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At an AlphaSense summit last week, a panelist described how the most sophisticated buy-side firms prepare for management meetings now. They stopped asking the sell side for conference questions. They have a model surface what management has been asked before, where they hedged, and what the killer question is. Another line from my notes: compare how management guided the street with how performance actually turned out. Four days earlier I had suggested almost the same analysis to a board director preparing for a board meeting. Look for material changes in the numbers that management didn't highlight. Look for gaps between stated priorities and actual spend. Preparation used to mean reading everything. Now it can mean asking a sharper question of everything. The context is the past four quarters of what people said, and that context is sitting in documents you already have. This works for anyone who sits across from management: investors, board members, nonprofit trustees, a CEO reviewing a division head. Here is the version I would try:
Step 4 matters more than it looks. Models sometimes tighten a quote or place it in the wrong quarter, and a misquoted line in front of management costs you the room. Ten minutes of checking protects the whole exercise. I find the "hardest question" ask useful because it forces a ranking. Without it you get a list of fifteen decent questions and no sense of which one to lead with. If you try it, I'd love to hear which question came back. Was it one you would have asked yourself? Alex |
As an AI Coach, Advisor, and Agent Builder, I help organizations and business leaders harness the power of artificial intelligence to boost productivity and streamline operations. I enable organizations to navigate the transformative landscape of AI, educating teams, identifying operational and strategic opportunities with AI and creating a framework for safe and transparent use of data in the organization.
Nate B. Jones wrote something last week that I've been chewing on. He described research (from Epoch and METR) where the same Claude model rebuilt a roughly 16,000-line scientific toolkit from its behavior alone, without seeing the code. Then, asked to customize a smaller library, it produced something unusable. Same model. Wildly different results. His explanation: AI improves fastest where results are cheap to check, like proofs and tests. It improves slowest where feedback is slow, like...
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