Is the red pill / blue pill approach our best idea for efficiency?

Over two million federal workers facing an uncertain future weighed heavily on me all day today. I had so many questions but no real answers and the crushing feeling of helplessness.

Below is a brief version for context in case you missed it.

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WASHINGTON ― Thursday was supposed to be decision day. More than 2 million federal employees across the country faced an end-of day deadline to choose whether to resign or stay in their jobs.

But a federal judge in Massachusetts on Thursday afternoon temporary blocked the buyouts, suspending the deadline until at least Monday, when a hearing has been set.

In a push to drastically reduce the workforce, President Donald Trump last week offered a mass buyout plan to the entire federal workforce, setting off fear and anxiety among federal workers as they contemplate what to do.

The judge's decision in a court showdown over the legality of the buyouts has now created more uncertainty.

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Having done multiple AI, data science transformation at huge scale, the learning over and over has been that no amount of speed in implementing technological change can just work overnight without learning from the domain experts. Most new tech implementation will only work 60% to 70% of the time. To get to 90% or higher levels the domain experts are critical and this is where a wonderful symbiotic exchange happens; the machine benefits from humans expertise in dealing with nuances and anomalies and the humans start to acquire new skills that help evolve these jobs. I love this ATM story because as financial institution today face another major shift where consumers have gone completely cashless it is insightful to learn how they have constantly evolved with time. I am a huge fan of borrowing learning across domains. I wonder what can the government learn from these massive high stake institutions that have successfully evolved over decades.

I don’t quite understand the rush to suddenly leave the future of 2 million workers completely up in the air. Efficiency cannot be bought overnight or in a few months with a red pill/blue pill approach. What if we flip the situation around and look at it with the lens of there are over two million people willing to work, what can we do if those two million folks can work with 10x more powerful machines? Why do we always consider cutting the human side of the equation in the name of efficiency? We have finite human resources, efficiency could also mean that we give the same people better tools for them to do more and support their growth. Why not rethink the jobs with technology being far more accessible, AI models becoming commoditized? Above all, why are layoff so normalized now?