Surviving AI – Navigating AI Job Displacement and Automation

Join Carlo Thompson on Surviving AI, your definitive resource for understanding AI job displacement and mastering AI survival strategies. This podcast breaks down complex artificial intelligence trends affecting jobs and offers practical guidance on skill development and navigating job automation challenges. With expert insights and structured content, listeners are equipped to protect their careers and capitalize on new opportunities in the changing economy. Surviving AI delivers: ✓ Early warning signs your job is vulnerable ✓ Skills that AI can't replicate (yet) ✓ Career pivots that protect your income ✓ Geographic arbitrage strategies for the AI economy ✓ Real case studies from the automation frontlines ✓ The truth about "AI will create more jobs than it destroys." This is a structured, season-by-season curriculum — not a news recap. Seasons 1–2 cover the foundations: automation risk, protected careers, sk

Surviving AI – Navigating AI Job Displacement and Automation is a business podcast hosted by Surviving AI with Carlo Thompson, with 73 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Surviving AI with Carlo Thompson hosts Surviving AI – Navigating AI Job Displacement and Automation, a business show with 73 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

• 

  Surviving AI – Navigating AI Job Displacement and Automation

  Entry-Level Hiring Fell 80% Since ChatGPT Launched. It Wasn't Layoffs.

  Aug 19, 202635 min

  Send us Fan Mail Entry-level hiring has fallen roughly 80% per quarter at companies adopting generative AI since 2023, and Carlo and Ainsley open this episode by clearing up the biggest misread of that number right away: it's not a layoff wave, it's a quiet freeze. A Harvard working paper covering 66 million workers and 280,000+ firms found actual entry-level employment only fell about 9% over six quarters. The real story is companies cutting hiring almost immediately after ChatGPT launched, betting on automation before it had fully arrived. From there, the data gets harder to wave away: Stanford's Digital Economy Lab, working from actual ADP payroll records, finds a 19% employment gap for 22-25 year-olds in AI-exposed occupations, up from 15% just a year ago, while workers in less-exposed jobs grew employment by about 10% over the same stretch. The mechanism Stanford names is the episode's most useful idea: "codified knowledge" (standardized, manual-teachable tasks) is what's disappearing fastest. Judgment work, the stuff you can't fully write down, is holding steady or growing. That's the filter this episode hands you to run over any job posting. Then the honest parts: a hard look at the newly announced Khan TED Institute (TED, Khan Academy, and ETS), a genuinely different, competency-based model that's also unlaunched and reportedly priced around $10,000, held up against what's already working at Northeastern and SUNY. And the global picture, because this show doesn't flatten it: 12.4% youth unemployment worldwide, but only 6.1% of youth-held jobs globally sit in the high-AI-exposure category the ILO tracks, concentrated almost entirely in rich countries, a different, not smaller, crisis for the roughly 90% of young workers in developing economies working informally. If you're locked out of your first job right now, this episode ends with the one reframe that actually changes what you apply for next. Chapters: 00:00 Cold Open: Entry-Level Hiring Fell 80% (Not a Layoff) 01:54 Inside the Harvard Study: 66 Million Resumes, 280,000 Firms 03:31 Why Companies Cut Early: Anticipatory Cost-Cutting 05:34 Stanford's Data: The Gap Widens to 19% 07:45 Codified vs. Tacit Knowledge, Explained 09:24 Higher Ed's Answer: Inside the Khan TED Institute 14:09 The Global Picture: ILO's 12.4% Youth Unemployment 17:38 Proof of Work, Not Task Completion 22:42 Even "Safe" Careers Have a Codified Half 26:01 Score Your Own Job's Risk This Week 28:03 The Gaps We're Naming Out Loud 32:17 The Close: A Season 6 Finale Tease Full sources and data referenced this episode are listed with links in the episode's Research Document. Ask in the comments if you want it. Surviving AI publishes every Monday and Wednesday. Subscribe: Apple Podcasts | YouTube | Spotify Please visit our website for more information - Surviving AI: Navigate the Future

  ListenShow Notes
• 

  Episode #8

  The Governance Gold Rush: Who Gets Paid to Say No to AI

  Aug 17, 202646 minS6

  Send us Fan Mail Somebody in your building, probably in privacy, legal, or IT, is about to get handed a job that didn't exist two years ago. This week, Carlo and Ainsley open Season 6's final Opportunity Map tier, AI governance, with the number that carries the whole episode: in a survey of 671 organizations across 45 countries, 98.5% say they need more AI governance staff in the next 12 months. Hiring has held flat at roughly 71 new US postings a week since January, including straight through the week everyone calls "the EU deadline," with no spike. That flat line is the thread Ainsley pulls hardest. What actually activated August 2, 2026, was the EU AI Office's enforcement power over general-purpose AI models and Article 50's transparency rules, not the broader high-risk system regime, which the Digital Omnibus pushed to December 2, 2027. This is the third time the show has corrected that exact date on air. Carlo and Ainsley trade real (composite, disclosed) stories along the way: a privacy manager in Ontario whose title changed without a single job posting, a compliance team in the Netherlands that sprinted for a deadline that wasn't the real one, and a risk analyst in São Paulo who used a consulting job as the door the in-house market wouldn't open for her. The honest breakdown of who's actually getting hired: 22% privacy, 22% legal and compliance, 17% IT, and over 60% of governance leadership coming from three functions that have never touched a line of model code. Professional Services firms, not tech companies, are the single biggest hirer, at 35% of all postings, and the concrete, less-hyped ISO 42001 audit track pays $95,000–$140,000 in-house. This closes the four-tier Opportunity Map that opened Season 6: infrastructure and operations, AI training and red-teaming, AI-augmented professional work, and now governance. Carlo and Ainsley are honest that the math doesn't fully add up: four real, hiring-now doors are nowhere near the World Economic Forum's much larger jobs-created projection, and the entry-level rung is getting thinner for people with no adjacent experience to redirect. But for anyone already doing risk, compliance, or process work today, the door is open now, wider than it's likely to stay. Wednesday: the tactical, 90-day version of walking through it. 00:00 Intro — Three Functions Now Run AI Governance 01:44 98.5% of Companies Say They're Understaffed 03:58 The Privacy Manager Who Became the Accidental Governance Chief 05:51 71 Postings a Week, No EU Deadline Spike 09:26 The Third Correction: What Actually Activated August 2nd 11:06 Who Has the Standing to Say Stop 13:21 The ISO 42001 Audit Backlog 16:55 The Netherlands Company That Prepped for the Wrong Deadline 19:07 The Real Cost of Panic Hiring 20:37 Why Consulting Is the Fastest Door In 23:36 The 22-22-17 Breakdown 28:02 The Honest Gap — Four Doors, Not 170 Million Jobs 31:21 Get In First, Certify Second 37:59 No Computer Science Degree Required 40:11 Four Doors, One Question: Who Gets to Say Stop Subscribe: Apple Podcasts — https://podcasts.apple.com/us/podcast/surviving-ai-job-automation-workforce-future-insights/id1864360631 YouTube — https://www.youtube.com/@SurvivingAIRisk/videos Please visit our website for more information - Surviving AI: Navigate the Future

  ListenShow Notes
• 

  Episode #4

  GPT-Red Beats Human Red-Teamers 84% to 13%. The Job Still Pays $300,000.

  Aug 10, 202635 minS6

  Send us Fan Mail OpenAI built an automated red-teamer called GPT-Red whose entire job is trying to break OpenAI's own models. In a head-to-head test, it succeeded 84% of the time. Human red-teamers, on the identical test, succeeded 13% of the time. Carlo and Ainsley open with that number on purpose, because the honest version of "AI trainer, evaluator, red-teamer" — a real job category paying $95,000 to $300,000+ a year — has to include the fact that the machine is already winning at the highest-volume layer of the exact work this episode is about. What it doesn't win at is judgment: a nurse who can catch a subtly wrong medication interaction in an AI triage tool, a loan officer who knows what discriminatory pricing language actually looks like, a Kenyan evaluator who understands which failure modes are specific to Kenyan social and legal context. That's the real hiring picture — Microsoft's AI Red Team includes a neuroscientist and a linguist alongside engineers, and the accessible entry points (AI Safety Evaluator, AI Governance Analyst) start at $95,000 and don't require a coding background. Carlo also walks through a three-pillar way to think about where this risk actually lives inside a company: infrastructure AI, employee AI, and customer AI, each with its own blind spots. The episode doesn't stop at the good news. Data annotators and content moderators in Kenya earn $1.46 to $3.74 an hour for work that, on a resume, sits in the same broad job category as red-team work paying $21 to $27 an hour in the US — a roughly 12-times gap for comparable work. Kenya's own government has a draft occupational-protection policy open for public comment right now. Same industry, wildly different economics, depending entirely on where you live. Ainsley closes with the actual first step: pick one AI tool you already use at work, break it on purpose, and write up five documented findings — that portfolio reportedly opens more doors than a certification. If you've got real depth in a non-tech field, this might be the clearest on-ramp into AI work we've mapped this season. Tell us in the comments: what's the one thing you'd document first? Subscribe: Apple Podcasts | YouTube | Spotify — Surviving AI publishes every Monday and Wednesday. Please visit our website for more information - Surviving AI: Navigate the Future

  ListenShow Notes
• 

  Episode #4

  Elon Musk Says Money Is Obsolete by 2036. His Companies Still Employ 180,000 People.

  Aug 5, 202651 minS6

  Send us Fan Mail Elon Musk told The Economist that money becomes obsolete by 2036 — AI and robots producing more than anyone can consume, prices collapsing toward zero, work becoming optional the way gardening is optional. Carlo and Ainsley spend this episode taking that prediction apart piece by piece: what Musk's own chess-and-Stockfish analogy accidentally proves against him, why "universal basic high income" has never been tested anywhere near the scale he's describing (Stockton and Finland's pilots ran a few hundred dollars a month, not a livelihood floor), and the demand-side hole almost nobody is naming automate away the income people need to buy things, and you haven't built abundance, you've built a factory that ships to an empty room. They also dig into the parts of the interview getting less attention: Musk's peer-review proposal for frontier AI labs (rivals get a one-to-two-week early look at a new model before release, government only as backstop), the sovereignty gap in every AI governance summit since Bletchley Park, and why the U.S. power grid or chip supply may be the real bottleneck standing between here and 2036. And underneath all of it, Tesla and SpaceX together still employ well over 100,000 people, including at the company owned by the man predicting the irrelevance of human labor. Carlo is at the AI-4 conference this week. Hinton, Fei-Fei Li, and Andrew Ng are all on one stage on a fact-finding mission to ask the people building this future the one question this episode keeps circling back to: what are we building the economy around once the technology can do everything? Please visit our website for more information - Surviving AI: Navigate the Future

  ListenShow Notes
• 

  Surviving AI – Navigating AI Job Displacement and Automation

  Amazon Cut AI Jobs and Funded 340,000 New Ones. Most People Don't Know They Exist.

  Aug 3, 202637 min

  Send us Fan Mail Monday, we asked where the AI infrastructure money went. Today we name the seats it created: 340,000 data center positions sitting open in the US right now, against a total build-out need of roughly 650,000 across construction and operations not a 2030 forecast; open jobs the industry can't fill today. Carlo and Ainsley trace the real career ladder within that number, from entry-level data center technician roles through engineer, MEP, and AI infrastructure specialist roles, and show why Microsoft, Google, AWS, and Meta have all dropped the four-year degree requirement for entry-level technician hiring. Stargate, the Oracle/OpenAI buildout that just added 4.5 gigawatts of capacity and an estimated 100,000+ jobs, becomes the single clearest proof point: a named project, a named partner, workers already on site. The back half goes wider and harder: a "silver tsunami" retirement wave that's pulling out roughly a third of the current technical workforce at the same moment the industry needs them most, a workforce where half of all data centers report women make up less than five percent of staff, and a global build-out where high-income countries hold the overwhelming majority of capacity while the regions with the fastest-growing populations are still years behind and why the physics of latency means that has to change. The through-line from this season: the capital moved, the jobs moved with it, and almost nobody got the memo. Chapters: 00:00 Intro: Picking Up From the $700 Billion Question 00:38 The Real Number: 340,000 Open Seats Right Now 01:51 Does Your Career Port Over? Trades, PMs, and the "Wrong Room" Problem 04:49 Stargate: One Named Project, National Footprint 09:01 Who Actually Gets Hired: The Real Build-Out Labor Force and Salary Ladder 11:38 The Global Picture: Who Gets Left Out 14:24 Physics, Latency, and Why Local Data Centers Have to Exist 16:09 The Silver Tsunami: A Retirement Crisis Hiding Inside the Shortage 18:10 Less Than 5%: Where Are the Women? 20:56 The On-Ramps Nobody Tells You About 23:33 It's Not Just America: The Global Jobs Case 27:02 Structural Exclusion, Not a Pipeline Problem 29:28 Call to Action: Do This Before the Week Is Out 32:09 What's Next: Inside the Machine Subscribe: Apple Podcasts, YouTube, and Spotify — new episodes every Monday and Wednesday. Please visit our website for more information - Surviving AI: Navigate the Future

  ListenShow Notes

Recent guests on Surviving AI – Navigating AI Job Displacement and Automation. Study who booked and why before you pitch.

Based on semantic analysis of episode topics and host coverage, this show is a strong guest fit for executives in:

Industry fit is computed by PitchCentric using vector embeddings of the show's episode catalog.