Workers are spending over 6 hours a week botsitting AI, fueling job frustration

Botsitting and workplace frustration

• Many see “botsitting” as low-skill, low-satisfaction work: supervising LLMs, correcting errors, and triaging AI-generated slop from coworkers.
• A recurring complaint is coworkers and managers passing off unchecked AI output (e.g., PRDs, specs) as their own thinking, eroding trust and respect for the craft.
• Some feel AI is working “instead of” them, with humans reduced to babysitters/assistants to the model.

Debated productivity impact

• Reported individual gains range from modest (~20%) to dramatic (2–3x) for a small subset of power users, especially in coding/sysadmin tasks.
• Others argue real throughput is flat or negative once you include debugging, rework, and “prompt fiddling.”
• Links to observational studies (e.g., Faros) are cited; some interpret them as showing net gains, others as evidence of lower effective productivity due to quality issues.
• Distinction is drawn between “effort productivity” (same output with less effort) and “business productivity” (more valuable output per dollar).

Changing nature of knowledge work

• Several compare botsitting to factory work or Amazon warehouses: humans in the loop only where machines fall short.
• Some enjoy the new “manager of agents” role, focusing on architecture and direction; others hate being turned into code reviewers and project overseers.
• People note that models increasingly jump straight to “solutions,” making them worse as investigative assistants.

Tooling, workflows, and guardrails

• Power users stress careful setup: sandboxed agents, explicit guardrails, planning steps, and permission checks to veto bad actions.
• Others find the planning/agent cycle slow, context-switch heavy, and less fun than direct coding.
• There is concern about opaque model changes by vendors and a push toward eventual self-hosting for stability and cost control.

Job satisfaction, identity, and mental health

• Many report sharp drops in joy and meaning at work when AI automates the parts they liked (craft, problem-solving, relationships).
• Some accept AI and reorient toward ideation and shipping; others contemplate quitting tech for trades or retiring early.
• Alienation, loss of pride in skill, and fears of depression or even suicidality among displaced creatives are explicitly raised.

Management incentives and labor dynamics

• Commenters see management and investors as primary AI boosters, sometimes even mandating AI use under threat of firing.
• There is skepticism that token-based business models incentivize efficiency; some suspect subtle pressure to use more tokens.
• Broader worries appear about layoffs of “incompetent” or merely middling workers, degradation of quality of life, and the need for unions or new social arrangements as automation expands.