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Urban Innovation and Growth — Evolving Cities and Culture | by jurvetson
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Urban Innovation and Growth — Evolving Cities and Culture

Each of us submitted an essay on innovation and growth in advance for the Gruter Institute Conference on Growth. I’ll append mine below.


(photo by John Chisholm. More below).


Discussion ensued over lunch, and one of my favorite authors, Matt Ridley wrote a summary for the WSJ “Why Can't Things Get Better Faster (or Slower)?”



Innovation and Growth — Evolving Cities and Culture

By Steve Jurvetson


Innovation is critical to economic growth, progress, and the fate of the planet. Yet, it seems so random. But patterns emerge in the aggregate, and planners and politicians may be able to promote innovation and growth despite the overall inscrutability of this complex system. To tap the wisdom of crowds, we should shift the locus of learning from products to process. Leadership is not spotting the next growth industry, but tuning the parameters of human communication.


One emergent pattern, spanning centuries, is that the pace of innovation is perpetually accelerating, and it is exogenous to the economy. Rather, it is the combinatorial explosion of possible innovation-pairings that creates economic growth. And that is why cities are the crucible of innovation.


Geoffrey West of the Santa Fe Institute argues that cities are an autocatalytic attractor and amplifier of innovation. People are more innovative and productive, on average, when they live in a city because ideas can cross-pollinate more easily. Proximity promotes propinquity and the promiscuity of what Matt Ridley calls “ideas having sex”. This positive network effect drives another positive feedback loop - by attracting the best and the brightest to flock to the salon of mind, the memeplex of modernity.


Cities are a structural manifestation of the long arc of evolutionary indirection, whereby the vector of improvement has risen steadily up the ladder of abstractions from chemicals to genes to systems to networks. At each step, the pace of progress has leapt forward, making the prior vectors seem glacial in comparison – rather we now see the nature of DNA and even a neuron as a static variable in modern times. Now, it’s all about the ideas - the culture and the networks of humanity. We have moved from genetic to mimetic evolution, and much like the long-spanning neuron (which took us beyond nearest neighbor and broadcast signaling among cells) ushering the Cambrian explosion of differentiated and enormous body plans, the Internet brings long-spanning links between humans, engendering an explosion in idea space, straddling isolated pools of thought.


And it’s just beginning. In the next 10 years, three billion minds will come online for the first time to join this global conversation (Diamandis).


But why does this drive innovation and accelerating change? Start with Brian Arthur’s observation that all new technologies are combinations of technologies that already exist. Innovation does not occur in a vacuum; it is a combination of ideas from before. In any academic field, the advances today are built on a large edifice of history. This is the foundation of progress, something that was not so evident to the casual observer before the age of science. Science tuned the process parameters for innovation, and became the best method for a culture to learn.


From this conceptual base, come the origin of economic growth and accelerating technological change, as the combinatorial explosion of possible idea pairings grows exponentially as new ideas come into the mix (on the order of 2^n of possible groupings per Reed’s Law). It explains the innovative power of urbanization and networked globalization. And it explains why interdisciplinary ideas are so powerfully disruptive; it is like the differential immunity of epidemiology, whereby islands of cognitive isolation (e.g., academic disciplines) are vulnerable to disruptive memes hopping across, much like South America was to smallpox from Cortés and the Conquistadors. If disruption is what you seek, cognitive island-hopping is good place to start, mining the interstices between academic disciplines.


So what evidence do we have of accelerating technological change? At DFJ, we see it in the diversity and quality of the entrepreneurial ideas arriving each year across our global offices. Scientists do not slow their thinking during recessions. For a good mental model of the pace of innovation, consider Moore’s Law in the abstract – the annual doubling of compute power or data storage. As Ray Kurzweil has plotted, the smooth pace of exponential progress spans from 1890 to 2012, across countless innovations, technology substrates, and human dramas — with most contributors completely unaware that they were fitting to a curve.


Moore’s Law is a primary driver of disruptive innovation – such as the iPod usurping the Sony Walkman franchise – and it drives not only IT and communications, but also now genomics, medical imaging and the life sciences in general. As Moore’s Law crosses critical thresholds, a formerly lab science of trial and error experimentation becomes a simulation science and the pace of progress accelerates dramatically, creating opportunities for new entrants in new industries. And so the industries impacted by the latest wave of tech entrepreneurs are more diverse, and an order of magnitude larger — from automobiles and rockets to energy and chemicals.


At the cutting edge of computational capture is biology; we are actively reengineering the information systems of biology and creating synthetic microbes whose DNA was manufactured from bare computer code and an organic chemistry printer. But what to build? So far, we largely copy large tracts of code from nature. But the question spans across all the complex systems that we might wish to build, from cities to designer microbes, to computer intelligence.


As these systems transcend human comprehension, will we continue to design them or will we increasingly evolve them? As we design for evolvability, the locus of learning shifts from the artifacts themselves to the process that created them. There is no mathematical shortcut for the decomposition of a neural network or genetic program, no way to "reverse evolve" with the ease that we can reverse engineer the artifacts of purposeful design. The beauty of compounding iterative algorithms (evolution, fractals, organic growth, art) derives from their irreducibility. (My Google Tech Talk goes into some detail on the dichotomy of design and evolution).


The corporation is a complex system that seeks to perpetually innovate. Leadership in these complex organizations shifts from direction setting to a wisdom of crowds. And the process learning is a bit counterintuitive to some alpha leaders: cognitive diversity is more important than ability, disagreement is more important than consensus, voting policies and team size are more important than the coherence or comprehensibility of the decisions, and tuning the parameters of communication (frequency and fanout) is more important than charisma.


The same could be said for urban planning. How will cities be built and iterated upon? Who will make those decisions and how? We are just starting to see the shimmering refractions of the hive mind of human culture, and now we want to redesign the hives themselves to optimize the emergent complexity within. Perhaps the best we can do is set up the grand co-evolutionary dance, and listen carefully for the sociobiology of supra-human sentience.

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Taken on October 12, 2012