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Explore why trust, not returns, is the true currency of financial services as AI and passive investing erode traditional advantages.
AI is dissolving the informational edge institutional money managers relied on for decades. Passive funds are also narrowing the performance edge.
What survives, once both fade, is something that never appeared on a fact sheet: belief.
Every institution that manages other people’s money says it is in the business of returns. That is only partly true.
The actual product a financial institution sells is the feeling that someone is watching your money as carefully as you would. Performance numbers may justify that feeling later, but they rarely create it. Once trust is broken, returns alone seldom rebuild it.
That distinction has become more urgent. AI is closing the information gap. Passive products are shrinking the performance gap. Geopolitical volatility is weakening the certainty that once anchored long-term allocation.
What remains is a deeper question: would the institution behind the money make the same decision if the money were its own?
Nilesh Shah, Managing Director of Kotak Mahindra Asset Management and a part-time member of the Prime Minister’s Economic Advisory Council, made this case on KNOLSKAPE’s Clearing the BLUR podcast, hosted by Rajiv Jayaraman and Deepak Sharma.
His formulation is simple: the job is trust management. Everything else sits downstream of that distinction.
Financial institutions often treat trust as a brand attribute communicated through advertising, testimonials, and reassuring client letters.
That misses where trust actually comes from.
Trust is the residue left behind by specific decisions, especially when the institution’s short-term interest conflicts with the client’s interest.
In 2003, when Indian interest rates had fallen from 14% to 5%, a large fixed-income fund faced such a choice. The fund had been built and marketed on returns that could no longer be sustained.
The firm could continue collecting fees on expectations it knew it could not meet. Or it could tell investors to leave.
Shah, who was running the fund, recommended the latter. The fund eventually shrank from roughly ₹6,000 crore to ₹200 crore.
Reflecting on the episode years later, he said:
Measured through AUM, revenue, and market share, the decision looked disastrous. Measured through the ability to survive multiple cycles, it may have been one of the firm’s most valuable decisions.
Every “client first” promise must be earned when doing the right thing carries a real cost.
The 2026 Edelman Trust Barometer places trust in financial services at 63% globally, its strongest sustained level since the 2008 crisis. Yet it also describes a wider retreat into smaller circles of trust, with authority shifting from institutions toward people’s immediate networks.
Trust in finance is therefore rising in an environment that makes institutional trust harder to earn.
For most of modern financial history, institutional advantage was informational.
A fund manager had access to a Bloomberg terminal, analyst teams, management meetings, and years of pattern recognition built through previous crises. The retail investor had none of it.
AI is removing that gap.
Real-time data on cement production, GST collections, auto sales, and other indicators is now widely accessible. Pattern recognition that once took decades to build can now be approximated by models trained on historical datasets.
Shah used a cricketing analogy to describe the uncertainty this creates.
The greater danger may lie with institutions and professionals who remain undecided about how they will use the technology.
Yet closing the information gap does not automatically improve investor outcomes.
Behavioral finance has shown that access to data was rarely the main constraint for retail investors. The harder problem was the discipline to act calmly on that data.
When informational barriers fell during the 2020 to 2021 retail trading boom, speculative and momentum-driven behavior rose alongside access. More information did not necessarily produce better long-term allocation.
AI may even widen the behavior gap. A model can generate a convincing investment thesis on demand, making impulsive decisions feel more rigorous than they are.
The scarce resource in investing was never information alone. It was discipline.
That is why the advice that survives an AI-driven leveling of information remains unglamorous: invest regularly, diversify, and remain invested longer than feels comfortable.
Every investing cycle returns to the same challenge: making consistent decisions as available signals change.
Shah offered a sailor’s image for this. Near the shore, steer by the lighthouse. In open water, steer by the North Star.
The source of guidance changes with the conditions, even when the destination remains the same.
The metaphor also points to a useful test of investor honesty. Whether someone bought, sold, or froze during a genuine crisis, with COVID being the most recent shared example, reveals more about actual risk tolerance than a questionnaire.
Risk tolerance often remains a story people tell themselves until a crisis reveals the truth.
India’s investment case is often described as the convergence of talent, capital, and infrastructure. Shah calls it a Triveni Sangam, a coming together in which a good idea no longer needs the surname of an established business family to attract capital.
That marks a structural shift from the India of fifteen years ago.
The share of household savings allocated to equities and mutual funds rose from roughly 2% to more than 15% between FY12 and FY25.
International capital has been less enthusiastic. Foreign institutional investors have continued to pull back even as domestic institutions absorb much of the selling.
The contrast suggests a communication gap.
Domestic conviction in India’s growth story has moved faster than global conviction, partly because the work of explaining why India matters has lagged behind the numbers supporting the case.
Strong fundamentals can still underperform in global perception when the story is poorly distributed.
Shah’s position on active and passive investing is direct:
If taken seriously, this rule would require active managers to acknowledge their own irrelevance when the data no longer supports their fees.
It is the same principle behind the 2003 fund decision, applied at an industry level. Client interest must come before institutional self-preservation.
Passive assets in the Indian mutual fund industry have grown from roughly 3% of total AUM in 2017 to close to 19% by early 2026. Active equity funds also saw their share of total industry assets decline for the first time on record in FY26.
India has not yet followed the US, where passive funds account for more than half of total fund assets.
One reason may be talent. Many strong US active managers have moved to hedge funds and private equity, leaving thinner teams to compete against the index.
India’s active-passive balance may therefore depend on whether the industry can retain genuinely alpha-generating fund managers.
Shah credits Kotak founder, Uday Kotak with a two-by-two grid for judging consequential decisions.
Noble ends achieved through noble means sit in one quadrant. Noble ends achieved through means that require some flexibility sit in another. A fixation on means that causes the original purpose to be forgotten sits in a third. Disregard for both ends and means sits in the fourth.
The framework separates two leadership failures that are often confused.
The first is bending a process for a genuinely good reason. The second is following every process while producing an indefensible outcome.
Many governance failures in finance, including incorrect selling and valuation shortcuts, appear procedurally clean until the consequences emerge.
A useful framework should help leaders identify which quadrant they are operating in before the outcome exposes them.
Across the examples, metaphors, and market shifts, the argument is consistent.
In a business built on other people’s money, the only asset that can compound without limit and disappear in an instant is belief.
AI is dissolving the informational moat that protected incumbents. Passive products are dissolving the performance moat. Geopolitical volatility is dissolving the certainty moat.
What remains is the moat that was never based on information or returns.
It does not appear in a fact sheet. It cannot be marketed through a five-year CAGR. It is earned through decisions made when the client’s interest and the institution’s interest diverge.
As information and performance become easier to access and compare, trust becomes more valuable precisely because it cannot be commoditized.
