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Technology and investing: this time may be different

I am often reminded of the sage advice from Sir John Templeton: “The four most dangerous words in investing are ‘this time it’s different’.” As investors, I think we need to question whether we are entering a new technological and machine age over the next 10-25 years that could disrupt most businesses and possibly society as we know it. In this regard, the new technological and machine age may be more important than The Industrial Revolution. Quite possibly, this time it is different and whilst heeding Sir John’s advice, as prudent investors we believe it would be neglectful to ignore the technological developments that are almost certain to provide substantial threats and opportunities to businesses.

In a recent TED interview, Charlie Rose asked Larry Page (Co-Founder of Google) what is his most important lesson from business. He said that he has studied why many large businesses fail and he concluded: “They missed the future.” As investors, can we afford to miss the future? In our view, there is mounting evidence that we are approaching a tipping point of exponential technological advancement, particularly through accelerating improvements in artificial intelligence, 3D printing, genomics, computing power and robotics.

We have numerous recent powerful lessons on the rapid disruption of businesses from technological innovation:

  • In 1998, Kodak had 145,000 employees and sold 85% of all photographic film. In 1999, Kodak’s stock price peaked and in January 2012 it filed for bankruptcy. What is surprising about the Kodak story is that it invented the digital camera in the 1970s and yet the company was effectively destroyed by its own invention.
  • In 1998, Nokia overtook Motorola to become the world’s largest mobile phone manufacturer. By 2007, Nokia controlled in excess of 40% of the mobile phone market and was highly profitable. In July 2005, Google bought Android and in January 2007, Apple launched the iPhone. In September 2013, Nokia sold its loss-making mobile phone business to Microsoft.
  • Google was founded in September 1998. In 1999, newspapers’ share of global advertising revenue was approximately 35%. In 2015, Google generated advertising revenues of over US$67 billion, or 14% of global advertising. Meanwhile, newspapers’ share of global advertising revenue had fallen to approximately 12%.

Another lesson is that large scale/global disruption from technological advancements appears to be occurring at a faster and faster pace. Uber was founded in March 2009 and is now the world’s largest ‘taxi company’, with operations in 429 cities in 71 countries. Facebook was founded in February 2004 and has in excess of 1.6 billion monthly active users. The company is expected to generate advertising revenues in excess of US$20 billion this year. Airbnb was founded in August 2008 and is now the world’s largest accommodation company, with over two million listings in 34,000 cities in over 190 countries.

Exponential versus linear growth

It is difficult to comprehend that we could rapidly face a radically different world from the advancement of technology, when our own experience suggests that fundamental change is occurring incrementally and at a gradual pace. A reason why we may be underestimating the impact of technological change is that most changes in our life (like ageing, learning, career progression, etc.) occur in a well-established linear trajectory whereas technological progression is exponential.

In exponential growth, a measurement is multiplied by a constant factor for a given unit of time (e.g. computation power doubles every year), whereas for linear growth the measurement is added to incrementally and by a constant factor (i.e. we grow older by one year per year). Early on, it is difficult to feel the difference between linear and exponential growth (i.e. from 1,2,3,4 … to progressions of 1,2,4,8…); however, after 30 iterations the linear sequence is at 30 whereas the exponential sequence is over 500 million. In an exponential world nothing is perceived to be changing in the early stages and then suddenly change starts occurring at an explosive rate.

There are numerous examples of technology progressing at an exponential rate. Three well-cited examples are:

  • Computational power - In 1965, Gordon Moore, Co-Founder of Intel, predicted that the number of transistors in an integrated circuit would double every two years (the so-called Moore’s Law). Over the last six decades, computation power has increased over one trillion times per integrated circuit. An iPhone 5 released in 2013 has twice the processing power of the 1985 Cray-2 supercomputer, which at the time was the world’s most powerful computer. At the current rate of progression, a mobile phone is likely to have the processing power of the current largest supercomputer – China’s Tianhe 2 – in around 15 years.
  • Genome sequencing - When the project to sequence the human genome was started in 1990, given the speed at which the genome could be scanned at that time, it was thought it would take thousands of years to sequence the entire human genome (six billion bases). The full genome was sequenced 10 years later. In 2000, the cost to sequence an entire human genome was around US$100 million and by 2015, the cost had fallen exponentially to US$1,000.
  • Data - It has been estimated that the amount of digital data in the world is doubling every two years. To put it another way, estimates suggest that more data has been created in the past two years than in the previous history of the human race.

In order to predict what will happen in the future through technological change, you need to extrapolate and think exponentially. Ray Kurzweil, a natural language processing pioneer and entrepreneur, a renowned futurist and currently Director of Engineering at Google, wrote in a March 2001 paper titled, ‘The Law of Accelerating Returns’:

“An analysis of the history of technology shows that technological change is exponential, contrary to the common-sense intuitive linear view. So we won’t experience 100 years of progress in the 21st century, it will be more like 20,000 years of progress (at today’s rate).”

“It is important to ponder the nature of exponential growth. Toward this end, I am fond of telling the tale of the inventor of chess and his patron, the Emperor of China. In response to the Emperor’s offer of a reward for his new beloved game, the inventor asked for a single grain of rice on the first square, two on the second square, four on the third and so on. The Emperor quickly granted this seemingly benign and humble request. One version of the story has the Emperor going bankrupt as the 63 doublings ultimately totalled 18 million trillion grains of rice.”

“As exponential growth continues to accelerate into the first half of the 21st century, it will appear to explode into infinity, at least from the limited and linear perspective of contemporary humans. The progress will ultimately become so fast that it will rupture our ability to follow it. It will literally get out of control.”

Bill Gates has commented that “we always overestimate the change that will occur in the next two years and underestimate the change that will occur in the next 10.” This tendency to overestimate change in the short term and underestimate the long term creates an interesting (and possibly dangerous) paradigm for an investor – acting too early by selling or short selling businesses that are most likely to be disrupted may well be detrimental to short-term returns, whereas waiting too long could be very costly, as in the end disruption may occur very rapidly. Judging where we are on the exponential path of technological development is becoming critical for any longer term investor. In thinking about the investment impact of exponential growth, it is instructive that five of the world’s 10 largest companies by market capitalisation are currently technology companies (Apple, Alphabet, Microsoft, Facebook and Amazon) and three of these companies did not exist less than 25 years ago.

Are we nearing a tipping point?

We believe there is evidence that technology may be nearing a tipping point – technology is now advancing at such a rate that a breakthrough in Artificial General Intelligence (AGI) may be rapidly approaching (AGI is a computer system that is as smart as a human across any intellectual task).

Firstly, we believe that the world’s major technology companies are collectively assembling the equivalent of the ‘Manhattan Project’ that led to the development of the atomic bomb in World War II. Companies such as Alphabet (Google), Facebook, Microsoft, IBM, Alibaba, Baidu, Amazon and Apple are investing unprecedented amounts of money in artificial intelligence research and development, expansion of computational power, collation of the world’s data and knowledge and assembling the world’s leading intellectual capital by hiring leading graduates, researchers and scientists in fields of artificial intelligence and computer engineering from the world’s leading universities.

Secondly, over the last few years there have been dramatic advances in machine learning, voice and image recognition, machine understanding of language (machines can now read and understand documents) and the early development of quantum computers. Each of these areas appear important in the development of AGI and it seems reasonable to expect accelerating advances in the years ahead.

Finally, March 2016 may well be remembered as a seminal moment in the advancement of artificial intelligence, when AlphaGo (a computer program developed by Google DeepMind) beat the Go world champion, Lee Sedol, in four out of five games. Experts had predicted that a computer program would not master Go, an ancient Chinese board game still played today, for another decade given the complexity of the game. There are apparently more possible moves in a game of Go than there are atoms in the universe. The breakthrough with AlphaGo is that it is a self-learning algorithm that learns from raw data. AlphaGo taught itself to play by playing itself 30 million times. Google DeepMind’s website states:

“The algorithms we build are capable of learning for themselves directly from raw experience or data, and are general in that they can perform well across a wide variety of tasks straight out of the box.”

An algorithm that learns for itself is a fundamental building block of developing AGI. The winners in the AGI arms race are likely to have access to the best intellectual capital, massive computing power and vast data across all areas (personal, written documents, image/video).

In our view, disruptive and profound changes to businesses, industries and economies from exponential advances in technology appear to be ever closer to our doorstep. As investors, we need to carefully weigh up nearer-term investment opportunities against the likelihood of exponential progress and be prepared and positioned for fundamental and disruptive change over the longer term. The risk is that we will fail as investors if we fail to see the future. This time it may well be different.

 

This is an extract from Magellan Asset Management’s Annual Investor Report for June 2016

Hamish Douglass is CEO, CIO and Lead Portfolio Manager at Magellan Asset Management. This material is for general information purposes only and must not be construed as investment advice. It does not take into account your investment objectives, financial situation or particular needs.

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