In 1959, a physicist named Richard Fyenman took the stage at CalTech for the annual meeting of the American Physical Society. A short bit into his presentation, he asked a transformative question.
“Why cannot we write the entire 24 volumes of the Encyclopaedia Brittanica on the head of a pin?”
He theorized that making things smaller could and SHOULD have a significant impact on the world. He asked important questions that in many ways set the multi-disciplinary trajectory that we’ve been on over the past 55 years.
On making computers smaller, Fyenman expanded:
“I don’t know how to do this on a small scale in a practical way, but I do know that computing machines are very large; they fill rooms. Why can’t we make them very small, make them of little wires, little elements – and by little, I mean little. For instance, the wires should be 10 or 100 atoms in diameter, and the circuits should be a few thousand angstroms across. Everybody who has analyzed the logical theory of computers has come to the conclusion that the possibilities of computers are very interesting – if they could be made to be more complicated by several orders of magnitude. If they had millions of times as many elements, they could make judgments. They would have time to calculate what is the best way to make the calculation that they are about to make. They could select the method of analysis which, from their experience, is better than the one that we would give to them. And in many other ways, they would have new qualitative features.
If I look at your face I immediately recognize that I have seen it before. (Actually, my friends will say I have chosen an unfortunate example here for the subject of this illustration. At least I recognize that it is a man and not an apple.) Yet there is no machine which, with that speed, can take a picture of a face and say even that it is a man; and much less that it is the same man that you showed it before – unless it is exactly the same picture. If the face is changed; if I am closer to the face; if I am further from the face; if the light changes – I recognize it anyway. Now, this little computer I carry in my head is easily able to do that. The computers that we build are not able to do that. The number of elements in this bone box of mine are enormously greater than the number of elements in our “wonderful” computers. But our mechanical computers are too big; the elements in this box are microscopic. I want to make some that are sub-microscopic.
If we wanted to make a computer that had all these marvelous extra qualitative abilities, we would have to make it, perhaps, the size of the Pentagon. This has several disadvantages. First, it requires too much material; there may not be enough germanium in the world for all the transistors which would have to be put into this enormous thing. There is also the problem of heat generation and power consumption; TVA would be needed to run the computer. But an even more practical difficulty is that the computer would be limited to a certain speed. Because of its large size, there is finite time required to get the information from one place to another. The information cannot go any faster than the speed of light – so, ultimately, when our computers get faster and faster and more and more elaborate, we will have to make them smaller and smaller.
But there is plenty of room to make them smaller. There is nothing that I can see in the physical laws that says the computer elements cannot be made enormously smaller than they are now. In fact, there may be certain advantages.”
And, so with that, nanotechnology was effectively born.
Today, we DO have computers who can recognize faces, or voices, and/or other biometric footprints. We are also making headway on computers who can think on their own. (see the latest on deep learning)
Not only have computers with magical powers themselves become smaller (and will continue to do so), but our understanding of the world has become ever more granular.
In no other place is this becoming more apparent than in our understanding of the human body, and specifically the human brain. The rapidly advancing fields of neuroscience, genomics, and optogenetics are allowing us to understand things that were inconceivable just 10 years ago. They are allowing us unprecedented visibility to the brain “sources of emotions, memory, and consciousness for the first time”.
But, this understanding isn’t just limited to the field of medicine. Google Maps continues to provide more granular details about the physical world.
Behavioral tracking via smart meters, GPS navigation systems, sensors of all kinds, wearables, thermostats, video cameras, ceiling fans, and all other sorts of devices allow us to connect the dots of every action into a larger narrative of what’s happening. The feedback loops are getting shorter and more accurate.
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But, we’re not just understanding the world in a deeper and more microscopic level that we ever have before. We’re also able to participate in ever narrowing snippets of activity. Prior to the creation of Mechanical Turk (or CrowdFlower, Clickworker, CloudCrowd and dozens of smaller crowdsourcing platforms), the friction associated with finding, hiring, training, evaluating, paying, and firing an employee was significant. Not so much anymore.
“Before the Internet, it would be really difficult to find someone, sit them down for ten minutes and get them to work for you, and then fire them after those ten minutes. But with technology, you can actually find them, pay them the tiny amount of money, and then get rid of them when you don’t need them anymore.” – Crowdflower CEO Lukas Biewald
(There is an entirely separate conversation around what this means for the future of work and institutional models, but we’ll need to save that for another day.)
Decoupling of capabilities and offers is becoming more common place, as consumers increasingly desire it.
Good Lord!!!! It’s true – Chris Rock (aka Cheap Pete) can finally get what he’s asking for. (one of my favorite In Living Colour skits by the way).
Instead of buying, or even committing to long term leases, cars, games, movies, office space are increasingly available in ever shorter time increments.
While other technology vendors seek to bundle increasingly large capabilities into a common platform, Zoho has created lots of micro-applications that are easily consumable for a fraction of the price as individual stand-alones.
Based on some estimates, each Facebook like is worth about $.03. However, in the structure of the network economy, scaling $.03 lots of times across the globe adds up to billions.
Even entire business models are even built on extracting fractions of pennies from millisecond scale network inefficiencies in financial markets.
The implications of modularizing everything down into small micro-offerings is evolving the way that commerce is done, allowing for more robust and complex models of value exchange.
And the devices keep getting smaller too. Tiny robots the size of insects can now fly. A slew of exploratory instruments keep getting smaller, like this high resolution endoscope that is no larger than the size of a human hair.
The questions that Richard Fyenman were asking have been answered. His vision is being rendered before our eyes. So what does all of this mean?
1. Just like a scientist, we have the ability to understand more about our world. In the context of corporations, it means having the ability to ask better and more difficult questions about our customers, our employees, our partners, and our entire supply chain. These questions can likely only be answered through new instrumentation, and new information systems to gather, analyze,and present this information to us.
2. Our customers are living in the same reality. Generally speaking, they are highly interested in reducing the friction around consuming your products and services (when and how they want them). They’d rather pay $1 for exactly what they need, than $5 for lots of capabilities that they can’t immediately leverage. Distribution channels for just about everything are evolving. Payment systems, new devices, advances in logistics capabilities, evolving consumer behavior are rapidly evolving what’s possible, and customers have shown a great propensity to adjust rapidly.
3. Connecting points 1 and 2 above are where the greatest work is to be done. Institutionalizing the methods for gathering greater customer understanding, while building the core competencies and infrastructure to support rapidly evolving go to market strategies is where great opportunity and challenges exist. Spend your time envisioning and executing towards these ends.