivot is the big story. Germany’s €1 trillion spending bill marks a historic shift, with broader European infrastructure investment accelerating. Fiscal deficits globally may weigh on currencies. (4) Economic nationalism is reshaping global dynamics. US effective tariff rates have reached levels not seen since 1934, creating a new trade order that markets must price in. (5) China’s Tech sector remains a top global opportunity despite tensions. Stimulus measures are supporting equities, and yuan appreciation is expected as growth stabilizes. (6) Attractive entry point for quality bonds. 10-year UST yields around 4.2% in December 2025 offer compelling returns, with better starting valuations than recent years. (7) Elevated geopolitical risks persist: Russia-Ukraine, Middle East tensions, and broader great power competition remain market-moving factors. (8) Bitcoin with institutional adoption accelerating ETF inflows continue and corporate treasury allocations are expanding. Regulatory clarity improving in the US, though enforcement actions remain a wildcard. Leverage buildup in derivatives markets. Watch for Bitcoin halving aftermath effects and macro liquidity conditions as primary drivers.

A short note on my analysis process: (1) I combed through insights and data from analyst and research outlooks by Goldman Sachs Asset Management, J.P. Morgan Asset Management, Morgan Stanley, and UBS Investment Research. (2) I wrote a script to convert large PDFs to Markdown and optimize them for LLM processing. (3) I then used Claude agents to look for differences and similarities in the reports, which created an extensive overview. (4) This served, together with my own thoughts and opinions, as the basis for my 2026 allocation.

Not Logan Roy: Netflix vs. Paramount's Bidding War

me@philippdubach.com (Philipp D. Dubach) — Tue, 09 Dec 2025 00:00:00 +0000

In the HBO series Succession, billionaire Logan Roy’s children spent four seasons scheming, backstabbing, and making offers to inherit a media empire. This week, the real version played out with more zeros and a $252 billion Oracle stake. Time for a closer look:

On Friday, Warner Bros. Discovery’s board agreed to sell the company to Netflix for $72 billion. By Monday, Paramount had launched a hostile tender offer directly to shareholders at $30 per share, all cash. In this post I will be going into the gap between those two numbers, streaming economics, aggregator theory, and hostile deal mechanics.

The Netflix offer breaks down into three pieces: $23.25 per share in cash, $4.50 per share in Netflix stock subject to a collar, and shares in a spun-off entity called Discovery Global containing CNN and the cable networks that Netflix doesn’t want. Analysts value that stub somewhere between $2 and $5 per share, which puts the total package at roughly $29.75 to $32.75. Paramount is offering $30 per share in cash for the entire company, including the cable assets. Warner’s stock closed Friday at $26.08 and opened Monday around $27.64, which tells you the market expects a bidding war but isn’t fully convinced either deal closes.

We’re sitting on Wall Street, where cash is still king. We are offering shareholders $17.6 billion more cash than the deal they currently have signed up with Netflix.

David Ellison’s arithmetically correct. Warner’s board took the Netflix deal anyway. This gets at something I learned in a dealmaking class in University: Boards weight speculative ideas of long-term value. They believe the Discovery Global spinoff might be worth $5, that Netflix stock has upside, that strategic fit matters. Shareholders, particularly the arbs and institutional holders who actually vote, prefer certainty. Thirty dollars in cash is just $30 in cash. As Matt Levine noted, “$30 in cash is worth more than, well, again, the stock closed at $26.08 on Friday.” The board’s job is to maximize long-term shareholder value. The shareholders would like their value now, please.

The strategic logic behind Netflix’s offer deserves examination. Both companies began as distributors. The Warner brothers opened a movie theater in Pennsylvania in 1907 before moving into film production; Netflix mailed DVDs before becoming a streaming giant. The crucial difference: physical distribution has capacity constraints, while internet distribution has none. A theater seat that goes unsold is lost revenue forever. The marginal costs of Netflix to deliver to one more subscriber, whether that subscriber is in Zurich or Tokio, are essentially zero. This asymmetry explains why Netflix is worth $425 billion and the combined legacy studios are worth a fraction of that. Consider what Netflix does to content it doesn’t own. Drive to Survive transformed Formula. Apple is now paying $150 million annually for F1 broadcast rights that ESPN once carried for free. NBCUniversal’s Suits sat dormant on Peacock until Netflix licensed it and turned it into a streaming phenomenon. In each case, Netflix created enormous value but captured little of it. The logical next step: own the IP instead of renting it.

This is what Ben Thompson calls aggregation economics. Hollywood executives spent years insisting that content was king, and for decades they were right. When distribution required owning theaters, securing broadcast licenses, or negotiating cable carriage, the studios held leverage. The internet eliminated those bottlenecks. Now the scarce resource isn’t access to content but attention, and the companies that own the customer relationship capture most of the value. Netflix grasped this early; the legacy studios chased streaming without understanding why Netflix was winning. The result: Netflix commands a market cap of $425 billion, while Paramount’s standalone value sits around $15 billion.

Paramount’s financing structure is worth looking at. The tender offer filing is backed by $54 billion in debt commitments from Bank of America, Citigroup, and Apollo, plus a $40.4 billion equity backstop from Larry Ellison’s trust. That trust holds approximately 1.16 billion Oracle shares worth around $252 billion at current prices. Additional equity comes from Saudi Arabia’s Public Investment Fund, Abu Dhabi’s L’imad Holding, Qatar Investment Authority, and Affinity Partners. To avoid CFIUS jurisdiction, the foreign investors have waived all governance and voting rights.

Hostile Tender Mechanics

In a friendly deal, the target’s board negotiates terms and recommends shareholders accept. In a hostile tender, the acquirer goes directly to shareholders with a public offer, bypassing the board. Warner’s board has 10 business days to respond with a recommendation. Defense mechanisms exist (poison pills, enhanced breakup fees) but all invite litigation. The best defense is usually more money from the preferred bidder.

The antitrust arguments on both sides are instructive. Ellison argues that combining Netflix (#1 in streaming) with HBO Max (#3) is anticompetitive: “It’s like saying Coke could buy Pepsi because Budweiser sells a lot of beer.” Netflix counters by pointing to Nielsen’s TV viewing data, which shows Netflix at 8% of total TV usage, slightly below Paramount’s 8.2%. By that measure, Netflix ranks sixth overall, with YouTube at #1 and Disney at #2. The relevant market definition will determine whether this deal survives regulatory review.

If regulators define the market narrowly as “subscription video on demand,” combining Netflix with HBO Max looks troubling. If they define it as “all video consumption,” Netflix is one player among many, competing against YouTube’s bottomless catalog of free content. This framing matters because YouTube already exceeds Netflix in total viewing time. The existential threat facing Hollywood isn’t consolidation among paid streamers. It’s the democratization of content creation itself. Every teenager with a smartphone is a potential competitor for audience attention. The hours flowing to TikTok and YouTube creators don’t flow to HBO. From this vantage point, Netflix absorbing Warner Bros. looks less like monopolization than like circling the wagons. Ellison offered his counter-narrative on Monday: the Netflix deal means “the death of the theatrical movie business in Hollywood.” He promised to put 30 movies a year in theaters exclusively and to combine CBS News with CNN into what he called a news service “in the trust business, the truth business” that “speaks to the 70% of Americans that are in the middle.” Whether you find this vision compelling probably depends on your priors about theatrical distribution and centrist news. The deal timeline matters. Netflix’s offer is expected to take 12-18 months to close, driven by antitrust review. Paramount claims its offer has a faster path to regulatory approval. If Warner’s shareholders ultimately take Paramount’s offer, Warner owes Netflix a $2.8 billion breakup fee. If Netflix’s deal collapses after the review period, Netflix owes Warner $5.8 billion, one of the largest breakup fees on record.

Breakup Fee Economics

Breakup fees serve two functions: compensating the jilted bidder for deal expenses and transaction costs, and creating a hurdle for competing offers. A $5.8 billion reverse breakup fee equals roughly $2 per Warner share, meaning any competing bid needs to clear that hurdle to be economically equivalent. The size of Netflix’s fee signals both confidence and a willingness to pay for optionality.

Warner’s stock trading below both offers reflects the compounded uncertainties: antitrust risk, timeline risk, financing risk, and the possibility that both deals fall apart. The 12-18 month window creates a lot of room for things to change. Interest rates could move. The administration’s antitrust priorities could shift. Netflix’s stock could fall further, reducing the value of the stock component. Paramount’s financing consortium could develop cold feet.

What happens next is procedurally straightforward. Warner’s board will respond to Paramount’s tender offer within 10 business days. Netflix will likely raise its bid; Ellison signaled Monday that $30 “wasn’t best and final.” The arbs will push for whichever deal offers better risk-adjusted value. Whoever wins will spend the next year in antitrust review while the other side’s lawyers look for grounds to challenge.

Hollywood’s century-old industrial structure is unwinding regardless of which bid prevails. The studio system emerged when controlling both production and distribution created durable advantages. The internet dissolved those advantages by making distribution essentially free and universally accessible. Warner Bros. spent a century building an integrated media empire; Netflix spent two decades proving that owning the customer relationship matters more than owning the soundstages. The question isn’t whether legacy media consolidates into tech platforms. It’s which platform, at what price, and whether inherited wealth can rewrite the outcome. I doubt it. On the internet, aggregators tend to win, and Netflix is the aggregator in video.

Nike's Crisis and the Economics of Brand Decay

me@philippdubach.com (Philipp D. Dubach) — Tue, 02 Dec 2025 00:00:00 +0000

Nike’s $28 Billion Value Destruction

What it sounds like is that the CEO has the wrong people making the wrong decisions across the strongest brand or one of the strongest brands in consumer history.

This quote by Scott Galloway on his podcast is from July 2024. In March 2025, Nike reported its worst revenue decline in nearly five years: an 11.5% drop to $11.01 billion. Digital sales fell 20%, app downloads decreased 35%, and store foot traffic declined 11%. Nike’s crisis reveals how competitive advantages work, and how quickly they can disappear when the company that once captured roughly half of the US athletic footwear market systematically weakens its own foundations.

Nike spent decades building dominance through complementary assets: product development, athlete partnerships, and marketing that reinforced premium positioning. These three worked together to create what Porter would call a sustainable competitive advantage. When all three are strong, you can charge premium prices and maintain gross margins above 40%. When you systematically weaken each pillar simultaneously, the advantage collapses.

The Direct-to-Consumer Strategy Failure

Nike’s gross margins peaked around 45% in the mid-2010s. By fiscal 2025, margins had compressed to 42.7%, a decline of 190 basis points, primarily due to higher discounts and unfavorable sales channel mix. The direct-to-consumer shift that was supposed to improve margins actually made things worse because it reduced retail presence at exactly the wrong moment. Competitors filled the shelf space Nike vacated.

The most significant strategic shift came in 2020 when Nike hired John Donahoe, a former Bain consultant and eBay CEO, to replace Mark Parker. Donahoe accelerated the direct-to-consumer transition, terminating hundreds of wholesale accounts. The theory was sound: wholesale margins are 30-35% after retailer markups, while direct sales can reach 50% or higher. But retail shelf space is a zero-sum game. When Nike pulled out, competitors immediately filled the void. Running brands like On and Hoka, which had been developing new sole technology and cushioning systems, suddenly had access to prime retail real estate. On’s CloudTec and Hoka’s maximalist cushioning gained visibility precisely when Nike was reducing its retail presence. Nike improved its direct-to-consumer margins but reduced its total addressable market. The company assumed consumers would follow it online, but many didn’t. Instead, they discovered alternatives in physical stores.

Product Innovation Decline and Organizational Restructuring

The product development problem was structural. Donahoe reorganized Nike from sport-specific teams to general categories: Men’s, Women’s, and Kids’. This destroyed the organizational capabilities that had driven product development. The running team understood biomechanics and materials science. The basketball team understood court dynamics and performance requirements. When you collapse these into general categories, you lose the specialized knowledge that creates functional differentiation. Senior designers and executives left for competitors. The company began relying more heavily on retro basketball shoes, which worked during the pandemic. But when trends shifted toward lower-profile shoes like Adidas Sambas, Nike was left holding excess inventory. Inventory turnover deteriorated, and gross margins compressed further.

The combination of reduced retail presence and weaker product development created a gap that competitors exploited. On’s revenue grew from $330 million in 2020 to $1.8 billion by 2025. Hoka’s parent company, Deckers, saw Hoka revenue increase from $352 million in 2020 to $1.4 billion. Moody’s has noted that emerging brands like On and Hoka have intensified competition, contributing to Nike’s 10% revenue drop and 42% decline in EBIT.

The athlete partnership strategy collapsed. Roger Federer left for On, which he partially owns. Harry Kane signed with Skechers. Simone Biles went to Athleta. Josh Allen moved to New Balance. Tiger Woods left to start his own brand. These departures matter because athlete partnerships aren’t just marketing expenses. They’re product development inputs and distribution channels. When Michael Jordan worked with Nike in the 1980s, the collaboration produced the Air Jordan line, which generated over $5 billion in annual revenue by 2023. The real problem isn’t that athlete deals are more expensive today. It’s that Nike lost athletes because it was no longer the clear leader in product development. Federer left because On was developing better running shoes.

Nike’s Marketing Strategy Failure and Brand Erosion

The marketing shift was perhaps the most visible change, and the most damaging to brand positioning. Nike’s historical advertising was unapologetically about winning. The tagline “Just Do It” was aggressive. The tone was confident, sometimes arrogant. This worked because it matched the product and the athletes. When you have the best products and the best athletes, you can be arrogant. When you don’t, arrogance becomes empty posturing. Under Donahoe, the messaging softened. Ads became more whimsical, focused on participation rather than victory, and emphasized social issues. A strategy that worked for many brands but for Nike, this represented a departure from what had made the brand distinctive. More importantly, it didn’t match the product. Without new products and top athletes, the softer messaging couldn’t sustain premium positioning.

Then came the tariffs. In early 2025, the Trump administration implemented “reciprocal tariffs” on imports from various countries. Nike manufactures 95% of its shoes and 60% of its apparel in Southeast Asia, particularly in Vietnam, China, Indonesia, and Cambodia. While rates were later adjusted downward, Nike still faces an estimated $1 billion to $1.5 billion in additional tariff costs over the next few years. Nike has announced plans to reduce China’s share of its U.S. footwear imports from 16% to single digits by fiscal 2026, but this requires building new supplier relationships, retooling factories, and establishing new logistics networks. The transition will take years and cost billions.

Supply Chain Risk and Path Dependency

The tariff situation highlights a broader issue with Nike’s supply chain strategy. The company has concentrated manufacturing in a small number of countries to achieve scale economies and cost efficiency. This works well when trade policy is stable, but when trade policy shifts, the concentration becomes a vulnerability. This is what economists call path dependency. Past decisions constrain future options. Nike focused on cost efficiency, but this created exposure to trade policy risk that the company can’t easily unwind. When Vietnam shut down factories during COVID-19, Nike lost three months of production.

Nike’s Turnaround Strategy Under Elliott Hill

The company’s response has been to return to its original formula. In September 2024, Nike replaced Donahoe with Elliott Hill, a 30-year company veteran. Hill is pushing product development as part of Nike’s “Sport Offense” strategy. There are early signs this might work, though Nike has faced multiple consecutive quarters of declining sales as it works to stabilize the business.

The sport offense realignment will focus on driving distinction within key sports, building a complete product portfolio, creating stories to inspire and connect with consumers, and elevating and growing the entire marketplace.

Nike’s competitors have used the past few years to build stronger positions. On has established itself in running with new sole technology and Federer’s endorsement, growing from $330 million to $1.8 billion in revenue. Hoka has gained market share with its maximalist cushioning, growing from $352 million to $1.4 billion. The competitive environment has fundamentally changed. In the 2010s, Nike could dominate through scale and brand power alone. Today, smaller brands can compete effectively by focusing on specific sports or product categories. Social media and direct-to-consumer platforms have lowered barriers to entry. Nike can’t simply return to its old formula and expect to regain dominance because the structural advantages that made the old formula work no longer exist.

Why Complementary Assets Explain Nike’s Brand Decline

Nike’s crisis wasn’t caused by a single mistake or even a series of mistakes. It was caused by a fundamental misunderstanding of how competitive advantages work. The company treated its brand, its products, and its athlete partnerships as separate assets that could be managed independently. But they’re not separate. They’re complementary assets that create value only when they work together. When you weaken one, you weaken all of them. When you weaken all of them simultaneously, the advantage collapses completely. The direct-to-consumer transition, organizational restructuring, and marketing shift each made sense in isolation. But together, they destroyed the system that created Nike’s competitive advantage.

The company focused on efficiency and margins while ignoring the fact that its real advantage came from being the best at product development, athlete relationships, and brand positioning at the same time. The tariffs added pressure at the worst possible time, but they’re not the root cause. Nike systematically weakened its competitive advantages and then tried to maintain premium pricing without the product foundation to support it. The market responded predictably and retailers quickly filled shelf space with other brands. Athletes left for companies that were actually developing new products. Recovery will be harder than the decline because the structural advantages that made Nike dominant might no longer exist.

Michael Burry's $379 Newsletter

me@philippdubach.com (Philipp D. Dubach) — Fri, 28 Nov 2025 00:00:00 +0000

Michael Burry (who in your head probably looks like Christian Bale thanks to The Big Short), the investor who famously predicted the 2008 housing crash, has launched a Substack newsletter after deregistering his hedge fund. The $379 annual subscription capitalizes on the 1.6 million followers he’s built on X, offering what he describes as his “sole focus” going forward.

The newsletter’s inaugural post takes (which he kindly enough made accessible for free as a Thanksgiving gift today) readers back to 1999, when Burry was a 27-year-old neurology resident at Stanford making $33'000 annually while carrying $150'000 in medical school debt. There he wrote his Valuestocks.net article “Buffett Revisited”. A fellow resident casually mentioned making $1.5 million on Polycom stock. Physicians crowded around terminals checking stocks while patients waited. In that environment, Burry was writing investment analysis late at night, getting paid $1 per word by MSN Money under the pen name “Value Doc.” His VSN Fund returned 68.1% in 1999, and by February 2000, the San Francisco Chronicle noted he had shorted Amazon. Fourteen days after that article appeared, the NASDAQ topped. It was a peak it wouldn’t revisit for 15 years.

Burry’s approach today is notably personal and reflective. His analysis of Apple in 1999 exemplifies his contrarian thinking. He bought it for the VSN Portfolio despite pushback, writing that great companies like Coca-Cola, American Express, and Disney had all experienced 11-year periods of negative real returns. Unlike the skeptics who simply dismissed the internet as a fad in 1999, Burry recognized the technology was transformational; he just believed the infrastructure was being overbuilt relative to near-term demand. He’s making the same argument about AI today. He believes markets are deep in bubble territory, drawing parallels between the late 1990s tech mania and today’s AI boom. His X post’s often echoed familiar warnings:

Feb 21, 2000: SF Chronicle says I’m short Amazon. Greenspan 2005: ‘bubble in home prices … does not appear likely.’ Powell ‘25: ‘AI companies actually… are profitable… it’s a different thing.’

The comparison is deliberate. Burry highlighted then-Fed Chair Alan Greenspan’s 2005 insistence that U.S. housing prices showed no signs of a bubble. This was just two years before the subprime implosion validated Burry’s famous “Big Short.” Today, he’s openly bearish on AI poster children Nvidia and Palantir, suggesting history is rhyming once again. Shortly after the newsletter was out, Nvidia circulated a seven-page memo to Wall Street analysts explicitly naming Burry in its opening, a rare move for a company of Nvidia’s stature. The memo sought to refute his claims about stock-based compensation, depreciation schedules, and what he calls “circular financing.”

Mainly, Nvidia disputed Burry’s depreciation argument. Burry contends that customers overstate GPU useful lives to justify massive capex, claiming the hardware becomes obsolete in two to three years. Nvidia counters that its A100s, released in 2020, continue running at high utilization rates with “meaningful economic value” well beyond that timeframe, justifying the standard four-to-six-year depreciation schedule. The memo also rejected suggestions of “circular financing,” noting that Nvidia’s strategic investments represent a small fraction of revenue and that AI startups raise capital predominantly from outside investors. Burry responded on Substack:

I stand by my analysis. I am not claiming Nvidia is Enron. It is clearly Cisco.

He argues Nvidia now occupies the exact position Cisco held in 1999-2000. It’s the key hardware supplier powering a massive capital investment cycle built on optimistic demand forecasts. Just as telecom companies spent tens of billions laying fiber optic cable based on projections that “internet traffic doubles every 100 days,” today’s hyperscalers are promising nearly $3 trillion in AI infrastructure spending over the next three years. The problem? In the early 2000s, less than 5% of U.S. fiber capacity was operational. Burry believes today’s AI buildout rests on similarly flawed assumptions about data center power and GPU longevity. “And once again there is a Cisco at the center of it all, with the picks and shovels for all and the expansive vision to go with it. Its name is Nvidia,” Burry wrote. The analogy might resonate with market observers who remember how that story ended. Cisco’s stock peaked above $80 in March 2000. It wouldn’t return to that level for nearly 24 years. The company survived and remained profitable, but shareholders who bought at the top experienced a generational loss. One key difference is worth noting: Cisco’s forward P/E in 2000 was around 200; Nvidia’s is under 40.

In my opinion the technical argument around depreciation matters more than it might appear. If hyperscalers must depreciate GPUs over three years instead of six, companies like Alphabet would see roughly a 10% hit to net profit. More importantly, it would signal that the economic returns on AI infrastructure spending are weaker than advertised. Alphabet, for example, is currently guiding $90 billion-plus of AI spending this year. Using 5-year straight line depreciation, you get $18 billion per year in expenses. Add $9 billion for a conservative 10% WACC. That’s $27 billion, and assuming a 70% blended margin, you need about $40 billion per year in incremental revenue directly attributable to AI to make the infrastructure spending justifiable. Therefore, the question remains: how much of Alphabet’s $60 billion annualized revenue increase is actually attributable to AI versus normal growth? Burry closes his first newsletter with characteristic understatement:

I doubted if I should ever come back. I’m back.

Everything is a DCF Model

me@philippdubach.com (Philipp D. Dubach) — Sun, 19 Oct 2025 00:00:00 +0000

A brilliant piece of writing from Michael Mauboussin and Dan Callahan at Morgan Stanley that was formative in what I personally believe when it comes to valuation.

[…] we want to suggest the mantra “everything is a DCF model.” The point is that whenever investors value a stake in a cash-generating asset, they should recognize that they are using a discounted cash flow (DCF) model. […] The value of those businesses is the present value of the cash they can distribute to their owners. This suggests a mindset that is very different from that of a speculator, who buys a stock in anticipation that it will go up without reference to its value. Investors and speculators have always coexisted in markets, and the behavior of many market participants is a blend of the two.

Gambling vs. Investing

me@philippdubach.com (Philipp D. Dubach) — Fri, 30 May 2025 00:00:00 +0000

Kalshi, a prediction market startup, is using its federal financial license to offer sports betting nationwide, even in states where it’s not legal. The move has earned them cease-and-desist letters from state gaming regulators, but CEO Tarek Mansour isn’t backing down:

We can go one by one for every financial market and it would fall under the definition of gambling. So what’s the difference?

It’s a question that cuts to the heart of modern finance. The founders argue that Wall Street blurred the line between investing and gambling long ago, and casting Kalshi as the latter is inconsistent at best. They have a point—if you can bet on oil futures, Nvidia’s stock price, or interest rate movements, why is wagering on NFL touchdowns more objectionable?

Benefiting from the Trump administration’s hands-off regulatory approach, with the CFTC dropping its legal challenge to their election contracts, the odds might be in their favor. Even better, a Kalshi board member is awaiting confirmation to lead the very agency that was previously their biggest antagonist.

The technical distinction matters: Kalshi operates as an exchange between traders rather than a house taking bets against customers. But functionally, with 79% of their recent trading volume being sports-related, they’re forcing us to confront an uncomfortable reality about risk, speculation, and what we choose to call “investing.”

Whether you call it innovation or regulatory arbitrage, Kalshi is exposing the arbitrary nature of the lines we’ve drawn around acceptable financial speculation.

_ _

(17/06/2025) Update: Matt Levine - one of the finance columnists I enjoy reading most - just published a long piece “It’s Not Gambling, It’s Predicting” in his newsletter on exactly this issue:

Kalshi offers a prediction market where you can bet on sports. No! Sorry! Wrong! It offers a prediction market where you can predict which team will win a sports game, and if you predict correctly you make money, and if you predict incorrectly you lose money. Not “bet on sports.” “Predict sports outcomes for money.” Completely different.

Passive Investing's Active Problem

me@philippdubach.com (Philipp D. Dubach) — Sat, 15 Feb 2025 00:00:00 +0000

(1) A new academic paper suggests the rise of passive investing may be fueling fragile market moves. (2) According to a study to be published in the American Economic Review, evidence is building that active managers are slow to scoop up stocks en masse when prices move away from their intrinsic worth. (3) Thanks to this lethargic trading behavior and the relentless boom in benchmark-tracking index funds, the impact of each trade on prices gets amplified, explaining how sell orders can induce broader equity gyrations

Passive investing, the supposedly boring strategy of buying and holding index funds, might actually be making markets more volatile. A new study set to be published in the American Economic Review finds that active managers are slow to scoop up stocks when prices move away from their intrinsic worth. Meanwhile, the relentless boom in benchmark-tracking index funds means that each trade gets amplified, explaining how sell orders can induce broader equity gyrations. Justina Lee for Bloomberg writes that this week’s AI-fueled market swings perfectly illustrate the phenomenon. Big equity gauges plunged on Monday over fears about an AI model, before swiftly rebounding.

Thanks to this lethargic trading behavior and the relentless boom in benchmark-tracking index funds, the impact of each trade on prices gets amplified.

The researchers from UCLA, Stockholm School of Economics, and University of Minnesota have identified what they call “Big Passive”—a financial landscape that’s proving less dynamic and more volatile. When most investors are on autopilot, the few remaining active traders have disproportionate influence. This doesn’t invalidate passive investing’s core benefits—lower costs and better long-term returns for most investors remain compelling. But it does suggest that our increasingly passive financial system has some unintended consequences.

Crypto Mean Reversion Trading

me@philippdubach.com (Philipp D. Dubach) — Mon, 11 Nov 2024 00:00:00 +0000

In late 2021, Lars Kaiser’s paper on seasonality in cryptocurrencies inspired me to use my Kraken API Key to try and make some money. A quick summary of the paper: (1) Kaiser analyzes seasonality patterns across 10 cryptocurrencies (Bitcoin, Ethereum, etc.), examining returns, volatility, trading volume, and spreads (2) Finds no consistent calendar effects in cryptocurrency returns, supporting weak-form market efficiency (3) Observes robust patterns in trading activity - lower volume, volatility, and spreads in January, weekends, and summer months (4) Documents significant impact of January 2018 market sell-off on seasonality patterns (5) Reports a “reverse Monday effect” for Bitcoin (positive Monday returns) and “reverse January effect” (negative January returns) (6) Trading activity patterns suggest crypto markets are dominated by retail rather than institutional investors.

The paper’s main finding: crypto markets appear efficient in terms of returns but show behavioral patterns in trading.

The efficient-market hypothesis (EMH) is a hypothesis in financial economics that states that asset prices reflect all available information. A direct implication is that it is impossible to “beat the market” consistently on a risk-adjusted basis since market prices should only react to new information.

The EMH has interesting implications for cryptocurrency markets. While major cryptocurrencies like Bitcoin and Ethereum have gained significant institutional adoption and liquidity, they may still be less efficient than traditional markets due to their relative youth and large audience of retail traders (who might not act as rationally as larger, institutional traders). This inefficiency becomes even more pronounced with smaller altcoins, which often have: (1) Lower trading volumes and liquidity (2) Less institutional participation (3) Higher information asymmetries (and/or greater susceptibility to manipulation). These factors create opportunities for exploiting market inefficiencies, particularly in the short term when prices may overreact to news or technical signals before eventually correcting.

Unlike Kaiser’s seasonality research, I didn’t focus on calendar-based anomalies over longer time horizons. After reviewing further research on cryptocurrency market inefficiencies [1] [2] [3] [4], I was intrigued by predictable patterns in returns following large price movements. This led me to develop a classic mean reversion strategy instead (mean reversion suggests that asset prices tend to revert to their long-term average after extreme movements due to market overreactions and subsequent corrections).

First, I had to find “change points.” The PELT algorithm efficiently identifies points in ETH/EUR where the statistical properties of the time series change significantly. These changes could indicate market events, trend reversals, or volatility shifts in the cryptocurrency price.

I then implemented an automated mean reversion trading strategy following this logical flow: Continuous monitoring → Signal detection → Buy execution → Hold period → Sell execution. The script continuously monitored prices for certain cryptocurrencies on Kraken exchange. It executed buy orders when the price moved more than four standard deviations over a 2-hour period, then automatically sold after exactly 2 hours regardless of price movement. The strategy used fixed position sizes and limit orders to minimize fees. It assumed that large price drops represent temporary market overreactions that will reverse within the holding period.

This little script earned some good change, but then again, it was 2021.

My First 'Optimal' Portfolio

me@philippdubach.com (Philipp D. Dubach) — Fri, 15 Mar 2024 00:00:00 +0000

My introduction to quantitative portfolio optimization happened during my undergraduate years, inspired by Attilio Meucci’s Risk and Asset Allocation and the convex optimization teachings of Diamond and Boyd at Stanford. With enthusiasm and perhaps more confidence than expertise, I created my first “optimal” portfolio. What struck me most was the disconnect between theory and accessibility. Modern Portfolio Theory had been established since 1990, yet the optimization tools remained largely locked behind proprietary software.

Nevertheless, only a few comprehensive software models are available publicly to use, study, or modify. We tackle this issue by engineering practical tools for asset allocation and implementing them in the Python programming language.

This gap inspired what would eventually be published as: A Python integration of practical asset allocation based on modern portfolio theory and its advancements.

My approach centered on a simple philosophy:

The focus is to keep the tools simple enough for interested practitioners to understand the underlying theory yet provide adequate numerical solutions.

Today, the landscape has evolved dramatically. Projects like PyPortfolioOpt and Riskfolio-Lib have established themselves as sophisticated open-source alternatives, far surpassing my early efforts in both scope and sophistication. Despite its limitations, the project yielded several meaningful insights:

First, I set out to visualize Modern Portfolio Theory’s fundamental principle—the risk-return tradeoff that drives optimization decisions. This scatter plot showing the efficient frontier demonstrates this core concept.

The results of my first optimization: maintaining a 9.386% return while reducing volatility from 14.445% to 5.574%, effectively tripling the Sharpe ratio from 0.650 to 1.684.

By varying the risk aversion parameter (gamma), the framework successfully adapted to different investor profiles, demonstrating the flexibility of the optimization approach. This efficient frontier plot with different gamma values illustrates how the optimization framework adapts to different investor risk preferences.

Perhaps most importantly, out-of-sample testing across diverse market conditions—including the 2018 bear market and 2019 bull market—demonstrated consistent CVaR reduction and improved risk-adjusted returns.

We demonstrate how even in an environment with high correlation, achieving a competitive return with a lower expected shortfall and lower excess risk than the given benchmark over multiple periods is possible.

Looking back, the project feels embarrassingly naive—and surprisingly foundational. While it earned some recognition at the time, it now serves as a valuable reminder: sometimes the best foundation is built before you know enough to doubt yourself.

Investing on Philipp D. Dubach | Quantitative Finance & AI Strategy

Midyear Portfolio Review: The Rotation Worked. Europe Didn't.

Portfolio performance so far

What worked, what didn’t

Valuations got more extreme

The Europe trade is alive but not paying

Dollar, AI, bonds

H2 Outlook

Je ne regrette rien

Two Anthropics

Race to the top, on paper

What scaling produced

Why both cannot fully be true

Three scenarios

What this means for pricing AI labs

AI Models Are the New Rebar

What the steel mills can tell us

The cost decline eats strategy

Switching costs that approach zero

What OpenAI’s numbers actually require

Who actually wins when the model layer is a commodity

AI Capex Arms Race: Who Blinks First?

The LBO

What would have to be true

Depreciation time bomb

Arms race logic

Every Bulge Bracket Bank Agrees on AI

Concerning consensus

What’s happening vs. what might happen

Dotcom comparison

Sell side

Second-order AI beneficiaries

Power

What the reports don’t say

The Absolute Insider Mess of Prediction Markets

Three cases, three months, zero enforcement

Now there’s an AI that hunts them

Regulation

Why regulation matters: the lemons problem

Prediction markets as macroeconomic forecasting tools

The regulatory picture, fractured

The SaaSpocalypse Paradox

The bear case for enterprise software

The bull case for software stocks

The hyperscaler AI capex question that connects both sides

Bifurcation, not extinction: the SaaSpocalypse resolved

Buying the Haystack Might Not Work This Year

The Market Can Stay Irrational Longer Than You Can Stay Solvent

Praise by Name, Criticize by Category: Warren Buffett Retires at 95

How AI is Shaping My Investment Portfolio for 2026

Not Logan Roy: Netflix vs. Paramount's Bidding War

Nike's Crisis and the Economics of Brand Decay

Nike’s $28 Billion Value Destruction

The Direct-to-Consumer Strategy Failure

Product Innovation Decline and Organizational Restructuring

How On and Hoka Captured Nike’s Athletic Footwear Market Share

Nike’s Marketing Strategy Failure and Brand Erosion

Supply Chain Risk and Path Dependency

Nike’s Turnaround Strategy Under Elliott Hill

Why Complementary Assets Explain Nike’s Brand Decline

Michael Burry's $379 Newsletter

Everything is a DCF Model

Gambling vs. Investing

Passive Investing's Active Problem

Crypto Mean Reversion Trading

My First 'Optimal' Portfolio