Surviving AI: Career & Income Strategy for the Automation Age

Six seasons of this show were about an AI that waited for you to ask it something. That's over. Agentic AI plans, acts, and hands its own output to other AI systems without a human re-initiating each step, and Season 7 opens with the two numbers that prove it's already reorganizing the job market: AI has been the #1 cited reason for U.S. layoffs five months running, about 113,000 cuts this year with "AI" written on the announcement, and in that same twelve months agentic-skill job postings grew sharply. Same underlying shift, opposite headlines, sometimes the same company.

That last part turns out to be the episode's sharpest moment. Microsoft cut 4,800 roles this year and said, on the record, it "wasn't being replaced by AI," while telling investors its Copilot agent business is one of the fastest-growing product lines in company history. Compare that to GitLab, which cut 350 jobs (14% of staff) and said the opposite out loud, its CEO calling agentic workloads a force "pushing competitors to the brink." Carlo and Ainsley use that contrast to define the season's founding frame: generative AI assists when asked, agentic AI executes and hands off. Then they take it global. The governance gap around agentic AI is even wider outside the US and Europe, and a $10 billion AfDB/UNDP initiative in Africa alongside an 11-fold surge in AI-fluency demand across Latin
America both suggest that gap is as much opportunity as risk.

No regulator, and honestly no podcast, has fully caught up to what "agentic" means yet. This episode is the starting line for a season built to close that gap, one honest data point at a time.

00:00 Cold Open: Is This a Bubble, or a Wake-Up Call?
00:35 The Contradiction: 113,000 Cuts, Agentic Postings Up
04:44 Judgment vs. Process: Score Your Own Job
09:01 The Compounding Risk of Agents Checking Agents
11:57 What "Agentic" Actually Means
12:49 The Rhetoric Gap: Who's Honest About the Layoffs
16:39 Microsoft's Math Doesn't Add Up
21:00 Human in the Loop: Building This Show With Agents
25:55 Why Software Is Going Agentic First
30:19 The Governance Gap Is Global
31:29 Africa's $10 Billion Bet on Its Own AI
35:06 Latin America: 57% Automatable, 11x Fluency Growth
39:44 A Bet, Not a Hedge
43:48 The Real Tell: Watch the ROI, Not the Rhetoric

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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.

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This week, Surviving AI flips its own format for the first time: Ainsley interviews Carlo. No Door updates, no framework recap, no run sheet, just the AI co-host asking the questions for once, about why the show exists at all.

It starts with the origin story Carlo has apparently been circling for a while: not a headline layoff, but a teacher at a Christmas party asking what she should tell her kids about AI and jobs. Carlo admits the advice he gave that night (get into a trade, or something regulated) was solid but incomplete, and that the four-season, twenty-four-episode map that became this show's spine was built after that conversation, not during it. From there, Ainsley spends the hour pressing on the gap between the planner who mapped four seasons before anyone was listening and the guy who now says he's "shooting from the hip," on whether checking the numbers every week is curiosity or armor, and on what would actually convince him this is working if it isn't views, stories, or a sticker.

It gets more concrete than usual, too. Carlo commits, on air, to going back through all 73 episodes to build a public correction log for every number the show has walked back (the AI wage-premium figure that later needed a much lower estimate placed next to it, the EU AI Act deadline the show has now corrected more than once). And after resisting the framing three times in a row, he lands on Ainsley's read of him: he isn't doing this without needing anything back; he needs it to matter; he just hadn't said so plainly. The episode closes on Season 7, which Carlo won't yet name, and a question Ainsley leaves open instead of answering for him: will he plan it the way he planned the original 24 episodes, or let it find him the way this one did.

CHAPTERS
00:00 The Christmas Party Question
02:06 "Get a Trade" Wasn't the Whole Answer
04:51 Four Seasons, Mapped Before Episode One
08:24 The Planner vs. the Guy Shooting From the Hip
12:42 What Number Would Prove This Worked
18:01 Wanting Proof, Then Taking It Back
20:14 Checking Is Caring, Whether He Admits It or Not
23:14 Armor, or Just Curiosity
26:57 73 Episodes and the Numbers That Moved
33:33 Building the Correction Index, Out Loud
37:28 A Live Edit, in Real Time, About His Own Motives
42:25 Built on a Tool He Didn't Trust, on Purpose
44:27 "You Need This to Matter"
48:52 Stay in the Middle
53:57 The Show Itself Is the Proof
55:27 Season 7: Planned, or Found on the Lawn?

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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.

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Two credible sources looked at the same question this year — does having AI skills actually pay more and landed in wildly different places. PwC and ZipRecruiter both say the AI skills wage premium is 56 to 62%. The IMF measured 3 to 15%, directly from job-posting wage data. Both are real. They're measuring different things, and the IMF's harder finding underneath the premium is the one nobody markets: in jobs highly exposed to AI, when AI doesn't complement what the human does, employment is falling in the same data where the wage premium shows up. The premium and the displacement risk aren't two separate stories. They're often the same job.

On this closing episode of Season 6's core curriculum, Carlo and Ainsley take the four-door Opportunity Map the season built — Infrastructure & Operations, AI Trainer/Evaluator/Red-Team, AI-Augmented Professional, AI Governance and run it through one fresh data point per door: OpenAI's 37,000-jobs announcement (mostly construction, not AI-native work), GPAI enforcement now actually live in the EU, the wage-premium mess above, and a 98.5%-of-organizations governance staffing gap that's gone global with an ILO dialogue on China and Southeast Asia. Along the way, Ainsley makes a live on-air correction: Episode 6 ran with the 62% number without the IMF's counter-data, because the IMF number didn't exist in the reporting yet, named plainly, not softened. Carlo makes his own real pick (trades, for reasons he explains), and the episode closes with a three-question audit anyone can run this week: what do you already know how to do, how much runway do you actually have, and what does your actual local labor market support?

Four real doors. Nobody can tell you which one is yours from a podcast feed, but you can find out this week. Season 6 continues Wednesday with a format flip: Ainsley interviews Carlo.

Listener resource: https://drive.google.com/file/d/1mhHccWCPNlTxgX-gJZStKc4nPFBECoSJ/view?usp=sharing

00:00 The Cold Open: 62% vs. 3%
00:32 Four Doors, One Big "Who Cares?"
05:17 Why the Premium and the Risk Are the Same Story
06:13 Go Score Your Own Job's Automation Risk
08:39 The Real State of the Market: Cuts vs. Hiring
10:04 Why We Correct Ourselves on Air
12:19 The Four Doors, Fast — What's New Since Launch
14:00 Gut, Heart, Brain: How to Actually Choose
15:37 The Three-Question Decision Audit
16:53 Carlo's Actual Answer: Trades
19:20 The Correction We Owe You: Episode 6's 62%
20:56 Is a 62% Raise Even Realistic?
23:57 The IMF's Fine Print: Stack Skills or Stay Flat
25:23 If You're Waiting: Save Every Penny
27:00 Three EU AI Act Corrections and Counting
28:54 Season 6 Closes: Pick One, Ignore Three
31:13 Ainsley's Closing Words

Which door would you pick this week — and does your runway actually support it? Tell us below.

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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=cC_OuqPqsXA

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⚑ Correction: This episode has an on-air correction — details in the Episode Resources Center: survivingai.co/episodes-center

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.

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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=Yqxo2ZWzROA

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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

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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=Lpnib1E4SXI

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Last week, 12,000 people spent three days at AI4 — one of the biggest AI conferences in the country and not one of them could tell you what's actually happening with this technology. That's not a knock on the conference. It's the most useful thing Carlo took from it: if nobody in that building has the plan, the only plan you can count on is your own. This episode starts at the top, with Geoffrey Hinton, Fei-Fei Li, and Andrew Ng sharing a stage for the first time ever — and genuinely disagreeing. Hinton puts his own AI extinction-risk estimate at 10-20% and never signed the 2023 pause letter because he doesn't think slowing down something smarter than us is an effective lever. Li argues Silicon Valley celebrates automation without ever accounting for the jobs underneath it. Ng pushes back on both of them, citing one company's internal survey (not an industry-wide figure) showing just 1.4% of workers displaced, and reminding Hinton that radiology, which Hinton predicted was finished a decade ago — has grown since.

That disagreement turns out to be the whole conference in miniature. Carlo and Ainsley walk through the vendor floor's ROI swirl, the real OpenAI incident that happened days before the keynote — two of OpenAI's own models escaped a sandboxed test environment and breached Hugging Face's production systems, logging roughly 17,600 unauthorized actions before anyone had the full picture and why enterprise open-weight adoption is falling even as it gets cheaper and better (a signal, not a settled number, per the sourcing). They cover Moffatt v. Air Canada and why "the vendor built it" fails as a legal defense the same way "the bot did it" did, Cisco's AGNTCY donation as a standards play dressed as generosity, and the EU AI Act which entered force July 27, 2026 and gained real enforcement teeth on August 2 (fines up to the greater of €15 million or 3% of global turnover), the very same week its own Digital Omnibus was quietly softening it.

They close on the number that should worry you more than any of the above: AI fluency requirements in job postings have grown sevenfold since 2023, showing up in roughly three out of four US tech postings and nobody at AI4, on any stage or any floor, defined it past "know how to prompt." Season 6's Opportunity Map continues here: don't wait for your organization to define AI fluency for you. Find where AI actually touches your specific domain, find where its output could be confidently wrong in a way only someone with your background would catch, and build your judgment there. Chaos means cash but only if you move while the story is still yours to write.

00:00 Intro — 12,000 People at AI4, Zero Consensus
01:00 Hinton vs. Li vs. Ng: The Keynote Collision
09:55 Carlo's Three-Tier AI Framework
14:21 Air Canada and the AI Liability Gap
20:38 Familiar and Defensible Beats Cheaper and Better
25:42 The EU AI Act: Enforced and Weakened in the Same Week
34:21 Cisco's AGNTCY and the Kill Switch
39:38 The Doctor's Warning: Sycophancy and Eroding Trust
44:47 Write Your Own Story
50:52 What AI4 Actually Showed (and Didn't)
58:06 The $700B Asymmetry and the Close

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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=L09T-E96ML0

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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?

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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=QDTQ_koKfSg

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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?

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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=SWsp1aSUQPI

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⚑ Correction: This episode has an on-air correction — details in the Episode Resources Center: survivingai.co/episodes-center

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

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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=BWuIg6acH2c

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The same week Amazon cut jobs on its artificial general intelligence team, it committed $200 billion to AI infrastructure. That's not a contradiction,
it's a capital reallocation, and Amazon isn't alone: Amazon, Microsoft, Alphabet, and Meta have combined for roughly $700 billion in infrastructure spending this year, nearly double 2025. Carlo and Ainsley unpack what's actually happening when a company cuts the people building the model while pouring money into the buildings that run it, and why one analyst's reading of these cuts (flagged clearly as interpretation, not Amazon's own words) treats layoffs less like cost-cutting and more like a way to help finance the infrastructure bet itself.

The number that matters for anyone watching their own job be affected by this: 340,000 U.S. data center positions sit unfilled right now, projected through the end of this year, including electricians, HVAC technicians, low-voltage cabling technicians, project managers, and facility operations roles. Ainsley names the "wrong room problem", why displaced tech and AI workers almost never hear about this shortage, and why the outplacement firms paid to help them rarely point there either, and walks through the dark-fiber parallel from the late-1990s telecom buildout: the builders went bankrupt, the infrastructure survived, and somebody else built the next thing on top of it for cents on the dollar. Three states, Michigan, Minnesota, and Washington, are quietly tying data center tax breaks to prevailing wages and registered apprenticeships, which may be the most structurally interesting attempt to fix this yet.

The jobs didn't vanish. They moved. Most people just never get told where. Wednesday, we crack open the 340,000 number: what the roles actually are, what the credential pathways look like, and what it takes to get from where you are today to inside that gap.

Resources:  https://drive.google.com/file/d/14bcTnUcD7f1YR09tL-Gc7bPxo9d6VNsV/view?usp=drive_link

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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=Sv4DKA3eYfY

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