Unraveling the Employee Empowerment Myth

The Critical Choice Companies Face

As AI deployment accelerates across industries, companies face a fundamental choice: Will they use AI to amplify human competency or to extract ever-more value from workers? Last week saw two major corporations announce AI workforce programs that exemplify exactly opposite approaches to this question, and the contrast reveals everything about the future of work.

Uber's AI Microtask Program (announced Oct 16, 2025) Uber is piloting a program allowing drivers to earn money doing AI-related microtasks like uploading photos to train AI models when they're not actively driving. The company is also using AI agents to help customer service representatives be more productive by summarizing communications with users and surfacing context from previous interactions.

Toyota's Factory Worker AI Platform (updated October 9, 2025) Toyota implemented an AI platform using Google Cloud's infrastructure that enables factory workers themselves to develop and deploy machine learning models, resulting in a reduction of over 10,000 man-hours per year while increasing efficiency and productivity. The workers aren't being replaced; they're being empowered to create the AI tools they need.

Why Uber's Approach Is Troubling

Uber's program exemplifies exactly what Andus Labs founder and scholar-in-residence Douglas Rushkoff has been cautioning against in his recent work on AI and deskilling.

It commodifies the workers themselves as the product. Drivers aren't using AI to become better drivers or to enhance their core expertise. Instead, they're being turned into data laborers. What does that look like? It means doing the repetitive, low-value work (image labeling, voice recordings) that trains AI systems. This is literally the "busywork" that AI should be doing for humans, but flipped backwards.

It creates a precarious income stream with no skill development. When drivers aren't driving, instead of resting or developing skills that enhance their primary work, they're doing microtasks that: a) don't build any lasting expertise, b) pay based on task difficulty with no transparency, and c) keep them tethered to Uber's platform with no transferable value.

It deepens Uber's extraction model. Uber already classifies drivers as independent contractors while controlling most aspects of their work. Now they're extending that model to squeeze additional value out of their "downtime,” essentially saying "we'll monetize every minute of your availability."

The comparison to Toyota is stark: Toyota empowers factory workers to create ML models that improve their own jobs. Uber is asking drivers to feed ML models that may eventually automate them out of existence (remember, this is separate from their self-driving work, but trains AI systems generally).

This is deskilling masquerading as opportunity. It's the gig economy logic applied to AI training—fragmenting labor into smaller and smaller units while eliminating any path to expertise or autonomy.

Toyota's Approach: Workers as Domain Experts

What makes Toyota's approach exemplary is that it inverts the typical AI implementation hierarchy. Rather than having data scientists or engineers impose AI solutions from above, Toyota treats factory workers as the domain experts they are. These workers understand the nuances of their processes, the pain points, and the opportunities for improvement in ways that no external consultant could. By giving them the tools to build their own ML models, Toyota:

• Preserves and enhances expertise rather than extracting and replacing it
• Creates upward skill mobility as workers learn valuable technical capabilities
• Generates better AI solutions because they're designed by people who intimately understand the problems
• Builds worker agency and investment in both the technology and the company's success

The 10,000 man-hours saved aren't from replacing workers. Rather, they're from workers eliminating their own friction points and inefficiencies. That's the difference between using AI as a partner in bettering human competence versus AI as a replacement for people.

The Bottom Line

Uber's program is deskilling masquerading as opportunity. Uber's approach applies gig economy logic to AI training, fragmenting labor into smaller and smaller units while eliminating any path to expertise or autonomy. Toyota's approach does the opposite: it aggregates capability, builds competency, and treats workers as the irreplaceable architects of their own productivity gains.

For companies designing AI workforce programs, the question isn't just "Can AI do this task?" but "Does this implementation make our people more capable, more expert, and more valuable? Or does it reduce them to interchangeable components in a system that will eventually discard them?" The answer to that question will determine not just the ethics of your AI strategy, but its long-term sustainability and competitive advantage, extending beyond a six-month opportunity window.

‍