Key Takeaways
- The asset‑management industry has built a highly effective system for producing consensus, rooted in shared educational backgrounds, the CFA curriculum, and uniform analytical tools.
- This consensus‑forming mechanism creates intellectual herding, where managers arrive at similar portfolios despite describing their strategies differently.
- Artificial intelligence is amplifying the tendency toward uniformity because many firms rely on the same off‑the‑shelf models and overlapping data sets.
- Mercer’s 2026 survey shows that 55 % of asset managers already use AI in at least one investment process, with 91 % planning to expand its use, while 63 % employ off‑the‑shelf tools and 58 % use vendor‑provided data.
- The paradox is that AI, which promises to democratize intelligence, may actually reduce differentiation if everyone feeds similar questions into similar models.
- To preserve a competitive edge, firms must deliberately cultivate intellectual diversity—hiring people with varied training, encouraging independent observation, and designing research processes that probe second‑ and third‑order consequences rather than merely identifying what works today.
- The future advantage in asset management will lie not in who uses AI most aggressively, but in who uses AI to broaden the range of questions while retaining independent human judgment capable of spotting overlooked trends.
The Roots of Consensus in Asset Management
The article begins by observing a striking pattern: despite different firms, brands, and investment philosophies, portfolio constructions often look remarkably similar. This is not a coincidence but the outcome of a system that has become extraordinarily effective at producing consensus. The foundation of that system lies in the talent pipeline. As the piece notes, “Many investment professionals come from the same relatively concentrated group of undergraduate institutions and then attend the same leading MBA programs.” After school, they undergo essentially identical professional training through the globally recognized CFA curriculum and examination framework. Consequently, thousands of analysts are taught to analyze markets, value securities, and think about risk in a shared language. While these common standards create competence, they also set the stage for common conclusions when the system that establishes foundations starts to shape outcomes.
How Intellectual Herding Emerges
Because analysts are schooled in similar frameworks, they tend to examine the same securities and ask comparable questions. The article describes this as “a form of intellectual herding. Managers may describe their strategies differently, but they frequently work from similar frameworks, examine similar securities, and ultimately arrive at similar portfolios.” The industry excels at spotting what is attractive “now,” yet it is less adept at rewarding imagination about what might matter next. This tendency toward uniformity is reinforced by the homogeneity of education and credentialing, which creates a feedback loop where similar inputs generate similar outputs.
Artificial Intelligence as an Amplifier
The piece then turns to artificial intelligence, arguing that AI is not the source of the problem but a magnifier of existing tendencies. It notes, “The issue is not that AI lacks depth or discipline. It’s that investment professionals are increasingly using the same tools, trained on overlapping information, to answer similar questions.” When thousands of professionals pose similar queries to similar models that have been trained on overlapping data sets, the resulting insights inevitably begin to converge. This convergence creates an uncomfortable paradox: AI promises to democratize intelligence, yet widespread adoption of the same intelligence infrastructure could reduce differentiation across the industry.
Survey Evidence on AI Adoption
To substantiate the claim about AI’s growing role, the article cites Mercer’s 2026 survey of 131 asset managers. According to the survey, “55% had already integrated AI into at least one investment process, while 91% expected to increase their use over the next year.” Moreover, “Sixty-three percent reported using off-the-shelf AI tools, while 58% use some vendor-provided data.” These statistics illustrate that a majority of firms are relying on standardized AI solutions and external data vendors, further narrowing the diversity of inputs that feed into investment decisions.
The Paradox of Democratized Intelligence
The article highlights the tension between AI’s potential to broaden access to sophisticated analysis and its risk of homogenizing thought. When the same models are applied to similar data sets by a large pool of professionals, the likelihood of converging insights rises. This scenario undermines the very advantage that AI could offer—namely, the ability to surface novel, non‑obvious opportunities. The author, drawing from personal experience as a top‑quartile CIO and portfolio manager, notes that his own edge came not from following the crowd but from bringing a different perspective: “I did not attend any of those schools, and although I paid for the entire team to earn their CFAs, I never studied for or took the exams myself.” By valuing individual thought and observation as highly as formal training, he was able to generate performance that stood out.
Strategic Implications for Asset Managers
Given these dynamics, the article argues that the strategic question for asset managers should shift from “How do we use AI?” to “How do we use AI without becoming more like everyone else?” The answer lies in deliberately cultivating intellectual diversity. Organizations need people who have been trained differently, think differently, and are willing to challenge the consensus. Analysts and portfolio managers must design research processes that go beyond identifying what works today to explore second‑ and third‑order consequences: What is changing beneath the surface? Which behaviors are emerging? What assumptions embedded in today’s models may prove wrong? By expanding the range of questions asked, firms can harness AI’s power while preserving the independent judgment necessary to spot overlooked trends.
The Future Competitive Advantage
Historically, the industry’s competitive advantage has been framed in terms of information, technology, and analytical horsepower—resources that are now becoming commoditized. The article concludes that “What’s becoming a scarce resource now might just be original thought.” In a world where data, models, and even AI tools are widely accessible, the ability to generate genuinely novel insights will differentiate the best performers. The most successful investment organizations of the future will not necessarily be those that deploy AI most aggressively, but those that use AI to broaden their inquiry while retaining a culture that prizes independent, contrarian thinking. In doing so, they can avoid the pitfalls of intellectual herding and turn AI into a tool for true innovation rather than a conduit for conformity.
https://www.wealthmanagement.com/artificial-intelligence/ai-in-asset-management-has-made-it-more-homogeneous-