AI companies market autonomous agents as workplace collaborators, but this framing obscures a fundamental truth. These systems are tools, not peers. They lack agency, consciousness, and accountability in ways that matter legally and ethically.
The industry habit of naming AI agents and referring to them as "coworkers" creates a misleading mental model. When a company deploys "Alex" to handle customer service or data analysis, employees begin treating the system as a colleague with autonomy and judgment. This is false. Alex executes patterns learned from training data. It makes no decisions in any meaningful sense.
This distinction matters operationally. Real coworkers take responsibility for their work. They advocate for themselves, push back on bad requests, and grow from feedback. AI agents do none of this. They fail silently or catastrophically. When an AI system makes an error, accountability vanishes. Did the tool malfunction? Did the trainer inject bad data? Did a user misuse it? The answer determines who bears responsibility, and companies exploit this ambiguity to dodge liability.
The naming convention carries deeper problems. It encourages anthropomorphization in workplaces already anxious about job displacement. Calling a system a coworker suggests it has desires, preferences, and a stake in outcomes. It doesn't. It has parameters and a loss function. The psychological effect is real: humans who personify AI agents report lower job satisfaction and higher anxiety about automation.
There's also a labor dimension. Treating AI as a coworker legitimizes replacement rhetoric. If the tool is a team member, then hiring people becomes inefficient. The language primes companies to substitute humans with systems, framing it as a neutral technological upgrade rather than a choice to eliminate jobs.
The honest frame is simpler. AI agents are software. They are capital equipment, like spreadsheets with more compute behind them. They require human oversight, error correction,