Why the future of work isn’t about making hierarchies more efficient, but building mission-driven communities
The McKinsey Mirage
The latest McKinsey report on people management promises a revolutionary future: hyper-personalized employee experiences, AI-driven insights, and streamlined operations. It sounds transformative until you realize what it’s really proposing—using artificial intelligence to optimize the same industrial-era management structures that have defined corporate life for the past century.
This is optimization masquerading as innovation. While the tools are new, the fundamental assumptions remain unchanged: employees are resources to be managed, hierarchies exist to control decision-making, and success comes from predicting and directing human behavior through data and incentives.
What McKinsey is offering is not forward looking nor thought leadership. The report fails to acknowledge that our world is fundamentally shifting, and if the AI revolution is as big as they want us to believe, jobs will mean something different in the future, and our organizations need to adapt. That means tearing down and rebuilding not incremental gains on antiquated systems.
The Ecosystem Reality
Organizations don’t compete in isolation, they compute in ecosystems. Value creation increasingly happens at the intersections, through emergent collaborations that can’t be captured in org charts or predicted by algorithms. Yet our management structures still treat companies as closed systems that can be optimized through better internal processes.
As AI capabilities accelerate, this disconnect becomes fatal. When machines can handle predictable, measurable work, the very tasks that hierarchical management was designed to coordinate, human value shifts to the unmeasurable: creativity, empathy, innovation, and the ability to navigate complexity and ambiguity.
The goal should not be to use AI to make human resources more efficient. It should be to organize human potential. With AI we can remove the fundamental constraints of information scarcity, coordination costs, and automate standardized processes, and that leaves little need for hierarchical management.
From Control to Autonomy
The industrial model succeeded because it solved the problems of its era: how to coordinate large numbers of people doing predictable work with limited information and slow communication. It created hierarchies where decisions flowed down and information flowed up, with managers serving as filters and coordinators.
But in an AI-abundant world where the needed information is readily available the power dynamic can shift from: “who has the information to decide” to “who cares enough to act.”
This creates the possibility for distributed agency at unprecedented scale, what we might call “superminds” emerging from collaborative, creative workforces.
This isn’t about eliminating structure. It’s about evolving from imposed control to emergent coordination. Instead of rigid hierarchies, imagine fluid networks organized around shared missions. People self-organize based on passion and capability, with natural selection determining which missions gain traction and which fade away.
From Theory to Reality
This isn’t theoretical. We already see it working in real-world contexts. On GitHub, no one waits for permission to fix a bug or contribute to a feature. Leaders emerge because they act, not because they’re assigned. Rules are created, followed, revised—not from headquarters, but from the ground up. The clearest signal of leadership is contribution, not control.
When Hurricane Katrina hit New Orleans, much of the effective rescue effort came not from the official chain of command, which was slow and disjointed, but from ordinary individuals stepping up. Ad hoc teams formed, neighbors shared resources, and people adapted in real time. The most impactful coordination often came from those who took responsibility without formal authority.
Open source projects routinely solve problems that stumped large corporate teams. Linux competes with Microsoft. Wikipedia replaced Encyclopedia Britannica. These successes come from distributed intelligence, not centralized planning. When AI acts as connective tissue enabling knowledge sharing, pattern recognition, and coordination without central command organic coordination patterns become possible at unprecedented scale.
MIT’s Thomas Malone calls these “superminds”. Collective intelligence systems that combine human creativity with technological coordination to solve problems no individual or traditional hierarchy could tackle alone. What we’re describing isn’t revolutionary. It’s the natural evolution of patterns that already work when conditions allow them to emerge.
Natural Selection for Value
In hierarchical systems, advancement comes from managing up. Impress your boss, claim credit, and avoid blame. The skills that lead to promotion are often orthogonal to the skills that create value. This creates perverse incentives where political players rise while contributors remain stuck.
Mission-driven organizations operate on entirely different principles. Instead of centralized resource allocation creating power bottlenecks, imagine each person receiving a baseline allocation of organizational resources (budget, tools, access) that they can deploy based on their judgment of what creates value.
This creates a natural venture capital system within the organization. In a food production company someone working on soil optimization might invest part of their allocation in their own team, part in logistics networks that help distribute their innovations, and part in community initiatives that provide testing grounds. Teams that consistently make smart investments and generate results attract more resources from colleagues who see their success as tied to their own.
The system becomes self-correcting through visible resource flows. People who hoard resources find themselves isolated and struggling. Those who invest wisely in ecosystem success build track records that attract collaboration. Missions that can’t demonstrate traction naturally lose support and dissolve, freeing resources for more promising initiatives.
Gaming this system requires actually creating value. Exactly the behavior you want to incentivize. The political players who thrived in captive hierarchical audiences find themselves leading empty rooms when people can simply walk away to more effective collaborations.
This creates natural accountability. When people rotate between different missions and teams, their absence reveals their true impact. The person who prevents conflicts or catches problems early might not generate visible outputs, but when they’re gone, team performance deteriorates. This organic measurement system surfaces contributions that traditional metrics miss.
Real-world Evidence
The above is not new. We’re already seeing the emergence of this new model. At Valve, one of the most successful game companies in the world, there are no formal managers. Employees move between projects based on interest and perceived value. The act of joining a team is itself a resource allocation signal, a vote of confidence. Projects that attract energy thrive. Others wither.
Meanwhile, in China, Haier has turned an 80,000-person appliance company into a federation of 4,000 micro-enterprises. Each unit contracts with others in an internal marketplace and competes for resources by delivering measurable value. This creates natural accountability and evolutionary pressure. Only the most adaptive teams survive.
Haier specifically shattered its organizational structure, and rebuilt with future technological shifts in mind. Their Rendanheyi has taken them from the walled garden (of Western inspiration) to a rainforest, where parts of the organization organically die off and others grow up.
These models are not fringe. They are functional prototypes of a post-industrial organizational paradigm. Ones that use technology to decentralize power, surface real contribution, and let value, not title, determine influence.
Sidenote: the Haier example is also from a McKinsey interview – shouldn’t that data point have been included in their thinking
The Special Forces Model
The best analogy might be special forces units or elite team sports: elite individuals who succeed through collective excellence. Everyone is highly capable, but their real strength comes from how they elevate each other and function as a unit under pressure. Individual heroics that compromise the mission or leave teammates exposed aren’t celebrated; they’re seen as failures.
This balance between individual capability and team success could define the new organizational model. You want the best individuals on your team because they’ll make you more effective, and they want to be on teams where they can have maximum impact. It becomes positive-sum: everyone’s success is interconnected.
The continuous rotation between missions would help surface both dimensions. Someone might be individually brilliant but consistently leave teams more fractured. That pattern would become visible. Conversely, someone who consistently helps teams overachieve, even if their individual contributions are less flashy, would build an equally valuable reputation.
The Cultural Foundation
Technology enables this transformation, but culture drives it. The shift from following orders to autonomous responsibility requires new social muscles that most of us haven’t developed. We’ve been conditioned by industrial-era thinking: wait for instructions, optimize within your lane, let someone else worry about the big picture.
Mission-driven organizations will be more conflict-rich, not conflict-free. When passionate people work on things they deeply care about, tensions rise quickly and differences of opinion surface immediately. Most of us have been trained to see conflict as failure, something to avoid or suppress. But productive disagreement becomes essential when teams need to make consequential decisions without hierarchical authority to impose resolution.
This requires developing conflict resilience. The ability to advocate strongly for ideas without attacking people, to receive criticism without defensiveness, to pivot when wrong. The educational system must evolve from manufacturing compliant workers to cultivating creative problem-solvers who can handle the discomfort of sustained disagreement in service of better outcomes.
The natural selection pressure helps here too. In fluid systems where people can walk away, toxic leaders who create destructive rather than productive conflict find themselves leading empty rooms. Meanwhile, those who can channel passionate disagreement into innovation attract the best collaborators.
Beyond the Industrial Dream
We stand at an inflection point. We can use AI to optimize industrial management. Making hierarchies more efficient, predictions more accurate, and control more sophisticated. Or we can use it to enable entirely new forms of human organization based on autonomy, purpose, and distributed creativity.
The first path leads to the McKinsey vision: personalized employee experiences delivered through algorithmic management, where humans become more efficient resources in an optimized machine. It’s seductive because it builds on familiar structures and doesn’t threaten existing power arrangements.
The second path leads to something unprecedented: organizations that harness human potential rather than constrain it, where people self-organize around missions they care about, where influence flows to those who create value, and where work becomes an expression of purpose rather than just a paycheck.
This isn’t naive idealism, it’s a possible future. As AI handles more of the predictable work, human value increasingly lies in creativity, empathy, and innovation. These emerge from autonomy and purpose, not control and optimization. Organizations that embrace this reality will outcompete those that don’t.
The question isn’t whether this transformation will happen, it’s whether we’ll have the courage to create it. The future belongs to those bold enough to imagine work as something more than efficient resource management, and creative enough to build the systems that make it possible.
As John Lennon sang, “You may say I’m a dreamer, but I’m not the only one.” The tools exist. The models are emerging. The future of work isn’t about making humans more efficient. It’s about making human potential more possible.