Why is Meta destroying its engineering organization?

Hi – 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:Many subscribers expense this newsletter to their learning and development budget. If you have such a budget, here’s 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’s existence, it had a “move-fast-and-break-things” culture, and in the early 2020s this shifted to a “move-fast-with-stable-infra” 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’ve been sharing how bad things are inside the social media company for engineers in one of Silicon Valley’s most prestigious workplaces. In this article, we walk through what’s happened, and ask what’s 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’s 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 it just Meta, or are other companies also acting irrationally?I’d split Meta’s engineering culture into two eras: “move fast and break things”, and then “move fast with stable infra.”In the 2010s, Facebook’s 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’ desks.

Presented with retro propaganda design, it was dubbed the “little red book”, co-opting the name of a famous volume of the thoughts of Chairman Mao, (1964).At around 70 pages long, Facebook’s version codified its engineering culture: speed, fearlessness, taking ownership, and thinking outside of the box.Back then, mantras in Facebook’s 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’t 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’s Little Red BookIn 2022, I did what is one of the longest deepdives we’ve published on the topic of Meta’s 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’s how I described Meta’s engineering culture then:“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’t even try to compare it to Amazon’s engineering culture and the countless formal processes there. But even compared to companies like Google, Microsoft or Uber, Facebook’s 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’ll 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’s not the case at Google or Apple. There are good examples of smaller companies like Cloudflare, but they’re 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.”Also, 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 – and the metaverse – could be massive. Billions was spent on ensuring Meta would be the market leader in this space. But once again, VR didn’t 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’t miss it: he assembled the internal FAIR group (Fundamental AI Research team), and released a series of open-weight AI models:Llama 1: released in Feb 2023, three months after ChatGPTLlama 2: in June 2023Llama 3: in April 2024. This model was Meta’s 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 Scale AI to reboot its AI efforts for a whopping $14.8B, and brought Scale AI’s CEO, Alexandr Wang to take over Meta’s 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, it’s pretty clear that Meta – and Zuckerberg – 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’s 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 “human in the loop” 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’s 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’s new AI.

There’s 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’re 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’s concerns there are finally being heard:“Meta is dialing back elements of its plan to collect employee mouse movements, keystrokes and other actions for use as AI training data, it said in an internal memo on Tuesday, following weeks of angry pushback from staffers.New controls will allow employees to pause the data collection for up to 30 minutes at a time and request exemptions from the initiative, according to the memo, authored by Stephane Kasriel, a vice president in Meta’s AI model-building Superintelligence Labs unit.”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).“Forceful” reassignment is very relevant here because of Meta’s 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’s 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’ tenure knows that previously they chose what to work on, and of course, could pick the most impactful thing to work on.