The Allocation Economy: Manage Models Like a First-Time Manager
AI turns everyone into a manager — the valuable skills become the ones first-time managers must learn
- Difficulty
- Moderate
- Time to result
- ~ongoing to results
- Steps
- 4
- Confidence
- 88%
Shipper's allocation-economy thesis: we're shifting from a knowledge economy (paid to do a task) to one where the scarce, valuable skills are management skills. Working with AI is literally managing — communicating the problem, gathering context, choosing which model, dividing the task, giving feedback, judging output. Today only ~8% of the workforce manages; soon everyone will 'manage models,' so the classic first-time-manager dilemmas become universal skills to learn.
Origin
Based on Dan Shipper's essay 'the allocation economy,' written ~2.5 years earlier (pre-agents), from observing how he actually used GPT-3/4 day to day.
Core principles
- 01The scarcest valuable skills become manager skills: evaluating talent, vision, taste, and knowing when to dive into details
- 02Working with a model is managing — you communicate, contextualize, allocate, and give feedback
- 03'I can't trust it to do it well so I'll do it myself' is exactly the first-time-manager trap
- 04Management skill is currently rare because managing was expensive; AI makes it cheap, so it spreads
- 05The best predictions come from using the tools yourself every day
How to run it
- 1
Reframe AI work as management, not doing
Recognize that prompting is the manager's loop: communicate the problem, gather and shape the right context, pick the right model, divide the task, give feedback, hold a vision and success criteria.
Pro tip If you catch yourself thinking 'that's just managing' — that's the signal you're in the allocation economy.
- 2
Resist the do-it-myself reflex
When output isn't what you wanted, don't retreat to doing it yourself (zero leverage). Treat it as the delegation-vs-micromanage judgment every new manager must learn.
Watch out Doing it yourself feels safer but forfeits all leverage — the whole point of the shift.
- 3
Learn when to lean in vs. delegate
Practice the core management judgment: when to micromanage a little, when to trust and delegate, and how to divide a task across models based on their strengths.
- 4
Invest in the durable skills
Build the skills the essay flags as rising in value: evaluating talent, vision, taste, and knowing when it makes sense to get into the details.
Pro tip Entry-level people now learn to do the work AND to manage simultaneously, effectively starting one level above entry.
In the wild
Using GPT-3/4 daily, Shipper noticed he spent his time deciding how to communicate the problem, gather the right information, pick which model, divide the task, and give feedback — then realized 'Oh, that's just managing.' The common objection 'I can't trust AI to do it well so I'll do it myself' is, he notes, exactly what every first-time manager says.
→ A framework naming the shift and predicting which skills — evaluating talent, vision, taste, when to dive in — become most valuable.
Common mistakes
Falling into the first-time-manager trap
Refusing to delegate because output isn't perfect yields no leverage; the skill is learning when to trust, when to micromanage, and how to divide the work — not reverting to doing it all yourself.
Is it for you?
Best for
Knowledge workers and early-career people orienting their skill development for an AI-mediated career
Not ideal for
Those seeking a concrete step-by-step process rather than a strategic lens on which skills to build
From the transcript
“one big group of skills are the skills of managers. Today they're human managers. Tomorrow everyone's a model manager”
“And I was like, "Oh, that's just managing."”
“that's exactly what every first time manager says”
“If I do it myself, I get no leverage”
From the episode
The AI-native startup: 5 products, 7-figure revenue, 100% AI-written code
Dan Shipper (co-founder/CEO of Every)