I recently finished reading “The Second Machine Age”, which was recommended to me by one of our board advisors.   The authors Eric Brynjolfsson and Andrew McAfee make a convincing argument that we are in the midst of a paradigm shift every bit as transformative as the industrial revolution. While the industrial revolution was about harnessing physical power, this new machine age is about harnessing cognitive power.

The authors identify the steam engine as the key catalyst for change in the industrial revolution. Between 1765 and 1776, James Watt, in partnership with Matthew Bolton, made fundamental improvements to the efficiency of the existing steam engine that would see its widespread adoption across a range of industries. What was so important about this invention is that it radically changed the relationship between human labor and output. Production was no longer limited by the physical capability of the number of humans on site (or the animals they employed), and it led to an explosion in productive capacity.

This new machine age is not about our physical constraints, but the constraints of a single human mind. Again we are experiencing a fundamental decoupling between our cognitive capacity and output. This change is being driven by the emergence of smart machines and the networks that connect us to these machines and to each other:

“The second machine age will be characterized by countless instances of machine intelligence and billions of interconnected brains working together to better understand and improve our world.” The Second Machine Age

The industrial revolution spread from one industry to the next, creating huge disruption as well as new opportunities – in agriculture, textiles, mining and transport to name a few. The same is happening now in this new machine age. The past 20 years has seen disruption moving between communication, media and retail. As the size and power of networks increases exponentially, more industries are now falling to this paradigm shift.   As a member of Level39, London’s fintech accelerator and an alumni of Startupbootcamp Fintech we have witnessed first hand how the second machine age is playing out in the financial services industry. Reading “the Second Machine Age” I realized how many of the themes are relevant not only in our own business, StockViews, but across the entire Fintech space.



It is hard to overstate the exponential growth in the power of the internet over the last 20 years. The world internet population has grown from 280 million users in 1999 to 3.3 billion users today. In the film “Limitless” Eddie Morra (played by Bradley Cooper) takes a pill (“NZT”) that enables him to unlock 100% of his brain function, not just the 20% that we typically access. Within a few days this power enables him to master several new languages, work out how to beat the markets and learn Kung Fu. In many ways the Internet is like a real-world NZT pill for the planet’s combined brain power.

“In addition to powerful and useful AI, the other recent development that promises to further accelerate the second machine age is the digital interconnection of the planet’s people.   There is no better resource for improving the world and bettering the state of humanity than the world’s humans – all 7.1 billion of us”  The Second Machine Age

This network of billions of minds is a formidable force that we are only just beginning to comprehend. The so-called “Sharing Economy” started out in physical goods and services, but arguably it is even more powerful when applied to cognitive tasks. There are countless examples of what this network has already been able to achieve. In 2010 NASA launched an open-source competition that asked contestants to create a reliable model to predict the onset and duration of solar particle events (events which can be damaging to people and equipment in space). Despite 35 years of research, NASA still had no reliable model for predicting these events. The winner of the competition was a retired radio frequency engineer living in New Hampshire (not a profile that would typically have been recruited to NASA). There are now numerous other examples of success for platforms like Innocentive (crowdsourcing scientific problems) and Kaggle (crowdsourcing data science problems). Time and time again the “crowdsourcing” of these problems to a wider set of contributors generates a better result than a smaller group of experts.

Here at StockViews we are doing the same for stock research – accessing a broader and more diverse set of minds than those employed on Wall Street. We can already see how the unique insights and combined wisdom of these connected minds is delivering a result that is far superior to the status quo. In terms of cognitive capacity it is the difference between a high-speed locomotive and a horse and cart. At some point in the near future it will seem quaint and outmoded that at one time we relied simply on the opinions of a small sample of “experts” on Wall Street to garner views on a stock.


Machine Versus Human

The impact of these networks alone is powerful enough. But when we add smart machines into the mix, the power of these minds is amplified even further. Contrary to the popular fears of many, machines are better used as a complement to humans and not as a replacement. In a 2005 freestyle chess contest, machines were pitched against humans and teams of humans with machines. The results showed that the human + machine combination was by far the most powerful:

“The teams of human plus machine dominated even the strongest computers. The chess machine Hydra, which is a chess-specific supercomputer like Deep Blue, was no match for a strong human player using a relatively weak laptop. Human strategic guidance combined with the tactical acuity of a computer was overwhelming” Gary Kasparov

Computers are extremely good in narrow frame environments – where there are a finite number of variables, choices and outcomes. This makes the game of chess a natural environment for a computer. The game of chess is restricted to 32 pieces and 64 squares. A lot of combinations of moves, but calculable.   Computers find it much tougher in broader frame environments, where there are infinite numbers of variables, choices and outcomes.   Stock analysis is an obvious example here. The analyst needs to make judgments on a wide variety of topics including the credibility of management, the outlook for the market, the loyalty of customers and the impact of market psychology on the stock. Each one of these is an epic challenge in itself, but the combination of all these, and the judgment as to which is most important in each case represents too broad a frame. Simply put, a machine just wouldn’t know where to start – it needs human guidance.

However we see a huge amount that machines can do for the stock analyst. Checking through 10-K statements for important changes, comparing recent numbers to competitors and picking out anomalies, or screening transcripts for important statements are all things we can train a machine to do. In fact much of our research effort at StockViews is targeted towards these very things.


Organizational Change

One of the interesting claims made in the book “the Second Machine Age” is that the greatest productivity improvement comes not from a direct substitution of the new technology, but from organizational change. Put simply, using the new machine in the same way won’t result in significant change.

“the best ways to use technology is usually not to make a literal substitution of a machine for each human worker, but to restructure the process” The Second Machine Age

One example provided is when factories began to replace steam engines with electrical motors. Perhaps surprisingly, there was little change in productivity at first. In the steam age it was most efficient to place all machinery close to a central engine in order to maximize power. When factory owners introduced electric motors, they simply replaced the existing steam engine with an equally large electric motor. It took 30 years for organizational change – smaller electric motors attached to each piece of equipment in a decentralized structure around the factory floor. And with this change finally came the real and substantial productivity benefits of adopting electric motors.

The same is true of the power of networks and smart machines. A bank can adopt all the innovation it likes, but without organizational change it will likely have little impact on productivity.  In fact a bank, as it is organized today, may struggle to find much value at all in adopting this next wave of technology. In contrast, peer to peer lending and crowdfunding platforms are structured from the beginning to benefit from the power of networks. This is one of the key reasons why the future belongs to the Fintech disruptors – not because financial institutions aren’t investing in technology but because they aren’t able to re-organize their structure fast enough.

 Nearly 30 years after the birth of the Internet we see these three components (networks, machine intelligence, and organizational change) converging and new business models emerging to take advantage. It is the combination of these components that is unlocking our cognitive potential and driving rapid change in multiple industries. From what we are seeing in the Fintech industry, this is likely to be just the beginning and the impact will be just as revolutionary as the first machine age.

“You see, Tom, the world goes on at a smarter pace now than it did when I was a young fellow. Why, sir, forty years ago, when I was much such a strapping youngster as you, a man expected to pull between the shafts the best part of his life, before he got a whip in his hand. The looms went slowish, and fashions didn’t alter quite so fast; I’d a best suit that lasted me six years. Everything was on a lower scale, sir…It’s this steam, you see, that has made the difference; it drives on every wheel double pace and the wheel of fortune along with ‘em” Mr Deane, George Eliot’s ‘The Mill on the Floss’

Tom Beevers and Sandeep Bathina are the Co-Founders of StockViews, a platform that connects fund managers with independent equity analysts.  Tom is an ex fund manager and Sandeep is an expert in artificial intelligence.


Join the conversation! 2 Comments

  1. Great article Tom, thanks.


  2. […] Source: Fintech in the Second Machine Age […]



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