Research shows that instead of reducing work, AI often increases it through 'task expansion.' Employees use AI to take on work they previously delegated or outsourced, such as a product manager writing code, blurring roles and intensifying their workload.

A randomized controlled trial revealed a nearly 40% perception gap in developer productivity. While experienced developers using AI tools were measurably 19% slower, they self-reported feeling 20% faster. This highlights the unreliability of self-reported metrics for assessing AI's impact.

Human intuition is a poor gauge of AI's actual productivity benefits. A study found developers felt significantly sped up by AI coding tools even when objective measurements showed no speed increase. The real value may come from enabling tasks that otherwise wouldn't be attempted, rather than simply accelerating existing workflows.

Some engineering teams use AI in a way that produces a high volume of code riddled with mistakes. This forces them to rewrite large portions, sometimes without AI assistance, ultimately slowing them down. The issue is not the tool, but the lack of best practices for its application.

While AI coding assistants appear to boost output, they introduce a "rework tax." A Stanford study found AI-generated code leads to significant downstream refactoring. A team might ship 40% more code, but if half of that increase is just fixing last week's AI-generated "slop," the real productivity gain is much lower than headlines suggest.

Developers using AI agents report unprecedented productivity but also a decline in job satisfaction. The creative act of writing code is replaced by the tedious task of reviewing vast amounts of AI-generated output, shifting their role to feel more like a middle manager of code.

A Meta study found expert programmers were less productive with AI tools. The speaker suggests this is because users thought they were faster while actually being distracted (e.g., social media) waiting for the AI, highlighting a dangerous gap between perceived and actual productivity.

AI coding assistants are creating a massive productivity gap among engineers. This leads to a bimodal distribution where one group fully leverages the tools and becomes massively effective, while another falls far behind. Hiring must now select for this new skillset.

Despite AI's promise to reduce menial work, developers still spend 23-25% of their week on repetitive tasks. The nature of this "toil" has simply changed from writing boilerplate code to the more complex and time-consuming task of validating and debugging plausible-looking AI-generated code.