TTL 59
In December 2021, I wrote an article about the future of work.
Today, I’m updating that article, particularly looking at how AI has since modified that vision and how, in some ways, it has made it even more urgent.
This was, in extreme synthesis, my thesis:
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We measure the value of work in three ways — by input, output, or outcome.
- Input measures the time spent working. It’s a legacy from another era and a weak metric: it is easy to count but rewards low productivity and triggers a race to the bottom.
- Output measures the volume of tasks completed and things shipped. It’s a vanity metric, because making things doesn’t automatically create value. A focus on output pushes teams to ship useless features just to look productive, and usually ends up confusing the very users they were meant to serve.
- Outcome measures the real impact on customers and the problems solved for them. It rewards fast iteration, reduces risk, and multiplies a team’s chances to learn.
Outcome, as you might have guessed, is my favorite.
Adopting outcomes, however, requires one structural condition: autonomy. By putting teams in a position to decide their own course of action, leaders can evaluate success based on the actual impact of those actions. This shift requires a shared system of values and principles, allowing a leader to trust their team. Ultimately, managers only succeed by becoming leaders who people want to follow.
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But today, something has drastically changed: AI is now managing my calendar, organizing my trips and programming my apps — and tools like these are now available to everyone.
Artificial Intelligence has transformed outcome-driven leadership into a requirement.
Input metrics are obsolete for systems that do not tire: obviously, no one counts how many hours an AI works or how many lines of code it produces. The only measure that matters is the outcome.
You cannot tell an AI agent to “work harder” or “produce more features”. These instructions are nonsense to a machine. You must specify the outcome.
You must grant autonomy, establish shared values so the system makes good decisions within your principles, measure success against objectives. Just like I stated in 2021.
This has an immediate and counterintuitive consequence: management experience becomes more valuable than technical expertise.
A developer skilled only at writing code loses their advantage to an AI that writes faster and never sleeps. The same engineer skilled at organizing resources, setting clear goals, granting autonomy, and iterating based on outcomes becomes irreplaceable.
In 2021, I did not anticipate how quickly AI would make this change inevitable, but it’s quite obvious now: the future of work is based on outcomes.
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I also publish on paolo.blog and monochrome.blog.
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