Winners Take All

Anand Giridharadas: Winners Take All : The Most Important Book on Tech in 10 Years

We don’t post book reviews often on this blog. Sometimes we recommend books we think our startups should read. We have a reading list, which deserves an update as well. More often we talk about books with our startups in person, and many that the management team recommend are standard fare for startups and business. Dale Carnegie, Al Ries, and business classics like Zero to One, Crossing the Chasm, or books and articles by Malcolm Gladwell. 

This post is a bit different, because the critical consensus on Anand Giridharadas’s new book Winners Take All : The Elite Charade of Changing the World, has not yet been formed. Still Giridharadas’s well known views, first as a columnist for the New York Times, and then as a member of the speaker circuit he himself criticizes in his new book, are rare harsh words from a tech industry insider who knows of what he speaks better than most.

In truth, the book is a kind of validation for us as well. StartupYard has been publicly critical of “Thought Leadership,” and the incestuousness of the idea space that tech startups and corporations tend to occupy. We have been publicly in favor of many of the solutions that Giridharadas suggests, including Universal Basic Income. Still, his book comes as something of a wake up call for our industry. One that should not surprise anyone, but probably will.

“Making The World a Better Place”

When we refocused StartupYard 5 years ago on global projects with an emphasis on novel technologies, part of our thesis was that it would not be enough for us to “make the world a better place.” This is not least because “making the world a better place,” often functions as an excuse for failing to take on important, and yet smaller issues. Instead, we focused on developing the ecosystems in which we live and work, be they the city of Prague (in multiple cooperative hackathons with the city, and by investing in local startups), the Czech Republic, CEE, or finally, the globe. We also live on the globe.

We also tried to focus on problems with a deep impact on the world that could be widely felt. That led to our investments in companies like Gjirafa, Neuron Soundware, OptioAI, Rossum, or TurtleRover. Each of these companies does more than simply allow large companies to increase profit margins. They create new markets and new ways of looking at old problems. They create opportunities for small actors who can’t afford to compete with the bigger players. They drive competition, not individual competitiveness.

What we tried hard not to do was to choose startups and make investments solely based on the idea that these companies might be financially successful. Moreover, would their impact on real people’s lives be meaningful, measurable, and tangible, ultimately, to the founders and to us?

That might sound like “Making the World a Better Place,” but it’s not the same thing. To develop and push the world towards a more just, more fair, and more prosperous future is not to simply “make the world a better place.” In fact, it should not surprise the reader that new technologies can make the world a harder place to live in.  These are issues that we must face as moral actors in the world. Thus: will the automation, or new ways of working and living our startups are trying to invent, make the lives of the individuals it is most important to, better in a meaningful way to them?

When we talk about a better world, we often just mean a better world for us. One in which we feel better about ourselves. Yet, this is not enough to help a person sleep at night. At least not to help this investor sleep at night.

Giridharadas gives shape to these thoughts in his book. He asks us: who suffers so that we may succeed? These are the people to whom a debt is truly owed, no matter who they are. Our responsibility to the world around us does not end with the interests of our shareholders. It extends to all who our technologies touch, directly or indirectly. It extends to promoting a moral worldview to the founders we work with – one in which greed is not good. Giridharadas also asks something more: what happens when success does come? What will we do when and if we do win?

Facing Win-Lose Scenarios

One of the themes of Winners Take All, is pointing out and challenging the insidious idea that any technology company can do essentially anything it wants to do, and still one can find “win-win” scenarios, in which the impact of their choices can be seen as positive for everyone involved. It’s ok that Uber destroys the taxi industry, because the net impact will be good. It’s ok that Airbnb might raise property prices, because in the long run, it will all balance out. Having personally met with and heard talks from executives at these companies, along with many other major tech players, I can assure you that they do not have a concrete view of how this balancing act will work

In tech, most do not take responsibility for this balancing out. We disrupt, but we don’t fix the problems we create.

Worst of all, investors and startups may justify their negative impacts on the world around them by spending the money they earn on fixing the very problems they created, via charities, or via yet new startup businesses. This can be like treating a hangover with more alcohol. It tends to lead to similar results. New approaches are needed.

Thus tech companies can disrupt and invalidate systems that employ and support large numbers of people, and they can justify this disruption by pointing to a possible future in which their impact will be seen as positive by those who suffer today. Often the worldview is passed down from the investor, to the startup, to the individual team member: “it doesn’t matter if we wreck a few lives… eventually they’ll thank us for it.”

This is not good enough. That is why StartupYard is in favor of Universal Basic Income research, among other governmental and regulatory approaches to protecting individuals, classes of people, and regions from the negative effects of the very innovation we invest in. We simply will not profit from a world that we are destroying. That’s also why we have consistently criticized governmental approaches to innovation that idealize the tech industry as a cure-all for social and economic inequality. We can do many things, but we cannot replace democratic institutions, or be the voice of unheard people.

Sometimes winners win, and losers lose. In a just world, and in a world we do want to live in, the losers don’t have far to fall. Losing is, after all, what almost all of us have to do at some point before winning. The waste and the pity of a winner take all world is that so many great things come from those who have lost something before – people who fail know the value of succeeding.

Survivorship Bias – The Nose Goes

Finally Giridharadas asks important and difficult questions about what it means to actually be a winner. Can we ever know, if we do succeed, that this success is due more to our abilities and our hard work, than to luck, or to the simple fact of who we are?

He reminds us of something that is also part of StartupYard’s DNA: the belief that someone’s CV is not their life. In our business, it is easy and in some ways much simpler to follow the signals of success that follow certain people through their lives. Did the person go to Harvard? Did they have a good position in a previous company? Did their past startup succeed?

We focus very little on these details, because they don’t tell us that much about people. There are plenty of idiots in C-level positions. There are plenty of bad founders from Yale or Oxford. Hard experience has shown us that a person’s CV doesn’t always have a strong relationship with their individual potential when it comes to founding and building a company. If we were to only look at Ivy Leaguers from prestigious tech companies as founders, we’d miss people who have no such credentials, but have far more important qualifications than that. Qualifications like knowing their customers better than anyone else. Like being more devoted to what they do than anyone else in the world.

What is interesting about the culture of startups is that we “celebrate failures,” but actually we celebrate learning from mistakes. True failures, like not working hard enough, not getting good enough marks in school, or failing to even act on an idea you had in the past, are not celebrated this way, because we don’t tend to learn from them. A story of real failure is not interesting, because it doesn’t have a happy ending. It just ends. Somebody who failed at 25 is inspiring. Somebody still failing at 50 is pathetic. We celebrate people who almost succeed, and probably will succeed in the future. That isn’t the same as failing.

The heuristics you use for making decisions about people should be based on your own experiences, and not those of others. This is what we call “instinct,” which for some reason has become taboo in the technology world, where everything is data and metrics driven. We believe in following your nose. Your nose tells you things your eyes and ears will never quantify.

Read the Book

Enough of my opinion on the topic. Read the book! I promise you’ll at least learn something you probably didn’t know before.

Ramez Naam: 3 Ways We Fail At Predicting the Future

Last week in Prague, the famous inventor, sci-fi author and speaker Ramez Naam gave a talk about one of our favorite topics: Exponential Innovation, under the auspices of DirectPeople. Ramez Naam is not only an award winning author, he is also a veteran of Microsoft, the holder of 19 separate patents, and the founder of Apex Nanotechnologies, which is the first company to create software tools for molecular design.

The partnership is fitting, as DirectPeople works with companies and startups to realize innovative new projects, building teams around new ideas and executing them for banks, energy companies, and telcos, as well as smaller firms. (Full disclosure: Petr Sidlo, CEO at DirectPeople, is our mentor, and Phillip Staehelin their Innovation Evangelist is our mentor and investor.)

The talk centered around an introduction to exponential thinking, much like those we have written before. What really stood out from the talk however, was Naam’s particular attention to the reasoning that so often fails to predict radical changes in the business and technology landscapes. As important as the exponential innovation framework is, we must put it in context with the logic that has failed to match it over and over again.

The talk was a long one, so I’ve chosen to focus on 3 key errors that perpetually lead industries as well as individuals to dramatically underestimate the effects of innovation over long periods. As Naam pointed out in his talk,

3 Ways we Fail at Predicting the Future:

First: Disruptive Innovation Comes “Bottom Up”

One of the key analogies Naam used was the broad history of world empires beginning around A.D. 1400-1500, with two key events: the European discovery of America by Columbus’s fleet, and the exploration of South Asia by Zheng He of China.

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To scale: the flagship of the Chinese fleet of 1400 (larger), and the Santa Maria (smaller).

Though the two events bear remarkable similarity in their timing and boldness, there the similarities end. Columbus’s voyage was an accidental success. Attempting to circumnavigate the globe with too little food and resources, his fleet was saved by the accidental discovery of Cuba, a fraction of the total distance to his actual target, East Asia and India.

Zheng He, on the other hand, commanded the most technologically advanced navy in the world. His ships made Columbus’s tramp fleet of the Nina, Pinta, and Santa Maria look like bath toys in comparison. His expedition was well planned and financed, took much less risk, and made much more important near term discoveries for China in the form of large potential trading partners.

Yet, Columbus’s voyage set off an imperial age in Europe that lasted for nearly 5 centuries, and saw the colonization and Europeanization of most of the world. Zheng He’s 25 year exploration of South Asia and East Africa ended in the burning of his fleet, and the closing of China from the outside world for another 4 centuries.

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Why these two vastly dissimilar outcomes? Moreover, why did the results of a well funded, professional, and government backed expedition (that of Zheng He), result in nothing of significance, when the poorly funded, badly planned expedition of Columbus ended in Spain’s domination of South America for hundreds of years, and Britain’s eventual domination of half the world?

Top Down V. Bottom Up:

There is one key differentiator: China’s exploration of the known world was a top-down affair, instigated by the emperor, and carried out on behalf of China’s leadership. It was essentially a government program/ Meanwhile, Columbus’s voyage was backed by Queen Isabella of Spain (after other monarchs turned it down), but she served as little more than a financier. The project was from the beginning a bottom-up effort by Columbus, and would have been doomed to failure and the death of the entire fleet, if not for America conveniently sitting between Europe and Asia. Columbus lucked into a discovery that eventually made him rich and famous. He took the key risk (with his life), and so he also owned his share of the rewards.

Again, Naam directs our attention to the effects that leadership structure had on the resulting discoveries. While Zheng He returned to China a hero, his discoveries and fleet were the property of the Emperor, who chose simply to dismantle them, rather than to take the risk of engaging with foreign trade and potentially coming into conflict with foreign powers. To China, the fleet represented a cost and risk that was evaluated based on the status quo. Since the voyage proved that China was the most powerful nation on Earth, the Chinese saw no more reason to engage with the outside world and risk their already safe position.

Stability Becomes a Liability

Columbus was in a weaker position from the beginning. So why did he win? Naam argues that it is because of the inherently competitive nature of European politics at the time. While China was unified under the Ming Dynasty for 3 centuries (from 1368-1644), Europe’s political landscape was inherently unstable, and composed of hundreds of individual state-like entities, with nobles fighting for position and consolidating control of their regions.

It was that instability which made it difficult for any one government to back Columbus, and it was the need for a competitive advantage that drove Isabella, who had only just united Spain under one ruler, to fund him. The price of not having state control of the expedition was that Columbus himself (along with the explorers and conquistadors who followed), personally profited from their efforts. A traditional of entrepreneurial exploration was established because Europe could not afford to publicly fund it.

Thus, Columbus’s voyages were highly disruptive to the established order because Europeans were desperate to gain competitive advantages over neighbors. Meanwhile China, the strongest nation in the world at the time, had no such need. In fact, quite the opposite, as opening itself to trade only served to destabilize China when it eventually did realize it could no longer afford to remain closed. It was finally Britain that forced the opening of China and established Hong Kong as its trading colony in the late 19th century.

One does not have to look too hard here to recognize the tech startup and the modern corporation. Stability for its own sake can turn to a liability over time, as hungry younger competitors adapt too quickly and are too numerous to be stopped.

Second: Exponential Innovation is Deceptive

Naam spent a lot of time covering the ways in which exponential innovations deceive people into seeing them as always just about to slow down. This is because the fundamental drivers of innovation are often mistaken as being the same as they were before the exponential growth curve took effect. He shared this graph, for example:

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The graph shows a classic exponential curve in the electrical capacity of solar power from roughly 1998 to 2014, along with the World Energy Organization predictions of future growth. As is plain to see, every single prediction over 12 years shows static growth, but realigned from the new actual baseline in any given year. Every prediction is not only wrong, it’s wrong in exactly the same way.

Why would that continue to happen for 16+ years in a row? Surely someone at the WEO might notice it happening and try to figure out why?

Exponential Innovations are on a broad front:

Naam provides a partial answer in this blog post on the topic. He points out that predictions typically take a snapshot of the industry at the present moment, and extrapolate expected advances based on what appear to be limitations to growth that are non-surmountable without a step change in the technology or the underlying economy.

This works most of the time, because most of the time the specific conditions for exponential innovation aren’t met. Namely: that advances in different aspects of a technology produce feedback to affect growth in other areas. Innovation is happening on a “broad front.”

In this particular case, the reason the predictions are wrong is not because their underlying assumptions are inaccurate, but because they are about the wrong things.

For example, predictions of the slowed growth of solar energy may point to theoretical limitations in the materials being used to make solar panels. Indeed, solar panel efficiency is not growing as fast as it did in the past. Instead, as Naam points out in his post, the manufacturing methods are improving faster instead, which is compensating for the diminishing returns of material science advances.

This effect repeats itself over and over again. If it isn’t manufacturing, it’s software. If it isn’t software, it’s infrastructure changes. If it isn’t that, it’s changes in the consumer business model that accelerate growth. The growth in total wattage keeps going up exponentially because all these advances are driving each other forward at once.

Third: Exponential Innovation is Democratizing

On the last point about innovation around the economic models of new technologies, Ramez Naam took special care. He argues that exponentiality of innovation depends on the technology’s ability to be democratized over time.

In fact, in reference to solar power, he used Nuclear energy as a counter-example. Despite the enormous efficiency and effectiveness of modern nuclear power, it is failing to keep up with solar energy in the growth of its footprint. In fact it is declining. Predictions by Isaac Asimov and many others about a nuclear future in which atomic cores power watches and handheld computers, or even body implants, are now severely dated.

Why is that? Naam argues that it is because Nuclear power is difficult or impossible to democratize. Simply because of how it works, and its ability to be weaponized, atomic power must remain under the control of governments. This means that the core technologies involved with nuclear power have fewer chances to influence other areas of technology growth, and vice versa. Nuclear grows in a kind of parallel, where advances don’t greatly benefit other technology areas, and so other technologies don’t arise to create demand for new innovations.

Nuclear is not doomed because the technology doesn’t work, but rather because it can’t be enmeshed with other businesses and technologies. Like China’s great navy in 1400, nuclear is a state operation: it can’t be democratized because it is too dangerous.

If we go back to the previous example of Columbus and Zheng He, we see that a classical prediction would state that the Chinese technical superiority would allow them to predominate or to catch up quickly with the west. But what actually ended up happening was that the west innovated around the business model of exploration first, and the attractive monetary incentives led a revolution in technology that profited from the scalable finance model that colonization had created.

Consider the fate of nuclear vs. solar power in that context. Nuclear is far and away more complex technology. The only problem is that might not matter. The innovation that advances on a broad front and permeates society will win out.

By the time the British Empire came into direct conflict with China, it was the English who had developed the use of colonies as markets for their own goods, rather than a source of resources for the use of their own governments or ruling classes. Thus the English could afford conflict with China, because colonization was immediately profitable to those involved, rather than to established players only.

Indeed, this innovation is what made the American revolution the opposite of a disaster for England. Because England had repurposed itself as the workshop of the world, the growth of American power and economic strength helped the British empire to finance itself through trade with America. Britain had innovated away from the old definition of an Empire through mercantilism and trade. The state did not have to directly fund exploration and colonization because it shared the profits with the colonies themselves.

The English democratized economics itself. That was the innovation. It was one China still struggles to match.

So too with solar energy: solar power can afford to compete directly with traditional sources because it brings the benefits of the new technology closer to those who will profit from it. Even when solar was not at cost-parity with traditional fossil fuels (which is no longer true), it was cost effective for those who adopted it, because they saw the benefits personally, and over the long term. The risk of adoption had an appropriate reward.

Unlike with nuclear power or coal, which require long-term up-front investments far from view of those who pay for them, solar is a source of energy consumers and businesses can own themselves. This means they’re increasingly likely to invest in it, and to see less risk in doing so. The technology is thus democratized, and suddenly it can be the basis for knew, previously unimagined products or businesses.

The innovation can be in the business model this makes possible: not always in the technologies that underwrite the growth. Exponential Innovation shifts streams and goes around obstacles, rather than charting a straight line in one area. Thus predictions from the WEO may accurately represent technological limitations, but still fail to predict growth because they are using an outdated economic model.

Is There a Better Way to Predict the Future?

On the one hand, if predicting the future were as easy as a new mental framework, then everyone would be doing it already. Yet on the other, we can avoid making the same wrong predictions over and over by paying attention to what causes an exponential growth cycle in a particular industry or technology.

One of the key takeaways for me from Ramez Naam’s talk was that exponential change occurs primarily where there are multiple axes of advance being explored at once. If one hits a wall, another can cause the technology’s adoption to grow any way. What is inherent in an exponential growth model is that growth never depends on one particular characteristic of a technology, but rather on the overwhelming incentives it creates. People want it to grow, and so it does. That was the fundamental insight of Moore’s Law decades ago. Limitations we recognize as crucial today can become meaningless tomorrow, because the incentive structure for innovation is self-feeding.

Singularity University uses a framework called the 6 “D”s  (also used by Ramez Naam), namely: Digitized, Deceptive, Disruptive, Demonetized, Dematerialized, and Democratized. In broad strokes, they argue that an organization or industry becomes exponential when the following conditions occur:

  1. Advances are based on an already exponentially scaleable platform (ie: computing, or energy generation)
  2. Advances appear to be insignificant or even slow at first, although capabilities are doubling and redoubling on small scales.
  3. Advances begin to outperform existing solutions at sub-scale (eg: Solar vs. Coal)
  4. Cost falls as demand rises.
  5. Advances become commoditized and ubiquitous.
  6. The underlying technology becomes commonly achievable and can be applied anywhere.

Given these conditions, a technology may reach an exponential growth curve, and if we recognize a technology that begins to fall into this particular virtuous cycle, we must closely examine whether or not traditional predictive frameworks of the future remain useful.

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What Exponential Innovation Really Means

Last week, we announced a new series on the topic of Exponential Innovation. The piece began with a clear premise, which we restate here:

The central premise of our series on Exponential Innovation will be this: exponential growth in the complexity of technology, reflected in increasing computing power and capacity, the explosion of data and increasingly complex and powerful material sciences, is a reality in our society, and will have an ever increasing influence over society and the world economy for the foreseeable future.”

Exponential and Linear Innovation

An important part of talking about technological trends is addressing how and why exponential trends differ from linear trends. Why is technological progress exponential, and why does that make such a big difference in how we talk about the future?




It’s common in ordinary speech and thinking to envision most trends as being linear- in part because most of the “trends” we encounter on a daily basis appear to be linear in nature. Linear trends are easy to recognize: the population grows at a more or less steady rate, the price of a liter of milk increases fairly regularly over the years, and one’s age steadily increases.

So we are good at understanding linear trends, but not good at dealing with exponential ones. That’s because evolution has optimized the human brain for dealing with linear functions. These are far more important to our immediate survival than exponential functions are. Worse still, because our brains have been adapted to viewing trends in a linear way, we can very easily make the mistake of assuming that any trend we observe is a linear one.

As Ray Kurzweil put it in his book The Singularity is Near: “the subjective experience is the opposite of the objective reality:” what we experience in a linear fashion subjectively is different from what the data actually says. Because we think in terms of only one or two “steps” on a trend line, trends always appear to us to be more stable than they are.

Thus we predict the future based on an incomplete view of the data- assuming that the current rate of change is going to be continued, without noting that the previous rate of change has been accelerating.

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Tim Urban also highlighted this predictive problem in his recent piece on the subject, and produced a helpful graph:

Kurzweil used a concrete example: In 1985, what then comprised the internet had about 2,000 “nodes,” or servers in its network. That was more than double what the number had been only a few years before.  If you asked computer specialists in 1985, how fast the internet was likely to grow over the next ten years, you would likely get some function of the past rate of change, projected into the future. If in 1985 there were 2,000 nodes, then we could expect that there would be up to 10,000 nodes by 1985, and perhaps 20,000 by 1995.

In the event, there were millions of nodes by 1995. How could the predictions be so wrong? Well, engineers in 1985 were dealing with the complexity of growing the internet using 1985 technology. But by 1990, new technology had effectively octupled the effectiveness of computers on a cost basis, and the internet had grown exponentially, doubling its own size every few years- rather than growing by 2000 nodes a year, it grew by 4,000, then 8,000, then by 16,000, and so on.

The engineers can be forgiven for seeing that as it concerned them and their work, they did not have the capacity to double the size of the internet in only two years. But because the growing size of the internet also allowed more people and more computer power to be applied to growing it even further, their predictions were based on incomplete assumptions. It would have been very hard indeed to octuple the size of the internet with 1985 technology. But every day that passed, the very same technology was also making that work easier to do.

Imagining An Exponential Future

So we understand that exponential trends make our normal predictive powers pretty weak. So what does your gut tell you about technology in the near future? How do you imagine the world of 2060?

Let’s try a thought experiment: suppose there are 3 Billion smartphones in the world today. Now suppose that all those smartphones have an average computing power of about 2 Gigaflops. An exponential progress curve suggests that within about 45 years from today, a device of the same cost of a modern smartphone should surpass the computing power of all smartphones in the world today. This means that by 2060, every human being should have access to the computing equivalent of the entire world’s personal computers from 2016 combined.

And that level of advancement will actually arrive much sooner than 2060: that year is only the time at which such computing power will be generally available at low cost.

Now, how do you imagine that people will be using that technology in 2060? Will it actually be that we will have smartphones capable of all the computational power of the entire world’s computers, in our pockets? What would we do with all that computational power?

You already can’t keep track of what your own personal computer is doing most of the time- it already does many things of which you are not actively aware. So in a world where all of those processes that are happening today, outside of human observation or awareness, all over the planet, could be happening in a device as small as a smartphone, what would a smartphone actually be used for? In a world of pervasive human-level or superhuman level AI, would it even be necessary to own such a device?

The answer for Kurzweil, and many other futurists, is no. A person would certainly not interact with future technologies in any of the same ways that we currently do. In the same way that we do not operate computers today using punchcards and levers, so too will we stop interacting with computers using keyboards, screens, mouses, or even our voices. At some point in the not too distant future, computers will cease to be treated as mere tools, and will instead become an extension of everything we do and interact with- they will be just another part of us.

The Exponential Is All Around Us

Exponential change is hard to see, because it also takes place over incredibly long time scales. While we can observe exponential trends that are on a human time scale, like the size of the internet, we can’t very well appreciate exponential trends that we are a smaller part of.

Kurzweil makes a compelling argument that the current exponential growth in technology and computing capacity is part of a trend that dates back to almost the beginning of the universe, but has concrete origins in the development of human civilization. The only difference is that we just happening to be living in a time when that trend has started to become a part of our perceptible reality.

Tim Urban, in that same piece on WaitButWhy, asks us to consider the life of a farmer from 1750. If you brought such a person to the world of 2016, what would his reaction to the modern world be? The modern world would be a fantastical, baffling, and overwhelming nightmare. Bright colored capsules that roar down the streets and overhead. Glowing boxes that speak and display images and make sounds, windows of light into other parts of the world, where you can talk to distant people instantaneously. Weapons that can bring the power of the sun to the surface of the world and open gateways to hell.

Would the shock of all that overwhelm him? Would he die from fright?

Next, he asks us to consider the following: if that farmer were to bring someone of a similar remove in history to his own time, taking a peasant from 1500 to the world of 1750, what would that farmer’s reaction be? Surely he would be mightily impressed, but nothing the farmer of 1750 could show that peasant would cause him to die of shock. There would be no apparent magic in anything he saw. Just impressive and exciting new technologies.

The fact is that if exponential innovations in technology continue, then the periods in which such technological changes occur will continue to grow shorter. The world of 2060 may be as shocking to a normal person of 2016, as the world of 2016 would be to a farmer from 1750. And Kurzweil’s contention is that superintelligent AI is the inevitable result of that progress.

Exponential Innovation: Then and Now 

In our next post, we’ll discuss the evolution of technology from the distant past to the present, and talk about what technology is capable of doing today. If we’re going to be discussing what technology’s effects will be on society in the near future, it will be important to touch on what we are already capable of doing.

If you’ve got an opinion, please share with us on Twitter, Facebook, or LinkedIn. We will include your reactions in future posts.

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Exponential Innovation: Preface

We are on the edge of change comparable to the rise of human life on Earth.” – Vernon Vinge

Last week, StartupYard managing director Cedric Maloux spoke about “Exponential Innovation” at the “What About Innovation” meeting of the American Chamber of Commerce in Pristina, Kosovo, on the invitation of StartupYard alum Gjirafa.

The talk dealt with a simple, but broad based question: Is Exponential Innovation an Opportunity or a Threat to Society?

The talk proved to be very popular, and raised ideas that the StartupYard team felt should be explored in more depth. So, during spring and summer 2016, StartupYard will be working on a series of blog posts around this topic.

Exponential Innovation

The central premise of our series on Exponential Innovation will be this: exponential growth in the complexity of technology, reflected in increasing computing power and capacity, the explosion of data and increasingly complex and powerful material sciences, is a reality in our society, and will have an ever increasing influence over society and the world economy for the foreseeable future.

Exponential innovation raises important questions and concerns:

“What role will humans play in a society where most existing jobs can be done more efficiently by machines?”

“Is the current 40 hour model of work-based employment viable going forward?”

“What will be the roles of work and employment in the near future?”

“Which jobs are immediately at risk? Which ones are at risk long term?”

“What will be the role of education in a world where intellectual labor is increasingly automated?”

“How will innovation change the role of money in our lives, as the need for a traditional workforce decreases?”

“How will governments and societies adapt to rapid advances in artificial intelligence, and its growing role in business decision making?“


For more on this topic, check out this fascinating weekly newsletter from Azeem Azhar:

The Exponential View


Defining Our Purpose

For centuries, conversations around technological progress have been surrounded by, on the one hand, fear and apprehension about change, and on the other, excitement at the prospect of a better, happier, safer and more fulfilling life.

The world in the midst of the industrial revolution in 1840, and the world of 2016 are very different places, and yet these dual feelings of apprehension and expectation have not changed. In both eras, automation is viewed as both a threat and a deliverance. But whereas the industrial revolution replaced much routine manual work, the current technological revolution will replace many non-routine cognitive tasks.

At the same time, many of our startup founders and mentors express a certain fatalism about innovation. They say, in one form or another: “The robots will take our jobs, and so you’ll either have to own the robots, or you’ll have nothing.”

Is that really the case? Is it the inevitable outcome of rapid innovation?

Then, as now, governments and society had the capacity to evolve the function of money, ownership, and work to adapt to a changing technological reality. We are already seeing a rise in extreme political movements, from the mid-east to the Americas, fueled in large part by the diminishing role of the middle class in the new economy.

Will we see political changes as profound as those of the industrial revolution in the near future? Will innovations like Universal Basic Income (UBI), become essential elements of the new, post AI economy?

The primary purpose of this series will be not to argue in favor of one particular view of modern society, or to espouse one particular political or economic agenda. Instead, it will be to inspire conversations about pressing topics for the many millions of people, in Europe and around the world, who are facing a future that they find difficult to understand, and even harder to predict.

What are we, as workers, as entrepreneurs, as citizens, or as members of society and actors in our economy, working towards? What future are we building for ourselves? These questions are highly relevant to the work of StartupYard, and to every one of our members, alumni, mentors, partners, and investors, as well as the millions of people who will be touched by the technology we invest in, and help to foster.