The active versus passive investing paradigms are becoming irrelevant. Indeed, even the popular belief that passive investing dominates active investing is wrong. As measured by worldwide AUM, passive investing only dominates active investing for one region and in one asset sub-class – US Large Cap Equity Blend.
Technological change (both software and hardware) and the associated economics are driving the impetus to customization for a large swath of investors, an alteration well beyond the constructs of active and passive. An ever-increasing number of client portfolios will be personalized to meet their specific requirements and priorities. This change is being manifested today. This movement from asset management as a product business to asset management as a service business represents a fundamental shift and presents both new opportunities and pitfalls for the industry.

Historically, asset management has been defined by two distinct investment approaches: discretionary (fundamental) investing, led by human judgment and deep research, and systematic (quantitative) investing, driven by models and data. The rise of AI, particularly large language models, is dissolving the boundaries between these paradigms.
Discretionary investors gain unprecedented breadth through scalable analysis, while systematic investors achieve new levels and depth by incorporating qualitative insights. This convergence is redefining active investing — alpha generation will be driven by unique datasets, expert application and customization of LLMs through prompting and fine-tuning, and the efficient delivery of actionable insights. The winners will be those who fuse technological innovation with the enduring principles of economic intuition and sound judgment, unlocking the full potential of human plus machine.

The asset management industry is in the midst of a marked transformation. The long and measured evolution of asset management that now provides investors with broad access to low-cost, diversified, efficient portfolios is now converging with notable advancements in computing power, the proliferation of new data sources, the ongoing development of novel analytical methods, and the adoption of artificial intelligence. While these innovations offer the promise of dramatic improvements across the economic landscape, they have also resulted in uncertainty becoming an increasingly prominent feature of the financial markets.
As such, managing uncertainty, along with risk and return, will need to be a key focus for investment practitioners. This will require investors to re-consider how they think about the different types of information that drive asset prices, passive investing, the role of active management, diversification, portfolio construction, and even the potential impacts of artificial intelligence on investing.
In this presentation we revisit the practical and theoretical ideas that have shaped modern asset management and contemporary views on active and passive investing. We then explore how those ideas have evolved and re-introduce ideas from visionary thinkers in economics, finance, and computing that are relevant to managing both information and uncertainty. Finally, we discuss the key implications and challenges for the practice of investment management.

Harry Markowitz showed that any manager’s stock selection skill is most accurately reflected in their fund performance by an efficient portfolio. Despite this fact, many managers use inefficient heuristic portfolio construction methods, since portfolio optimisation is widely regarded as problematic. This talk argues that the underlying problem is not with the idea of optimisation, but with its standard implementation, which simply ignores the inevitable uncertainty in the expected returns.
It then describes a new way of taking uncertainty explicitly into account, which removes the usual problems of optimisation, and also ensures the greatest increase in portfolio efficiency for the least amount of turnover. It then introduces two new metrics: the Implied Minimum Inefficiency Risk Aversion parameter, which makes the current portfolio look as efficient as possible, and the Efficiency Coefficient, which measures a manager’s portfolio construction skill just as an Information Coefficient measures their stock selection skill.
Asset management is currently undergoing a paradigm shift from a product business to a service-oriented business. In this brave new world, asset management firms will need to demonstrate value added in any dimension available, including skill in constructing better, and more efficient, portfolios.

As capital flows more freely across asset classes and technology reshapes markets, the lines between public, private, and crypto investments are increasingly blurred. We explore modelling risk jointly across these domains, not just at a single moment in time, but across investment horizons.
What macro forces bind them? What risk factors differentiate them? This talk examines cross-asset risk modelling across timescales including liquidity timelines, valuation cycles, and systemic shocks, and proposes a framework for identifying common and divergent risk drivers across traditional and digital domains.