Musk's Way Of Streamlining Tech Startups Is Wrong For Government

It's true that eliminating waste is foundational to industrial and systems engineering. It's an approach often associated with the Lean production philosophy , which has its roots in post-war Japan. A fundamental tenant of Lean is that waste should be identified by workers and leaders should support them in eliminating wasteful tasks. Unlike Musk's top-down productivity algorithm, it's designed to be a bottom-up approach.

Musk's approach was developed for tech startups where failing is expected, common and largely inconsequential to everyone but stockholders. If SpaceX doesn't get humans to Mars, it's inconsequential for most people. If Tesla, PayPal or Twitter/X fail, alternatives will fill the void.

However, this model doesn't easily translate to government, where failure has more direct, far-reaching consequences on people's lives.

Musk's efficiency-driven approach has had a notable impact on the companies he's led. Shortly after taking over Twitter/X, Musk went from eliminating tasks to eliminating people. Over the course of roughly a year, Musk laid off approximately 80 per cent of Twitter's staff .

Since identifying“wasteful” employees is more complex than cutting unnecessary tasks, new tools were needed. Software engineers were asked to submit code for evaluation , but when this didn't result in deep enough cuts, employees were given an ultimatum: those who didn't opt in to keep their jobs would be fired , placing the onus on workers to declare their willingness to stay.

A similar approach was used at the FBI in February . In an email, Musk instructed federal workers to explain what they had done in the past week with a warning that non-responses would be treated as a resignation. In less than 48 hours, this was quashed and responses were made voluntary.

This startup mentality of“failing fast” also didn't translate well at the National Nuclear Security Administration, where a rapid round of firings led to concerns that national security was being jeopardized. Within 48 hours, most of the firings were rescinded and 322 of 350 fired employees were hired back.

Similarly, at USAID, DOGE-led firings“accidentally” cut Ebola prevention during an outbreak in Uganda - a mistake that could have had catastrophic consequences.

One of the flaws in Musk's efficiency algorithm applied to people is the assumption that fired employees can always be rehired if needed. But people are not tasks that can be removed and replaced without consequence.

The National Nuclear Security Administration struggled to contact dismissed personnel. At the US Food and Drug Administration , one of the fired scientists asked,“How are you going to be able to hire good people when you're not offering Silicon Valley stock or pay and you've taken away their stability?”

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While this method may have worked in the fast-paced, high-reward world of tech startups, its application in government has been chaotic at best and dangerous at worst. Furthermore, early reports indicate the cuts are hardly making an impact on spending .

No luxury of trial and error

Lean manufacturing has often been described as transformative and credited with turning ailing companies into fierce streamlined competitors, but Musk's version of efficiency engineering lacks consideration of long-term consequences.

Even apostles of Lean would not call it disruptive or take an overzealous“shoot first and ask questions later” approach. Efficiency is not synonymous with cutting; it should be implemented with foresight, careful attention to value creation and consultation with those involved.

Musk in his approach to government so far seems more like the merciless corporate downsizer that George Clooney plays in Up in the Air than any real-life efficiency pioneers such as Henry Ford , Joseph Juran or Apple CEO Tim Cook .

Government agencies don't operate like tech startups, public servants are not tasks and public services don't have the luxury of trial and error when national security or public health are on the line.

Peter Vanberkel is a professor in the Department of Industrial Engineering at Dalhousie University .

This article is republished from The Conversation under a Creative Commons license. Read the original article .

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