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Voyagers Beyond

Work & AI

What were my fifteen years for?

A tool produced in minutes something that resembles work you spent years learning to do. The question is not rhetorical. It deserves a real answer about what those years actually built.

A first orientation

The years built four different things that usually get counted as one. They built the ability to produce the output. They built familiarity with the segment of the workflow where that output lives. They built judgment: knowing when the output is wrong, what it will cost, and who will pay. And they built a market price that was attached to the bundle.

The tool reproduced the first of those and part of the second. The third is what you use to check the tool. The fourth is now being renegotiated, which is painful, but it is a different problem from the years being wasted.

The distinction

Fused
  1. 01Output
  2. 02Workflow segment
  3. 03Judgment and responsibility
  4. 04Market price
Related. Not identical. The years live mostly in the third.

Verify

Evidence notes

Working paper

Generative AI at Work

Erik Brynjolfsson, Danielle Li, Lindsey Raymond · National Bureau of Economic Research · NBER Working Paper 31161; later published in The Quarterly Journal of Economics · 2023-04

What the source supports

  • In this setting, a generative AI assistant increased average productivity, measured as issues resolved per hour.
  • The gains were concentrated among novice and lower-skilled agents. Experienced, high-skill agents saw minimal improvement.
  • The authors interpret the tool as spreading the practices of more able workers to newer ones.

What it does not support

  • That experienced workers in other fields will see the same pattern. The study covers one company and one kind of work.
  • That AI assistance makes experience worthless. The tool learned from the experienced agents' own behavior.
  • Any claim about job loss, wages, or long-term careers. The study measures task performance, not employment outcomes.

What Sougata is doing with it

Sougata uses this as evidence that the visible output gap between novice and expert can shrink quickly, while noting that the expert's judgment is what the tool was trained to imitate. That distinction between output and judgment is central to Episode 1 and is Sougata's synthesis, not the paper's claim.

Source note

Working paper

Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality

Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, Karim Lakhani · Harvard Business School · HBS Working Paper 24-013; forthcoming in Organization Science · 2023-09

What the source supports

  • AI capability is uneven across tasks that look similar in difficulty. The authors call this the jagged frontier.
  • For tasks inside the frontier, consultants with the tool performed measurably better on speed and rated quality.
  • For a task outside the frontier, tool use was associated with worse accuracy.

What it does not support

  • That the frontier is fixed. The authors describe it as moving and hard to see from the inside.
  • That professionals can reliably tell in advance which side of the frontier a task sits on.
  • Any general claim about consulting careers, hiring, or the value of experience over time.

What Sougata is doing with it

Sougata uses this to support one specific point: the segment of a workflow that a tool handles well is not the whole workflow, and knowing where the tool fails is itself a form of experienced judgment. The framing of workflow segment versus judgment is Sougata's, not the paper's.

Source note