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A Spotter’s Guide to the Coming Revolution

Working day-in and day-out with associations across every spectrum of American society, you get a pretty good sense for the pulse of America and the collective mood of its business leaders—often finding yourself privy to emerging trends at their source.


Sometimes, you even find yourself with a front row seat to a revolution.


It’s that moment when technology or technological development comes along that is so radical and so disruptive, it transforms virtually every aspect of society, spawning complementary innovation and creating entirely new supply chains, industries, and unimagined new markets—while bringing others to an inglorious close.


Today, we find ourselves at just such a moment of transformative change, where the promise of the new opportunities unleashed is only rivaled by the danger that comes from failing to adapt and embrace the new way of doing business.


It’s called the platform revolution

Platforms are what enabled Uber to send the taxi industry to the curb, Amazon to close the chapter on bookstores, and Netflix to roll credits on the video rental industry.


Oddly enough, platforms themselves are not revolutionary at all. In fact, if you are familiar with newspapers or shopping malls, you’re already a platform expert. Because, simply put, platforms merely bring together producers and consumers, fostering mutually beneficial exchanges.


A mall is a platform that brings together consumers and retailers, serving nothing more than a vehicle for their exchanges to be made. Similarly, a newspaper is a platform that pairs advertisers with consumers, while providing news content to both.


But what makes today’s platforms different than those legacy platforms is that today’s leverage other concurrent technological revolutions to optimize their performance, delivering on the promise of giving consumers what they want the very moment they want it.


Phenomenal leaps in the collection and interpretation of data, along with the interconnectivity of the Internet of Things (IOT), has allowed platform-based companies to anticipate and answer the needs of their consumers more quickly than ever before. These companies have proven so nimble and quick, they’ve been able to do an end-around of traditional pipeline-based companies who’ve relied on sheer size and control of the supply chain to maintain market dominance.


Companies that have been quick to adopt the new platform model have not only been able to seize market share from traditional pipeline-driven companies, but entire markets. At the time of the introduction of Apple’s iPhone, for instance, four major telecom companies had a stranglehold on the mobile phone market, sharing 90% of the industry’s profits among themselves. Yet, in just eight short years after introducing Apple’s iPhone platform, Apple had virtually wrestled away the entire market, earning 92% of all its profits, with just one of the giants recording any profit at all.


Platforms have proven so successful at disrupting traditional supply-chain business models, the Harvard Business Review notes, “While plenty of pure pipeline businesses are still highly competitive, when a platform enters the market of a pure pipeline business, the platform virtually always wins.”


The importance of network effects

Ultimately, the success of a platform is determined by its proficiency in generating network effects—or its ability to increase in value for all those participating on a platform as the number of actors joining the platform increases.


Gaming consoles are a perfect example of a platform utilizing network effects. The consoles serve as middlemen between gamers eager for the next thing in gaming and game developers eager for a loyal fan base for their products. The greater the number of gamers using a console, the greater the appeal and value of the platform to game developers. When more game developers are drawn to a platform, the number of games available on the console becomes that much more appealing and valuable to gamers. Theoretically, the cycle intensifies exponentially until this symbiotic relationship fuels the network effects sufficient for the platform to capture the market.


Ultimately, the platform revolution is being accelerated and enhanced by a series of other concurrent revolutions, each equally important and disruptive in their potential impact on traditional business models.


Early adopters quick to embrace these new technologies are likely to gain considerable advantages in their respective marketplaces that could lead to a decisive edge in the new economy. Those that don’t are just as likely to end up as a bullet point in the food-chain of the next great disrupter fattening itself on corporate giants for lunch.


Where does blockchain fit in?

Like digital DNA, blockchain (distributed ledger technology) is revolutionizing the reliability of data by creating digital fingerprints that can always be traced back to its original source.


It is a decentralized way to digitally verify the veracity of commercial transactions, using algorithm-based transactions, called smart contracts, that limit the need for human data entry and, therefore, reducing the chance of human error. By automating typically manpower-intensive processes, blockchain creates new efficiencies while reducing costs and mistakes.


The efficiencies of blockchain will be most valuable for new and future digitized transactions that are added to the chain as they are produced. Before blockchain can be of real value to the paper transactions of the past, each of those transactions must first be digitized and added to its database, and each associated transaction entered, too, to eliminate gaps in data chain.


Currently, the costs associated with digitizing legacy records dating back hundreds of years is proving to be prohibitively expensive. So, predictions that blockchain will render industries overseeing legacy data obsolete are premature. On the contrary, blockchain cost-savings and efficiencies may make these industries more valuable and indispensable than ever as the sole keepers of these essential records.


The nascent world of Artificial Intelligence

Artificial Intelligence (AI) is when machines are programmed to “learn” from experience and use the data collected to create algorithms on-the-fly that mimic human problem-solving and learning.


Today, AI can outperform humans in very narrowly-defined tasks like chess. However, in the very foreseeable future, it is expected that AI will surpass human ability in nearly every cognitive task assigned.


Conquering the Internet of Things (IOT)

As broadband internet becomes more accessible and the price of connecting to it more affordable, more and more devices, sensors and household appliances are being Wi-Fi enabled.  The Internet of Things (IOT) is the resulting network of billions of interconnected devices, connecting people to devices, devices to devices and people to people.


It’s this interconnectivity of things that has the potential to make platforms that much more versatile and valuable.


Imagine if, after a quick scan, your refrigerator notices you are running low on milk, eggs, and one of the herbs key to the casserole you’ve scheduled on your calendar for dinner. Your refrigerator immediately sends a text to you, your car and Amazon. Your car’s built-in GPS calculates the quickest route to Whole Foods and notifies you that your items will be waiting for you when you arrive.


Why Big Data is still a big deal

As you know, since the dawn of the digital age, humanity has collected and perfected ways to collect gobs and gobs of data. Once basic information became digitized, it became remarkably easy to access and utilize.


Suddenly, mundane data could be effortlessly combined with other related data, broadening understanding and contributing to something much bigger than itself. Increases in computer processing power and cheap data storage not only allowed us to effortlessly store huge amounts of data, but organize it and make sense of it as well.


Today, this abundance of information and the ability to easily insert it into powerful analytical models is what is fueling revolutions in everything from AI and data science to platforms and the IOT.


Like gold to the prospectors of old, the digital fingerprints of consumers—from online browsing histories, the interactions between their Wi-Fi-enabled appliances, text messages, GPS-assisted trips to the grocery store—have become an invaluable commodity to marketers. With them, they’re able to create predictive models that anticipate how consumers will react in any given situation with a high degree or reliability, gain new insight and understandings by looking for patterns and relationships in structured and unstructured data harvested from desperate databases, and pinpoint tends, threats and opportunities as they materialize in real time.


Big data gets exponentially bigger every day. Consider that between the time you read this and two days from now, we—the global community—will produce as much data as was created in the span between the first written word and the year 2000.


Deep Learning is powering breakthroughs

Under the umbrella of Artificial Intelligence (AI) and a subset of machine learning, deep learning is the phenomenon behind recent breakthroughs in AI that have allowed everything from SIRI and ALEXA to become household names, to the strides made by self-driving cars and package delivering drones.


Powered by the immense processing power of Graphical Processing Units (GPU) with nearly limitless storage and seas of raw data (Big Data), Deep Learning, is patterned after the neural networks in our brains, where any one neuron connects with any of millions near it to process data.


In Deep Learning, although unable to duplicate the flexibility of neurons perfectly, it mimics the brain’s thought processes by creating algorithms of layered processing, where each layer is informed by the “learning” of the one before it.


As data is entered into the system, Deep Learning makes initial assumptions about that data and measures the probability of whether its conclusions are accurate or not. If it is deemed to be accurate, the data is passed to the next layer for even closer scrutiny. If its assumptions are deemed to be inaccurate at any layer, a record is made so the error is not repeated.


This allows the process to create “probability vectors,” where data is judged by its probability to be accurate. The data with the highest probability of being accurate is passed down layer after layer until it is found to have a high probability of being 100% accurate.


Over time, as the Deep Learning GPU is exposed to hundreds of millions of scenarios, it becomes trained, or “learns,” to provide analysis with accuracy surpassing human ability in certain cases.


Already, Deep Learning and advancements in artificial intelligence have contributed to huge strides in medicine, able to analyze medical samples and quickly pinpoint anomalies that could be signs of illness. It is not beyond the realm of possibility that in the very near future AI enlightened machines will be able to scan the entire genetic code in a person’s DNA and quickly find and repair maladies that have confounded doctors and scientists for generations.


The new frontier

Someday in our lifetimes, it’s widely believed we’ll  experience singularity—the moment when the intelligence of machines overtake that of humans. Until then, I take pleasure in the fact that there’s still a role for us mere humans in imagining what was unimaginable just a few short years ago and setting into motion the next great revolution that undoubtedly will transform our very lives and livelihoods.