We didn’t see this coming—but we should have. Just days after Xbox CEO Asha Sharma announced the layoff of 3,200 employees (the largest restructuring in Microsoft gaming history), she joined the Federal Reserve’s new AI & Employment Task Force. The timing wasn’t a coincidence. It was a signal. Not about AI’s impact on jobs, but about the foundational flaw in how we govern transformative technology: centralized decision-makers sitting on both sides of the table—creating the disruption and then studying how to manage it.
Context: The Same Old Multi-Sig Problem
In blockchain, we’ve learned the hard way that “code is law” only works when no single entity controls the upgrade keys. DAOs that start with decentralized voting often end up controlled by a few multi-sig admins who decide when to patch the smart contract. The Fed’s AI task force is exactly that: a centralized committee with broad authority to shape AI employment policy, composed of insiders from the very industries driving the displacement. Microsoft’s own AI strategy—powered by OpenAI, Azure, and Copilot—is the engine behind Xbox’s restructuring. Asha Sharma will now help write the rules for the game she’s already playing. This is not conspiracy. It’s the logical outcome of a system that conflates power with expertise.
Truth in blockchain isn’t found in whitepapers, but in the code that runs. Here, the code is the organizational structure. The Fed’s task force has no independent audit, no public ledger of its discussions, no veto power for the workers most affected. It’s a closed DAO with a single multi-sig: the members themselves.
Core: Analyzing the Structural Centralization
Let’s break down the ethical math. The Fed’s mandate is to ensure maximum employment and stable prices. AI-driven job displacement is a direct threat to both. Yet the task force’s composition—academics, tech executives, policy insiders—mirrors the very networks that profit from efficiency gains. Xbox’s 3,200 layoffs are part of a broader trend: Microsoft, Google, Meta have collectively cut over 150,000 jobs since 2023, with AI cited as a driver in many cases. The narrative goes: “We need to reorganize to stay competitive in the AI era.” But who decides what “competitive” means? The same executives whose bonuses depend on stock price increases driven by cost-cutting.
I’ve seen this pattern before. In 2020, during DeFi Summer, I lost $15,000 AUD to a yield farming protocol that claimed to be community-governed but had a single admin key. The team promised to fix the exploit, but the damage was done. The lesson: centralization of decision-making—whether in DeFi or employment policy—creates a feedback loop where the architects of harm also control the narrative of repair. The Fed’s task force is that admin key. It will publish reports, recommend training programs, maybe even suggest a “robot tax.” But as long as its members come from the same pool that benefits from layoffs, the recommendations will never challenge the underlying assumption: that efficiency and shareholder value should trump human livelihoods.
Technical parallel: The Layer2 sequencer trap. In blockchain, Layer2 solutions promise scalability while inheriting Ethereum’s security. But in practice, most sequencers are centralized nodes controlled by a single company. Decentralized sequencing has been a PowerPoint for two years. Similarly, the Fed’s AI governance is a centralized sequencer: it processes policy inputs from a small group and outputs a single set of recommendations. No transparency, no audit trail, no mechanism for the affected workers to challenge the ordering of priorities.
The real driver of this centralization isn’t malice—it’s a philosophical failure. We treat AI as a technical problem that can be managed by experts, rather than a political one that demands inclusive deliberation. I’ve been guilty of this myself: in 2017, I wrote a 40-page thesis on “Code as Law,” believing that smart contracts could replace trust. I’ve since learned that technology can only encode values if the values themselves are agreed upon first. The Fed’s task force hasn’t agreed on any values—they’re just trying to keep the system from exploding.
Contrarian: Maybe the Task Force Is a Good Thing
Here’s the counter-intuitive angle: the Fed’s involvement is actually a sign that the US government takes AI’s labor impact seriously. A task force with high-level access could accelerate funding for retraining programs, unemployment insurance reform, and universal basic income pilots. Asha Sharma, as a gaming executive, might bring insights about creative work that other members lack. The task force could produce a roadmap that prevents a Weimar-style job crisis.
I want to believe that. But my pragmatism—earned from three years of watching DAO governance fail—won’t let me. The problem isn’t intent; it’s structure. Any centralized body that both creates and manages a crisis will prioritize its own stability over radical solutions. The Fed’s primary goal is to maintain confidence in the financial system, not to maximize human flourishing. They will recommend policies that smooth transitions, but won’t question the root cause: that AI is being deployed in a corporate governance model that treats labor as a cost, not an asset. Until the people making the decisions are accountable to the people losing their jobs—through transparent voting, real stake, and revocable authority—any “solution” will be a band-aid.
Takeaway: The Blockchain Alternative
We didn’t build crypto because we hate money. We built it because we saw that centralized money creates centralized power that corrupts. The same logic applies to AI governance. The only way to prevent a future where a few executives decide who works and who doesn’t is to decentralize the decision-making process itself. That means: AI audit DAOs with worker representatives, transparent algorithms for layoff algorithms, and federated governance where local communities can opt out of AI deployment that harms them.
The Fed’s task force is a warning. It shows that even the best-intentioned centralized actors will recreate the same power imbalances. The real work—building decentralized, resilient governance for the age of intelligent machines—hasn’t started yet. But the clock is ticking.