Episode Overview

Title: How Not to Lose Your Job to AI
Podcast: 80,000 Hours Podcast
Date: July 31, 2025
Host/Author: Benjamin Todd (with the 80,000 Hours team)

This episode is a deep dive into Benjamin Todd’s article "How Not to Lose Your Job to AI." He explores crucial questions about how AI-driven automation is transforming the labor market, which skills will gain or lose value, and how individuals can position themselves to thrive in the coming era of rapid technological change. Drawing from economic theory, historical trends, and an up-to-the-minute understanding of AI progress, Benjamin breaks down practical strategies to future-proof your career.

Key Discussion Points & Insights

1. Why Are People Worried About Losing Jobs to AI?

• AI's Current Capabilities:
  AI is increasingly able to handle sophisticated tasks—real-world coding, generating photorealistic videos, driving taxis, medical diagnoses (00:35).
• Automation Paradox:
  While automation can threaten some jobs, it can also drive up the value of complementing human skills—notably, those AI cannot easily replicate (02:00).

Notable Quote:

"While AI drives down the value of skills it can do, it drives up the value of skills it can't. Wages on average will most likely increase before they fall as automation generates a huge amount of wealth."
— Benjamin Todd, 02:10

2. Common Misunderstandings About Automation

• The ATM Example:
  Introduction of ATMs reduced routine tasks for bank clerks, freeing them to focus on customer interaction—employment rose until later waves of online banking automation (03:40).
• Cycle of Automation:
  Partial automation often increases demand for human labor by lowering costs; full automation eventually lowers demand when humans are no longer needed for critical tasks (06:26).
• AI’s Unprecedented Potential:
  Unlike past technology, advanced AI and robotics may automate almost all economic activities—a prospect even tech experts treat as possible, if not imminent (09:05).

3. How Could Automation Affect Your Wages and Job Prospects?

• Possible Outcomes:
  If AI automates most work, wages might spike initially (as remaining human roles become bottlenecks), then dramatically fall as final "human-in-the-loop" needs are eliminated (11:52).
• Key Advice:
  "Learn the skills most likely to increase in value in the immediate future so you can maximize your contribution and wages in the time between now and full automation." (13:30)

4. Framework: Four Types of Skills Likely to Become More Valuable

(14:40)

• 1. Hard-for-AI Skills:
  Messy, long-horizon, "person-in-the-loop" tasks where AI lacks sufficient training data or the ability to effectively perform.
• 2. AI Deployment Skills:
  Organizing, auditing, and applying AI systems; understanding their strengths and weaknesses; complementary industries like data center construction.
• 3. Skills for “Expanding” Goods & Services:
  Sectors where demand could greatly grow with economic abundance: healthcare, housing, luxury goods, research.
• 4. Scarce Expertise:
  Skills that are hard for others to learn or acquire, creating bottlenecks (e.g., specialized trades or leadership roles).

5. Skill-by-Skill Analysis: What to Focus On (And Why)

A. Using AI to Solve Real Problems (28:20)

• What It Entails:
  Understanding and leveraging AI tools to create real-world outcomes; specifying project requirements; UX for AI systems; coordinating outputs.
• How to Develop:
  Use latest AI tools in your job or side projects. Work at an AI startup or dynamic organization for “on-the-job” skills.

Notable Quote:

"Maybe even eventually a lot of the economy could become figuring out what instructions to give AI systems."
— Benjamin Todd, 29:02

B. Personal Effectiveness (30:00)

• General Productivity & Agency:
  Setting goals, motivating yourself, organizing and completing tasks.
• Social Skills:
  Relationship-building, teamwork, emotional understanding; increasingly important as routine work is automated.
• Learning How to Learn:
  Adapting to rapid change, using AI as a learning aid.

C. Leadership Skills (34:30)

• Entrepreneurship:
  Spotting opportunities, launching and leading projects.
• Management:
  Overseeing both people and AI agents; project, people, and product management.
• Strategic Decision-Making:
  High-level prioritization; setting mission and analyzing what to tackle given massive AI leverage.
• Developing True Expertise:
  Deep understanding in growing fields (AI, robotics, cybersecurity, government policy).

D. Communication and Taste (43:15)

• Essence:
  Judging design, aesthetics, what audiences like, branding, PR, storytelling.
• Why It Matters:
  As content creation gets automated, curation and personality-driven engagement are more prized.

E. Getting Things Done in Government (45:30)

• Valued Skills:
  Political strategy, passing policies, navigating bureaucracies.
• Rationale:
  Many government functions will still require human discretion and physical presence; likely to adopt AI tools more slowly.

F. Complex Physical Skills (46:50)

• Examples:
  Surgery, advanced construction, hands-on repair, data center tech, electrician.
• Why Valuable:
  Robotics lags in dexterity and learning; demand for skilled physical workers in expanding industries.

6. Skills with a More Uncertain Future (48:20)

• Routine Knowledge Work:
  Writing, admin, basic analysis, translation, recall of information—best automated by current AI.
• Coding and STEM:
  While still valuable today, these are areas where AI is rocketing ahead. The bar for human contribution is rising quickly.
• Visual Creation:
  Image/video generation improving fast; human roles may become more about oversight and direction.
• Routine Manual Skills:
  Highly predictable manual labor (e.g., driving, warehouse work) is next in line as robotics improves.

Notable Analysis:

"The largest effects [of AI] will be on white collar jobs around the 70th to 90th percentile of income, which in the US is about $100,000 to $200,000."
— Benjamin Todd, 49:20

7. Career Strategies in a Rapidly Changing Labor Market

(59:20)

• Leapfrogging Entry-Level Roles:
  Proactively seek leadership, communication, or AI deployment experience—even via side projects or smaller organizations.
• Be Cautious About Long Training:
  Extended programs (PhDs, medicine) risk being outdated by the end of training; weigh alternatives or accelerated paths.
• Build Resilience:
  Diversify skills, save more money, invest in adaptability and mental health.
• Stay Agile:
  Constantly monitor AI progress, adapt your skill set, and identify new human bottlenecks as technology evolves.

Notable Quotes & Memorable Moments

• AI raises, then lowers wages:
  "Wages initially increase about tenfold, only to plunge in the late 2030s as the final human bottlenecks are removed." (11:59)
• On messy, long-horizon skills:
  "Messy long horizon tasks are our best bet at what AI is most likely to struggle with. And it's possible the ability to do the most messy long horizon skills is still decades away." (20:30)
• On government jobs:
  "I expect AI to be adopted faster than previous technology waves. But still many organizations will be slow to apply AI tools, meaning humans stay in important jobs for longer." (24:40)
• On skill agility:
  "The goal isn't to find a single job that will always be resistant to automation, but rather to stay one or two steps ahead of it." (1:05:55)

Timestamps for Major Segments

• 00:03 — Introduction by Benjamin Todd
• 03:40 — ATMs and the misunderstanding of automation
• 11:52 — What could full automation mean for wages?
• 13:30 — Key advice on staying valuable
• 14:40 — Four types of valuable skills
• 28:20 — Using AI to solve real problems
• 34:30 — Leadership skills in an AI world
• 45:30 — Value of government and complex physical skills
• 48:20 — Skills likely to decline in value
• 59:20 — Career strategy wrap-up
• 1:05:55 — Final advice: staying one or two steps ahead

Actionable Takeaways

• Focus on skills that are difficult for AI to master: messy, novel, and long-horizon tasks.
• Learn to use, deploy, and manage AI systems—not just technical skills, but the judgment and teamwork to apply them well.
• Prioritize adaptability, learning, and personal effectiveness for resilience in fast-changing times.
• Seek out growth environments (startups, small orgs, leadership roles, side projects) instead of routine, entry-level jobs.
• Build deep expertise and strong networks in sectors likely to expand with AI.
• Stay informed—continually test, update, and re-focus your skills according to where technology creates new human bottlenecks.

For Further Learning

• 80,000 Hours Career Guide (especially on personal effectiveness)
• Article: “How to be More Agentic” by Kate Hall
• Profiles on AI deployment, founding orgs, and government policy careers

This episode is essential listening for anyone rethinking their career strategy in the face of accelerating AI progress, offering both high-level frameworks and immediately actionable career advice.