Free To Obey
Leveraging AI to reduce human resources… using human resources is an irony I couldn’t help but pass through the BomaliQ analytical engine. Applying the “Free to Obey” standard as a managerial gamble to enter the race for the Frontier Firm model, mid-market companies force their employees to develop AI solutions that produce a sense of grandeur, better enabling them to plunge into the decadence of the Silicon Kolkhoz.
Let’s unpack the mechanics at stake and get ready for a plot twist that only the “free to obey” managerial gamble can offer on a silver platter.
Fernand Léger’s 'Le Mécanicien' (1920) depicts the ultimate sovereignty sink: a world in which the manager is no longer the operator but a standardized gear within the platform's algorithmic machinery.
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This briefing is part of the BRS Foundation Series (Signals 1 through 10).
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1. News from the battlefield
Here are three case studies that illustrate the “free to obey” managerial gambling among the mid-market companies.
Case study one: Steel company
A sales manager at a steel company in Canada recently reached out to say employees are experimenting with AI to achieve their goals. Curious I always have been, curious I’ll always be. I promptly asked questions to audit the case. Top management insists on increasing sales by leveraging AI. An objective as bright as the full moon on a summer night. Sales. Profits. But how? Leveraging AI is vague, to say the least. What about boundaries? Framework? Governance? Accountability? Training? The list goes on. The answers don’t. There is no corporate policy for using and leveraging AI to achieve the company’s goals. Just slogans: Be bold, maverick, innovate! And employees execute, each according to what they think is relevant.
The sales manager showed me the dashboard she created using a personal licence of Claude, mostly in her free time. A couple of refined prompts and Anthropic’s magic produced a shiny HTML page that consolidates clients’ data, deadlines, contact information, and the like into a single interactive space. Does the dashboard significantly increase steel sales? What about productivity? It is hard to tell; labour cost margins aren’t easy to compute in the shade of shadow AI. But the sales manager is confident the dashboard saved time and augmented her productivity. A METR study recently showed that this feeling is common but deceptive: AI tools slowed experienced software developers by 19%, even though those same developers believed they were 24% faster.
Nonetheless, the steel company’s management schedules regular department meetings where employees share their new AI-based tools and solutions in a friendly, team-building competition. And the shiny dashboard is about to move up the company’s list of proprietary tools and be deployed across the sales teams. It goes without saying that the workflow and dataflow are now captured and owned by the hyperscalers, but I already covered this in previous editions of the BomaliQ Risk Signal, so I’ll leave it for now.
Case study two: Automotive software company
A couple of weeks ago, I invited a guest speaker to my Advanced Marketing Management course at the Ted Rogers School of Management (yes, you read it right: besides being the Principal of BomaliQ, I’m a tenured associate professor). My guest speaker, a product manager at an automotive software company, shared her company’s current plan to leverage AI to reduce entry-level headcount. Spoiler alert: the pattern is the same as at the Steel company: total focus on the objective—reducing the entry-level headcount by two-thirds—and total freedom in the means, provided AI is used.
The management’s instructions are a pristine illustration of the McDonaldization of corporate labour: automating, streamlining, and standardizing the workflow. Every week, middle managers who monitor entry-level employees’ efforts to leverage AI to create the conditions of their own replacement report their progress and show how their AI-based tools contribute to achieving the objectives. The atmosphere, the guest speaker said, is competitive as the clock ticks. Top management pressures middle managers to deliver significant results by the deadline and to organize the scale-up of the homemade AI solutions.
In this high-pressure phase, the middle manager is the indispensable architect of the transition. They serve as the high-velocity relay between top management’s vague AI mandates and the technical output of the entry-level workforce. By meticulously organizing the scale-up of homemade solutions, these managers believe they are securing their status as the new elite of the AI-driven firm. In reality, they are acting as the foremen of a new industrial logic, building the very infrastructure of coordination that will eventually render their own supervisory role obsolete.
Case study three: The customer loyalty software company
Not surprisingly, this third and final case study reveals the same pattern. The company develops and sells customer-loyalty software and intends to leverage AI (i.e., human-labour-free, automated, scalable, highly productive, etc.) to compete in the market. The CEO reported on LinkedIn the corporate strategy: give every employee a blank afternoon each week with only one instruction: build something with AI. The “free-afternoon-build-with-AI” is packaged with catchy branding, so employees feel emboldened and part of a great adventure. The expectation? None! At least, in the first phase of the experiment, as we will see later. At the time, it was a big brainstorming session.
A quarter later, solutions emerged—mostly automated searches in internal databases, along with scoring, scheduling, and data-entry automation. Nothing revolutionary, really. What about the labour-cost margin before and after the experiment? How much was invested in building these automated solutions, and how long would it take for the investment to become profitable? The CEO is not a fool, and the conclusion he drew is subtler than the numbers: corporate culture!
What an interesting leap in the narrative, isn’t it? This transition from joyful grassroots experimentation to top-down industrial capture is the modern revival of a specific architecture of power based on the delegation of responsibility.
2. “Free to Obey” or the managerial culture of the Silicon Kolkhoz
Free to Obey is a concept developed byJohann Chapoutot, a French historian who studied contemporary management rooted in the nazi architecture of power. Free to obey relies on the Bad Harzburg model developed by the former SS officer Reinhard Hohn, who argued that modern management must break away from the authoritarian Prussian hierarchy (which was too rigid) and move toward leadership through delegation of responsibility.
According to the “Free to Obey” managerial culture, workers are not passive subjects but collaborators (Mitarbeiter) within a community of purpose (Volksgemeinschaft as applied to the workplace). Giving workers time and space to build AI solutions without clear constraints is the very essence of delegation, according to Höhn. Workers are given the illusion of freedom (“Do whatever you want with the AI”). But this freedom is directed toward a single goal: the company’s vital performance.
In the “Free to Obey” managerial culture, managers set the goal, and subordinates are “free” to figure out how to achieve it. Here, the company leverages its employees’ tacit knowledge to have them automate their own processes. It’s essentially crowdsourcing its own obsolescence.
As illustrated in the three case studies I reported above, and consistent with what Chapoutot described in his book, once solutions have emerged from the grassroots through creativity, the structure captures them, standardizes them, and makes them mandatory. This marks the transition from joyful experimentation to the industrial imperative of results.
3. The mechanism of the “Free to Obey” managerial framework
The employee’s acceptance
In the “Free to Obey” managerial culture, workers produce the solutions that will eventually displace them through the fundamental rule of membership. The condition for membership is the recognition and acceptance of the organization’s expectations. If a member refuses a single demand—such as the CEO’s instruction to “build something with AI” during a “free” afternoon—it is interpreted as a rebellion against the entire system and all its formal expectations. It is a means for organizations to test what people are willing to do to prevent their own self-exclusion. Employees feel compelled to participate in these AI experiments to remain part of the great adventure or the company’s vital performance.
The zone of indifference
A CEO giving employees a blank afternoon with no initial expectations, which eventually leads to automated solutions that the company then captures, is called the “zone of indifference”. Upon entering an organization, members issue a blank check, pledging general allegiance to instructions that are not yet specified in detail. This zone allows management to adjourn and still secure decisions on indeterminate subjects. What begins as joyful experimentation without clear constraints is a mechanism by which the company crowdsources its own obsolescence by leveraging employees’ tacit knowledge.
The paradox of voluntary commitment
There is a psychological explanation for why workers don’t quit when the illusion of freedom to figure out how to achieve management’s goals becomes extractive. First, because the decision to join and participate is voluntary, workers feel a high degree of self-commitment. To quit the experiment or refuse the AI mandate would be embarrassing, as the individual would have to disavow their previous decision to join the “adventure”. Second, organizations based on voluntary membership can go further in formalizing extreme expectations than those that use forced membership. This reinforces the democratic culture praised in AI crowdsourcing, which allows the quick reassertion of the extractive nature of the Frontier Firm.
The “foot-in-the-door”
The shift from joyful experimentation to mandatory industrial imperatives is a subtle transition and relates to the “foot-in-the-door” principle. Once an employee agrees to a small, fun task (like a friendly brainstorming session), it becomes increasingly difficult to refuse subsequent, more demanding steps in the automation process. Workers continue because it allows them to avoid having to justify why they were prepared to do the previous task but not the current one, leading to the industrial imperative of results.
Brain fry vs. “resistenz”
Brain fry is the friction created when workers must work more to make AI solutions work. Workers are exposed to exponential output while their cognitive capacity remains unchanged. As we mentioned in the BRS 7, “The agentic platform successfully doubles the speed and volume of the process. But it does not double the speed of human judgment.” But there is a parallel behaviour, recently documented and measured, showing that 80% of white-collar workers refuse to adopt AI mandates. We call it “resistenz,” or covert resistance. When demands become unreasonable, members often do not openly oppose them; instead, they exploit control gaps to evade demands through mistakes, misunderstandings, or reduced output while still appearing to comply with the basic membership rule.
This covert resistance makes calculating the true “labour cost margin” of AI adoption difficult, as much of the human labour (and the resistance to that labour) happens in shadow adoption or through the babysitting of agentic solutions. Far from being a liberation, this covert refusal acts as an invisible tax that renders the expected productivity gains of the Frontier Firm mathematically insolvent.
4. Grandeur and decadence of the “Free to Obey” managerial culture
The risk of liquidating tacit knowledge
In the “Free to Obey” managerial culture, the “how” is delegated to the collaborators. This amounts to asking employees to transform a unique asset (employees’ expertise and tacit knowledge; their “know-how”) into a standardized commodity owned by the platform provider (Microsoft, OpenAI, Google) through the company’s (or the vendor’s) AI model. The moment an expert automates their logic into an agentic platform, the value of that logic shifts from the company’s balance sheet (human capital) to the AI provider’s balance sheet (cloud capital). The company then begins paying a perpetual cognitive annuity to access its own intelligence. This is the mechanics of the Silicon Kolkhoz in plain sight.
The opportunity cost of shadow AI
The “Free to Obey” management culture touts “AI Builders Day” as a burst of creativity, but from a strategic perspective, it’s a nightmare for coordination costs, i.e., the ratio of time spent on experimentation (Q1) to the success rate of scaling up (Q2). Historically, Höhn’s delegation model fails during high-friction phases because it leads to fragmented procedures. In practice, you end up paying 45 experts to create 45 incompatible micro-solutions. The cost of cleaning up, integrating, and securing these makeshift solutions often exceeds the initial productivity gain. This is the antithesis of McDonaldization’s calculability.
The risk of strategic blindness
As Chapoutot points out, the “Free to Obey” managerial culture transforms the leader into a referee who no longer understands the technical work of his subordinates. This creates a dangerous structural split: middle management—the traditional translators who turn strategy into technical execution—is systematically erased as their functions are absorbed by the platform. If the AI infrastructure goes down or the platform raises its prices by 300%, the mean time to recover increases, and the company cannot roll back because it has released (i.e., laid off or downsized) the people who understood the process. This leaves behind a blind top tier of sovereign executives who pilot a corporate black box, disconnected from the technical reality of a now fully platformized workforce. Sovereignty is lost because the bridge between intent and execution has been outsourced to a hyperscaler’s algorithm. And here is the plot twist!
5. The strategic erasure of management
The “Free to Obey” model does not merely delegate tasks; it initiates a transformation of the employee’s very identity through what Michel Foucault calls “governmentality”—the “conduct of conduct”. By adopting the Bad Harzburg model of “delegation of responsibility,” corporations move away from rigid Prussian hierarchies toward a system in which power is exercised through the “entrepreneurial” freedom of subordinates. This is the root cause of a chain of effects that shifts the worker from a traditional employee to a prosumer, and finally, to a platform worker.
The prosumer phase: Crowdsourcing the obsolescence of tacit knowledge
Under the guise of “joyful experimentation” (e.g., the CEO’s “blank afternoon” for AI building), employees issue a “blank check” of allegiance within their zone of indifference. They are encouraged to become “entrepreneurs of the self,” responsible for optimizing their own productivity. In doing so, they become prosumers: they produce the very automated solutions they must then consume to perform their jobs. This process effectively captures the employee’s tacit knowledge—their unique “tour de main”—and converts it into standardized algorithms owned by hyperscalers.
The platform worker: Hooked into the hyperscaler logic
Once this tacit knowledge is “aspired” into the platform, the transition to platform worker is complete. The business logic, workflows, and tools are now locked into an external infrastructure—Cloud Capital. The worker is no longer an asset of the mid-market firm but a component hooked into a platform of time where tasks per unit of time are accelerated by the platform’s own imperatives, while human capacity to analyze, sort out, and debug stagnates, if not collapses due to emotional exhaustion. As Foucault noted in his analysis of neoliberalism, the individual becomes a “behavioristically manipulable being” reacting to an artificially arranged environment of “rational choices” (the AI mandates).
Plot twist: The death of the manager
The prevailing corporate fear is that AI will replace entry-level workers; however, the “Free to Obey” logic points to the obsolescence of the manager. The transition from prosumer to platform worker is driven by the hyperscalers’ need for a frictionless connection to human cognitive output. Any layer of human management—with its inherent delays of social nuance, labour coordination, and ethical judgment—represents a bottleneck in the value-extraction chain. To maximize the perpetual cognitive annuity paid to the Silicon Kolkhoz, the system must bypass the manager entirely. The algorithm becomes the sole ‘conduct of conduct,’ establishing a direct, unmediated link between the worker’s effort and the hyperscaler’s ingestion engine.
Ultimately, the “Free to Obey” managerial culture is an inventory of liquidation before entering the Silicon Kolkhoz. By leveraging the democratic culture of AI crowdsourcing, executives are inadvertently building a system in which the management layer is the primary barrier to the platform’s industrial imperative: results. By removing the human translator—the manager—the organization loses its capacity to debug technical reality, leaving the executive alone with an unmediated and uncontrollable algorithm.
The Bottom line
The mid-market companies I audited believe they’re demonstrating agility. By asking everyone to tinker with AI, they’re throwing open the vault of sovereignty. They treat their secret recipe like a commodity. But crowdsourcing AI in the corporate world turns out to be the precondition to enter the Silicon Kolkhoz; it’s a clearance sale. You’re asking your best people to map the value they bring, so you can lease it to Microsoft at a premium. It’s the Bad Harzburg management approach applied to data: we give you the freedom to invent the rope with which the platform will hang you. In the Silicon Kolkhoz, it is the manager, not the worker, who is the final ghost in the machine.
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About the Author & BomaliQ
This newsletter is authored by Mathieu Lajante, PhD, Founder and Architect of BomaliQ Inc. BomaliQ provides specialized strategic intelligence for the algorithmic frontline, helping corporate leaders navigate the behavioural and political frictions of high-tech organizational transformation.
Nature of Intelligence
The insights provided in this publication are based on the stress-testing of publicly available industry reports, market data, and proprietary analytical frameworks. This content is intended for informational and strategic signalling purposes only. While every effort is made to ensure the accuracy of the analysis, the algorithmic frontline is a volatile environment.
Limitation of Liability
The BomaliQ Risk Signal does not constitute professional consulting advice, legal counsel, or a formal business diagnosis. Readers should not make critical strategic decisions based solely on this newsletter without a rigorous, organization-specific assessment. BomaliQ Inc. and Mathieu Lajante shall not be held liable for any business outcomes or losses resulting from the use of this general intelligence.