A viral social media thread by VC industry figure Deedy Das has ignited a fierce debate about engineering productivity at some of America’s largest technology companies. This comes as one of many efficiency arguments after billionaires Elon Musk and Vivek Ramaswamy—co-heads of the Trump administration’s Department of Government Efficiency (or “DOGE”)—say they want to slash federal spending by $2 trillion.
The revelation? Many software engineers at major tech firms are making between $200,000 and $300,000 dollars annually while pushing as few as “two code changes a month.” The companies in question aren’t limited to small players or struggling startups. According to the viral thread, this pattern of low productivity appears at some of tech’s most established names, including: Oracle, Salesforce, Cisco, Workday SAP, IBM, VMware, Intuit, Autodesk, Veeva.
The “Quiet Quitting” Playbook VCs Discovered
What’s particularly striking is the systematic nature of low productivity. The viral thread outlines what it calls the “quiet quitting playbook,” a series of tactics from the perspectives of employees used to maintain the appearance of productivity while minimizing actual work:
- Marking oneself as “in a meeting” on Slack
- Scheduling communications (Slack messages, emails, and code commits) for off-hours to appear active
- Blocking out private calendar time
- Using mouse-movement software to appear online, which was even highlighted by VC icon Marc Andreesen in a recent Joe Rogan podcast episode, again going viral on X.com
- Artificially extending project timelines (claiming two weeks for one-day tasks)
- Citing unclear specifications as blockers
- Making numerous small code refactors
- Blaming build system issues
- Claiming blockages from other teams
- Using technical jargon about “race conditions” to justify delays
- Deflecting tasks by requesting formal ticketing (“can you create a JIRA for that?”)
The Scale of the Issue and Lesson from Twitter after Elon Musk Fired 80% of the Workforce
The impact of this phenomenon becomes clear when considering the historical precedent set by major tech companies. As noted in the thread, “Most people in tech were never surprised that Elon could lay off 80 percent of Twitter,” suggesting that similar workforce reductions might be possible at various large tech organizations without proportional drops in productivity. Industry veterans responding to the thread point to management oversight as a critical issue. As one HR professional noted on X, “Quiet quitting is only ‘quiet’ if management isn’t paying attention.” This suggests that ineffective management structures may be enabling the continuation of these practices.
The Amplification Effect of Modern Tools, Poor Job Market
The situation may be getting easier to maintain with modern technology. As one commenter points out, developers can now use AI to “write unit tests, refactor useless functions, add minor improvements that look ‘big’ in commits to non-tech managers. One of the more nuanced observations from the discussion suggests that the issue isn’t just about individual underperformers. As one commenter noted, “A big part of the reason why this works is all the people who work full time yet fail to be much more productive than the quiet quitters.” This points to a broader question about how we measure and value productivity in software development.
In a deeper exploration of this trend David Stepania, a bootrstaped Bay Area-based AI founder, highlighted in his LinkedIn post that conversations with over a dozen tech recruiting veterans with 30+ years of experience reveal a more systemic transformation. The tech industry is experiencing what can only be described as the “White Collar Recession,” a transformative period marked by mass offshoring, AI-driven productivity, and a ruthless efficiency paradigm that has turned traditional employment models on their head. With major players like Microsoft achieving a staggering $50 billion revenue growth while maintaining a flat headcount, and Meta multiplying its market capitalization by four while simultaneously reducing its workforce by 20%, we are witnessing the most profound restructuring of knowledge work since the internet’s inception.
The numbers are stark: hiring freezes are ubiquitous, salaries have plummeted 30-40%, quality candidates face systematic rejection, and 6-month job searches have become the new normal. This isn’t merely a tech phenomenon—banking, media, marketing, and other white-collar sectors are rapidly adopting a similar playbook of aggressive optimization. As companies increasingly plan 10-15% annual US headcount reductions, leveraging AI and global talent pools, the fundamental question remains: Are we experiencing the birth of a new economic paradigm, or is this just another cyclical boom-and-bust tech narrative?
Looking Forward: Tech Productivity during the AI Boom Remains An Open Question
The viral nature of this discussion – garnering hundreds of thousands of views and thousands of engagements – suggests this is a topic that resonates deeply within the tech industry. While some respondents argued that certain described behaviors are simply “normal dev processes,” the broader conversation points to a growing awareness of potential inefficiencies in how large tech companies structure and manage their engineering teams.
It’s worth noting that not everyone agrees with the characterization. Some developers in the thread argued that what appears as “quiet quitting” might sometimes be normal development processes being misinterpreted by those outside the technical realm. As one respondent noted, it’s “basically the ‘water is bad because Hitler drank water’ argument,” suggesting that some of these behaviors might be normal aspects of software development when viewed out of context.
The revelation of these practices, the significant engagement with the discussion as well as the fact that 28% of global employees are ready to change jobs, suggests that the tech industry may be at an inflection point regarding how it evaluates and measures engineering productivity.
In 2024, were AI is revolutionizing the way we work and we see startups founded by teenagers moving at lightning-fast speed, employees of VC-funded scale ups and public companies will have to face new realities.
On the other hand, as companies face increasing pressure to optimize their operations and reduce costs, the conversation about engineering team efficiency is likely to continue evolving.
