Exponential Famous Quotes

The exponential growth of this industry was correlated with the phenomenon famously discovered by Moore, who in 1965 drew a graph of the speed of integrated circuits, based on the number of transistors that could be placed on a chip, and showed that it doubled about every two years, a trajectory that could be expected to continue. This was reaffirmed in 1971, when Intel was able to etch a complete central processing unit onto one chip, the Intel 4004, which was dubbed a “microprocessor.” Moore’s Law has held generally true to this day, and its reliable projection of performance to price allowed two generations of young entrepreneurs, including Steve Jobs and Bill Gates, to create cost projections for their forward-leaning products.

The exponential growth of this industry was correlated with the phenomenon famously discovered by Moore, who in 1965 drew a graph of the speed of integrated circuits, based on the number of transistors that could be placed on a chip, and showed that it doubled about every two years, a trajectory that could be expected to continue. This was reaffirmed in 1971, when Intel was able to etch a complete central processing unit onto one chip, the Intel 4004, which was dubbed a “microprocessor.”Moore’s Law has held generally true to this day, and its reliable projection of performance to price allowed two generations of young entrepreneurs, including Steve Jobs and Bill Gates, to create cost projections for their forward- leaning products.

The exponential growth of this industry was correlated with the phenomenon famously discovered by Moore, who in 1965 drew a graph of the speed of integrated circuits, based on the number of transistors that could be placed on a chip, and showed that it doubled about every two years, a trajectory that could be expected to continue. This was reaffirmed in 1971, when Intel was able to etch a complete central processing unit onto one chip, the Intel 4004, which was dubbed a “microprocessor.” Moore’s Law has held generally true to this day, and its reliable projection of performance to price allowed two generations of young entrepreneurs, including Steve Jobs and Bill Gates, to create cost projections for their forward-leaning products. The

Moore’s Law, the rule of thumb in the technology industry, tells us that processor chips—the small circuit boards that form the backbone of every computing device—double in speed every eighteen months. That means a computer in 2025 will be sixty-four times faster than it is in 2013. Another predictive law, this one of photonics (regarding the transmission of information), tells us that the amount of data coming out of fiber-optic cables, the fastest form of connectivity, doubles roughly every nine months. Even if these laws have natural limits, the promise of exponential growth unleashes possibilities in graphics and virtual reality that will make the online experience as real as real life, or perhaps even better. Imagine having the holodeck from the world of Star Trek, which was a fully immersive virtual-reality environment for those aboard a ship, but this one is able to both project a beach landscape and re-create a famous Elvis Presley performance in front of your eyes. Indeed, the next moments in our technological evolution promise to turn a host of popular science-fiction concepts into science facts: driverless cars, thought-controlled robotic motion, artificial intelligence (AI) and fully integrated augmented reality, which promises a visual overlay of digital information onto our physical environment. Such developments will join with and enhance elements of our natural world. This is our future, and these remarkable things are already beginning to take shape. That is what makes working in the technology industry so exciting today. It’s not just because we have a chance to invent and build amazing new devices or because of the scale of technological and intellectual challenges we will try to conquer; it’s because of what these developments will mean for the world.

Reid Hoffman famously said, ‘If you’re not embarrassed by the first version of your product, you’ve launched too late.’ 

A sobering denouement had to come...exponential growth is a potent delusion-maker, and in 1999, 10 years after the Nikkei’s peak, I was thinking about the Japanese experience as we were waiting to claim our rental car at San Francisco airport. Silicon Valley was years into its first dotcom bubble, and even with advance reservations people had to wait for the just-returned cars to get serviced and released again into the halting traffic on the clogged Bayshore freeway. Mindful of the Japanese experience, I was thinking that every year after 1995 might be the last spell of what Alan Greenspan famously called irrational exuberance, but it was not in 1996 or 1997 or 1998. And even more so than a decade earlier, there were many economists ready to assure American investors that this spell of exponential growth was really different, that the old rules do not apply in the New Economy where endless rapid growth will readily continue.

Tony Hseih, CEO of Zappos, helped disrupt the retail space by emphasizing mastery, making the “pursuit of growth and learning” central to his corporate philosophy and famously saying: “Failure isn’t a badge of shame. It is a rite of passage.

The early to mid-1780s were years of exponential growth for Mozart, not only in terms of his family and career but in his style and exposure as a composer and musician. He met Gottfried van Swieten, a Viennese government official who was a keen patron of musicians at this time. He gave Mozart access to his formidable library of compositions, and Mozart delved into study of the works of some famous predecessors, most notably Johann Sebastian Bach and George Frideric Handel. Access to the breadth of their work highly influenced many of Mozart’s works in the year to come, as he shifted to a more Baroque style in many of his compositions. This influence can most clearly be heard in his opera The Magic Flute, as well as Symphony No. 41. It was also at this time, and perhaps influenced by his study of the greats that came so recently before him, that Mozart wrote one of his greatest liturgical pieces, Mass in C minor. It was performed for the first time in 1783 when Wolfgang and Constanze traveled to Salzburg in order to visit Mozart’s father and sister.

Correlation is enough,” 2 then-Wired editor in chief Chris Anderson famously declared in 2008. We can, he implied, solve innovation problems by the sheer brute force of the data deluge. Ever since Michael Lewis chronicled the Oakland A’s unlikely success in Moneyball (who knew on-base percentage was a better indicator of offensive success than batting averages?), organizations have been trying to find the Moneyball equivalent of customer data that will lead to innovation success. Yet few have. Innovation processes in many companies are structured and disciplined, and the talent applying them is highly skilled. There are careful stage-gates, rapid iterations, and checks and balances built into most organizations’ innovation processes. Risks are carefully calculated and mitigated. Principles like six-sigma have pervaded innovation process design so we now have precise measurements and strict requirements for new products to meet at each stage of their development. From the outside, it looks like companies have mastered an awfully precise, scientific process. But for most of them, innovation is still painfully hit or miss. And worst of all, all this activity gives the illusion of progress, without actually causing it. Companies are spending exponentially more to achieve only modest incremental innovations while completely missing the mark on the breakthrough innovations critical to long-term, sustainable growth. As Yogi Berra famously observed: “We’re lost, but we’re making good time!” What’s gone so wrong? Here is the fundamental problem: the masses and masses of data that companies accumulate are not organized in a way that enables them to reliably predict which ideas will succeed. Instead the data is along the lines of “this customer looks like that one,” “this product has similar performance attributes as that one,” and “these people behaved the same way in the past,” or “68 percent of customers say they prefer version A over version B.” None of that data, however, actually tells you why customers make the choices that they do.

This is what happened when I cofounded LinkedIn. The key business model innovations for LinkedIn, including the two-way nature of the relationships and filling professionals’ need for a business-oriented online identity, didn’t just happen organically. They were the result of much thought and reflection, and I drew on the experiences I had when founding SocialNet, one of the first online social networks, nearly a decade before the creation of LinkedIn. But life isn’t always so neat. Many companies, even famous and successful ones, have to develop their business model innovation after they have already commenced operations. PayPal didn’t have a business model when it began operations (I was a key member of the PayPal executive team). We were growing exponentially, at 5 percent per day, and we were losing money on every single transaction we processed. The funny thing is that some of our critics called us insane for paying customers bonuses to refer their friends. Those referral bonuses were actually brilliant, because their cost was so much lower than the standard cost of acquiring new financial services customers via advertising. (We’ll discuss the power and importance of this kind of viral marketing later on.) The insanity, in fact, was that we were allowing our users to accept credit card payments, sticking PayPal with the cost of paying 3 percent of each transaction to the credit card processors, while charging our users nothing. I remember once telling my old college friend and PayPal cofounder/ CEO Peter Thiel, “Peter, if you and I were standing on the roof of our office and throwing stacks of hundred-dollar bills off the edge as fast as our arms could go, we still wouldn’t be losing money as quickly as we are right now.” We ended up solving the problem by charging businesses to accept payments, much as the credit card processors did, but funding those payments using automated clearinghouse (ACH) bank transactions, which cost a fraction of the charges associated with the credit card networks. But if we had waited until we had solved this problem before blitzscaling, I suspect we wouldn’t have become the market leader.