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.

The discipline of writing down your thought process is crucial for decision analysis. AI now amplifies this by creating a searchable, analyzable record of your thinking over time, helping you identify blind spots and get objective feedback on your reasoning.

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.

Beyond simple quantitative screens, AI can now identify companies fitting complex, qualitative theses. For example, it can find "high-performing businesses with temporary, non-structural hiccups." This requires synthesizing business model quality, recent performance issues, and the nature of those issues—a task previously reliant on serendipity.

In high-stakes fields like pharma, AI's ability to generate more ideas (e.g., drug targets) is less valuable than its ability to aid in decision-making. Physical constraints on experimentation mean you can't test everything. The real need is for tools that help humans evaluate, prioritize, and gain conviction on a few key bets.

AI validation tools should be viewed as friction-reducers that accelerate learning cycles. They generate options, prototypes, and market signals faster than humans can. The goal is not to replace human judgment or predict success, but to empower teams to make better-informed decisions earlier.

The most effective way to use AI is not for initial research but for synthesis. After you've gathered and vetted high-quality sources, feed them to an AI to identify common themes, find gaps, and pinpoint outliers. This dramatically speeds up analysis without sacrificing quality.

Advanced AI tools can model an organization's internal investment beliefs and processes. This allows investment committees to use the AI to "red team" proposals by prompting it to generate a memo with a negative stance or to re-evaluate a deal based on a new assumption, like a net-zero mandate.

AI models tend to be overly optimistic. To get a balanced market analysis, explicitly instruct AI research tools like Perplexity to act as a "devil's advocate." This helps uncover risks, challenge assumptions, and makes it easier for product managers to say "no" to weak ideas quickly.