This is an interview about J.P. Morgan Asset Management’s core equity research process and how it handles disruption, especially AI. The guest argues that a long-tenured, research-heavy, five-year valuation framework helps the team stay disciplined, update quickly when facts change, and separate real disruption risk from market overreaction.
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The conversation centers on how J.P. Morgan Asset Management’s U.S. Core Equity team maintains conviction when markets are being reshaped by AI and other disruptive forces. Danielle Hines says the team’s edge starts with structure: senior research analysts are career analysts, not future portfolio managers, and on average have 25 years of experience covering their sectors. That depth, she argues, helps them navigate periods of structural change because they have seen multiple cycles, built industry relationships, and can think beyond quarter-to-quarter noise. A second pillar of the process is long-term valuation discipline. Hines explains that the analysts forecast earnings five years out, and often think beyond that, using a five-year expected return framework that has been in place for close to four decades. …
Tactically, the market is still vulnerable to broad-brush AI winner/loser trades, so the immediate setup is differentiation rather than blanket exposure. The risk is paying too much for consensus enablers or selling protected incumbents too aggressively before company-specific evidence is clear.
Over the next few quarters, the more likely path is a widening gap between true beneficiaries of AI spend and names the market has over-discounted as disrupted. The view holds if earnings estimates keep getting revised with real evidence rather than narrative alone; it weakens if disruption proves faster and more universal than company moats can absorb.
Structurally, the transcript argues that AI is a regime shift in industry structure, but not a uniform one. The lasting implication is that long-horizon active research still has an edge when it can pair human judgment with better tools and disciplined valuation.
Five years from now, some companies that exist today will not survive AI disruption, but many incumbents will adapt and innovate using their distribution and proprietary data — similar to what played out with e-commerce and brick-and-mortar retailers.
The speaker draws a historical analogy to e-commerce disruption: some retailers went away (Bed Bath & Beyond), but others adapted to omnichannel and succeeded.
The market underestimated the amount of AI spend versus a year ago, and has been framing it too narrowly in terms of hyperscaler free cash flow.
The speaker notes that they and the market have had to adapt upward their estimates of AI capital spending as reality exceeded prior expectations.
The market has been indiscriminate in derating perceived AI losers across many sectors, but this derating is overdone for insurance brokers like Aon and Arthur Gallagher.
The speaker argues the market is wrong to lump insurance brokers into AI-loser bucket because they have proprietary data, client relationships, and complex claims processes that AI cannot easily replicate.
How does the equity platform know when you're early versus when you're wrong, given that you're taking a five-year view in a rapidly changing world?
Danielle says the key is understanding the difference between being early and being wrong. Their Research Analysts write down their investment thesis and the specific things they're looking for to tell whether they're on track. In periods with new information coming every day, they roll up their sleeves, evaluate whether their earnings forecasts are the right probabilistic forecasts based on what they now know, and adjust if needed.
How do you think about assessing AI disruptors and practically get that disruption risk into the numbers?
Danielle uses insurance brokers (Aon and Arthur Gallagher) as an example. The market put them in the perceived AI loser bucket, but Danielle's team did extensive primary research: meeting with disruptors at the Insurtech conference, meeting with the brokers to see their technology platforms, meeting with consulting companies, and demoing platforms. They concluded the market was missing the brokers' proprietary data, distribution/client relationships, and claims complexity. For names where confidence varies, they probability-adjust their forecasts and terminal values.
How does the framework stress test whether the AI enabler stories are underwriting something real with durable earnings power versus a cycle that fades, especially with euphoric consensus around the idea?
Danielle says the process is the same on both sides — it applies to disruptors and enablers alike. They cannot be afraid to adapt to new information and change numbers when facts change. She notes that even versus a year ago, they underestimated AI spend. They also leverage cross-sector collaboration: their utilities analyst works with industrials and technology teams to understand bottlenecks, power capacity, and what it means for enablers.
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