Automating industrial labour is the key to unlocking Star Trek levels of prosperity. In a recent interview, Scott Phoenix, the co-founder of the AI/robotics startup Vicarious, paraphrased a client’s observation about industrial labour:

T here’s no such thing as material costs. There’s only labour costs. The material is just stuff in the ground that you need to pull out with labour, and move around with labour.

When raw materials are abundant and accessible in the Earth’s crust, the cost of the materials is just the cost of mining them. Automating mining can, in theory, bring down the cost of raw materials.

So too with manufacturing: the process of transforming raw materials into a finished product. The cost of manufacturing is the cost of labour: human labour and the “labour” of machines, and the indirect human labour required to support both. Automating manufacturing can, in theory, bring down the cost of finished goods.

After manufacturing comes freight transport, warehousing, and delivery. Autonomous freight trucks, warehouse robots, and autonomous delivery vans, drones, and robots can automate these processes too. The whole sequence from raw materials in the ground to a finished product in the customer’s hand could, in theory, be fully automated. So, over the long term, the cost of finished goods depends largely on progress in AI and robotics.

If we want to live in a world of universal material prosperity like Star Trek, AI and robotics is how we’re going to get there. Jobs will become obsolete, sure. But it was only 230 years ago that 90% of the U.S. workforce were farmers. Either new jobs will be created to replace the old, as has happened in the past, or they won’t.

If we run out jobs permanently, then we’ll need a policy response. Perhaps a universal basic income based on the cost of a comfortable life, or based on a share of a country’s GDP. This would give each person the freedom to pursue whatever they want in life — whether that’s competitive gaming, a life of prayer or meditation, making music, or starting a company. I think this is a good outcome. That’s a society I want to live in.

Another idea is to create massive government programs that employ people in jobs with a social good, but no short-term profitability. Hundreds of millions of people worldwide could be employed in science, philosophy, and art. Public service, education, medicine, and social work could draw on an expansive labour pool. The more labour is freed up from mining, manufacturing, freight transport, warehousing, and delivery, the more labour that we can consume in the form of service to our communities, in the form of creativity and research, and as therapy, care, and healing.

So, here are two visions of a highly automated future without jobs: one with individual freedom and one with government paying workers to perform socially beneficial tasks. These two ideas can be mixed together, too. We could have a universal basic income and government jobs that provide extra income.

There’s also a highly automated future with jobs: one where new needs and wants for human heads and hands rush in to replace the old ones once they’re satisfied by machines. This is a good future, too. It’s an organic, market-driven version of the government-driven ideas above.

Rather than seeing automation as a threat, we should see it as a beautiful, exciting opportunity to increase the economic prosperity of human civilization. We should prepare a policy response in case jobs start disappearing and aren’t replaced, or if the newly unemployed need extra help making it from obsoleted jobs to new jobs. But we should understand that, with the right policy response, automation is a good thing. It can enable us to live in a world where we are free to pursue meaning, purpose, creativity, imagination, possibility, passion, spirit, and love — not just economic self-sustenance.

Automation has been misportrayed as an emissary of economic strife. Really, automation is the cure for economic strife. Perhaps recent political paralysis and dysfunction in the U.S. has left Americans feeling hopeless about government’s ability to deploy an effective policy response to any crisis. And the U.S. has long had an allergy to major redistributions of wealth, such as universal healthcare. Maybe that’s why so many Americans despair about automation. But this is a problem with U.S. government, and U.S. government specifically (as opposed to say, Canadian government), not a problem inherent to labour automation. U.S. government vetocracy will create panic and despair anytime there is a crisis to deal with — an opioid crisis, a climate crisis, or a labour crisis. Americans, don’t lay your structural political problems at the feet of robots.

I’m going to zoom in from this big picture, aerial view of automation down to one particular product: the Tesla Model Y. The Model Y is scheduled for production sometime in 2020, and Tesla CEO Elon Musk recently expressed his intention to make the Model Y production system a “manufacturing revolution”. Elon has been going back and forth on how much to simply copy the existing manufacturing process for the Model 3 versus trying something new, untested, and ambitious. For now at least, it sounds like Elon wants to do the latter. Details are supposed to be announced later this year when the Model Y is unveiled.

From a business standpoint, there is a good argument to be made for either choice. Copying the Model 3 production system would presumably minimize delays, an unforeseen run-up in costs, and the risk that the new system just won’t work. It would, in theory, allow Tesla to quickly and assuredly launch its crossover SUV version of the super popular Model 3 sedan. Since crossovers are more popular than sedans, it stands to reason that the Model Y will be even more popular than the Model 3.

On the other hand, innovation in manufacturing automation can reduce costs, increase production speed, and provide an avenue of sustainable competitive advantage for Tesla. By fusing its competence in car manufacturing and its competence in AI, Tesla can create a combination that is unique in the world: a car factory designed by a Silicon Valley AI company. This is so much more exciting to me than getting the Model Y to market sooner, more cheaply, and with less technology risk. It’s more exciting to me both as a Tesla investor, and as a human being living in the post-Industrial Revolution, pre-Star Trek era of our civilization.

I don’t care if the Model Y takes two extra years to make. If Tesla can use innovations in AI and robotics to make the Model Y production system a “manufacturing revolution”, it’s worth it. This isn’t just important for the company, or for the auto industry. It’s important for humanity. Tesla has served as the proof of concept for electric cars, and in so doing it has catalyzed the whole auto industry to transition from gasoline to electric propulsion. A proof of concept for a new level of factory automation would probably have a similar catalyzing effect. Manufacturers across the world, throughout industries, would want to emulate Tesla.

Why should we believe this dream is possible? That’s a fair question. It might not be. There is no guarantee it will work. But without risk, there is no innovation.

Some people argue that it is foolish to even try for two reasons. The first reason is that a new level of factory automation was already tried by GM and it failed abjectly. The second reason is that, supposedly, folks in the auto industry do not think it is possible—perhaps for the first reason.

The first argument is not credible, in my opinion. GM tried fully automating manual or semi-automated production processes in the 1980s. That’s ancient history in the timeline of AI and robotics. The technologies used back then are not the technologies used today. This case study is as irrelevant as the observation that you can’t go to space with a steam engine. The failure of steam engine-based space travel in the Victorian era would tell you nothing about the feasibility of the Apollo program. GM’s failure in the 1980s is not instructive as to Tesla’s chances of success in the 2020s.

Deep learning only gained prominence in 2012, and only as recently as 2015 outperformed the human benchmark on the ImageNet Challenge for image classification. The advancements that embolden AI and robotics proponents today are all very recent.

We should split AI into two eras, like we split history into B.C. and A.D.: there should be the pre-deep learning era prior to 2012, and the deep learning era of 2012 onward. This helps disambiguate all the various things people mean when they say “AI”.

To illustrate the difference, see how much object detection has changed from the pre-deep learning era to the deep learning era: