AI is passing the bar exam, acing medical licensing tests, and crushing coding challenges. So why does research show these same systems fail more than 90% of the time on tasks lasting over four hours?

The answer lies in how AI gets trained—and the limitations that process bakes in from the start.

In this episode of Surviving AI, we go deep on the training problem: the gap between benchmark performance and real-world reliability that creates both risks and opportunities for your career.

What you'll learn:

• How Reinforcement Learning from Human Feedback (RLHF) optimizes AI to sound right rather than be right—and why this creates "sycophantic" systems that tell you what you want to hear
• The "4-Hour Rule": why AI succeeds on quick tasks but struggles with complex, sustained work—and what that means for job vulnerability
• Four predictable failure modes you can learn to spot: temporal blindness, distribution shift, benchmark theater, and inherited bias
• Why 98% of companies feel urgency to deploy AI while only 13% are actually ready—and what happens in that gap
• The three emerging roles that become MORE valuable as AI capabilities grow: the Validator, the Translator, and the Accountability Layer
• Specific questions to ask when AI enters your workplace for hiring, strategy, or workforce decisions

Key research discussed:

• "Open Problems and Fundamental Limitations of RLHF" (Casper et al.)
• METR's research on AI task completion by duration
• Cisco's 2024 AI Readiness Index
• Stanford's Foundation Model Transparency Index
• Deloitte's findings on executive decisions based on AI hallucinations

The bottom line: The gap between what AI benchmarks measure and what work actually requires is your competitive advantage. This episode shows you exactly where to find it.

550,000 plumber shortage by 2027. An 80,000+ electrician shortage doubling by 2030. Median age in the trades: 46–48, with mass retirements coming. Meanwhile, AI threatens millions of white-collar jobs—but the infrastructure powering AI creates massive demand for the very work robots can't do.

As Nvidia's CEO put it: electricians, plumbers, and carpenters are in "astronomical demand."

In this episode, we cover: • Why electricians, plumbers, and HVAC techs face only 5–15% automation risk through 2040+ • The data center boom—one large facility needs 100+ electricians to build, 20–40 for ongoing maintenance • Real earnings: $40K–$50K during apprenticeship → $60K–$80K journeyman → $100K–$200K+ as a business owner • The complete career path from apprenticeship to master license to ownership • Field service trades you've never considered: telecom tower techs, wind turbine technicians, elevator mechanics ($80K–$100K+), medical equipment engineers • The hybrid strategy: electricians who understand solar, IoT, and building automation earning $150K+ • Why the stigma is wrong—this is knowledge work that happens to be physical

Plus: the exercise to research your local IBEW or UA union, find apprenticeship requirements, and shadow a tradesperson for a day.

If you scored Critical or High risk in our assessment, and you're not opposed to physical work, this is the fastest path to a six-figure, recession-proof, AI-proof career. And you get paid while you learn.

CES 2025 showed us the promise. CES 2026 delivered production timelines, price tags, and deployment dates. In this special AI Dispatch bulletin, we break down the seismic shift that just happened in Las Vegas—from Boston Dynamics announcing factory deployment by 2028 to the $25,000 EngineAI T800 shipping this summer.

We cover the nine humanoid robots that dominated the show floor, LG's autonomous home robot that actually thinks for itself (no hidden human operators), and why every major chipmaker is now positioning itself as a robotics company. Plus, what all of this means for your career in the next three to five years.

The question has shifted from "if" to "when"—and increasingly, the answer is "soon."

If you want a job in 2035, look at who's hiring in 2025. Healthcare added 528,500 jobs last year. Home health aides are up 21%. Nurse practitioners? Up 52%—the fastest-growing occupation in America.
This episode breaks down WHY healthcare and public safety are AI-resistant and how to break into these fields.
Healthcare careers analyzed:

Registered Nurses (3.2M employed, 6% growth, $81K median)
Nurse Practitioners (52% growth, $121K median, can open own practice)
Home Health Aides (21% growth, low entry barrier, career ladder to RN)
Licensed Clinical Social Workers (crisis intervention, court testimony)
Mental Health Counselors (therapeutic alliance requires human empathy)

What's NOT protected in healthcare: Medical transcription (99% automated), medical coding (40% by 2025), health information technicians, medical records clerks.
Public Safety careers: Police, firefighters, EMTs—physical presence essential, split-second decisions, community trust cannot be automated.
Career pathways included: How to transition from non-healthcare into these protected fields, including timeline and cost estimates.

9.6% vs. 3.5%. That's the percentage of women's jobs vs. men's jobs in the highest AI automation risk category.  Nearly 3x the exposure.  Source: The UN's International Labor Organization and Poland's National Research Institute (NASK) - the most comprehensive study on AI workforce disruption to date, released May 2025.  The findings challenge everything we thought we knew about who's at risk:  Women face IMMEDIATE displacement from white-collar clerical and administrative roles.  Men face MEDIUM-TERM displacement from blue-collar physical labor roles (1.5M trucking jobs, 2M manufacturing jobs by 2030).  Both groups need to adapt. Just on different timelines.  And here's the compounding problem: Harvard Business School found women use AI tools at 25% lower rates than men. They're in more vulnerable roles AND less likely to be building skills that could help them transition.  This isn't about politics. It's about understanding risk factors for career planning.  New episode of Surviving AI breaks it all down with actionable strategies for everyone.  Link in comments.  #AI #WorkforcePlanning #Automation #CareerStrategy #FutureOfWork

Season 1 Break - AI Physical Infrastructure and adoption

We've spent years warning about AI taking jobs. The predictions have been dire—knowledge work, creative work, analytical work, all at risk. The consensus: it's coming fast, maybe faster than we can adapt.
But there's a massive variable that most AI forecasters are ignoring: the physical infrastructure required to actually run these systems at scale. And that infrastructure is hitting some very real walls.
Today, we're doing a deep dive into the data. The question: Could infrastructure constraints—power shortages, water scarcity, community opposition, supply chain bottlenecks—actually delay AI's impact on the workforce? Let's look at what the numbers actually tell us.

By the end of this episode, you'll have a number—your personal automation risk score. Then we'll tell you exactly what to do about it.
Episode 6 of Surviving AI gives you the comprehensive assessment framework combining:

O*NET task analysis
The Four Protection Factors
Stanford exposure metrics
Microsoft AI applicability scores

THE 100-POINT ASSESSMENT:
SECTION 1: Task Analysis (40 points)
→ Routine vs. creative work
→ Digital vs. physical
→ Predictable vs. variable environment
→ Problem-solving requirements
→ High-stakes judgment
SECTION 2: Credential Barriers (30 points)
→ Licensing requirements
→ Preparation time
→ Continuing education
SECTION 3: Human Elements (20 points)
→ Empathy requirements
→ Non-verbal cue reading
SECTION 4: Liability & Authority (10 points)
→ Personal legal liability
SCORE INTERPRETATION:

81-100: VERY LOW RISK (protected through 2040+)
61-80: LOW RISK (protected through 2035)
41-60: MEDIUM RISK (transformation 2028-2032)
21-40: HIGH RISK (major impact 2025-2027)
0-20: CRITICAL RISK (displacement happening NOW)

SPECIFIC ACTION PLANS FOR EACH TIER:
→ Critical risk: Immediate transition required (6-12 month window)
→ High risk: Emergency career pivot (12-24 months)
→ Medium risk: Urgent upskilling needed
→ Low risk: Enhancement actions
→ Very low risk: Leverage AI to thrive
📊 Full assessment walkthrough included—pause and complete it as you listen.
Re-assess every 6 months. Your score isn't static.
#careertest #automationrisk #AIassessment #jobsecurity #careerplanning #futureproof

AI threatens 300 million jobs globally. But 5.5 million new US businesses launched in 2023—the highest ever recorded. What's going on?

Episode 5 of Surviving AI explores the entrepreneurship explosion happening alongside automation—and why business ownership might be your best defense.

THE NUMBERS THAT DON'T ADD UP:

• 19% of US adults now identify as entrepreneurs (highest ever)
• 92% of global economies saw increased business formation
• 68% of small businesses already using AI
• People's response to automation: "If AI is coming for my job, I'll start a business"

THE BINARY OUTCOME FOR BUSINESS OWNERS:

PATH 1 - AI Adopters (Winners):

• 40% productivity gains
• 37% lower operational costs
• Solo founders with team-level capabilities
• Case study: Atlanta florist—35% booking increase with AI chatbot

PATH 2 - AI Resisters (Losers):

• Outpaced by competitors
• Unable to match prices or speed
• Eventual market exit

HOW AI LEVELS THE PLAYING FIELD: What used to require a team, one person with AI can now do: → Marketing department → One person + AI tools → Customer service team → AI chatbot + one supervisor → Content team → AI generation + human editing

THE DARK SIDE:

• 60% cite cost as a barrier
• 77% lack technical knowledge
• Big Tech controls the infrastructure

📊 Exercise: The Business Owner Decision Tree—are you better off employed or starting something?

Employment vs. entrepreneurship isn't the main decision anymore. AI adoption is.

#entrepreneurship #smallbusiness #AItools #startups #businessgrowth #automation

You spent $200K on a degree to do work that ChatGPT does in 3 seconds. How does that feel?

Episode 4 of Surviving AI delivers the uncomfortable truth about white-collar automation—with specific data for data scientists, lawyers, and accountants.

DATA SCIENTISTS:

• Microsoft study: On the "40 most vulnerable jobs" list
• 35-50% of tasks at immediate risk
• The irony: BLS projects 36% growth—but the entry ladder is disappearing
• Survival path: Pivot to AI engineering, governance, or deep domain expertise

LAWYERS:

• Paralegal work: 80% automation risk by 2026
• Junior associates: HIGH risk (research/doc review was their entire job)
• Senior partners: LOW risk (relationships, rainmaking, strategy)
• Timeline: 2025-2027 junior reductions, 2028-2035 firm restructuring

ACCOUNTANTS/CPAs:

• 50-70% of routine tasks automatable
• Entry-level: 70-95% automation risk by 2027
• CPA license provides minimal protection for routine work
• What's protected: Strategic advisory, forensic accounting, complex tax

THE COMMON PATTERN: → Entry-level positions disappearing fastest → Mid-level squeezed → Senior relatively safe—but fewer total positions

The traditional career ladder is broken. AI does the grunt work that juniors used to do to learn and advance.

📊 Exercise: List your daily tasks. Mark each as Routine/Creative, Digital/Physical, Predictable/Unpredictable. What percentage is "Routine + Digital + Predictable"? That's your danger zone.

#knowledgeworkers #datascience #lawyers #accountants #whitecollarjobs #AIautomation

Everyone asks "Will AI take my job?" Wrong question. The right question is "WHEN?" Here's the month-by-month breakdown through 2035.

Episode 3 of Surviving AI gives you the specific timeline for every major industry—so you can plan, not panic.

PHASE 1 (NOW - 2027):

• 76,440 jobs already lost to AI in 2025
• IBM: 8,000 layoffs (AI replaced HR)
• Customer service: 80% automation by 2025
• Medical transcription: 99% automated already
• 491 people losing jobs to AI every single day

PHASE 2 (2028-2030):

• 30% of work hours automated (McKinsey)
• 85 million jobs displaced globally
• Junior lawyers, accountants, software engineers squeezed
• Manufacturing: 50-70% of assembly roles automated

PHASE 3 (2031-2035):

• Truck driving crisis: 1.5-4.4M jobs at risk
• Law firms restructured—junior positions rare
• Healthcare admin largely automated

PHASE 4 (Post-2035):

• Service trades remain 90%+ human
• New roles we can't imagine yet

📊 Industry-by-industry countdown chart included in this episode.

Exercise: Create your personal 36-month timeline. What tasks get automated first? Will your position still exist? Start building your next move NOW.

#futureofwork #AIjobs #careertimeline #automation2025 #jobdisplacement