Future Ready Leadership With Jacob Morgan

October 28,2026: I unpack Marissa Mayer's viral moment when she asked her AI assistant Dazzle to tell her what made her 10-year-old daughter special, why the internet erupted over whether parents should outsource that kind of thinking, and the three questions you need to ask before handing anything off to AI. Then I head to Nevada where AI is building jobs in the north and replacing them in the south, why more than 100,000 acres of desert east of Reno are becoming one of the world's largest concentrations of data centers providing work for thousands of electricians and pipe fitters, and why 400 miles away on the Las Vegas Strip companies like Zoox and Waymo are replacing human-driven taxis while MGM's call center cuts hundreds of jobs using AI. Finally, I look at SAP replacing expensive AI models inside its software with cheaper alternatives, why their CFO says the move could save more than 2 billion euros in efficiency gains over the next two years even as overall AI spending rises, and how this works like a flat rate plumber who charges $300 to fix a leak no matter which parts go in. Chapters 00:00:00 The Cake Mix Paradox: Why Effort Matters in the AI Age 00:11:03 The Three Questions: Deciding Which Eggs to Keep Cracking Yourself 00:16:42 The Myth of Permanent Jobs: What Construction Workers Already Know 00:21:42 SAP's $2 Billion AI Efficiency Play: Swapping Models Without Cutting Spending 00:24:15 The AI Model Commodity Shift: What It Means for Workers, Vendors, and Customers 00:26:42 Jevons Paradox Returns: Why Cheaper AI Means More AI, Not Less

October 27, 2026: I unpack Anthropic's IPO filing showing revenue that jumped from under $1 billion a quarter last year to $11.6 billion this quarter, why Wall Street is valuing the company at up to $2 trillion despite $80 billion in reported losses that are mostly accounting charges tied to how they raised money, and what it means when more than half the Fortune 500 are paying six figures or more but spending less than 0.1% of their operating budgets on AI. Then I get into Microsoft offering voluntary buyouts to 60,000 employees including teams in Xbox, Windows, Copilot, and Finance while excluding data centers and sales, why this is a trade of buildings and chips in and people out as the company spends $175 billion on AI infrastructure this year, and why I think we're about to see a massive influx in hiring for communication, PR, and marketing roles because companies are doing a terrible job explaining what AI means to employee. Finally, I look at a Bentley University and Gallup survey showing 84% of Americans say companies should avoid big pay gaps between CEOs and employees but only 15% think their companies are doing a good job of it, why I don't think you should ever care how much your CEO makes as long as you're being paid what you're worth, and the four situations where pay gaps actually do matter. I break down when the explanation doesn't match the decision, when accountability only runs in one direction, when your pay doesn't match your worth, and when leaders' incentives shape your daily life, and why Elon Musk's pay package tied to goals like hitting $8.5 trillion valuation and delivering 1 million humanoid robots shows what happens when compensation is structured around ridiculous astronomical milestones that actually get achieved. Chapters 00:00:00 Google's IPO Warning and the Privacy-to-Scrutiny Trade: What Anthropic's Public Debut Reveals 00:01:10 Anthropic's IPO Filing: The Numbers Behind Claude and What They Tell Us About AI Adoption 00:12:04 The Bear Case: High Valuation, Fixed Costs, and the Dependency Dilemma 00:15:15 Investment Strategy: Who Should Buy Anthropic and Who Should Stay Away 00:24:10 The Bigger Economic Shift: From Tech Hubs to Trades and the 700 Billion Dollar Infrastructure Wave 00:26:48 CEO Pay Gaps Get Failing Grades: When Fairness Matters and When It Doesn't 00:35:54 The Four Times You Should Actually Pay Attention to Pay Gaps and Executive Compensation

October 23, 2026: I unpack two very different approaches to sharing AI wealth after Anthropic pledged 5% of its shares to a public benefit fund it controls while Elon Musk announced he'll give one SpaceX share directly to every kid in America, why one approach hands people ownership and the other hands them a promise, and what history teaches us about foundations that drift away from their founders' vision like the Ford Foundation that eventually told Henry Ford's grandson that hospitals weren't part of the program. I explore why Musk's companies support somewhere between 400,000 and a million jobs worldwide, why more than 4,400 SpaceX employees could become millionaires when the company went public, and why giving 73 million children actual equity respects people enough to let them own something instead of waiting for experts to decide who deserves help. I break down the effective altruism philosophy behind Anthropic's approach, why it feels dystopian when the company building technology that might displace millions of jobs also gets to decide which displaced people receive relief, and why Milton Friedman said nobody spends somebody else's money as carefully as he spends his own. Then I get into why AI is everywhere except in the GDP numbers, why companies spent hundreds of billions on AI in 2025 but 95% of projects produced no measurable profit impact, and why the stock market just had its worst day since June with the NASDAQ dropping 2.6% as investors panic about two opposite things at once. I explore why Meta fell 7% after saying it will spend up to $145 billion on capital projects, why CoreWeave's stock crashed from $163 to $54 as local governments put moratoriums on new data centers, and why the real bottleneck isn't chips but power with the U.S. short about six Californias worth of electricity by 2050. I break down why only 2% of U.S. households pay for AI tools, why the most important jobs in the AI era are linesmen and electricians not prompt engineers, and why leaders need to stop asking how to do the same work cheaper and start asking what they'd build if they could build 10 times as much. Finally, I look at ASML, Europe's most valuable company and the only firm that can build the machines needed to make cutting edge chips, tying executive pay to employee satisfaction by measuring whether people understand their roles, whether teams are organized, and whether managers care about them as people, and why this shifts leadership from lagging indicators like revenue to early warning signs that show up before the numbers drop. Chapters 00:00:00 Ownership Versus Promises: Musk's SpaceX Shares and Anthropic's Public Benefit Fund 00:13:23 The Ford Foundation Warning: When Founders Lose Control of Their Legacy 00:23:16 The Real AI Bottleneck: Power Plants, Not Processors 00:26:54 Beyond Cost Cutting: Moving from Bucket One to Bucket Two AI Strategy 00:33:54 The Efficiency Trap: Why Measuring Everything Turns People Into Line Items

October 20, 2026: I get into why AI is launching a brand new first phase that's landing on knowledge workers who are already in phase three, why Jensen Huang says companies cutting employees due to AI just lack imagination, and why the near future isn't less work but more ambition per person with human attention and judgment becoming the scarce resource. Then I explore who actually owns the AI agents inside your organization, why 82% of enterprises discovered agents in the past year they didn't even know they had, and why employees not IT or HR are going to own the bots because they're spinning them up without asking permission just like they did with iPhones and Dropbox. I break down the coming shift toward bring your own agent policies, why interviews will soon include questions like what can your agents do, and why an agent trained on your expertise will follow you from job to job like a carpenter's tools or a chef's knives. Finally, I look at the Wall Street Journal story about a Princeton PhD in chemical engineering who applied to 2,300 jobs over two years and still doesn't have one, why his story is painful but may not reflect the rule, and why unemployment at 4.2% with low layoffs and rising demand in healthcare and construction suggests the market isn't closed but shifting toward different kinds of work that require flexibility, relocation, and real world networking instead of spray and pray online applications. Chapters 00:00:00 Jevons Paradox: Why AI Makes Us Work More, Not Less 00:06:05 The Industrial Revolution Lesson: When Better Tools Meant Longer Hours 00:11:52 The Ambition Multiplier: Jensen Huang's Imagination Argument 00:20:09 Agent Fatigue: Why My Team Ignores Our Slack Bot 00:21:49 The Princeton PhD Who Applied to 2,300 Jobs and Still Can't Get Hired 00:28:45 When Robots Talk to Robots: AI Applications Meet AI Screening

October 16, 2026: I unpack why Accenture, one of the world's largest consulting firms that sells AI transformation to thousands of companies, just told its employees it will only pay $29 a month for Claude after some consultants ran up bills between $500 and $1,000 monthly, and why this marks the end of the era where companies assume one frontier model works for everything. I explore why AI costs keep rising even as intelligence gets 10 times cheaper every year, how 73% of organizations say their AI costs came in above projections, and why companies are learning that summarizing a meeting doesn't require the same model as building multi-step agents. I break down how AT&T cut costs by 56% with only a 2% quality dip by routing requests to cheaper models, why AI policies inside organizations are starting to look like corporate travel policies with tiers and escalation rules, and how being good at AI is no longer just about prompting but knowing which tool to use for what task. Then I get into Bank of America hiring a thousand contractors to patch software vulnerabilities exposed by Anthropic's Mythos model, why a tool so capable at finding problems actually created jobs instead of eliminating them, and how AI didn't replace the security team but handed them a much bigger to-do list. I explore the radiologist lesson from 2016 when Geoffrey Hinton said people should stop training them because AI would replace them, why the field instead became one of the most in-demand careers with salaries rising faster than others, and how AI-related roles at major banks jumped 49% this year to almost 140,000 listings. Finally, I look at a 16-year-old in New York City who spent $500 to plaster flyers across the city asking to shadow people at their jobs because he's so anxious about his future he lies awake at night, why this points to a real gap in how young people learn about work today, and why we need to bring back apprenticeship programs that let people see what work actually looks like beyond a laptop screen. Chapters 00:00:00 Accenture's AI Budget Cap: When the Consulting Giant Hits the Brakes 00:05:40 The AI Cost Paradox: Cheaper Intelligence, Higher Bills 00:10:20 Bank of America Hires 1,000 Contractors to Fix What AI Found 00:13:45 The Radiology Lesson: When AI Creates Jobs Instead of Destroying Them 00:17:46 The 16-Year-Old Who Plastered NYC with 'Let Me Shadow You' Flyers