We’ve been analyzing the data, and while headline numbers look robust, the engine driving them is becoming dangerously narrow. We are witnessing the return of the “K-shaped” economy, but this time, the divergence is creating a structural fragility that markets are underpricing.

Economist Peter Atwater calls it a “Jenga tower.” We think that’s the perfect analogy. The structure keeps getting taller, but the base is being pulled out block by block.

Here is our thesis on why the US economy is more top-heavy, and fragile, than it appears.

Structural inequality is now a systemic risk

The divergence between the “haves” and “have-nots” isn’t just a social issue anymore, it’s a macro-critical risk factor.

For decades, we have watched the income gap widen. But in the post-pandemic era, this trend has accelerated to unsustainable levels. The top 10% of income earners now capture nearly 50% of all income in the United States (chart 1).

This matters because it dictates who is actually keeping the economy alive.

With inflation eating into purchasing power and the labor market softening, the bottom of the K (the lower and middle income earners) are tapped out. Their wage increases are barely keeping pace with the cost of living.

Meanwhile, the top of the K is experiencing a completely different reality, fueled by asset price inflation in equities and real estate.

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The concentration of consumption

Because income is concentrated, spending is concentrated. This is where the risk lies.

Here, the divergence is even more pronounced. The wealthiest 10% of Americans now account for approximately 50% of all consumer spending (chart 2), a record high. The top 20% control 63% of total consumption. Meanwhile, the bottom 80%, which represented 42% of spending before the pandemic, has been compressed to just 37%.

This chart explains the “mystery” of the resilient economy. The economy hasn’t avoided recession because the average American is doing well. It avoided recession because the top 10% are spending aggressively, buoyed by the wealth effect of record-high stock markets.

But this creates a single point of failure.

If equity markets correct, the “wealth effect” evaporates. The top 10% will pull back. Since the bottom 80% has no capacity to step up and fill the gap, even a moderate drop in asset prices could trigger a sharp, consumer-led recession.

The Fed’s blunt tool is worsening the split

The Federal Reserve is inadvertently exacerbating this bifurcation.

Jerome Powell has admitted that rates are a blunt tool. They cannot target specific demographics. However, restrictive monetary policy is hitting the economy from the bottom up, not the top down.

High interest rates punish borrowers. Low-income consumers rely on credit cards to bridge the gap between wages and inflation. As the Fed hiked rates, credit card interest rates skyrocketed in lockstep.

Investment implications

From a positioning perspective, this analysis suggests several conclusions.

First, equity market stability has become the critical variable for US growth. Monitoring consumer confidence among high-income households matters more than traditional labor market indicators. We’re watching discretionary spending patterns at premium retailers and luxury goods sales as leading indicators.

Second, the wealth effect works both ways. If equities correct 15-20%, we’d expect a sharp, sudden shift in consumption patterns, not a gradual adjustment. Portfolio construction should account for this tail risk through either defensive positioning or explicit hedges.

Third, this environment favors businesses with pricing power and market positions insulated from mass-market consumer weakness. Companies relying on broad-based consumer demand face structural headwinds.

The policy response options are limited. The Fed’s toolkit can’t address this bifurcation. Fiscal policy could theoretically redistribute income or support lower-income consumers, but the political will seems absent. That leaves markets vulnerable to the whims of wealthy households’ spending decisions.

We’re not predicting imminent recession. But we are arguing that the US economy’s stability rests on a narrower foundation than the headline data suggests. The “Jenga tower” stands tall, for now. But the blocks supporting it are fewer than they appear, and the consequences of removing the wrong one have never been greater.

The headline number tells part of the story. Alphabet is up 70.74% YTD. Nvidia, for context, is up 28.57% over the same period (chart 1).

But the more interesting narrative isn’t about relative returns. It’s about what happens when monopolies become vulnerable.

The monopoly paradox

Here’s what we’ve learned watching tech markets for the past decade: monopolies don’t fall because competitors build better products. They fall because the monopolist’s own customers become desperate for alternatives.

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Nvidia has owned the AI accelerator market. Their GPUs became the de facto standard. Their CUDA software created switching costs that felt insurmountable. Every major AI lab (Meta, OpenAI, Anthropic) ran on Nvidia infrastructure. Jensen Huang could essentially name his price.

But monopoly pricing creates its own problems. When Nvidia chips become so expensive that cloud providers like Oracle struggle to generate decent gross margins renting them out, something has to give. When your biggest customers are spending tens of billions annually and facing supply constraints, they start looking for alternatives regardless of switching costs. That’s where Google enters the picture.

The TPU strategy

For years, Google has used its tensor processing units internally to train its own models. They’ve also rented TPUs to cloud customers through Google Cloud. But the new development is more aggressive: Google is now pitching companies on using TPUs in their own data centers, not just in Google’s cloud infrastructure.

Meta is reportedly in talks to spend billions on TPUs for deployment in Meta’s own facilities in 2027, plus renting Google chips from Google Cloud in 2026. Given Meta’s projected capex of at least $100 billion for 2026 (with an estimated $40-50 billion allocated to inferencing chip capacity) we’re talking about a material shift in procurement strategy.

This isn’t just about one customer. Google previously secured a deal with Anthropic for up to one million TPUs. Financial institutions and high-frequency trading firms are in discussions about TPU deployments. The pitch is straightforward: TPUs are cheaper than Nvidia chips, and for sensitive data, running them in your own data centers meets higher security and compliance standards.

Some Google Cloud leaders have suggested internally that this strategy could capture as much as 10% of Nvidia’s annual revenue. That’s not a small number. Nvidia’s trailing revenue is in the vicinity of $130 billion annually. Ten percent would represent roughly $13 billion in annual revenue for Google, a meaningful addition to Google Cloud’s existing business.

Why this works now

Timing matters. Three factors have converged to make Google’s TPU push credible.

First, Google’s AI capabilities have legitimately caught up. The Gemini 3 release earlier this month received enthusiastic reviews from prominent tech figures who believe Google has eliminated the gap with OpenAI. You can’t sell AI chips if customers doubt your AI expertise. Google’s recent technical progress gives TPUs credibility they might have lacked two years ago.

Second, developers increasingly believe Google has narrowed Nvidia’s lead in the dense server clusters needed to train large models. Meta isn’t just talking to Google about inference workloads, they’re also discussing using TPUs to train new AI models. If TPUs can handle training, not just inference, the addressable market expands dramatically.

Third, Google has developed software called TPU Command Center to make it easier for developers to work with TPUs. This matters because Nvidia’s biggest moat has always been CUDA, the software layer that AI developers know intimately. If Google can reduce switching friction, price becomes a more decisive factor.

The leverage game

Here’s where it gets interesting from a game theory perspective. Even if Google captures only modest market share, the mere existence of a credible alternative creates leverage for Nvidia’s biggest customers.

Look at what happened after Google’s Anthropic deal became public. Jensen Huang immediately announced a multi-billion dollar investment in Anthropic and secured a commitment from them to use Nvidia GPUs. When OpenAI’s plans to rent Google TPUs leaked, Huang struck a tentative deal to invest up to $100 billion in OpenAI for data center development.

Nvidia is reacting defensively. They’re using their balance sheet to lock in customers. That’s rational behavior when you’re defending a dominant position, but it’s also expensive. And it suggests Nvidia recognizes the competitive threat is real.

From the customer perspective, this dynamic is valuable even if they never fully switch to TPUs.

Having Google as a credible alternative vendor strengthens their negotiating position with Nvidia. It caps how much pricing power Nvidia can exercise. It ensures they have supply optionality if Nvidia faces manufacturing constraints.

What we are watching

The Meta-Google negotiations are the key near-term catalyst. If that deal closes, it validates the entire TPU strategy. Meta is sophisticated. They understand the technical tradeoffs. If they’re willing to commit billions to TPUs for both training and inference, it signals Google has achieved technical parity where it matters.

The longer-term question is whether Google can sustain the engineering investment required to keep TPUs competitive. Nvidia doesn’t stand still. They’re iterating rapidly on new architectures. Google needs to match that pace while simultaneously building the software ecosystem that makes TPU adoption frictionless.

Whether TPUs capture 10%, 15%, or 20% of the accelerator market matters less than the structural change underway. The AI chip market is fragmenting. Monopoly pricing power is eroding. And Google has positioned itself as the primary beneficiary of that transition.

That’s why we are staying long Alphabet. The TPU opportunity alone could drive material upside to Google’s growth trajectory. Combined with their improving AI model performance and entrenched position in search, the risk-reward remains compelling.

Both bitcoin and gold recently touched psychological milestones, $100,000 and $4,000 respectively. Both are now pulling back from all-time highs. And both get marketed as “insurance” against monetary debasement. The comparison is everywhere: bitcoin as “digital gold,” gold as “analog bitcoin.”

But something about this equivalence bothered us. So we spent the past few days thinking through what portfolio insurance actually means in practice.

What insurance really insures against

Portfolio insurance isn’t about hedging normal volatility. It’s about extreme scenarios, the kind where financial infrastructure itself becomes fragile.

The 2008 crisis. The 1970s stagflation. Argentina’s currency collapse. These aren’t hypothetical thought experiments for academic papers. They’re historical events where counterparty risk materialized and payment systems broke down.

Gold has served this insurance function for millennia because of fundamental physical properties: it’s scarce, it doesn’t corrode, it has limited industrial uses, and critically, it exists outside the financial system. In a fiat monetary regime where central banks can expand balance sheets at will, an asset with no counterparty dependency has real value.

The recent surge in gold demand from emerging market central banks illustrates this. They’re not just hedging inflation. They’re seeking alternatives to a US dollar system that increasingly attaches political conditions.

The $28 trillion question

The market cap comparison gets cited constantly: gold at roughly $30 trillion, bitcoin at $2 trillion. The bull case flows naturally from these numbers. Both assets should benefit from rising global wealth and rising demand for monetary insurance. But bitcoin has an additional tailwind, it can capture share from gold’s massive installed market cap.

We get the logic. We hold bitcoin at Arkevium. A meaningful portion of the fund is in it.

But here’s where the comparison breaks down completely.

If bitcoin and gold truly served interchangeable portfolio insurance functions, we’d expect their returns to move together. The data suggests otherwise (chart 1).

The physical/digital divide in extremis

Portfolio insurance is specifically about tail scenarios. The scenarios where everything is going wrong simultaneously.

In those moments, we can buy physical gold coins, store them in a safe, and barter them if the banking system fails. This sounds dramatic (perhaps even paranoid) but insurance is precisely about dramatic scenarios. During the 2008 crisis, demand for physical gold spiked as investors questioned whether “paper” gold certificates would actually be redeemable. The physical premium expanded significantly.

Bitcoin offers no equivalent. We cannot hold it physically. We cannot barter it at a local level if payment rails collapse. Yes, the blockchain continues operating. Yes, we can memorize a seed phrase and cross borders. These are valuable properties, but they’re different properties, serving different use cases.

Our point is that in extremis, we’re not sure any digital asset can fulfill the same role as a physical one. The idea that bitcoin and gold are almost interchangeable as portfolio insurance strikes us as fundamentally wrong.

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This isn't about bitcoin's technology. It's about what happens when digital infrastructure itself becomes unreliable. In a genuine financial system crisis, the kind where electricity grids are unstable, internet access is restricted, or governments actively interfere with digital transactions, a physical asset has properties that no digital asset can replicate.

The position sizing implication

This distinction has direct consequences for how we think about allocations.

If bitcoin truly serves as equivalent portfolio insurance to gold, then the market cap delta suggests enormous upside. A doubling of bitcoin’s share of the combined “monetary insurance” market would imply substantial appreciation.

But if bitcoin serves a different function, perhaps as a speculative monetary alternative or a hedge against financial censorship rather than systemic collapse, then the comparison is meaningless.

They’re not competing for the same role in portfolios.

We’re not dismissing bitcoin. We think it has genuine value. But we’re skeptical it’s a direct substitute for gold in the insurance role. The physical versus digital distinction isn’t technophobia. It’s a fundamental difference in how these assets function when systems break down.

This is why we treat them as complementary positions rather than interchangeable ones. Gold as insurance against systemic financial failure. Bitcoin as insurance against monetary debasement and financial censorship. Related concerns, but not identical.

Maybe we’re wrong. Maybe the next crisis looks different (more cyber than physical, more infrastructure failure than banking collapse). Maybe bitcoin’s digital properties prove more valuable than gold’s physical ones. We’re genuinely open to that possibility.

But for now, the historical pattern is clear: when financial systems fail, physical assets matter. And that distinction affects how we size these positions.