As platforms like AlphaSense automate the grunt work of research, the advantage is no longer in finding information. The new "alpha" for investors comes from asking better, more creative questions, identifying cross-industry trends, and being more adept at prompting the AI to uncover non-obvious connections.

Even in hyper-quantitative fields, relying solely on logical models is a failing strategy. Stanford professor Sandy Pentland notes that traders who observe the behavior of other humans consistently perform better, as this provides context on edge cases and tail risks that equations alone cannot capture.

High-frequency trading firms are expanding into medium-frequency horizons (days to weeks). They use their sophisticated short-term AI models, which can predict optimal prices within the next hour, to inform the execution strategy for their longer-term positions, creating a cascading effect where intraday precision enhances multi-day trading performance.

By being the first clients for "invest-tech" and alternative data companies, hedge funds are training technologists to identify market inefficiencies. This process will ultimately commoditize their unique edge and lead to their disruption.

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.

Cliff Asnes explains that integrating machine learning into investment processes involves a crucial trade-off. While AI models can identify complex, non-linear patterns that outperform traditional methods, their inner workings are often uninterpretable, forcing a departure from intuitively understood strategies.

The future of financial analysis isn't job replacement but radical augmentation. An analyst's role will shift to managing dozens of AI agents that perform research and modeling around the clock, dramatically increasing the scope and speed of idea generation and validation.

Firms that meticulously document the reasoning behind trading decisions are building a proprietary dataset for future AI agents. This intellectual property, capturing the firm's unique philosophy, will be invaluable for training AI that can truly understand and operate within its specific context, forming a powerful competitive advantage.

Contrary to the hype around alternative data, the most crucial input for intraday trading AI is standard market data feeds from exchanges. This raw, high-volume data on quotes and trades is the truest expression of market intent, far outweighing the predictive value of news or social media feeds.