Sign up to save your podcasts
Or
Share Multitasking was always a lie, AI made it more believable
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Multitasking was always a lie, AI made it more believable
Multitasking is a false promise for productivity, but AI's form factor and speed make it the default path, especially as you become an AI power user. Eric and John explore why deep focus still wins.
Summary
Eric and John revisit a problem that predates AI but has been deepened by it: multitasking. Before ChatGPT, we had email, Slack, Skype, and multiple monitors pulling attention in different directions. The science was clear then: context switching makes everything take longer and degrades quality.
Now AI adds a new layer. The tools are structured around waiting: issue a prompt, the agent works, you wait. That waiting window naturally encourages more tasks, more tabs, more jobs in parallel. What was already a productivity killer has found a faster engine.
Eric and John agree the antidote hasn't changed: prioritization, limited work in progress, and the discipline to finish one thing before starting another. But resisting the pull toward constant context switching is harder than ever when the tools themselves reward it.
Key takeaways
Multitasking is context switching, not parallel work: Humans can only do one cognitive task at a time, so rapid toggling between tasks makes everything take longer and reduces quality.
AI incentivizes more context switching, not less: The async latency of AI agents (prompt, wait, review) naturally encourages running multiple jobs in parallel, compounding the productivity problem.
More concurrent tabs means less quality output: Running 10 AI tasks at once fragments attention. Reviewing and integrating work from all of them without discipline degrades the final result.
Managing AI agents is like managing a team, and most people are not great at it: Moving AI into Slack does not solve the problem. Great managers are rare because prioritization, reviewing, and limiting work are hard skills.
Long-horizon AI tasks are still a future promise: Agents that can work independently for days are not yet reliable. Most AI work needs check-ins every 30 to 60 minutes, which keeps you in a high-frequency context switching loop.
The fundamentals have not changed: Prioritize, limit work in progress, and protect deep focus. The tools have changed, but the principles of productive work remain the same.
Notable mentions and links
Eric’s blog post, Fragmented focus in the age of AI, outlines the science behind the damaging effects of multitasking on productivity.
Claude Tag, Anthropic's new feature for using Claude asynchronously inside Slack, enters the conversation as a potential solution to multitasking — letting AI work in the background while humans focus elsewhere.
Personal Kanban, a book by Jim Benson and Tonianne DeMaria Barry, provides the framework of limiting work in progress and visualizing tasks to improve personal throughput.
The Phoenix Project, a DevOps novel by Gene Kim, Kevin Behr, and George Spafford, is referenced as a bridge between manufacturing throughput concepts and modern software workflows.
Scrum, the agile methodology popularized by Jeff Sutherland, originally drew on multitasking research to argue that humans can only do one thing at a time and that rapid switching is inefficient.
View all episodes
By Eric Dodds & John WesselMultitasking is a false promise for productivity, but AI's form factor and speed make it the default path, especially as you become an AI power user. Eric and John explore why deep focus still wins.
Summary
Eric and John revisit a problem that predates AI but has been deepened by it: multitasking. Before ChatGPT, we had email, Slack, Skype, and multiple monitors pulling attention in different directions. The science was clear then: context switching makes everything take longer and degrades quality.
Now AI adds a new layer. The tools are structured around waiting: issue a prompt, the agent works, you wait. That waiting window naturally encourages more tasks, more tabs, more jobs in parallel. What was already a productivity killer has found a faster engine.
Eric and John agree the antidote hasn't changed: prioritization, limited work in progress, and the discipline to finish one thing before starting another. But resisting the pull toward constant context switching is harder than ever when the tools themselves reward it.
Key takeaways
Multitasking is context switching, not parallel work: Humans can only do one cognitive task at a time, so rapid toggling between tasks makes everything take longer and reduces quality.
AI incentivizes more context switching, not less: The async latency of AI agents (prompt, wait, review) naturally encourages running multiple jobs in parallel, compounding the productivity problem.
More concurrent tabs means less quality output: Running 10 AI tasks at once fragments attention. Reviewing and integrating work from all of them without discipline degrades the final result.
Managing AI agents is like managing a team, and most people are not great at it: Moving AI into Slack does not solve the problem. Great managers are rare because prioritization, reviewing, and limiting work are hard skills.
Long-horizon AI tasks are still a future promise: Agents that can work independently for days are not yet reliable. Most AI work needs check-ins every 30 to 60 minutes, which keeps you in a high-frequency context switching loop.
The fundamentals have not changed: Prioritize, limit work in progress, and protect deep focus. The tools have changed, but the principles of productive work remain the same.
Notable mentions and links
Eric’s blog post, Fragmented focus in the age of AI, outlines the science behind the damaging effects of multitasking on productivity.
Claude Tag, Anthropic's new feature for using Claude asynchronously inside Slack, enters the conversation as a potential solution to multitasking — letting AI work in the background while humans focus elsewhere.
Personal Kanban, a book by Jim Benson and Tonianne DeMaria Barry, provides the framework of limiting work in progress and visualizing tasks to improve personal throughput.
The Phoenix Project, a DevOps novel by Gene Kim, Kevin Behr, and George Spafford, is referenced as a bridge between manufacturing throughput concepts and modern software workflows.
Scrum, the agile methodology popularized by Jeff Sutherland, originally drew on multitasking research to argue that humans can only do one thing at a time and that rapid switching is inefficient.