The Age of Information
By Empirical Asset Management
About the Authors
Glenn Caldicott is the Chief Investment Officer and a Portfolio Manager at Empirical Asset Management, where he leads the firm’s investment strategy. The original creator of The Insider Portfolio® and Rules Based Investing®, he has over four decades of investment experience. A CIMA® charterholder, Glenn is a graduate of Babson College.
Pete Martin is the Managing Director and Head of Client Portfolio Management at Empirical Asset Management, where he leads client relationships and distribution. With over 33 years of industry experience, he was previously Head of Distribution at Diffractive Managers Group and held senior distribution and client roles at AlphaSimplex Group and Natixis Global Asset Management. He holds a B.S. from the University of New Hampshire.
What Will the World Look Like in 2035—and What Happens to Investing?
We are entering what may ultimately be called the Age of Information.
The internet gave humanity access to extraordinary amounts of information. Artificial intelligence is beginning to give us the ability to process that information, connect it, interpret it and act upon it at a scale that is fundamentally different from anything that came before.
That distinction matters.
By 2035, AI will likely be embedded into nearly every major aspect of life. Healthcare, energy, transportation, education, manufacturing, science and finance will not simply be more efficient versions of today’s industries. Their underlying business models and workflows may be fundamentally different.
PwC’s vision of healthcare in 2035, for example, includes continuous biometric monitoring, AI-enabled diagnosis before symptoms emerge, more care delivered at home, and physicians increasingly acting as “data orchestrators” rather than spending their time on administrative tasks.
Imagine the implications for an average person.
A person in 2035 may have an AI assistant that continuously monitors finances, health, travel, insurance, taxes, education and household spending. A physician may know more about a patient’s health before the patient walks into the office. Cars will anticipate rather than simply respond. Education may become highly personalized. Routine administrative work may largely disappear from many professions.
The average human may have access to an extraordinary amount of intelligence that today is available only through expensive specialists.
Intelligence itself may become abundant.
That may be the defining economic characteristic of the next decade.
And if intelligence becomes abundant, industries built around the scarcity of intelligence will have to change.
The uncomfortable question for finance
This is where the financial industry should become uncomfortable.
Modern Portfolio Theory dates to Harry Markowitz’s seminal work in 1952. It was revolutionary. Markowitz demonstrated that a portfolio could not be understood simply by evaluating securities individually; correlations and interactions among securities mattered. His work fundamentally changed portfolio construction and established diversification as a central principle of modern investing.
There is no reason to diminish that achievement.
But perhaps we should stop confusing a revolutionary framework for 1952 with a permanent framework for 2035.
Think about the information environment in which MPT was developed.
Computing was primitive. Data was scarce. Historical databases were limited. Securities were analyzed individually. Portfolio optimization was computationally difficult. Human beings had to make enormous numbers of assumptions because they simply did not have the ability to process information at scale.
Today, those constraints are disappearing.
Yet we continue to build portfolios using many of the same conceptual building blocks.
Stocks.
Bonds.
Large cap.
Small cap.
Value.
Growth.
Domestic.
International.
Morningstar’s Style Box, introduced in 1992, created a nine-square framework for categorizing investments by size and value/growth characteristics. It was enormously useful in a world where investors needed simple frameworks to understand what they owned.
But consider the absurdity of applying a 1992 nine-box framework to an investment world increasingly capable of analyzing thousands of variables simultaneously.
Why should a security have to live in one box?
Why should a portfolio be constructed by deciding in advance that we want 20% value, 20% growth, 10% small cap, 30% international, or some other predetermined combination?
Those categories were useful partly because information was limited.
AI changes that equation.
From nine boxes to one box
The next evolution of investing may therefore not be another factor.
It may be the elimination of the boxes themselves.
At Empirical Asset Management, we have spent more than three decades creating and working with Rules Based Investing® and the evolution of systematic portfolio construction. Our current work with AI partners is built around what we call Smart Alpha—using AI, quantitative research and rules based portfolio construction to evaluate multiple factors simultaneously rather than forcing investments into predetermined categories.
The concept is simple but potentially profound:
What if the portfolio didn’t care whether a stock was technically value or growth, large or small, domestic or international?
What if the only question was:
Given everything we know today, what combination of securities provides the best expected opportunity for return relative to risk?
That is one-box investing.
Every security starts in the same universe.
AI evaluates the characteristics.
The model determines the opportunity.
The portfolio changes as the information changes.
Instead of allocating capital because an investment fits a category, capital is allocated because the investment fits the objective.
That is a fundamentally different philosophy.
And it is why we believe the future may not be about choosing between active and passive.
It may be about using AI to create adaptive portfolios that can intelligently move across the entire opportunity set.
The industry’s intellectual arrogance
This leads to an uncomfortable observation.
Finance has developed an enormous amount of intellectual capital around the belief that sophisticated humans can identify securities, construct portfolios and manage risk better than everyone else.
We have analysts.
Portfolio managers.
Chief Investment Officers.
Economists.
Strategists.
Asset allocators.
Quantitative researchers.
And we have built an industry around their collective expertise.
But why do we assume that because someone is highly intelligent, highly educated, and highly experienced, they will necessarily be better at processing information than an AI system?
This is not an argument against human intelligence.
It is an argument against intellectual arrogance.
The question isn’t whether today’s AI can replace today’s portfolio manager.
The more important question is:
Why would we assume that the portfolio manager of 2035 will be better than the AI available to them?
Morningstar’s own 2026 research on AI in active management describes a transition that is already underway: AI is accelerating research and analysis, although actual investment decision-making remains concentrated among more technologically advanced managers.
McKinsey estimates that AI, generative AI, and agentic AI could ultimately affect 25%–40% of the cost base of an average asset manager, while potentially transforming distribution, investment processes and compliance.
The industry is beginning to see the direction.
But I suspect the eventual change will be much larger than simply reducing costs.
The Advisor of 2035
The financial advisor may be transformed even more dramatically.
Today, an advisor may spend significant time determining asset allocation, selecting investments, analyzing portfolios, producing reports, researching securities and building financial plans.
Much of that work could become automated.
The advisor of 2035 may instead become something closer to a financial quarterback.
AI could continuously analyze a client’s portfolio, taxes, spending, liabilities, goals, market conditions and thousands of potential scenarios.
The human advisor interprets the recommendations, challenges the machine, understands the client’s psychology and—perhaps most importantly—keeps the client from making terrible decisions.
Because there is one thing AI may not solve:
Human behavior.
AI can tell you what to do.
It cannot guarantee that you will do it.
It cannot eliminate fear during a 40% market decline.
It cannot make someone comfortable with uncertainty.
And it cannot replace trust.
The value of the advisor therefore may migrate from information provider to decision partner.
What happens to the average investor?
This could be one of the most democratizing consequences of AI.
The individual investor of 2035 could have access to capabilities that today require an entire investment team.
Imagine asking your personal AI:
“Here are my assets, income, taxes, liabilities, spending, family objectives and risk tolerance. What is the optimal strategy for the next 30 years? Now show me 10,000 scenarios. What happens if inflation remains at 4%? What happens if I live to 105? What happens if equities fall 50%? What if interest rates remain at 7%? What if I retire five years early?”
The machine may be able to answer those questions almost instantaneously.
The competitive advantage of the professional investor therefore changes.
Information becomes less valuable.
Interpretation, proprietary data, implementation, risk management, behavioral insight and trust become more valuable.
The real investment question for 2035
Perhaps the most important question investors should ask today is not:
“What is my allocation to large-cap growth, small-cap value, international equities and bonds?”
It is:
“Why should those categories determine my portfolio in the first place?”
Those categories describe the market.
They do not necessarily describe the optimal portfolio.
MPT taught us that diversification matters because securities interact with one another.
AI may take that insight to its logical extreme.
Instead of diversifying according to predefined boxes, we may be able to continuously evaluate the entire investment universe and determine where the best combination of expected return, risk, valuation, quality, momentum, fundamentals, sentiment and other characteristics exists at any particular moment.
That is not abandoning diversification.
It is making diversification dynamic.
That is not abandoning rules.
It is making the rules intelligent and adaptive.
And that may be the difference between traditional systematic investing and what we call Smart Alpha—investing with AI rather than simply investing in AI.
2035
We should not pretend to know exactly what 2035 will look like.
But we should recognize that the direction is becoming increasingly clear.
Healthcare will become more predictive.
Science will accelerate.
Energy systems will become more intelligent.
Machines will perform more cognitive tasks.
Individuals will have access to extraordinary analytical capabilities.
And financial markets will increasingly be populated by machines capable of processing information at speeds and scales humans cannot match.
The biggest mistake may be to assume that the future will simply be a more sophisticated version of today.
It won’t.
The financial industry should be particularly careful here.
Markowitz did not fail because he was wrong.
Morningstar’s boxes did not fail because they were wrong.
They succeeded because they were appropriate for the information environment of their time.
The question is whether we are willing to recognize when that environment has changed.
If intelligence becomes abundant, the old definitions of expertise become less relevant.
The investment professional of 2035 may not be the person who knows the most.
It may be the person who knows how to harness the most intelligence.
And perhaps the greatest competitive advantage will belong to the investor willing to abandon the boxes altogether.
The future of investing may not be more boxes.
It may be one box: the entire opportunity set.
Based in Wellesley, Massachusetts, Empirical Asset Management is a registered investment advisor that operates as both a wealth manager for individual clients and an asset manager offering portfolios to individuals, institutions, and other financial advisors. The firm specializes in “Investing with AI” and currently manages five AI-driven portfolios. While this piece contains forward-looking statements, any such statements are not guarantees of any future outcomes. Any projections, outlooks, or estimates should not be construed as indicative of actual events that will occur.
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