The publication is now organized around three distinct formats, each serving a different purpose and written with a different time horizon in mind.

Perspectives are the ongoing work. Timely observations on markets, macroeconomic conditions, and current events in capital allocation. Shorter in form, closer to the moment.

Letters are longer and meant to last. Thesis-driven writing on investment philosophy, capital allocation, and how I think about markets across cycles. The goal is for a Letter published today to be just as useful to read in five years.

Investment Theses are fundamental research on specific businesses and industries. Deep work on individual companies, competitive positioning, and long-term value.

The reason for the structure is simple: not everything worth saying belongs in the same format. A reaction to this week’s Fed commentary is a different kind of work than a framework for thinking about capital allocation over a decade. The format should reflect that distinction.

The content and voice remain the same. The thinking is ongoing. This is just a cleaner way to organize it.

As always, all notes, letters, and theses are open for discussion. Thoughtful questions and pushback are welcome.

— Michele Chiacchio

Many managers, however, are not good at this. In fact, a large portion of corporate executives are capital destroyers. They invest capital in projects that are negative returning or are unattractive at the margin, meaning they aren’t the highest options compared to the required return on capital of the shareholders.

A key measure to watch how management invests capital is Return on Invested Capital, or ROIC. Here is the formula:

NOPAT is another measure operating income if the company was unlevered (it had no debt). You can get there by taking EBIT (Earning Before Interest and Taxes) * (1-Tax Rate).

Invested Capital is the total capital supplied to the company by debt holders and equity shareholders (i.e. capital invested in the company).

Fundamentally, ROIC answers the question “How much in returns is the company earning per dollar of invested capital?” It is a question management of a company should ask themselves, frequently.

Crucially, the return on invested capital should be higher than the companies cost of capital, or WACC. WACC is the average rate a business pays to finance its assets, representing the minimum return required by lenders and shareholders.

If ROIC > WACC, value is being created. If the returns are is less than the cost of capital, value is being destroyed. This rule of thumb is one measure to determine if a company’s management is effective at capital allocation. It can be another way to determine if a company has a sustainable competitive advantage. However, the high return alone does not tell you that, whether the company is able to generate high returns on invested capital consistently over long periods of time tells you the company must have some form of a defensible moat, since consistent higher returns will drive others to try and get in on the action. If the company can continue to keep market share over new entrants, or new entrants can’t enter, the company has a formidable barrier.

These types of companies typically don’t need great management teams, since the business model is so strong, but they can’t have bad management teams that destroy value. At least, that’s how it should work. Unfortunately, there are a lot of great businesses out there with bad management teams.

This is where incremental return on invested capital becomes the more important measure.

While looking at historical ROIC, it can tell you about where the company has been, but doesn’t tell you where the company is going. In other words, historical ROIC tells you what a business has earned on the capital already deployed. Incremental ROIC, or ROIIC, tells you what the business is earning on each new dollar of capital it puts to work. The distinction matters, and the market frequently misses it.

A company can carry an impressive historical ROIC of 20% or 25% while quietly destroying value on every incremental investment it makes. This happens when management, flush with cash from a strong core business, reaches for growth in adjacent markets, makes acquisitions at inflated prices, or builds capacity into saturating demand. The headline ROIC looks fine for years. The damage only surfaces later, when those bets don’t pay off.

The best capital allocators understand this intuitively. They ask not just “what are we earning?” but “what will we earn on the next dollar we deploy and is that better than returning it to shareholders?” Companies that can consistently reinvest at high incremental returns are rare. When you find one, the compounding effect over a long holding period is extraordinary.

This is what makes a business worth owning forever (or until the company no longer has this advantage). The market tends to price current earnings and near-term growth. It frequently underestimates the duration of a company’s reinvestment runway at attractive incremental returns. That gap between market pricing and the true long-term compounding power of a high-ROIIC business is, in our view, one of the most consistent sources of alpha available to a patient, research-driven investor.

So when you evaluate a company and its management team, the ROIC history is the starting point. The harder question is what they will do with tomorrow’s capital. A strong business model earns you time, but it doesn’t make the capital allocation decisions for you. Hold management to the same standard you would any steward of your money: Are they deploying capital where it earns the most, or where it’s most convenient?

The best managers think like investors. They understand that saying no to a low-return project, even a strategically appealing one, is itself an act of value creation. They treat the hurdle rate as a real constraint, not a formality. And when reinvestment opportunities are scarce, they have the discipline to return capital to shareholders rather than empire-build.

These are not common traits. But when you find a management team that operates this way, sitting inside a business with durable competitive advantages and a long reinvestment runway, you have found something worth holding for a long time.

That is what incremental return on capital is really measuring. Not just a number on a page but the quality of the decision-making behind every dollar the business touches.

— Michele Chiacchio

March 31, 2026

Disclaimer

All publications are provided for informational and educational purposes only and do not constitute financial, investment, or economic advice. Investing involves risk, including the possible loss of principal. The views expressed are solely those of the author and do not necessarily reflect those of any affiliated organizations.

Demand Outlook

In 2021, the US Department of Energy projected world energy consumption would grow by 50% of 2020 levels by 2050. To be fair, 2020 saw a record drop in consumption due to the pandemic but this meant that global energy was going to climb from roughly 560 exajoule (EJ) to a projected ~830-850 EJ. Not necessary to understand how much this is exactly but this will help provide a base for rate of change discussed later.

According to the EIA’s International Energy Outlook 2021:

“Energy consumption continues to rise through 2050 in both OECD and non-OECD countries, largely as a result of increasing GDP and population. As standards of living increase, most notably in non-OECD Asian countries, demand for goods and the energy needed to manufacture those goods increase.”

In 2023 and 2024, the EIA revised the outlook.

It is important to note; there are numerous institutions that exist with wide differences in projections. Their forecasts vary on policy assumptions, fuel mix changes, efficiency gains, and overall methodology. But I want to include the IEA, as I want to be clear the demand forecast is projected to increase significantly regardless of the forecasting model.

According to the International Energy Agency, as of 2024, global energy demand was ~650 EJ and by 2035 it will be 690 EJ and 2050 we’ll reach 710 EJ.

Here is a comparison table:

Either way, global demand for energy is expected to have increased 30-50% from 2020 levels by 2050, and 10-25% from today’s levels.

What is more important is that current estimates are most likely underestimating the increased demand from AI and AI-adjacent technologies. One key ingredient for AI to work, apart from LLMs and intelligent code, is energy. AI is far more energy-intensive than traditional computing, with a single query using 10–50 times more electricity than a standard web search.

It is not just the computation. Data centers require massive cooling systems, networking, and backup power, further increasing demand for energy.

There are a few reasons why this is being miscalculated:

First, most forecasts implicitly assume a linear scaling of compute demand, while recent trends suggest something closer to an exponential path. Training runs for frontier models have historically grown by orders of magnitude over relatively short timeframes, and there is little evidence this trend is slowing. If model capability continues to scale with compute, demand for energy may accelerate faster than currently embedded in baseline scenarios.

Second, estimates often rely on efficiency gains offsetting demand growth. While improvements in chip design (e.g., GPUs, ASICs) and data center optimization are real, history suggests that efficiency gains tend to be reinvested into higher performance rather than reducing total energy consumption, a dynamic similar to Jevons’ paradox1. In practice, lower cost per unit of compute may expand usage rather than constrain it.

Third, projections may understate the breadth of AI adoption. Current assumptions tend to focus on hyperscalers and large centralized data centers. However, as AI becomes embedded across enterprise workflows, consumer applications, and edge devices, aggregate compute demand could expand well beyond current centralized infrastructure forecasts.

Finally, there is a timing mismatch embedded in many projections. Announced data center capacity and power purchase agreements already point to a sharp increase in near-term electricity demand, suggesting that realized consumption could outpace modeled trajectories over the next several years.

Taken together, these factors suggest that even relatively bullish forecasts may represent a floor rather than a ceiling for AI-driven electricity demand.

Supply-Side Constraints

It does not take much data to support a simple point: demand for AI scales far faster than the supply chain required to support it.

This is not about the speed of inference, AI systems can generate responses almost instantaneously, despite the immense compute behind them. The constraint lies elsewhere: in the physical infrastructure required to deliver that compute.

Expanding AI capacity requires a long and complex chain of inputs: semiconductors, data centers, power generation, cooling systems, and grid infrastructure, each with its own bottlenecks. Fabrication plants take years to build and bring online. Data centers require land, permitting, and reliable access to electricity. Power supply, in turn, depends on generation capacity, transmission buildout, and in some cases fuel logistics, whether natural gas, uranium, or oil (see Strait of Hormuz crisis).

This stands in stark contrast to the demand side. Deploying AI is fast, distributed, and capital-light at the margin. A new application can be launched in days, and adoption can scale globally almost immediately. Multiply that dynamic across thousands of firms and millions of users, and demand for compute, and therefore electricity, can accelerate far more quickly than the underlying infrastructure can adjust.

The result is an inherent asymmetry: AI demand compounds on a software timeline, while supply responds on an industrial one.

That mismatch is the core of the constraint.

What This Means

Now of course this relies on a rate of adoption to a new technology that could be argued is not showing up in the data. That is a fair counterargument, Torston Slok at Apollo has shown data suggesting this.

The Nobel Prize–winning economist Robert Solow said in 1987, “You can see the computer age everywhere but in the productivity statistics.” This observation is the so‑called Solow productivity paradox.

The same can be said today: AI is everywhere except in the incoming macroeconomic data.

Today, you don’t see AI in the employment data, productivity data or inflation data. Similarly, for the S&P 493, there are no signs of AI in profit margins or earnings expectations.

Maybe there is a J‑curve effect for AI, where it takes time for AI to show up in the macro data. Maybe not.2

History suggests that technological change is not linear, but accelerative, with each wave of innovation increasing the pace of the next.

“The pace of technological change is much faster now than it has been in the past, according to Our World in Data. It took 2.4 million years for our ancestors to control fire and use it for cooking, but 66 years to go from the first flight to humans landing on the moon.”3

If AI adoption proves slower than expected, then the outlook remains familiar: energy demand rises alongside population and economic growth, and the challenge is simply scaling supply to meet a richer, more energy-intensive world.

But if AI continues on its current trajectory, scaling rapidly and diffusing across the economy, then the constraint shifts. In that world, the limiting factor is no longer innovation or capital, but the speed at which physical energy and infrastructure systems can expand.

Where the Opportunity Lives

If energy is transitioning from a cyclical input to a structural constraint, then the opportunity does not sit in the most obvious places, it sits in the parts of the system that are scarce, slow to scale, and increasingly indispensable.

First, it lives in power generation and firm capacity. As demand becomes more persistent and less elastic, the value of reliable, dispatchable energy increases. Not all megawatts are equal, intermittent supply does not solve for continuous, high-load demand from data centers and industrial usage. Assets that can deliver consistent power, particularly in constrained regions, may see both higher utilization and improved pricing dynamics.

Second, it lives in grid infrastructure. In many markets, the bottleneck is no longer generation but transmission. The ability to move electricity, from where it is produced to where it is needed, has become a binding constraint. Investment in transmission, distribution, and grid modernization is likely to be a necessary condition for any meaningful supply response.

Third, it lives in energy-adjacent infrastructure. Cooling systems, backup power, and data center buildout are not peripheral, they are integral to scaling compute. As AI deployment accelerates, these components move from supporting roles to critical infrastructure.

More broadly, the opportunity lies in recognizing that energy is no longer just a sector, it is becoming a cross-asset driver. Industries and companies with secured access to power may gain a structural advantage, while those exposed to rising and volatile energy costs may face increasing pressure.

The common thread is scarcity. In a system where demand can scale quickly but supply cannot, value accrues to the parts of the chain that are hardest to replicate and slowest to expand.

In this environment, the question is no longer who can build the best AI, but who can power it.

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Published by Michele Chiacchio, CFA

Notes on the Market is a letter on investing, financial markets, and global macroeconomic and business conditions. It reflects ongoing research and investment thinking centered on long-term value creation and disciplined capital allocation.

If you would like to receive future letters, you may subscribe here. Prior editions are available in the archive. Extended research memos and the full archive are available to paid subscribers.

All letters are open for discussion. Comments and questions are welcome. You can leave one below or send me a direct message.

This letter is provided for informational and educational purposes only and does not constitute financial, investment, or economic advice. Investing involves risk, including the possible loss of principal. The views expressed are solely those of the author and do not necessarily reflect those of any affiliated organizations or employers.

That pretty much covers it, but if you’d like to read Part 1 (and take in the beautiful charts I provided) you can do so here:

Okay… now where could the risks be mispriced?

Inflation

I believe the upside risks to inflation are still being miscalculated. There are still a few headwinds (or I suppose tailwinds?):

• A dovish Fed pushing interest rates down too low and overheating the economy which reactivates inflation.

• Tariffs began to take a toll (or Trump follows through with the really impactful ones he threatened but backed away from), causing a cost-push effect to prices by raising production costs.

• The One Big Beautiful Bill tax cuts will put more money in people’s pockets, possibly sparking a demand-pull effect to prices (and the stimulus checks Trump keeps mentioning will only increase this risk).

• The US federal government continuing to run huge deficits coupled with easing monetary policy that strain the economy even further.

Unemployment

Unemployment tends to “rise gradually, then suddenly” according to data from the Richmond Fed1. They didn’t include the Covid recession (for obvious reasons), but the chart below shows a common theme over the last 11 recessions. Of course, this data assumes we see a recession.

Recession Probability

Will we see a recession? after a sharp increase last year after “Liberation Day”, the probability based on forecasts and surveys from Bloomberg have declined to less than 30% chance. (please ignore the dark tint to the graph)

Growth

To be sure, the economy does look good. The US GDP annual growth rate was 2.3% as of 3Q25. Profit growth continues. Consumer spending is strong, growing 2.6%. Business investment is strong? Fixed investment was 2.6% but as I’ve discussed here and here, AI certainly has everyone excited and they’re investing gigantic sums to build it, but we need clarity on the business models.

Risk Assets

Analysts across the street have the S&P 500 moving higher. The Shiller Cape ratio shows US equity valuations are stretched (and it’s not just Big Tech; Costco’s forward P/E is 46x, Walmart’s is 42x, Starbuck’s is 38x and they’re expected earnings growth is 12%, 11%, and -14%, respectively).

Credit spreads are ultra tight, which signals complacency and possible miscalculations of the risks to the economy. If this reverses, it could lead to a feedback loop:

1. Spreads widen, borrowing costs rise

2. Investment and hiring fall

3. Growth slows

4. Defaults increase

5. Spreads widen further

So where does this leave us?

Taken together, this is not an environment screaming “imminent recession,” but it is one where risks appear asymmetrically skewed. Markets are priced for continued growth, benign inflation, and orderly disinflation in rates, all at the same time. That’s a narrow path.

If inflation re-accelerates even modestly, the Fed’s room to cut disappears just as growth momentum slows. If unemployment follows its historical pattern and rises “suddenly,” credit spreads are not remotely prepared for it. And if fiscal dominance continues, large deficits alongside easier monetary policy, longer-duration assets may struggle regardless of near-term growth.

In other words, the base case looks fine, but the tails look fatter than markets are pricing.

That doesn’t mean risk assets will sell off tomorrow. It does mean expected returns are likely lower, volatility is underpriced, and diversification matters more than it has in years. When everything is expensive, “being right” matters less than not being wrong in the same way as everyone else.

The market may well grind higher from here, but if it does, it’s doing so with very little margin for error.

And that’s where I think the real risk lies.

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Disclaimer

The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

By subscribing to and reading this newsletter, you acknowledge and agree to this disclaimer.

There are a couple reasons I like to have an outlook for the year:

(1) My investment philosophy is based on fundamental value investing, but I believe in a hybrid approach to analysis and researching ideas in my investment universe. I primarily make investment decisions based on a company’s true value and its long-term outlook, while looking to pay a reasonable price. I believe in order to understand a company’s long-term outlook and whether you are paying the right price, you must be well researched on the current market environment.

(2) I do make some investment decisions based on tactical opportunities, which are shorter-term special situations for alpha typically driven by temporary dislocations, liquidity conditions, or macroeconomic shifts, as opposed to company-specific long-term value.

(3) Because just like I believe it’s important to set goals personally and professionally, I think it’s important to have a viewpoint based on convictions, while being objectively open and honest with yourself.

*I can guarantee you things will change as the year goes on1. This is more of a lay of the land than an outlook.

Definition: lay of the land

1. the features or characteristics of an area.

   • the current situation or state of affairs.

What happened in 2025

Is it time to start smelting your sterling silverware? Silver and Gold broke records reaching their highest values in history (in dollar terms).

Bitcoin didn’t act like digital gold at all…at least not compared to actual gold.

Emerging markets and European stocks outperformed the S&P 500. Actually, the world2 outperformed the S&P 500 (which still returned 16.4%, its third straight year of double-digit growth).

What about the tariffs?

No seriously, what about them? We made $30 billion, sure, but let’s remember the U.S. treasury had $5.23 trillion in revenues for 2025. So far this year (fiscal year 2026, which started in October 2025), they’ve pulled in $740 billion3. Not to mention China, our largest trade partner, has been unaffected. They’ve redirected most trade to other countries that are happy to fill the gap (and use them as middlemen to sell to the US at lower tariffs rates).

The Macro Regime

Unemployment

Well… it’s not terrible (historically speaking) but as I said, we’re doing trend analysis here and it’s clear the job market is deteriorating. The unemployment rate returned to its pre-COVID level and now rising higher again.

Inflation

We’re still at 2.7% YoY as of November 2025. The Fed targets an average annual 2% and they seem to have paused around their own calculation4 of the natural rate of interest* (r-star). With R-star at 1.1% and inflation at 2.7%, the nominal natural rate is 3.8%. The Fed Funds rate target is currently 3.5% - 3.8%.

*The natural rate of interest (r*) is the theoretical real (inflation-adjusted) interest rate where the economy is at full employment with stable inflation, acting as a neutral point for monetary policy. In English: the healthy level where the economy can operate and grow without the need for rate hikes or rate cuts from the Fed (i.e. it’s balanced).

Another good sign for the inflation trends (but not so much for the bottom 90% in the K-shaped economy) is wage growth is declining to pre-COVID levels.

Fiscal Budget

Investor are starting to demand more in term premium (the yields for longer term bonds) to fund governments, but they haven’t been as pushy in the U.S., yet.

Market Implications

Wall Street’s Forecasts

Everyone on the street seems happy to forecast more boom times in 2026.

What are they most excited about? That beautiful upward trend in billions upon billions (to trillions) in capital spending on AI infrastructure.

Valuations

I won’t beat a dead horse here, but things are expensive.

Where Risk is Mispriced

To be continued… Part 2 coming next week.

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Appendix

A trend I was really surprised to come across, pizza restaurants are on the decline. What is happening to the world…

Disclaimer

The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

By subscribing to and reading this newsletter, you acknowledge and agree to this disclaimer.

Technology companies typically do not have or require many tangible assets on their balance sheets (property, factories, machinery, trucks, etc.) like an industrial or manufacturing business would. Instead, their economics are driven by intangible assets such as code, data, networks, and intellectual property.

This structural difference has traditionally given tech companies better unit economics, faster scalability, and higher returns on capital, all of which justify higher multiples. Historically, technology firms have commanded 20x–40x earnings multiples, whereas manufacturing and other asset-heavy businesses typically trade at 10x–20x. The wide range largely reflects differences in expected growth, investors pay more for companies that can grow faster.

To illustrate how well this asset-light, high-growth model has paid off, the charts below capture it effectively. It also shows how much investors continue to a high premium for these companies.

Big Tech’s Move Toward More Asset-Heavy Models

I’ve discussed previously the significant level of capital expenditures these companies have undertaken and the risks involved. To read more, see here: What If the Massive AI Spend Doesn’t Pay Off?

The figure the clearest way to show how Big Tech’s strategy is shifting is to break down a few of the largest companies one by one. The list below is not exhaustive, but it highlights how major technology firms have evolved into far more capital-intensive businesses in recent years.

Amazon – Warehouses, Logistics & Transportation

This one is a little bit of an older story, as Amazon has been doing this for the last decade, but Amazon, once the poster child for asset-light scalability, have become one of the most capital-intensive companies in the world.

• They’ve built hundreds of fulfillment centers, sortation centers, last-mile delivery stations and expanded their transport fleet: planes (Amazon Air), trucks, vans, and maritime logistics.

• For inside the warehouses, they’ve invested heavily in robotics and automation.

• They have a massive ongoing data center build-out for AWS, one of the largest in the world.

Amazon is now closer to a vertically integrated industrial and infrastructure operator than a software-only company. Its annual capital expenditures rivals the largest global industrial conglomerates.

Microsoft – Data Centers and AI Infrastructure

Microsoft has transitioned from a predominantly software company to a capital-heavy cloud and AI infrastructure powerhouse.

• Microsoft Azure (their cloud business) requires enormous global data center deployments (land, buildings, networking gear, cooling).

• AI acceleration increased demand for GPU clusters, specialized silicon, fiber networking, and power.

• Hardware products (Surface, Xbox) add further manufacturing and inventory complexity, but this is nothing new as they’ve had this business for a while.

The result? Capex has exploded, largely due to cloud and AI infrastructure. Microsoft’s FY 2025 Capex was projected by the company to be $80 billion. Capex now makes up 25% of the companies revenue, more than 10 times what it was a decade ago. Microsoft today looks like a digital utility, with cost structures resembling industrial-scale infrastructure providers.

Alphabet (Google) – Data Centers, Servers, Fiber, and AI Compute

Google was an early adopter of the in-house infrastructure strategy, but the past few years have seen a dramatic acceleration in Capex driven by AI requirements, YouTube scale, and cloud expansion. Some of the major asset-heavy shifts:

• Global data center campuses requiring land acquisition, power, and cooling.

• Investment in subsea and terrestrial fiber networks, sometimes fully owned.

• Custom semiconductors (TPUs), meaning hardware R&D and manufacturing partnerships.

• AI compute clusters built for large-scale training and inference.

Meta – AI Training Infrastructure and the Metaverse Hardware Push

Meta historically ran a very asset-light model (ad delivery, software, social apps). This has changed. What’s changed:

• Significant investments in AI data centers and GPUs. Not to mention the huge compensation packages they’ve been paying out to poach top talent from its tech rivals (they’ve grabbed senior leaders from Apple, OpenAI, Google and others.)

• Massive capex for reality labs, including AR/VR headsets, experimental hardware platforms, sensors, optics, and chip development.

• Scaling global infrastructure for Reels, video, encryption, and WhatsApp messaging.

The company that made $80bn in operating income in 2024, is planning to spend $600bn over the next 3 years making Meta one of the largest Capex spenders in the S&P 500, surpassing many industrial companies.

Apple – Vertical Integration & Infrastructure to Support Services

Apple has always been primarily a hardware company (e.g. Apple computer, iPod, iPhone, iPad, MacBooks, etc.), but their primary change in strategy is their move to be vertically integrated. They primarily outsourced most of the manufacturing to partners, but have been making key changes over the couple of years to bring this manufacturing in-house.

Key asset-heavy moves:

• They moved more chip design in-house (kicking Intel to the curb), requiring massive long-term investments in silicon R&D and physical testing facilities.

• They’ve been building out data center infrastructure for iCloud, Apple TV+, Apple Music, and recently (famously behind everyone else) their AI-related workloads.

Apple’s model has become more capital-intensive as it builds out broader ecosystems and services that require physical infrastructure.

The Valuation Implication

Typically, as a business becomes more asset-heavy, its marginal returns on capital decline. Physical assets require massive, continuous capital expenditures leading to higher depreciation, tighter margins, and the need to reinvest just to maintain capacity. This dilutes overall returns compared with asset-light models, which can scale with far less incremental capital. As a result, valuation multiples naturally compress toward those of industrial, telecom, or infrastructure companies.

However, Big Tech is making these investments because it believes the potential returns from AI leadership will be extraordinary. Whether this is a race to the top or a race to the bottom* remains unclear.

Time will tell whether this historic level of capital spending produces meaningful profits or becomes one of the most expensive capital allocation mistakes in corporate history.

*In brief, the downside scenario (the “race to the bottom”) is as follows:

• Each company is forced to outspend rivals to try and achieve AI leadership,

• Capex escalates beyond what is rational (if it hasn’t already),

• Returns on those investments compress sharply,

• And the industry collectively drives itself into poor economics despite chasing growth

By the way, they’ve also been borrowing heavily to make these investments.

Disclaimer

The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

By subscribing to and reading this newsletter, you acknowledge and agree to this disclaimer.

Wall Street CEOs have all been voicing their concerns about the frothy market, and it’s something I have been thinking about (and positioning my portfolio) for months. Torston Slok, Chief Economist at Apollo, plotted the Warren Buffett indicator for overvaluation (comparing US equity market cap to GDP) next to the Shiller CAPE ratio (which is a Cyclically Adjusted PE ratio averaged and inflation-adjusted over the past 10 years).

As a refresher, I discussed the Shiller CAPE and the sky-high valuations for AI in the following note:

Notes on the Market

What If the Massive AI Spend Doesn’t Pay Off?

Last month, MIT researchers released a study showing that 95% of organizations—out of the 300 they analyzed—receive no return on their investment in AI. This is despite enterprises pouring an estimated $30–40 billion into generative AI. If accurate, the finding is significant: it implies billions of dollars are being misallocated rather than put to prod…

Read more

6 months ago · 2 likes · 1 comment · Michele Chiacchio, CFA

AI valuations are surely a large contributor to the overall market’s extreme valuations, but is it the only froth? The following charts from Current Market Valuation show where we are:

Quick note: The following charts are as of June 30th, 2025 but these are great charts to illustrate where things sit within the historical context. I provide current numbers where available and the story is the same.

Buffett Indicator

Here again, the Buffett Indicator— a ratio that fluctuates over time since the value of the stock market can be volatile, but GDP tends to grow much more predictably— is currently at 217% using market cap and GDP as of 6/30/2025, which is about 68.63% (or ~2.2 standard deviations) above the historical trend line (since 1950), suggesting that the stock market is strongly overvalued relative to GDP.

10-Year P/E Ratio

Today, the 10-year price to earnings ratio (aka CAPE) is 39.7x, over 2 standard deviations away from the historical average since 1950 of 20.5x—this suggests a significant overvaluation on an earnings basis. In the chart below, CAPE was 37.1x as of 6/30/25.

Price to Sales Ratio

Another indicator showing strong overvaluation is the price to sales ratio for the S&P 500. As of June 30th it was at 3.1x, vs its average of 1.8x since 2000. Today, the ratio is 3.3x.

S&P 500 Market Cap Relative to Interest Rates

Today, the 10-year treasury yield is 4.1% (relatively the same since June) and the S&P 500 is 6,735, up 8.5% since June, so the relative value position of the S&P 500 has moved closer to strongly overvalued territory.

Methodology: What this measurement and chart is showing is the composite of the S&P 500 and 10Y Treasury Yields relative position to their long-term trend lines (e.g., the S&P 500 is over 2 standard deviations above its long-term performance average, and the 10Y is 0.5 standard deviations below its average, therefore the S&P 500 is ~1.5 standard deviations above its trend line relative to the 10Y)

Mean Reversion

Statistics teach a basic truth: what goes up eventually comes down. That’s the essence of mean reversion. When the S&P 500 trades well above its long-term trend line, it signals something unsustainable: continued outperformance would require the market to compound at an ever-increasing growth rate. History shows that rarely lasts. Eventually, valuations, earnings, or sentiment normalize, and the index drifts back toward trend. Markets can defy gravity for long stretches, but never indefinitely.

Earnings Yield Gap

Another measure of overvaluation (but caution on this one as there is a lot of nuance) is the earnings yield gap, that is: the gap between earnings yield (the inverse of P/E ratio) and the 10Y Treasury yield. You can look at the earnings yield as an equivalent measure of a bonds interest rate, it’s the rate of return you can expect to earn off your principal (price paid for the stock).

The current gap is -0.81%, meaning stocks are relatively overvalued to bonds but not significantly.

These indicators paint a remarkably consistent picture: valuations across multiple lenses—market cap to GDP, cyclically adjusted earnings, price-to-sales, and relative value to interest rates—are all stretched beyond historical norms. Some metrics are flashing outright extremes, while others, like the earnings yield gap, offer a more nuanced signal. But the broader message is hard to ignore. Markets don’t need an imminent catalyst to correct; they simply need gravity to reassert itself. At these levels, forward returns are mathematically constrained, and the burden of proof shifts squarely onto continued earnings growth and AI-driven optimism. For investors, this isn’t a call for panic, but it is a call for discipline. When valuations sit this far above trend, mean reversion isn’t a risk scenario. It’s a matter of time and magnitude.

Disclaimer

The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

By subscribing to and reading this newsletter, you acknowledge and agree to this disclaimer.

Auto loans serve as a sort of canary in the coal mine for household credit stress. They’re smaller, shorter-term, and more income-sensitive than mortgages, making them one of the first places strain tends to appear when borrowing costs rise. And strain is exactly what we’re seeing: subprime auto delinquencies have surged to their highest levels since the financial crisis, while used-car values, the collateral behind much of this lending, continue to fall.

The result is a slow-motion squeeze across the auto ecosystem. Consumers are increasingly unable to keep up with payments, lenders are facing higher losses, and securitizations are now filled with riskier loans. The pressure is building across balance sheets, and it’s starting to test the resilience of the broader credit cycle.

Auto Lending

To understand how these pressures are building, it’s worth looking at the auto credit market itself.

Total balances of auto loans and leases for new and used vehicles have reached nearly $1.7 trillion in the U.S., a record high. Auto debt has become a meaningful burden on household finances. According to Experian, the average monthly auto loan payment has climbed to $662. With median household income at roughly $83,700 in 2024, that means the average household is spending about 10% of its gross income on car payments, an unusually high share for a depreciating asset.

Delinquencies on subprime auto loans have hit their highest levels since the Great Recession. From the first quarter of 2010 through the first quarter of 2025, delinquencies, defined as loans 60 days or more past due, have surged over 50%. Over the same period, the opposite trend is visible in credit cards, personal loans, and other forms of consumer credit.

If these rising delinquencies turn into a wave of defaults, they could spill over to investors in asset-backed securities (ABS), where auto loans make up a meaningful slice of underlying collateral. TriColor, for instance, was a major originator of subprime loans that were securitized into ABS deals.

To illustrate the current status of the auto loans collateralized in ABS portfolios, I will let the following charts mostly speak for themselves.

Subprime Annualized Net Losses are raising:

Subprime delinquencies are raising (higher than during the GFC):

Subprime recovery rates are falling and diverging from prime recoveries:

Depreciating Collateral

The core problem with auto lending, compared with mortgages, is that the underlying collateral loses value almost immediately. Unless you’re financing Ferraris, most vehicles, the Hondas and Fords, depreciate the second you drive them off the lot.

That creates a vicious cycle when borrowers fall behind. As car values decline faster than loan balances, negative equity builds, making it easier to walk away from the loan altogether.

Take the Ford Escape, a $28,000 vehicle that loses roughly 35% of its value within a year, about $9,000 in depreciation. If financed with a typical car loan, the borrower would still owe around $22,000 after one year, meaning they’re already $3,000 underwater. That kind of negative equity becomes especially painful when budgets tighten

The Macro Picture

The auto industry touches nearly every corner of the economy: consumer credit, industrial production, durable goods spending, and local employment. Rising delinquencies and tightening credit are already squeezing lenders. If the cycle turns here first, it could be an early signal that the broader consumer-driven expansion is running out of road.

I’ve written before about the “K-shaped” economy, where growth looks strong on the surface but is increasingly uneven underneath. The strain in auto lending is another symptom of that divide. It’s the middle- and lower-income households, those most sensitive to borrowing costs, who are feeling the pressure first.

Cracks in auto credit might not mark the start of a full-blown crisis, but they rarely appear in isolation. The auto sector sits at the fault line of the U.S. economy, where household leverage, production, and credit all converge. With stress building here, it could be foreshadowing the end of the easy-money phase of the credit cycle. The question now isn’t whether these cracks exist, but how far they’ll spread as higher rates and tighter liquidity test the durability of the consumer engine that’s kept growth running.

Disclaimer

The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

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“My antenna goes up when things like that happen,” Jamie Dimon, JPMorgan Chase & Co.’s chief executive officer, said on a call with analysts. “I probably shouldn’t say this, but when you see one cockroach, there are probably more. Everyone should be forewarned on this one.”

The bankruptcy filings of two-multibillion-dollar companies in the course of a month is not something to ignore. However, if we take a step back to look into what we know so far about these companies’ operations, it seems like it is pretty idiosyncratic.

First, and most importantly, allegations of fraud have been alleged at both companies. First Brands is apparently missing $2.3B that “simply vanished”, according to one of its largest creditors Raistone, a provider of short-term financing that FB relied heavily on. Tricolor, a company who provided auto financing for customers with no social security number or credit history (with interest rates upwards of 20%), has also been alleged of fraud with the DOJ having opened up a probe and creditors looking into whether the company double-pledged the same collateral.

It is completely fair to question whether these companies should have been given so much credit, and that the lenders should have seen red flags, but it seems the fault lies mostly with the companies’ leadership (also noteworthy is that the founder and CEO of First Brands had previously been accused of fraud, which he settled).

These companies were large borrowers in the asset-backed market. First Brands, which reported $11.6bn in total liabilities, was a big user of trade receivable financing, a type of short-term financing that collateralizes the accounts receivables the company is owed from its customers. If the term sounds familiar, it is a similar form of financing that Greensill Capital was providing*, the firm that blew up in spectacular fashion and was a precursor to the collapse of Credit Suisse in 2023. That is not to say these types of financings are inherently risky, but it’s clear it can be abused.

Quick Note: I won’t go deep into the complexities of this type of structured finance product, but what you should understand about trade receivable financing, or factoring, is given the company is collateralizing its receivables, the credit risk you are facing as a lender is not that of First Brands, but of its customers—which were retailers like Walmart and AutoZone and large automakers like Stellantis and General Motors. Again, the alleged fraud here is what seems to be the primary driver of the company’s downfall.

*Greensill Capital’s core product was actually reverse factoring, a much riskier form of trade finance, where they would essentially settle a clients accounts payables by paying off their supplier early and at a discount, then collect the full invoice from the client later down the road

Some have been quick to blame private credit as the culprit, but some of the largest on the creditor list are banks, or attached to banks—Jefferies’s asset management division, Leucadia is reportedly facing ~$750M in losses from FBG. Other major creditors include Norinchukin Bank, ING, and Katsumi Global who together are owed roughly $1B. When it comes to Tricolor: Fifth Third Bank, JPMorganChase, and Barclays are all facing losses in aggregate of over $200M. To be fair, private credit firms were surely involved and are also facing big losses—Raistone is looking for that $2.3B and below is a sort of private credit proxy list with the BDCs/Interval Funds that had the largest exposures to FBG.

Global Demand for Debt

What does this all mean? Are there systemic risks?

The global demand for debt is strong, with global fixed income outstanding at $145.1T in 2024, and it is clear from spreads tightening, across private credit and public debt, that competition is tough. Banks have shown a willingness to lend in this market just as much as private credit managers. Their balance sheets total $105.8T as of 2024, where private capital AUM is now around 16T.

Demand is strong and there seems to be plenty of supply to meet that demand. However, with increased competition some lenders will play loose in order to deploy capital and lighten up their underwriting standards. This can be seen in both the increase of covenant lite loans outstanding and the use of PIK, specifically “Bad PIK”. To define these terms, Cov-lite loans omit the periodic financial tests (such as leverage or interest coverage ratios) that historically gave lenders an early warning system and bargaining power when a borrower’s performance deteriorated. PIK stands for payment-in-kind, a form of interest payment not made in cash but by issuing additional debt (or increasing the principal balance), effectively compounding the amount owed. These types of payments don’t necessarily mean trouble, they can provide borrowers with flexibility and preserve cash. However, Bad PIK, loosely defined as loans that did not have this option at origination, and are instead given as an option to the company during a restructuring of the loan, presumably when they’ve run into trouble.

All of these factors look as if there are risks in the current debt cycle, but is there widespread contagion? Are there risks to the system? I don’t think so. Especially, when considering that the private credit model is just built better than the banking model. In the typical bank business model, you have an institution using very short-term financing or liabilities (i.e., depositor money) to fund long-term assets, such as 30-year mortgages, 10-year corporate/commercial loans, or some complex illiquid trade that their trading desk might do. Private credit, on the other hand, is funding long-term assets, typically middle market commercial loans, with long-term liabilities from investors who are aware of the risks and the illiquidity of their investments, something most bank depositors are not. The industry is better off because it doesn’t have the brutal duration mismatch that the banking model does. If you were to come up with the idea for a bank today, you could surely do better.

I’m not suggesting there are no risks in the debt markets, but when push comes to shove, private credit managers are likely to fare better than banks (helped in part by the absence of mark-to-market valuations).

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The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

By subscribing to and reading this newsletter, you acknowledge and agree to this disclaimer.

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A K-shaped recovery occurs when, following a recession, different parts of the economy recover at different rates, times, or magnitudes.

The chart below illustrates this pattern in the post–COVID-19 recovery. The reason it’s called K-shaped is that different segments of the economy diverged sharply—one rising while another declined—forming the shape of the letter “K.”

Over time, the term has evolved beyond sectoral performance to describe the widening gap between high earners and low earners. A similar “K-shape” emerges when charting the U.S. wealth divide.

Unfortunately, it’s beginning to look like a K-shape is also forming in the labor market—between large and small firms. That’s concerning because small businesses have long been the backbone of U.S. job creation. According to data from the U.S. Bureau of Labor Statistics (BLS), small businesses accounted for roughly 55% of total net job creation between 2013 and 2023.

However, recent data from ADP shows this job creation engine is starting to stall. In the most recent report, companies with fewer than 50 employees shed about 40,000 jobs, while those with between 50 and 499 employees lost another 20,000.

Meanwhile, consumption continues to be disproportionately driven by wealthier households. According to Moody’s Analytics, consumers in the top 10% of the income distribution accounted for 49.2% of total spending in the second quarter—the highest share since 1989. In contrast, the bottom 80% have seen their spending merely keep pace with inflation since the pandemic.

This means that as long as consumer spending props up the economy, it’s higher-income Americans doing most of the heavy lifting. Last week, I noted how AI-related capital expenditure may be masking underlying weakness in growth; similarly, strong aggregate consumer data could be concealing a more uneven, K-shaped reality beneath the surface. Remember, consumer spending makes up about 70% of U.S. GDP—and if only 10% of Americans are driving nearly half of that spending, it signals a structural weakness in the economy’s growth engine.

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While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

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The study did note, however, that companies purchasing AI tools tended to see more success than those building internally. That suggests the projected $364 billion in spending by AI superscalers this year could generate returns, as more firms opt to outsource instead of building in-house.

Even so, the sheer magnitude of superscaler spending is striking. Capex-to-sales ratios for the largest players now exceed 20%, compared with roughly 5% for the rest of the S&P 500. By some measures, the $155 billion spent in the first half of 2025 outpaced total U.S. consumer spending during the same period. Such levels of investment may be masking the true health of U.S. GDP growth. As I noted last week, stagflation risks are emerging via sticky inflation and rising unemployment. If growth is being propped up by technologies that fail to deliver productivity gains, those risks rise considerably.

To be clear, I am no AI skeptic. The technology is revolutionary. My concerns lie in two areas: (1) companies chasing hype and funding negative-NPV projects—often under faulty assumptions or to inflate share prices—and (2) investors chasing hype and bidding up the stock of any company linked to AI, regardless of fundamentals.

Today, AI infrastructure spending is roughly 1.2% of U.S. GDP. This is not unprecedented: in the 1880s, railroads—then a revolutionary technology—accounted for about 6% of GDP.

AI will almost certainly be the next railroad (and more), linking data and systems in ways we cannot yet fully grasp. Its potential is enormous. But it is precisely this excitement that risks fueling a dot-com–style bubble, one that only pays off after the hype fades and discipline returns—as it did in the aftermath of the 2000 tech bust.

Valuations are high

According to Apollo, forward P/E ratios—a widely used valuation measure—are now higher than they were during the 1990s tech bubble. Valuations are clearly at historic highs. What remains unclear is whether they are justified. That is yet to be seen. GenAI will undoubtedly create vast wealth over the coming decades, but the winners and losers of this race are still to be determined.

The Shiller CAPE ratio—developed by economist Robert Shiller—offers a more reliable gauge of market valuation. Rather than focusing on a single year’s earnings, which can be unusually high or low, it averages inflation-adjusted earnings over the past decade. This helps smooth business cycles and provides a clearer picture of whether stocks are cheap or expensive relative to long-term earnings power. Today, the ratio stands near 38x, approaching the 44x peak reached in January 2000, just before the market crash that began in March of that year.

Ultimately, the story of AI will be written over years, not quarters. The technology is revolutionary, but revolutions in productivity rarely happen on Wall Street’s timeline. For investors and corporate managers alike, the challenge is to separate hype-driven spending from sustainable value creation. If today’s massive capital outlays generate the productivity gains many hope for, the economy could enter a new era of growth. But if they don’t, the risks of inflated valuations, wasted capital, and stagflationary pressures will only grow. Either way, the stakes are big and worth watching closely.

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The content provided in this newsletter is for informational and educational purposes only and does not constitute financial, investment, or economic advice. The views expressed are solely those of the author and do not necessarily reflect the opinions of any affiliated organizations or employers.

While efforts are made to ensure the accuracy of the information presented, no guarantee is given regarding its completeness, reliability, or suitability for any particular purpose. Readers are encouraged to conduct their own research and consult with a qualified financial advisor before making any investment decisions.

Past performance is not indicative of future results. All investments carry risk, and the value of investments may go down as well as up. The author is not liable for any losses or damages arising from the use of this content.

By subscribing to and reading this newsletter, you acknowledge and agree to this disclaimer.

The way I see it, there are three ways to generate a superior risk-adjusted return:

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Stagflation is a situation where the inflation rate is elevated, unemployment is rising, and economic growth is slow or non-existent. In a normal economic cycle, growth typically occurs alongside a healthy labor market, which can lead to inflation. However, this isn’t necessarily a concern, as prices tend to increase naturally with higher productivity—people are earning more and spending more. As long as inflation remains moderate (the Fed targets an average rate of 2%), it’s considered manageable.

In a stagflationary environment, however, prices continue to rise while unemployment increases, and growth stagnates. This combination hurts consumer spending, as more people are out of work and have less purchasing power. Companies face higher input costs amid weaker demand from consumers, which hurts profit margins. Investors tend to pull back, affecting both stocks and bonds—slow growth weighs on equities, while higher inflation erodes fixed income returns.

Inflation Risks

Torsten Slok, Chief Economist at Apollo Global Management, had two great charts this morning that does a great job illustrating a key theme I have been communicating over the past week. That is, disinflation has stalled and there is a real risk it remains that way or we see a reignition.

According to the data from Apollo, almost two thirds of the items used to calculate the CPI inflation rate are rising faster than the Fed’s 2% target.

The PCE data isn’t showing a different story (PCE stands for personal consumption expenditure and is another inflation measurement—one that the Fed prefers to look at)

Unemployment Risks

The unemployment rate in the U.S. is rising; the U-3 rate increased to 4.3% in August 2025. If we zoom out and look at the U-6 unemployment—the U-6 rate differs from the more commonly reported U-3 rate in that it also includes workers that are discouraged and underemployed—the number is naturally larger, at 8.1% and on a similar trajectory as the U-3 rate.

What is more concerning is the rate of increase in long-term unemployment, which jumped to 25.7% of total unemployed in August 2025 from 21.5% in August 2024.

Though these labor market trends are worth monitoring, I would note that the current levels are not historically high.1

Growth Risks

The key here is whether or not unemployment trends are going to lead to a slowdown in growth. At the moment, the Atlanta Fed’s GDPNow cast has been raising over the past few months and is now at an estimated 3.3% for Q3.

There is an important disclaimer to this estimate:

GDPNow is not an official forecast of the Atlanta Fed. Rather, it is best viewed as a running estimate of real GDP growth based on available economic data for the current measured quarter. There are no subjective adjustments made to GDPNow—the estimate is based solely on the mathematical results of the model.2

In conclusion, stagflation risks should not be ignored. Historically, it has proven to be a difficult cycle to break, often requiring aggressive interest rate hikes by the Federal Reserve (monetary tightening) and austerity-like measures—such as reduced government spending and/or increased taxes—to lower deficits and control inflation (fiscal tightening). While this combination doesn't guarantee a recession, it has often led to one. The last time the U.S. experienced a stagflation cycle, in the early 1980s, the resulting downturn was both deep and prolonged, thus making the following chart all the more sobering.

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The majority of FOMC members think inflation will remain above the 2% target through 20271

They also see unemployment rising above the current 4.3% in August 20252

This puts the Fed between a rock and a hard place as they manage their dual mandate—controlling inflation and unemployment

The Fed cut rates by 25bps this week to a range of 4.00-4.25% and according to the dot plot, 9 voting members are forecasting a 3.50-3.75% target range by the end of the year.

The market is expecting the same.

Why did the Fed cut when its own projections point to a prolonged period of higher inflation? It framed the move as a “risk-management cut”—meaning, like any prudent risk manager, it will continue to reassess. The market, however, isn’t pricing in any reversal.

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Whether or not you believe tariffs are inflationary (a debate currently being argued amongst economists), there is evidence to suggest that we haven’t yet seen most of them pass down through price increases to the consumer. However, producers of goods and services have been paying for them.

The Producer Price Index (PPI) is the inflation index for domestic producers. It is the inflation on raw materials, intermediate goods, and finished products at the wholesale level. Think of it this way: if the price of steel increases, car manufacturers must pay more for steel, which in turn increases the cost of the car they make with that steel. Whether or not producers pass on their increase in costs to the consumer is up to them (well, it’s up to Porter’s 5 forces—such as the bargaining powers of the consumer).

The fact of the matter is producers are experiencing higher prices almost across the board and soon they may begin passing those price increases onto consumers (see the first chart).

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Welcome to Notes on the Market

Seeing that this newsletter’s “coming soon” post was published over a year ago, it’s safe to say I’ve wanted to launch this Substack for quite some time. I could blame the delay on a lack of time, but the truth is I wasn’t sure anyone would care to read it. Eventually, I realized that doesn’t matter. I enjoy writing, and I’m always reading about the markets and the economy—so I figure why not put my notes down here and let anyone who’s interested follow along. Welcome again, and please subscribe and share as you please.

1. The economy ain’t so bad

The U.S. GDP growth rate is the highest amongst most developed nations and the highest across G-7 nations. Given the “long-term neutral” or the potential real GDP growth rate for the U.S.—i.e. what the growth of the economy can sustain over the long run, when operating near full capacity and without generating inflation pressures—is estimated by the Fed to be 1.85% (see the following chart) the fact that the U.S. Real GDP growth rate as of 2Q25 is +3.3%, according to the U.S. Bureau of Economic Analysis, the economy seems to be growing well above the long-term sustainable rate. Which leads to my next point….

2. There is a risk of inflation re-emerging

When an economy is overheated—growing faster than its potential— this can lead to inflation. Since one of the main drivers behind the U.S. GDP growth is from consumer spending—its the largest component of the equation and accounts for about 65-70% of total U.S. GDP—which remains supportive, we could see a re-ignition of inflation if the job market doesn't fall off and the Fed cuts interest rates.

Next week I will continue with the risks on re-inflation and how tariffs may or may not be a key driver

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All of my notes shared here are open for review. Please share any comments, questions, or rebuttals.

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