How not to lose your job to AI (article by Benjamin Todd)

About half of people are worried they’ll lose their job to AI. They’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.

But what’s less appreciated is that 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 increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. ATMs actually increased employment of bank clerks — until online banking automated the job much more.

Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. This article covers what those skills are, as well as tips on how to start learning them.

Check out the full article for all the graphs, links, and footnotes: https://80000hours.org/agi/guide/skills-ai-makes-valuable/

Chapters:

• Introduction (00:00:00)
• 1: What people misunderstand about automation (00:04:17)
• 1.1: What would ‘full automation’ mean for wages? (00:08:56)
• 2: Four types of skills most likely to increase in value (00:11:19)
• 2.1: Skills AI won’t easily be able to perform (00:12:42)
• 2.2: Skills that are needed for AI deployment (00:21:41)
• 2.3: Skills where we could use far more of what they produce (00:24:56)
• 2.4: Skills that are difficult for others to learn (00:26:25)
• 3.1: Skills using AI to solve real problems (00:28:05)
• 3.2: Personal effectiveness (00:29:22)
• 3.3: Leadership skills (00:31:59)
• 3.4: Communications and taste (00:36:25)
• 3.5: Getting things done in government (00:37:23)
• 3.6: Complex physical skills (00:38:24)
• 4: Skills with a more uncertain future (00:38:57)
• 4.1: Routine knowledge work: writing, admin, analysis, advice (00:39:18)
• 4.2: Coding, maths, data science, and applied STEM (00:43:22)
• 4.3: Visual creation (00:45:31)
• 4.4: More predictable manual jobs (00:46:05)
• 5: Some closing thoughts on career strategy (00:46:46)
• 5.1: Look for ways to leapfrog entry-level white collar jobs (00:46:54)
• 5.2: Be cautious about starting long training periods, like PhDs and medicine (00:48:44)
• 5.3: Make yourself more resilient to change (00:49:52)
• 5.4: Ride the wave (00:50:16)
• Take action (00:50:37)
• Thank you for listening (00:50:58)

Audio engineering: Dominic Armstrong
Music: Ben Cordell