Forcing businesses to pay a mandated high wage for a low-value job creates a powerful incentive to automate that role, especially with the rise of AI. A better approach is bottom-up regulation that fosters a competitive labor market, forcing companies to increase wages naturally to attract talent.

Digital platforms can algorithmically change rules, prices, and recommendations on a per-user, per-session basis, a practice called "twiddling." This leverages surveillance data to maximize extraction, such as raising prices on payday or offering lower wages to workers with high credit card debt, which was previously too labor-intensive for businesses to implement.

The minimum wage effectively makes it illegal for an employer to hire a worker whose skills are not yet worth the mandated hourly rate. This prevents young or unskilled individuals from accepting lower-paying jobs that would provide crucial skills and experience, trapping them in a cycle of unemployability.

Platforms can algorithmically profile workers based on their acceptance behavior. Drivers who accept low-paying orders quickly are tagged with a high "desperation score." The system then deliberately stops showing them high-paying orders, saving those to hook casual drivers while grinding down the full-timers who are most reliant on the income.

The most significant weakness of a multi-component model isn't price sensitivity, but the deep customer resentment it fosters. This reputational damage is difficult to quantify on a balance sheet but leads to long-term customer churn and incentivizes users to find alternatives.

Lyft maintains a 29-point advantage over competitors in driver preference. A key factor is their guarantee that drivers will never make less than 70% of what riders pay weekly, after insurance. This fosters loyalty and pride, acting as a competitive moat in the gig economy.

Unprofitable AI models mirror Uber's early strategy. By subsidizing services, they integrate into workflows and create dependency. Once users rely on the tool (e.g., a law firm replacing an associate), prices can be increased dramatically to reflect the massive value created, ultimately achieving profitability.

Contrary to the common view, algorithms charging different prices based on a consumer's wealth can be beneficial for market efficiency. The real harm occurs when algorithms exploit a lack of information or behavioral biases, not simply when they adjust prices based on a person's ability to pay.

Uber's initiative to offer drivers short, digital tasks for money while they wait for passengers marks a new phase in the gig economy. It aims to monetize every moment of a worker's time, effectively merging the roles of gig worker and crowdsourced data labeler to maximize platform labor efficiency.