deal architect

The Comparative Advantage of Nations applied to technology

As Washington, London and other world capitals debate and revisit classical global trade thinking, a remarkable change has been happening in the technology world. SaaS, PaaS and IaaS competencies are becoming increasingly pronounced and vendors are recognizing the sheer scale and competitive advantage that specialization is bringing.

I have been projecting for a decade we would start to see such specialization and it is now starting to accelerate, even as politicians throw regional roadblocks using privacy and data storage and retention requirements.

Workday CEO Aneel Bhusri appeared today at Amazon's re:Invent event to announce Workday has selected AWS as its preferred public cloud infrastructure provider for customer production workloads. As it navigates complex regulatory requirements across countries, the first set of customers to enjoy this choice will be in Canada next year.

It follows the path of Infor which committed to AWS a few years ago (CEO Charles Phillips famously said “Friends don’t let friends build data centers”), and Unit4 and IFS which leverage Microsoft’s Azure cloud. That frees up SaaS vendors to invest more in application feature/functions – their areas of comparative advantage.

Oracle, IBM, Google and others are similarly gearing up to be IaaS vendors in their own right. It is a game of capex at scale of in billions of dollars a year.

Just as interesting is the PaaS game. Oracle and Microsoft enjoy massive historical developer support but as interest moves to Artificial Intelligence, the Internet of Things and Virtual Reality, Amazon with services like Lex and Polly, Salesforce with Edison, GE with Predix and many other players are looking to build platform ecosystems. Indeed Amazon today pointedly praised a bunch of “disrupters” (in slide below) which are changing many industries, as it seeks to become their provider of choice.

It’s a tale of two cities for a vendor CTO – growing number of platform and infrastructure choices to decide from, tempered by growing nationalism and politicians who want to narrow their choices.

Enterprise processes and systems in a world of growing automation

In recent blog posts you have seen me combat what I consider alarmist scenarios about job losses from automation being painted by many analysts, academics and folks in Silicon Valley. My perspective is automation targets individual tasks done by humans, not complete jobs. Human workers will keep moving to higher value activities. And, as a result, business processes and enterprise applications will similarly need to evolve.

Here are some scenarios from the 50+ work settings across industries in my new book, Silicon Collar :

Expense management and compliance

Patrick Taylor is the CEO of Oversight, which sells solutions designed to assist travel, purchasing card and accounts payable managers in uncovering possible misuse, fraud, and compliance violations. He says they analyze nearly $2 trillion in expenditures annually at Fortune Global 5000 companies and government agencies.

As he says

“A lot of attention is focused on when a credit card is stolen, duplicated, or used by the wrong person. Our automation focuses on the instance where the right person is using their credit card, but are they doing the wrong thing. It’s authorized users doing something inappropriate."

His clients can move from sample-based reviews to 100% automated review with an emphasis on policy effectiveness, employee behaviors, and root causes of policy violations.

Real estate

Lots of simple white collar tasks will be done by bots. And that will allow for rethink of physical spaces. Mihir Shukla, CEO of Automation Anywhere is quoted in the book

“I can take what would need offices in six large buildings and put them on two racks. I can help consolidate global offices. A cognitive bot can process an invoice in 30 languages—I don't need centers in the Philippines and other centers in Japan to deliver diverse language skills.”

Advanced Manufacturing/Logistics

The shop and warehouse floor of tomorrow will be dotted with Baxter and Kiva robots, workers with wearables, 3D printers and sensors everywhere. As Jason Blessing, CEO of Plex told his user conference earlier this year

“What we're doing, the Plex community, is someday going to be taught in classrooms. In a sense, we're the pioneers who are leading the fourth industrial revolution—leveraging technology to make manufacturing plants more intelligent, safe, and efficient than ever."

Legal and risk management

Bill Hewitt, CEO of Exari is quoted

“Our vision is a world where the contract process is virtual. Companies negotiate with each other in cyberspace, taking best practices from a universal library of clauses and precedents or recommendations from a contract ‘machine.’ Automatic scoring of risk gives the negotiator visibility into how exposed they are, allowing them to ensure they are putting proper protections in place. Finally, the contract is analyzed against similar previous contracts and the libraries are updated even as the contract whizzes through cyberspace with electronic counterparty signatures. The contract and its core data are stored, and now are ready to inform downstream and upstream systems of the new rights and obligations.”

Asset management, predictive maintenance and field service

GE is a prime example of a company with plenty of “smart” assets. “Workers at airport hangars, wind turbine farms, hospitals, and many other settings are seeing the growing impact of smarter GE machines that generate loads of data which is then acted upon.”

Jeremiah Stone, general manager for Asset Performance Management (APM) at GE Digital, is cited

"Companies that wait for equipment to fail spend an average of 13% of replacement asset value annually on maintenance. Companies that employ proactive condition-based maintenance, by contrast, face a slim cost of just 2%.”

HCM/talent management

In the book, I expand on Charles Handy’s “shamrock organization” (internal staff, long term contracted talent and contingent labor) to make it a “clover-leaf” with the fourth leaf covering machines, robots, and other automation. “Actually, clovers can have many more leaves. The Guinness World Records says one was found with 56 leaves.” I describe talent enterprises are increasingly using in the form of outsourcing, franchises, platforms and customer self-service.

I conclude 

“As a result, very few organizations have a complete map of all the talent they are leveraging. That's remarkable, considering that for many companies 80% to 90% of their talent is now "off balance sheet"—not directly on their payroll.”

There are scores of other areas where enterprise processes and software will need to evolve as the man/machine mix changes. Over the next few months I will focus on more of those in my posts.

Automate this!

In my new book, Silicon Collar I looked at automation and impact on jobs in 50+ work settings. On my innovation blog I have been excerpting weekly from those settings — you can see some of them linked below. In each case, practitioners are applying AI, robotics, wearables and other technologies to automate tasks.

The simplest job I profiled was that of a garbage collector in our county. He used to dangle at the back of the truck, pick up bags at each house and throw them into the compactor. The job did not need any formal education or much training.

To be able to automate that job we had to retrofit the garbage trucks with a robotic arm. Each of our houses had to be delivered 2 sturdy, large new carts — 95 gallon size for the regular garbage, 65 gallon one for recyclables. It is estimated over a half a million carts were delivered to residents. The drivers had to be retrained to operate the arm. Each household had to be trained how to position the carts. We had posters and billboards with instructions in neighborhoods, brochures in the mail and public service announcements on TV, social media and the Web. Quite a significant investment in time and money.

Informal polls showed 2/3 of our residents opposed the move. Some found the carts too cumbersome to store and drag to the street, others felt guilty about the potential job losses. We went ahead anyways. Unlike the city of Toledo, OH which gave up after a year, we have persisted and our streets look neater, our recycle rates have improved and we see other benefits.

However, our use case was relatively straightforward given our mostly sunny weather and suburban neighborhoods. The Volvo Group is working on an all-weather truck with a collection robot at back and a drone to communicate with sensors on the garbage cans and guide the robot to each can. Think of the incremental capital commitment that will need.

Now think about automating more elaborate jobs like some below which require multiple skills during the course of a workday — hand dexterity, frequent walking, visual acumen, judgment calls, social interaction, ability to sense hot and cold and many others.

Just for one occupation — accountants, the O*Net (the country’s primary source of occupational information) lists 13 skills. For garbage collectors, they list 6.

Try to find machines which can duplicate these skills, preferably from a single source vendor not a “Frankensoft” solution which require many custom manufacturers and systems integrators. The reality is most automation can replace a task or two, not complete jobs.

Next think through what regulators would need to certify such automation in healthcare, transportation, aviation and many other industries. Finally factor the ROI analysis corporations would need to conduct, the comfort consumers would need to develop to adopt such machines. In this article, Slow-Motion Automation I describe some of the “circuit breakers” to societal adoption of automation.

Now factor that the 50 settings I looked at for the book barely scratch the surface of the 840 occupations the Bureau of Labor Statistics currently tracks. An update to that is due in 2017. Many new occupations are emerging every year. FastCompany expects new ones in the next decade like neuro-implant technicians and urban farmers.

Still feeling pessimistic and that we are moving soon to jobless societies due to machines doing all our jobs?

I will make you an offer to cheer you up. I am taking bets on specific job loss scenarios in the next decade or two. In fact I have a dinner scheduled on my calendar for September 1, 2026. I will pick up the tab if in that year 20% or more of all car sales/leases in the U.S. have fully autonomous capability. Otherwise my friend who I have the wager with will buy the nice meal.

Ping me if you want to wager something similar. I am that confident humans will be doing all kinds of work even decades from today and that machines will be our peers. not our replacements.

In the meantime, enjoy these excerpts from the book.

Accounting

Advertising

Architecture

Banking

Basketball

Financial Adviser

Food and Beverage

Garbage Collection

Hospital

Hotel

Oil and Gas

Restaurants

Research and Development

Roofing

School teachers

Uber driver

UPS driver

Urban Planning

Warehouse

Winery

cross posted at Medium

What do it do?

I remember as a young PwC consultant, a manager who used to walk around and joke “what do it do?” as he reviewed our specs and our code. It was his way of reminding us to deliver what the customer wanted, not a “solution looking for a problem”.

As I review the inflated expectations people have around today’s automation technologies (AI, robotics etc.) that is very useful advice to bear in mind.

Take autonomous cars. When someone spots Google testing a driverless concept or when Elon Musk says “ you’ll be able to summon your car from across the country” people jump to driverless Uber scenarios. Within a year or two, no less. If displacing a few Uber drivers is the end goal we have missed the mark by a wide margin. The promise of driverless cars is much nobler — to reduce over 30,000 car accident deaths a year in the US, and over a million around the world.

I, for one, do not think we will see autonomous cars en masse for decades — and describe in Silicon Collar selected scenarios where we will see them. While the technology is evolving nicely, our road infrastructure, our driving laws, ethical discussions and incumbent interests will slow down progress toward broader deployment.

You know what? So long as the technology keeps evolving, we should still celebrate. In a decade, if most of our cars have blind spot sensors, auto-braking, distracted driving alarms and many of the 30+ Audi Q7 safety features we will be so much safer.

Indeed, Honda’s newest Civic LX, equipped with an advanced driver assistance system, recently navigated a 25-mile Detroit area road course nearly hands-free. With price points starting at a little over $20,000, it may be “driverless enough” for many customers.

Or when McKinsey says “about 60 percent of occupations could have 30 percent or more of their constituent activities automated.” again people jump to conclusions. Stop and think about the technologies needed to cover the breadth of the 2,000 activities across 800 occupations that are mentioned by McKinsey “from fine motor skills and navigating in the physical world, to sensing human emotion and producing natural language.” Think of the specialized sensors, robotic limbs, image capture and other technologies that would need to coalesce and allow for the vast sweep of capabilities.

While you consider that wide palette of technologies, enjoy this joke: An accountant, a lawyer, and an automation engineer are led to the guillotine, having been convicted of grievous crimes. The accountant is first in line, but the falling blade jams halfway down. The executioner tells him, “You may be unethical but my professional ethics require me to spare your life.” The lawyer is second in line, and again the falling blade sticks and the man is spared. Finally, the automation engineer is walked up, and he confidently addresses the executioner, “I know what your problem is. It’s the actuator.”

With the range of technologies described above, it is highly unlikely any single engineer could be that specific. You also hope he is less exuberant:)

Contract manufacturers and systems integrators will need to work together to develop customized machines. Similarly, on the customer side, process and automation engineers will need to collaborate closely. None of this will be cheap.

I look at my county and how much planning and investment it took to put a robotic arm on our garbage trucks. Every household had to adopt sturdy bins which the county subsidized. The drivers had to undergo significant re-training. All that to eliminate maybe 100 jobs of which used to pick up garbage bags at each house and toss them into the compactor on the truck. Not a complex job by any stretch of the imagination.

So, before you go and plan for such “Frankensoft” automation solutions, check if customers are truly asking for it and at what cost? Best I can tell customers want automation that can target what I call 3D — dull, dirty and dangerous — tasks, not complete jobs. Deliver that and we will have happy customers and grateful workers.

Moral of the story — don’t over-engineer. Keep asking the wise question “What do it do?”

As for those who confidently project jobless societies, perhaps they should go talk to practitioners, regulators and others with first hand experience with automation. The expectations are fairly modest after years of hype cycles around AI, robotics and other technologies.

Cross-posted at Medium.

Forget Luddite workers. Luddite customers are bigger roadblocks to automation

In researching my recent book, Silicon Collar I expected resistance to automation to come from workers and unions, and from politicians trying to protect jobs. I was surprised major resistance actually comes from customers for a wide range of reasons

Corporate customers worry about poor payback

I interviewed practitioners in 50+ work settings across industries using AI, robotics, wearables and other forms of automation. The broad consensus about automation — cautious optimism.

Many categories of automation have been hyped in the past. In some cases, every 5 years. So many corporate customers worry about project failures and poor payback.

Take AI, even after decades of promises. We keep making progress, and yet there is so much farther to go. As IBM executive Vijay Vijayasankar wrote on his Facebook page:

“To the world that worries about AI, machine learning and assorted algorithms becoming our overlords — I present to you ….LINKEDIN! A trained monkey will pick better job recommendations than what LinkedIn manages to send me in these emails.”

Yale computer science professor David Gelernter thinks we have only scratched the surface. In his book The Tides of Mind, he calls it “the spectrum of consciousness,” which is “essentially a range of mental states through which all humans cycle each day. The cyclical element is crucial and underlies his metaphor of tidal motion. At the upper end of the spectrum, mental high tide, we are focused on the outer world, biased towards logical and abstract reasoning, and more likely to remember our experiences later. But as we drift down through the middle and into the lower reaches of the spectrum, we become increasingly conscious of the inner worlds of memory, prefer narrative to logic, and cross eventually into the difficult-to-remember realms of dreams.”

We still don’t understand much about the lower reaches of that spectrum.

Or take robotics

The first humanoid robot appeared in Japan in 1928. It could do simple motions like move a pen with its right hand. Today, Japan is the leading maker and consumer of robots, accounting for half of the world’s production. Naturally, it has the world’s largest concentration of robot engineers. Yet, these world-leading experts have tried for five years following the Tohoku earthquake to use robots to clean the radiation at the Fukushima nuclear plant. So far, all the robots sent into the reactors have failed to return.

Forget nuclear clean up — how about tidying your hair? David Bruemmer, president and CTO of 5D Robotics, recently described how intimidating a hairdresser’s job looks to a robot.

Consumers often complicate automation decisions

Most companies offer to mass customize their products for consumers. That complicates automation. An example from Bloomberg “the intricate options offered on the new S-Class — from heated and cooled cupholders to four different types of tire valve stem caps — overwhelm traditional automotive assembly robots. Accordingly, Mercedes has begun replacing them with human workers.”

With many products, the machine touch actually leads to discounted prices — cigars, Persian rugs, silk ties and fine foods are some that come to mind. Many consumers pay premiums for the artisan version.

In his book, The Revenge of Analog, David Sax highlights a move back to vinyl, paper, board games, brick and mortar for a number of customers. That often means small batch production or dusting off old equipment and bringing back labor with those skills.

A vivid example of slow adopter customers comes from the auto industry. Oscar H. Banker was granted the patent for the automatic transmission way back in 1940. In 1965, Playboy magazine screamed “Bye-Bye Stick Shift.” Now, over 50 years later, more than half of the light cars sold outside the U.S. still have manual transmission. How many decades before these customers adopt driverless cars? And when they are ready to, they may find some of today’s leading brands missing

Mazda, for example, is counting on humanity’s continued love for driving — what it has branded “Zoom-Zoom.”. Along with Porsche, Ferrari and a few other car makers, Mazda has shown little interest in investing in driverless cars.

Many consumers are passive-aggressive about self-service automation

While some consumers eagerly embrace self-service for its speed and convenience, many others expect a modest discount, considering that they are reducing labor costs of banks, retailers, or airlines. In fact, many are annoyed as banks tack on fees for using ATM machines and airlines charge for printing boarding passes.

This comment on an ABC News story reflects the attitude of some consumers:

“Part of the cost of an item is to pay for the service of checkout. I’m not going to waste my time and effort, and take someone’s job in the process, by checking myself out. If self checkout gave you a 5% discount on the total bill, I’d consider it, but otherwise I’d rather not take someone’s job.”

Still others resent kiosks at Home Depot and elsewhere that capture a video of their transaction. It is meant to reduce fraud, but consumers worry that their privacy is also being compromised.

Taunt them as “Luddite” if you dare. These customers are a source of plenty of revenues for a number of businesses. Automation can wait.

Cross posted at Medium

Corporate investment in enterprise software

I have long wondered when corporations, especially conglomerates, would become enterprise software/service providers in their own right. The track record is patchy. You see financial service companies like Bank of America and Fidelity offer payroll and benefits support. You see vertical investments – Siemens with PLM and CAD, ABB with Mincom. You see auto companies cross license plenty of technology. But in many cases these services become invisible – a small part of the corporate product mix.

We may be a seeing a bit of a change. Koch Industries, the privately held conglomerate is investing $ 2.5 billion in Infor. Koch owns stakes in INVISTA, Georgia-Pacific, Molex, Flint Hills Resources, Koch Pipeline, Koch Fertilizer, Koch Minerals, and many other entities with consolidated revenues in excess of $ 100 billion. Infor management is not expected to change much.

GE has been building out its Digital unit and Predix platform over the last couple of years with acquisitions like Wurldtech and Meridium. It announced this week the acquisition of ServiceMax, for field service support for $ 900m as it expands coverage of the Industrial Internet.

The difference, this time, is the investments in software are much larger – the chances they will be “forgotten” are slim. They open up opportunities for the software companies within their own portfolios, and with that the hope that the software will become functionally stronger with operational demands of those wide spread industries.

Probably a bit much to expect but most corporations have seen first hand the massive overruns and failures around enterprise software. If they can bring their Six Sigma and other quality disciplines to software for the benefit of broader customers, there will be much more to celebrate.

What do people do all day?

Do you remember Grocer Cat, Farmer Alfafa or Jason the Mason? You don’t? Shame on you!

Not really. I don’t blame you for not remembering what you may have read when you were very young. But in many ways, we should have adult versions of those wonderful characters from Richard Scarry’s illustrated Busytown.

In writing my recent book, Silicon Collar, I had a chance to profile workers and their machines in over 50 industries — including at accounting firms and banks, the battlefront and digital agencies, the oil patch and restaurants, R&D labs and shop floors, warehouses and wineries.

I thought I had covered wide ground, but then was floored the Bureau of Labor Statistics classifies workers into one of 840 detailed occupations, in accordance with the 2010 Standard Occupational Classification (SOC) system. The next list due in the next couple of years will show even greater variety as STEM disciplines create a new generation of jobs. FastCompany’s projection of jobs in the next decade includes Urban Farmers, Neuro-Implant Technicians and Virtual Reality Experience Designers.

CareerPlanners.com does an even more granular listing of job descriptions, and lists 12,000 separate jobs. And they do not even begin to profile countless other jobs TV hosts like Mike Rowe showcase like sewer inspector and shark suit tester.

Why is this important for all of us? I find most of us, in our networks and neighborhoods, only seem to know of 30–40 such jobs and as a result have a narrow world view.

The more we understand of the amazing, modern day job economy, the more pragmatic we can be. We have way too many workers angry at politicians and corporations for layoffs, offshoring, and automation.

We can gain confidence to reskill ourselves every few years, and change occupations several times a career. Our parents enjoyed (or suffered) through a life time career which came with a 25 year plate and a pension. In many ways, they would be envious at the amazing choices we have in what to do all day.

When you see the breadth of jobs, each of which requires multiple skills, you can sit back and realize how alarmist it is for academics and economists to predict machines (AI, robotics, wearables etc) will destroy all jobs. Machines will take over individual tasks and in the process reshape jobs, but not destroy tens of millions of jobs anytime soon.

When you explore the breadth of the job economy, you realize how pessimistic the studies are which say the middle class has been decimated. The middle 2/3 of returns filled with the IRS show adjusted gross income of $ 6 trillion. It’s real money being earned by the hundreds of occupations you did not even know existed.

I could go on. But instead of poring through boring BLS and IRS data like I did, spend some time browsing cable TV and look at workers and entrepreneurs in shows like Cake Boss, Fast N’ Loud, Hell’s Kitchen, Inside Man, Shark Tank, Top Gear and Undercover Boss. Or even better strike up conversations with your neighbors, at the barber shop, on the train or the plane about what people do all day.

It will help you understand we have a wide and a rapidly changing labor economy. Something we can be proud of and be comforted by.

Image credit

Cross posted at Medium

Can you acquire cloud DNA?

Zach Nelson, CEO of NetSuite tells Diginomica

And so I actually believe Oracle’s going to behave more like NetSuite in the future than NetSuite’s going to behave like Oracle. Every company wants cloud DNA. It’s the only DNA we have. I think we’re going to have enormous influence on the Oracle side of the house.

So it had me thinking, how easy is it “acquire” such cloud DNA? I think Oracle was well on its way with a decade of Fusion development, with acquisitions like Taleo and with growing investments in its infrastructure as a service (so Larry Ellison felt bold enough at OpenWorld to challenge the market leader, Amazon). And yet, at times even they still act like they long to stay in the on-premise world

SAP’s acquisitions of SuccessFactors, Ariba and others and its own experience with Business ByDesign do not appear to have instilled the cloud DNA yet. Jon Reed reports they are just ramping up their S/4HANA public cloud. The new head of that division seems almost casual about a core tenet of cloud computing.

Today, the fact that we’re not multi-tenant in the application level has zero impact on my ability to envision the product, zero impact on the security of my product, zero impact on the cost of my product – at the scale that I’m at today. But: I want to fix it, because when I hit a billion euros annually, it’s going to make a difference.

IBM’s acquisition of SoftLayer does not appear to have instilled a cloud DNA either. The bulk of their revenues – outsourcing, software and hardware revenues still persist with older hosting and on-premise models. With the Watson Developer cloud that may change somewhat in coming years. But then again I already have waited for 5 years since IBM started pitching Watson and it’s still not even 5% of its revenues.

Took Microsoft for a while to grow its cloud DNA and there it was more organic. I wrote in The New Polymath in 2010 about their Azure data centers. Even though billions of dollars had been poured in them it took a few more years before Office 365 started to take off and for Microsoft to actively push its platform to developers and ISVs.

I recently wrote about a spate of cloud acquisitions by large outsourcers. It will be a decade this month when I expected them to start bringing in cloud principles like Software as a Customized Service. And still, the vast majority of their revenues come from support of on-prem solutions.

If you were not born in the cloud like NetSuite or Workday or Appirio, it is very difficult to change your spots – leopard like.

Oracle may just be a exception to that, now with NetSuite’s help. Other large vendors will need to do a lot more than just acquire "born in the cloud" entities. 

Tech was a big loser in this election cycle

There will be plenty of post-mortems from this highly unusual election season. As a technologist, we should face up to fact we did not do well in this election cycle

• Neither campaign really talked about science and tech futures and next-gen opportunities for the country. Even though folks like Peter Thiel and Mark Cuban were visible in the campaigns, in the debates and countless other appearances the candidates did not talk with much pride about our STEM capabilities
• One of the biggest fears in the workforce is that automation will lead to massive job losses. If you read my book Silicon Collar or an abridged version here, the fear is irrational. But neither candidate addressed the issue.
• The Wikileaks and other security breaches were embarrassing and reduced faith in our IT folks.
• Lost in the politics was the fact that the State Dept IT should have made it easier for their chief executive to get email on her fingertips, not have her implement her own solution. Too many CIOs have outdated email infrastructure and more and more of us turn to “rogue” solutions like Gmail and communicating via social and mobile apps 
• The massive miss of the pollsters will make people wonder if Big Data and other analytics have been over hyped

Let’s learn from this. We need to put our best foot forward and we need to get much more actively involved in making sure STEM topics get the policy attention they deserve.

Automation Amnesia

Some of you know Denis Pombriant as a top-notch CRM analyst. I also know him as a historian. I look forward to meals with him where I can hear him talk about N. American history over the centuries.

He recently wrote two economic history pieces at Diginomica focused on the Kondratiev wave, here and here. These are long term cycles which everyone in technology would benefit reading about given the heavy capex and long time scales that IT initiatives continue to demand.

I interviewed him for my book, Silicon Collar and he made a striking comment about why we are so panicked about job losses from automation:

"The people crying loudest that the sky is falling can be separated into two buckets. The first bucket contains all those who have never experienced a wave change, which is to say all of us. We’ve lived our productive lives in one paradigm and we need to figure out what to do when we no longer make metaphorical picture tubes.”

His comments made me look at “societal amnesia” when it comes to automation and job losses. We panic every 3-4 decades – because the current generation has not experienced something similar in our lifetimes. I also found the panic is often accompanied by other traumatic changes in our societies.

The current panic, I believe, was triggered by the scary, deep global recession of 2007-2008. No job seemed secure with the financial freefall we saw.

But if some of us had been in the workforce in the 60s, we would likely have remembered that President Lyndon Johnson set up a blue ribbon commission to explore  growing panic about automation. The trauma back then could be explained by the turmoil around civil rights and Vietnam that the country experienced during his term.

Go back another few decades. Palo Alto, with its VCs and startups, is today the capital of the technology world. But would you believe the mayor of that city sent President Herbert Hoover a letter warning that industrial technology was a “Frankenstein monster” that was “devouring our civilization.”? The trauma back then came from the Great Depression of the 1930s.

You can go back every few decades all the way back to the Luddites and you find similar panic attacks. The Luddites, of course, had the ultimate panic attack. They were bands of English workers in the 1810s who destroyed newly introduced machinery, especially in cotton and woolen mills, fearing their jobs would be lost.

Denis and I are collaborating on research on these periodic panic attacks.

The important thing here to take away is none of us was working during the previous Johnson panic. If we had,  we could calm ourselves. The sky did not fall last time. It will not this time, either.