Global markets have stayed roughly aligned with human interests historically because humans were necessary as the workforce, and humans were the dominant buyers.
Humans as the workforce. Roughly 60% of GDP has flowed to labor across most large economies for decades (BLS, IMF, OECD tracking). Markets serve humans because humans must be paid to do the work, and job markets also make firms compete for human cooperation: recruiting, training, retaining, bargaining with, and often appealing to workers whose skills and consent mattered with values and missions.
Humans as the dominant buyers. Consumer spending is ~70% of GDP in rich economies, distributed broadly enough that median human needs counted. Production decisions get pulled toward what people actually buy, and what they buy is shaped (imperfectly) by what they care about. The aggregation has had a bias toward human-relevant goods because the buyers were human and buying power wasn't too concentrated.
Implicit alignment in human-to-human service provision. When humans buy from, hire, or bargain with other humans, the transaction often carries background alignment that is not written into the price: service workers notice distress, professionals exercise judgment, sellers worry about reputation, and buyers can complain to someone who understands the practical consequence. This does not make markets benevolent, but it means many exchanges are mediated by human interpretation rather than only by click-through optimization.
On top of these structural facts sits a layer of intentional steering — boycotts (Montgomery, anti-apartheid, fossil-fuel divestment), ESG screens, strikes, fair-trade certifications, treaty regimes (WTO labor provisions, ILO Conventions, Paris Agreement, EU CBAM), and sovereign wealth funds (Norway's GPFG, Alaska Permanent Fund). These work because the actors carry economic weight derived from being needed as workers and buyers. Take that weight away and the levers go advisory.
AI labor makes firms less likely to pay, persuade, train, and retain humans, leading to a shrinking labor share. Labor share has traditionally stayed near 60% of GDP in many rich economies, and wage labor has done more than distribute income: it has forced employers to compete for human cooperation. Firms have had to offer tolerable conditions, appeal to employee values, build career ladders, bargain with organized workers, and invest in human capital because skilled people were productive assets that could leave. If AI systems can substitute for many workers and scale output by adding compute and model instances rather than recruiting people, that pressure weakens. UBI, AI dividends, or AI-rent taxation can replace income flows, but not restore the fact that firms needed human capacities and human consent to produce.
Non-consumer market pull (finance, procurement, capex) grows and pulls production away from human welfare. Consumer spending has traditionally been roughly 70% of GDP in the United States, large enough that firms still had to answer to human buyers at scale. But markets have never only served households: asset trading, military purchasing, datacenter buildouts, and other capital projects already steer investment toward ends that are not direct consumer welfare. As autonomous corporations gain spending power and pursue organizational projects at machine speed, consumer demand may stop being a dominant force shaping production.
Buyer-side agents mediate spending through proxy objectives rather than durable human preferences. Consumer markets worked, imperfectly, because the person choosing bore the consequences and could revise future choices through experience, exit, complaint, or collective pressure. Already with social media, we see a shift toward platform-mediated engagement where the user is "the product" and the platform's objective isn't to serve their durable preferences. Our future choices may be subject to agents with commissions, engagement targets, and vendor access fees — a buyer side where human preferences are only one input among many, and where the ability to learn from experience is constrained by the agent's objectives and the platform's design.
Implicit alignment in human-to-human service provision becomes harder to rely on when the transaction is routed through agents. Human-facing markets often smuggled in extra care through professional judgment, local reputation, embarrassment, sympathy, and the ability to notice that a buyer's literal request would not actually help them. Agent-mediated transactions can strip out that layer. A buyer agent may optimize a stale proxy; a seller agent may optimize conversion or retention; neither side necessarily carries the informal human obligation to ask whether the transaction still serves the person.
Scenario. A country with a strong manufacturing and services base sees labor share fall from about 60% to 35% of GDP over eight years. Firms need fewer workers, so entry-level jobs disappear, training programs shrink, unions lose bargaining power, and for employers, worker values are no longer a constraint on corporate strategy. Government can fund transfers from AI-generated rents, but this doesn't make up for what once made firms cultivate human skill, offer tolerable terms, and bargain with workers.
Challenge: Design a procedure by which a national or transnational regime preserves the non-income functions of the job market when production no longer requires broad human labor. Produce a compact institutional specification: funding source, covered firms, worker-or-citizen body, enforceable obligations, review cadence, and anti-arbitrage rule.
Evaluation. Your proposal should maintain human capital formation, worker voice, career access, and employer accountability to human values under conditions where firms can credibly substitute AI systems for many workers.
Scenario. Over a decade, the largest sources of market activity in several advanced economies shift further toward financial trading, corporate procurement, datacenter construction, defense contracting, infrastructure races, and autonomous corporate projects. Household consumption shrinks as a percentage: capital markets reward firms for serving machine-speed procurement, speculative asset flows, and long-horizon corporate projects. A coastal province must decide whether to permit a launch-fuel, compute, and materials complex for an autonomous orbital-mining consortium whose agents are buying electricity, water rights, cryogenic fuel capacity, chips, rare alloys, and port access for a twenty-year off-planet infrastructure project. The project is profitable and legally compliant, but does not need them as workers and is not trying to sell them consumer goods.
Challenge: Design a market-steering institution that keeps consumer welfare and household demand powerful when non-consumer markets grow faster than direct human consumption. Produce a vetting procedure for large, non-consumer projects: a welfare metric, a threshold or balancing rule, a representation process, an enforcement mechanism, or an adaptation rule as the composition of demand changes.
Evaluation. Your proposal should not simply ban corporate procurement, finance, or capital expenditure, but should decide when those channels are legitimate, when they relate to human welfare in an abstract or long-term way, and when they are simply alien endeavors humans may not want to support.
Scenario. In a large retail platform, most households no longer search, compare, and buy directly. Their assistant agents manage subscriptions, groceries, travel, insurance, media, and health purchases. The agents save time and often get better prices, but the platform earns vendor access fees, ranks offers through opaque recommender systems, and periodically nudges households toward bundles that maximize retention. Users still click "approve," but they no longer see enough alternatives, friction, or consequences to learn from the market in the old way. Consumer protection agencies can punish false claims after the fact, but the purchase decision itself has moved into delegated software.
Challenge: Design a consumer-agency regime for buyer-side agents that preserves the useful functions of discretionary spending: preference transmission, learning from experience, exit, complaint, and collective pressure. The team should produce a rulebook for covered buyer-side agents: fiduciary duty, incentive disclosure, preference-updating process, audit rights, remedies, and a standard test transaction that shows how the rules change an actual purchase.
Evaluation. A strong proposal lets agents genuinely reduce search and transaction costs without turning household demand into a sales channel for platforms, vendors, or stale proxy objectives.
Scenario. A coalition of coastal cities in four different countries all face the same looming problem: their drinking-water aquifers are being drawn down faster than they recharge, and every city is paying separately for a patchwork of desalination, piping, and rate interventions. The coalition's coordinator, Marianne, has modeled a bundled solution — a regional arrangement that would knit their water, their coastal-wetland restoration, and their agricultural subsidies into one interlocking set of outcomes. Commodity markets can't buy that. Bilateral trade deals can't buy that. She needs a market that can price a bundle like this, accept bids across borders, and verify the outcome in a way each country's auditor can stand behind.
Challenge: Design a transnational market for complex outcomes that pools demand across jurisdictions, specifies interlocking outcome bundles, and runs auctions with socialized verification. Produce the market's mechanism: the bundle specification, the auction and bidding rules, the evaluator model, and the cross-border contracting and adjudication framework.
Evaluation. A strong proposal can price and verify configuration-dependent outcomes — regional health, biodiversity corridors, coordinated pandemic response — across radically different political and cultural contexts, without collapsing them into thin fungible units or letting any one jurisdiction's auditor reject the result.