Essays on AI and investing, from inside the industry
Panel notes, the Quantamental 2.0 thesis, and field lessons from building AI for lean investment teams.
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The machine consumer breaks seat-based licensing. The real question is not whether licensing survives, but where the edge goes once the pipe is cheap. Notes from the Neudata TMDS London closing panel.
Why the discretionary side moved first, why the quant hesitation was rational and wrong, and where the role goes next. Notes from RavenPack's Exponential Summit London panel.
Notes from the Eagle Alpha London panel on what's actually breaking in quant, and why it isn't the factors.
SimCorp's AI Stress Testing Is the Right Move — for Firms That Already Have the Infrastructure
SimCorp announced AI-powered stress testing in Axioma Risk. It's a genuinely useful capability — for firms that already have the plane. The more interesting story is what becomes possible for everyone else.
Five Things I Learned About AI Implementation from Practitioners at Man Group, H2O, and Beyond
Five lessons on AI implementation from practitioners at Man Group, H2O, and beyond. From the Neudata London Data Summit panel on what happens when AI theory meets investment reality.
How agentic AI expands institutional capabilities for lean investment teams. The economics of building bespoke risk infrastructure have changed — and that changes what is feasible for boutique funds.
On February 23, 2026, Bloomberg officially announced ASKB, a conversational AI interface embedded directly into the Bloomberg Terminal. Why agentic AI inside a platform is not the same as AI embedded across an investment firm.
Anthropic's 2026 Agentic Coding Trends Report reveals that roughly 27% of AI-assisted work consists of tasks that would not have been done otherwise. For boutique investment firms, this finding has direct implications.
Generative AI is accelerating a convergence that has been underway for years. As analytical capabilities become more widely accessible, differentiation inevitably shifts. Context increasingly becomes the source of sustainable differentiation.
Generic AI tools like ChatGPT weren't built for institutional investors. BigData.com offers a different approach: purpose-built financial research infrastructure with premium sources, audit trails, and model routing control.
95% of enterprise GenAI projects show no measurable impact on P&L. The report doesn't simply tell us that 'AI isn't working.' It explains why most projects fail, and what differentiates the few that succeed.
BlackRock recently released a paper introducing AlphaAgents, a multi-agent, LLM-powered framework for equity research and portfolio construction. Are we heading toward AI-powered investment committees becoming the new standard?