From Wikipedia:

The classic example used to describe the paradox is fuel consumption. William Stanley Jevons himself observed the phenomenon from Britain rise during the industrial revolution as steam engines become more efficient. The assumption was that greater efficiency in steam engines use of coal would decrease consumption, however the total amount of coal demand had actually increased instead of declining.

A more modern example: the increased fuel efficiency of cars on the road, i.e. more miles per gallon, has actually increased the total amount of miles a person drives therefore increasing total demand / consumption, on average. Economists call this the rebound effect, and to be precise about the distinction driving did increase, but not by enough to raise total fuel consumption. The efficiency gain was partially offset, not reversed. The full paradox, where consumption actually rises, is the extreme case and in the 160 years since Jevons first wrote about it, it has been rarely observed.

The theory is having a resurgence because it is being used to describe what AI might do to the economy. The argument goes: compute is getting radically cheaper per unit of intelligence, cheap intelligence will find unlimited uses, therefore total demand for compute, and for the energy, chips, land, and grid capacity behind it, explodes. Buy everything upstream of the datacenter.

I think the people making that argument are right about the mechanism and have not noticed what it implies.

The condition nobody prices

For the paradox to occur, three things must be true. (1) There has to be a technological change that increases efficiency, (2) the increased efficiency has to result in a lower price for the good or service, and (3) that lower price has to drastically increase demand.

Conditions two and three are critical in understanding what type of product it is, one where the provider does not have pricing power and which the consumer has very low switching costs. Efficiency gains only pass through to price when the provider cannot hold onto them (the consumer has the bargaining power), a company with pricing power keeps efficiency gains as margin. A company in a commodity market hands it to the customer, otherwise their competitors will (high elasticity of demand). Their product is not differentiated so competition is fierce.

Every time someone invokes Jevons to justify infinite compute demand, they are also asserting, whether they notice or not, that intelligence is a commodity and the model layer has no pricing power. The two claims travel together. You cannot have exploding demand driven by collapsing prices and durable margins at the layer where the prices are collapsing. The same premise that makes the paradox bullish for the inputs makes it bearish for the companies spending trillions to win the market.

If Jevons paradox holds for AI, that is great for the consumer of intelligence, but terrible for the big tech companies spending trillions of dollars to compete in the market of artificial intelligence.

Testing the conditions

The first condition of the phenomenon is overwhelmingly met. The cost of a unit of model output has fallen significantly in the past three years, through better chips, better models, and better inference.

The second condition has been met so far. Efficiency gains have flowed to price rather than margin. Frontier model pricing has fallen relentlessly, open-weight alternatives sit underneath every closed model as a ceiling on what anyone can charge, and switching costs at the model layer remain low. Today, the switching costs for a developer are low. The model layer is behaving like a commodity. A clean example is OpenAI’s recent price cuts to take back market share from Anthropic. That is not the behavior of a company with a competitive advantage.

Condition three is an open question. Consumption has increased as prices fall (though there have been some signs of stagnating demand). But the historical cases where the full paradox held involved goods where demand was effectively bottomless at the right price: mechanical work in an industrializing economy, artificial light, miles of personal mobility. Whether demand for intelligence behaves the same way, whether every drop in the price of a token durably creates more than a proportionate rise in tokens consumed, is not something three years of data can settle. It is the load-bearing assumption, and it is unproven.

What the companies are doing about it

I don’t think the people running these companies are dumb. My argument is not that these companies don’t see what is happening. I think they all want to avoid the current path of destructive pricing wars on tokens so they are building products to break the condition. They want high switching costs, they want pricing power. That is easier said than done, and it could be unavoidable. I am not making any such call, just sharing my perspective on where we are and where this might be going.

What I am watching

First, the variable costs. To avoid the paradox, there needs to be meaningful increases in token margins. Does cost of tokens continue to decline relative to the price of/revenue from tokens or is it the opposite? This is critical, otherwise it’s a commodity. Which, by the way, seems to be the route Apple and Microsoft are taking. I think they see the writing on the wall and are heading towards model agnostic products. They both have a gigantic installed base (the iPhone, Microsoft 365) and they intend to allow all models on their platforms, even the open-weight ones. Microsoft is doing both at once, funding a frontier lab while installing other models into their products. That is a hedge, and it tells you something that the company closest to the spending is the one least willing to bet on it.

Second, the fixed costs. Can pricing power show up before the money runs out? Less of a problem from Google, Microsoft, and Meta, as they have their core profit engines to continue funding the investment, even though trillions in wasted capital will not go over well. Anthropic, OpenAI, and, to a lesser but still significant extent, SpaceX (xAI) will need to both differentiate their product offerings while stabilizing the costs of infrastructure. I am not so sure the capital sources they have had so much success tapping into will run forever.

Third, whether usage keeps growing faster than prices fall. That is condition three, and it is the one I said the whole thing rests on. If that gap ever closes, the paradox was never here, and a lot of capacity got built for demand that does not show up.

I am not making a call on how this ends. But the two halves of the Jevons argument go together. If intelligence gets cheap enough that we use it without thinking about the cost, that is because nobody managed to hold the price. Jevons was writing about the coal, not about the people selling engines.

—

Michele Chiacchio

August 18, 2026

Important Disclosures

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.

The private market has been incredibly good to SpaceX. They likely could have raised the ~$75 billion privately without any issue (they raised roughly $10–15 billion over the last two years alone), so it’s not entirely clear why they chose to go public. The typical explanation is VC pressure—funds need to return capital and push for liquidity—but that doesn’t seem to be the case here. SpaceX’s shareholder base is unusually long-duration, with insiders, sovereigns, and crossover capital that have been willing to roll forward rather than exit. There have also been structured secondary programs offering periodic liquidity to employees and early investors, which reduces the urgency to IPO.

So, if not liquidity, then what? A few possibilities:

• Currency: Public equity can be used for acquisitions, compensation, and broader strategic optionality

• Access to retail demand: Expands the buyer base meaningfully

• Narrative timing: Peak enthusiasm + scarcity = premium valuation

• Benchmark inclusion: Forces passive flows over time

Still, none of those feel strictly necessary. It may simply be that at a certain scale, going public is less about need and more about inevitability, or maybe it’s just fun to IPO.

Anthropic and OpenAI have also set the stage to IPO, and considering how the SpaceX issuance performed, I’d expect both to at least accelerating timelines to capture momentum.

First off, I agree AI will change the world. The productivity gains could be enormous. But that doesn’t mean any price is justified.

To illustrate how stretched valuations are, it helps to go back to basics. A trillion-dollar annuity, discounted at today’s risk-free rate (~4.5%), pays roughly $45 billion per year in perpetuity. That’s guaranteed cash flow. Equities are the opposite, cash flows are uncertain, outcomes are path-dependent, and competition, execution, and leverage matter. So, you start with that $45 billion baseline and then demand a risk premium on top. You should be earning meaningfully more than that to justify owning something with real downside.

Now layer in reality, these companies are not producing stable cash flow, they are currently deeply loss-making, and the capital intensity is massive. (See: The Tech Giants Are No Longer Asset-Light Businesses, and What If the Massive AI Spend Doesn’t Pay Off?)

Yet investors are underwriting outcomes where these businesses become some of the most profitable companies ever created. Last week, SpaceX traded at something like ~2,800x 2028E earnings. That projects a future where revenue scales massively, margins expand, competition is limited, and capital intensity declines, all the while the three companies all are in an intense competition for what is arguably a commoditized product (See: CEO of Microsoft on AI Agents & Quantum | Satya Nadella).

The bigger issue is that they’re losing money today, and not small amounts. Based on leaked and disclosed figures, OpenAI and Anthropic are burning tens of billions. The losses for these three companies were upwards of ~$40 billion in 2025, and capex (compute, infrastructure, energy) is still accelerating.

This isn’t SaaS with negative working capital and 80% margins. This is industrial-scale AI infrastructure, with economics that are still very much unproven. You could argue that costs will collapse with scale, models will monetize better over time, and winners will emerge with dominant positioning, but those are assumptions, not facts.

Separately, I would really like to understand how SpaceX received an investment-grade rating from S&P. Seriously, how?

You have:

• Enormous capex requirements

• Lumpy and long-dated project economics (Starship, Starlink)

• Heavy reinvestment needs

• Limited visibility into sustainable free cash flow

The rating only makes sense if you underwrite:

• Durable, quasi-monopolistic positioning in launch and satellite internet

• Long-term contracted revenue streams from governments and enterprises

• Significant operating leverage as Starlink scales

• A very high tolerance for execution risk

In other words, the rating appears to reflect less what SpaceX is today and more what it is expected to become. That may ultimately prove correct. I understand credit ratings are based on expected debt repayment capacity, not current profitability alone. Still, the decision is striking. Companies such as Amazon, Tesla, and Netflix spent years demonstrating profitability, cash-flow generation, and balance-sheet strength before achieving investment-grade status. SpaceX appears to have crossed that threshold largely on the basis of its market position, strategic importance, and expected future economics rather than a long history of proven free cash flow generation.

Whether that proves prescient or premature remains to be seen.

But it's a useful reminder of where we are in the cycle: we're not valuing these businesses based on what they are today; we're valuing them based on what they could become under highly optimistic assumptions.

The internet changed the world too. It also went through a period where capital was effectively free, expectations overshot reality, and valuations disconnected from fundamentals.

History didn’t say the technology was wrong. It just repriced the timeline.

—
Michele Chiacchio

June 23rd, 2026

Important Disclosures

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.

Investment Summary

We recommend a long position in Tetra Tech, Inc. at $30.19 per share with a weighted 5-year price target of $85.11, representing a total return of 181.9% (23.0% IRR). The stock trades at 14.1x EBITDA versus a 19.4x five-year average, a de-rating driven by misplaced fears around AI disruption of the consulting sector and the USAID contract cancellations. We believe the market prices TTEK as a cyclical government contractor when it is, in fact, a mission-critical advisory platform with durable competitive advantages and structural margin expansion ahead.

• Durable competitive moat in water and environmental engineering. TTEK is the federal government’s institutional knowledge base: its EPA models, datasets, and workflows are structurally embedded across all 10 EPA regions. The company has served the EPA since its founding in 1970 and owns 50 years of proprietary contaminant data on the Great Lakes. When five-year RFPs are issued, there is no credible alternative bidder. State and local water spending and emerging contaminants represent a decade-long structural demand runway the market has not yet priced.

• Core compounder with high-ROIC capital allocation. FY2025 ROIC of 13.4% against a cost of capital consistently below 10%. Over the past decade, ROIC has ranged from 11.4% to 16.1%, consistently exceeding WACC. Incremental margins across FY2021 to FY2025 ranged from 11.4% to 29.6%, reflecting the operating leverage embedded in the business as digital tools scale and the revenue mix shifts toward higher-value work.

• 26 consecutive years of free cash flow generation. Total levered FCF from 2000 through 2025 was $3,882M. FCF grew at a 12.2% 10-year CAGR, driven by rising operating cash flow, a reduction in days sales outstanding from 70 to 57, and lower capex following TTEK’s exit from its construction business. FY2025 FCF was $439M.

• 13 consecutive years of dividend growth. Quarterly cash dividends have grown at a 12% compound rate. Current dividend yield is 0.86% ($0.26 per share annually). With a payout ratio of only 26% and minimal capex requirements, the dividend has substantial room to continue growing.

• Structural secular tailwinds in global water and environmental services. The global water and wastewater treatment market is valued at $348B (2024) and projected to reach $652B by 2034 at a 6.5% CAGR. PFAS regulatory mandates are bipartisan and court validated. International frameworks in the UK and EU mirror the US regulatory trajectory with a 3-to-5-year lag, creating a second wave of demand.

• AI is a margin tailwind, not a displacement risk. TTEK trades at a blanket sector discount applied to all consulting firms. Unlike peers, TTEK sits at the far end of the data-defensibility spectrum. Clients fund its R&D while TTEK retains the IP. Proprietary tools like Volans have reduced work that previously required ten engineers over six months to one engineer over three months. The margin benefit accrues to TTEK.

Variant View: What the Market Is Missing

Read more

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.

Subscribe now

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.

Read more

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

(1) My investment philosophy is ba…

Read more

Technology companies typically do not have or require ma…

Read more

Wall Street CEOs have all been voicing their concerns about the frothy market, and it’s something I have been thinking about (an…

Read more

Read more

“My antenna goes up…

Read more

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 decl…

Read more

Read more

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

1. LBO - take majority control through a buyout (attractive given the large discount and practically zero debt on the company’s balance sheet)

2. Active Value - Buy it low on its cheap market price to fundamentals, however you will need to be active in creating a catalyst to close the Price/Value gap as the board and management are idle.

3. The third option is to buy in at the current discount and wait for another investor to come with a plan for one of the two previous options

Read more

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 health…

Read more

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

Read more

Investment Summary:

Strong Balance Sheet with $82mm in cash and no debt

Long history of paying dividend

• 21 consecutive years of regular quarterly cash dividends and dividend growth (2 years negative growth rate, 2022 and 2011)

• Current dividend yield of 1.97% ($1.22) per year.

Consistent track record of generating free cash flow for 30 consecutive years

• Total FCF less CAPEX from 1996 through 2024 was $346.4 million (average of approximately $11.9 million per year), with no negative cash flow years.

Longtime key supplier to neonatal intensive care units, labor and delivery departments, women’s health centers in hospitals, outpatient clinics, and physicians’ offices

• Diversified revenue sources across a diversified distribution network

Strong secular tailwinds in global neonatal care equipment and services market

Read more

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…

Read more

Subscribe now

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’…

Read more

Read more