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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. The useful answer is not a defence of those years. It is a way to find out which lessons in them are still worth something to somebody else.

A first orientation

The years gave you problems you recognise, questions you have learned to ask, and lessons you have used often enough to trust. That is not the same thing as the output a tool can now produce quickly.

There is a way to find out what one of those lessons is worth without arguing about it. Take a problem people already ask you for help with, write down the questions you ask, and let one person try it on something small. What they actually do with it, and where they get stuck, tells you more than any prediction about AI can.

The distinction

Fused
  • Something you know
  • Something someone else can use
One person trying it is what moves a lesson from the first to the second.

Verify

Evidence notes

Working paper · 2023-04

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

Evidence

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.

Boundary

What it does not establish

  • 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.

Interpretation

What Sougata is doing with it

Sougata uses this as context for why Episode 1 exists: in this setting the visible output gap between newer and more experienced workers narrowed quickly. That is the situation the Second Door Plan responds to. The paper does not evaluate the Second Door Plan, and nothing in it supports a claim that writing a lesson down and testing it produces any career outcome. The connection between the two is Sougata's reasoning, not the paper's finding.

Source note

Working paper · 2023-09

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

Evidence

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.

Boundary

What it does not establish

  • 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.

Interpretation

What Sougata is doing with it

Sougata uses this for one specific point: a tool's competence is uneven in ways that are hard to see from inside a task, so knowing where it fails is worth something. This is context about AI and work. It is not evidence that the Second Door Plan helps anyone, and the paper makes no claim about that method.

Source note