发表机构
LUISS; EIEF; London Business School; CEPR（卢伊斯自由大学; 意大利经济与金融研究所; 伦敦商学院; 欧洲经济政策研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究将AI引入职业信号模型，分析代币交易对工人努力、技能投资和福利的影响，发现附带转售时信号与努力脱钩，工人获益取决于福利改善。
AI 中文摘要
早期职业工作既传递了工人的能力信号，又培养了长期技能。我们将人工智能行业引入职业信号模型。该行业出售代币，工人将其转化为与自身产出无法区分的产出。我们比较了三种制度：无代币交易、仅购买、以及附带转售的开放交易。在无代币交易下，市场了解每种类型，信号激励造成“鼠赛”，工人工作超过完全信息水平。在仅购买代币下，较低类型购买产出，但市场仍了解每种类型。较低类型的购买提高了较高类型必须发布的信号，因此高类型可能比引入AI前更努力地工作。购买者使用AI后工作更少，甚至低于完全信息水平。在附带转售且AI充足的情况下，信号不再依赖于努力，每种类型都低估了技能投资。工人仅当福利改善时才能从代币交易中获益，绝无相反情况，差额即为AI行业的利润。在教育领域，产出无价值时，分离均衡中每个工人都受损；但当产出有价值且代币便宜时，每个人都受益。代币税、工资税和水印对工人收益和福利的影响具有模糊性，且其效果取决于工人是否能在旁侧出售产出。
英文摘要
Early-career work both signals a worker's ability and builds long-run skills. We introduce an AI industry into a career signaling model. The industry sells tokens, which workers convert into output indistinguishable from their own. We compare three regimes: no token trade, purchases only, and open trade with side sales. With no token trade, market learns every type, signalling incentives create a rat race and workers work above the full information level. With token purchases only, lower types buy output but the market still learns every type. Buying by lower types raises the signal higher types must post, so the high types can work harder than before AI. Buyers work less with AI, even below the full-information level. With side sales and abundant AI, the signal no longer depends on effort and every type under-invests in skill. Workers gain from token trade only when welfare improves, never the reverse, and the difference is the AI industry's profit. In education, where output has no value, every worker loses in the separating equilibrium, but with valuable output and cheap tokens everyone gains. Token taxes, wage taxes, and watermarking have ambiguous effects on workers' payoffs and welfare, and their effects depend on whether workers can sell output on the side.
