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Highly relevant for software engineers, AI engineers and engineering leaders, useful for those working in tech. Over 1,100,000 subscribers (Enter your email...) Subscribe By subscribing, you agree Substack's (https://substack.com/tos) Terms of Use , and acknowledge its (https://substack.com/ccpa#personal-data-collected) Information Collection Notice and (https://substack.com/privacy) Privacy Policy . Already have an account? Sign in (https://newsletter.pragmaticengineer.com/s/deepdives/?utm_source=substack&utm_medium=menu) Deepdives Why is Meta destroying its engineering organization? Leadership at the social media giant has been on an AI-fueled rampage through its engineering org. We report what\u2019s happened (https://substack.com/@pragmaticengineer) (Gergely Orosz) (Gergely Orosz's avatar) (https://substack.com/@pragmaticengineer) Gergely Orosz Jun 16, 2026 976 15 87 Share Hi \u2013 this is Gergely with a free issue of the Pragmatic Engineer Newsletter. In every issue, I cover challenges at Big Tech and startups through the lens of senior engineers and engineering leaders. Subscribe to get deepdives like this in your inbox, weekly: (https://newsletter.pragmaticengineer.com/p/ai-fakers) () (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering) (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering) () (subscribe-widget) (Type your email...) Subscribe Many subscribers expense this newsletter to their learning and development budget. If you have such a budget, here\u2019s (https://blog.pragmaticengineer.com/request-to-expense-the-pragmatic-engineer-newsletter/) an email you could send to your manager . For two decades, Meta had a unique, high-performance engineering org; right up until around April of this year. For the first 20 years of the company\u2019s existence, it had a \u201cmove-fast-and-break-things\u201d culture, and in the early 2020s this shifted to a \u201cmove-fast-with-stable-infra\u201d one. Engineers I know at the company were empowered to do good work, focus on impact, and to balance business interests with solid engineering. But in the past few weeks, all that has changed, as if the leadership has been following detailed blueprints on how to demolish a proven, successful engineering culture in the most ruthlessly efficient way possible. For the past few weeks, I\u2019ve been (https://newsletter.pragmaticengineer.com/i/196795826/3-meta-forcefully-assigns-engineers-to-data-labelling-pre-layoff) sharing how bad things are inside the social media company for engineers in one of Silicon Valley\u2019s most prestigious workplaces. In this article, we walk through what\u2019s happened, and ask what\u2019s going through the minds of leadership who are reducing software engineering there from (https://newsletter.pragmaticengineer.com/p/profit-centers-cost-centers) the profit center that it was between 2004 until very recently, to the disdained cost center that it has become in just a few weeks. We cover: Meta\u2019s pre-AI engineering culture Investing in AI and pressing engineers to always use it Core engineering folks feel treated like trash Most embarrassing-ever outage Internal mess Self-inflicted wounds Is \u201cAI psychosis\u201d just a Meta issue? 1. Meta\u2019s pre-AI engineering culture I\u2019d split Meta\u2019s engineering culture into two eras: \u201cmove fast and break things\u201d, and then \u201cmove fast with stable infra.\u201d \u201cMove fast and break things\u201d In the 2010s, Facebook\u2019s unconventional engineering culture had grown somewhat legendary in the tech industry, as the company went against conventional best practices and succeeded massively. In 2012, when Facebook hit the billion-users landmark, the company produced a small physical book about its culture which was placed on employees\u2019 desks. Presented with retro propaganda design, it was dubbed the \u201clittle red book\u201d, co-opting the name of a famous volume of the thoughts of Chairman Mao, (1964). At around 70 pages long, Facebook\u2019s version codified its engineering culture: speed, fearlessness, taking ownership, and thinking outside of the box. (https://substackcdn.com/image/fetch/$s_!4O-q!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fe21ca23a-f069-489a-9bf0-fa882e94b9b6_1968x1518.png) () From the Little Red Book. Source: (https://v1.benbarry.com/project/facebooks-book) Ben Barry Back then, mantras in Facebook\u2019s little red book were also in print across campus, and included: Move Fast and Break Things Done is Better Than Perfect Fail Harder What Would You Do If You Weren\u2019t Afraid? Every Day Feels Like a Week The Wright Brothers Did Not Have Pilot Licenses The Foolish Wait Fortune Favors the Bold There was genuine focus on building good products. Also from the book: (https://substackcdn.com/image/fetch/$s_!4-Pa!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Facc1bcd4-cad8-4d13-882a-b9e9bbd8ff55_2048x1522.png) () More from Facebook\u2019s Little Red Book \u201cMove fast with stable infra\u201d culture In 2022, I did what is one of the longest deepdives we\u2019ve published on the topic of (https://newsletter.pragmaticengineer.com/p/facebook) Meta\u2019s engineering culture . By then, things had evolved, and much of any former recklessness was gone, replaced by the principle of moving fast, but with stable infra. Here\u2019s how I described Meta\u2019s engineering culture then: \u201c The culture is incredibly engineering-centric: much more than most of Big Tech. This might come from Mark Zuckerberg being an engineer himself, or because much of the innovation in the early days of Facebook came from engineers. Focus on individual impact. Impact has been the bread and butter of the focus at Facebook. This is very true since the early days, and the focus on generating impact remains. One detail in common with most Big Tech firms is that both the engineering culture and general culture focus so much on individual impact. This results in some people focusing on short-term, measurable wins and assuming that teamwork and split wins between groups might be less rewarded. The lack of rigid processes. Facebook seems to have the least amount of processes or standardization across all of Big Tech. Don\u2019t even try to compare it to (https://newsletter.pragmaticengineer.com/p/amazon?s=w) Amazon\u2019s engineering culture and the countless formal processes there. But even compared to companies like Google, Microsoft or Uber, Facebook\u2019s processes are much looser. Most of this comes from the engineering-centric nature of the company and engineers disliking processes. Surprisingly little emphasis on testing, documentation or code comments. You\u2019ll find shockingly little automated testing and documentation at Facebook, compared to the rest of Big Tech. Inline code comments are also very rare. A founder-engineer driven company. Facebook is one of the few Big Tech firms whose founder is an engineer, and still is the CEO. Netflix is the other one where founder and co-CEO Reed Hastings was also a software engineer before starting the company. Amazon was the other example of this until recently, but it\u2019s not the case at Google or Apple. There are good examples of smaller companies like Cloudflare, but they\u2019re all younger than Facebook. Bootcamp . A unique onboarding process, unlike what any other Big Tech firms offer. We cover this more in the (https://newsletter.pragmaticengineer.com/i/55330545/bootcamp-and-onboarding) Bootcamp & onboarding section. \u201d Also, Facebook, as a product, has one of the (https://newsletter.pragmaticengineer.com/p/shipping-to-production) most sophisticated auto rollout systems in the industry. Instagram has a battle-tested infrastructure where it was almost trivial (https://newsletter.pragmaticengineer.com/p/building-the-threads-app) to launch a new social network (Threads) with 100 million users served in its first week. Engineers whom I knew inside the company are capable, motivated, and product-minded, and their work was appreciated. CEO, Mark Zuckerberg, was influential: he personally coded the first version of Facebook, had stayed close to engineering, and valued software engineers very much. Engineers there felt they were working inside a profit center. 2. Investing in AI and pressing engineers to always use it Meta has been the only company among the big five of Apple, Microsoft, Amazon, Google, and itself not to own a hardware platform or operating system. Apple has the iPhone, iPad and Macs, Google has Android, ChromeOS and Pixel phones, Microsoft has Windows, and Amazon has the Kindle. Stepping back, it looks as though the Mark Zuckerberg of today has resolved not to miss a platform opportunity, after the company failed to build its own mobile OS or mobile phone during the 2010s. This is one reason for investing so much in virtual reality (VR) with Oculus, and in augmented reality with the Meta Glasses. Facebook changed its name to Meta in 2021, back when it looked like VR \u2013 and the metaverse \u2013 could be massive. Billions was spent on ensuring Meta would be the market leader in this space. But once again, VR didn\u2019t go mainstream; since the end of the pandemic, popular interest in the segment has died down considerably. When it became clear that AI would become a mega-trend in 2022, Zuckerberg didn\u2019t miss it: he assembled the internal FAIR group (Fundamental AI Research team) as well as a GenAI product organization and released a series of open-weight AI models: Llama 1 : released in Feb 2023, three months after ChatGPT, built by FAIR Llama 2 : in June 2023, built by the GenAI product organization (as well as all subsequent Llama models) Llama 3 : in April 2024. This model was Meta\u2019s most competitive LLM of all, and gained momentum in adoption across the industry Llama 4 : in April 2025. This model was (https://newsletter.pragmaticengineer.com/i/161033486/metas-latest-llama-release-disappoints) deeply disappointing In June that year, Meta acquired a 49% stake in Scale AI to reboot its AI efforts for a whopping $14.8B, and brought in Scale AI\u2019s CEO, Alexandr Wang to take over Meta\u2019s AI strategy. The acquisition of Chinese startup Manus AI for $2B is currently in question after China (https://newsletter.pragmaticengineer.com/i/196004322/china-blocks-meta-from-buying-manus) blocked the deal from being completed. Based on the investment made into Scale AI and Wang, it\u2019s pretty clear that Meta \u2013 and Zuckerberg \u2013 is determined to build a state-of-the-art LLM that can be competitive with the latest versions of Claude and ChatGPT. But Meta has to start pretty much from scratch, and it\u2019s up to Alexandr Wang to deliver. Scale AI brings in a very specific kind of expertise to Meta, as one of the best in the industry in: Training data and labeling: Scale started, and is still best known, as a provider of high-quality labeled datasets for machine learning and AI training, including code, text, image, video, etc. RLHF and fine-tuning: A RLHF (reinforcement learning from human feedback) flow which Scale runs, where people give feedback for foundation models, as a \u201chuman in the loop\u201d data engine that many leading AI labs use to create better LLMs. Wang seems to have a very broad reign to do what he has been an expert in: creating training data, doing data labeling and RLHF. This is being pulled off with the labor of Meta\u2019s engineering workforce, and by surveilling it. Problem #1: Tracking keystrokes and mouse clicks, with no option to opt out. In late April, Meta told engineers they were being enrolled into a system that tracks every keystroke and click, to produce training data for Meta\u2019s new AI. There\u2019s no way to opt out. Needless to say, this is invasive and raises privacy questions: If you log into your personal bank account, does the tool track you? What about when you\u2019re writing a personal email, or responding to a personal call? Meta held no consultation and there are no workarounds; just a top-down decision being pushed through. This month, Reuters (https://www.reuters.com/world/meta-scales-back-ai-mouse-clicks-tool-citing-employee-concerns-2026-06-02/) reported that people\u2019s concerns there are finally being heard: \u201cMeta is dialing back elements of its plan to collect employee mouse movements, keystrokes and other actions for use as AI \u200btraining data, it said in an internal memo on Tuesday, following weeks \u200bof angry pushback from staffers. New controls will allow employees to pause \u2060the data collection for up to 30 minutes at a time and \u200brequest exemptions from the initiative, according to the memo, authored by Stephane Kasriel, \u200ba vice president in Meta\u2019s AI model-building Superintelligence Labs unit.\u201d From talking with current Meta engineers, I understand the logging system has not been rolled out in the UK due to data protection regulation. Problem #2: 30-50% of engineers on core teams have been forcefully reassigned to data labeling and RLHF, upsetting folks even more. Also starting in late April, product engineering teams received a mandate from above, whereby 30-50% of engineers were to leave the team and join the ADO org (Agent Data Optimisation). \u201cForceful\u201d reassignment is very relevant here because of Meta\u2019s traditional engineering culture. Between its founding in 2004 and until last year, Meta gave engineers autonomy to choose where they work and what they work on. This was structural to how the company worked: Engineers were not hired for a specific team (save for at the Staff+, levels, in some cases). They were hired to the company During a 6-week bootcamp, new hires got familiar with Meta\u2019s engineering culture and chose a team Team matching meant talking with multiple teams who had headcount, doing small work with them, and finding a match Internal transfers were easy, and often initiated by engineers Team selection via bootcamp started to die down in around 2024, but any Meta engineer with at least two years\u2019 tenure knows that previously they chose what to work on, and of course, could pick the most impactful thing to work on. And then, out of the blue, they\u2019re assigned to a division where the impact is not clear, the work is menial, and doing it too long will surely hurt their career prospects. \u201cData labeling\u201d is more involved work, even though a bit repetitive. There are labeling tasks, where you create a website, then look at it and decide if it looks good or not, and give this feedback: this is the \u201ctypical\u201d data labeling. But this is the less frequent type of work. But then there are more involved AI training tasks, which looks like this: Come up with a task that the AI should do Then write the tests that confirm the result Package all of this up into a Docker container, using (https://github.com/harbor-framework/harbor) Harbor framework Then read the code that the AI writes - often doing this based on feedback from several models, and give it feedback This work is not easy to do \u2014 and you can see why you need good software engineers to do it! \u2014 but it gets repetitive really quickly. Most engineers who I talked to said that they found it hard to do this work motivated day-in, day-out. Then again, I did talk with an engineer inside the org who said that by varying the technologies they use, and challenging themselves, they found this work motivating and interesting! This engineer expects there to be more software engineering work coming up, after the phase of training passes. There\u2019s a lesson here: when life gives you lemons, you can either complain about it, or you can make lemonade, like this dev has done so. This training work is secretive across the AI industry, and vendors are offering $100+ per hour for devs to do this type of work. There are rumors across the industry that OpenAI and Anthropic spends more on these training environments (to make the models great at coding) than the model training run itself! Infrastructure and security teams were hit especially hard by reassignments. I talked with several engineers in infra orgs, who had 30-50% of their teams drafted into the ADO org. And in some cases, it was the best engineers who left. One engineer told me that the whole situation feels like the movie, The Hunger Games, when tributes are randomly selected and then removed from their environment, to something completely different. Except, at Meta, many more folks are being affected, with between three and five from a 10-person team going from building products used by hundreds of millions, to giving human feedback on AI-generated GitHub repos, over and over. So, a wider impact than in the Hunger Games, but with less drastic consequences. Around 6,500 people are in the ADO org, more than at OpenAI and Anthropic. Roughly four to five thousand of these are software engineers. Meta has around 25,000 engineers, meaning that one in every 5-6 software engineers may now find themselves doing data labeling full time. As you can imagine, people are actively open to new positions, and nobody is updating their job title on LinkedIn and elsewhere to \u201cdata labeling at Meta.\u201d I\u2019ve spoken with people in this role and they don\u2019t like doing it, and feel upset about the top-down decision making. The silver lining is that they still have a job, have retained their salary, and were not part of layoffs. They still have time to leave Meta for something that pays comparably and is not a data labeling job. 3. Core engineering folks feel treated like trash Problem #3: a month-long waiting game, stoking fear across the company. On 20 April, Reuters (https://www.reuters.com/world/meta-targets-may-20-first-wave-layoffs-additional-cuts-later-2026-2026-04-17/) reported that Meta planned to lay off 10% of staff in a month\u2019s time, and Meta confirmed the news, meaning there was a period of four weeks when everyone knew that they could be unemployed very soon. Forced reassignments to data labeling started to happen. As I covered (https://newsletter.pragmaticengineer.com/i/196795826/3-meta-forcefully-assigns-engineers-to-data-labelling-pre-layoff) at the time: \u201cUnderstandably, there are mixed feelings about this redeployment [to data labeling], with layoffs coming soon. On Wednesday, 20 May, Meta will announce layoffs. Perhaps those moved to do data labeling could actually be \u201csafer\u201d than colleagues on product teams. Of course, this is speculation, but it would be cruel if Meta cut devs reassigned to data labeling.\u201d Problem #4: Performance review is hyper-aggressive at Meta, so devs optimize all metrics. The internal performance review process, PSC (Performance Summary Cycle), is very stringent, compared to Google and Apple, I\u2019ve learned. Managers inside Meta \u201cfight\u201d over the pay packets of their employees, which involves \u201cknocking down\u201d the packet of engineers on other teams, so their direct reports are ranked higher. It\u2019s common to weaponize metrics in this process \u2013 be that business impact, the number of code reviews, number of lines of code written, pre-AI (see (https://newsletter.pragmaticengineer.com/p/the-coding-machine-at-meta) our Coding Machine archetype podcast on this.) Quotas are handed down to managers for the splits of the workforce to be put in each \u2018bucket\u2019, and the internal politics gets heated as managers try to get their reports into higher buckets. After a few years, engineers at Meta learn that the best way to not get a bad PSC rating is to have all metrics \u2013 impact, code committed, and other numbers \u2013 higher than their peers\u2019 are. Learn more about (https://newsletter.pragmaticengineer.com/p/performance-calibrations) the internal politics of performance calibrations. Problem #5: tokens are measured as part of perf, so devs aggressively optimize for it. When layoffs were confirmed, engineers also learned that managers shall inspect token count during perf reviews. This raised worries that those with low token counts might be marked as underperformers and dismissed. So, what is the natural reaction to this as an engineer at Meta? They started using AI tools for the sake of generating more tokens. This happened while Meta had an internal token leaderboard, encouraging tokenmaxxing. As I wrote (https://newsletter.pragmaticengineer.com/i/194426825/1-tokenmaxxing-weird-new-trend) on 16 April: \u201cAs per The Information, Meta employees (http://reported) used a total of 60.2 trillion AI tokens (!!) in 30 days. If this was charged at Anthropic\u2019s API prices, it would cost $900M. Of course, Meta is likely purchasing tokens at a discount, but that could still come in at $100M+ \u2013 in large part from senseless \u201ctokenmaxxing\u201d.\u201d The biggest problem: people stop caring about real work and focus on performative work. Let\u2019s check the four ingredients that Meta\u2019s leadership has decided to introduce to their workplace: Tracking the keyboards and mouse clicks of all engineers, where legally possible Reassign a good chunk of engineers to fulltime data labeling Let staff know that 10% of them will be laid off Have a culture where devs optimize for any and all metrics measured during PSC Measure token usage as part of PSC Shake this mix up well, and what do you get? Two things: Everyone overuses AI to boost their personal stats. An engineering workforce that pretends to work with as much AI, and as little human input, as possible. It\u2019s a strange incentive where an outage caused by a failure to review code properly is not grounds for dismissal, but writing code by hand \u2013 instead of having an AI agent write it \u2013 could cost you your job Every longer-tenured engineer is seeking a new job, or at least considering it. Those who have been around at Meta longer term have seen enough. Let me describe this visually: (https://substackcdn.com/image/fetch/$s_!rXjq!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F9a19e01d-9126-456c-8369-f2663ff69cdc_1590x1074.png) () Why pretty much every engineer at Meta is looking for a way out, visualised Fresh data seems to confirm that starting in May, a lot more engineers at Meta are looking for an \u201cout.\u201d Here\u2019s how signups to leading Big Tech interview preparation service (http://interviewing.io) interviewing.io from Meta have changed over the last year and a half: we can see a massive jump in May vs the year before, as shared by founder and CEO (https://x.com/alinelernerLLC) Aliner Lerner with us: (https://substackcdn.com/image/fetch/$s_!fEQS!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F6ad3ffe5-21c8-4052-bf15-50c7e286e550_1614x1160.png) () Signups to interviewing.io\u2019s interview prep service from Meta. Source: (http://interviewing.io) interviewing.io . (We covered more data from interviewing.io and other interesting data sources in the deepdive (https://newsletter.pragmaticengineer.com/p/the-job-market-in-2026-part-2) State of the software engineering job market in 2026 ) To its credit, Meta has given out generous retention equity packages to several engineers considered key on the remaining teams. These packages make it harder to get matching compensation elsewhere. Still, I talked to one engineer who got an equity top-up, and said that this approach helped him decide to leave as soon as possible because he feels bitter about the lack of autonomy and having no control over things. 4. Most embarrassing-ever outage Meta\u2019s core infra and security teams have suddenly found themselves severely understaffed. Most folks are pushing AI-generated code merged with AI-only reviews, without paying much attention to quality. After all, they\u2019re dealing with the possibility of unemployment, while firefighting to operate a team without its best engineers whose headcount has been cut in half, all with the knowledge that AI usage could affect their own job security. Two weeks ago, on 30 May, the most embarrassing outage in Meta\u2019s history happened. Here\u2019s software engineer Siddharth Sundharam\u2019s (https://www.0xsid.com/blog/meta-account-takeover-fiasco) summary (emphasis mine): \u201cYesterday, a slew of Instagram accounts, including some high profile ones like the Obama White House account, seemingly got hacked. Look, I\u2019m no spring chicken. I\u2019ve spent almost a decade and a half identifying vulnerabilities and exploits at unicorn scale, but this is hands down the most unserious, \u201calmost too stupid to be true\u201d of them all. The Takeover Flow: Step 01: Faking the Location & Initiating Support. All the attacker needs to kick this off is your account username. Then, they hop on a VPN or proxy close to your city so Instagram\u2019s security algorithms don\u2019t suspect a thing. (You can quite easily get this from your public profile or \u201cAbout\u201d section or a hundred other ways.) Once it looks like the request is coming from the correct region, they tell the Meta support AI that the account is hacked and ask it to send the verification codes to an arbitrary email address they control. Step 02: That\u2019s It. Really, that\u2019s it. The first proper zero auth password reset I\u2019ve seen in production. There appears to be no additional check as to whether the email being given is actually something the user has used before. Once the AI sends the security code to the attacker\u2019s email, the attacker passes it right back to complete the verification. The platform hands over a fresh password reset link, granting full ownership to the attacker.\u201d This is a security breach in which Meta left its extra-secure, reinforced front door unlocked, so that anyone could come in, and there was no alarm to notify anybody when it happened! It seemed Meta only noticed when users started reporting it on social media! From talking with folks inside Meta, I\u2019ve learned that AI was at the heart of this outage. AI-generated, AI-reviewed code, and security teams being gutted were together the cause of this beyond-embarrassing incident. I poked around, and here\u2019s what I gathered: Instagram\u2019s Trust and Safety Team lost around 50% of its staff to data labeling and layoffs. Some of the most senior folks were drafted onto AI training tasks. AI-generated changes that saw no human input, just another AI code review, were very common during the last two months, across the codebase. The change that caused this outage looked like one of these Normally, the Trust and Safety team would be on top of monitoring and alerting of security breaches, but it is currently in full disarray due to rapid, internal disorganization. Meta\u2019s Chief Security Officer resigned the very next day. The outage was resolved on Monday, 1 June, and an investigation started as part of the SEV process. On Tuesday, Meta\u2019s Chief Information and Security Officer (CISO), Guy Rosen, (https://www.bloomberg.com/news/articles/2026-06-02/meta-s-rosen-former-head-of-election-integrity-to-depart) announced his departure. Coincidence? I suspect not: the CISO might have stepped down if they warned against the Security org being gutted but were then ignored, and so no longer trusts leadership. I also imagine the CISO didn\u2019t have the idea to move a good half of Instagram\u2019s security team over to data labeling. As a note, the previously worst outage was all Meta services going down for seven hours (https://en.wikipedia.org/wiki/2021_Facebook_outage) in 2021 , due to a DNS / BGP configuration issue. It was a bad outage, but Meta handled the follow up well, in my opinion. After that 2021 outage, Meta (https://engineering.fb.com/2021/10/04/networking-traffic/outage/) shared a postmortem and apology . It has not done so for the latest Instagram account takeover outage. 5. Internal mess Wired (https://www.wired.com/story/mark-zuckerberg-meta-employee-meeting-interrupt-ai/?_sp=2e37591b-dcf6-4632-bbf6-bc7c570a4b65.1781609124066) shares more details on just how bad the situation is inside Meta, right now: \u201cSomeone interrupted a livestreamed, employee-only presentation at Meta earlier this week with an expletive-filled outburst about \u201cbeing the company\u2019s bitch,\u201d according to a recording heard by WIRED. The individual then asked the people leading the call to write to a specific Meta AI executive and \u201ctell him that he\u2019s a piece of shit.\u201d The incident, which took place on a call open to thousands of employees, reflects growing frustration inside the company\u2019s Applied AI team, which was formed in March to support the work of AI researchers at Meta Superintelligence Labs. Three current employees tell WIRED there is widespread dissatisfaction with how Meta assembled the unit of about 6,500 engineers and product managers and the drudgework they allege they have been assigned to improve AI models. \u201cIt\u2019s literally the gulag,\u201d one of the employees claims. \u201cYou have zero purpose in life all of a sudden, you barely interact with anyone, you just have these tasks every week.\u201d There\u2019s more: Meta\u2019s Chief Product Officer, Chris Cox, reportedly admitted to staff that Meta\u2019s upper leadership (the folks above him, meaning the C-level at Meta) created the mess. Also from Wired: During a meeting this week open to all employees at Instagram, Meta chief product officer Chris Cox addressed the \u201cdifficult\u201d and \u201cbrutal\u201d environment created by the \u201cinsanity of this company\u201d in the past few months, according to a recording heard by WIRED. Cox applauded Instagram employees for launching features and serving around 2 billion users amid what he compared to \u201crunning a marathon in the middle of a hailstorm and then, like, your teammate gets replaced and then we\u2019re recording you.\u201d \u201cIt\u2019s like what the fuck,\u201d he said, drawing laughs, before repeating himself. \u201cIt is like what the fuck.\u201d 6. Self-inflicted wounds So, is there an ultimate source of the \u201cinsanity of this company\u201d, as CPO Chris Cox put it? Engineers whom I talked to point the finger at two individuals: Mark Zuckerberg and Alexandr Wang. Zuckerberg has full control over the business, and has made the decisions to reallocate a good part of engineering folks to data labeling, to roll out tracking software, and to lay off 10% of staff when Meta achieved record revenue and profits. As the CEO, the buck clearly stops with him. But it\u2019s hard to unsee that \u2013 outside of layoffs \u2013 everything that Meta is doing is taken from the Scale AI playbook, and that surely comes from Wang: Mandatory keystroke and mouse tracking to generate training data Forced data labeling with 4,500+ engineers is to generate high-quality RLHF, surely for Meta\u2019s under-construction coding LLM Taking away the best engineers from the heart of the business is surely signed off by Mark Zuckerberg, believing that it is more important for Meta to train a coding AI than it is to operate its core business like Instagram, Facebook or Messenger reliably. Oh, did I mention that on Saturday (12 June) Facebook and Instagram (https://www.tomsguide.com/news/live/meta-instagram-facebook-messenger-down-6-12-2026-live-updates) had another SEV0 , that is, a full-on outage? Before all this happened, Meta (https://newsletter.pragmaticengineer.com/i/195258143/3-morale-at-meta-hits-all-time-low) was on track to overtake Google as the world\u2019s #1 ads business by the end of the year. But for some reason, Mark Zuckerberg decided that building a coding LLM is more important. Meta\u2019s leadership is now trying to undo all the damage they have done. Wired reports that Meta CTO, Andrew Bosworth, (https://www.wired.com/story/andrew-bosworth-meta-employees-unrest/) admitted to staff that the AI reorg was atrocious and committed to better communication in the future. To me, it looks obvious that Zuckerberg doesn\u2019t care how engineers feel about the massive changes, and that Bosworth likely ignored the chaos, all while engineers know for a fact that the next AI model matters more than they do to the business. Bosworth also said that employees will have access to AI coaching tools. Very considerate, given the situation! Based on all I\u2019ve learned, Meta\u2019s engineering culture is dead because leadership has made it clear that engineering at the company is (https://newsletter.pragmaticengineer.com/p/profit-centers-cost-centers) a cost center . (https://substackcdn.com/image/fetch/$s_!uzPs!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2F220f78ee-73a9-4d1a-bfbd-371f36315d97_1678x1092.png) () Nice while it lasted Needless to say, I hope my assessment is way off, but I\u2019ve seen nothing yet from Mark Zuckerberg and Alexandr Wang \u2013 the two executives creating this current mess \u2013 to suggest it is. There may be a short time period where, if major changes like data labeling assignments and staff tracking are undone, then things at Meta could return to normal. The longer the current conditions persist, the more tenured engineers will surely leave. 7. Is \u201cAI psychosis\u201d just a Meta issue? It\u2019s tragic to see a technical founder at Meta so focused on AI that he neglects the engineers who built the heart of his company. But is Meta a one-off exception? Mitchell Hashimoto (creator of Ghostty, founder of HashiCorp) (https://x.com/mitchellh/status/2055380239711457578?s=20) says he is seeing similar behavior by other founders (emphasis mine:) \u201c I strongly believe there are entire companies right now under heavy \u201cAI psychosis\u201d and it\u2019s impossible to have rational conversations about it with them. I can\u2019t name any specific people because they include personal friends I deeply respect, but I worry about how this plays out. I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now it\u2019s... the whole software development industry (maybe the whole world, really). It\u2019s frightening, because \u2018psychosis folks\u2019 operate under an almost absolute \u201cMTTR is all you need\u201d mentality: \u201cit\u2019s fine to ship bugs because the agents will fix them so quickly and at a scale humans can\u2019t do!\u201d We learned in infrastructure that MTTR is great but you can\u2019t yeet resilient systems entirely. The main issue is I don\u2019t even know how to bring this up to people I know personally, because bringing this topic up leads to immediate dismissals like \u201cno no, it has full test coverage\u201d, or \u201cbug reports are going down\u201d or something, which just don\u2019t paint the whole picture. We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happen so fast that nobody notices the underlying architecture decaying. I worry.\u201d The takeover outage at Instagram was exactly like this: the engineering team dropped the quality bar for AI-generated and AI-reviewed code, probably expecting that they could recover quickly from failures. And they did indeed recover\u2026 after the damage was done, high-profile Instagram accounts were hacked, and the system was compromised, all very publicly. Mitchell highlights the specific concern of founders over-estimating the capabilities of AI, and consequently casting aside sensible safeguards when shipping code to production. Takeaways Most of us probably have something to learn from the disastrous events at Meta caused by hyper-focus on AI to the exclusion of people who are the lifeblood of that company. In some good news, I\u2019m hearing that in the UK, some of the 10% layoffs have suddenly been cancelled: at the end of the mandatory consultation period, several infra and security teams are learning that no engineers on their team will be let go, as originally expected. Meta has a booming business, and is already a beneficiary of AI via increased ads revenue. Meanwhile, my Facebook feed is filled with fake, AI-generated videos, with hundreds of comments from bots and people who seemingly don\u2019t realize it\u2019s AI. It all seems like just more content for Meta to show ads next to. And yet, despite business booming, Meta\u2019s leadership has gone on a crusade to inflict the most damage possible on its engineering org. Apparently, they\u2019re now learning that most of it was pointless. If you\u2019re in a leadership position and feeling the temptation to make drastic org changes for AI-related reasons, take a deep breath and see where it left Meta. Meanwhile, If you\u2019re an engineer at a company whose leadership is over-indexing on AI, consider forwarding this article as additional context. If you\u2019re hiring standout engineers who are extremely hands-on with AI, then it\u2019s never been easier to get talent from Meta, than right now. Every engineer I know at the company is an extremely early adopter of AI, and knows how to build products and AI infra. These folks have soured on the company and its leadership. Meta\u2019s loss of talent will be the gain of other startups and the rest of Big Tech; it\u2019s one benefit of AI that\u2019s probably a bit unexpected \u2013 not least of all by Meta! It seems like the old mantra of \u201cmove fast and break things\u201d has now extended to Meta\u2019s engineering org itself, with the company\u2019s rush to over-invest in AI, so it will avoid missing the latest mega-trend in the tech industry. Subscribe to The Pragmatic Engineer Tens of thousands of paid subscribers Big Tech and startups, from the inside. Highly relevant for software engineers, AI engineers and engineering leaders, useful for those working in tech. (https://newsletter.pragmaticengineer.com/p/ai-fakers) (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering) (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering) (post-end-cta) (Type your email...) Subscribe By subscribing, you agree Substack's (https://substack.com/tos) Terms of Use , and acknowledge its (https://substack.com/ccpa#personal-data-collected) Information Collection Notice and (https://substack.com/privacy) Privacy Policy . (https://substack.com/profile/138860717-wonderbird) (WonderBird) (WonderBird's avatar) (https://substack.com/profile/15945213-john-edwards) (John Edwards) (John Edwards's avatar) (https://substack.com/profile/130582678-jtocold) (Jtocold) (Jtocold's avatar) (https://substack.com/profile/123002278-isaac-harwell) (Isaac Harwell) (Isaac Harwell's avatar) (https://substack.com/profile/6863640-david-fu) (David Fu) (David Fu's avatar) 976 Likes \u2219(https://substack.com/note/p-202307236/restacks?utm_source=substack&utm_content=facepile-restacks) 87 Restacks 976 15 87 Share Discussion about this post Comments Restacks (User) (User's avatar) (Write a comment...) (https://substack.com/profile/100948949-matej-vitasek?utm_source=comment) (Matej Vit\u00e1sek) (Matej Vit\u00e1sek's avatar) (https://substack.com/profile/100948949-matej-vitasek?utm_source=substack-feed-item) Matej Vit\u00e1sek (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering/comment/277402278) (Jun 16, 2026, 4:44 PM) Jun 16 I mean... this is very much not nice to the people on the ground, but let's face it, this is not healthcare or utilities or food production. Even if whole Meta burns itself to the ground I don't see too many people shedding tears for it \ud83e\udd37\u200d\u2642\ufe0f Like (67) Reply Share (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering/comment/277402278) 3 replies by Gergely Orosz and others (https://substack.com/profile/45963189-abel?utm_source=comment) (Abel) (Abel's avatar) (https://substack.com/profile/45963189-abel?utm_source=substack-feed-item) Abel (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering/comment/277514505) (Jun 16, 2026, 7:55 PM) Jun 16 Liked by Gergely Orosz From a perspective I've always admired the balls they have going all in a direction. Have worked/know about a lot of companies whom have lost their moat because they never developed any risk appetite, instead they were clinging on what they had. You cannot sustain your moat by clinging, or at least not to the same margin, which meta knows very well. However, if you invert the problem and ask how could you asap loose your moat, you could say that exactly by alianating the people getting you that mote. More specifically your employees and your customers. Like (35) Reply Share (https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering/comments) 13 more comments... Top Latest Discussions (https://newsletter.pragmaticengineer.com/p/when-ai-writes-almost-all-code-what) When AI writes almost all code, what happens to software engineering? 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In every issue, I cover challenges at Big Tech and startups through the lens of senior engineers and engineering leaders. Subscribe to get deepdives like this in your inbox, weekly:Many subscribers expense this newsletter to their learning and development budget. If you have such a budget, here\u2019s an email you could send to your manager.For two decades, Meta had a unique, high-performance engineering org; right up until around April of this year. For the first 20 years of the company\u2019s existence, it had a \u201cmove-fast-and-break-things\u201d culture, and in the early 2020s this shifted to a \u201cmove-fast-with-stable-infra\u201d one. Engineers I know at the company were empowered to do good work, focus on impact, and to balance business interests with solid engineering.But in the past few weeks, all that has changed, as if the leadership has been following detailed blueprints on how to demolish a proven, successful engineering culture in the most ruthlessly efficient way possible.For the past few weeks, I\u2019ve been sharing how bad things are inside the social media company for engineers in one of Silicon Valley\u2019s most prestigious workplaces. In this article, we walk through what\u2019s happened, and ask what\u2019s going through the minds of leadership who are reducing software engineering there from the profit center that it was between 2004 until very recently, to the disdained cost center that it has become in just a few weeks.We cover:Meta\u2019s pre-AI engineering cultureInvesting in AI and pressing engineers to always use itCore engineering folks feel treated like trashMost embarrassing-ever outageInternal messSelf-inflicted woundsIs \u201cAI psychosis\u201d just a Meta issue?I\u2019d split Meta\u2019s engineering culture into two eras: \u201cmove fast and break things\u201d, and then \u201cmove fast with stable infra.\u201dIn the 2010s, Facebook\u2019s unconventional engineering culture had grown somewhat legendary in the tech industry, as the company went against conventional best practices and succeeded massively.In 2012, when Facebook hit the billion-users landmark, the company produced a small physical book about its culture which was placed on employees\u2019 desks. Presented with retro propaganda design, it was dubbed the \u201clittle red book\u201d, co-opting the name of a famous volume of the thoughts of Chairman Mao, (1964).At around 70 pages long, Facebook\u2019s version codified its engineering culture: speed, fearlessness, taking ownership, and thinking outside of the box.Back then, mantras in Facebook\u2019s little red book were also in print across campus, and included:Move Fast and Break ThingsDone is Better Than PerfectFail HarderWhat Would You Do If You Weren\u2019t Afraid?Every Day Feels Like a WeekThe Wright Brothers Did Not Have Pilot LicensesThe Foolish WaitFortune Favors the BoldThere was genuine focus on building good products. Also from the book:More from Facebook\u2019s Little Red BookIn 2022, I did what is one of the longest deepdives we\u2019ve published on the topic of Meta\u2019s engineering culture. By then, things had evolved, and much of any former recklessness was gone, replaced by the principle of moving fast, but with stable infra. Here\u2019s how I described Meta\u2019s engineering culture then:\u201cThe culture is incredibly engineering-centric: much more than most of Big Tech. This might come from Mark Zuckerberg being an engineer himself, or because much of the innovation in the early days of Facebook came from engineers.Focus on individual impact. Impact has been the bread and butter of the focus at Facebook. This is very true since the early days, and the focus on generating impact remains.One detail in common with most Big Tech firms is that both the engineering culture and general culture focus so much on individual impact. This results in some people focusing on short-term, measurable wins and assuming that teamwork and split wins between groups might be less rewarded.The lack of rigid processes. Facebook seems to have the least amount of processes or standardization across all of Big Tech. Don\u2019t even try to compare it to Amazon\u2019s engineering culture and the countless formal processes there. But even compared to companies like Google, Microsoft or Uber, Facebook\u2019s processes are much looser. Most of this comes from the engineering-centric nature of the company and engineers disliking processes.Surprisingly little emphasis on testing, documentation or code comments. You\u2019ll find shockingly little automated testing and documentation at Facebook, compared to the rest of Big Tech. Inline code comments are also very rare.A founder-engineer driven company. Facebook is one of the few Big Tech firms whose founder is an engineer, and still is the CEO. Netflix is the other one where founder and co-CEO Reed Hastings was also a software engineer before starting the company. Amazon was the other example of this until recently, but it\u2019s not the case at Google or Apple. There are good examples of smaller companies like Cloudflare, but they\u2019re all younger than Facebook.Bootcamp. A unique onboarding process, unlike what any other Big Tech firms offer. We cover this more in the Bootcamp & onboarding section.\u201dAlso, Facebook, as a product, has one of the most sophisticated auto rollout systems in the industry. Instagram has a battle-tested infrastructure where it was almost trivial to launch a new social network (Threads) with 100 million users served in its first week.Engineers whom I knew inside the company are capable, motivated, and product-minded, and their work was appreciated. CEO, Mark Zuckerberg, was influential: he personally coded the first version of Facebook, had stayed close to engineering, and valued software engineers very much. Engineers there felt they were working inside a profit center.Meta has been the only company among the big five of Apple, Microsoft, Amazon, Google, and itself not to own a hardware platform or operating system. Apple has the iPhone, iPad and Macs, Google has Android, ChromeOS and Pixel phones, Microsoft has Windows, and Amazon has the Kindle.Stepping back, it looks as though the Mark Zuckerberg of today has resolved not to miss a platform opportunity, after the company failed to build its own mobile OS or mobile phone during the 2010s.This is one reason for investing so much in virtual reality (VR) with Oculus, and in augmented reality with the Meta Glasses. Facebook changed its name to Meta in 2021, back when it looked like VR \u2013 and the metaverse \u2013 could be massive. Billions was spent on ensuring Meta would be the market leader in this space. But once again, VR didn\u2019t go mainstream; since the end of the pandemic, popular interest in the segment has died down considerably.When it became clear that AI would become a mega-trend in 2022, Zuckerberg didn\u2019t miss it: he assembled the internal FAIR group (Fundamental AI Research team) as well as a GenAI product organization and released a series of open-weight AI models:Llama 1: released in Feb 2023, three months after ChatGPT, built by FAIRLlama 2: in June 2023, built by the GenAI product organization (as well as all subsequent Llama models)Llama 3: in April 2024. This model was Meta\u2019s most competitive LLM of all, and gained momentum in adoption across the industryLlama 4: in April 2025. This model was deeply disappointingIn June that year, Meta acquired a 49% stake in Scale AI to reboot its AI efforts for a whopping $14.8B, and brought in Scale AI\u2019s CEO, Alexandr Wang to take over Meta\u2019s AI strategy. The acquisition of Chinese startup Manus AI for $2B is currently in question after China blocked the deal from being completed.Based on the investment made into Scale AI and Wang, it\u2019s pretty clear that Meta \u2013 and Zuckerberg \u2013 is determined to build a state-of-the-art LLM that can be competitive with the latest versions of Claude and ChatGPT. But Meta has to start pretty much from scratch, and it\u2019s up to Alexandr Wang to deliver.Scale AI brings in a very specific kind of expertise to Meta, as one of the best in the industry in:Training data and labeling: Scale started, and is still best known, as a provider of high-quality labeled datasets for machine learning and AI training, including code, text, image, video, etc.RLHF and fine-tuning: A RLHF (reinforcement learning from human feedback) flow which Scale runs, where people give feedback for foundation models, as a \u201chuman in the loop\u201d data engine that many leading AI labs use to create better LLMs.Wang seems to have a very broad reign to do what he has been an expert in: creating training data, doing data labeling and RLHF. This is being pulled off with the labor of Meta\u2019s engineering workforce, and by surveilling it.Problem #1: Tracking keystrokes and mouse clicks, with no option to opt out. In late April, Meta told engineers they were being enrolled into a system that tracks every keystroke and click, to produce training data for Meta\u2019s new AI. There\u2019s no way to opt out.Needless to say, this is invasive and raises privacy questions: If you log into your personal bank account, does the tool track you? What about when you\u2019re writing a personal email, or responding to a personal call? Meta held no consultation and there are no workarounds; just a top-down decision being pushed through.This month, Reuters reported that people\u2019s concerns there are finally being heard:\u201cMeta is dialing back elements of its plan to collect employee mouse movements, keystrokes and other actions for use as AI \u200btraining data, it said in an internal memo on Tuesday, following weeks \u200bof angry pushback from staffers.New controls will allow employees to pause \u2060the data collection for up to 30 minutes at a time and \u200brequest exemptions from the initiative, according to the memo, authored by Stephane Kasriel, \u200ba vice president in Meta\u2019s AI model-building Superintelligence Labs unit.\u201dFrom talking with current Meta engineers, I understand the logging system has not been rolled out in the UK due to data protection regulation.Problem #2: 30-50% of engineers on core teams have been forcefully reassigned to data labeling and RLHF, upsetting folks even more. Also starting in late April, product engineering teams received a mandate from above, whereby 30-50% of engineers were to leave the team and join the ADO org (Agent Data Optimisation).\u201cForceful\u201d reassignment is very relevant here because of Meta\u2019s traditional engineering culture. Between its founding in 2004 and until last year, Meta gave engineers autonomy to choose where they work and what they work on. This was structural to how the company worked:Engineers were not hired for a specific team (save for at the Staff+, levels, in some cases). They were hired to the companyDuring a 6-week bootcamp, new hires got familiar with Meta\u2019s engineering culture and chose a teamTeam matching meant talking with multiple teams who had headcount, doing small work with them, and finding a matchInternal transfers were easy, and often initiated by engineersTeam selection via bootcamp started to die down in around 2024, but any Meta engineer with at least two years\u2019 tenure knows that previously they chose what to work on, and of course, could pick the most impactful thing to work on. And then, out of the blue, they\u2019re assigned to a division where the impact is not clear, the work is menial, and doing it too long will surely hurt their career prospects.\u201cData labeling\u201d is more involved work, even though a bit repetitive. There are labeling tasks, where you create a website, then look at it and decide if it looks good or not, and give this feedback: this is the \u201ctypical\u201d data labeling. But this is the less frequent type of work. But then there are more involved AI training tasks, which looks like this:Come up with a task that the AI should doThen write the tests that confirm the resultPackage all of this up into a Docker container, using Harbor frameworkThen read the code that the AI writes - often doing this based on feedback from several models, and give it feedbackThis work is not easy to do \u2014 and you can see why you need good software engineers to do it! \u2014 but it gets repetitive really quickly. Most engineers who I talked to said that they found it hard to do this work motivated day-in, day-out. Then again, I did talk with an engineer inside the org who said that by varying the technologies they use, and challenging themselves, they found this work motivating and interesting! This engineer expects there to be more software engineering work coming up, after the phase of training passes. There\u2019s a lesson here: when life gives you lemons, you can either complain about it, or you can make lemonade, like this dev has done so.This training work is secretive across the AI industry, and vendors are offering $100+ per hour for devs to do this type of work. There are rumors across the industry that OpenAI and Anthropic spends more on these training environments (to make the models great at coding) than the model training run itself!Infrastructure and security teams were hit especially hard by reassignments. I talked with several engineers in infra orgs, who had 30-50% of their teams drafted into the ADO org. And in some cases, it was the best engineers who left.One engineer told me that the whole situation feels like the movie, The Hunger Games, when tributes are randomly selected and then removed from their environment, to something completely different. Except, at Meta, many more folks are being affected, with between three and five from a 10-person team going from building products used by hundreds of millions, to giving human feedback on AI-generated GitHub repos, over and over. So, a wider impact than in the Hunger Games, but with less drastic consequences.Around 6,500 people are in the ADO org, more than at OpenAI and Anthropic. Roughly four to five thousand of these are software engineers. Meta has around 25,000 engineers, meaning that one in every 5-6 software engineers may now find themselves doing data labeling full time.As you can imagine, people are actively open to new positions, and nobody is updating their job title on LinkedIn and elsewhere to \u201cdata labeling at Meta.\u201dI\u2019ve spoken with people in this role and they don\u2019t like doing it, and feel upset about the top-down decision making. The silver lining is that they still have a job, have retained their salary, and were not part of layoffs. They still have time to leave Meta for something that pays comparably and is not a data labeling job.Problem #3: a month-long waiting game, stoking fear across the company. On 20 April, Reuters reported that Meta planned to lay off 10% of staff in a month\u2019s time, and Meta confirmed the news, meaning there was a period of four weeks when everyone knew that they could be unemployed very soon.Forced reassignments to data labeling started to happen. As I covered at the time:\u201cUnderstandably, there are mixed feelings about this redeployment [to data labeling], with layoffs coming soon. On Wednesday, 20 May, Meta will announce layoffs. Perhaps those moved to do data labeling could actually be \u201csafer\u201d than colleagues on product teams. Of course, this is speculation, but it would be cruel if Meta cut devs reassigned to data labeling.\u201dProblem #4: Performance review is hyper-aggressive at Meta, so devs optimize all metrics. The internal performance review process, PSC (Performance Summary Cycle), is very stringent, compared to Google and Apple, I\u2019ve learned. Managers inside Meta \u201cfight\u201d over the pay packets of their employees, which involves \u201cknocking down\u201d the packet of engineers on other teams, so their direct reports are ranked higher. It\u2019s common to weaponize metrics in this process \u2013 be that business impact, the number of code reviews, number of lines of code written, pre-AI (see our Coding Machine archetype podcast on this.)Quotas are handed down to managers for the splits of the workforce to be put in each \u2018bucket\u2019, and the internal politics gets heated as managers try to get their reports into higher buckets.After a few years, engineers at Meta learn that the best way to not get a bad PSC rating is to have all metrics \u2013 impact, code committed, and other numbers \u2013 higher than their peers\u2019 are. Learn more about the internal politics of performance calibrations.Problem #5: tokens are measured as part of perf, so devs aggressively optimize for it. When layoffs were confirmed, engineers also learned that managers shall inspect token count during perf reviews. This raised worries that those with low token counts might be marked as underperformers and dismissed.So, what is the natural reaction to this as an engineer at Meta? They started using AI tools for the sake of generating more tokens. This happened while Meta had an internal token leaderboard, encouraging tokenmaxxing. As I wrote on 16 April:\u201cAs per The Information, Meta employees used a total of 60.2 trillion AI tokens (!!) in 30 days. If this was charged at Anthropic\u2019s API prices, it would cost $900M. Of course, Meta is likely purchasing tokens at a discount, but that could still come in at $100M+ \u2013 in large part from senseless \u201ctokenmaxxing\u201d.\u201dThe biggest problem: people stop caring about real work and focus on performative work. Let\u2019s check the four ingredients that Meta\u2019s leadership has decided to introduce to their workplace:Tracking the keyboards and mouse clicks of all engineers, where legally possibleReassign a good chunk of engineers to fulltime data labelingLet staff know that 10% of them will be laid offHave a culture where devs optimize for any and all metrics measured during PSCMeasure token usage as part of PSCShake this mix up well, and what do you get? Two things:Everyone overuses AI to boost their personal stats. An engineering workforce that pretends to work with as much AI, and as little human input, as possible. It\u2019s a strange incentive where an outage caused by a failure to review code properly is not grounds for dismissal, but writing code by hand \u2013 instead of having an AI agent write it \u2013 could cost you your jobEvery longer-tenured engineer is seeking a new job, or at least considering it. Those who have been around at Meta longer term have seen enough. Let me describe this visually:Why pretty much every engineer at Meta is looking for a way out, visualisedFresh data seems to confirm that starting in May, a lot more engineers at Meta are looking for an \u201cout.\u201d Here\u2019s how signups to leading Big Tech interview preparation service interviewing.io from Meta have changed over the last year and a half: we can see a massive jump in May vs the year before, as shared by founder and CEO Aliner Lerner with us:Signups to interviewing.io\u2019s interview prep service from Meta. Source: interviewing.io.(We covered more data from interviewing.io and other interesting data sources in the deepdive State of the software engineering job market in 2026)To its credit, Meta has given out generous retention equity packages to several engineers considered key on the remaining teams. These packages make it harder to get matching compensation elsewhere. Still, I talked to one engineer who got an equity top-up, and said that this approach helped him decide to leave as soon as possible because he feels bitter about the lack of autonomy and having no control over things.Meta\u2019s core infra and security teams have suddenly found themselves severely understaffed. Most folks are pushing AI-generated code merged with AI-only reviews, without paying much attention to quality. After all, they\u2019re dealing with the possibility of unemployment, while firefighting to operate a team without its best engineers whose headcount has been cut in half, all with the knowledge that AI usage could affect their own job security.Two weeks ago, on 30 May, the most embarrassing outage in Meta\u2019s history happened. Here\u2019s software engineer Siddharth Sundharam\u2019s summary (emphasis mine):\u201cYesterday, a slew of Instagram accounts, including some high profile ones like the Obama White House account, seemingly got hacked.Look, I\u2019m no spring chicken. I\u2019ve spent almost a decade and a half identifying vulnerabilities and exploits at unicorn scale, but this is hands down the most unserious, \u201calmost too stupid to be true\u201d of them all.The Takeover Flow:Step 01: Faking the Location & Initiating Support. All the attacker needs to kick this off is your account username. Then, they hop on a VPN or proxy close to your city so Instagram\u2019s security algorithms don\u2019t suspect a thing. (You can quite easily get this from your public profile or \u201cAbout\u201d section or a hundred other ways.) Once it looks like the request is coming from the correct region, they tell the Meta support AI that the account is hacked and ask it to send the verification codes to an arbitrary email address they control.Step 02: That\u2019s It. Really, that\u2019s it.The first proper zero auth password reset I\u2019ve seen in production. There appears to be no additional check as to whether the email being given is actually something the user has used before. Once the AI sends the security code to the attacker\u2019s email, the attacker passes it right back to complete the verification. The platform hands over a fresh password reset link, granting full ownership to the attacker.\u201dThis is a security breach in which Meta left its extra-secure, reinforced front door unlocked, so that anyone could come in, and there was no alarm to notify anybody when it happened! It seemed Meta only noticed when users started reporting it on social media!From talking with folks inside Meta, I\u2019ve learned that AI was at the heart of this outage. AI-generated, AI-reviewed code, and security teams being gutted were together the cause of this beyond-embarrassing incident. I poked around, and here\u2019s what I gathered:Instagram\u2019s Trust and Safety Team lost around 50% of its staff to data labeling and layoffs. Some of the most senior folks were drafted onto AI training tasks.AI-generated changes that saw no human input, just another AI code review, were very common during the last two months, across the codebase. The change that caused this outage looked like one of theseNormally, the Trust and Safety team would be on top of monitoring and alerting of security breaches, but it is currently in full disarray due to rapid, internal disorganization.Meta\u2019s Chief Security Officer resigned the very next day. The outage was resolved on Monday, 1 June, and an investigation started as part of the SEV process. On Tuesday, Meta\u2019s Chief Information and Security Officer (CISO), Guy Rosen, announced his departure.Coincidence? I suspect not: the CISO might have stepped down if they warned against the Security org being gutted but were then ignored, and so no longer trusts leadership. I also imagine the CISO didn\u2019t have the idea to move a good half of Instagram\u2019s security team over to data labeling.As a note, the previously worst outage was all Meta services going down for seven hours in 2021, due to a DNS / BGP configuration issue. It was a bad outage, but Meta handled the follow up well, in my opinion. After that 2021 outage, Meta shared a postmortem and apology. It has not done so for the latest Instagram account takeover outage.Wired shares more details on just how bad the situation is inside Meta, right now:\u201cSomeone interrupted a livestreamed, employee-only presentation at Meta earlier this week with an expletive-filled outburst about \u201cbeing the company\u2019s bitch,\u201d according to a recording heard by WIRED. The individual then asked the people leading the call to write to a specific Meta AI executive and \u201ctell him that he\u2019s a piece of shit.\u201dThe incident, which took place on a call open to thousands of employees, reflects growing frustration inside the company\u2019s Applied AI team, which was formed in March to support the work of AI researchers at Meta Superintelligence Labs. Three current employees tell WIRED there is widespread dissatisfaction with how Meta assembled the unit of about 6,500 engineers and product managers and the drudgework they allege they have been assigned to improve AI models.\u201cIt\u2019s literally the gulag,\u201d one of the employees claims. \u201cYou have zero purpose in life all of a sudden, you barely interact with anyone, you just have these tasks every week.\u201dThere\u2019s more: Meta\u2019s Chief Product Officer, Chris Cox, reportedly admitted to staff that Meta\u2019s upper leadership (the folks above him, meaning the C-level at Meta) created the mess. Also from Wired:During a meeting this week open to all employees at Instagram, Meta chief product officer Chris Cox addressed the \u201cdifficult\u201d and \u201cbrutal\u201d environment created by the \u201cinsanity of this company\u201d in the past few months, according to a recording heard by WIRED. Cox applauded Instagram employees for launching features and serving around 2 billion users amid what he compared to \u201crunning a marathon in the middle of a hailstorm and then, like, your teammate gets replaced and then we\u2019re recording you.\u201d\u201cIt\u2019s like what the fuck,\u201d he said, drawing laughs, before repeating himself. \u201cIt is like what the fuck.\u201dSo, is there an ultimate source of the \u201cinsanity of this company\u201d, as CPO Chris Cox put it? Engineers whom I talked to point the finger at two individuals: Mark Zuckerberg and Alexandr Wang. Zuckerberg has full control over the business, and has made the decisions to reallocate a good part of engineering folks to data labeling, to roll out tracking software, and to lay off 10% of staff when Meta achieved record revenue and profits. As the CEO, the buck clearly stops with him.But it\u2019s hard to unsee that \u2013 outside of layoffs \u2013 everything that Meta is doing is taken from the Scale AI playbook, and that surely comes from Wang:Mandatory keystroke and mouse tracking to generate training dataForced data labeling with 4,500+ engineers is to generate high-quality RLHF, surely for Meta\u2019s under-construction coding LLMTaking away the best engineers from the heart of the business is surely signed off by Mark Zuckerberg, believing that it is more important for Meta to train a coding AI than it is to operate its core business like Instagram, Facebook or Messenger reliably. Oh, did I mention that on Saturday (12 June) Facebook and Instagram had another SEV0, that is, a full-on outage?Before all this happened, Meta was on track to overtake Google as the world\u2019s #1 ads business by the end of the year. But for some reason, Mark Zuckerberg decided that building a coding LLM is more important.Meta\u2019s leadership is now trying to undo all the damage they have done. Wired reports that Meta CTO, Andrew Bosworth, admitted to staff that the AI reorg was atrocious and committed to better communication in the future.To me, it looks obvious that Zuckerberg doesn\u2019t care how engineers feel about the massive changes, and that Bosworth likely ignored the chaos, all while engineers know for a fact that the next AI model matters more than they do to the business. Bosworth also said that employees will have access to AI coaching tools. Very considerate, given the situation!Based on all I\u2019ve learned, Meta\u2019s engineering culture is dead because leadership has made it clear that engineering at the company is a cost center.Nice while it lastedNeedless to say, I hope my assessment is way off, but I\u2019ve seen nothing yet from Mark Zuckerberg and Alexandr Wang \u2013 the two executives creating this current mess \u2013 to suggest it is. There may be a short time period where, if major changes like data labeling assignments and staff tracking are undone, then things at Meta could return to normal. The longer the current conditions persist, the more tenured engineers will surely leave.It\u2019s tragic to see a technical founder at Meta so focused on AI that he neglects the engineers who built the heart of his company. But is Meta a one-off exception?Mitchell Hashimoto (creator of Ghostty, founder of HashiCorp) says he is seeing similar behavior by other founders (emphasis mine:)\u201cI strongly believe there are entire companies right now under heavy \u201cAI psychosis\u201d and it\u2019s impossible to have rational conversations about it with them. I can\u2019t name any specific people because they include personal friends I deeply respect, but I worry about how this plays out.I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now it\u2019s... the whole software development industry (maybe the whole world, really).It\u2019s frightening, because \u2018psychosis folks\u2019 operate under an almost absolute \u201cMTTR is all you need\u201d mentality: \u201cit\u2019s fine to ship bugs because the agents will fix them so quickly and at a scale humans can\u2019t do!\u201d We learned in infrastructure that MTTR is great but you can\u2019t yeet resilient systems entirely.The main issue is I don\u2019t even know how to bring this up to people I know personally, because bringing this topic up leads to immediate dismissals like \u201cno no, it has full test coverage\u201d, or \u201cbug reports are going down\u201d or something, which just don\u2019t paint the whole picture.We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happen so fast that nobody notices the underlying architecture decaying.I worry.\u201dThe takeover outage at Instagram was exactly like this: the engineering team dropped the quality bar for AI-generated and AI-reviewed code, probably expecting that they could recover quickly from failures. And they did indeed recover\u2026 after the damage was done, high-profile Instagram accounts were hacked, and the system was compromised, all very publicly.Mitchell highlights the specific concern of founders over-estimating the capabilities of AI, and consequently casting aside sensible safeguards when shipping code to production.Most of us probably have something to learn from the disastrous events at Meta caused by hyper-focus on AI to the exclusion of people who are the lifeblood of that company. In some good news, I\u2019m hearing that in the UK, some of the 10% layoffs have suddenly been cancelled: at the end of the mandatory consultation period, several infra and security teams are learning that no engineers on their team will be let go, as originally expected.Meta has a booming business, and is already a beneficiary of AI via increased ads revenue. Meanwhile, my Facebook feed is filled with fake, AI-generated videos, with hundreds of comments from bots and people who seemingly don\u2019t realize it\u2019s AI. It all seems like just more content for Meta to show ads next to.And yet, despite business booming, Meta\u2019s leadership has gone on a crusade to inflict the most damage possible on its engineering org. Apparently, they\u2019re now learning that most of it was pointless.If you\u2019re in a leadership position and feeling the temptation to make drastic org changes for AI-related reasons, take a deep breath and see where it left Meta. Meanwhile, If you\u2019re an engineer at a company whose leadership is over-indexing on AI, consider forwarding this article as additional context.If you\u2019re hiring standout engineers who are extremely hands-on with AI, then it\u2019s never been easier to get talent from Meta, than right now. Every engineer I know at the company is an extremely early adopter of AI, and knows how to build products and AI infra. These folks have soured on the company and its leadership. Meta\u2019s loss of talent will be the gain of other startups and the rest of Big Tech; it\u2019s one benefit of AI that\u2019s probably a bit unexpected \u2013 not least of all by Meta!It seems like the old mantra of \u201cmove fast and break things\u201d has now extended to Meta\u2019s engineering org itself, with the company\u2019s rush to over-invest in AI, so it will avoid missing the latest mega-trend in the tech industry." ], "output": "readability/", "pwd": "/data/archive/1782599161.650065", "schema": "ArchiveResult", "start_ts": "2026-06-27T22:26:40.429729+00:00", "status": "succeeded" } ], "screenshot": [], "singlefile": [], "title": [ { "cmd": [ "/usr/bin/curl", "--silent", "--location", "--compressed", "--proxy", "socks5://tor-socks-proxy:9150", "--max-time", "60", "--user-agent", "Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/128.0.0.0 Safari/537.36 ArchiveBox/{VERSION} (+https://github.com/ArchiveBox/ArchiveBox/)", "https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering" ], "cmd_version": "8.10.1", "end_ts": "2026-06-27T22:26:39.783186+00:00", "index_texts": null, "output": "Why is Meta destroying its engineering organization?", "pwd": "/data/archive/1782599161.650065", "schema": "ArchiveResult", "start_ts": "2026-06-27T22:26:39.723112+00:00", "status": "succeeded" } ], "wget": [] }, "icons": null, "is_archived": true, "is_static": false, "latest": { "archive_org": "https://web.archive.org/web/20260627222713/https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering", "dom": "output.html", "favicon": "favicon.ico", "git": null, "media": "ArchiveError: Failed to save media", "pdf": null, "screenshot": null, "singlefile": null, "title": "Why is Meta destroying its engineering organization?", "warc": null, "wget": null }, "link_dir": "/data/archive/1782599161.650065", "newest_archive_date": "2026-06-27T22:27:08.715203+00:00", "num_failures": 1, "num_outputs": 8, "oldest_archive_date": "2026-06-27T22:26:06.233641+00:00", "path": "/p/why-is-meta-destroying-its-engineering", "schema": "Link", "scheme": "https", "snapshot_abid": "snp_01KW5K15V40714A31E01SCH4XZ", "snapshot_id": "5cb9be91-df7e-4d65-a023-d25d32c893bf", "sources": [ "/data/sources/1782599159-import.txt" ], "tags": null, "tags_str": "", "timestamp": "1782599161.650065", "title": "Why is Meta destroying its engineering organization?", "url": "https://newsletter.pragmaticengineer.com/p/why-is-meta-destroying-its-engineering" }