Despite the wide availability of powerful AI models, a sustainable edge in the zero-sum game of investing comes from a combination of unique, curated data sets, bespoke technology for scale, and the experienced human context to ask the right questions of the models.

While AI seems a natural fit for systematic strategies, its most immediate impact at Marshall Wace has been on the discretionary side. AI tools for distilling vast amounts of information give fundamental managers a speed and depth of analysis that was previously impossible, transforming their workflow.

Historically, investment tech focused on speed. Modern AI, like AlphaGo, offers something new: inhuman intelligence that reveals novel insights and strategies humans miss. For investors, this means moving beyond automation to using AI as a tool for generating genuine alpha through superior inference.

An AI-native VC firm operates like a product company, developing in-house intelligence platforms to amplify human judgment. This is a fundamental shift from simply using tools like Affinity or Harmonics, creating a defensible operational advantage in sourcing, screening, and winning deals.

Bridgewater's AI compute consumption has increased 200-fold in about a year. Their AI-driven fund is profitable, generating revenue to cover this cost and reinvest in making the intelligence more powerful, creating a self-sustaining competitive moat.

To create a truly innovative AI, Bridgewater established its "artificial investor" as a separate venture. This prevented the AI from simply inheriting the biases and flaws of the existing human-driven system. The goal was for the AI to develop its own independent, uncorrelated ideas rather than becoming a digital copy of Bridgewater itself.

An AI-powered simulation loads a team's actual portfolio and subjects it to stressful, AI-generated news headlines. This "war game" allows managers to rehearse their strategy for volatile markets, identifying weaknesses before real money is on the line.

Hudson River Trading shifted from handcrafted features based on human intuition to training models on raw, internet-scale market data. This emergent approach, similar to how ChatGPT is trained, has entirely overtaken traditional quant methods that relied on simpler techniques like linear regression.

Advent created an AI trained on its entire investment history, including deals they passed on. This 'IC Robot' analyzes new proposals and flags assumptions—like margin growth—that deviate from historical precedent, serving as a powerful, data-driven check on the investment committee's biases.