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Considerations for the Future of Education, Re-Training and Choosing the Right Career in the Era of Artificial Intelligence

AI Superpowers: China, Silicon Valley and the New World Order - Kai-Fu Lee


Summary and Commentary by Lawrence J. Danks

Assistant Professor of Business, Camden County College


( 31, 2019)--Kai-Fu Lee, a major venture capitalist, appeared on Sixty Minutes in July 2019. He described major changes that will be brought about through the rise of Artificial Intelligence (AI). Any student, worker and educational administrator should Guest opinion 4familiarize themselves with what is likely to happen in the not too distant future, to assist them in proper planning  for what’s coming.

What follows is an amalgam of Dr. Lee’s (Columbia, Carnegie-Mellon) thoughts accompanied by my commentary. There is no substitute for reading this book however. The purpose of the summary is simply to provide an impetus to do so, in your own self-interest and that of entities you are associated with.


In 2017, Chinese venture-capital investors poured record sums into artificial intelligence startups, making up 48 percent of AI venture funding globally, surpassing the United States for the first time.

The threat to jobs is happening far faster than most experts anticipated, and it will not discriminate by the color of one’s collar, instead striking the highly trained and poorly educated alike.

“Deep learning” is what’s known as “narrow AI” – intelligence that takes data from one specific domain and applies it to optimizing one specific outcome. While impressive, it is a far cry from “general purpose technology” that can think just as a human being could.

We stand at the precipice of a new era, one in which machines will radically empower and/or violently displace human beings.

Deep learning pioneer Andrew Ng has compared AI to Thomas Edison’s harnessing of electricity; a breakthrough technology on its own, and one that once harnessed can be applied to revolutionizing dozens of different industries, just as nineteenth-century entrepreneurs soon began applying the electricity breakthrough to cooking food, lighting rooms, and powering industrial equipment.

Successful AI algorithms need three things: big data, computing power, and the work of strong—but not highly talented, engineers to develop algorithms. Lee says that Silicon Valley looks downright sluggish compared to its competitor across the Pacific. (This was not a slight to the technical intelligence of Silicon Valley, but a tribute to the relentlessness of the Chinese.)The only way to survive this battle is to constantly improve your business model and build a “moat” around your company to protect it.

Dr. Lee believes China’s world-class entrepreneurs and proactive government will continue its quest for superiority in AI and that that lead will translate into productivity gains on a scale not seen since the Industrial Revolution.


Significant as this jockeying between the world’s two superpowers, it pales in comparison to the problems of job losses and growing inequality as deep learning washes over the global economy: Poised to wipe out billions of jobs up and down the economic ladder: accountants, assembly line workers, warehouse operators, stock analysts, quality control inspectors, truckers, paralegals, and even radiologists, just to name a few. Within fifteen years, artificial intelligence will technically be able to replace around 40 to 50 percent of jobs in the United States. (This figure has been prominently mentioned in the media. While Lee said this is possible, he says actual estimates will more likely be in the wide range from about 10% -38% and that there will be natural pushback against this through political action, appearance of new careers and general inertia. He says this trend has already started and its growth will likely be faster than we might imagine. Other sources predict that while jobs will be lost to AI, they will be supplanted by new ones, at least to some degree. In any case, it is important for anyone involved in future employment to pay close attention to what’s happening.

Lee says that occupations and industries threatened may not be eliminated entirely, but nevertheless may be substantively affected. It is part of our job to determine which ones are at risk and how best to prepare for the new future of employment and competition. The author has made it clear that new jobs created by this phenomenon will be far short of those that will be lost.)

How valuable would Uber become, he says, if in the span of a couple of years, the company was able to replace every single human driver with powered self-driving vehicles  or that lenders with algorithms could make smarter loans with much lower default rates — all without human intervention? Similar combinations will soon play out across industries like trucking, insurance, manufacturing, and retail. (This would add to the already existing challenges faced by brick and mortar retailers, making mass in store retailing a risky long term career choice.  A quick thought might be that workers should move to online retainers like Amazon instead, but while they are now increasing their workforce, there will continue to be an increased use of robots inside and drones outside to facilitate deliveries.

Similarly, autonomous (driverless) trucks and drones will dramatically slash the cost of shipping physical goods. Unions have made inroads into having Congress work against subsidizing driverless truck technology, likely to be in the long term as effective as trying to save jobs by reducing foreign imports. It’s virtually impossible to stop the inevitable. Career plans and policies of educational institutions need to be based on today’s likely realities, recognizing that some current programs and courses should cease and be replaced by new, more relevant ones.)


AI isn’t just going to hurt the US, but China and developing nations too. Al-driven automation in factories will undercut the one economic advantage developing countries historically possessed: cheap labor. Robot-operated factories will be built to be closer to their customers in large markets, pulling the upward ladder away from developing countries like China, used in becoming high-income, technology driven economies.

Lee believes the real underlying threat posed by artificial intelligence will be tremendous social disorder and political collapse stemming from widespread unemployment and gaping inequality.

Many of us have also been conditioned to develop a sense of self-worth from our daily work. The rise of artificial intelligence will challenge these values and threatens to undercut that sense of life purpose in a vanishingly short window of time.  (Lee offers some proposals to deal with these problems near the end of the book, many of them socially beneficial focused on human care and education for example, but such options likely to pay less than what many are earning today.)


Chinese companies are first and foremost market-driven. Their ultimate goal is to make money, and they’re willing to create any product or service, and to do whatever it takes to do it –including the pirating of technology. All that matters is whether a financial profit can be made. The grand prize is riches. It doesn’t matter how they get there.


While US organizations are largely mission driven, Chinese companies are market driven, but that can become a real burden rapidly changing markets. What does a company do when there’s a divergence between what the market demands and what a mission dictates? China is a strong believer in the “lean start-up” method: Companies don’t know what the market needs – the market knows what product the market needs. Start-ups should move quickly to release a “minimum viable product” (MVP) that can tease out demand for different functions. This instant feedback lets them immediately begin iterating on the product (or service), discarding unwanted features, tacking on new functions and constantly testing the waters of market demand. Organizations which focus on mission can prevent themselves from quickly responding to needed change.



In the great economic growth of China in the past thirty years, it created its rise and made its living by focusing on being an engine of productivity, working off the product ideas created by others. Today’s China will both build things and create ideas. The author’s newly founded company, “Sinovation Ventures” is an early-stage innovator and angel investment fund for Chinese start-ups.


The evolution of Chinas technology ecosystem, and that ecosystems greatest asset is its tenacious entrepreneurs. If artificial intelligence is the new electricity, big data is the oil that powers the generators.

WeChat is the dominant social app in China that has evolved into a digital “Swiss army knife” capable of letting people pay at the grocery store, order a hot meal, book a doctor’s visit or a roving masseuse or rent short term use of a bike. Shared bike use in China outpaces that in the US by 300 to 1. Paying by smartphone turned Chinese cities into the first cashless environments since the days of the barter economy. It has become the world’s largest internet of things (and services) network, and in the process created massive amounts of data about who is buying what, where they are buying it and when.

Training successful deep-learning algorithms requires computing power, technical talent and lots of data. But of those three, the volume of data will be the most important.

The country’s massive number of internet users – greater than the US and Europe combined – gives it the quantity of data, but it’s what those users do online that give the data its quality.

By 2010 only around one-third of the Chinese population had access to the internet. When  cheap smart phones hit the market, waves of ordinary citizens leap-frogged over personal computers entirely and went online for the first time by way of their phones.

By WeChat’s two-year anniversary in January 2013, it had 300 million users. Along the way it had added voice and video calls and conference calls, allowed users to hail taxis, unlock shared bikes, manage investments, book appointments, and have meals and prescriptions delivered to the door.

Early smartphone users had more than doubled between 2009 and 2013 from 233 million to a whopping 500 million.

Added to all this is the Chinese government’s broad direction and support of entrepreneurship, principally through incentivized local mayors who compete to build successful business environments. Obviously, in a system like this there were mistakes and misfires, but the Chinese philosophy was that overpaying in the short term can be the right thing to do, when the upside is so monumental, creating a system that is both highly inefficient but extraordinarily effective.

All this contributed to the “020 Revolution” short for “online to offline”, bringing e-commerce convenience to the purchase of real world services, things that can’t be shipped in a cardboard box and shipped like hot food, a ride to the bar, having children picked up from school, or getting a haircut. Manicurists gave up their storefronts and starting booking house calls though online apps. Round the clock condom delivery was also available.


Food delivery has led the way. By the end of 2014, Chinese spending on food delivery had grown over 50%. China’s 20 million daily online food orders equaled ten times the total across the US. (China’s population is about 4 times that of the US, so the growth was accounted for by far more than larger numbers of people.) By the end of 2016, it was hard to find a shop in a major city that did not accept mobile payments. By the end of 2017, 65% of China’s over 735 million smartphone users had enabled mobile payments. The market research firm iResearch estimated in 2017 that Chinese mobile payment spending outnumbered that in the US by a ratio of 50 to 1. All this has created one of the richest maps of consumer activity the world has ever known.

(This is what we are competing against for the future of our economy and our jobs. It’s not just a matter of preparing our workers for what is in their best interests to be doing, but trying to stay up with a relentless adversary. In the meantime, government spending in the US for AI has not kept pace. Instead, the daily news is filled with tweets about inane nonsense. The stock market has seen unprecedented growth recently, but it can hardly be expected to hold up with this kind of onslaught and lack of better attention to our futures.)

The Light Touch Versus Heavyweights

Silicon Valley start-ups build the information platform, but let the businesses themselves handle the on-the-ground logistics. In China, companies tend to “go heavy”. They don’t just build platforms, they recruit sellers, run the delivery team, supply the scooters for delivery, repair the scooters and control payment. In addition, the companies gain far deeper sources of regular data that help them remain even more competitive.

(Given this type of unprecedented assistance, what small business wouldn’t want to sign up? And virtually all of them do. The deeper into the nitty-gritty, the harder it will be for competitors to copycat, effectively building a “moat” around the business, protecting it from challenge. Warren Buffett and Charlie Munger, the brains behind Berkshire Hathaway and its legendary success, stress that the presence of moats are one of the major factors they use in selecting their corporate investments.)


Massive productivity gains will come from the automation of profit-generating tasks, but they will also eliminate jobs for huge numbers of workers. These layoffs won’t discriminate by the color of one’s collar, hitting highly educated white-collar workers just as hard as for traditional manual laborers.

A college degree- even a highly specialized professional degree – is no guarantee of job security when competing against machines that can spot patterns and make decisions on levels the human brain can’t fathom. They will be competing against machines that can spot patterns based on millions of pieces of data that the human brain couldn’t recognize or calculate.

(This won’t necessarily mean wholesale replacement of occupations, although sometimes it may, such as perhaps drones being using for crop spraying and fire fighting, instead of airplanes. AI, automation and robotics aren’t going to replace attorneys in court, but legal research might be able to be done more efficiently by AI instead of by attorneys and paralegals - and at a fraction of the cost.

Accounting and radiology have long been thought to be highly desirable career paths, and obviously we are still going to need the output they provide, but more routine aspects of these professions may be done more efficiently and expeditiously by AI, reducing the overall number of opportunities in those fields, as well as the cost of them and the salaries paid for them.

AI will revolutionize manufacturing. This will add to the pressure already caused by automation – augmenting workers with machines – and robotics, that sometimes can completely replace humans as producers. AI’s big advantage over both of these is that it can be easily replicated and shared quickly at low cost, without requiring development, shipping, installation, maintenance and other factors required by automation and robotics.

Hardest hit can be industries that involve high volume work paired with external marketing or customer service: such as fast food, financial services, security and even radiology, where the result will be steep — although not total —reductions.


AI is projected to have major negative impacts on employment and income distribution. Dr. Lee says that AI will produce a mixed bag of winners and losers, depending on the content of the jobs performed. While getting a college education or training for fields in demand makes sense, the selection of fields, and what educational institutions offer, is going to be more important.

Beginning on page 155, Lee provides charts showing “Risk of Replacement: Cognitive Labor and Risk of Replacement: Physical Labor”. A few takeaways:

Cognitive Labor:

Safe: Wedding Planner, Teacher, Doctor (General Practitioner),Tour Guide, Financial Planner, Remote Tutor, Concierge, Criminal Defense Attorney, Social Worker, CEO, Psychiatrist, PR Director – Obviously occupations that require lots of hands on. Such jobs are difficult to automate and outsource. They are the types of jobs workers should be seeking. Note that not all the occupations mentioned above are high paying. A classic example of another is home health aides. There is going to be continuing demand for them, but it pays poorly.

Danger Zone: Customer Service Rep, Radiologist, Personal Tax Preparer, Insurance Adjuster, Consumer Loan Underwriter, Basic Translator, Telemarketer – In-the-ear translation devices may obviate the need for as much language instruction and translation services in the future.

Slow Creep: Facing some threats, but likely to take a while: Columnist, Medical Researcher, Graphic Designer, Legal/Financial Analyst, Scientist, Artist. Note that other threats can impact these occupations, as the internet has dramatically affected newspaper employment, so all avenues need to be examined, and local advice sought, to get a full picture of occupational prospects.

Physical Labor

Safe: Caterer, Bartender, Luxury Hotel Receptionist, Café Waiter, Elderly Home Caretaker, Home Health Aides, Physical Therapist, Hair Stylists, Dog Trainer – Note that while some of these occupations may not be threatened, a number of them do not pay well or otherwise have undesirable elements to them, so it shouldn’t be a knee-jerk reaction to run to these fields just because they seem “safe”. As a portent of possible things to come, I saw a news story from China where customers placed their food order in a restaurant with tables next to a conveyor belt. When the order came from the kitchen, the conveyor took the food right to the customer’s table, obviating the need for human servers. Smartphones enabled payment.

Danger Zone: Teller/Cashier, Fast Food Preparer, Restaurant Cook, Garment Factory Worker, Dishwasher, Fruit Harvester, Truck Driver, Assembly Line Inspector – Other than trained restaurant chefs, the long term prospects in these occupations are not good. Additionally, banking faces frequent mergers than can also reduce job opportunities. On the other hand, some banks are growing rapidly and provide excellent opportunities. Bank teller training is also a way to gain some very responsible skills.

Slow Creep: Taxi Driver, House Cleaner, Plumber, Home Construction/finish worker, Nightwatch Security, Aerospace Mechanic – Autonomous vehicles cause a real threat to taxi and Uber drivers, house cleaners will always be needed and such work is difficult to automate, but it is work that typically has no benefits, including no Social Security contributions being made to the worker’s account. (Social Security is not automatic. There must have been a required contribution for the mandated number of “quarters” of the year in order to qualify, unless someone can qualify under another’s employment. This is far more important than someone avoiding taxes by working “under the table”.) Construction is up and down and faces challenges from pre-fabrication, including homes being “xeroxed”. Night watch security can largely be replaced by motion detectors and other technologies and  additionally has threats to personal safety.

Learning Hard Lessons About Life

While the author has had an amazingly successful career, he had a real wake-up call and found that we should recognize that there is nothing more valuable in this world than the simple act of sharing love with others. If we start from there he says, the rest will begin to fall in place. “It’s the only way we can truly become ourselves…I wouldn’t seek to be a productivity machine. A loving human would be enough… Instead of seeking to outperform the human brain, I should have focused to understand the human heart.”

Facilitating for the Future

It is never too early to start educating students on what artificial intelligence is, how it works and what its ramifications are for future employment and daily life. This education should begin  in middle school through college. On the college level, a program, or at a minimum, a basic course in Artificial Intelligence, Robotics and Automation should be offered.

Colleges should also review their current program offerings in light of the impact of artificial intelligence for the purpose of determining which programs should be ended and which new ones should be created. Students need to be able to rely upon the wisdom of their colleges and universities to prepare them for viable long term futures, not to be inadvertently led toward careers that face the threat of being dramatically, negatively impacted in the future. Students should not be directed down unfulfilling and risk laden holes.

Moving Forward

Traditional wisdom of long standing has held that getting a four year college degree is most important for students to prepare themselves to be competitive in today’s economy. Community colleges in particular have also taken the lead in preparing students for many career offerings requiring education and/or training of two years or less. While obtaining a bachelor’s degree or above is often thought to be a career advantage, it is certainly not the only benefit of getting such education. Education has value in and of itself in creating an improved insight into life.

Nothing mentioned here is intended to suggest that it is still not a good idea for students to obtain academic degrees or to obtain training in careers in demand. What has changed now, in the light of the face of AI, is that educational institutions and students must make more informed choices about what fields of employment they intend to enter.

Career Planning and Academic Advising offices need to familiarize themselves with this trend too so that their students can be properly counseled. It is essential that this type of informed guidance be provided. Recently graduated high school students, and those in their early  twenties, can’t be expected to have sufficient current and future purview to recognize the challenges that AI presents. They need to be able to rely on their administrators, academic advisors, career planning offices, departments and professors. Their need is to familiarize themselves with the potential impacts of AI on their areas of competence. This should extend to flat out advising students that some majors, occupations and areas of employment have varying degrees of risk.

Students also have a responsibility to do some investigation on their own through family, friends, those working in an industry that they have an interest in and through information they can glean from their own course and program selection. Because of the high likelihood that the future of AI is going to influence employment far into the future, colleges should ask themselves what mechanism they have for staying current on these trends and why they aren’t offering a program or an overview course on AI so students will have a better understanding of the challenges they face.


Recommended Sources of Additional Information

Artificial Intelligence To Create 58 Million New Jobs By 2022, Says Report

Will robots and AI take your job? The economic and political consequences of automation

Jobs lost, jobs gained: What the future of work will mean for jobs, skills, and wages


Will Robots and AI Take Your Job

The outlook for machine learning in tech: ML and AI skills in high demand

Good News for Job Seekers With Machine Learning Skills: Talent Shortage

How Technology Will Affect U.S. Jobs Over the Next 10 Years - Cisco

  • Shows displacement and income effects by industry

Indeed: AI job-posting rate slows and interest dips


4 ways AI will impact the financial job market Impact of Artificial Intelligence – Widespread Job Losses


The Rise of AI and Employment: How Jobs Will Change to Adapt