ICYMI 2026-08-15: Bootstrapping Understanding
Our weekly roundup of signals from the AI noise, for humans leading change.
AI employment gap?
Stanford published an update to their study on the impact of AI on the labor market. While it doesn’t state anything definitively, it highlights an important (if expectable) insight: the most affected jobs seem to be those affecting young people (i.e., novices) in fields that require highly codified knowledge. That is, jobs that require tacit knowledge seem to fare better. Ask yourself: what parts of your business run on codified knowledge? Tackle those first.
Understanding is the new bottleneck
Your agents are writing code, but do you understand what they’re doing? Geoffrey Litt argues we can use agentic systems to help us understand their output. Basically, bootstrapping our understanding of the system. I distinguish between the work we do and the meta-work that enables it: AI can expedite both.
AI math breakthrough
Seemingly, a contradiction to the value of tacit knowledge when interacting with LLMs: Jarred Sumner, an Anthropic employee, coaxed Claude to make progress with “the most notorious problem in all of math,” the 167-year-old Riemann hypothesis. The kicker: Sumner himself didn’t understand the problem. Instead, his contribution was giving Claude pep talks. I take most everything from the frontier labs as potential propaganda, but this case raises interesting questions. Perhaps tacit knowledge doesn’t matter as much? (WSJ gift link)
LLMs for theory-building
There are three kinds of reasoning, and LLMs aren’t equally good at all three. Alas, one of them — abductive reasoning — is essential for theory-building, and therefore, strategic foresight. Gordon Brander explores how we may architect systems to compensate for LLMs’ shortcomings in abductive reasoning. Bookmarking this one for some work I’m doing now.
The first Unfinishe Conversation
Greg and I hosted author Stef Hutka, PhD for the first Unfinishe Conversation, a new series on how leaders can steer through this time of change. The subject of our first conversation was Stef’s new book, What Your Machines Should Do. TL;DR: AI is an accelerant, but that doesn’t mean you’ll move faster in the right direction. And yet, as my friend Peter Van Dijck put it, you must still move faster.