Maintaining conviction through disruption | 23 Jun 2026 | Center for Investment Excellence

Nuggets

Maintaining conviction through disruption | 23 Jun 2026 | Center for Investment Excellence | J.P. Morgan Asset Management · Nuggets

Architecture of US Core Equity Research [00:01:07 - 00:03:02]
- The Core Argument: Institutional outperformance relies on a highly specialized organizational design that decouples foundational research from the immediate trading pressures of portfolio management.
- Mechanics & Nuance: By ensuring the research analyst position is a career destination rather than a transitional stepping stone, the platform retains deep institutional knowledge. Analysts focus heavily on forecasting a clear 5-year window of earnings power rather than near-term quarterly cycles. Mispricings are systematically targeted via a standardized, time-tested 5-year expected return evaluation framework.
- Anecdotes & Evidence: This specific valuation architecture has been continuously utilized across the platform for close to four decades [00:02:33].
Differentiating Between Being "Early" vs. "Wrong" [00:03:02 - 00:04:02]
- The Core Argument: Active managers taking an extended 18-to-36-month investment horizon must maintain an explicit, written tracking framework to dynamically determine whether a lagging position is a timing issue or a broken core thesis.
- Mechanics & Nuance: To prevent cognitive bias and thesis drift, analysts must explicitly document their investment thesis and detail the precise milestones or metrics required to keep the trade on track. When high-frequency market information or disruptive data enters the cycle, analysts must re-calculate their probabilistic earnings forecasts rather than dogmatically digging into their original views.
Underwriting the AI Disrupted ("AI Losers") [00:05:02 - 00:08:55]
- The Core Argument: The equity market behaves indiscriminately during major tech-inflection cycles, derating strong legacy compounders based on perceived, rather than verified, disruption risk.
- Mechanics & Nuance: Sectors like business services, wealth management, and insurance brokers are often labeled as "AI losers" under the assumption that automated solutions will seamlessly replace human intermediaries. However, the market frequently underappreciates three core barriers: extensive proprietary data monopolies used for specialized pricing optimization, sticky client distribution footprints, and deep transactional complexity in post-sale processes like claims management.
- Anecdotes & Evidence: The research team conducted primary boots-on-the-ground due diligence on insurance brokers like Aon and Arthur Gallagher. By attending specialized insurtech conferences and directly interviewing both technology disruptors and incumbent CEOs, the analyst confirmed that underlying data moats remained entirely intact [00:05:56]. In less certain cases, the team systematically adjusted down their terminal values and probability-weighted their long-term forecasts [00:08:22].
Stress-Testing the AI Enablers ("AI Winners") [00:08:55 - 00:11:34]
- The Core Argument: Capturing sustainable upside in high-momentum secular themes requires active managers to aggressively adjust numbers upward when empirical data changes, while checking market euphoria against real-world physical bottlenecks.
- Mechanics & Nuance: Initial consensus models severely underestimated total AI infrastructure capital expenditure because they incorrectly capped spending assessments within historical hyperscaler free cash flow parameters. To accurately underwrite these parabolic trajectories, analysts must break down traditional sector silos. For instance, tech spending models cannot be fully evaluated without directly integrating industrial and utility analytics regarding power availability.
- Anecdotes & Evidence: The team's utilities analyst collaborated directly with the industrials and technology teams to thoroughly map out power grid bottlenecks, calculating exactly how much physical power can come online to realistically sustain projected AI data center expansions [00:10:43].
Historical Parallels of Structural Disruption [00:11:34 - 00:15:55]
- The Core Argument: The current velocity of AI anxiety mimics previous technological transitions, which ultimately resulted in bifurcated industry outcomes rather than total sector obliteration.
- Mechanics & Nuance: High-momentum disruptions follow a predictable pattern: the market initially assumes an incoming digital giant will entirely eliminate legacy physical operators. Over a multi-year horizon, structural winners and losers emerge. Total failures occur primarily among low-scale operators who refuse to pivot, while massive incumbents with superior scale and distribution successfully adapt via omnichannel or integrated models.
- Anecdotes & Evidence: During the initial rise of Amazon and e-commerce, the market quickly derated nearly all brick-and-mortar retailers. While players like Bed Bath & Beyond and traditional department stores faced severe structural declines, scale-advantaged retail operators successfully adapted to an omni-channel environment [00:12:15]. A similar pattern occurred historically when commodities analysts had to thoroughly evaluate B2B e-commerce impacts on global energy companies [00:14:39].

Maintaining conviction through disruption | 23 Jun 2026 | Center for Investment Excellence | J.P. Morgan Asset Management

1. Executive Briefing (TL;DR)

References

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Maintaining conviction through disruption | 23 Jun 2026 | Center for Investment Excellence | J.P. Morgan Asset Management

1. Executive Briefing (TL;DR)

References

More nuggets

Robert Kaplan Exclusive | India's Growing Economic Potential Amid Global Challenges

BofA: The Consumer’s Summer Spending Surge | 15 Jul 2026

Must Read Research: SpaceX; The Next AI Winners; Europe’s Earnings Momentum; Buy Value | 14 Jul 2026

2. Speaker Profiles & Context

3. Thematic Deep Dives

Architecture of US Core Equity Research [00:01:07 - 00:03:02]

Differentiating Between Being "Early" vs. "Wrong" [00:03:02 - 00:04:02]

Underwriting the AI Disrupted ("AI Losers") [00:05:02 - 00:08:55]

Stress-Testing the AI Enablers ("AI Winners") [00:08:55 - 00:11:34]

Historical Parallels of Structural Disruption [00:11:34 - 00:15:55]

4. Forward Looking Indicators & Risks

5. Data & Macro Matrix

Capital Group: 2026 Midyear Outlook | 16 July 2026