If you squint a little, you can see the Apple Store clerk as a cyborg, a hybrid of human and machine. Each store is flooded with smartphone-wielding salespeople who are able to help customers with everything from technical questions and support to purchase and checkout. There are no cash registers with lines of customers waiting with products pulled from the piles on the shelves. The store is a showroom of products to explore. When you know what you want, a salesperson fetches it from the back room. If you’re already an Apple customer with a credit card on file (and as of 2014, there were 800 million of us), all you need to provide is your email address to walk out the door with your chosen product.

Rather than using technology to eliminate workers and cut costs, Apple has equipped them with new powers in order to create an amazing user experience. By so doing, they created the most productive retail stores in the world.

Even the very first advances in civilization had this cyborg quality. The marriage of humans with technology is what made us the masters of other species, giving us weapons and tools harder and sharper than the claws of any animal, projecting our strength at greater and greater distance until we could bring down even the greatest of beasts in the hunt, not to mention engineer new crops that produce far more food than their wild forebears, and domesticate animals to make us stronger and faster.

In short, there are two types of augmentation, physical and mental, in a complex dance. One frontier of augmentation is the addition of sensors to the physical world, allowing data to be collected and analyzed at a previously unthinkable scale. That is the real key to understanding what is often called the “Internet of Things.” Things that once required guesswork are now knowable. (Insurance may well be the native business model of the “Internet of Things” in the same way that advertising became the native business model of the internet, because of the data-driven elimination of uncertainty.) It isn’t simply a matter of smart, connected devices like the Nest thermostat or the Amazon Echo, the Fitbit and the Apple Watch, or even self-driving cars. It’s about the data these devices provide. The possibilities of the future cascade in unexpected ways.

When Monsanto bought Climate Corporation, the big-data weather-insurance company founded by former Google employees David Friedberg and Siraj Khaliq, and paired it with Precision Planting, the data-driven control system for seed placement and depth based on soil composition, they showcased that the new focus of productivity in agriculture is in data and control. Less seed, less fertilizer, and less water are needed when an eye in the sky can tell the farmer with precision the state of his land and the progress of his crop, and automatically guide his equipment to act on that knowledge.

This is true in engineering and materials science as well. The inventor Saul Griffith has told me, “We replace materials with math.” One of Saul’s companies, Sunfolding, sells a sun-tracking system for large-scale solar farms that replaces steel, motors, and gears with a simple pneumatic system made from an industrial-grade version of the same material used for soft-drink bottles, at a tiny fraction of the weight and cost. Another project replaces the giant carbon-containment vessels for natural-gas storage with an intestine of tiny plastic tubules, allowing natural-gas tanks to fit any arbitrary shape as well as reducing the risk of catastrophic rupture. It turns out that when you properly understand the physics, you can indeed replace materials with math.

The new design capabilities go hand in hand with new manufacturing techniques like 3-D printing. 3-D printing doesn’t just provide low-cost prototyping and local manufacturing. It makes possible different kinds of geometries than traditional manufacturing. That requires software that encourages human designers to explore possibilities far afield from the familiar. The future is not just one of “smart stuff,” tools and devices infused with sensors and intelligence, but of new kinds of “dumb stuff ” made with smart tools and better processes for making that stuff.

Autodesk, the design-software firm, is all over that concept. Their next-generation tool set supports what is called “generative design.” The engineer, architect, or product designer enters a set of design constraints—functionality, cost, materials; a cloud-based genetic algorithm (a primitive form of AI) returns hundreds or even thousands of possible options for achieving those goals. In an iterative process, man and machine together design new forms that humans have never seen and might not otherwise conceive.

Most intriguing is the use of computation to help design radically new kinds of shapes and materials and processes. For example, Arup, the global architecture and engineering firm, showcases a structural part designed using the latest methods that is half the size and uses half the material, but can carry the same load. The ultimate machine design does not look like something that would be designed by a human.

The convergence of new design approaches, new materials, and new kinds of manufacturing will ultimately allow for the creation of new products as astonishing as the Eiffel Tower was to the world of 1889. Might we one day be able to build the fabled space elevator of science fiction, or Elon Musk’s Hyperloop transportation system?

The fusion of human with the latest technology doesn’t stop there. Already there are people trying to embed new senses—and make no mistake of it, GPS is already an addition to the human sensorium, albeit still in an external device—directly into our minds and bodies.

Might we one day be able to fill the blood with nano-bots—tiny machines—that repair our cells, relegating the organ and hip replacements of today, marvelous as they are, to a museum of antiquated technology? Or will we achieve that not through a perfection of the machinist’s art but through the next steps in the path trod by Luther Burbank? Amazing work is happening today in synthetic biology and gene engineering.

George Church and his colleagues at Harvard are beginning a controversial 10-year project to create from scratch a complete human genome. Ryan Phelan and Stewart Brand’s Revive and Restore project is working to use gene engineering to restore genetic diversity to endangered species, and perhaps one day to bring extinct species back to life. Technologies like CRISPR-Cas9 allow researchers to rewrite the DNA inside living organisms.

Neurotech—direct interfaces between machines and the brain and nervous system—is another frontier. There has been great progress in creating prosthetic limbs that provide sensory feedback and respond directly to the mind. On the further edges of innovation, Bryan Johnson, the founder of Braintree, an online payments company sold to PayPal for $800 million, has used the proceeds to found a company whose goal is to build a neural memory implant as a cure for Alzheimer’s disease. Bryan is convinced that it’s time for neuroscience to come out of the labs and fuel an entrepreneurial revolution, not merely repairing damaged brains but enhancing human intelligence.

Bryan is not the only high-profile neurotech entrepreneur. Thomas Reardon, the creator of Microsoft’s Internet Explorer web browser, retired from Microsoft to pursue a Ph.D. in neuroscience and in 2016 cofounded a company called CTRL-Labs to produce the first consumer brain-machine interface. As Reardon noted in an email to me, “Every digital experience can and should be controlled by the neurons which deliver the output of your thoughts, those neurons which directly innervate your muscles.” This is a brilliant combination of neuroscience and computer science. “The kernel of our work is held in the machine-learning models which translate biophysical signals—yes, even at the level of individual neurons—to give you control over digital experiences.”

Elon Musk joined the parade in 2017 with a company called Neuralink that is, according to Elon, “aiming to bring something to market that helps with certain severe brain injuries (stroke, cancer lesion, congenital) in about four years.” But as Tim Urban, the author of the Wait But Why blog, who was given extensive access to the Neuralink team, explains, “When Elon builds a company, its core initial strategy is usually to create the match that will ignite the industry and get the Human Colossus working on the cause.” Proving that a profitable, self-sustaining business can be created in an untried area is a way to get everyone else piling onto the new opportunity. That is, like Bryan Johnson, Elon’s vision is not just to build a company, but to build a new industry.

In the case of Neuralink, that new industry is a generalized brain-machine interface that would allow humans and computers to interoperate far more efficiently. “You’re already digitally superhuman,” Elon notes, referring to the augmentation that our digital devices already give to us. But, he notes, our interfaces to those devices are painfully slow—typing on keyboards or even speaking aloud. “We should be able to improve that by many orders of magnitude with a direct neural interface.”

These technologies raise questions and fears as profound as any in the world of artificial intelligence. Like other tools of enormous power, they may come into common use through a tumultuous, violent adolescence. Yet I suspect that in the end, we will find ways to use them to make ourselves live longer, happier, more fulfilled lives.

AI is not some kind of radical discontinuity. AI is not the machine from the future that is hostile to human values and will put us all out of work. AI is the next step in the spread and usefulness of knowledge, which is the true source of the wealth of nations. We should not fear it. We should put it to work, intentionally and thoughtfully, in ways that create more value for society than they disrupt. It is already being used to enhance, not replace, human intelligence.

“We’ve already seen chess evolve to a new kind of game where young champions like Magnus Carlsen have adopted styles of play that take advantage of AI chess engines,” notes Bryan Johnson. “With early examples of un-enhanced humans and drones dancing together, it is already obvious that humans and AIs will be able to form a dizzying variety of combinations to create new kinds of art, science, wealth, and meaning.”

Like Elon Musk, Bryan Johnson is convinced that we must use neurotech to directly enhance human intelligence (HI) to make even more effective use of AI. “To truly realize the potential of HI+AI,” he says, “we need to increase the capacity of people to take in, process, and use information, by orders of magnitude.” But even without direct enhancement of human intelligence in the way that Bryan envisions, entrepreneurs are already building on the power of humans augmented by AI.

Paul English, the cofounder of Kayak, the travel-search site that helped put many travel agents out of work, has a new start-up called Lola, which pairs travel agents with an AI chatbot and a back-end machine-learning environment, working to get the best out of both human and machine. Paul describes his goal with Lola by saying, “I want to make humans cool again.” He is betting that just as a human chess master paired with a chess computer can beat the smartest chess computer or the smartest human grand master, an AI-augmented travel consultant can handle more customers and make better recommendations than unaugmented travel agents—or travelers searching for deals and advice on their own using traditional search engines.

The arc between travel agents and Kayak and Lola, the embedding of what was once the specialized knowledge of a travel agent into ever-more-sophisticated tools, teaches us something important. Kayak used automation to replace travel agents with search-enabled self-service. Lola puts humans back into the loop for better service. And when we say “ better service,” we usually mean “more human, less machinelike service.”

Sam Lessin, the founder and CEO of Fin, an AI-based personal-assistant start-up, makes the same point: “People in the technology community frequently ask me ‘how long will it take to replace the Fin operations team with pure AI?’” he wrote in an email. “At Fin, however, our mission is not automation for its own sake. Our guiding principle is providing the best experience for users of Fin ... Technology is clearly part of the equation. But people are also a critical part of the system that results in the best possible customer experience. And the role of technology at Fin is largely to empower our operations team to focus their time and effort on the work that requires decidedly human intelligence, creativity, and empathy.”

In addition to enabling better, more human service, automation can expand access by making other jobs cheap enough to be worth doing. After receiving what he believed was an unfair parking ticket, Josh Browder, a young British programmer, took a few hours to write a program to protest the ticket. When the ticket was cleared, he realized he could turn this into a service. Since then, DoNotPay, which Josh calls “the robot lawyer,” has cleared more than 160,000 parking tickets. Josh has since moved on to building a chatbot in Facebook Messenger to automate the application for asylum in the United States, Canada, and the United Kingdom on behalf of refugees.

There are many jobs—like protesting unfair parking tickets—that don’t get done because they are too expensive, and making the job cheaper conflicts with the business model of existing companies. Tim Hwang, a programmer who is also trained as a lawyer, told me that when he worked at a law firm, he set out to make himself obsolete. “Every day, I’d get a set of tasks to do, and each night I’d go home and write programs to do them for me the next time I got asked to do them,” he said. “I got more and more efficient at doing the work more quickly, and this started to become a problem for the law firm because their business model depends on billable hours. I quit just ahead of getting fired.”


This post is adapted from O’Reilly’s recent book, WTF? What's the Future and Why It's Up to Us.


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