Tag Archives: Silicon Valley

We Must Break Free of the Engineering Mindset

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We Must Break Free of the Engineering Mindset

GUEST POST from Greg Satell

In 2014, when Silicon Valley was still largely seen as purely a force for good, George Packer wrote in The New Yorker how tech entrepreneurs tended to see politics through the lens of an engineering mindset. Their first instinct was to treat every problem as if it could be reduced down to discrete variables and solved like an equation.

Despite its romantic illusions, the digital zeitgeist merely echoed more than a century of failed attempts to generalize engineering approaches, such as scientific management, financial engineering, six sigma and shareholder value. All showed initial promise and then disappointed, in some cases catastrophically.

Proponents of the engineering mindset tend to blame its failures on poor execution. Surely, logic would suggest that as long as a set of principles are internally consistent, they should be externally relevant. Yet the problem is that reality is not simple and clear-cut, but complex and nonlinear, which is why we need be ready to adapt to the unexpected and nonsensical.

The Rise of the Engineering Mindset

In the 1920s, a group of intellectuals in Berlin and Vienna, much like many of the Silicon Valley digerati today, became enamored with the engineering mindset. By this time electricity and internal combustion had begun to reshape the world and Einstein’s theory of relativity, confirmed in 1919, had reshaped our conception of the universe. It seemed that there was nothing that scientific precision couldn’t achieve.

Yet human affairs were just as messy as always. Just a decade before Europe had blundered its way into the most horrible war in history. Social scientists still seemed no more advanced than voodoo doctors and philosophers were still making essentially the same arguments the ancient Greeks used two thousand years before.

It seemed obvious to them that human endeavors could be built on a more logical basis and saw a savior in Ludwig Wittgenstein and his Tractatus, which described a world made up of “atomic facts” that could be combined to create “states of affairs.” He concluded, famously, that “Whereof one cannot speak, thereof one must remain silent,” meaning that whatever could not be proved logically must be disregarded.

The intellectuals branded their movement logical positivism and based it on the principle of verificationism. Only verifiable propositions would be taken as meaningful. All other statements would be treated as silly talk and gobbledygook. Essentially, if it didn’t fit in an algorithm, it didn’t exist.

A Foundational Crisis

Unfortunately, and again much like Silicon Valley denizens of today, the exuberant confidence of the logical positivists belied serious trouble underfoot. In fact, while the intellectuals in Berlin and Vienna were trying to put social sciences on a more logical footing, logic itself was undergoing a foundational crisis.

At the root of the crisis was a strange paradox, which can be illustrated by the sentence, “The barber shaves every man who does not shave himself.” Notice the problem? If the barber shaves every man who doesn’t shave himself, then who shaves the Barber? If he shaves himself, he violates the statement and if he does not shave himself, he also violates it.

It seems a bit silly, but the Barber’s Paradox is actually a simplified version of Russell’s Paradox involving sets that are members of themselves, which had baffled mathematicians and logicians for decades. Clearly, for a logical system to be valid and verifiable, statements need to be provably true or false. 2+2 for example, needs to always equal four. Yet the paradox exposed a hole that no one seemed able to close.

Eventually, the situation came to a head when David Hilbert, one of the most prominent logical positivists, proposed a program that rested on three pillars. First, mathematics needed to be shown to be complete in that it worked for all statements. Second, mathematics needed to be shown to be consistent, no contradictions or paradoxes allowed. Finally, all statements need to be computable, meaning they yielded a clear answer.

The hope was that the foundational crisis would be resolved, the hole at the center of logic could be closed and the logical positivists could move along with their project.

The System Crashes

Hilbert and his colleagues received and answer faster than most had expected. In 1931, just 11 years after Hilbert proposed his foundational problems, 25-year-old Kurt Gödel published his incompleteness theorems. It wasn’t the answer anyone was expecting. Gödel showed that any logical system could be either complete or consistent, but not both,

Put more simply, Gödel proved that every logical system will always crash. It’s only a matter of time. Logic would remain broken forever and the positivists hopes were dashed. Obviously, you can’t engineer a society based on a logical system that itself is hopelessly flawed. For better or for worse, the world would remain a messy place.

Yet the implications of the downfall of logic turned out to be far different, and far more strange, than anyone had expected. In 1937, building on Gödel’s proof, Alan Turing published his own paper on Hilbert’s computability problem. Much like the Austrian, he found that all problems are not computable, but with a silver lining. As part of his proof, he included a description of a simple machine that could compute every computable number.

Ironically, Turing’s machine would usher in a new era of digital computing. These machines, constructed on the basis that they would all eventually crash, have proven to be incredibly useful, as long as we accept them for what they are — flawed machines. As it turns out, to solve big, important problems, we often need to discard up our illusions first.

We Need to Think Less Like Engineers and More Like Gardeners

The 20th century ushered in a new era of science. We conquered infectious diseases, explored space and unlocked the genetic code. So, it was not at all unreasonable to want to build on that success by applying an engineering mindset to other fields of human endeavor. However, at this point, it should be clear that the approach is far past the point of saving.

It would be nice if the general well-being could be reduced to a single metric like GDP or the success of an enterprise could be fully encapsulated in a stock price. Yet today we live, as Danny Hillis has put it, in an age of the entanglement, where even a limited set of variables can lead to the emergence of a new and unexpected order.

We need to take a more biological view in which we think less like engineers and more like gardeners that grow and nurture ecosystems. The logical positivists had no idea what they were growing, but somehow what emerged from the soil they tilled turned out to be far more wondrous—not to mention exponentially more useful—than what they had originally intended.

As I wrote at the beginning of this crazy year, the time has come to rediscover our humanity. We are, in so many ways, at a crossroads. Technology will not save us. Markets will not save us. We simply need to make better choices.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Silicon Valley Has Become a Doomsday Machine

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Silicon Valley Has Become a Doomsday Machine

GUEST POST from Greg Satell

I was working on Wall Street in 1995 when the Netscape IPO hit like a bombshell. It was the first big Internet stock and, although originally priced at $14 per share, it opened at double that amount and quickly zoomed to $75. By the end of the day, it had settled back at $58.25 and, just like that, a tiny company with no profits was worth $2.9 billion.

It seemed crazy, but economists soon explained that certain conditions, such as negligible marginal costs and network effects, would lead to “winner take all markets” and increasing returns to investment. Venture capitalists who bet on this logic would, in many cases, become rich beyond their wildest dreams.

Yet as Charles Duhigg explained in The New Yorker, things have gone awry. Investors who preach prudence are deemed to be not “founder friendly” and cut out of deals. Evidence suggests that the billions wantonly plowed into massive failures like WeWork and Quibi are crowding out productive investments. Silicon Valley is becoming a ticking time bomb.

The Rise Of Silicon Valley

In Regional Advantage, author AnnaLee Saxenian explained how the rise of the computer can be traced to the buildup of military research after World War II. At first, most of the entrepreneurial activity centered around Boston, but the scientific and engineering talent attracted to labs based in Northern California soon began starting their own companies.

Back east, big banks were the financial gatekeepers. In the Bay Area, however, small venture capitalists, many of whom were ex-engineers themselves, invested in entrepreneurs. Stanford Provost Frederick Terman, as well as existing companies, such as Hewlett Packard, also devoted resources to broaden and strengthen the entrepreneurial ecosystem.

Saxenian would later point out to me that this was largely the result of an unusual confluence of forces. Because there was a relative dearth of industry in Northern California, tech entrepreneurs tended to stick together. In a similar vein, Stanford had few large corporate partners to collaborate with, so sought out entrepreneurs. The different mixture produced a different brew and Silicon Valley developed a unique culture and approach to business.

The early success of the model led to a process that was somewhat self-perpetuating. Engineers became entrepreneurs and got rich. They, in turn, became investors in new enterprises, which attracted more engineers to the region, many of whom became entrepreneurs. By the 1980’s, Silicon Valley had surpassed Route 128 outside Boston to become the center of the technology universe.

The Productivity Paradox and the Dotcom Bust

As Silicon Valley became ascendant and information technology gained traction, economists began to notice something strange. Although businesses were increasing investment in computers at a healthy clip, there seemed to be negligible economic impact. As Robert Solow put it, “You can see the computer age everywhere but in the productivity statistics.” This came to be known as the productivity paradox.

Things began to change around the time of the Netscape IPO. Productivity growth, which had been depressed since the early 1970s, began to surge and the idea of “increasing returns” began to take hold. Companies such as Webvan and Pets.com, with no viable business plan or path to profitability, attracted hundreds of millions of dollars from investors.

By 2000, the market hit its peak and the bubble burst. While some of the fledgling Internet companies, such as Cisco and Amazon, did turn out well, thousands of others went down in flames. Other more conventional businesses, such as Enron, World Com and Arthur Anderson, got caught up in the hoopla, became mired in scandal and went bankrupt.

When it was all over there was plenty of handwringing, a small number of prosecutions, some reminiscing about the Dutch tulip mania of 1637 and then everybody went on with their business. The Federal Reserve Bank pumped money into the economy, the Bush Administration pushed big tax cuts and within a few years things were humming again.

Web 2.0. Great Recession and the Rise Of the Unicorns

Out of the ashes of the dotcom bubble arose Web 2.0, which saw the emergence of new social platforms like Facebook, LinkedIn and YouTube that leveraged their own users to create content and grew exponentially. The launch of the iPhone in 2007 ushered in a new mobile era and, just like that, techno-enthusiasts were once again back in vogue. Marc Andreessen, who founded Netscape, would declare that software was eating the world.

Yet trouble was lurking under the surface. Productivity growth disappeared in 2005 just as mysteriously as it appeared in 1996. All the money being pumped into the economy by the Fed and the Bush tax cuts had to go somewhere and found a home in a booming housing market. Mortgage bankers, Wall Street traders, credit raters and regulators all looked the other way while the bubble expanded and then, somewhat predictably, imploded.

But this time, there were no zany West Coast startup entrepreneurs to blame. It was, in fact, the establishment that had run us off the cliff. The worthless assets at the center didn’t involve esoteric new business models, but the brick and mortar of our homes and workplaces. The techno-enthusiasts could whistle past the graveyard, pitying the poor suckers who got caught up in a seemingly anachronistic fascination with things made with atoms.

Repeating a now-familiar pattern, the Fed pumped money into the economy to fuel the recovery, establishment industries, such as the auto companies in Detroit were discredited and a superabundance of capital needed a place to go and Silicon Valley looked attractive.

The era of the unicorns, startup companies worth more than a billion dollars, had begun.

Charting A New Path Forward

In his inaugural address, Ronald Reagan declared that, “Government is not the solution to our problem, government is the problem.” In his view, bureaucrats were the enemy and private enterprise the hero, so he sought to dismantle federal regulations. This led to the Savings and Loan crisis that exploded, conveniently or inconveniently, during the first Bush administration.

So small town bankers became the enemy while hotshot Wall Street traders and, after the Netscape IPO, Internet entrepreneurs and venture capitalists became heroes. Wall Street would lose its luster after the global financial meltdown, leaving Silicon Valley’s venture-backed entrepreneurship as the only model left with any genuine allure.

That brings us to now and “big tech” is increasingly under scrutiny. At this point, the government, the media, big business, small business, Silicon Valley, venture capitalists and entrepreneurs have all been somewhat discredited. There is no real enemy left besides ourselves and there are no heroes coming to save us. Until we learn to embrace our own culpability we will never be able to truly move forward.

Fortunately, there is a solution. Consider the recent Covid crisis, in which unprecedented collaboration between governments, large pharmaceutical companies, innovative startups and academic scientists developed a life-saving vaccine in record time. Similar, albeit fledgling, efforts have been going on for years.

Put simply, we have seen the next big thing and it is each other. By discarding childish old notions about economic heroes and villains we can learn to collaborate across historical, organizational and institutional boundaries to solve problems and create new value. It is in our collective ability to solve problems that we will create our triumph or our peril.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Stop Fooling Yourself

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Stop Fooling Yourself

GUEST POST from Greg Satell

Early in my career I was working on a natural gas trading desk and found myself in Tulsa Oklahoma visiting clients. These were genuine roughnecks, who had worked their way up from the fields to become physical gas traders. When the NYMEX introduced “paper” contracts and derivatives into the market, however, much would change.

They related to me how, when New York traders first came to town offering long-term deals, they were thrilled. For the first part of the contract, they were raking in money. Unfortunately, during the latter months, they got crushed, losing all their profits and then some. The truth was that the trade was pure arbitrage and they never had a chance.

My clients’ brains were working against them in two ways. First, availability bias, caused them to value information most familiar to them and dismiss other data. The second, confirmation bias, made them look for information that would confirm their instincts. This, of course, isn’t at all unusual. It takes real effort to avoid believing the things we think.

Becoming a Square-Peg Business in a Round-Hole World

When I was researching my book, Mapping Innovation, I spoke to every great innovator I could find. Some were world class scientists, others were top executives at major corporations and still others were incredibly successful entrepreneurs. Each one shared with me how they were able to achieve incredible things.

What I found most interesting was that the story was different every time. For every one who told me that a particular approach was the secret to their success, I found someone else who was equally successful who did things completely differently. The fact is that there is no one “true path” to innovation, everybody does it different ways.

Yet few organizations acknowledge that in any kind of serious way. Rather, they have a “way we do things around here,” and there are often significant institutional penalties for anyone who wants to do things differently. Usually these penalties are informal and unspoken, but they are very real and can threaten to derail even the most promising career.

You can see how the same cognitive biases that lost my gas trader friends money are at work here. In a profitable company, the most available information suggests things are being done the “right” way and everybody who wants to get ahead in the organization is heavily incentivized to embrace evidence to support that notion and disregarding contrary data.

That’s how organizations get disrupted. They stick to what’s worked for them in the past and fail to notice that the nature of the problems they need to solve has fundamentally changed. They become better and better at things that people care about less and less. Before they realize what happened, they become square-peg businesses in a round-hole world.

Silicon Valley Jumps the Shark

Nobody can deny the incredible success that Silicon Valley has had over the past few decades. Still mostly a backwater in the 1970s and 80s, by the end of 2020 four out of the ten most valuable companies in the world came from the Bay Area (not including Microsoft and Amazon, which are based in Seattle). No other region has ever dominated so thoroughly.

Yet lately Silicon Valley’s model of venture-funded entrepreneurship seems to have jumped the shark. From massive fraud at Theranos and out-of control founders at WeWork and Uber to, most recently, the incredible blow-up at Quibi, there is increasing evidence that the tech world’s “unicorn culture” is beginning to have a negative impact on the real economy.

One clue of where things went wrong can be found in Eric Ries’s book, The Startup Way. Ries, whose earlier effort, The Lean Startup, was a runaway bestseller, was invited to implement his methods at General Electric and transform the company to a 124 year-old startup. Much like with the “unicorns,” it didn’t end well.

The fundamental fallacy of Silicon Valley is that a model that was developed for a relatively narrow set of businesses—essentially software and consumer electronics—could be applied to solve any problem. The truth is that, much like the industrial era before it, the digital era will soon end. We need to let go of old ways and set out in new directions.

Unfortunately, because of how brains are wired for availability bias and confirmation bias, that’s a whole lot easier said than done.

Breaking Out of the Container of Your Own Experience

In 1997, when I was still in my twenties, I took a job in Warsaw, Poland to work in the nascent media industry that was developing there. I had experience working in media in New York, so I was excited to share what I’d learned and was confident that my knowledge and expertise would be well received.

It wasn’t. Whenever I began to explain how a media business was supposed to work, people would ask me, “why?” That forced me to think about it and, when I did, I began to realize that many of the principles I had taken for granted were merely conventions. Things didn’t need to work that way and could be done differently.

I also began to realize that, working for a large corporation in the US, I had been trained to work within a system, to play a specific part in a greater whole. When a problem came up that was outside my purview, I went to someone down the hall who played another part. Yet in post-Communist Poland, there was no system and no one down the hall.

So I had to learn a new outlook and a new set of skills and I consider myself lucky to have had that experience. When you are forced to explore the unknown, you end up finding valuable things that you didn’t even know to look for and begin to realize that many perspectives can be brought to bear on similar problems with similar fact patterns.

Learning How to Not Fool Yourself

In one of my favorite essays, originally given as a speech, the great physicist Richard Feynman said “The first principle is that you must not fool yourself—and you are the easiest person to fool. So you have to be very careful about that,” and goes on further to say that simply being honest isn’t enough, you also need to “bend over backwards” to provide information so that others may prove you wrong.

So, the first step is to be hyper-vigilant and aware that your brain has a tendency to fool you. It will quickly grasp on the most readily available data and detect patterns that may or may not be there. Then it will seek out other evidence that confirms those initial hunches while disregarding contrary evidence.

Yet checking ourselves in this way isn’t nearly enough, we need to actively seek out and encourage dissent. Some of this can be done with formal processes such as pre-mortems and red teams, but a lot of it is cultural, hiring for diversity and running meetings in such a way that encourages discussion by, for instance, having the most senior leaders speak last.

Perhaps most of all, we need to have a sense of humility. It’s far too easy to be impressed with ourselves and far too difficult to see how we’re being led astray. There is often a negative correlation between our level of certainty and the likelihood of us being wrong. We all need to make an effort to believe less of what we think.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Building an Innovation Ecosystem: Lessons from Silicon Valley

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Building an Innovation Ecosystem: Lessons from Silicon Valley

GUEST POST from Chateau G Pato

Innovation has become the driving force behind economic growth and societal progress in today’s highly competitive global landscape. As the birthplace of countless revolutionary technologies, Silicon Valley has emerged as the epicenter of innovation, setting a blueprint for other regions aspiring to foster their own vibrant ecosystems. In this thought leadership article, we will explore the key elements that have made Silicon Valley thrive. By examining the pivotal role collaboration, access to venture capital, and a culture of experimentation have played, we will extract valuable lessons that can be applied when building innovation ecosystems elsewhere. To illustrate these principles, we’ll highlight two compelling case studies that demonstrate successful implementation beyond the confines of Silicon Valley.

Case Study 1: Singapore’s Rise as “Asia’s Silicon Valley”

Singapore, once regarded as a financial hub, has leveraged its favorable regulatory environment and strategic partnerships to create a thriving innovation ecosystem. The city-state’s pivotal initiative, “Smart Nation,” emphasizes collaboration between academia, industry, and the government. By fostering close relationships between research institutions such as Nanyang Technological University, startups, and multinational corporations through collaborative projects, Singapore has developed a dynamic exchange of ideas and knowledge. Furthermore, the government’s proactive involvement, manifested in unique initiatives like the Data Innovation Lab, has facilitated access to resources and intellectual support, mirroring Silicon Valley’s approach.

Case Study 2: Tel Aviv’s “Startup Nation” Success

Tel Aviv, Israel’s vibrant tech hub, has earned international recognition as the “Startup Nation.” Its tremendous achievements can be attributed to a unique blend of collaboration and a culture of experimentation. Tel Aviv’s success began with the establishment of the first technology incubator program, Yozma, in the 1990s. It attracted venture capital funds from abroad, providing startups with the necessary financial backing they needed to thrive and turning Israel into a hotbed of innovation. Additionally, the Israeli Defense Forces’ Unit 8200, known for its exceptional technological prowess, has served as a breeding ground for entrepreneurs, contributing to a robust talent pipeline. By cultivating a supportive network where government, startups, academia, and investors collaborate, Tel Aviv has successfully emulated Silicon Valley’s recipe for innovation.

Key Lessons for Building Innovation Ecosystems:

1. Collaboration is Key: Facilitating collaboration among academia, industry, and government creates a vibrant exchange of knowledge and resources. Implementing initiatives like innovation hubs, incubators, and public-private partnerships can foster collaboration and create synergistic relationships, ultimately driving innovation forward.

2. Access to Venture Capital: A well-developed venture capital ecosystem is crucial. Governments can incentivize venture capital investments through tax breaks, subsidies, and the establishment of government-backed funds. Encouraging institutions to invest in promising startups promotes growth and attracts talent, mirroring the success of Silicon Valley and Tel Aviv.

3. Cultivating a Culture of Experimentation: Encouraging risk-taking and embracing failure as valuable learning experiences are fundamental aspects of nurturing innovation. Governments and organizations should provide a supportive environment for entrepreneurs and allow room for experimentation, empowering individuals to push boundaries and disrupt existing industries.

Conclusion

Silicon Valley’s innovative ecosystem has demonstrated that collaboration, access to venture capital, and a culture of experimentation are key ingredients for success. By examining Singapore’s “Smart Nation” and Tel Aviv’s “Startup Nation,” it becomes evident that these principles can be adapted and applied in other locations, spurring their own innovation ecosystems. Building a dynamic environment that brings academia, industry, government, and investors together can unlock tremendous potential and accelerate progress towards a more prosperous future. Emulating these lessons from Silicon Valley will undoubtedly create a fertile ground for innovation to thrive, establishing a legacy that will endure for generations to come.

SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.

Image credit: Unsplash

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