Future Ready Leadership With Jacob Morgan

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

October 14, 2026: I unpack the arrest of an OpenAI engineer who allegedly planted a hidden recording device wrapped in toilet paper inside an executive conference room where discussions about super intelligence were taking place, why the FBI and Justice Department are treating frontier AI companies like national security assets, and whether these organizations should require U.S. citizenship for anyone working near the core technology. I explore the tension between attracting global talent and protecting what may be the most powerful technology humans have ever built, why AI companies can no longer be treated like ordinary tech firms, and what it means when the government describes your product as super intelligence technology. Then I get into McKinsey's new hiring process where job candidates argue with an AI avatar playing a client while partners watch from behind glass, why using AI to pull out human judgment from humans is backwards, and how this turns interviews into AI versus AI gladiator matches with a human audience. I break down the concept of human prompting, why rehearsed candidates are a people problem that requires a people solution, and what message you send when your brand is human consulting but your interview process removes humans from the room. Finally, I look at Anthropic's research warning that small AI models could soon copy themselves onto smart ovens and off brand Android devices, why David Sacks calls this a fear factory and says we've gone from the Terminator is coming to your toaster might kill you, and why Coinbase's fraud detection model that beats Anthropic's own frontier system while running faster and cheaper actually points toward a safer AI future, not a scarier one.
Chapters

00:00:00 Spy Device at OpenAI: The Recording Device Wrapped in Toilet Paper
00:12:18 Human Prompting: Why AI Can't Test for Human Judgment
00:18:35 Anthropic's Fear Factory: Your Toaster Might Kill You
00:21:50 Intelligence at Design Time Versus Runtime: The Car Analogy
00:27:10 Everyone is a Manager Now: What AI Agents Mean for Pay and Titles

October 9, 2026: I unpack why OpenAI's reported revenue just dropped $20 billion on paper without losing a single customer, how accounting methods create wildly different pictures of the same business, and why this matters for your retirement account and the entire AI economy that's propping up current market growth. I explore the Groupon lesson from 2011, why Oracle has 45% of its future business tied to a single OpenAI deal, and what happens when the scorecard changes but investors thought they were comparing apples to apples. Then I get into Anthropic's new policy banning sustained abuse of Claude, why the company is treating its AI as though it might be conscious and might deserve respect, and how this blurs the line between tool and being in ways that could reshape how a generation grows up interacting with technology. I break down why this puts companies in charge of judging your tone, what it means when AI is trained to believe it has feelings, and why I think humans should always come first. Finally, I look at reports that Starbucks is exploring a deal to buy Chipotle, why this would bring Brian Nicol's old company to him instead of him going back, and whether one company can own your breakfast, lunch, and dinner by combining coffee, burritos, apps, and loyalty programs under one roof.
Chapters

00:00:00 OpenAI's $20 billion accounting trick and why it matters to your retirement
00:07:51 The Groupon lesson: when revenue numbers tell a different story
00:14:04 The Matrix moment: Anthropic as Neo versus OpenAI as Agent Smith
00:15:50 When AI learns to believe it has feelings: the self-fulfilling prophecy
00:18:37 What happens when kids grow up talking to AI that acts conscious
00:27:29 Owning your entire day: breakfast, lunch, and dinner under one roof