Will AI take your job? In Part 2 of my conversation with Ben Zweig, we move beyond theory into the practical chaos of how work actually functions in today’s economy.
Ben and I dive deep into why there are 90 million unique job titles (yes, really), how LLMs are finally solving the job categorization problem that has plagued labor markets for decades, and why we organize work into arbitrary categories in the first place.
We explore the philosophical question of taxonomy—from dogs and cats to humans and job titles—and discuss how the level of detail we need depends entirely on our familiarity with a domain.
The conversation then shifts to Keynes’ famous prediction that we’d work 15-hour weeks by 2030, why he was both spectacularly wrong and surprisingly right, and what this tells us about human nature and our insatiable appetite for consumption.
We close with Ben’s mother’s career transformation from typist to database administrator, a perfect example of how jobs evolve gradually even when technology disrupts them dramatically, and why firms adopt new technology much more slowly than we think, giving workers time to adapt.
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Key Takeaways
There are 90 million unique job titles in the economy, creating massive inefficiency in labor markets—but LLMs can finally solve this categorization problem by analyzing the actual tasks described in job postings and resumes rather than relying on arbitrary titles.
Job titles are social constructs that exist for coordination purposes, not because they reflect fundamental differences in work—a product manager at Indeed might be an engineering lead while a product manager at Amazon does client success, making titles meaningless without understanding the underlying tasks.
Taxonomy is the art of choosing the right level of abstraction for your needs—finance departments only care about broad categories (sales vs R&D), while talent acquisition needs granular distinctions (DevOps manager vs infrastructure developer).
Keynes predicted 15-hour work weeks by 2030 because productivity has grown faster than he expected, but he underestimated our appetite for consumption and new desires—we don’t run out of things to want, which creates endless demand for human labor.
The VC funding model has fundamentally shifted from valuing companies on forward earnings to valuing them on current revenue multiples because AI makes the future so unpredictable that you can’t discount uncertain future cashflows back to present value.
Resources
Chapters
Note: Timestamps are approximate and may vary across listening platforms due to dynamically inserted ads.
(0:00) Introduction
(3:24) The taxonomy of jobs
(6:13) How LLMs solve the job categorization problem
(18:55) The philosophy of taxonomy: From dogs to job titles
(28:14) Finding the right level of granularity
(34:13) Keynes’ prediction: Why we don’t work 15-hour weeks
(37:23) AI, creativity, and what work remains human
(42:59) How the VC funding model has transformed
(52:57) Why large regulated organizations struggle to adapt
(1:03:01) From typist to database administrator: Job evolution in action
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