The work you never did because it never paid is where the largest AI return sits. · VANDAM

This page as Markdown llms.txt llms-full.txt services.json sitemap.xml
# The work you never did because it never paid is where the largest AI return sits. · VANDAM

> The largest AI return is the expert work a mid-market firm skips today. What Jevons, ATMs and translation show, and how to pick the first three.

URL: https://vandam.ai/notes/work-you-never-did  
Contact: hendrik@vibelab.be

# The work you never did because it never paid is where the largest AI return sits.

Most AI business cases count the hours of work a company already does, which caps the return at today's labor cost. The evidence from Jevons to bank tellers says cheaper work expands where demand was rationed, and I set out how a leadership team lists, prices and picks the expert work it skips.

Hendrik De Winne 26 October 2026 8 min read

Last spring, Austin Lau, then a growth marketer at Anthropic, described a Google Ads workflow he had built with Claude. Once a company spends enough on search, he wrote, “no marketer is ever going to go through tens of thousands of search terms daily, let alone EVERY single search term to identify negative keywords.” So the agent does it, every day. In [his own summary](https://x.com/helloitsaustin/status/2041218668072403356), it “was building a workflow for something I’d never do.” Of the four ways he used AI, he called this one “the one that most people overlook.”

That is the gap in almost every AI business case I read. The shortlist starts from the work people do today, and the saving is capped at what that work costs today. If your company spends 400 hours a year on contract review, the best automation can ever return is 400 hours. The review you skip on 80% of your deals has no hours attached, so it never appears on the shortlist, even though the money it touches can be far larger than the hours.

Economics has a precise answer for when cheaper work turns into more work, and it is useful to a CFO. It depends on whether demand was being held back by price. Demand for a mid-market company’s own expert work is held back about as hard as demand gets, because the company rations it: contract review on the big deals, churn analysis on the top accounts, a pricing review once a year. Rationed demand is exactly the kind that expands when the price falls.

- **2013**
  Tellers needed per urban bank branch, 1988 to 2004, as ATMs spread, per [James Bessen](https://www.imf.org/external/pubs/ft/fandd/2015/03/bessen.htm) (2015).
- **43%**
  Growth in urban bank branches over the same years, so teller jobs did not disappear.
- **61%**
  Share of New York-area service firms using AI in 2026, up from 25% in 2024, per the [New York Fed](https://libertystreeteconomics.newyorkfed.org/2026/09/businesses-are-using-ai-to-transform-work-not-cut-jobs/).
- **4%**
  Share of those service firms that laid off workers in response to AI in the past six months.

## Cheaper work has usually meant more of it

The idea is old. In 1865 the English economist William Stanley Jevons argued in [The Coal Question](https://energyhistory.yale.edu/files/2023/06/Jevons-The-Coal-Question.pdf) that more efficient steam engines would raise Britain’s coal use. “It is wholly a confusion of ideas to suppose that the economical use of fuel is equivalent to a diminished consumption. The very contrary is the truth.” Cheaper coal per unit of work made coal worth using in places it had never paid. Economists now call this the Jevons paradox.

The service version I find most useful is the bank teller. ATMs spread through American banking from the mid-1990s, and the obvious forecast was that tellers would vanish. [James Bessen](https://www.imf.org/external/pubs/ft/fandd/2015/03/bessen.htm), an economist at Boston University, showed in 2015 that it went the other way. The number of tellers needed to run an average urban branch fell from **20** to **13** between 1988 and 2004. A branch became cheaper to run, so banks opened more of them, and urban branches grew by **43%**. “Fewer tellers were required for each branch, but more branches meant that teller jobs did not disappear.”

Aaron Levie, CEO of Box, makes the same case for marketing in his essay [Jevons Paradox for Knowledge Work](https://x.com/levie/status/2004654686629163154). He estimates that the US had a few hundred thousand people in marketing-related jobs in the 1970s and has low millions today, because software let almost any small business “justify the ROI of doing more sophisticated marketing.” He calls his figure back of the envelope math, and he runs a company that sells AI to businesses, so I read it as his estimate. The direction matches the teller story.

## It fails where demand was already full

The paradox has a condition, and it decides where your money should go. In a [2019 paper in Economic Policy](https://ideas.repec.org/a/oup/ecpoli/v34y2019i100p589-626..html), Bessen traced two centuries of US textile, steel and auto employment. Automation first added jobs in all three, then cut them. In his words, “What changed? The elasticity of demand.” While people wanted much more cloth than they could afford, cheaper cloth meant more cloth and more workers. Once people had enough, each new machine simply removed labor.

Translation shows the failure case today. Carl Benedikt Frey and Pedro Llanos-Paredes of [Oxford](https://www.oxfordmartin.ox.ac.uk/publications/lost-in-translation-artificial-intelligence-and-the-demand-for-foreign-language-skills) studied 695 US local labor markets after Google Translate launched as an app, and published in January 2025. Areas that adopted machine translation more heavily saw translator employment fall, and employer demand for foreign language skills fell with it. In that market the cheap version substituted for paid work instead of adding to it.

The rule I take from the two cases: falling prices create new work where demand was rationed by price, and remove work where demand was already met. That turns the question for a leadership team into a concrete one: which expert work does it ration today because the full version costs too much.

## Mid-market companies ration expert work the hardest

A company of 400 people rations expert work all the time without calling it that. Legal reads the master agreements on the large deals and lets the rest go out on the template. Customer success studies the accounts that are already shouting. Pricing gets reviewed when someone has a week free. Invoices are audited by sample. Win and loss interviews happen after the deals people remember. Each of those is a decision that the full version is not worth an analyst’s salary.

A Fortune 500 company makes far fewer of those trade-offs, and Levie puts the difference plainly in the same essay.

> For most large companies today, they can effortlessly move resources around between projects, afford to experiment on new ideas, hire the top lawyers or marketers for any new project they need
>
> Aaron Levie, CEO, Box, December 2025, [Source](https://x.com/levie/status/2004654686629163154)

His claim is that “AI agents bring democratization to every form of non-deterministic knowledge work,” and his list starts with reviewing contracts, market research and 24/7 support. I covered his budget argument in [the note on pricing AI per finished task](https://vandam.ai/notes/cost-of-the-i). The part that matters here is where he points: to the work a smaller company has never been able to afford. In April he gave the operating test in [a post on agent roles](https://x.com/levie/status/2043883641366032638): look for tasks where agents let you “either execute it 100X faster or do it 100X more times than before.” Most shortlists only look for the first.

## So far, companies are reorganizing more than cutting

The early data fits this picture better than the layoff headlines do. In the New York Fed’s [regional business survey](https://libertystreeteconomics.newyorkfed.org/2026/09/businesses-are-using-ai-to-transform-work-not-cut-jobs/), published in September,, **61%** of service firms used AI, yet only **4%** had laid off workers because of it in the previous six months. About 15% said they had hired fewer people than they otherwise would, and 13% said they had hired more. Among AI users, just over a third of service firms were retraining staff. The authors conclude that “existing workers are much more likely to be retrained than replaced by AI.”

Denmark offers the deepest look so far. Anders Humlum and Emilie Vestergaard linked chatbot adoption surveys to national employment records in an [NBER working paper](https://www.nber.org/papers/w33777) revised this March. They find “precise null effects on earnings and recorded hours” two years after ChatGPT, while “new AI-related tasks are widespread,” including content generation, AI oversight and AI integration. Employers are absorbing the technology by changing the work.

None of that happens on its own. [Nikita Bier](https://x.com/nikitabier/status/2004985707870445855), former head of product at X, noticed in December that once the cost of making software and video fell, “all the same people are doing it.” Cheaper tools changed what was possible and left the question of who starts untouched. The same holds inside a company: the skipped contract review stays skipped until someone decides it is now worth doing, gives it an owner and a budget, and acts on what it finds.

## How to list, price and pick the work you skip

This is the exercise we use. It takes one leadership meeting and a week of follow-up.

**1. List.** Each executive brings five items, answering three prompts. What do we check by sample that we could check in full? What do we do yearly or quarterly that would pay monthly or weekly? What does a company twice our size do that we skip? Typical answers: contract review on every deal, a churn read on every account every month, a monthly pricing and discount review, a full audit of vendor invoices, win and loss interviews on every closed deal, competitor tracking on every product line.

**2. Write down the decision.** For each item, name the decision its output would change and the person who makes that decision. An item with no decision attached is a report, and it comes off the list.

**3. Price it twice.** First, what it would cost with people at today’s rates. That number explains why you skip it. Second, what it costs with agents: the run cost plus the human hours to review the output and act on it. The review hours are usually the larger line, and they are the one most cases leave out.

**4. Size the money it touches.** Estimate the revenue, leakage or exposure the item reaches: the renewal base the churn read covers, the spend the invoice audit covers, the contract value that goes out unreviewed. Then make a conservative guess at the share you would act on. Write the guess down so you can test it later.

**5. Pick three.** Rank by money touched times your conservative share, divided by the agent-era cost. Take the top item from three different categories, ideally one about revenue, one about leakage and one about risk, so a single bad assumption cannot sink the whole program. My rule for the shortlist: an item makes it only if a named person has agreed to act on its output within a week of receiving it, and the output is something a person can check and mark as wrong.

Run each of the three for 60 days alongside current practice. The test is simple: what did the full version find that the sampled version missed, and what was it worth? At the end you will know whether your demand for that work was rationed, which is the only condition under which the Jevons logic pays.

## What to do this month

If you run the company, put the exercise on the next leadership agenda and ask every function head for their five items in writing beforehand. If you run finance, add a second section to the AI business case template, next to hours saved, titled work we will now do, with the decision it feeds, the owner, and the money it touches. If you run operations, take the first item on the list that runs on data you already have, such as invoice lines or support tickets, and run the full version once by hand with an agent this week, so the leadership meeting starts from a real result.

## More notes

29 October 2026 6 min read

## Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.