LPL Financial ($LPLA) is the largest independent broker-dealer in the United States, supporting more than 32,000 financial advisors and roughly 11 million customer accounts, with about $2.4 trillion in advisory and brokerage assets on its platform at the end of 2025.

It is not an asset manager.

It provides the plumbing to independent advisors to run a modern wealth management practice: technology, clearing, custody, compliance, research, and practice-management support.

The bulk of its profits come from advisory fees, commissions, and interest on client cash.

Its dominant position is built on three main factors: immense switching costs, scale advantages, and barriers to entry.

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Switching Costs

Switching costs are LPL Financial’s single strongest competitive advantage.

LPL has retained roughly 97% of platform assets on an average annual basis over the past decade. This translates into a useful life of client assets around 35 years.

Advisors and their clients, once on the platform, simply do not leave.

Why don’t they leave?

Because leaving is genuinely expensive, on several fronts.

The first cost is client attrition.

Industry data from Cerulli suggests that advisors who change firms lose upwards of 20% of their assets in the transition—clients who don’t want to make another move and accounts that fall through the cracks during repapering.

When your revenue and earnings are based on assets under management, losing clients is a large haircut to earnings and it could take years to replace those lost assets.

The second is the economics of the relationship.

LPL’s average advisor manages about $74 million in assets and generates roughly $470,000 in annual revenue. At LPL’s payout rate of nearly 87%, that advisor keeps around $416,000 before their own overhead. If that same advisor jumped to a wirehouse, where captive payouts cluster near 45%, they’d take home closer to $188,000. The independent model pays more than twice as much and a competitor would struggle to replicate those economics.

The third cost is operational entanglement.

LPL layers on roughly $21,000 per advisor of a la carte services. This includes marketing support, bookkeeping, paraplanning, integrated technology for running a practice. Leaving means an advisor would lose immediate access to this support harming how the business is ran and then they would have to spend time and money finding sourcing replacements from multiple vendors.

Leaving means an advisor would lose immediate access to this support, harming how the business is run, and then they would have to spend time and money sourcing replacements from multiple vendors.

And the only off-ramp that increases payouts is going fully independent as a registered investment advisor. But this means SEC registration and building a compliance and risk infrastructure from scratch. This is a daunting proposition for the typical advisor.

LPL even takes care of the most natural form of attrition, retirement. LPL has a liquidity and succession program that transitions retiring advisors’ books to other advisors and keeps the assets in-house.

All these stacked together create immense switching costs for an advisor.

Scale and Cost Advantage

As the largest independent broker-dealer in the country — after absorbing Commonwealth Financial Network — LPL spreads its fixed costs (technology, clearing infrastructure, back office) across a far larger base than any direct competitor.

Measured the same way a custodian like Schwab would report it, LPL’s expense on client assets runs around 0.20%, well below large banks such as Morgan Stanley (0.33%) and Bank of America (0.41%). Schwab’s is 0.11%. LPL is, in its own words, the lowest-cost solution for independent advisors on the market.

Taking a play book from Costco, LPL chooses to pass much of that scale benefit back to advisors in the form of industry-high payouts rather than keeping it as margin.

This is their flywheel: greater scale lowers the unit cost to serve each advisor, which funds the highest payouts in the business, which attracts and retains advisors, which adds scale.

The result over the past decade is an advisor headcount that has compounded at about 8.7% annually. Industry-wide advisor growth is roughly 0.3%. This means LPL is not only growing with the market but taking market share from competitors.

Passing these savings on through to its advisors instead of maximizing near-term profit, leaves little room for a well-capitalized competitor to undercut LPL. You might have to destroy your own economics to beat LPL on price.

Barriers to Entry

The barriers to entry in this business are high and rising.

There are a lot of regulations in this business. Operating a self-clearing broker-dealer and an RIA at national scale requires registration, net-capital reserves, and a compliance apparatus that is expensive to build and expensive to maintain.

Then there are financial barriers to compete. LPL reinvests north of $465 million a year into its technology platform, smaller independent broker-dealers cannot match this spend without eroding their profitability.

Probably the largest barrier is the economics of its advisor deals.

To lure LPL’s advisors away, a challenger would need to match LPL’s nearly 90% payout and its low cost base and its breadth of services. Smaller independent peers can’t afford the payout; wirehouses won’t cannibalize their captive model; and pure-play custodians like Schwab and Fidelity are aimed at a different customer. Meaningful encroachment on LPL’s core market looks unlikely, which is exactly why the firm keeps compounding share.

Network Effects

It’s tempting to describe LPL’s flywheel as a network effect, and you’ll see it called one, but it isn’t. The distinction matters for assessing the strength of its competitive factors.

A true network effect compounds because each new user makes the platform more valuable to other users. Think of a marketplace or an exchange, where more buyers attract more sellers, which attracts more buyers.

LPL doesn’t work that way. One advisor joining LPL doesn’t make the platform meaningfully more valuable to another advisor; advisors don’t transact with each other.

What LPL has instead is a scale flywheel: more advisors lower the per-advisor cost of running the platform, which funds better economics, which attracts more advisors. The loop has the same self-reinforcing shape as a network effect, but a different engine driving it: cost economics, not cross-user value.

That’s why LPL’s strongest competitive factor is its switching costs plus cost advantages, not network effects.

Putting It Together

Scale produces the cost advantage; the cost advantage funds the payouts and services that build switching costs; switching costs lock in the assets that sustain the scale.

This flywheel is why LPL’s adjusted ROIC has averaged around 20% over the last five years and consistently exceeds its cost of capital.

Also, the independent advisor industry is fragmented and consolidating. Being the lowest-cost platform with scale advantages makes LPL an attractive home for advisors—and an acquirer of other competitors (LPL just bought Commonwealth for $2.7B)—to plug into its flywheel. That gives the company a long runway of growth and reinvestment to strengthen and widen its “moat.”

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It was the best of quarters. It was the worst of quarters.

It was the best of quarters because Assisted Tax, aka TurboTax Live, grew 36%, Intuit/QuickBooks’ mid-market expansion grew nearly 40%, and the money platform grew 30%. Management raised full-year guidance for the second time this fiscal year, bought back a record amount of stock, and raised the dividend 15%.

It was the worst of quarters because CEO Sasan Goodarzi admitted Intuit “lost on price” at the low end of the consumer tax base, total IRS filings shrank for the season, the company announced a 17% reduction in force, and the $12 billion Mailchimp acquisition was demoted to cash management.

The market overwhelmingly sided with the second story.

The stock fell roughly 20% on the print and now sits around $302 per share. Far below its 52-week high of $813.70

Intuit’s fiscal third quarter contained two companies. A quality business solidifying its market position and growing by moving upmarket, and a business under AI-and-pricing pressure at the low end.

I’ve written before that I think the market is overstating the AI disruption risk to Intuit and underweighting AI’s potential to reinforce its position. I added to the position on the way down, bringing it up to a two-thirds weight. Then they released their Q3 2026 earnings and the stock dropped another 20%. Goldman also recently downgraded it to Sell, and Intuit now sits roughly 62% below its 52-week high of $813.70.

So this is a thesis check — a deep dive into the two different companies that exist inside Intuit. It’s also a reexamination of what I wrote in March, and a reverse DCF valuation update.

This is not a trade decision. Not yet, but one will have to be made very soon.

The Best of Times Company

If Intuit were only the company described in the first half of its own press release, the 56% drawdown would be a tremendous gift. Three engines are firing, and all three are the ones I argued AI would strengthen.

Assisted Tax = Freestyle Chess

TurboTax Live revenue grew 36% to roughly $2.8 billion. More than double management’s own long-term 15–20% framing. Customers were up 38% and new customers (excluding one-time offers) were up 29%.

Live is now 53% of total TurboTax revenue, up eleven points year over year, and retention ticked up two points.

Upselling to TurboTax Live has been a margin enhancer and now it is also looking like a strong acquisition channel.

In March I compared this to freestyle chess.

The AI does the calculation, the human owns the strategy and the liability. Goodarzi essentially said the same thing on the call: customers in this segment “buy confidence, not code,” and they spend roughly seven times more on experts than on software alone.

Mid-Market Expansion

Combined online-ecosystem revenue for QuickBooks Online Advanced plus the Intuit Enterprise Suite grew about 38% in the quarter, and IES contracts grew 37% sequentially.

The direct sales force is scaling up 30%, and in August they’ll launch consumption-based pricing for AI agents paired with existing subscriptions. Potentially a new catalyst for FY27.

Recently, a fractional CFO stated that they’ve seen a 200% year-over-year increase in IES adoption, and in some instances reverse migration, where growing clients came back to the Intuit/QuickBook ecosystem from the more expensive ERPs like NetSuite and Sage.

Money Platform

The money platform and consumer cross-sell are scaling too.

Total online payment volume grew 30% (18% excluding bill pay), with bill-pay adoption the standout.

On the consumer side, the cross-monetization is working too.

Customers using both TurboTax and Credit Karma carry roughly 30–35% higher ARPU than single-product users, more than 35% of TurboTax customers now adopt fast-money offerings, and TurboTax filers who start in Credit Karma are guided to grow 54% year over year. Credit Karma itself grew 15%, with the full-year guide raised to +19%.

Increased Capital Returns

Intuit repurchased $1.6 billion of its stock in Q3. That’s more than double from the same quarter a year-ago. Over the last nine-month, buybacks are up over 60% to $3.34 billion. Management also authorized a new $8 billion buyback plan, and raised the dividend 15%.

The Worst of Times Company

Then there is the other company.

Low End Disruption

The most exposed part of TurboTax’s customer base is price-sensitive DIY filers, who typically earn under $50,000 per year.

Earlier, management guided for total paying TurboTax Online units to grow 2%, with ARPU and mix doing most of the work. But total online units, including the pay-nothing base, were revised to −2%. That pay-nothing base fell from 8 million to about 7 million, and TurboTax’s share of e-files declined by a point. The headline +7% TurboTax revenue growth came from price and mix, not units.

The strategic response is to shift from complexity-based SKUs (one price for a W-2, more for a W-2 plus charitable donations) to value-based pricing: let simple filers in cheaply or for free, then monetize them through Credit Karma and the money platform.

The main question is whether this guidance change is due to price (management’s story), AI substitution (part of Goldman’s Sell thesis), or broader macro forces.

On the macro side, total IRS filings are down about 30 bps versus an expected +1%. That equates to about 2 million “missing” filers, mostly manual filers. According to Goodarzi, this is the worst category contraction since the post-COVID season.

Management is adamant it is price and macro, but that is what you would expect them to say. They are not going to come out and admit to AI disruption; it would hammer the stock even more than it already has.

Either way, it means fewer new customer acquisitions that could later be upsold as incomes rise and filings become more complicated. Management can overcome pricing and macro with the right strategy. Low end disruption by AI means these customers are never coming back.

Mailchimp Overpay Confirmed

Back in March I argued the real Mailchimp risk was never AI killing email; it was Intuit overpaying and then aggressively raising prices to justify the $12 billion purchase price, pushing current and future customers away.

Q3 made it official.

Mailchimp revenue is down slightly year over year, and the asset has been openly demoted to cash management.

In Aujla’s words, its cash-flow profile “will generate more value for Intuit than a third party is likely to pay for that asset in the current environment.”

Translation: Intuit can’t sell Mailchimp without taking a big loss so they’ll run it for cash and minimize reinvestment.

17% RIF

Intuit is cutting about 3,000 employees, roughly 17% of the workforce.

Goodarzi was explicit that the cuts are not AI-driven, breaking them into four buckets: fewer management layers, fewer coordination roles, removal of TurboTax/Credit Karma integration duplication, and right-sizing Mailchimp. Maybe that’s it?

But this is the second reduction in two years, and the 2024 framing that Intuit doesn’t “do layoffs to cut costs” didn’t last past 2026.

And Aujla’s clarification that the majority of savings will flow to the bottom line (versus 2024, when savings were reinvested) is management stating that they are now optimizing for cash and EPS rather than reinvestment like they said back in 2024.

Mid Market Ceiling

The same IES that’s winning the low-to-mid market reportedly keeps losing the high-mid. Once a client needs serious multi-entity consolidation and audit readiness, evaluations tip to NetSuite, Sage, or SAP. “Moving upstream” is a big growth lever but upstream growth may have a ceiling.

Reconciling the Two

Does monetizing beyond tax — Credit Karma plus the money platform, a 30–35% ARPU lift — more than offset price compression at the low end?

Can lower share and/or lower ARPU at the low end be more than offset by a higher mix in Assisted Tax?

Intuit currently has only about 10% of the ~$23B assisted tax prep market, and filers in this category generate, on average, 2.6x the ARPU vs DIY.

Intuit could lose at the low end and still grow if it takes assisted share from less technology-forward competitors faster than its low-end base erodes.

Because this is unknowable for several quarters, it increases uncertainty around the name, and market participants do not like uncertainty. They demand a lower multiple to compensate for the wider range of outcomes. Intuit’s current stock price reflects this rerating.

My framework from March still holds up. Who bears the liability when the AI is wrong, what regulatory barriers exist, how real switching costs are, and how proprietary the data is. But the answers have moved unevenly since March.

Credit Karma’s lead-gen model remains the highest structural risk. Even though TurboTax concerns garner all the attention.

TurboTax got worse at the low end and better in Assisted.

Mailchimp is being run for cash and not a top of funnel priority now.

And QuickBooks’ core is intact, and making great inroads up market.

Valuation

I updated the reverse DCF and my Bear, Base, and Bull cases around its new numbers.

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TCI cut Microsoft from 10% of the portfolio to 1% in the first quarter, and it’s probably at 0% by now.

Egerton Capital, a woefully underfollowed fund, fully exited its position too.

Meanwhile, Bill Ackman initiated a brand-new ~$2.1 billion MSFT position.

Three concentrated, high-conviction stock pickers came to opposite conclusions in the same quarter.

TCI and Egerton see Microsoft losing the AI race. Ackman sees a fortress that’s mispriced.

The Bear Case: “AI Introduces Uncertainty Over Microsoft’s Competitive Position”

Chris Hohn was explicit in TCI’s Q1 2026 investor letter:

“We reduced our investment in Microsoft because the rapid progress in AI introduces uncertainty over Microsoft’s competitive position in the future.”

He singled out two specific concerns: the Office productivity franchise, and what he called “some risks in Azure.”

TCI then redeployed the capital from MSFT into Alphabet. Raising it to 5% of the portfolio and making it their largest tech position.

TCI rode Microsoft through a near-400% return over nine years. So when a high-conviction, long-term holder sells the position completely, you have to stop and ask: what do they see? Especially when MSFT is part of your own portfolio.

Productivity

The bear case on Office isn’t that ChatGPT, Claude, or other LLMs replace Word or Excel. It’s that AI-native productivity tools chip away at the Office bundle one workflow at a time. The bundle has been Microsoft’s moat for 25+ years. If AI-native tools fragment the workflow, its not one death blow but a death by a thousand cuts.

Recent data points have been unpleasant.

Microsoft 365 Copilot’s Net Promoter Score landed at -19.8 in early 2026 and it’s market share among paid AI subscribers collapsed from 18.8% to 11.5% in six months. And after more than 18 months on the market, Copilot sits at roughly 20 million paid seats. That’s about 4% penetration of the M365 installed base. Seat additions are accelerating. The most recent quarter was the fastest net-add quarter since launch, but the absolute share losses is showing up in the data.

“Just bolt AI onto the legacy suite” isn’t a winning product strategy. And I think that’s what Hohn believes MSFT’s strategy is right now.

“Some Risks” in Azure

Hohn didn’t elaborate on Azure, but it might be the growth rate and the quality of its CRPO backlog.

Google Cloud grew 63% year-over-year in Q1 and accelerated 16 points sequentially. AWS grew roughly 21% and accelerated 4 points. Azure grew 40% in constant currency and accelerated 1 point. All three hyperscalers are running faster, but Azure is running the slowest right now.

For roughly a year, Azure has been range-bound between 35–39% constant-currency growth—which, don’t get me wrong, is still great. But the narrative was always “Azure is the AI cloud.” If that’s the case, then its growth rate should’ve shown up stronger this quarter when all the other hyperscalers’ growth rates picked up. But it didn’t.

Microsoft’s guidance for the next quarter calls for modest acceleration, supported by capacity coming online roughly six weeks earlier than expected at the Fairwater, Wisconsin facility. So they are still capacity constrained, not demand constrained. But this is the third quarter where MSFT has said “we couldn’t keep up with demand,” and at some point investors stop accepting it.

The quality of MSFT’s backlog could also be a concern.

Microsoft’s Commercial Remaining Performance Obligation is $627 billion, up 99% year-over-year and bigger than Google Cloud’s $462 billion backlog. But size isn’t the same as quality. Roughly 45% of Microsoft’s CRPO is concentrated in OpenAI commitments. Google’s backlog is more broadly distributed across enterprise customers.

The workload mix is probably Microsoft’s most defensible point.

Google Cloud is winning AI training workloads thanks to TPUs and the native Gemini stack. Azure is winning enterprise AI inference layered onto existing M365 and identity infrastructure. This should be stickier with a higher-margin profile.

The growth gap is an interesting short-term data point, but Azure is also growing 40% off a base roughly 2.5x larger than Google Cloud’s. This is a risk to monitor, but not a thesis-breaker, yet. If Azure dips below 35% while GCP stays above 50% for two consecutive quarters, it becomes worrisome.

The Bull Case: The Fortress Is Mispriced

Ackman built his position in MSFT during the Q2 selloff. This continues a recent pattern: Alphabet during its “AI loser” panic, Amazon after Liberation Day, Meta during its capex panic, and now Microsoft on AI skepticism.

MSFT’s valuation is at a multi-year low.

At roughly 21x forward earnings, Microsoft trades in line with the S&P 500. This is below its own three-year average and at the bottom of its NTM EV/EBITDA range.

Buying the most dominant enterprise software franchise in the world at a market multiple has historically been a profitable trade.

The core of Ackman’s thesis is that M365 is a fortress.

Microsoft 365 isn’t a single application; it’s a bundle tied together with Microsoft’s security, compliance, and governance infrastructure. He describes it as “tightly integrated into the daily workflow of nearly every large enterprise.”

An AI startup can replicate Word. It cannot replicate years of embedded enterprise identity, authentication, and data-governance infrastructure. M365 and Office are the industry standard, the common language across businesses. Take Excel: it’s the common denominator across all of finance. LLMs like Claude aren’t trying to replace it; they’re trying to become a valuable tool within it.

The Copilot share-loss data is real, but it has mostly come from individual consumers, not enterprise, which really drives the economics. Claude and Excel work great together, and it’s what Copilot should’ve been from the start. It’s also why MSFT formally designated Anthropic as a sanctioned sub-processor for MSFT’s Online Services in early 2026.

Ackman also believes the market is mispricing MSFT’s OpenAI stake.

MSFT’s ~27% economic interest in OpenAI is worth roughly $200 billion at OpenAI’s most recent funding-round valuation. That’s roughly 7% of Microsoft’s current market cap. The total Microsoft investment across several rounds was roughly $13 billion. The stake is now worth approximately $228 billion. That’s a 17.6x return.

Ackman’s counter to recent worries about capex and cash flow is that MSFT’s current capex is in a classic J-curve.

Hyperscaler capex keeps ratcheting upward and MSFT’s FY26 capex is now expected to be $190 billion. But that’s $190B against $627B of contracted demand and an AI business running at a $37B annual run rate, up 123% year over year. Though frighteningly high, the capex looks like growth investment against visible demand, not speculative overbuild. The thesis breaks if demand declines.

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Back in January Anthropic shipped Claude Cowork.

Within hours, software stocks started bleeding, and by the end of the month, $170 billion in market cap had evaporated.

It was The SaaS-pocalypse.

If an agentic AI can write code, draft emails, summarize documents, and handle structured workflows at near-zero marginal cost, then the application layer is at risk. Anything that was or looked like a “SaaS” company was vulnerable until proven otherwise.

Paychex got dragged in with them.

Down around 40% from its most recent 52-week high.

But Paychex isn’t a SaaS company.

It’s a payroll processor with a PEO business stapled on that has a regulatory moat built in.

The AI-replacement story that wrecked SaaS companies doesn’t map cleanly onto a business whose customers pay it to not get audited.

But there is an AI adjacent element to the bear case and AI disruption is the loudest story in the market and the one getting the most attention even for companies like Paychex.

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The Four Bear Narratives

The bear case against Paychex is composed of four narratives.

AI and Advisory Pricing

Paychex sells payroll at the core, but the margin lives in the HR consulting, compliance guidance, and PEO advisory wrapped around it. That’s where the take rate sits.

The concern is that agentic AI eats away at the HR, compliance, and PEO advisory business.

Routine HR questions like “what’s the I-9 deadline?”, “how do I classify this 1099?”, “what’s the FLSA threshold this year?” are exactly the kind of structured, rule-based queries large language models handle. You don’t need 80% of Paychex’s advisory function to get replaced. 30% of its highest-margin layer being competed away is enough to change the future value of the business.

Even though AI disruption is the most salient risk right now across the market. I don’t see it as an immediate risk to Paychex’s advisory business because of behavioral inertia and major legal barriers.

If an SMB is using Paychex and it solves all their payroll and/or compliance needs, then why switch? Yes, there are cheaper payroll companies, which I’ll talk about below, but then you’ll also need to replace your HR and compliance. It’s a hassle, and unless a small business is really at their breaking point with Paychex, they’re unlikely to get distracted by a new project that doesn’t grow their business.

For the AI disruption narrative, the biggest hurdle is liability.

Paychex’s HR and PEO business is not just selling a service; it’s selling indemnification.

Someone has to sign the 941.

Someone has to be the named defendant when an employee files an EEOC complaint.

Someone has to hold the workers’ comp master policy and carry the ERISA fiduciary duty on the 401(k).

The IRS is not going to accept “the AI told me to” as a defense. AI cannot be sued, cannot be deposed, cannot be an insurance counterparty, cannot owe fiduciary obligations to a plan participant. These are structural properties of being a legal person with assets.

With PEO or co-employment, Paychex becomes the employer of record on the W-2. The IRS certified them as one of fewer than 150 CPEOs in the country, which requires quarterly bonding, audited financials, and confers sole liability for federal employment taxes on the PEO itself.

Sure, an LLM can answer basic questions and handle basic consulting, but it can’t sign the return, post bond, hold the policy, and absorb a lawsuit.

Increased Payroll Competition

Payroll, Paychex’s original business, has been commoditized, and you can argue it is losing ground to newer software-native companies like Rippling, Gusto, and Deel. That is one reason Paychex bought Paycor: to strengthen its position at the upper end of the market. But at the low end, the new-business-formation cohort is increasingly turning to modern, tech-native payroll platforms with better UIs.

The AI disruption narrative is also at play here, with the assumption that SMBs will vibe-code their own system.

Again, this skips over the legal and liability hurdles.

Every state or local jurisdiction has its own filing frequency, deposit schedule, withholding tables, unemployment insurance rate, and rules for sick leave accruals, tip credits, and supplemental wage rates. New York City alone has roughly 40 distinct payroll-related compliance touchpoints. The IRS assesses about $13 billion annually in payroll tax penalties. Roughly 40% of small businesses get hit with at least one payroll-related penalty per year if they self-file.

Paychex’s major selling point is that it will eat the penalty if it messes up. Again, it’s selling SMBs indemnification. A vibe-coded solution opens up the SMB to increased liability.

But a cheaper software native solution that provides the same payroll efficiency and liability protection is a bigger immediate concern.

Management Solutions ARPU growth, the closest available proxy for advisory pricing per client, decelerated from +6.9% in FY23 to +2.3% in FY24, before the Paycor deal closed.

Paychex does not disclose ARPU directly, but it does disclose total revenue and approximate client count. The three proxies break PAYX’s estimated ARPU into three parts: total revenue per client (broadest, includes float), service revenue per client (ex-float), and Management Solutions per client (the closest thing to an advisory ARPU). The acquisition lines flag Lessor, Oasis, and Paycor, where the denominator gets rebased.

Paychex is not dying.

The PEO business is growing.

Payroll tax compliance across 50 states is not getting easier.

But the ARPU deceleration suggests its pricing power is fading.

PAYX’s acquisition strategy is about buying growth and moving upmarket because the organic flywheel is slowing at the lower end. It’s a defensive move to combat the encroachment of software-native competitors.

The Small Business Jobs Index Is Below 100

Paychex publishes its own Small Business Jobs Index. The SBJI baseline is 100.

It printed 99.30 in January 2026 and 98.81 in March. Below 100 means SMB employment is contracting on a relative basis.

Paychex revenue is partly priced per check. Fewer checks per client means less revenue. Yes, AI could increase layoffs at SMBs but the more immediate threat is a general economic contraction.

The SBJI is Paychex’s own data and based on their customers, but this is Paychex publicly warning that small businesses are getting hit by the current administration’s economic policies: tariffs, inflation, and high gas prices crimping the US consumer.

Things could change in the back half of the year, if tariff uncertainty dissipates, oil starts flowing through the Strait of Hormuz again, and inflation subsides enough to warrant further rate cuts.

Interest Rates

Paychex holds client funds between collection from the employer and disbursement to employees. Around $4.5 billion. Paychex gets to earn interest off this float at near-zero cost. It flows right to PAYX’s bottom line.

In FY25, interest was $167 million. In FY26, on management’s guidance, it’s tracking toward $205 million. Roughly 3% of total revenue and more than 3% of operating income.

Float income is rate-sensitive. Every 100 basis points of Fed cuts compresses the yield Paychex earns on those funds by approximately the same amount. Rates fall, float income declines, and earnings decline.

The operating-business EPS and the reported-EPS line have moved together for four years because rates have been a tailwind. But that has changed recently, and if the new Fed chief Warsh can get the board to lower rates further, then that is another earnings drag.

This is cyclical, and Paychex has been through many different rate environments, but a rate-cutting cycle occurring when market participants are pricing in business disruption increases the pressure on PAYX’s stock price.

What’s Priced In?

When I run a basic reverse DCF on Paychex at its current price, with a 9% discount rate and a 2.5% terminal growth rate, the market is pricing in roughly a 3.7% revenue CAGR with flat margins. Or if I keep revenue growth flat, it’s pricing in 650 basis points of margin compression.

Either way, the multiple has rerated to a slow-growing cyclical with float headwind and modest competitive erosion business.

The bears arguing AI is killing Paychex are over-arguing the case. The market doesn’t need that to be true. It only needs small business contraction and falling interest rates to play out, which looks to be the case, to justify the rerating of Paychex.

The bull counter is in the next 6-12 months SBJI inflects above 100 and the Fed pauses cuts, this could cause the multiple to snap back because the regulatory moat is durable. The PEO cross-sell flywheel kicks back in post Paycor acquisition. And now that 3.7% implied growth is too low.

The Real Threat

The SaaSpocalypse and AI disruption narrative is the loudest narrative, and it’s affecting Paychex’s stock price, but it’s not the immediate threat to Paychex’s core business.

It’s increased payroll processing competition on the low end, economic pressure on Paychex’s core small business customers, and declining float income in a rate-cutting cycle.

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How Fast Can Gulf Production Recover After Reopening?

The base case is a few months but the longer the wells stay put in the harder it gets to bring them back online. The worst case right now is a multi quarter process.

FICO Part 3: Update & Framework

The reality behind the wild market moving news surrounding FICO, the GSEs, and the FHFA.

Wealthfront Corporation (WLTH)

A look at the capital light software driven investment management platform. With 10-20% growth, >40% EBITDA margins, and 1/3rd of its market cap in cash and no debt.

SpaceX IPO: Why This Hedge Fund Manager is Fading the Hype | Moez Kassam

Although he’ll not short SpaceX directly, there are plenty of me too space companies to look at and who’s flows may reverse and go to SpaceX when it comes to m market.

Why Saudi Arabia’s $5B LIV Golf Experiment Failed | WSJ What Went Wrong

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In 1887, the German mathematician Carl Gustav Jacob Jacobi gave a piece of advice that Charlie Munger would repeat throughout his life.

Man muss immer umkehren.

Invert. Always invert.

Jacobi was talking about hard math problems that were resistant to the usual direct assault. But when you flip them around and ask a different question, the problem becomes much easier to solve.

Munger took the same idea and applied it to investing.

Don’t ask how to get rich. Ask how to stay poor, and then avoid that.

Don’t ask which stocks will win. Ask which stocks tend to lose, and start by not owning those.

It’s a powerful reframing.

What to Avoid

In 2008 Cooper, Gulen, and Schill published their paper looking into the asset growth anomaly. What they found was firms that grow their total assets aggressively go on to underperform the market by roughly 8 percentage points a year on a value-weight basis and this is sustained over five years.

The anomaly builds on two other anomalies. Pontiff and Woodgate found that net stock issuance predicts for returns. Then Titman, Wei, and Xie showed the same for abnormal increases in capital expenditures (Capex).

Companies that grow their asset base aggressively are usually doing one of three things: issuing equity at a rich multiple to fund growth they can’t fund internally, acquiring assets at poor prices, or increasing Capex into a trend they think is secular but is, in fact, cyclical (or less robust than they thought).

These companies disappoint because overzealous acquisitions get written down, the incremental returns on increased Capex trend toward the cost of capital (producing little excess value for shareholders), or the high-flying stock price used to fund growth re-rates lower, stunting future growth.

Taking Munger’s advice and inverting the investment puzzle, the companies and stocks we want to avoid are the aggressive overinvestors.

The Screen

I started with every name in the S&P 500 and 400 (midcap) and then filtered for companies with a market cap greater than $5 billion at the rebalance date.

I then applied three filters. First, organic asset growth, total assets ex-acquisitions, in the top quintile. Second, external financing as a percentage of prior-period assets in the top quintile. Third, EV/Sales in the top quintile.

The screen produces ~30 names per year. Each name had to be flagged using only data that would have been known at that moment. The basket was equal-weighted, rebalanced annually at year-end, and held for 12 months.

The backtest included names that subsequently delisted, held at last observed price, zeroed out if they went away entirely.

The look back window was the last nine years, from year-end 2016 through year-end 2025.

What actually happened

Over the last nine years, the Aggressive Overinvestors basket grew 2.66x vs 3.50x for SPY.

Annualized, that’s 11.5% for the basket against 14.9% for the market. The basket lagged SPY by about 3.4 percentage points a year.

And this is even during an extremely favorable period for aggressive corporate investing with AI infrastructure, GLP-1 buildouts, defense rearmament, and energy expansion.

And there were some strong years for aggressive overinvestors during this period.

The basket put up two enormous years: +65% in 2020, which encompassed the stay-at-home boom with Roku, Wayfair, Carvana, and Chewy leading the way. Then again at the start of the AI infrastructure boom in 2023-2024.

The basket’s worst year was 2022, when it lost 33% against SPY’s 18% drawdown.

The 2022 basket included Carvana, AppLovin, Wayfair, Roku, Block, Toast, Warby Parker, Qualtrics, Tandem Diabetes, Edgio, and Vicor — twelve names that lost between 48% and 98%.

The Disasters

Carvana, down 98%. AppLovin, down 89%. Wayfair, down 83%. Roku, down 82%. Block, Toast, Warby Parker, Qualtrics — all down between 48% and 80% in a single year. Almost all of them in 2022.

These were among some of the most-discussed, most-recommended, most-owned growth stocks of the prior cycle. They appeared in dozens of newsletter portfolios. They were core holdings of high-flying funds and the narrative for each of them was well articulated

But every single one of them tripped the same set of mechanical filters in the year before they collapsed. Aggressive asset growth, funded by external capital, at a rich revenue multiple.

The screen is not without its drawbacks.

The same screen also flagged Eli Lilly, Broadcom, Palantir, and several other names that performed well through the period. The screen is designed to identify a cohort that underperforms on average. It does not specifically tell you which individual name will collapse.

The goal is to prevent you from owning these dramatic blowups. From permanently impairing your capital.

The goal is to incrementally improve the long-term returns of your portfolio.

But if you’re using this screen for potential shorts, you can’t rely solely on it. It’s a starting point. You will need to dig deeper and create a wider mosaic to try to identify the potential blowups.

Behavioral Checkpoint

I’m not saying this is some binary choice between trying to pick winners or avoiding losers. It’s not a competition because both approaches complement each other.

They can protect you against different mistakes.

Let’s say you think you found your quality compounder. Returns on capital are high, the moat seems real, the management team has a credible plan.

But before you initiate a position you see if it makes the screen for aggressive overinvestors. Is this name in the top quintile of organic asset growth? Is it being funded primarily by external capital? Is it trading at the kind of revenue multiple where the math only works if everything goes right?

If the answer is yes to any of them, you don’t necessarily need to kill the idea. As I said above, it flagged Eli Lilly, Broadcom, and Palantir too. But you need to ask some harder questions and understand why the company is making this screen. Maybe you size the position smaller or have a tighter stop if your thesis is not playing out.

Munger’s advice to invert is essentially a behavioral checkpoint. It asks you to slow down and look at your next great investment a different way because maybe in your excitement about how great this investment is, you overlooked a crucial detail.

Creating a checkpoint slows our thinking down and causes us to engage our more rational thought processes.

Key Lessons

I see three key lessons from Munger’s inversion advice.

The first is that inversion is useful and widely underused. Most investors run elaborate processes to find what to buy with no formal process to identify what to avoid.

The second is that the screen’s real value isn’t as a standalone strategy. Its value resides in being a behavioral checkpoint.

The third is about portfolio management. The point is not to be right about every stock you avoid. It’s about being wrong less often about the stocks you own.

A quality book is a concentrated bet on a small number of companies. The cost of accidentally including a blow-up is enormous. We want to reduce those chances for the long-term health of our portfolio.

What’s in the Basket Today?

The current Aggressive Overinvestors basket is concentrated in three themes: AI infrastructure, GLP-1 manufacturing scale-up, and the post-IRA (Inflation Reduction Act) industrial build-out.

This doesn’t mean all the names in the basket will be losers. Some of them could turn out to be great compounders — especially if their asset growth is in highly productive Capex that generates returns above their cost of capital. But a few of them just might make the list of worst performers.

The full list of names is below for paid subscribers.

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Making the Marlboro Man

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Half of Google’s and Amazon’s Blowout ‘AI profits’ Came From Anthropic—Not Their Core Business

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Google and Amazon’s “AI profits” weren’t mostly from selling more ads or cloud—they were largely paper gains from marking up their Anthropic stakes after new funding rounds.

GLP-1s, Peptides, and The Trillion-Dollar Health Revolution

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AI Can Make Software Now. That Changes Everything, with Paul Ford

Despite the title it’s a much needed middle of the road discussion on AI, vibe coding, and the risks to software jobs.

Why Did Italian Football Fall?

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In 1992, most small businesses in America were still doing their books on paper.

Some had graduated to spreadsheets.

An enterprising few repurposed Quicken, a personal finance tracking software, as a business accounting tool.

They were already using Quicken to better understand their personal cash flow and balance sheet and immediately recognized the benefit of using an accounting program in their businesses. The problem was that none existed, and Quicken became their workaround—even though it wasn’t the best fit for business accounting.

Intuit recognized the opportunity and launched QuickBooks to immediate success. It soon became the platform for small business accounting. Accountants standardized their practice around it, and their clients—small businesses—followed them onto the platform.

Intuit continued to build upon its platform, entrenching its users further. They added payroll, payments, tax solutions, and third-party integrations. Each new addition made the ecosystem more valuable to its users and harder to leave.

Intuit digitized small business accounting and became the industry standard.

Thirty-four years later, I see a similar pattern and opportunity forming in a $13 trillion industry.

Construction is 13% of global GDP and McKinsey ranks it the second-least-digitized industry, behind only agriculture.

Project schedules live in spreadsheets. Requests for information (RFIs) sit unorganized in email inboxes. Budgets get reconciled manually across disconnected point solutions, sometimes weeks after decisions have already been made in the field.

The industry isn’t under-digitized because it doesn’t need software. It’s under-digitized because no one has solved the operating system yet, like Intuit did with Quickbooks.

Until this month’s selection.

The company has spent the better part of two decades making the same bet Intuit made in 1992.

And like Intuit, the core product is just the base to build incremental layers of operational value for its customers, increasing the value of the platform and increasing the costs to switch, while also making it hard for owners, contractors, and general contractors to operate their businesses without it.

The company is still in the early stages of building its competitive moat and becoming the industry standard. This creates a large opportunity, but also increased risks and a wider variance of potential outcomes.

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The Week that Meta Employees Became Training Data

Meta reportedly wants to record employees’ clicks and keystrokes to train AI agents. It’s Taylorism for knowledge work: turn judgment and autonomy into measurable workflow data, then optimize it—and potentially automate the job away.

Pro Poker Player Says New Tax Laws Forced Him Into Semi-Retirement

You always have to account for tax drag.

A 2026 tax change lets gamblers deduct only 90% of losses. For high-volume pros, that can mean owing tax even when breaking even or even losing money.

Constellation Software’s Influences: An Overview of High Performing Conglomerates

Mark Leonard built Constellation by studying “high performing conglomerates.” and their decentralized acquisition discipline, high switching-cost niches, and relentless focus on returns on capital.

Acquired: Ferrari

I always joke “is it a Ferrari?” when someone says they have a surprise for me and listening to deep dive into the history of Ferrari makes me dream of one even more.

How Private Equity Ate Britain

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The Investment Case for Listed Railroads

Actual physical network effects with extremely high barriers to entry for competitors. The UNP & NSC merger should unlock a lot of savings and increase demand for their transcontinental system.

Long Broadridge Financial

Broadridge was not immune to the Saaspocalypse but it brought the stock price of Wall Street monopoly down to a great price.

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“In a fireside chat, Murray Stahl discussed finding value in an increasingly indexed market, cryptocurrency, and unconventional compounders.”

Mitchell Green - Lessons from Cold Calling 10,000 Companies

An interesting angle on VC and growth investing.

2025 Tesla Cybertruck: Regular Car Reviews

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The Charlie Munger 200-week moving average screen is supposed to help find high-quality companies trading at a discount, where short-term sentiment does not reflect the business’s long-term earning power.

But like all value-focused screens, it will also surface value traps, or in this case, quality traps: businesses whose returns look great in the rearview mirror but whose future is slowly eroding.

The name that popped out on a recent run of that screen, and that struck me as a potential quality trap, is Morningstar (MORN) because it is facing two secular changes that should negatively affect its business.

The first has been grinding away for a decade: the migration from active mutual funds to passive ETFs, which cuts at the heart of Morningstar’s original franchise.

On a assets basis it looks gradual but when you look at flows, the secular shift is more pronounced.

Since 2015, long-term mutual funds have had net outflows in most years. Since 2020, those outflows have run in the hundreds of billions annually.

ETFs, meanwhile, have pulled in positive flows every year, hitting a record $1.1T in 2024.

Active mutual funds are a shrinking pool, losing $250–500B a year. The main things propping them up are market appreciation and 401(k) plans.

Morningstar is synonymous with mutual funds.

It famously started in 1984 at Joe Mansueto’s kitchen table to fill a void and provide institutional-quality research on mutual funds. Any business built on the rise of mutual funds should show signs of deterioration as mutual funds decline in popularity.

The second threat is generative AI and its potential to commoditize the kind of packaged research and data that Morningstar sells. That threat is the reason MORN used to trade at 36.6× EBITDA in early 2024 but now sits at ~10.5× today, a multiple last seen during the GFC.

I worked in labs throughout college because I originally planned to become a scientist. That obviously did not happen, but the scientific method is ingrained in my thinking. So, like any good scientific endeavor, the goal here is to try to disprove my hypothesis.

If I can’t, then the Munger screen might have surfaced exactly what it was supposed to: a high-quality company experiencing undue negative short-term sentiment relative to its long-term earning power.

The cleanest way to run this test is segment by segment. Morningstar reports five segments today — Morningstar Direct Platform, PitchBook, Morningstar Credit, Morningstar Wealth, and Morningstar Retirement — plus an Indexes business sitting inside “Corporate and All Other” that just got a lot bigger and will probably become Morningstar’s sixth segment.

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Morningstar Direct Platform

This is the segment people mean when they say “Morningstar.” It includes Morningstar Data, Morningstar Direct, and Advisor Workstation. These are the data feeds and analytical platforms that sit on the desktops of financial advisors and institutional analysts.

At roughly $820M of annual revenue, it is the largest segment. On paper, it is also the most exposed to both threats: the mutual fund database was built for an active-management world, and the GUI-based research workflow is exactly what an LLM should disrupt first.

So is it melting?

The first place to look is seats. Morningstar Direct licensed users are disclosed quarterly in the supplementals.

Direct licenses grew from ~17,421 at the end of 2021 to 18,562 by late 2023. Since then, growth has essentially gone flat. Over the next six quarters, users increased from 18,562 to 18,810.

If AI were going to strangle anything at Morningstar, it would start here, and the flattening licensed-user curve supports that story.

The AI threat is less about churn and existing users leaving to build their own solutions. It is more about slowing new customer acquisition, and the renewal rates support that.

In 2025, Morningstar Direct posted net retention of ~104% and Morningstar Data posted ~101%. Above 100% net retention means existing customers expanded contract value net of any churn.

Institutional data platforms in finance have always been workflow-sticky. Compliance-bound clients are slow to rip out incumbent systems, templates are embedded in downstream reporting, and Advisor Workstation in particular carries FINRA-reviewed audit trails that ChatGPT cannot replicate.

What the chart shows is Morningstar holding on tightly to existing customers while struggling to add new licensed seats. It is not just about AI. Last year was also a tough budget environment, and that showed up across several companies that serve the financial industry.

Right now, it is a decelerating business, not a melting one.

It is also what you would expect from a mature-category leader. Price protection and unit economics remain strong, but the top of the funnel gets harder.

Morningstar ≠ Mutual Funds

The idea that Morningstar is a mutual fund business misunderstands what Morningstar actually sells.

Morningstar’s customer is not the mutual fund investor. It is the financial professional who needs to analyze investments of any structure.

An advisor building a client portfolio in 2026 still has to compare expense ratios, holdings, factor exposures, tracking error, and historical performance. The advisor is agnostic to whether the asset is a mutual fund or an ETF.

It is easy to see why the decline of mutual funds did not coincide with the decline of Morningstar’s core business.

Morningstar Indexes

As we’ll see in the other segments, Morningstar’s management has a track record of recognizing where the business is headed, investing heavily in the right areas, and exiting the wrong ones.

They know ETFs are the future, and they have used their brand recognition to enter the index and ETF business.

So far, it has been a slow rollout. Morningstar Indexes is still reported inside “Corporate and All Other”.

But they just made a major investment with the acquisition of CRSP, and it should get its own reporting segment soon.

Morningstar Indexes revenue compounded from $37M in 2021 to ~$88M in 2025 before the CRSP acquisition. Then, in February 2026, Morningstar closed the $365M acquisition of CRSP, the University of Chicago-affiliated index provider whose US Total Market Index is the benchmark the Vanguard Total Stock Market Index Fund (~$2T AUM) is contractually required to track.

On a pro forma 2026E basis, the Indexes business jumps to roughly $143M, a ~60% revenue bump.

That growth is meaningful, but the acquisition also reshapes the competitive landscape.

Morningstar Indexes went from a subscale also-ran to #4 globally, with $4.2T of assets benchmarked.

Of that $4.2T, roughly $3T+ is already in ETFs, which is a higher ETF share than every other provider in the table except S&P Dow Jones.

The segment is not without risk.

Vanguard is now the single largest client relationship inside Morningstar Indexes, and it is a famously price-sensitive counterparty. Vanguard left S&P for CRSP in 2012 specifically over pricing. The renewed multi-year license is reassuring, but it is not permanent, and future Vanguard renegotiations will be the most-watched events in this segment.

Management knows the future is ETFs, and it needed to become a bigger player in the space. The acquisition of CRSP, and the resulting Vanguard relationship, is a huge win and a smart investment.

Granted, the immediate revenue contribution from Indexes post-CRSP acquisition is still small at 5.7%. But the margin contribution is much higher. MSCI’s index business runs 75% adjusted EBITDA margins, and if Morningstar’s Indexes is similar, that implies about $110M in segment profit and roughly 15% of total EBITDA.

PitchBook

PitchBook is the key segment that determines whether MORN is interesting or not. It’s the private-markets intelligence platform Morningstar bought in 2016.

Revenue grew from ~$207M in 2020 to $672M in 2025 — roughly 3.2× over five years — while adjusted operating margins moved from 26.6%, down to a 2022 investment-cycle trough of 16.0%, and back up to 31.3% in 2025.

Adjusted operating income went from $55M to $210M over the same window, nearly 4×. Even with growth decelerating, the profitability curve is moving in the right direction. Margins are expanding, not compressing, which is the opposite of what you’d expect from a business being commoditized.

The AI disruption narrative has the most superficial appeal with PitchBook because it is the closest thing Morningstar has to a “research portal” product.

But PitchBook’s value isn’t the UI. It’s the database behind it.

Decades of accumulated deal data, fund-level returns, LP commitments, portfolio company financials, and league tables. Private-markets data is not on the open web. An LLM cannot scrape it because it was never posted. It has to be sourced, verified, and maintained the way PitchBook has been doing since the mid-2000s. So when a new AI-native application needs canonical private-markets data, whose data does the AI call?

Management has been active in making sure that call goes to PitchBook. They’ve signed data and distribution partnerships with OpenAI, Anthropic, Microsoft, and most recently, Perplexity.

PitchBook is also rolling out MCP servers and AI APIs for generative summaries and model connectivity.

Morningstar is also trying to turn PitchBook from just a private-markets database into a broader research platform by adding more asset classes and analytical layers.

They’ve added analyst-driven public equity research to the database. It also aggregates third-party research from nine outside partners.

Morningstar bought Leverage Commentary & Data (LCD) from S&P in 2022. This brings leveraged loan and high yield bond data into PitchBook Credit. They also bought Lumonic to improve portfolio monitoring of credit positions.

On top of distribution deals with top AI companies, PitchBook is adding its own AI research layer, PitchBook Navigator.

The problem is that all this investment and expansion has not changed PitchBook’s revenue deceleration.

PitchBook’s organic growth decelerated from 30%+ in 2021 to 8.5% in 2025, and Q4 2025 came in at just 5.9% organic, the lowest quarter in the dataset.

Licensed user count was essentially flat in 2025 at 113,447 versus 113,451 the year before. To be fair, there was a methodology change in how users were counted (internal active users were removed from the count), but it is still a caution sign.

The combined PitchBook platform & direct data renewal rate also ticked down from 108% in 2024 to 103% in 2025.

I see two reasons for this.

The platform expansion is aimed at deepening wallet share with core investor/advisor clients, which pushes out smaller users and increases churn.

The easy upsell to current clients is gone. This is, again, a new user acquisition issue.

Either PitchBook is settling into a normal mature-SaaS growth rate (high single digits), with AI partnerships reaccelerating it as the distribution deals compound, or PitchBook is structurally breaking and heading for sub-5% organic growth as AI alternatives pick at the edges.

The next four quarters are important.

If PitchBook reaccelerates even modestly, the whole MORN thesis gets materially easier. If it slips further, the bear case on the stock gets a lot more credible.

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Morningstar Credit (DBRS)

Morningstar Credit is essentially DBRS, the fourth-largest credit rating agency in the world, plus Morningstar Credit Analytics.

It is one of roughly ten SEC-registered nationally recognized statistical rating organizations (NRSROs), and one of only four credit rating agencies the ECB recognizes as an external credit assessment institution. The other three are S&P, Moody’s, and Fitch.

An LLM cannot assign an NRSRO-recognized credit rating. No issuer is going to structure a CLO around ChatGPT’s or Anthropic’s credit opinion, because the regulatory framework that governs CLO tranching, bank capital treatment, insurance-company investment mandates, and a dozen other downstream uses of credit ratings requires a recognized, licensed rating from an approved body. The cost of entry is enormous: multi-year track record requirements, jurisdictional approvals, and issuer inertia. The list of approved bodies is essentially closed.

And the business itself has just come ripping out of a cyclical trough.

Credit revenue peaked at $271M in 2021, then fell for two straight years as the Fed hiked rates and CMBS / RMBS issuance collapsed.

Then the cycle turned.

This segment grew revenue 35.1% in 2024 and another 27.9% in 2025. It was the fastest-growing Morningstar segment for two years running. The growth was driven by strength in corporate ratings globally and structured finance ratings in the US and Europe.

DBRS has been taking share steadily for years by positioning itself as a credible “diversity alternative” to the big three. Post-2008, issuers have a structural incentive to get a second or third rating from a non-big-three agency for capital-markets-access reasons.

DBRS is the least likely business division to be disrupted by AI.

Morningstar Wealth

Morningstar Wealth was historically a TAMP (turnkey asset management platform) business, offering model portfolios and SMAs for financial advisors.

Full-year 2025 revenue was roughly flat, and adjusted operating margins were in the low single digits.

This business is a melting ice cube.

Not because of AI or the secular shift to ETFs, but because it is a highly competitive industry where Morningstar has no meaningful advantage versus larger platforms like Envestnet.

Management recognizes this and is unwinding the business. Through 2024 and 2025, Morningstar has been sunsetting TAMP assets and booking some gains on sales.

Morningstar Wealth is small (~$255M of revenue, roughly 10% of the company), and eliminating the drag is accretive to consolidated margins. Removing the Wealth drag could add another 100–200 bps to Morningstar’s consolidated adjusted operating margin.

Morningstar Retirement

Morningstar Retirement is the smallest segment, at roughly $125M in revenue, with mid-40s adjusted operating margins and mid-single-digit growth.

Morningstar Retirement sells managed-account and fiduciary services to 401(k) plans. It sits on top of employer-sponsored retirement plans and provides personalized allocation advice to participants. Revenue is asset-based and scales with market levels and participant adoption.

There is not much of a disruption story here.

The business is protected by regulation (managed accounts carry fiduciary responsibility, which consumer AI tools do not), the customer base is sticky (plan sponsors rarely switch recordkeepers because it is operationally painful), and the growth driver is plan-level participant adoption that compounds quietly.

What Expectations are Priced In?

There are no bad assets, only bad prices.

- Howard Marks

The final piece is what expectations the market is pricing in. If expectations are too pessimistic, then Morningstar could be a buy even if some AI disruption occurs.

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Who Is Satoshi Nakamoto? My Quest to Unmask Bitcoin’s Creator

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Critical Review of SpaceX’s Financials

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There Is Not Enough Claude

AI demand will keep increasing as models get more capable and spawn new “spillover” use cases. Like the internet, a general-purpose tech can keep compounding demand for decades. Leading to more compute scarcity.

Michael Mauboussin: Base Rates, AI Adoption, and Investing in the Intangible Economy

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Behind Chick-fil-A’s Unconventional Franchise Model

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“If all you ever did was buy high-quality stocks on the 200-week moving average, you would beat the S&P 500 by a large margin over time.”

- Charlie Munger

Taking Charlie’s advice I ran a screen for companies trading at or below their 200 week moving average.

I also added a Insider Buy column to see which companies had recent insider transactions that could act as a short-term catalyst.

The 22 companies with recent insider buys are listed below.

Some interesting names pop up like Microsoft, American Tower, and Broadridge Financial Solutions.

And then there are some other names that could be potential value/quality traps that I’d might explore in an upcoming issue.

The full screen of 122 companies is available below to paid subscribers.

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The HALO Effect: Heavy Assets, Low Obsolescence

Capital-intensive stocks have outperformed capital-light peers by ~35% since 2025.

AI is compressing software moats while repricing hard-to-replicate physical assets like grids, pipelines, and industrial infrastructure upward.

ServiceNow: Deep Dive Analysis

ServiceNow is the workflow operating system for big enterprises with 98% renewals and strong free cash flow.

It too got caught up in the SaaSpocalypse but AI increases the need for a governed orchestration layer, not less.

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How Nike Lost Its Way

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On January 19, 2026, the New York Stock Exchange announced it is building a blockchain-based platform for trading and settling tokenized versions of U.S. stocks and ETFs.

24/7 trading with instant settlement, funded by stablecoins.

Two months later, NYSE named Securitize — the BlackRock-backed tokenization firm — as its first digital transfer agent, eligible to mint blockchain-native versions of stocks and ETFs on the platform. Three weeks before that, NYSE’s parent company, Intercontinental Exchange, invested roughly $200 million in crypto exchange OKX at a $25 billion valuation, securing a board seat and a deal that will let OKX’s 120 million users trade tokenized NYSE-listed securities.

Last week, ICE completed a $600 million cash investment in prediction market Polymarket, bringing its total commitment to $1.6 billion as part of a broader $2 billion arrangement.

What’s happening here is a coordinated infrastructure play by the operator of the world’s most iconic stock exchange to rebuild market plumbing for the next era of capital markets.

For investors evaluating ICE as a long-term holding, this matters, not because tokenization will drive earnings next quarter, but because it represents asymmetric optionality that the market is currently pricing at zero.

What Is Tokenization & Why Should Equity Investors Care?

Tokenization is the process of representing ownership of a financial asset — a share of Apple stock, a Treasury bond, a unit in an ETF — as a digital token on a blockchain. The underlying asset doesn’t change. What changes is the infrastructure through which it’s recorded, traded, and settled.

Currently, when you buy a share of stock through your brokerage, the trade doesn’t truly settle for one business day (T+1). During that gap, a chain of intermediaries — clearing houses, depositories, custodians, transfer agents — reconcile records, move money, and update ownership.

Tokenized securities compress that entire process into near-instant, on-chain settlement. And they unlock capabilities the current system simply can’t offer:

24/7 trading. Global investors don’t stop wanting to trade U.S. equities at 4:00 PM Eastern. A tokenized market can operate continuously, capturing demand from every time zone.

Instant settlement. T+0 eliminates counterparty risk during the settlement window and frees up capital currently locked in the clearing process.

Fractional shares by design. Orders are sized in dollar amounts, not share quantities. A $50 investment in Berkshire Hathaway becomes trivially easy.

Stablecoin-based funding. Programmable digital cash enables settlement without dependence on traditional banking hours or wire transfer infrastructure.

The question isn’t if this will happen. It’s coming. The questions are how big will this be and, more importantly, who builds the infrastructure and gets to collect the tolls.

ICE’s Multi-Front Strategy

What distinguishes ICE’s approach from crypto-native tokenization experiments is that it’s working from a position with a structural advantage.

ICE already operates the matching engines, clearing houses, data networks, and regulatory relationships that underpin major global capital markets. It’s not building from scratch. It’s extending existing infrastructure onto new rails.

The strategy has four interlocking pieces:

1. The NYSE Digital Trading Platform

The core product. NYSE is building a new venue that supports trading of tokenized shares fungible with traditionally issued securities, as well as tokens natively issued as digital securities. It combines the Pillar matching engine — the same technology that handles billions in daily equity volume — with blockchain-based post-trade settlement across multiple chains.

Critically, tokenized shares retain full shareholder rights. This is not a synthetic derivative or a crypto wrapper. It’s a genuine share of stock, represented on-chain. That’s a fundamental distinction from every tokenized equity product currently available on offshore platforms.

ICE is also working with BNY and Citi to support tokenized deposits across its clearing houses, enabling margin obligations and funding transfers outside traditional banking hours. Launch is targeted for late 2026, pending SEC and FINRA approval.

2. Securitize: The Minting Partner

On March 24, NYSE signed an MOU with Securitize, naming it the first digital transfer agent eligible to create blockchain-native securities on the platform. Securitize — backed by BlackRock and Ark Invest, and SEC-registered — handled the issuance infrastructure for BlackRock’s tokenized fund (BUIDL). Its broker-dealer arm could participate in trading, giving it a role across both issuance and market activity.

This is where NYSE’s approach diverges from Nasdaq’s. Nasdaq recently received approval for tokenized trading, but plans to process it through traditional clearing infrastructure. NYSE is building a separate blockchain-native venue.

3. OKX: The Distribution Channel

ICE invested roughly $200 million in OKX at a $25 billion valuation, took a board seat, and established a partnership with two commercial dimensions.

First, ICE will license OKX’s spot crypto prices to launch regulated crypto futures which is a direct revenue opportunity for ICE’s exchange segment.

Second, and more strategically, OKX will offer its 120 million global users access to ICE’s U.S. futures and tokenized NYSE equities.

This solves ICE’s single biggest gap. The NYSE is the most iconic exchange brand in the world, but it has no direct distribution channel to international retail or crypto-native investors. OKX provides that channel overnight. If tokenized equities take off, ICE now has the matching engine, the regulatory framework, and the global retail distribution to capture it.

4. Polymarket: The Data Play

ICE’s $2 billion commitment to Polymarket makes it the global distributor of prediction market data to institutional investors.

In February, ICE launched the Polymarket Signals and Sentiment tool, normalizing real-time prediction market probabilities into structured data feeds.

This fits the broader strategy in two ways. First, prediction market data is a genuinely new data asset unavailable through conventional sources. Second, Polymarket runs on blockchain smart contracts, making it a natural adjacency for ICE’s on-chain infrastructure ambitions. ICE and Polymarket have explicitly agreed to collaborate on future tokenization initiatives.

What This Means for ICE Shareholders

Read more

In this episode, I break down each of Intuit’s four major business lines — Credit Karma, Mailchimp, TurboTax, and QuickBooks Online — and stress-test them against the AI disruption thesis. For each segment, I walk through the bear case (where AI agents pose a genuine threat) and the bull case (where Intuit’s data moats, switching costs, and platform strategy create durable advantages).

We cover Credit Karma’s conflict of interest and its new OpenAI partnership, why vibe-coding your own ESP is harder than it looks, TurboTax’s underrated structural advantages (including guarantees no LLM will match), and how QuickBooks could evolve from accounting software into a virtual business team powered by AI agents.

If you own Intuit, are thinking about buying it, or just want a framework for evaluating AI disruption risk in quality businesses — this one’s for you.

📖 Read the full article with valuation analysis and portfolio action plan: https://www.incrementalreturns.co/p/intuit-vs-ai-agents-threats-growth-engines

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