Intuit's badge readers went dark for three thousand people this week, and the memo blamed "AI." We ask how many of 2026's layoff headlines are real causation and how many are Just So Stories - tidy explanations built to end the questions, not answer them.

Along the way: Sam Altman's own admission that companies are "AI washing" their cuts, a European rule on algorithmic firing decisions that got quietly postponed one day before it was due to bind, and a fresh PwC report showing where the real shift is happening - judgment-heavy work pulling away from everything a model can commoditize.

Two Nobel-winning economists spent years telling everyone to stop panicking about "AI" and jobs. This week they signed a joint statement, alongside 200+ economists and 16 Nobel laureates, warning it could reshape the economy faster than the Industrial Revolution. We dig into what actually changed their minds: Verizon's 16,600 job cuts, a hiring freeze two-thirds of CEOs can't yet justify with data, a quiet insurance-sector layoff filing, and a generation of young software developers whose employment just fell 20%. Plus: the regulatory patchwork racing to catch up, and why "new AI jobs will replace the old ones" is still mostly a hope dressed up as a forecast.

After a way too long break, we're back with the story that made silence impossible: companies that laid off workers for AI are quietly, expensively rehiring them, and a new survey of 2,000 hiring managers puts a real number on it. We trace the "AI boomerang" back thirty years to a nearly identical management fad, sit inside a discrimination lawsuit alleging an algorithm targeted employees on medical and parental leave, and ask what it means that the last 40% of almost every job turns out to be the part no one figured out how to automate.

In this Episode, Koby Ofek dissects the conflicting narratives surrounding AI's impact on the workforce as of January 2026. He challenges the Oxford Economics report that calls AI layoffs "corporate fiction", arguing that companies use AI as a scapegoat for poor management and cost-cutting. Simultaneously, he presents the paradoxical case of Tailwind Labs, where the success and popularity of their open-source framework, accelerated by AI coding tools, led to an 80% revenue drop and 75% of engineers losing their jobs because developers no longer need to visit their documentation site to learn how to use the product. The episode highlights a massive gap between the hype and the implementation of AI and questions the sustainability of current open-source business models in an AI-driven world.

In this New Year kickoff episode, Koby Ofek tracks the first big labor-market pattern of 2026: the career ladder itself is starting to lose its bottom step. Geoffrey Hinton warns that automation is about to speed up, investors openly predict companies will swap headcount for AI budgets, and young workers respond by fleeing “first-rung” office jobs toward trades where reality still fights back. The result is not one clean wave of layoffs, but a quieter crisis: fewer on-ramps, fewer apprenticeships, and a workforce forced to prove value earlier, faster, and more publicly. Koby connects the dots, shares the core logic behind his latest 2026 predictions, and explains why the most important job story this year might be the disappearance of entry-level work, not the disappearance of work itself.

In this Episode, Koby Ofek discusses how late 2025 marked a shift from AI as a productivity story to AI as a staffing strategy, as companies increasingly cite “AI efficiency” to justify layoffs, reorganizations, and leaner org charts. He explores what record AI linked layoff figures really mean, how Big Tech is “replumbing” work by compressing middle layers and automating internal service functions, and why the most consequential battleground may be entry level roles, the training ground that turns juniors into future experts. Along the way, Koby connects the dots between corporate incentives, talent pipelines, and the emerging barbell shaped company, then closes with forward looking signals for what 2026 could bring for careers, hiring, and the structure of work.

In this episode, Koby Ofek explores how AI is quietly erasing the “first rung” of the career ladder, moving from abstract debates to concrete headcount decisions in banking, hiring, and seasonal work, and asks what happens when the entry-level path into good jobs starts to wobble. He connects candid remarks from major banks about “doing more with less people” to a broader shift where AI targets junior, repetitive tasks and reshapes traditional apprenticeship-style pipelines. Koby then examines a new federal push to standardize AI-in-hiring rules, showing how automated screening can narrow access and turn hiring into a black-box gatekeeping system just as companies experiment with “agent boss” org designs built around humans managing AI tools. He weaves in warnings from leading AI researchers and labor economists about mass unemployment, surplus workers, and the choice between an anti-worker automation route and a pro-worker augmentation route that uses AI to upgrade jobs instead of eliminating them. Finally, he offers workers, leaders, and policymakers a mental model for surviving this transition—rethinking skills, ladders, and “AI productivity” through the lens of where saved labor goes, and how to widen personal options before the doors into good work narrow further.​

In this episode, Koby Ofek discusses the week's major shift in Big Tech, which signals the end of the "Moonshot Era" and the start of the "Extraction Era," starting with Meta's move to slash the metaverse budget and reallocate those billions toward AI, effectively changing the "Aleph" from a physical destination to a computational tool. He then breaks down how Amazon's new Trainium 3 chip is directly assaulting the "cost of cognition," making automation accessible to mid-sized companies, and how Mistral's edge AI models are decentralizing this intelligence, removing "latency protection" for field workers. Finally, he examines HP's multi-year layoff plan as a "boiling frog" strategy that validates the "AI efficiency" thesis for the entire Fortune 500, concluding with a call to action for workers to stop managing friction and move to the parts of work that require humanity, context, and a clear sense of direction.