The Sunk Cost of Loud Working
This week the internet has been abuzz with the term “Loud Working.”
This week the internet has been abuzz with the term “Loud Working.” The term is a play on “Quiet Quitting” which trended hard a few years back. It referred to people who decided a job was not worth the compensation, so they simply stopped putting in effort and quietly looked around for a new and better job – one where they would be appreciated.
I am always fascinated by the rubber band effect you can observe in the pattern of trends – meaning you have one trend, and the next trend is a reaction to that trend, in many cases going the opposite direction of the original trend. The rubber band snaps back. The pendulum swings. There are really endless metaphors applied to this phenomenon.
Despite the rubber band effect, “Loud Working” is not a reaction to too much “Quiet Quitting.” It's a reaction to endless rounds of layoffs, and general anxiety that jobs will be replaced by AI. The result is lots of employees making sure they are visibly and clearly demonstrating value to their higher-ups. During the next round of layoffs, they will be safe as long as they have clearly demonstrated more value than their coworkers – in theory.
Most of the articles I’ve seen bouncing around the interwebs this week say loud working is now a necessity for career success. I disagree. I think working louder will more likely just result in sunk cost – a term for money or time already spent that doesn’t really have any relevance to decisions about the future.
My advice is to think critically about what actually has value to your employer moving forward. If you get caught up in performing value – especially value as you knew it before AI – then you might be too busy putting on a show to actually add any.
AI is often taking over the details and the grind – the bulk of production work. On a software team that’s the job of the software engineer: write code to match the spec. But AI has accelerated the coding process so much, I am seeing a lot more expectation from leadership around what a coder can contribute. Does this fit the users needs? Is the product delivering the value it promises. These were important questions before, but generally handled by product managers, or maybe handed off to a whole other team of experts to analyze and address. That meant more specialists, each one another head count and another salary.
With AI in the picture, I’m noticing some engineers step up because they finally have the time. They can help with the big picture instead of staying in their usual corner – a different kind of extra mile.
Who do you think would be more likely to make it through the next round of cuts? The person coding eighty hours per week on a product that got lukewarm reviews? Or the person who understood what the business needed, suggested features customers are raving about, and lifted up the whole team instead of burning out for show?
Whether you are in tech or not, a shift in the paradigms that define our workforce is here. Don’t just think about what was valuable before. Think about what is possible with the time that AI frees up for you in your own job — your own niche. And maybe you are thinking, “but I don’t know how my team leadership approaches those things?” Well, luckily AI is also good at breaking down processes you might not be familiar with. Ask it. Get out of your comfort zone. See what you can add to your team, and I’m willing to bet those authentic contributions will not go unnoticed by the people in charge at your company – those contributions are worth far more than staying late while they’re watching.