Execution Economy

The future of work will be defined by how directly a person, unit, or system can turn intent into outcome. Work that doesn’t contribute to this connection is increasingly seen as overhead.

Here’s the hypothesis: the future of work will be defined by how directly a person, unit, or system can turn intent into outcome. Work that doesn’t contribute to this connection is increasingly seen as overhead. ¹

A couple of years ago, I came across Bullshit Jobs by David Graeber, which argues that many roles aren’t designed to solve real problems, but to preserve appearances and justify structure. One of the ways he illustrates this is by asking: what material value does this job generate? At the time, I was managing a global team in a publicly listed company, shortly after its IPO — and the question stuck with me.

Later last year, Paul Graham articulated a distinction between Founder Mode — when decisions are fast and momentum comes from building — and Manager Mode, where work shifts toward coordination over creation. This transition from direct action to layered coordination is often treated as inevitable. If execution doesn’t scale — we scale coordination instead.

By combining Graeber’s lens — roles that generate or don’t generate material value — with Graham’s founder-versus-manager framing, I've come up with the mental model of Simulation Economy.

The Simulation Economy is the way of operating where a significant portion of effort is spent performing activities that communicates progress but don’t necessarily drive real outcomes.

In this model, meetings simulate alignment. Roadmaps simulate motion. Processes simulate progress. Entire layers of organizations exist to make activity look like momentum — without meaningfully advancing toward results.

Simulation wasn’t born from laziness. It was a rational response to complexity — a way to mitigate the risks of poor prioritization and failed execution, and to manage fragmentation, specialization, and information asymmetry. ²

But as AI increases the speed and capacity to build, the time spent coordinating what and how things are built begins to work against momentum. When a single person or a small unit can take a problem end-to-end, handoffs become overhead. What used to take months of development can now be tackled in days. What once required escalation or consensus can now be addressed closer to the problem. So the tradeoffs of simulation become harder to justify.

This isn’t about AI adoption in companies — it’s about how this new level of execution forces us to redesign the systems we once created.

In contrast to this Simulation Economy, exists what could be called an Execution Economy: a way of working where progress is driven by proximity to the problem, and value is measured by the ability to turn intention into outcomes. A model where trust comes not from visibility, but from ownership and forward motion.

Here’s an initial articulation of how the models compare

This shift doesn’t just affect how organizations operate — it reshapes how individuals think about the value of their work and how companies design incentives.

As Victor Popper once shared with me, we’re often taught to associate value with effort: how much time we’ve spent, how carefully we’ve followed the process, how well we’ve executed a specific task. But in the execution economy, the question that matters most is: did it solve the problem? Value is increasingly measured by outcomes, not inputs. That doesn’t mean care and craft are irrelevant — they matter, but only insofar as they contribute to resolution. ³

For individuals, this means rethinking their own relevance: not by how much they do or how well they report it, but by how directly they move work forward. For companies, it means designing systems that reward action, unblock execution, and trust those closest to the problem. The most relevant professionals — and the cultures they thrive in — will be those who reduce the distance between intention and result. Not by managing abstraction, but by owning the path to impact.

This can be unsettling, especially for those who built careers around coordination, rigor, and delivery. The rise of AI and automation, paired with institutional ambiguity, creates fears that human contribution is becoming obsolete. ⁴

Yet the same shift opens new possibilities. When applied intentionally, technology can unbundle the layers that kept people distant from impact. It can shorten the path from decision to execution. It can bring power closer to the problem — and to the people best positioned to solve it. Designers can build proof-of-concepts. Engineers can design high-quality interfaces. Marketers can make profound data analysis. A new generation of multi-skilled professional is rising. ⁵

This shift doesn’t promise that work will get easier. But it invites us to make it more meaningful. Less performance, more progress. Less role, more ownership. Less simulation, more action.

At its core, the distinction between Simulation and Execution reframes how we see work — not as the appearance of effort, but as the ability to generate outcomes. It challenges us to look at every task, every role, every system, and ask: is this helping us solve the problem, or just making it look like we are?

Outcome-based frameworks are not new. We’ve been talking about them for decades. The difference now is technological. What’s changing isn’t the aspiration — it’s our capacity to deliver on it. AI, automation, and better interfaces are finally making it possible to collapse the layers we once needed to bridge through process, specialization, and coordination. What was once a matter of alignment can now be a matter of execution. ⁶

Whether this becomes a path to liberation — or simply a sleeker way to reinforce old patterns — depends on what we choose to build, protect, and leave behind.

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PS: I recently stepped into the role of CEO at Loja Integrada. This newsletter won’t focus on the company, but I will apply — and test — these ideas from the inside out.

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1. I’m referring broadly to the corporate world — and more specifically, to the culture and practices of tech companies. Of course, "work" can mean very different things. The concept of work has historically been shaped as much by ideology and structure as by necessity or utility. What we define as 'valuable work' in a corporate or tech context — might be completely irrelevant or even absurd in other social, cultural, or economic settings.

2. Fragmentation refers to the splitting of work between different roles, squads and organizations, often leading to lagging processes and lack of ownership. Specialization reinforces this by narrowing the scope of what each individual or team is responsible for — creating efficiency, but also dependency. Information asymmetry emerges when access to relevant knowledge or context is unevenly distributed, often requiring layers of coordination to fill in the gaps.

3. In his essay The CSPO Pathology, Marty Cagan describes better than myself a common failure mode for Product Managers: focusing more on managing process, ceremonies, and alignment than on actually solving customer problems.

4. Graeber captured this fear with precision: instead of freeing people, technology is often used to maintain layers of managerial or performative labor that simulate productivity but contribute little to actual outcomes. This fear — that technology might entrench the meaningless rather than unlock the meaningful — is increasingly visible as automation and AI rise.

5. In Rethinking Work, Rishad Tobaccowala suggests that professionals must build a "portfolio of skills" rather than rely on a single area of expertise. He emphasizes that adaptability — across disciplines and contexts — is increasingly necessary in environments where change is constant. The most valuable individuals are those who can combine left-brain and right-brain thinking, logic and empathy, storytelling and analysis. These aren’t just multi-skilled workers — they’re integrators, connectors, and momentum-builders across functions.

6. For decades, frameworks like OKRs and Opportunity-Solution Trees have emphasized outcome-based thinking. These models have encouraged teams to start from the problem, not the solution; to measure value by impact, not activity.