In any algorithmically managed system of work, workers occupy one of two positions: above-the-algorithm or below-the-algorithm. Above-the-algorithm workers leverage the power of algorithms to gain outsized returns on their efforts. This includes engineers, designers, and data scientists who design and operate algorithmic coordination systems. This

In moving from Nike to Shein, we moved from managerial oversight to algorithmic coordination.

This wasn’t software eating the world - it was coordination eating complexity. Airbnb won by standardizing trust, Stripe by simplifying financial complexity, and Tesla by eliminating charging uncertainty. The most transformative companies don’t just build better tools; they solve coordination problems that unlock new forms of economic activity.

Finally, the rise of cloud-based services enables companies to access specialized operational capabilities as needed.

When technology levels the playing field, it often reduces the skill premium by making workers more interchangeable with one another.

When companies deploy AI for isolated task automation rather than organization-wide coordination, they introduce a paradox: the very technology meant to improve coordination fragments the organization and increases coordination costs.

Yet, shifting our focus from tools and task-level improvements to the broader system, and how it is being transformed, is far from easy. Technological solutionism is seductive; it offers quick fixes and makes for attention-grabbing headlines as companies claim to cut headcount and deliver process improvements using AI. In fast-moving environments,

The task-based framing suggests that jobs are bundles of tasks, some of which are more susceptible to automation than others. The key determinant of automation risk wasn’t the job title or education level, but whether the tasks performed were routine or non-routine.

The more fragmented the system and the more diverse the incentives of the players, the greater the value in aligning them to work together.