By Kaori
Edit | Sleepy
Even if Meta lays off 90% of its staff, apps like Instagram and Facebook will continue to operate normally.
Eva is a senior engineer at Meta, not on the layoff list, has strong performance, and is proactively embracing AI tools.
But he said, "No one is safe; it's all dangerous, just a matter of time."
This is a story about how performance is evaluated, how promotions occur, how management operates, and even how effort itself is defined—those caught in it, from Zuckerberg down to the newest junior engineer, cannot say when the storm will end.
The layoffs are real, but the reasons are fake.
Meta has laid off approximately 25,000 people since 2022.
In November 2022, 11,000 people were laid off, followed by another 10,000 in 2023, which Zuckerberg called the year of efficiency. In January 2025, Zuckerberg announced in an internal memo the elimination of the bottom 5% of performers, approximately 3,600 people. In March 2026, an additional 700 were laid off. According to Reuters, another 8,000 employees are set to be cut by late May, representing about 10% of the global workforce of nearly 79,000, with a second round planned for the second half of the year.
Layoffs are genuinely happening, but not necessarily because AI has taken these people’s jobs.
Eva believes that most people laid off at this stage would have left regardless of AI. "A few years ago, the entire CS industry hired far more people than actually needed—industry prosperity, capital overheating, stock prices rising steadily, and many companies hired large numbers of employees. After Musk bought Twitter and laid off most of the staff, the app still worked fine—back then, there was no AI at all."
In 2026, Meta’s capital expenditure guidance is $115 billion to $135 billion, nearly double that of 2025, all directed toward data centers, GPUs, and AI infrastructure. The money saved from layoffs has been channeled into computing power.
At this stage, AI plays the role of a respectable card that companies can use to claim improved efficiency and reduced staffing needs.
Small companies are nimble and agile; as they grow into large corporations, decision-making slows down, and they realize they can't compete with emerging unicorns and startups, so they begin to streamline operations, flatten structures, and focus on core products. AI is merely accelerating a cycle that was already underway.
When AI usage is included in performance evaluations
However, the involvement of AI has changed some of the rules surrounding layoffs.
Meta’s original performance evaluation method was quite unique among Silicon Valley giants. Managers did not assign direct scores; instead, they compiled a performance rating document based on your self-assessment, peer feedback, and their own observations.
Then, proceed to a Calibration Meeting, where roughly a dozen peers are grouped together; each manager takes turns presenting their team members' performance and explaining why each individual deserves a particular rating, followed by collective discussion to ultimately determine everyone's rating.
This process is tedious and time-consuming, but its value lies in incorporating multiple perspectives and peer-level comparisons, making it difficult for a single manager’s preferences to determine the outcome. Eva considers this relatively fair.
In early 2026, the Calibration Meeting was canceled. Eva explained, "The company reverted to biannual performance reviews, as with AI, managers can use AI to assist in writing self-assessments, eliminating the need for as many collaborative steps and speeding up the process."
Meanwhile, Meta has launched Checkpoint, an AI performance tracking system that automatically aggregates employee work data from internal systems such as Google Workspace to generate contribution summaries for managers. For software engineers, Checkpoint tracks more than 200 data dimensions, including the percentage of AI-generated code, while monitoring metrics such as error rates and the number of associated bugs.
Meta's Chief Human Resources Officer, Janelle Gale, stated in an internal memo at the end of 2025 that AI collaboration skills will become a core criterion for performance evaluations in 2026.
In addition, for every segment of code written by Meta engineers, the system automatically assigns a percentage indicating the proportion of the code assisted by AI, and this data has become part of the evaluation criteria.
Each team sets its own minimum threshold based on its circumstances, such as requiring 50% or 90% of the code to be generated by AI. You must meet this threshold, but after that, performance evaluations will still focus on how much actual value your work delivers. “The company’s idea is that you start using it first, and we’ll see how well you do later,” Eva said.
Incorporating AI usage into performance metrics acts as a form of mandatory promotion mechanism—it doesn’t reward those who use it more, but penalizes those who don’t.
This approach is not unique to Meta.
NVIDIA CEO Jensen Huang publicly stated at the GTC conference in March 2026 that, in the future, every engineer at the company will need an annual Token budget, with half of their base salary allocated specifically for AI consumption. He even said that if an engineer earning $500,000 per year spends less than $250,000 on AI annually, he would be “deeply concerned.”
Jensen Huang is selling tokens; it's natural for merchants to promote their own products, but Meta once also reached the extreme of this quantitative frenzy.
An employee spontaneously created an internal leaderboard called "Claudeonomics," named after Anthropic's Claude model, tracking AI token consumption by 85,000 employees. Within 30 days, the entire company consumed over 60 trillion tokens.
The leaderboard features badge tiers from Bronze to Emerald, and the top 250 participants earn titles such as Token Legend and Cache Wizard. The top-ranked employee consumed 281 billion Tokens within 30 days; some employees manipulated the rankings by letting AI agents run idle for hours without performing any actual tasks, solely to consume Tokens. Measuring productivity by Token consumption is like evaluating a truck driver by fuel usage—just because the engine is running doesn’t mean deliveries are being made.
Eva didn’t feel pressure from the leaderboard within her team: “Anyway, we don’t have any direct connection to this leaderboard—we just keep doing our work and took a quick, lighthearted look at it.” Her manager didn’t use it as a talking point, but even after the leaderboard website was taken down, the underlying logic remained. The percentage of AI-generated code is still being tracked, and the minimum threshold still exists.
And as everyone is pushed to use AI, and everyone’s output increases, the performance standards themselves will rise accordingly. “If 60% of people are performing better, the standard will inevitably go up. But it’s hard to say how much of that improvement comes from AI versus simply working longer hours.”
The wind of intense competition has reached Silicon Valley
Eva’s top boss is also under pressure: “All the other senior leaders are pushing their teams extremely hard—if he doesn’t succeed in doing the same, his position is at risk.”
According to The Wall Street Journal, Meta has newly established an AI engineering department with a manager-to-engineer ratio of 1:50, meaning one manager oversees 50 engineers—twice the traditional Silicon Valley upper limit of 1:25.
Gallup data shows that the average number of direct reports per manager in the U.S. rose from 10.9 in 2024 to 12.1 in 2025, but Meta’s 50:1 ratio is still more than four times the industry average.
Eva personally felt this change. In a typical large company, a manager oversees dozens of people, helping with career planning, having one-on-one conversations, and understanding your needs.
1:50 means that a team of five managers now only needs one, leaving four without jobs.
No one knows how this new department will operate, although outside voices believe the change will end in tragedy.
Other departments are still maintaining their original management rhythm, and managers will still have one-on-one conversations with you about career planning, but everyone expects this state won’t last much longer. Some teams have already started eliminating junior managers and leaving only the upper-level managers to oversee everyone directly.
The management team is also confronting the question of whether their own work has become meaningless. “Everyone is in the same situation, facing the question of whether their role is still necessary. This applies to leaders as well—their days haven’t become any easier.”
AI is indeed helping managers improve efficiency by automatically summarizing what their team members have recently coded, posted, or attended in meetings, and generating regular reports. Previously, managers had to spend time tracking this down themselves; now, with AI’s summaries, they only need to review the results.
But on the other side of improved efficiency, management becomes cheaper, and cheap things never lack alternatives.
The pressure of intense competition trickles down, and those bearing the most direct impact are ultimately the entry-level positions at the bottom.
As a senior engineer, Eva used to hand over even small bugs to junior engineers when planning projects. But now, if the issue is minor, he simply opens an AI window and fixes it in minutes. “No need to communicate with junior engineers—I take care of it myself in no time.”
Large projects still require human effort, but the mundane tasks that once made up the bulk of junior engineers' workloads are now being effortlessly handled by AI at the fingertips of senior engineers.
Eva speaks quickly: "If you can do it as early as possible—being an engineering manager, a product manager, an engineer, and a designer all at once, handling everything yourself to build a feature or even an entire team—you might have a slightly lower chance of being laid off."
Regarding how many people will ultimately remain, Eva said with a smile, “At this point, even if Meta retains only half of its staff, it can still keep running. If AI continues to develop at the pace advertised, eventually only about 10% of programmers might be left to review what the AI has produced and align product decisions—while the remaining 90% become unemployed. Even in that scenario, Meta could still keep going.”
No one is safe, not even Zuckerberg
No one feels safe.
Senior leaders feel pressure because other senior leaders are competing; managers feel pressure because their span of control may increase from 1:15 to 1:50; senior engineers feel pressure because standards are continuously rising; junior engineers feel pressure because their work is being effortlessly absorbed by AI tools used by senior engineers.
Even Zuckerberg himself is experiencing anxiety.
The uncertainty of the AI era is real; every new feature released by Claude Code could put a company out of business, Figma's stock price surged dramatically after the Claude Design announcement, and the entire SaaS industry is being dismantled piece by piece.
Social networks may seem to have barriers, but those barriers are never as thick as they appear. Eva believes the transition from QQ to WeChat took just one or two years.
Zuckerberg, while concerned about the company’s future, is aggressively cutting jobs. To Eva, this is a management strategy: “He wants to keep the most driven and the smartest people. What’s the best way to do that? He found that giving money isn’t the most effective approach—layoffs work better.”
Creating a sense of insecurity drives output more than issuing bonuses.
But this strategy comes at a cost. Top engineers won’t tolerate this pressure indefinitely—they’ll leave for places that value their employees more. Layoffs can push out underperformers, but they may also drive away those with the most options.
The reason Eva chose to stay is practical: although Silicon Valley has become more intense lately, it’s not as intense as China.
However, behind these individual choices, the industry-wide trend can no longer be ignored. "AI will replace most jobs, and the internet industry can never return to its former glory of earning substantial income without being overly busy."
If you can't beat them, join them
AI has reshaped how existing employees work, and has also transformed the entry points for new hires.
Meta's engineering interviews traditionally consist of three parts: Coding, Behavioral Questions, and System Design. Coding involves solving an algorithmic problem, such as sorting a set of data, testing your choice of algorithm and your considerations for performance and cost. Behavioral questions are subjective, asking how you handle feedback and conflict. System Design is typically reserved for senior-level candidates and involves architectural design questions.
In October 2025, Meta introduced an AI coding round in its interviews. What was previously two rounds of pure coding is now one traditional coding round plus one AI coding round. Candidates are given a multi-file complex project in a CoderPad environment, with an AI chat window on the right side, allowing them to switch between multiple AI models during the interview, including the GPT series, Claude series, Gemini, and Llama. Within 60 minutes, you must understand a codebase you’ve never seen before, break down the problem, and use AI to implement features or fix bugs.
It’s not about whether you can write code or craft prompts—it’s about your judgment in collaborating with AI. The AI’s output may be correct, incorrect, or partially correct; what matters is how you interact with the AI to achieve satisfactory results, and whether you can determine if the generated code is optimal. The interviewer will observe every prompt and interaction in real time.
Eva believes this closely resembles a real work environment, observing whether candidates can use the latest tools to solve complex problems in a short time.
The new entry standards mean that future entrants to this industry are expected to possess the ability to collaborate with AI from day one. A candidate who went through this round of interviews summarized in their reflection that AI did not make the interview easier; rather, it raised the bar. With AI assistance, interviewers expect you to solve more complex problems within the same time frame.
Faced with this situation, Eva chose the strategy of joining forces rather than fighting.
If this is the trend, you can't change it—resisting AI is pointless.
Eva's daily workflow has completely changed, opening multiple AI windows to handle different tasks in parallel. "You only have one brain and can only do one thing at a time. But the advantage of AI is that you can run ten of them to do different things for you."
It took about a month to go from trying it out to getting comfortable with it.
He uses AI across nearly every aspect of his work—from writing documents and brainstorming during project planning, comparing solutions, writing SQL to estimate potential impacts, and coding, to drafting summaries and posting social media updates to increase visibility after completing features.
Be among the first to use AI to its fullest potential—maybe you’ll also be among the last to be laid off. But how fast layoffs will happen, or whether you’ll truly avoid them, no one knows. All you can do is make the best of it.
Beyond this self-reassurance, AI offers vastly different value to people at different levels.
For senior engineers who have accumulated sufficient experience and can identify issues and grasp direction, AI is a tangible lever—what used to be a daunting two-week analysis can now be started immediately. But for those early in their careers, AI removes precisely the part of thinking and trial-and-error they most need.
Efficiency has improved, but learning opportunities have disappeared.
Eva is unwilling to label herself as either optimistic or pessimistic: “You can’t change this big trend—just like the workers laid off in Northeast China back then, you can only accept it. Some opened restaurants, others headed south to start businesses. Who knows? Life is too long to overthink it.”
By now, the only thing certain is that no one is a winner.
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