There’s a certain poetry in naming your flagship AI model after a fruit famous for going from “not ripe” to “completely rotten” in about six hours.

Meta’s next-generation model, codenamed Avocado, was supposed to prove that $135 billion in annual AI spending could buy a seat at the frontier table. Instead, it’s become the most expensive guacamole in history — and the fallout is reshaping the entire tech industry.

The Avocado Debacle

The New York Times reported last week that Meta has delayed Avocado’s release from March to at least May. The reason: internal testing revealed the model trails leading systems from Google, OpenAI, and Anthropic in logical reasoning, programming, and writing.

This isn’t a minor stumble. Meta spent two years rebuilding its AI organization. It bought a $14.3 billion stake in Scale AI and made founder Alexandr Wang its chief AI officer. It poached top researchers from Google DeepMind and OpenAI. It poured what the company itself called “buckets of cash” into hiring across every level of its AI labs.

The result? A model that beats Meta’s previous generation — not a high bar after Llama 4’s lukewarm reception — but still can’t match what competitors shipped months ago.

The Gemini Humiliation

Here’s the detail that should make every Meta investor wince: the company reportedly discussed temporarily licensing Google’s Gemini to power certain Meta products while Avocado keeps baking.

Let that land. The company that built its entire AI strategy around open-source models as a strategic alternative to proprietary systems from Google and OpenAI was considering renting its competitor’s brain.

It’s the AI equivalent of Ferrari calling Toyota to ask if they can borrow an engine for the weekend.

No final decision has been confirmed. Meta’s spokesperson told CNET the company is excited for people to see “what we’ve been cooking very soon.” But the fact that licensing Gemini was even on the table tells you everything about the internal panic level.

15,000 Jobs Gone

Days after the Avocado delay leaked, Reuters reported that Meta is planning layoffs affecting up to 20% of its roughly 79,000-person workforce — approximately 15,000 to 16,000 positions. If executed, it would be the company’s largest restructuring since the 2022-2023 “Year of Efficiency.”

Meta is reportedly targeting an unprecedented 50:1 employee-to-manager ratio, compared to the 7-to-15:1 range traditionally considered standard. Bernstein analyst Mark Shmulik estimates the cuts could save $2 billion to $4 billion this year.

But those savings aren’t going back to shareholders. They’re being funneled straight into AI infrastructure. Meta plans to spend $600 billion on data centers by 2028 and just acquired AI startup Manus for at least $2 billion.

The math is brutal: fire humans to fund the machines that are supposed to replace humans.

The Cascade Is Already Here

When Shmulik warned clients, he didn’t just flag Meta. He predicted a “cascade of hurried pivots, half-formed strategies, and reactive restructuring across the ecosystem.”

The evidence says that cascade has already begun:

  • Block laid off nearly half its 4,000-person workforce three weeks ago. CEO Jack Dorsey told investors most companies would reach the same conclusion within a year.
  • Amazon confirmed 16,000 job cuts in January.
  • Salesforce CEO Marc Benioff said he “needs less heads” after cutting 4,000 from customer support.
  • Oracle and TCS reported major reductions.

In the first 74 days of 2026 alone, 55,775 roles were eliminated across 166 tech companies. The positions most at risk: middle management, quality assurance, customer support, and internal IT — precisely the roles most susceptible to AI automation.

Economist Anton Korinek put it bluntly: this could mark “the beginning of a new era where white-collar jobs become threatened more seriously by AI.”

Is AI the Reason or the Excuse?

Shmulik raises the most important question in his analyst note — and conspicuously leaves it open: are these cuts genuinely AI-driven, or is AI providing convenient cover for belt-tightening that would have happened anyway?

“Fat exists in every organization,” he wrote, “but it’s usually not as clean as being concentrated in specific teams or individuals.”

If AI is genuinely enabling the same output with 20% fewer people, we’re witnessing the early stages of the most significant labor market transformation since industrialization. If companies are using “AI transformation” as narrative cover for cost-cutting, the reckoning will be different — but no less painful.

The truth is probably somewhere in between. AI can automate a growing number of white-collar tasks. But the idea that Meta — which can’t even build a frontier model matching its competitors — has figured out how to run a 50:1 manager ratio using AI tools is optimistic at best.

The Paradox at the Heart of the AI Boom

Meta’s situation crystallizes something uncomfortable: the companies spending the most on AI are often the ones furthest from making it work.

Google, OpenAI, and Anthropic have pulled ahead on model quality. Meta is spending $135 billion this year and still considering licensing a competitor’s technology. Meanwhile, it’s cutting the very workforce it needs to close the gap.

The managed IT services market is projected to reach $424 billion in 2026 as companies outsource the work they’re cutting internally. The talent isn’t disappearing — it’s being redistributed from full-time positions to contract roles, often at lower cost and with fewer benefits.

For AI researchers and engineers, the market remains white-hot. For everyone else in tech? The “Year of Efficiency” has become the Decade of Efficiency, and no one’s waiting for the AI to actually be ready before making the cuts.

The Bottom Line

Meta’s Avocado delay isn’t a product stumble. It’s a stress test for the entire thesis that spending tens of billions on AI infrastructure inevitably produces competitive models. Sometimes you spend $135 billion and still need to borrow your neighbor’s Gemini.

The 15,000 layoffs aren’t a response to AI success — they’re a response to AI pressure. The need to show investors the money is going somewhere productive, even when the flagship model isn’t ready.

The real question isn’t whether AI will transform the tech workforce. It will. The question is whether companies are making these cuts based on what AI can actually do today — or based on what they hope it’ll do tomorrow.

Right now, it looks a lot like the latter.