MIT researcher and People Analytics author Ben Waber joins Amy and Meg for one of the most myth-busting conversations about AI, productivity, and what actually drives enterprise value. From a 23-year-old grad student who discovered that billion-dollar companies don't know how their own teams communicate, to the lunch table experiment that changed programmer productivity by 20%, Ben brings 15 years of behavioral data to challenge everything you think you know about how organizations work — and why most AI claims should make you very skeptical.
⏰ TIMESTAMPS:
00:00 What percent of your company is a dumpster fire?
00:11 Introduction to Ben Waber
01:11 From Philly to MIT: How Ben started measuring how humans work
04:35 A paper that made a billion-dollar bank reorganize
06:31 The Japanese minor, a bestselling book, and being recognized on the street
08:42 The Academic Run Playlist: 2,500+ talks and counting
13:07 The big idea: Where is the real value in AI?
14:37 Why AI vendors and economists are both getting it wrong
16:20 The calculation machines story: 20 years to get 20% cheaper
18:06 Amazon's box-packing metric and why "quantitative" doesn't mean "objective"
20:11 Jack Dorsey, Block, and the rude awakening ahead
22:11 Klarna's AI rollback and the nuance problem
24:33 "Spin up 100,000 agents doing nothing" — the meaningless metrics trap
27:17 The three things you need to understand before deploying AI
29:37 Tripwires: Building permission to be wrong
31:22 How do you actually model work? Amy's HRIS thesis
35:48 What we're really good at measuring: what's awful
37:12 From dumpster fires to board-level accountability
38:20 AI is a sugar rush — and profit predicts 1% of your future
39:06 If the cows are limping, it's bad
39:28 The lunch table story: a 20% productivity difference from a $50 decision
44:22 Leadership Corner: Breaking through when a peer team is gatekeeping
51:44 Wrap-up: What we learned from Ben
🔑 KEY INSIGHTS:
- Most AI productivity claims are measuring activity, not value — "having a seizure on my keyboard outputs more lines of code"
- Companies can't define what performance actually means — and that's the root problem
- We can't predict what great looks like, but we're really good at identifying what's awful
- The "dumpster fire" reframe: measure what percent of your company is broken and put a dollar value on it
- AI adoption is a sugar rush — firing 40% of employees boosts quarterly profit but predicts nothing about the future
- Current profit predicts only 1% of future profit — people metrics predict far more
- A 20% difference in programmer productivity was driven by which cafeteria door people walked through
- The financial industry is starting to use workplace behavioral data in investment decisions
📚 RESOURCES:
Ben Waber's book, People Analytics: https://www.amazon.com/People-Analytics-Technology-Transform-Business/dp/0133158314
Ben's HBR piece on LLMs and organizational performance: https://hbr.org/2024/01/is-genais-impact-on-productivity-overblown
Patty Azzarello, Move: https://www.amazon.com/Move-Decisive-Strategy-Obstacles-Setbacks/dp/1119348374
Nate B. Jones on Klarna: https://www.youtube.com/@NateBJones
🔗 CONNECT:
Ben Waber: https://www.linkedin.com/in/benjaminwaber/
Submit Leadership Questions: [email protected]
Instagram: https://www.instagram.com/megandamyshow/
LinkedIn: https://www.linkedin.com/company/the-meg-amy-show
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