June 8, 2026

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THIS WEEK'S EDITION

 Why AI Isn't Coming For The Jobs You Think—Part 2

Last week, we showed you how economic efficiency gains, historically, have created more demand than they've destroyed—known as Jevons Paradox. In the job market, we're seeing this play out already. One example: cheaper software development means more things that we build with software, creating higher, not lower demand for software engineers. 

Jevons Paradox has suddenly changed the tune of many of the brightest minds in the country, who until even a few weeks ago were predicting an "AI jobs apocalypse." This week, we discuss another reason gaining traction that points against AI causing mass unemployment—the fallacy that there's only so much work to go around. Economists have a name for that mistake, and they've been poking holes in it for two hundred years. In today's short read, we explain the lump of labor fallacy, show you that most of today's jobs are relatively new, and discuss what public affairs jobs AI is likely to create.

The Fixed-Pie Mistake

The "lump of labor fallacy" is the belief that an economy holds a fixed amount of work—one pie—so every slice a machine takes is gone forever. It sounds like common sense. It's almost always wrong.

In 1900, roughly 40% of Americans worked on farms. As a result of agriculture mechanization, today that's under 2%. By the fixed-pie logic, nearly 40% of the country should have been thrown into permanent unemployment. Instead, those workers—and their kids, and their grandkids—went on to do jobs that hadn't been invented yet. Today we have nearly the lowest unemployment in history. The pie wasn't fixed. We kept baking new ones. 

Your Job Is Younger Than You Think

MIT economist David Autor has shown that roughly 60% of the jobs Americans hold today didn't exist in 1940. Sit with that. The majority of us earn a living doing things that hadn't been invented when our grandparents started working. Software developer. Physician assistant. Paralegal. Genetic counselor. Fiber installer. None of it existed before the war. When it became much easier to make food, we didn't just sit on the couch and eat it. We created new things that in turn created new jobs. Across eighty years of staggering automation, the "lump" of work didn't shrink—it grew, and it changed shape.

The Jobs AI Is Already Inventing

You don't have to wait eighty years for the next wave of jobs. It's already here. Below are just a few examples:

🔱 Forward Deployed Engineer. A year ago this title barely existed outside Palantir. Now OpenAI, Anthropic, Google, and hundreds of other companies are racing to hire thousands of them—engineers who embed inside your organization to drag AI from impressive demo to actually working. It's one of the fastest-growing roles in tech.

🔱 Context Engineer. The person who feeds a model the right institutional knowledge so it stops guessing and starts knowing—increasingly the line between a useful AI and a useless one.

🔱 AI Evaluation Specialist. Someone whose entire job is stress-testing models for errors, bias, and hallucinations before they ever reach the public.

A decade ago, none of these were jobs. Today people have them on their business cards.

This is in addition to countless existing categories of jobs that AI is creating increased demand for: electricians, construction workers, and natural gas plant operators building and powering the massive new AI data centers—just to name a few.

What This Means For Public Affairs

Public affairs won't be the exception to the lump of labor fallacy. Some tasks will absolutely get automated—first drafts, list-building, legislative and news monitoring, the grind of compliance. That's real, and pretending otherwise helps no one. But the same shift will create roles we're only beginning to name. Here are a few educated guesses on new public affairs jobs you are likely to see soon:

🔱 Forward Deployed Advocacy Engineer. The political-shop version of tech's hottest new hire—someone who embeds inside an association, agency, or campaign and wires AI into the actual workflows.

🔱 AI Advocacy Strategist. The person who designs and supervises campaigns that AI agents execute at a scale no human team could match.

🔱 Policy Context Engineer. Someone who trains models on a client's legislative history, coalition map, regulatory record, and countless other data sources so the output drives maximum value.

Three Ways To Operationalize This Email

🔱 Audit your lump of labor. List the tasks on your team that AI could automate this year—then ask what higher-value work that frees your people to do.

🔱 Hire for the new shape. Next time you add a role, write the description around judgment, strategy, and AI supervision—not the tasks a model now handles.

🔱 Name your future roles now. The teams that win will be the ones inventing job titles before their competitors realize the titles exist.

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