Get AI or Die Tryin’: Part 1

April 5, 2018
· 6 min read

Unlike the kind of demise 50 Cent was referring to in his debut album, Get Rich or Die Tryin’, death in industry* is something quite different. Unlike the ultimate fate of all humans — a meeting with the Grim Reaper (unless your name is Sam Altman) — we have the ability, as individuals and organizations, to take control of our own destinies when it comes to death in industry.

However, steering the fate of our careers, or the trajectory of the organizations that employ us, is not an easy task. We are all at the mercy of external market forces that are so powerful we must accept that some things are as inevitable as the ocean tides. Perhaps the most relevant examples of this inevitability in our lifetime is artificial intelligence (AI) & automation.

At DataRobot, we are on the front-lines of an impending AI divide that has seen, in just a few years, a handful of technology giants rise to dominance within their respective industries due largely to their armies of data scientists and data engineers. Now, after their stock prices have doubled and tripled, they are quickly searching for the next source of growth as they’ve saturated their incumbent markets as they continue to deepen competitive moats thanks to increasingly aggressive data aggregation and monetization strategies.

This saturation has forced them to not just turn on each other, but increasingly venture outside their current markets into more legacy-oriented verticals like healthcare and banking that are ripe for disruption. What they lack in domain expertise in these new frontiers they are more than making up for with the advantage of having the in-house resources to develop native AI solutions.

I will outline why this trend is relevant to nearly every individual contributor, manager, and executive within every corner of industry, and how early-adopter organizations outside of the technology giants are keeping up – keen to not fall behind (or die).

AI and Orders of Magnitude

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As large and inconceivable of a number as one trillion dollars is for most economic forecasts (with the exception of financial derivatives markets), I believe that Forrester is vastly underestimating the impact of AI. Global GDP clocked in around $127 trillion in 2017 (The Balance).

Over the next three years, we will continue to see a considerable shift in allocation of global human capital and currency due to AI and automation. It’s not inconceivable to think that Forrester could be off by an order of magnitude if you look at the aggregate impact of AI and machine learning in industry if we include global governments as well as private economies. If this turns out to be true, the coming decade will bring with it a rolling tide of transformation in B2C (business to customer), B2B (business to business), and municipal business models so swift and ruthless that thousands of organizations will become dinosaurs, leaving behind millions of fossilized jobs and business models.

A large number of tech leaders and visionaries have openly spoke of this coming 4th Industrial Revolution of AI and Automation with similar orders of magnitude:

“Companies have to race to build AI or they will be made uncompetitive. Essentially, if your competitor is racing to build AI, they will crush you.” – Elon Musk (The Drive)

“AI is probably the most important thing humanity has ever worked on.” – Sundar Pichai (CNN Tech)

“…the one who becomes the leader in this sphere [AI] will be the ruler of the world.” – Vladimir Putin (The International Forecaster)

“Artificial intelligence, deep learning, machine learning- whatever you’re doing if you don’t understand it – learn it. Because otherwise you’re going to be a dinosaur within three years.” – Mark Cuban (Machine Learning for Beginners – Ken Richards)

“Those who rule data will rule the entire world.” – Masayoshi Son (

“It is a renaissance. It is a golden age. We are now solving problems with machine learning and artificial intelligence that were… in the realm of science fiction for the last several decades.” – Jeff Bezos (Innovation Enterprise)

One of the most relevant pieces of research I have found on this subject, and something that served as a partial inspiration for this piece, was Artificial Intelligence, Automation, and the Economy (link) published by the Obama Administration in December 2016.

The Coming Divide

Consolidation comes as an inevitable byproduct of death in industry, and it’s not without its side-effects.

Since 2000, 52% of companies in the Fortune 500 have gone bankrupt, been acquired, or ceased to exist. (Forbes) In the same time frame, the total number of US-listed public companies now sits at less than 4,000 – down roughly 40% from the ~7,000 that existed back then (Yahoo Finance).

Thanks to persistent consolidation and loosening regulations in recent decades, there is now a small number of unique companies in each sector who continue to grow their share of an already large percentage of total available resources and profits. When we look at some of the fastest-growing and most valuable public and private companies in 2018, all of these market leaders and category creators have made significant investments in the allocation of their resources to prioritize AI and the monetization of data as a primary part of their business strategy.

The Data Giants

Transportation: Uber, Tesla

Media: Netflix, Spotify

Hospitality: AirBnb

Advertising: Facebook, Google

Retail: Amazon

Mobile OS: Apple

The growth and market leadership of these companies in recent years has given them a dominant amount of resources, capital, and talent-attracting culture relative to their peers and legacy companies. This inevitably allows them to continue to hire ever-growing armies of data scientists and data engineers to help them dig even deeper data-driven moats and steal market share from their competition – both large and small. It appears to be a compounding cycle of data-driven domination that does not appear to be slowing down anytime soon.

Death in Industry

As result of being the first movers within their industries to successfully execute on delivering data-driven products and data-centric business models, these market leaders and category creators were able to deliver an enormous amount of value to both consumers and investors, all while simultaneously gaining dominant market share in their respective markets. This execution on their part has swiftly and ruthlessly left hundreds of thousands of laggard competitors out to die. If they haven’t died already, they will very well soon be dust in the wind – either bankrupted entirely, acquired for pennies on the dollar, or merged together in a desperate hope to stave off competition and survive.

We cannot afford to tip-toe around this trend any longer. We should face this uncomfortable truth: that ‘progress’ as we view it from a both societal and business perspective inevitably means the end of the status quo and the death of inefficient processes and business models. An even more uncomfortable truth: what we are now seeing in the most recent trends of AI and automation is that billions of humans, potentially a full majority of the global workforce, will be completely blindsided by the fourth Industrial Revolution.

And unless we force a dramatic shift in education and training, the majority of the workforce will lack the necessary skills to be designers or operators of AI and automated systems. Instead, they will either be told what to do by these systems or their work and the need for human capital to be deployed in large aspects of their employer’s business will be replaced entirely.

Stay Tuned…

In Part 2 of this blog post, I will take a deeper-dive into the landscape of the handful of technology giants who have successfully leveraged AI and automation, and identify a few examples of their plans for industry expansion and cross-pollination. However, as dark and dystopic as some of these industry trends appear to be, I can assure you that it is not all doom and gloom. In the final conclusion, I will highlight how the rest of the market is fighting back and using automated machine learning to help level the playing field.

 * Industry in this case is an all-encompassing roll-up of public and private sectors, individual businesses and business models, human capital, and machine capital.

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About the Author:

Ben Solari is the Director of Inside Sales at DataRobot, where he now leads a team of 15 Corporate Account Executives spanning from Boston to Singapore. He started as the first Inside Sales hire at DataRobot in March 2016. His background is primarily in finance and analytics, with previous data and analysis roles at organizations like ESPN, UBS, Trillium Trading, and InsightSquared.

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