Machines will not be your new overlords. You will not have to answer to an artificial manager anytime soon. And we are nowhere near loading a human consciousness into a superior mechanical body. Your worst nightmares inspired by Hollywood fever-dreams are not going to be coming true in your lifetime, and possibly not anyone’s lifetime.
That said, machines are a real and vital part of the workplace. It is very possible that you will lose your job to one at some point. Even now, many jobs are being filled by machines. It is not just that humans once did these jobs and now they don’t. It is that many of these jobs wouldn’t have existed without machines.
It sounds worse than it is. The jobs being filled by machines free us up to do more important jobs that machines are not good at. As more machines come online, there will be better jobs for humans and even more opportunities for a better and more fulfilled life. It can’t come soon enough. There are a few things we can do to shepherd the process along. Here are the components to keep at top of mind:

Built from Quality Parts

From PCB antennas to oscillators, the machine will never exceed the quality of its best component. It is the builder’s job to make sure even the smallest component is the highest possible quality. It is often said that the most important part of the plane is the rudder. In high-tech, industrial machinery, the key components are often much smaller compared to the whole. Consider this description of oscillators from Suntzu:
A Crystal Oscillator is an electronic circuit that uses the mechanical resonance of a vibrating crystal of piezoelectric material to create an electrical signal with a very precise frequency. This frequency is commonly used to keep track of time, to provide a stable clock signal for digital integrated circuits, and to stabilize frequencies for radio transmitters and receivers.
Your machine is useless without stable frequencies. Timing at the smallest increment makes the difference between success and failure. From surgical robots to military communications satellites, uncompromising precision starts with the innermost parts you can’t see, and flow from there. Tomorrow’s machines will need to be even more exacting than the mechanical marvels of today.

Machine Learning

Unlike humans, machines don’t have to grow up. But they do have to learn. Machine learning (ML) is not a threat to humankind. If anything, it is the salvation. For humans to thrive, machines have to win. As we continue to evolve, we find ourselves facing challenges for which we are not well-equipped. Fortunately, with a little help, our machines are.
At the beginning of the pandemic, we had an even bigger crisis due to the lack of PPE supplies. Companies turned to ML to deal with the supply chain problem. Countless lives were saved because we had the ability to find 60,000 PPE manufacturers with the proper certifications within minutes. It has never been more critical that we train our computers with the best and most complete possible ML models. It is imperative we factor our human prejudices from those models so that the machines will be a force for good for all people and not just a small subset.

Adopt Humanity-First Policies

It is very possible that humans could join the million species of plants and animals in danger of going extinct right now. We have had 5 periods in history where extinctions were particularly pronounced. The way humans will go extinct is by placing profits, politics and personal preferences over people. If we don’t develop a human-first policy, the machines won’t, either.
We never have to worry about machines displacing humans from jobs because they will be deployed to help humans work better. We don’t have to worry about machines waking up and purging the human virus because they will be dedicated to helping us improve the human condition. And we will not have to fear the digital divide if we democratize technology so that it is a rising tide for everyone.
The machine future is not to be feared, but to be embraced. We do that by insisting on uncompromising quality, better machine learning, and human-first policies.