Zuckerberg’s Biggest AI Gamble Wasn’t a New Model — It Was Meta’s Own Workforce

When the Machines Joined the Team

Meta Considered Cutting Some Teams by 60% as Zuckerberg Bet on an AI-First Workforce

Mark Zuckerberg reportedly considered shrinking some Meta teams by as much as 60 per cent as part of an extraordinary attempt to rebuild one of the world's largest technology companies around artificial intelligence. The idea was simple but radical: smaller groups of highly capable employees would use autonomous AI agents to perform work that previously required much larger human teams.

What followed became one of the clearest real-world tests yet of the theory that generative AI can rapidly reduce the number of people required inside major corporations. Meta did cut around 10 per cent of its workforce in May, reorganised thousands more employees around AI-related work and flattened parts of its management structure, but the most aggressive version of the transformation appears to have collided with an uncomfortable reality: the AI agents were not yet delivering productivity improvements as quickly as executives had expected.

Zuckerberg Wanted Meta to Become AI-Native

The restructuring was not simply another cost-cutting exercise. Zuckerberg had spent months arguing that artificial intelligence would fundamentally change how Meta operated internally as well as what it sold to billions of users.

The company was already pouring enormous sums into models, computing infrastructure, data centres and elite AI talent. At the same time, executives were examining what a corporation designed around increasingly autonomous software might look like rather than merely adding AI tools to traditional teams.

That distinction matters. Giving an engineer an AI coding assistant is an incremental productivity improvement. Designing a company on the assumption that AI can absorb large portions of the engineer's workload is an organisational revolution.

The reported plan envisaged smaller groups or “pods” of workers, with AI systems taking over more repetitive or executable tasks. Some teams could have been reduced by up to 60 per cent under the most ambitious scenarios being considered.

Human employees would increasingly become what could loosely be described as builders: people setting objectives, reviewing machine-generated output, exercising judgement and directing AI rather than manually performing every part of the production process themselves.

It was an attempt to answer a question hanging over corporate America: if one skilled employee equipped with powerful AI can eventually produce the work of several people, why should companies preserve organisational structures designed for the pre-AI economy?

Meta Actually Started Rebuilding the Workforce

This was not confined to PowerPoint slides.

Meta announced a roughly 10 per cent workforce reduction in May, affecting about 8,000 employees, while thousands of others were reassigned towards AI-related initiatives. Around 7,000 workers were moved into new or reorganised areas connected with AI workflows as Meta tried to concentrate talent around the technology.

Management layers were also targeted. The company wanted flatter organisations, smaller groups and greater responsibility for individual contributors rather than traditional hierarchies containing large numbers of managers coordinating large numbers of employees.

Meta Chief People Officer Janelle Gale described organisations becoming capable of operating through smaller pods or cohorts that could move faster and take greater ownership.

The logic reflected something Zuckerberg had already been signalling publicly: AI could allow unusually capable individuals to accomplish work that once required much larger teams.

That is potentially far more important than a conventional round of Silicon Valley layoffs. A recession-driven job reduction can reverse when economic conditions improve. An AI-driven redesign of how many humans a company believes it needs could become structural.

The AI Agents Were Supposed to Change Everything

Central to the plan were AI agents capable of doing more than answering questions or generating paragraphs of text.

The industry is moving towards systems that can receive an objective, plan a sequence of actions, interact with software, analyse information and perform multiple steps with decreasing levels of human intervention.

Meta itself has increasingly pushed its consumer AI in this direction. Its latest systems can make plans, connect with applications, conduct research and follow through on tasks rather than simply producing individual responses.

Inside a company, the potential is enormous.

An effective autonomous agent might search documentation, write code, analyse performance data, prepare reports, complete administrative workflows, identify problems and make changes before presenting the result to a human worker.

Scale that across tens of thousands of employees and the theoretical productivity improvement becomes enormous.

It also explains why reducing team sizes by 40, 50 or even 60 per cent could have appeared plausible in sufficiently AI-exposed areas. If software genuinely absorbed half of the routine work, preserving the old organisational structure would make little economic sense.

There was only one problem.

The technology had to work.

The Productivity Revolution Did Not Arrive Fast Enough

By the summer, Zuckerberg was acknowledging internally that progress with AI agents had not accelerated as quickly as Meta's leadership had expected.

That admission cuts directly to the heart of the entire AI employment debate.

Modern AI can produce astonishing demonstrations. It can generate code, manipulate documents, conduct research and automate complicated chains of work. But completing an impressive isolated task is different from operating reliably inside a giant company where mistakes can affect products used by billions of people.

Corporate work contains context, unwritten assumptions, competing priorities and endless edge cases. AI may produce an answer quickly while creating additional work for the human who must verify whether the answer is safe, correct and consistent with everything surrounding it.

That review burden can quietly consume part of the productivity gain.

An engineer capable of producing substantially more code with AI may also have substantially more code to inspect. A system capable of completing ten tasks automatically is less useful if humans must carefully verify all ten because nobody knows which one contains the critical mistake.

This is the problem facing every company racing towards autonomous agents.

AI does not need to become perfect before it changes employment. But it must become reliable enough that supervising the machine requires materially less labour than doing the work manually.

Meta appears to have discovered how difficult that threshold can be.

The Human Backlash Became Another Problem

The restructuring also created a cultural challenge.

Thousands of employees found themselves confronting simultaneous layoffs, organisational transfers and growing pressure to incorporate AI into their work. Some were moved into newly created AI-focused teams while the company continued experimenting with ways to train systems to perform increasingly sophisticated workplace tasks.

That creates an unavoidable psychological tension.

Companies can describe AI as something that empowers workers. Employees may hear something different when they are simultaneously watching colleagues disappear and seeing management redesign jobs around automation.

The distinction between “AI helping you perform your job” and “AI helping the company need fewer people doing your job” can become extremely thin.

Meta was therefore attempting two transformations simultaneously: developing a new technological operating system for work while persuading the humans inside that system to embrace it.

Moving too quickly risked destroying morale before the technology was capable of replacing the productivity being lost.

Zuckerberg Has Not Abandoned the AI Bet

The important conclusion is not that Meta has turned against artificial intelligence.

Quite the opposite.

Zuckerberg continues to place AI at the centre of Meta's future. The company is spending extraordinary amounts on computing infrastructure and has explicitly framed its long-term ambition around delivering what it calls personal superintelligence.

Meta AI is being developed into a system capable of acting on users' behalf, conducting research, organising tasks and interacting with other applications. The company's massive distribution network across Facebook, Instagram, WhatsApp, Messenger and its AI products gives Zuckerberg something almost every specialist AI laboratory would envy: direct access to billions of potential users.

Meta's strategic conviction therefore remains intact.

What appears to have changed is the timetable.

There is a huge difference between believing AI will eventually transform the workforce and believing today's AI is sufficiently reliable to eliminate enormous portions of that workforce immediately.

The first proposition may prove correct even if the second proves disastrously premature.

Meta Is Spending Enormously to Make the Bet Work

There is also mounting financial pressure behind the experiment.

Meta has been committing extraordinary amounts of capital to AI infrastructure, including advanced processors, data centres, energy and networking equipment. Its 2026 capital expenditure forecast has risen into the region of $130 billion to $145 billion.

That spending can be justified if AI produces equally extraordinary economic returns.

Meta already generates significant value from machine learning across advertising and content recommendation. Generative AI creates a much bigger ambition: new consumer products, autonomous assistants and radically greater internal productivity.

But investors ultimately need to see the conversion from expenditure to economic output.

If the company spends more than $100 billion a year building the machinery of AI while simultaneously maintaining much of the workforce that AI was supposed to make dramatically more productive, the economics become harder.

That gives Zuckerberg a powerful incentive to make the AI-native organisational model work eventually, even if the first attempt proved too aggressive.

The 60% Figure Shows Where Corporate Leaders Think This Could Go

The most significant detail may therefore be the idea that reductions approaching 60 per cent were seriously contemplated for some teams at all.

It reveals the scale of productivity improvement leading technology companies believe AI might ultimately create.

Nobody needs to believe that 60 per cent of Meta employees are about to disappear. They are not.

But organisational planning normally reflects what executives think could become possible. A company with enormous technical expertise experimenting with dramatically smaller AI-powered teams should command attention far beyond Silicon Valley.

The threat to employment is also unlikely to appear as one spectacular moment in which millions of people are simultaneously replaced by machines.

It could happen through attrition.

Vacant roles remain vacant. Teams that once contained ten people are rebuilt around six. Junior positions disappear. Managers oversee larger organisations. Employees leaving voluntarily are not replaced. Productivity targets increase because management assumes everybody has access to AI.

The company produces the same amount with gradually fewer people.

That process would be less visible than mass technological unemployment but potentially more consequential over a decade.

Meta May Have Revealed the Next Phase of the AI Jobs Story

Meta's experiment ultimately exposes both the enormous potential of artificial intelligence and the danger of believing the technology's own hype too early.

Zuckerberg may eventually be proved right that AI allows exceptionally talented people to accomplish what previously required entire teams. The trajectory of the technology makes that possibility increasingly difficult to dismiss.

But corporations cannot restructure themselves around theoretical future capability. Software has to perform consistently in the messy environment of real organisations, and humans still carry responsibility when automated systems fail.

Meta tried to move towards that future at extraordinary speed. The result appears to have convinced its leadership that the future was arriving more slowly than anticipated.

That is reassuring for workers in the short term, but it should not be mistaken for the end of the story.

Meta is still spending tens of billions of dollars building more capable systems. AI agents are still improving. Zuckerberg is still trying to turn Meta into one of the defining AI companies of the next computing era.

The radical workforce experiment may therefore have failed primarily because it was early.

And that creates a much bigger question than whether Meta cuts another few thousand jobs this year: what happens when the technology finally becomes good enough for the organisational model Zuckerberg originally imagined?