Christoph Gum (CEO Private Alpha) during a keynote on financial AI.
The current SPIVA data illustrates a structural problem that we have been observing in the investment industry for years: over longer periods, the vast majority of active fund managers fail to beat their respective benchmarks. This pattern runs through every region, asset class and market cycle.
There are, of course, plausible explanations for this. Many indices are increasingly concentrated, driven by the dominance of a handful of large technology and AI companies. Traditional investment funds operate within regulatory and structural limits. In addition, not every active strategy is designed to mirror the risk profile of its benchmark exactly.
Nevertheless, one fundamental insight remains undisputed: The investment industry needs better research technologies, superior market intelligence and a more systematic integration of data, artificial intelligence and portfolio management.
The challenge: too much information, too few actionable signals
Professional investors today have access to an unprecedented volume of data, research reports, analyst opinions, market terminals and real-time information. Yet more information does not automatically lead to better investment decisions.
In fact, many investment processes are still heavily shaped by opinions, narratives, subjective forecasts and backward-looking valuation models. This is precisely where the performance gap arises. Success is not determined by how much data an investor holds, but by their ability to extract robust, validated and actionable signals from it.
In my view, the core problem is that many traditional research processes cannot systematically identify which factors genuinely generate alpha. Which key figures perform best under different market conditions? Which sectors show structural strength? When does a trend begin to reverse? When does a market leader become a risk? And when does an overlooked company turn into the next compelling investment opportunity?
The CaesarDPT perspective: AI-powered research intelligence instead of conventional research
At Private Alpha we developed CaesarDPT to solve exactly this challenge. Rather than simply aggregating capital market data, our platform uses artificial intelligence, pattern recognition and systematic analysis to translate complex market information into actionable investment intelligence.
CaesarDPT combines several analytical layers:
1. Market regime analysis and risk management
We continuously assess the current market environment — whether markets are in a risk-on or risk-off regime, working through a consolidation, approaching a trend reversal or entering a new upward phase. For active managers, understanding the overarching market regime matters just as much as valuing individual securities.
2. Sector and thematic intelligence
In the AI supercycle it is no longer enough to concentrate only on the obvious winners. Investors have to recognise early where capital is flowing and which segments show relative strength — whether in semiconductors, data centre infrastructure, enterprise software, robotics or agentic AI.
3. Pattern recognition and trend analysis
Portfolio performance is ultimately carried by securities that show positive long-term trends. The systematic identification of trend reversals, relative strength and momentum structures has therefore become a decisive component of modern portfolio management.
4. AI-generated investment commentary
CaesarDPT distils highly complex market data into clear, decision-oriented research findings. Our aim is not to replace the portfolio manager, but to provide a considerably stronger analytical basis for investment decisions.
The Private Alpha AI Leaders 140 Index: making the AI supercycle investable
One concrete result of this research philosophy is the Private Alpha AI Leaders 140 Index. In our view it represents one of the industry’s leading systematically curated equity universes with a focus on artificial intelligence.
The index comprises around 140 global companies along the entire AI value chain — from semiconductors, memory technology and cloud infrastructure through data centres, energy, enterprise software, cybersecurity and robotics to the platform leaders driving the next wave of AI innovation.
The index deliberately reaches well beyond the familiar mega-cap names. The AI supercycle has long since stopped being just an Nvidia story — it has grown into a broad, multi-year infrastructure, platform and productivity cycle. The Private Alpha AI Leaders 140 Index is designed to reflect that breadth and to make the entire AI ecosystem both transparent and investable.
Why this matters for active managers
The SPIVA results should be a wake-up call. If active managers consistently struggle to outperform their benchmarks, merely consuming further traditional research reports or subscribing to additional market data services is unlikely to close the performance gap.
The real paradigm shift is this:
- ✓From information to intelligence.
- ✓From opinions to validated signals.
- ✓From static research to dynamic, AI-powered portfolio management.
- ✓From conventional benchmarks to intelligent, thematic investment universes.
Active managers have to recognise new market leadership earlier, pick up strengthening trends faster and quickly identify which positions no longer work. In highly dynamic environments such as the AI supercycle, speed is increasingly becoming a decisive factor in investment performance.
Conclusion
The investment industry is entering a new technological era. Conventional research remains an essential part of the investment process, but on its own it is no longer sufficient. Generating alpha in future calls for systems able to interpret markets continuously, to recognise emerging trends early, to make risks transparent and to support investment decisions with validated market intelligence.
That is precisely why we developed CaesarDPT: an AI-optimised platform for research and market intelligence, designed for professional investors who want to understand not only what is happening in the financial markets, but also what those developments mean for portfolio construction, risk management and long-term performance.
Professional investors do not need yet more data. They need better signals — and intelligent indices that genuinely reflect the markets of tomorrow.