The third quarter brought an end to more than three years of the Federal Reserve holding interest rates steady. On September 16, the Federal Open Market Committee raised its target rate one quarter of a point, to a range of 3.75%-4.00%, the first increase since July 2023.1 Persistently elevated inflation forced policymakers’ hands even as the economy held up reasonably well, and the Fed’s own projections point to a real chance of one more increase before year end. For a market that spent three years underwriting a rate-cutting cycle, the return of a hike risk was an adjustment, and equities wobbled accordingly, giving back a good portion of the July and August gains in September. Despite the rate environment and multiple ongoing conflicts abroad, continued strength in corporate earnings has helped underpin the market this year: S&P 500 companies are on pace for their eighth-consecutive quarter of double-digit, year-over-year earnings growth in the third quarter.2
Another big story this quarter, as it has been for much of the year, is artificial intelligence and the market’s erratic attempts to sort its beneficiaries from its casualties. A company gets labeled an “AI winner” until, seemingly overnight, it becomes an “AI loser,” only to be reprieved weeks later when a single data point suggests otherwise. We have watched narratives flip multiple times within the same quarter, often on nothing more than a sentence from an earnings call. This impulse to sort companies into tidy winner and loser buckets is not new; the market exercised a similar mentality most memorably during the dot-com era. When the market resorts to labeling an entire group of companies this way, it is usually a sign that it is guessing rather than measuring, and for long-term value investors like us, that guesswork is often where opportunity hides.
Guilty by association
What has changed this year is not just the frequency of these swings but their starting assumption. In most environments, a company earns a negative label only after evidence accumulates against it. This year, for a wide swath of the market, the label comes first: enterprise software, healthcare data and analytics, and insurance brokerages have all been presumed guilty of being structurally impaired by AI, before a single dollar of revenue had disappeared. Some of that skepticism is reasonable, but in our view, a presumption of guilt is unfair.
Two examples from our own coverage this quarter illustrate how quickly a verdict can be reached—and how much work a company must do to overturn it. IQVIA, the healthcare data and analytics company, spent much of the year fighting off a narrative that generative AI would erode the value of its proprietary data and consulting work, the same fear investors have attached to much of the life-sciences research industry. The case against IQVIA was built almost entirely on inference: if AI can synthesize data and generate insights, why pay a premium for IQVIA’s? Yet when IQVIA reported second-quarter results, its Analytics & Consulting segment posted its fastest organic growth since 2022, evidence management pointed to directly as proof the AI-disruption fear was overstated, not confirmed. The company, in effect, took the stand in its own defense.
Salesforce faced a version of the same story. As “agentic AI” became this year’s watchword, investors grew anxious that AI agents would eventually shrink the seat-based software model Salesforce is built on. Instead, it is finding a way to get paid for the value it delivers to customers rather than the headcount it occupies. Agentforce and Data Cloud, priced on usage rather than seats, are growing annual recurring revenue in the triple digits, and subscription revenue growth actually accelerated last quarter rather than decelerating. We don’t know whether this trend will persist, and we aren’t claiming Salesforce is destined to be an AI winner any more than we’d claim it’s destined to be a victim. Instead, we are simply trying to evaluate this business through full economic cycles rather than bet on which way market perception will swing next.
Business value often moves more slowly than stock prices
The gap between what a business is worth and what the market is willing to pay is the foundation of our entire investment philosophy. A company’s intrinsic value is a function of many things: its competitive position, the durability of its cash flows, the quality of the people allocating its capital and so on. None of these things typically change in a quarter—or even in a year. What changes constantly is investor sentiment.
Benjamin Graham captured this divergence when he described the market as behaving like a voting machine in the short run, merely tallying popularity and mood, but like a weighing machine over the long run, ultimately reflecting what a business is actually worth. A presumption of guilt is, in effect, a vote cast well before any weighing has taken place. Too often, investors let that vote do their thinking for them, treating a stock’s daily price as a verdict on the enterprise rather than what it is: a constantly shifting, emotional opinion poll. When a business is convicted of being a “loser,” as both IQVIA and Salesforce were at points this year, and the fundamentals have not deteriorated, we see a potential wrongful conviction, and a mispriced opportunity, not a reason to sell.
We’ve been here before
The temptation to assume guilt before the evidence is in is not new. In the late 1990s, investors did precisely this with the internet. Any company associated with the “old economy” was presumed guilty of irrelevance and written off, while nearly anything with a “.com” attached to its name was presumed innocent, regardless of whether it had a viable business model behind it. Some of the speculative names were, in fact, guilty of having no real business at all, and they disappeared. But many of the presumed-guilty “old economy” names were wrongly convicted. The internet did not go away, and it went on to reshape retail, media, and communication far more thoroughly than even its believers predicted.
Few companies illustrate this better than Amazon. In May 1999, Barron’s ran its now-infamous “Amazon.bomb” cover, dismissing the company as little more than “just another middleman” whose stock still had further to fall. It did. A Lehman Brothers analyst piled on the following year, warning that Amazon’s finances looked “weak and deteriorating,” and by 2001 the stock had fallen more than 90% from its highs, trading in the single digits. The market had reached its verdict: guilty, another dot-com casualty that would not survive the unwind. It was wrong. The very spending used as evidence against Amazon—the warehouses, the money burned building out logistics—became the foundation of one of the most dominant retail and cloud-computing franchises in history.3
We suspect something similar is underway with AI. We do not dismiss the possibility that some business models will be meaningfully disrupted, and the market is right to demand answers. But there is a wide gulf between demanding evidence and assuming guilt in its absence, and a wider gulf still between appropriate caution and pricing a company as though it will never recover. History suggests wrongful convictions are more common than the market believes in the moment it hands down judgment.
Where this leaves us
As long-term, bottoms-up value investors, this environment can play directly to our strengths. A market that convicts first and asks questions later creates the dislocation between price and value that we look for: a business wrongly found guilty of being an “AI loser,” trading at a discount, with the evidence for its innocence sitting in its own financial statements. Our job is not to predict with precision which companies will emerge as the ultimate beneficiaries of AI, any more than it was our job to predict the internet winners of the early 2000s. It is to serve as a more patient, more skeptical jury than the market itself, willing to look past this quarter’s verdict, weigh the actual evidence across a full cycle, and act when the market’s judgment and our own diverge. The jury is still out on which companies will ultimately prove to be AI’s beneficiaries or victims, and we expect the verdict to keep shifting before it is finally settled.
As always, we thank you for entrusting us with your investment assets and for your continued support. Lastly, the best compliment we can receive is a referral from a satisfied client. We appreciate your referrals and handle them with the utmost care.
1https://www.federalreserve.gov/newsevents/pressreleases/monetary20260916a.htm
2https://insight.factset.com/sp-500-earnings-season-preview-q3-2026
3https://www.cnbc.com/2021/10/12/jeff-bezos-shared-a-22-year-old-article-predicting-amazons-failure.html
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