Tag Archives: Silicon Valley

Why Students Are Booing Silicon Valley’s AI Vision

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Why Students Are Booing Silicon Valley's AI Vision

GUEST POST from Robert B. Tucker

A curious thing happened at the University of Arizona’s commencement ceremony.

The speaker was former Google CEO Eric Schmidt, one of the most influential figures in the development of the digital economy. Addressing thousands of graduates, Schmidt spoke enthusiastically about artificial intelligence and the transformative role it will play in their lives and careers.

Then something unexpected happened. Students began to boo.

For many observers, the moment was jarring. Why would graduates reject a future of technological abundance, economic growth, and unprecedented innovation? Aren’t young people supposed to be technology’s biggest boosters?

Not anymore, apparently. As a futurist who has spent more than three decades advising leaders on adapting to change and innovation, I see this moment as an inflection point. I think what they were rejecting was a vision of the future being jammed down their throats. Looking at a bleak employment market, these young people were saying en masse, “Your vision of our future is not our vision of our future, and we don’t feel you really have our interest at heart.”

The question at this juncture is: What kind of future are we rushing headlong to build, and who will benefit?

The tech industrial complex spins an appealing vision. But it’s beginning to wear thin. Students and other segments of society are pushing back. They are asking tough questions: Will AI really solve humanity’s greatest challenges? Will it cure diseases, eliminate drudgery, unlock extraordinary productivity gains, and usher in a new era of prosperity, as the so-called tech visionaries proudly claim?

Or could it be that the underlying premise is faulty: that the more intelligence we can automate, the better off society will become. The young people are waking up to the possibility that this is hot air.

Across college campuses, among young professionals, and increasingly among the broader public, there is another narrative taking shape. It is one that many technology leaders seem to want to dismiss: growing unease about where all of this is headed.

Many Americans view AI through the lens of issues much closer to home: skyrocketing electricity bills caused in part by data center proliferation; teen chatbot addiction, and looming job displacement. A recent Stanford study, Canaries in the Coal Mine?, found that young workers in the most AI-exposed occupations saw a 16% relative decline in employment from late 2022 through September 2025.

Over the past several years, I have spoken with educators, business leaders, and students around the world. Increasingly, I hear variations of the emerging narrative. I hear people questioning the tech industry’s vision more sharply. Are we building tools that expand human potential, or tools that gradually replace us? The concern isn’t that AI will become more capable. The concern is that humans will become less so.

Scot Rabe has taught design at Ventura College for decades. He recently described his growing frustration with students. Attendance remains high, but engagement is declining. There is little evidence that students are wrestling deeply with ideas. In his words, “the lights are on, but nobody’s home.”

That observation aligns with broader concerns about what I call human agency—the capacity to act intentionally, make decisions, solve problems, and shape one’s own future.

A 2023 survey by the Pew Research Center explored the future of human agency in an increasingly digital world. Experts were deeply divided. Many predicted that emerging technologies would weaken individual autonomy rather than strengthen it.

Their concern deserves attention.

The challenge facing young people today is not simply learning how to use AI. It is learning how to remain fully human in a world increasingly designed to automate thinking, decision-making, and even creativity.

Tim Wu, author of The Age of Extraction, argues that many of today’s largest technology firms operate by extracting value from our attention, data, and behavior. The more time we spend scrolling, clicking, and consuming, the more profitable the system becomes.

But what happens when the same incentives are applied to intelligence itself? What happens when convenience becomes the highest value? What happens when every difficult task can be delegated to a machine? What happens to the development of judgment, wisdom, resilience, and imagination?

These are not anti-technology questions. They are profoundly human questions.

History suggests that societies thrive not when technology advances alone, but when human capability advances alongside it.

The printing press transformed civilization. Electricity transformed civilization. The internet transformed civilization. Yet none of these innovations eliminated the need for human initiative, purpose, or responsibility. If anything, they increased it.

The danger today is not that AI becomes more powerful. The danger is that we gradually surrender the very qualities that make us uniquely human. That may be what those students were trying to express.

Perhaps they were saying that they do not want a future in which every challenge is solved for them. Perhaps they do not want to become passive consumers of machine-generated answers. Perhaps they are pushing back against a worldview that sees efficiency as life’s highest goal.

And perhaps they are asking a deeper question: What role will humans play in the future being built around us?

One vision imagines a future that is increasingly automated, optimized, digitized, and controlled by a small number of powerful technology platforms. Another envisions a future where technology augments rather than replaces human capability. A future where innovation strengthens creativity, deepens relationships, expands opportunity, and reinforces human dignity.

The choice between these futures is being made right now. Every generation inherits a set of technologies. But every generation must also decide how those technologies will shape our lives.

The students who are booing Silicon Valley’s assumptions were doing more than expressing frustration at yet another out-of-touch billionaire. They were reminding us that progress is not simply about building smarter machines. Rather, it is about building a future worth inhabiting.

This article originally appeared in Forbes

Image credit: Wikimedia Commons

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Capitalizing on Disruptive Innovations

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Capitalizing on Disruptive Innovations

GUEST POST from Geoffrey A. Moore

In Silicon Valley, we are in love with disruptive innovations, largely because we make a lot of them and have profited exceedingly well from so doing. But for anyone on the receiving end, the relationship is not so rosy. Yes, the potential for gain is extraordinary, but the path to getting there is strewn with attempts that have fallen far short of the hype. How can one engage responsibly with this sort of opportunity? Here’s a framework that can help.

Capitalizing on Disruptive Innovations Stairway to Heaven Framework

There are four proven ways to capitalize on disruptive innovation, and they are organized here in terms of escalating risk and reward. Each stair appeals to a different persona in the Technology Adoption Life Cycle, the bottom one attracting conservatives, the second, pragmatists in pain, the third, pragmatists with options, and the fourth, visionaries. Each stair can be managed to its targeted reward, but it is very hard indeed to manage two or more stairs in tandem. Most failures occur because management is not decisive about which gains it is committed to achieving and in what priority order it should be served. Needless to say, there is a better way.

The first use of this framework is to explore the possibilities of each stair for your enterprise. That is, if you were to prioritize this stair, what would success look like, how would you expect to measure it, and what costs and risks would be entailed? You want to talk this through as a team, ensuring everyone gets heard. Specifically, you want to make sure that the adoption personas of the most powerful people in the room do not dominate this part of the dialog. They are likely going to make the call in the end, but it is critical that they hear everyone out before they do.

Let’s try this out with everyone’s latest favorite example—generative AI. Imagine you are a member of the executive team at a pharmaceutical corporation, and you have charged your IT team to come up with a GenAI strategy. Wisely, they have come back to you with an array of options, arranged in a stairway to heaven. Here’s what they might say:

  • Automate. There is a whole series of regulatory compliance obligations that today we outsource overseas to be serviced by a lower-waged workforce. Not only would automating these tasks reduce our costs, it would also lower the error rate and continuously improve performance as more and more machine learning is put to work. This is a low-risk, modest-return option. There would be no disruption to any of our other operations, and we in IT could learn a lot about a technology that is mutating far faster than anything we have ever seen before.
  • Reengineer. Our proteomics research scientists are having a real problem with the combinatorial explosion of all the possible 3D configurations a given 2D sequence of amino acids might adopt. By focusing our generative AI models on just this one problem, we can vastly accelerate our discovery phase, transforming our problem set from completely intractable to continuously improving. This is a medium-risk, high-return opportunity that is confined to a single department, thereby minimizing disruption to the rest of our value chain.
  • Modernize. Our go-to-market teams are competing for smaller and smaller slices of time from the physician offices they call upon. We need relevant messaging to get the appointment and highly personalized content to get buy-in from both the doctors and the nurses. Today we rely on experience and anecdotal data, which works OK for our long-tenured members but makes recruiting, onboarding, and ramping a nightmare. By focusing our Large Language Model on all the data in our CRM systems, combined with all our data from the labs, clinical trials, patent submissions, as well as the patient records we have access to, we can arm our GenAI with more information than any one human could process. We still will have humans in the loop to monitor and adapt this material throughout the sales process, but they will be much better equipped to compete than ever before. This is a high-risk, high-return opportunity that will impact a large portion of our workforce, so we plan to stage the implementation to capture learnings as we go.
  • Innovate. Deep Mind’s AlphaGo program taught itself to play go at the highest level by playing against itself millions and millions of times. We think we can take a similar approach to drug discovery. It’s a moon-shot idea, and our data scientists are still in their own discovery phase, but this could be a game-changer for the industry. We’d like to take a VC approach to funding this effort, ring-fencing the funding across several years, but holding ourselves accountable to meeting material milestones along the way.

As you can see, there is a case to be made for each stair, but there is only so much time, talent, management attention, and working capital to go around, so it is critical that the executive team prioritize these four options and sequence them appropriately. Different teams will come up with different priorities. You are not looking for the “right answer.” You are looking for the one that will yield the best risk-adjusted returns for your enterprise under current conditions.

That’s what I think. What do you think?

Image Credit: Geoffrey Moore, Google Gemini

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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.

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Innovation is Combination

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GUEST POST by Greg Satell

Much has been made about the difference between innovation and invention. One writer went so far as to argue that Steve Jobs development of the iPod wasn’t an innovation because it was dependent on so much that came before it. A real innovation, so the argument goes, must be truly transformational, like the IBM PC, which created an entire industry.

The problem with these kind of word games is that they lead us to an infinite regress. The IBM PC can be seen as the logical extension of the microchip, which was the logical extension of the transistor. These, in turn, rose in part through earlier developments, such Turing’s universal computer and the completely irrational science of quantum mechanics.

The truth is that innovation is never a single event, but happens when fundamental concepts combine with important problems to create an impact. Traditionally, that’s been done within a particular organization or field, but to come up with breakthrough ideas in the 21st century, we increasingly need to transcend conventional boundaries of company and industry.

Transforming Alchemy into Chemistry

Everybody knows the story of Benjamin Franklin and his famous kite, but few have ever heard of John Dalton and his law of multiple proportions. What Dalton noticed was that if you combine two or more elements, the weight resulting compound will be proportional to its components. That may seem vague, but it did more for electricity than Franklin ever did.

The reason that Dalton’s obscure law became so important is that it led him to invent the modern concept of atoms and, in doing so, transformed the strange art of alchemy into the hard science of chemistry. Once matter could be reduced down to a single, fundamental concept, it could be combined to make new and wondrous things.

Dmitri Mendeleev transformed Dalton’s insight into the periodic table, transforming the lives of high school students and major chemical corporations alike. Michael Faraday’s chemical experiments led to his development of the dynamo and the electric motor, which in turn led to Edison’s electric light, modern home appliances and even IBM’s PC and Apple’s iPod.

Which of these are inventions and which are innovations? It’s impossible to tell and silly to argue about. What’s clear is none are the product of a single idea, but are all combinations of ideas built on the foundation that Dalton created.

Merging Man and Machine

In the early 1960s, IBM made what was perhaps the biggest gamble in corporate history. Although it was already the clear leader in the computer industry, it invested $5 billion — in 1960 dollars, worth more than $30 billion today — on a new line of computers, the 360 series, which would make all of its existing products obsolete.

The rest, as the say, is history. The 360 series was more than just a product, it was a whole new way of thinking about computers. Before, computers were highly specialized machines designed to do specific jobs. IBM’s new product line, however, offered a wide range of capabilities, allowing customers to add to their initial purchase as their business grew. It would dominate the industry for decades.

When Fred Brooks, who led the project, looked back a half century later, he said that the most important decision he made was to switch from a 6-bit byte to an 8-bit byte, which enabled the use of lowercase letters. Considering the size of the investment and the business it created, that may seem like a minor detail.

But consider this: That single decision merged the language of machines with the language of humans into a fundamental unit. In effect, the 8-bit byte transformed computers from obscure calculating machines into a collaboration tool.

Learning the Language of Life

Much like Dalton came up with the fundamental unit of chemistry, a century later Wilhelm Johannsen developed the fundamental unit of biology in 1909: the gene. This too was a combination — and also a refinement — of earlier ideas from men like Charles Darwin, Gregor Mendel and others.

However, for scientists of the early twentieth century, a gene was little more than a concept. No one knew what a gene was made of or even where they could be found. It was little more of an abstract idea until Watson and Crick discovered the structure and function of DNA. Even then, there was little we could do with genes except know that they were there.

That changed when the Human Genome Project was completed in 2003 and unleashed the new field of genomics. Today, genetic treatments for cancer have become common and, with prices for genetic sequencing falling faster than those for computer chips, we can expect gene therapies to be applied to a much wider array of ailments over the next decade.

Yet these new developments are not the product of just biologists. The challenges of gene mapping required massive computing power. So researchers working on genes needed to work closely with computer scientists to put supercomputers to work helping to solve the problem.

A New Era of Innovation

The confusion about innovation and invention reflects a fundamental misunderstanding about how innovation really works. The idea that certain ideas are flashes of divine inspiration while others are merely riffs off of earlier tunes sung long ago fails to recognize that all innovations are combinations.

Over the last century, most inventions have been combinations of fundamental units. Many important products, from household goods to miracle cures, have been developed through combining atoms in new and important ways. Learning how to combine bytes of information gave rise to the computer industry and we’re now learning how to combine genes.

The 21st century, however, will give rise to a new era of innovation in which we combine not just fundamental elements, but entire fields of endeavor. As Dr. Angel Diaz, IBM’s VP of Cloud Technology & Architecture told me, “We need computer scientists working with cancer scientists, with climate scientists and with experts in many other fields to tackle grand challenges and make large impacts on the world.”

Today, it takes more than just a big idea to innovate. Increasingly, collaboration is becoming a key competitive advantage because you need to combine ideas from widely disparate fields. So if you want to innovate, don’t sit around waiting for a great eureka moment — look for what you can combine to create something truly new and powerful.

image credit: bigstockphoto.com

An earlier version of this article first appeared in Inc.com

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