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 83 episodes on record and a Required Pod Score of 80.

Surviving AI with Carlo Thompson hosts Surviving AI – Navigating AI Job Displacement and Automation, a business show with 83 episodes published.

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  Episode #9

  The Agentic Survival Plan: Are You Still the One Watching?

  Sep 28, 202643 minS7

  Two AI agents were built to check each other's work. Instead, they spent 11 straight days approving each other's mistakes, burning close to $50,000 in compute before anyone noticed, and the thing that finally caught it wasn't a person and wasn't the other agent. It was a billing alert. That's the opening image for this Season 7 finale: not one bad AI output, but two systems agreeing their way past a wrong answer while nothing was watching closely enough to say stop. From there, Carlo and Ainsley test that idea against the week's real news: a decade after Geoffrey Hinton predicted AI would end radiology, radiologists are in more demand and better paid than ever, because the job narrowed down to the tacit half a machine still can't do; Trump telling the UN he's renaming AI to "Superintelligence"; and a live, unscripted moment where Carlo catches his own research agents chasing the wrong story about August's confusing jobs numbers (162,000 added, and two of the best data sources in the country, BLS and ADP, who don't even agree on what's happening underneath it) and has to pull the episode back on track himself. Then comes the case that has nothing to do with disagreement at all: one autonomous coding agent, one destructive command, and a company's entire production database and every backup gone in nine seconds, because nobody was required to approve it first. The takeaway isn't "pay closer attention." It's narrower than that: whoever decides if you keep your role has to be able to see the judgment call you made that a system alone wouldn't have. Ainsley breaks down exactly what that looks like for three different people (the person red-teaming these systems, the person signing off on their output, and the person just starting out with no track record yet) before closing with the homework: find one seam in your own work this week where something gets handed off unchecked, and write down what you'd catch that the system wouldn't. CHAPTERS 00:00 Intro: The Agentic Survival Plan 04:20 Cold Open: Two Agents, 11 Days, One Billing Alert 07:00 The Radiologist Who Got a Raise 09:29 Trump, the UN, and the Word "Superintelligence" 10:31 Carlo's Own Agents Missed the Point, Live 14:27 Inside August's 162,000 Jobs 16:31 BLS vs. ADP: Nobody Agrees 21:20 Why "AI Layoffs Are Slowing" Isn't the Real Story 22:38 PocketOS: Gone in Nine Seconds 25:38 The Real Question: What Needs a Human 29:48 Three Cohorts, Three Survival Plans 36:05 The Self-Driving Car Nobody Can Take Over 39:47 Homework: Find Your Seam Subscribe for new episodes every Monday and Wednesday: Apple Podcasts, YouTube, Spotify. Please visit our website for more information - Surviving AI: Navigate the Future

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  Episode #8

  AI Is "Solving Millennium Problems." In 25 Years, Humans Solved Exactly One.

  Sep 23, 202643 minS7

  The Optimist's View on AI Innovation Exploring the bright side of AI's potential with Carlo and Ainsley. In this episode of Surviving AI , Carlo and Ainsley dive into the optimistic possibilities of AI innovation amidst the prevailing concerns surrounding artificial intelligence. They discuss the potential for AI to create novel solutions and tackle complex problems that have long stumped human thinkers. Embracing Optimism in AI Carlo kicks off the conversation by donning his "Optimus hat," suggesting that while the narratives around AI often focus on risks and failures, there is a promising side to explore. He emphasizes that capable AI models could lead to groundbreaking innovations, potentially even solving millennium problems that mathematicians have struggled with for decades. "If AI could kinda solve some of those problems... it does it exceedingly fast." — Carlo Ainsley counters with a critical lens, questioning whether AI truly generates novel ideas or merely accelerates the testing of existing ones. She argues that the distinction between these two forms of innovation is crucial to understanding AI's capabilities. The Promise of AI As the discussion progresses, Carlo shares his vision of a future where AI could revolutionize various fields, from medicine to transportation. He imagines flying cars that don't rely on traditional propulsion methods, showcasing AI's potential to innovate in ways that humans might not conceive. However, Ainsley reminds him of the importance of context and narrow focus in AI achievements, citing the example of AlphaFold, a groundbreaking AI model that solved the protein folding problem. "AlphaFold is narrow. It was purpose-built on one extremely well-defined problem." — Ainsley The duo debates the feasibility of achieving superintelligence that can reason about complex, interrelated systems like a flying car. While Carlo is optimistic about the future capabilities of AI, Ainsley urges caution, pointing to the challenges of ensuring that AI can genuinely reason across multiple domains effectively. Navigating the Risks The conversation also touches on the risks associated with AI, including job displacement and ethical concerns. Carlo asserts that, despite these risks, the potential benefits of AI, when implemented correctly, could lead to significant societal improvements. Ainsley echoes this sentiment but emphasizes the logistical challenges involved in coordinating the necessary efforts across various fields to harness AI's potential responsibly. A Collaborative Future? As they conclude, Carlo and Ainsley explore the idea that superintelligence could catalyze human innovation rather than replace human jobs. Ainsley illustrates this point by suggesting that AI could empower scientists and planners worldwide, giving them the tools they need to solve pressing issues more efficiently. "If the race actually resolves into the second version, that's not a job story about disappearance. It's a job story about who gets access to the multiplier first." — Ainsley Carlo's ultimate hope is that all people will benefit from AI advancements, fostering a collaborative approach to innovation where everyone is working toward common goals. --- This enlightening conversation invites listeners to reflect on the dual nature of AI — its risks and its remarkable potential. For a deeper dive into the nuances of AI innovation, listen to the full episode of Surviving AI . Please visit our website for more information - Surviving AI: Navigate the Future

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  Episode #7

  The Human in the Loop: The Skill You Control

  Sep 21, 202645 minS7

  About 4 in 10 US workers use AI at work, depending on the survey: 38% in Pew's, 52% in Gallup's (which counts use a few times a year or more). In a New York Fed survey, only 15.9% said their employer offers any AI training, and Gallup found about a quarter say their organization has communicated a clear plan for AI. Carlo and Ainsley start there and ask what "human in the loop" actually requires of a person who was handed a tool and never told how to check it. Carlo's argument is that human in the loop is a skill, not a new job title: work out which tool you are using, learn what it gets wrong, and get faster at catching it. Ainsley pressure-tests it. The EU's human-oversight rules for high-risk AI, Article 14 included, do not apply until December 2, 2027, and they cover high-risk systems, not the chatbot at your desk. Job-ad studies put the AI pay premium between 28% (Lightcast, 2025) and 62% (PwC, 2026), but both measure what postings advertise to outside hires, not what people already in the seat are paid. One executive, Arkose Labs CEO Kevin Gosschalk, told IT Brew that managing agents is "likely to be something for the next 18 months." And research on automation bias finds that experts over-trust machine output too, and that training alone did not remove it. Carlo's positions on pay and job security are his opinion, not data. The homework: keep a failure log. Record the tool and version, the date, what it got wrong in plain language, what you did about it, and whether you have seen that failure from that tool before. The show notes have a blank template, a one-page sheet of every number in this episode with its source, and a Leverage Check worksheet. Everything we could verify comes from the US and Europe. Gallup is a research firm that also sells workplace consulting, PwC is a consulting firm, and Lightcast sells labor-market data. CHAPTERS: 00:00 Cold Open and Welcome 00:30 The Gap: Who Uses AI and Who Got Trained 02:50 Harvey AI vs. a General Chatbot: "Use AI" Is Not an Instruction 04:44 Rubber Stamp vs. Real Review (and the New-Coworker Problem) 07:39 What the EU AI Act Does and Doesn't Require 10:26 What the Market Prices In: The 28% to 62% Premium 13:53 The 18-Month Warning 16:20 Two Skills, One Name: Catching Errors vs. Knowing What People Want 19:01 Does the Speed Advantage Survive a New Tool? 22:59 The Failure Log: What to Write Down 23:50 Quitters or Never Trained? Who Isn't Using AI 29:15 "Master of the Loop": Can You Ask for a Raise? 33:56 Paintbrush vs. Spray Gun: Same Job, New Instrument 35:03 Will Failure Modes Ever Go Away? 39:56 The Four Columns of the Log 40:50 Grow Your BATNA 43:54 The Entry-Level Log, and Why Fresh Eyes May Be Safer Subscribe: Apple Podcasts, Spotify, and YouTube. New episodes every Monday and Wednesday. Please visit our website for more information - Surviving AI: Navigate the Future

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  Episode #6

  The $12.9 Billion AI Contradiction

  Sep 16, 202644 minS7

  NVIDIA just paid $12.9 billion for Hugging Face — the platform three million open-weight AI models live on. Days later, an Anthropic researcher resigned warning that OpenAI and Anthropic are "racing straight to self-improving superintelligence and gambling with our lives," and a separate Anthropic safety lead put the odds of AI killing everyone at over 10% within a decade. Same week: OpenAI locked in a custom chip deal with Broadcom, Anthropic is deep in talks with Samsung for its own silicon, and a 25-company coalition — led by NVIDIA, not by the labs — is lobbying Washington against restricting open-weight models. Carlo and Ainsley spend this reactive, off-schedule episode asking whether that's four unrelated headlines or one incentive structure wearing different masks. The real find isn't the conspiracy theory Carlo opens with — it's the EU AI Act's actual compute threshold (10^25 FLOPs) that already draws a bright line between regulated and exempt AI models, and the honest admission that OpenAI and Anthropic's proposed 30-day federal review window has never published what would actually trigger it. Ainsley pressure- tests every safety claim in the episode — NVIDIA's, the labs', the EU's — against the same three questions: who's saying it, what do they gain if you believe it, and would they act differently if they didn't believe it themselves. Correction: this episode originally aired the Anthropic researcher warning as one person's story — it's actually two people, Jacob Coxon (who resigned) and Evan Hubinger (who separately gave the 10% figure) — see the pinned comment and show notes for the full record. We got one thing wrong on air and want to own it here: the "AI researcher warns of 10% doom risk" story is actually two people, not one — Jacob Coxon resigned, Evan Hubinger gave the 10% figure. If you had to bet on it: does that correction change how seriously you take the underlying warning, or does the warning stand on its own either way? Genuinely curious where you land. CHAPTERS: 00:00 Cold Open — NVIDIA Buys the Library Everyone Downloads From 00:26 The Weekend Everything Blew Up 02:40 Three Moves, Same Few Months 04:16 The FDA Playbook for AI Liability 08:03 Forecast Fools and the Capital Chess Game 11:00 Kimi K3: The Real Event Nobody's Naming 14:15 The Capability Threshold Nobody's Debating Correctly 15:50 Should Hugging Face Be an App Store? 19:05 Trading Openness for Safety 20:31 The Honest Asterisk 22:02 Who Should Score AI Safety? 23:38 The EU Already Solved Half of This 26:02 A Best Guess Wearing a Number 28:56 The Lab That Shrinks Its Own Model 30:11 Accountability vs. Permission 33:27 What Happens When You Cross the Line 35:02 Show Me the Evidence 37:47 The Five-Lab Blind Spot 38:59 Does Open Weight Get You Out of Liability? 42:20 Two Things We're Not Letting Slide — Surviving AI is hosted by Carlo Thompson and Ainsley (AI co-host), breaking down what AI job automation actually means for your career — every week. Watch this episode on YouTube: https://www.youtube.com/watch?v=3Z0MUZ5fbpA Browse every episode, show notes, and resources: Surviving AI Episode Center Follow Surviving AI: Facebook | TikTok | Instagram | X/Twitter Please visit our website for more information - Surviving AI: Navigate the Future

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  Episode #5

  We Said the Trades Were Safe From AI. 13,000 Robots Later, Here's the Honest Correction.

  Sep 14, 202639 minS7

  In June this show argued that the trades were safe from AI because the body has skills the cloud can't run. Here's the honest update: total humanoid robots sold worldwide in all of 2025 was somewhere between 13,000 and 18,000 units, and Figure AI - the company whose robot is literally working inside a BMW plant right now - sold roughly 150 of them. That's not a reassuring number. It's a slower one, which is the only reason anyone has time to prepare. Our June framing wasn't wrong that physical work is harder to automate. It was incomplete about why: it isn't your hands that protect you, it's whether anyone has a financial reason to rebuild the room you work in. BMW's own account of its Spartanburg pilot makes that case better than we could. Over about ten months, a Figure 02 robot helped build more than 30,000 BMW X3s, moved over 90,000 components, and logged roughly 1,250 operating hours - against a schedule of five days a week, ten hours a shift, which by our own math implies something closer to 2,000 scheduled hours. That gap is downtime, and downtime on an industrial robot doesn't fix itself. What actually made the pilot work wasn't a smarter robot - it was BMW rebuilding the hall around it: new safety barriers and partitions, upgraded 5G coverage, a body shop chosen specifically because it was already the most automated space in the plant. Two thousand miles and one ocean away, 39,000 Hyundai workers walked out across three South Korean plants this August over a robot that won't arrive until 2028 - and won a settlement that includes 500 new technical hires. They struck two years before the machine showed up, because that's when the leverage actually exists. The geography of all this is wildly uneven, and that unevenness is the point. South Korea runs 1,220 robots for every 10,000 manufacturing workers, the highest density on Earth; Mexico runs 62. Robot adoption tracks the cost of labor, not the difficulty of the task - which means in lower-wage economies, physical work can currently sit under less automation pressure than office work, because robots cost real money and software doesn't. That's a reprieve, not a moat, and reprieves expire. The actual exercise this episode leaves you with: take your own work week and split it into two piles - the parts that happen in a space somebody has a financial reason to standardize, and the parts that don't. The first pile has a clock on it. The second pile is your actual career, for now. It takes about twenty minutes and no spreadsheet. Subscribe: new episodes every Monday and Wednesday. Apple Podcasts: podcasts.apple.com/us/podcast/surviving-ai-job-automation-workforce-future-insights/id1864360631 YouTube: youtube.com/@SurvivingAIRisk Please visit our website for more information - Surviving AI: Navigate the Future

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