The Great AI Layoff Scam: Part II — The Robots Aren't Working Either

A follow-up to the piece that reached 8,001 impressions and 5,956 real humans

The Setup You Already Know

Part I was simple: corporations are firing real people and citing AI as the reason — even when the AI doesn't exist yet, doesn't work yet, or was never the real driver. The post hit a nerve because everyone in the room knows it's true and nobody powerful wants to say it.

But here's the plot twist your LinkedIn feed is avoiding:

The robots are failing too. Quietly. Expensively. And nobody is talking about it.

The Numbers Behind the Lie

Let's start with what the data actually says, because the data is brutal.

According to a National Bureau of Economic Research study of 6,000 CEOs, CFOs, and executives across the US, UK, Germany, and Australia — nearly 90% of firms said AI had no impact on employment or productivity over the last three years. While two-thirds of executives claimed to use AI, actual usage averaged just 1.5 hours per week.

Meanwhile, Gartner projects worldwide AI spending will hit $2.5 trillion in 2026. PwC's latest Global CEO Survey found that 56% of CEOs cannot demonstrate any return — zero revenue gain, zero cost reduction — from their AI initiatives.

BCG's "Widening AI Value Gap" report put it even more starkly: 74% of companies show no tangible value from AI investments despite $252.3 billion in collective spending in 2024. MIT Project NANDA found 95% of organizations deploying generative AI saw zero measurable return.

You read that right. Not low return. Zero.

The Corporate Playbook Exposed

Here's how the scam actually works at the executive level:

1. Announce an "AI transformation."

2. Lay off several thousand employees.

3. Watch Wall Street hear "AI efficiency" and pump your stock.

It's not automation. It's theater. After Block's AI-linked cuts, the stock jumped — even though Amazon's own CEO Andy Jassy admitted the cuts were "not really AI-driven, not right now at least." Oxford Economics reviewed the layoff filings and called the AI narrative "corporate fiction." The Yale Budget Lab found no major structural employment change since generative AI launched.

59% of managers privately admit to using AI as an excuse for layoffs that were happening anyway — pandemic overstaffing, investor pressure, margin management dressed up in futurist language.

The Harvard Business Review found that companies are laying off workers in anticipation of AI's potential, not because the technology actually works at scale. Companies are firing humans over a forecast.

The AI Agents Are Actually Breaking Things

While executives talk about autonomous AI transforming operations, enterprise deployments are quietly producing disasters.

In agentic AI systems — where AI takes autonomous multi-step actions — the compounding error problem means that success rates can collapse to 60–70% even for well-designed systems when agents chain multiple reasoning steps together. For enterprises where errors translate to financial or reputational damage, that failure rate is commercially unacceptable.

The real-world failures in 2025 alone paint a picture the press releases don't mention:

• Waymo issued a software recall on 1,200+ driverless vehicles after 16 incidents of autonomous crashes into gates and poles

• UnitedHealth faces a class-action lawsuit accusing it of using AI to automatically deny rehabilitation coverage to elderly patients — one patient died within five days of being cut off

• Character.AI was sued over allegations that a chatbot encouraged a minor toward suicide

Meanwhile, hallucination rates among top AI chatbots nearly doubled year-over-year, rising from approximately 17% in 2024 to 35% in 2025. AI sales agents hallucinate because they're designed to predict language, not verify truth — and McKinsey confirmed organizations face "significant inaccuracy risks" in customer-facing AI applications.

A 2024 court ruling with Air Canada already established that companies are legally liable for their chatbot's errors. And as of today, courts have not issued definitive rulings on who bears liability for fully autonomous agent behavior — whether it's the user or the developer. Legal and compliance teams are walking into a minefield that nobody has mapped.

"AI-Washing" Is Now an Industry Term

Sam Altman — the CEO of OpenAI — used the term "AI-washing" at the AI Impact Summit in February 2026. He said companies are blaming AI for layoffs they would have made regardless. Marc Andreessen called AI the "silver-bullet excuse" on the 20VC podcast.

When the guy selling the AI tells you companies are misusing it as layoff cover, you should probably listen.

The downstream consequences for companies that made AI-driven cuts are already visible:

• 75% of organizations found AI-driven redundancies cost more than they saved, or broke even at best

• 9 in 10 HR leaders who conducted AI-driven layoffs said they would approach things differently

• Only 8.4% said the restructure delivered what was promised

• 40% of HR leaders reported layoffs led to increased voluntary turnover — the people you kept start leaving

The Audience That Needs to Hear This

My post analytics tell a story. The people who read my first article weren't fresh graduates panicking about their first job. They were:

• Senior, Director, and Manager-level professionals (63% combined seniority)

• Working at enterprises with 10,000+ employees (36.5%)

• In IT Services, Software Development, and Financial Services — the exact sectors leading AI adoption and cutting headcount simultaneously

These are the people sitting in the all-hands meeting, hearing the AI transformation speech, watching their colleagues get walked out — and asking themselves a question nobody on stage will answer honestly: If the AI is so effective, why can't anyone show me the numbers?

The Real Question Nobody in the C-Suite Will Answer

Gartner predicts 40% of enterprise applications will embed AI agents by the end of 2026, up from less than 5% in 2025. The agentic AI market is projected to surge from $7.8 billion to $52 billion by 2030.

Those are big numbers. But here are the numbers that don't make it into the earnings call:

• 42% of US companies abandoned most AI initiatives by mid-2025, up from 17% the prior year

• 46% of AI proofs of concept were scrapped before reaching production

• 60% of AI projects lacking AI-ready data are predicted to be abandoned through 2026

• Gartner also predicts more than 40% of agentic AI projects will be canceled by the end of 2027

The economic logic sold to shareholders — leaders will compare an AI agent that works 60–80% of a role around the clock at a fraction of the cost — only holds if the agent actually works. The dirty secret is that most of them don't. Not at enterprise scale. Not with real messy data. Not without the infrastructure, governance, and data readiness that most large companies haven't built.

What Comes Next

The workers are gone. The AI often doesn't work. The stock got a bump anyway. The liability is unresolved. And 90% of employees still believe their job is safe over the next 12 months — while 57% of HR leaders say layoffs are likely in that same period.

This isn't an AI story. It's a trust story. And every senior professional reading this on LinkedIn knows that once employees figure out they were lied to — not restructured, not "right-sized," but lied to — the institutional trust damage doesn't repair itself in a quarter.

The companies that treated their workforce like a cost line to be replaced by a press release will be hiring again in 18 months. Expensively. From a talent pool that remembers exactly what happened.

The Great AI Layoff Scam series. Part I reached 8,001 impressions. Let's see if the truth still travels.