Wanted: High Performers for the Last Job You’ll Ever Have

Legend has it that in the winter of 1913, explorer Ernest Shackleton put out an ad for sailors to join him on an expedition to Antarctica:

Men wanted for hazardous journey. Low wages, bitter cold, long hours of complete darkness. Safe return doubtful. Honour and recognition in event of success.

Today, in September of 2023, I’d like to propose a similarly adventurous job ad for the age of AI: 

Talented engineers, designers, and copywriters wanted for a new agency. All work will be recorded, labeled, and organized for AI training. Your role will be progressively phased out. Salary paid and profits shared to you indefinitely. Failure likely. In the event of success: the last job you’ll ever have—or need.

Here’s the idea: I think there’s an opportunity to start an agency that recruits extremely talented people to train AI by promising them it will be the last job they’ll ever have. 

Agency employees will do client work, like engineering, design, or copywriting. They will also record and label the entire process from start to finish as input to model fine-tuning. The agency’s goal is to progressively phase out each employee by training the model on their work. And the employees are in on it! Everyone wants to be replaced because—if it works—they get to keep their salary and upside in the form of dividends, for the life of the business. 

A professional services firm that’s structured in this way might have a margin profile that looks a lot more like software (good) than consulting (bad). It could become what The General Partnership investor Ben Cmejla described to me recently as a “mullet consultancy”: from the front it looks like a services business with salespeople and account managers, and from the back, it looks like a software business because most of the actual work is done by the AI.

If a model like this works, it will have broad implications for the types of professional services businesses that can be built, how much they can scale, and how employment agreements should be structured. 

Professional services businesses with software margins

The professional services industry is a gigantic part of the economy—generating $2 trillion in revenue in 2019. It’s a ripe playing ground for first-time entrepreneurs looking to get a business off the ground. Tiny, Andrew Wilkinson’s now-public software-holding company, started as a consultancy. So did Jason Fried’s 37 Signals, makers of Basecamp. If you’re a talented and motivated individual, it’s usually a lot easier to sell your time for money than it is to make a product.

But this comes with a tradeoff: consultancies are maddeningly hard to scale. Despite decades of technological advancements, professional services have been more or less immune to the productivity gains that software promises. You’re trading your time for money, and each employee at a consultancy can only handle so many clients. 

Even if you do manage to get to scale, the business you can build isn’t great by software standards. For example, Accenture, one of the largest consulting firms in the world, has more than 700,000 employees and did $61 billion in revenue in 2022. That’s a revenue per employee of around $87,000. Meta, on the other hand, has 70,000 employees and $116 billion in revenue. That’s a revenue per employee of $1.6 million—almost 20x Accenture. 

It will probably be a long time before firms as large as Accenture are automated. However, there are probably hundreds of thousands of smaller professional services firms in the U.S. that could use AI to significantly change their revenue-to-employee ratio—and their margins. Over time,  certain categories of professional services may end up having a margin profile that’s more similar to software than it is to consulting.

If AI does create room for these kinds of consultancies, how do you make one that produces great work?