Alan Kay is widely known for the credo, “The best way to predict the future is to invent it.” For him, the phrase is not just a witty quip; it is a guiding principle that has yielded a long list of accomplishments and continues to shape his work.
Kay was a ringleader of the exceptional group of ARPA-inspired scientists and engineers that created an entire genre of personal computing and pervasive world-wide networking. Four decades later, most of the information-technology industry and much of global commerce depends on this community’s inventions. Technology companies and many others in downstream industries have collectively realized trillions of dollars in revenues and tens of trillions in market value because of them.
Alan Kay made several fundamental contributions, including personal computers, object-oriented programming and graphical user interfaces. He was also a leading member of the Xerox PARC community that actualized those concepts and integrated them with other seminal developments, including the Ethernet, laser printing, modern word processing, client-servers and peer-peer networking. For these contributions, both the National Academy of Engineering and the Association of Computing Machinery have awarded him their highest honors.
I’ve worked with Alan to help bring his insights into the business realm for more than three decades. I also serve on the board of Viewpoints Research Institute, the nonprofit research organization that he founded and directs. Drawing on these vantage points and numerous conversations, I’ll try capture his approach to invention. He calls it a method for “escaping the present to invent the future,” and describes it in seven steps:
- Smell out a need
- Apply favorable exponentials
- Project the need 30 years out, imagining what might be possible in the context of the exponential curves
- Create a 30-year vision
- Pull the 30-year vision back into a more concrete 10- to 15-year vision
- Compute in the future
- Crawl your way there
Here’s a summary of each step:
1. Smell out a need
“Everybody loves change, except for the change part,” Kay observes. Because the present is so vivid and people have heavy incentives to optimize it, we tend to fixate on future scenarios that deliver incremental solutions to existing problems. To reach beyond the incremental, the first step to inventing the future is deep “problem finding,” rather than short-term problem solving. Smell out a need that is trapped by incremental thinking.
In Alan’s case, the need that he sensed in the late ’60s was the potential for computers to redefine the context of how children learn. Prompted by conversations with Seymour Papert at MIT and inspired by the work of Ivan Sutherland, J.C.R. Licklider, Doug Engelbart and others in the early ARPA community, Kay realized that every child should have a computer that helps him or her learn. Here’s how he described the insight:
It was like a magnet on the horizon. I had a lot of ideas but no really cosmic ones until that point.
This led Kay to wonder how computers could form a new kind of reading and writing medium that enabled important and powerful ideas to be discussed, played with and learned. But, the hottest computers at the time were IBM 360 mainframes costing millions. The use of computers in educating children was almost nonexistent. And, there were no such things as personal computers.
2. Apply favorable exponentials
To break the tyranny of current assumptions, identify exponential improvements in technological capabilities that could radically alter the range of possible approaches.
In 1965, Gordon Moore made his observation that computing would dramatically increase in power, and decrease in relative cost, at an exponential pace. Moore’s prediction, which would become known as Moore’s Law, was the “favorable exponential” that Kay applied.
Today, the fruits of Moore’s Law such as mobile devices, social media, cloud computing, big data, artificial intelligence and the Internet of Things continue to offer exponential advances favorable for invention. As I’ve previously written, these are make-or-break technologies for all information-intensive companies. But, don’t limit yourself to those.
Kay is especially optimistic about the favorable exponential at the intersection of computer-facilitated design, simulation and fabrication. This is the process of developing concepts and ideas using computer design tools and then testing and evolving them using computer-based simulation tools. Only after extensive testing and validation are physical components ever built, and, when they are, it can be done through computer-mediated fabrication, including 3D printing.
This approach applies to a wide range of domains, including mechanical, electrical and biological systems. It is becoming the standard method for developing everything, including car parts and whole cars, computer algorithms and chips, and even beating nature at its own game. Scientists and engineers realize tremendous benefits in terms of the number of designs that can be considered and the speed and rigor with which they can do so. These allow, Kay told me, “unbelievable leverage on the universe.”
See also: To Shape the Future, Write Its History
3. Project the need 30 years out and imagine what might be possible in the context of the exponential curves
30 years is so far in the future that you don’t have to worry about how to get out there. Focus instead on what is important to have. There’s no possibility of being forced to demonstrate or prove how to get there incrementally.
Asking “How is this incremental to the present?” is the “biggest idea killer of all time,” Kay says. The answer to the “incremental” question is, he says, is “Forget it. The present is the least interesting time to live in.”
Instead, by projecting 30 years into the future, the question becomes, “Wouldn’t it be ridiculous if we didn’t have this?”
Projecting out what would be “ridiculous not to have” in 30 years led to many visionary concepts that earned Kay wide recognition as “the father of the personal computer.” He was sure, for example, that children would have ready access to laptop and tablets by the late 1990s — even though personal computers did not yet exist. As he saw it, there was a technological reason for it, there were user reasons for it and there were educational reasons for it. All those factors contributed to his misty vision, and he didn’t have to prove it because 30 years was so far in the future.
How might the world look relative to the needs that you smell out? What will you have ready access to in a world with a million times greater computing power, cheap 3D fabrication, boundless energy and so on? Remember, projecting to 2050 is intended as a mind-stretching exercise, not a precise forecasting one. This is where romance lives, albeit romance underpinned by deep science rather than pure fantasy.
4. Create a 30-year vision
A vision is different from a mission or a goal. If the previous step was about romance, a 30-year vision is more like a dream. It is a vague picture of a desirable future state of affairs in that 30-year future. This is the step where Kay’s recognition that computers would be widely available by the late 1990s turned into a vision of what form those computers might take.
That vision included the Dynabook, a powerful and portable electronic device the size of a three-ring notebook with a touch-sensitive liquid crystal screen and a keyboard for entering information. Here’s one of Kay’s early sketches of the Dynabook from that time.
The next illustration is Kay’s sketch of the Dynabook in use. He describes the scenario as two 12-year-olds learning about orbital dynamics from a version of “Space Wars” that they wrote themselves. They are using two personal Dynabooks connected over a wireless network.
Kay’s peers in the ARPA community had already envisioned some of the key building blocks for the Dynabook, such as LCD panels and an Internet-like, worldwide, self-healing network. (For a fascinating history of the early ARPA community, see Mitchell Waldrop’s brilliant book, “The Dream Machine.“)
For Kay, these earlier works crystallized into the Dynabook once he thought about them in the context of children’s education. As he described it,
The Dynabook was born when it had that cosmic purpose.
Laptops, notebook computers and tablets have roots in the early concepts of the Dynabook.
5. Pull the 30-year vision back into a 10- to 15-year lesser vision
Kay points out that one of the powerful aspects of computing is that, if you want to live 10 to 15 years in the future, you can do it. You just have to pay 10 to 20 times as much. That’s because tomorrow’s everyday computers can be simulated using today’s supercomputers. Instead of suffering the limitations of today’s commodity computers (which will be long obsolete before you get to the future you are inventing), inventors should use customized supercomputers to prototype, test and evolve aspects of their 30-year vision. Pulling back into the 10- to 15-year window brings inventors back from the “pie in the sky” to something more concrete.
Jumping into that “more concrete” future is exactly what Alan Kay did in 1971 when he joined the Xerox Palo Alto Research Center (PARC) effort to build “the office of the future.”
It started with Butler Lampson and Chuck Thacker, two of PARC’s leading engineers, asking Kay, “How would you like us to build your little machine?” The resulting computer was an “interim Dynabook,” as Kay thought of it, but better known as the Xerox Alto.
The Alto was the hardware equivalent of the Apple Macintosh of 1988, but running in 1973. Instead of costing a couple of thousand dollars each, the Alto cost about $70,000 (in today’s dollars). PARC built 2,000 of them — thereby providing Kay and his team with the environment to develop the software for a 15-year, lesser-but-running version of his 30-year vision.
6. Compute in the future
Now, having created the computing environment of the future, you can invent the software. This approach is critical because the hardest thing about software is getting from requirements and specification to properly running code.
Much of the time spent in developing software is spent optimizing code for the limitations of the hardware environment—i.e., making it run fast enough and robust enough. Providing a more powerful, unconstrained futuristic computing environment frees developers to focus on invention rather than optimization. (This was the impetus for another Kay principle, popularized by Steve Jobs, that “People who are really serious about software should make their own hardware.”)
The Alto essentially allowed PARC researchers to simulate the laptop of the future. Armed with it, Kay was a visionary force at PARC.
Kay led the Learning Research Group at PARC, and, though PARC’s mission was focused on the office environment, Kay rightly decided that the best path toward that mission was to focus on children in educational settings. He and his team studied how children could use personal computers in different subject areas. They studied how to help children learn to use computers and how children could use computers to learn. And, they studied how the computers needed to be redesigned to facilitate such learning.
The power of the Alto gave Kay and his team, which included Adele Goldberg, Dan Ingalls, Ted Kaehler and Larry Tesler, the ability to do thousands of experiments with children in the process of understanding these questions and working toward better software to address them.
We could have a couple of pitchers of beer at lunch, come back, and play all afternoon trying out different user interface ideas. Often, we didn’t even save the code.
For another example of the “compute in the future” approach, take Google’s driverless car. Rather than using off-the-shelf or incrementally better car components, Google researchers used state of the art LIDAR, cameras, sensors and processors in its experimental vehicles. Google also built prototype vehicles from scratch, in addition to retrofitting current cars models. The research vehicles and test environments cost many times as much as standard production cars and facilities. But, they were not meant for production. Google’s researchers know that Moore’s Law and other favorable exponentials will soon make their research platforms practical.
Its “computing in the future” platforms allow Google to invent and test driving algorithms on car platforms of the future today. Google greatly accelerated the state of the art of driverless cars and ignited a global race to perfect the technology. Google recently spun off a separate company, Waymo, to commercialize the fruits of this research.
Waymo’s scientists and engineers are learning from a fleet of test vehicles driving 10,000 to 15,000 miles a week on public roads and interacting with real infrastructure, weather and traffic (including other drivers). The developers are also taking advantage of Google’s powerful cloud-based data and computing environment to do extensive simulation-based testing. Waymo reports that it is running its driving algorithms through more than three million miles of simulated driving each day (using data collected by its experimental fleet).
See also: How to Master the ABCs of Innovation
7. Crawl your way there
Invention requires both inspiration and perspiration. Inspired by this alternative perspective of thinking about their work, researchers can much more effectively channel their perspiration. As Kay is known for saying, “Point of view is worth 80 IQ points.”
PARC’s success demonstrates that even if one pursues a 15-year vision — or, more accurately, because one pursues such a long-term vision — many interim benefits might well come of the effort. And, while the idea of giving researchers 2,000 supercomputers and building custom software environments might seem extravagant and expensive, it is actually quite cheap when you consider how much you can learn and invent.
Over five glorious years in the early 1970s, the work at PARC drove the evolution of much of future computing. The software environment advanced to become more user-friendly and supportive of communications and different kinds of media. This led to many capabilities that are de rigueur today, including graphical interfaces, high quality bit-mapped displays, what-you-see-is-what-you-get (WYSISYG) word processing and page layout applications. The hardware system builders learned more about what it would take to support future applications and also evolved accordingly. This led to hardware designs that better supported the display of information, network communications and connecting to peripherals, rather than being optimized for number crunching. Major advancements included Ethernet, laser printing, peer-to-peer and client server computing and internetworking.
Kay estimates that the total budget for the parts of Xerox PARC that contributed to these inventions was about $50 million in today’s dollars. Compare that number to the hundreds of billions of dollars that Xerox directly earned from the laser printer.
Although the exact number is hard to calculate, the work at PARC also unlocked trillions reaped by other technology-related businesses.
One of the most vivid illustrations of the central role that Xerox played was a years-later interchange between Steve Jobs and Bill Gates. In response to Jobs’ accusation that Microsoft was stealing ideas from the Mac, Gates tells him:
Well, Steve, I think there’s more than one way of looking at it. I think it’s more like we both had this rich neighbor named Xerox, and I broke into his house to steal the TV set and found out that you had already stolen it.
Kay cautions that his method is not a cookbook for invention. It is more like a power tool that needs to be wielded by skilled hands.
It is also a method that has been greatly enabled by Kay and his colleagues’ inventions. Beyond the technology industry that they helped spawned, their inventions also underscore discovery and innovation in every field of science and technology, including chemistry, biology, engineering, health and agriculture. Information technology is not only a great invention; it has reinvented invention. It powers the favorable exponential curves upon which other inventors can escape the present and invent the future.
See also: How We’re Wired to Make Bad Decisions
For his part, Kay continues to lead research at the frontiers of computing, with a continued emphasis on human advancement. In addition to his Viewpoints Research Institute, he recently helped to formulate the Human Advance Research Community (HARC) at YC Research, the non-profit research arm of Y Combinator. HARC’s mission is “to ensure human wisdom exceeds human power, by inventing technology that allows all humans to see further and understand more deeply.”
That is a future worth inventing.