Although our intellect always longs for clarity and certainty, our nature often finds uncertainty fascinating — Baron Carl Von Clausewitz
The world IS data. It is very obvious that the more relevant data a platform possesses, the more powerful it becomes at understanding the world.
For example, medicine has already moved to a crowdsourcing model of compiling the wisdom from specialists and doctors around the world. This is because it is the most efficient way of compiling the collective knowledge of the best doctors mankind has to offer. The benefits don’t have an upper limit. Better delivery of medicine, efficiency in conducting surgeries, remote medical procedures and cost effective delivery of healthcare are some of the benefits. New doctors will learn from global experts and will drastically shorten their learning curve on global medicine and practices. Ultimately, the end consumer i.e. the patiens can expect to live a healthier life and to live longer than before.
Hedge funds are crowdsourcing algorithms from data scientists around the world to create the ultimate algorithm that yields them the elusive alpha. Crowdsourcing is also a form of diversification so that a collectively sourced algorithm can provide higher returns while minimizing downside risk. In fact, the Winton group in London, United Kingdom gathers data from as far back in human history as it can. The premise of that approach is that the history of financial markets tends to rhyme if not repeat itself. It wants to leverage human history to improve its understanding of the future. Thirdly, think of Initial Coin Offerings (ICO’s), although in their infancy, as a way of crowdsourced venture capital.
By now, everyone understands that the best way to remove bias in decision making is to have as many people touching the proverbial elephant as possible in the hopes of finding the true nature of the beast. Of course, the beast in this case could be the future. This brings us to the question this article attempts to address: how important is multidisciplinary thinking to a business? In my humble opinion, it’s the difference between survival and extinction. The very future of many industries depends on this approach, not in the very least — that of financial services.
A Meeting Of Minds In New York
Last week, I was in a meeting hall packed with executives from well established financial services incumbents (multinational and national mega banks), fintech entrepreneurs and leaders from consulting firms to listen to a futurist talk about his vision for financial services. It was an eclectic mix of people. Like any other event, people were attending the meet to gain knowledge and insights and to network with various objectives. The term ‘networking’ was always an umbrella term for various agendas. Young and middle aged professionals and entrepreneurs furiously exchange their business cards in the hopes of drumming up business or to meet prospective vendors for their services. Often, the people trying to sell their services are employees or founders of FinTech startups that are trying to make their case to financial services professionals about how their technology is unique and can be tailored to specific needs. The incumbents, on the other hand, are trying to keep themselves abreast of technology to avoid obsolescence and to spot that one unique solution to their primary problems. Most problems end up being data oriented problems.
This dance is not new. Many Incumbents think that home grown innovation may end up being fancy bullet points on MS PowerPoint slides because they are extremely hard to execute. The speaker mentioned a good analogy. According to him, innovation is like a virus. The corporate body sees it as a threat and its immune system kills it before implementation. Obviously, incumbents need FinTech startups to provide them with their innovations. The FinTech startups, in turn, form partnerships with incumbents to understand scalability of their ideas creating a symbiotic partnership. However, make no mistake, all companies will be technology companies in the future.
Thus, mega banks will have to transform themselves into technology led business companies. In a likely end game, incumbents, FinTech and TechFin (the likes of Amazon, Apple) should be able to compete with propietary algorithms as their primary differentiators. This scenario makes a direct case for including AI experts and data scientists on the board of directors of financial service companies. As per a 2015 report by Accenture, only 6% of board of directors and 3% of CEO’s of leading banks have technology experience. Further, the research indicates more than two-fifths of banks have no board members with professional technology experience.
The Mechanics Of Data
The interface of the future can take three forms : voice based semantic search of data, augmented reality for playing with more visual experiences and a very distant future where humans will be cybernetic organisms capable of doing the very same computing that mechanical marvels do today.
Today, a data engineer is exactly that. She looks at disparate sources of data ie legacy systems, sensors on the ground and other IT systems and creates a pipeline from each of those systems using API’s to supply data to an algorithm. The data scientists, many possess PhD in statistics, design the algorithm by understanding the data pipelines setup by the data engineer and the output needed by a business analyst or the management to solve their business problems. The role of the management is to define specific business problems as best as they can.
Practically, a manager can then ask a voice based assistant in his colloquial language (let’s say English in this case) to provide him with the earnings before tax over ten years of a particular business unit in a bar chart form. The algorithm will then recognize the voice, make sure the person is authorized to access the data and then provide the output on a dashboard with appropriate labels for the chart.
Once this setup ie the full stack is established, the machine can learn from itself using reinforcement learning to keep evolving and becoming more intelligent. Thus, a variety of disciplines ie programming, statistics and user interface design (UX) come together to create a humanoid version of data.
In modern business, cloud computing for storage, AI algorithms for marketing, cybersecurity, Blockchain for decentralized record keeping and Augmented Reality all coalesce to transform brick and mortar to its digital avatars.The problem with many technologies is that they are short lived. Newer technologies with better economics eat older technologies often giving an advantage to second movers rather than early adopters.
For example, the security of Blockchains which are based on asymmetric encryption techniques could become obsolete with quantum computing. As per the journal National Review, quantum computing is the new arms race.
As per NASDAQ, the market for quantum computing is projected to be around $5 billion to $10 billion a year by Morgan Stanley in the next 10 years, while a report by Homeland Security Research estimates that the global market for quantum computing and technologies will grow at a CAGR of 24.6% throughout 2018–2024. Quantum computers can theoretically break all the encryption used by classical computers today. Similarly, augmented reality could be replaced by chips in our brain providing exponential computing power inside the brain obviating the need to rely on computers for complex calculations.
Of course, Venture Capital (VC) investment in quantum computing is a trickle compared to investments in other technologies. The primary reason is the fact that building the infrastructure i.e. quantum computers is a hardware intensive and lower gross margin business.
Highly disruptive and life changing technologies often develop in growths and spurts. For instance, the development of Artificial Intelligence (AI) went through two AI winters i.e. decreased interest and spending on research. Similarly, quantum computing could grow in spurts. This trend does not take away from the fact that CEO’s have to be mindful of what’s coming down the pike. While there are very important impediments to the development of the Blockchain (e.g. scalability), other technologies are developing simultaneously. Most of the businesses are focused on cybersecurity and understandably so. On Friday, there were reports of a massive data breach of client data on the Starwood hotel chain (Marriott) database. However, with technologies such as Quantum Key Decryption (QKD), the hackers are also thinking of breaking quantum encryption.
All these facts lead to a very complicated future for CEO’s . A multidisciplinary panel of experts is needed to help a CEO prioritize a business’ short term and long term challenges.
AI algorithms are already biased. That’s because we are replicating biases embedded in the programmers’ brain into the algorithm. The solution is to include more women, ethicists, disenfranchised and the government in removing bias towards humans. This is one of the most profound examples of multidisciplinary vision.
An even bigger one comes to mind when you think of genetic editing. CRISPR has made gene editing so simple that it has been democratized to anyone with a lab and understanding of the technology. In the case of gene editing techniques, the most powerful concept is the exponential rate of diffusion of the technology. Many gene editing and splicing techniques were available before. The power of CRISPR is its incredibly low resource requirements making it extremely fast to propagate. Last week, He Jianqui in China claimed to have created CRISPR babies. Gene editing needs powerful regulation lest it becomes a weapon of mass destruction.
So, the next time a CEO says we need to consult a quantum physicist to understand how long it will take for quantum computers to break down the current encryption safeguarding our clients’ and business’ data, he is not joking.
Just as quantum physics appended classical physics, quantum computing can break all the encryption algorithms we use today using Shors’ algorithm. In fact, quantum key distribution is already a reality. The data, the algorithm working on the data and the output (e.g.semantic voice search) are all going to be crowdsourced. Its not a question of if but when.
The immediate solution before business leaders is to diversify its board of directors, say in case of a financial services company, to include technology entrepreneurs and CEOs as well as futurists who draw upon various experts to craft their vision of the future.
Another solution is to create a panel of advisors which can include quantum physicists, AI ethicists and labor reform specialists to help retrain displaced labor to avoid civil strife and social unrest. Of course , training has become an instrument of avoiding civil war. The moment middle class and upper middle class jobs are threatened by AI, social unrest will be extremely visible.
A third solution is to form alliances with competitors and partners to pool resources for cost efficiencies and to design industry grade scalable solutions.
Ultimately, business works for human societies and not vice versa. Every business is personal and empathy is extremely powerful. The existing problems of humanity are existential, technology intensive and spreading rapidly across the globe. Forget utopia, we need to ensure healthy diffusion before we begin to build a beautiful garden. Lastly, I leave you with these words by Dante Alligheri:
‘The darkest places in hell are reserved for those who maintain their neutrality in times of moral crisis’
As businessmen, let’s not make technology in business the wellspring of another moral crisis.