AI layoffs are dominating the news, but the story being told isn't the full story. In this episode, Jess and Kyle break down what's really driving the cuts, and what it means for your job and your business.

When Meta announced 8,000 job cuts, the coverage landed the same way it always does: AI is replacing people, the future is here. But the numbers don't support that story. Meta's savings from the cuts amount to roughly three billion dollars. Their AI infrastructure spend this year runs to multiples of that. So what's actually going on?

In this episode, Jess and Kyle work through the real economics behind the layoff headlines, from the infrastructure bets driving the cuts, to the compute costs that are now exceeding what companies spend on their own people, to the quietly alarming data on what's happening to early-career workers. They also cover the Musk v. Altman verdict, what it means for OpenAI's upcoming IPO, and why Anthropic keeps coming out looking like the adult in the room.

The episode closes with practical guidance on what founders, team leaders, and employees can actually do right now, including why waiting to feel ready is the worst strategy available.

What You'll Learn:

Why Meta's 8,000 job cuts are better understood as a budget-clearing exercise
What AI washing is and how to spot it in a layoff announcement
Why infrastructure spending at Amazon, Meta, and Microsoft is projected to exceed total payroll costs by $50 billion this year
What MIT research actually found when it tested whether AI is economically viable compared to keeping humans in the role
Why 43% of CEOs plan to reduce junior roles over the next two years
Why IBM's contrarian bet on junior hiring may look very smart in ten years
Why smaller businesses are better placed than large firms to make the same move
What AI fluency actually means in practice

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Timestamps:

00:00 Introduction and Personal Updates

04:58 Why the AI Layoff Headlines Don't Tell the Whole Story

08:58 When Companies Cut Staff to Fund AI and Call It Efficiency

18:16 How Meta Extracted Its Employees' Knowledge Before Letting Them Go

22:40 Why the Productivity Gains Don't Justify the Scale of the Cuts

28:12 When Running AI Costs More Than Paying Your Team

29:58 MIT Research: AI Is Only the Cheaper Option in 23% of Cases

32:14 The Disconnect in AI Implementation