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How Tech Created a New Industrial Model

With a connected device for every acre of inhabitable land, we are starting to remake design, manufacturing, sales. Really, everything.

With little fanfare, something amazing happened: Wherever you go, you are close to an unimaginable amount of computing power. Tech writers use the line “this changes everything” too much, so let’s just say that it’s hard to say what this won’t change.

It happened fast. According to Cisco Systems, in 2016 there were 16.3 billion connections to the internet around the globe. That number, a near doubling in just four years, works out to 650 connections for every square mile of Earth’s inhabitable land, or roughly one every acre, everywhere. Cisco figures the connections will grow another 60% by 2020.

Instead of touching a relatively simple computer, a connected smartphone, laptop, car or sensor in some way touches a big cloud computing system. These include Amazon Web Services, Microsoft Azure or my employer, Google (which I joined from the New York Times earlier this year to write about cloud computing).

Over the decade since they started coming online, these big public clouds have moved from selling storage, network and computing at commodity prices to also offering higher-value applications. They host artificial intelligence software for companies that could never build their own and enable large-scale software development and management systems, such as Docker and Kubernetes. From anywhere, it’s also possible to reach and maintain the software on millions of devices at once.

For consumers, the new model isn’t too visible. They see an app update or a real-time map that shows traffic congestion based on reports from other phones. They might see a change in the way a thermostat heats a house, or a new layout on an auto dashboard. The new model doesn’t upend life.

For companies, though, there is an entirely new information loop, gathering and analyzing data and deploying its learning at increasing scale and sophistication.

Sometimes the information flows in one direction, from a sensor in the Internet of Things. More often, there is an interactive exchange: Connected devices at the edge of the system send information upstream, where it is merged in clouds with more data and analyzed. The results may be used for over-the-air software upgrades that substantially change the edge device. The process repeats, with businesses adjusting based on insights.

See also: ‘Core in the Cloud’ Reaches Tipping Point  

This cloud-based loop amounts to a new industrial model, according to Andrew McAfee, a professor at M.I.T. and, with Eric Brynjolfsson, the coauthor of “Machine, Platform, Crowd,” a new book on the rise of artificial intelligence. AI is an increasingly important part of the analysis. Seeing the dynamic as simply more computers in the world, McAfee says, is making the same kind of mistake that industrialists made with the first electric motors.

“They thought an electric engine was more efficient but basically like a steam engine,” he says. “Then they put smaller engines around and created conveyor belts, overhead cranes — they rethought what a factory was about, what the new routines were. Eventually, it didn’t matter what other strengths you had, you couldn’t compete if you didn’t figure that out.”

The new model is already changing how new companies operate. Startups like Snap, Spotify or Uber create business models that assume high levels of connectivity, data ingestion and analysis — a combination of tools at hand from a single source, rather than discrete functions. They assume their product will change rapidly in look, feel and function, based on new data.

The same dynamic is happening in industrial businesses that previously didn’t need lots of software.

Take Carbon, a Redwood City, CA maker of industrial 3D printers. More than 100 of its cloud-connected products are with customers, making resin-based items for sneakers, helmets and cloud computing parts, among other things.

Rather than sell machines, Carbon offers them like subscriptions. That way, it can observe what all of its machines are doing under different uses, derive conclusions from all of them on a continuous basis and upgrade the printers with monthly software downloads. A screen in the company’s front lobby shows total consumption of resins being collected on AWS, the basis for Carbon’s collective learning.

“The same way Google gets information to make searches better, we get millions of data points a day from what our machines are doing,” says Joe DeSimone, Carbon’s founder and CEO. “We can see what one industry does with the machine and share that with another.”

One recent improvement involved changing the mix of oxygen in a Carbon printer’s manufacturing chamber. That improved drying time by 20%. Building sneakers for Adidas, Carbon was able to design and manufacture 50 prototype shoes faster than it used to take to do half a dozen test models. It manufactures novel designs that were previously theoretical.

The cloud-based business dynamic raises a number of novel questions. If using a product is now also a form of programming a producer’s system, should a company’s avid data contributions be rewarded?

For Wall Street, which is the more interesting number: the revenue from sales of a product, or how much data is the company deriving from the product a month later?

Which matters more to a company, a data point about someone’s location, or its context with things like time and surroundings? Which is better: more data everywhere, or high-quality and reliable information on just a few things?

Moreover, products are now designed to create not just a type of experience but a type of data-gathering interaction. A Tesla’s door handles emerge as you approach it carrying a key. An iPhone or a Pixel phone comes out of its box fully charged. Google’s search page is a box awaiting your query. In every case, the object is yearning for you to learn from it immediately, welcoming its owner to interact, so it can begin to gather data and personalize itself. “Design for interaction” may become a new specialization.

 The cloud-based industrial model puts information-seeking responsive software closer to the center of general business processes. In this regard, the tradition of creating workflows is likely to change again.

See also: Strategist’s Guide to Artificial Intelligence  

A traditional organizational chart resembled a factory, assembling tasks into higher functions. Twenty-five years ago, client-server networks enabled easier information sharing, eliminating layers of middle management and encouraging open-plan offices. As naming data domains and rapidly interacting with new insights move to the center of corporate life, new management theories will doubtless arise as well.

“Clouds already interpenetrate everything,” says Tim O’Reilly, a noted technology publisher and author. “We’ll take for granted computation all around us, and our things talking with us. There is a coming generation of the workforce that is going to learn how we apply it.”

Don’t Be Distracted by Driverless Cars

Because of the arrival of autonomous vehicles, a good number of prognosticators are loudly heralding the end of auto insurance and the insurers that rely on this income stream. While there is no doubt that the auto insurance will change over time because of autonomous vehicles, the doomsday folks paint a picture of an almost certain — and right-around-the-corner — cataclysmic change, with the general population cheering wildly about not having to pay for auto insurance anymore.

Disappointingly for those people, this is a highly unlikely scenario.

At the most fundamental level, autonomous vehicles are expensive. Until the bulk of the population can afford autonomous cars and trucks, we will be living in a world with a foot on each side of the driving paradigm. This means that, while the autonomous vehicle might not hit the car in front of it, the vehicle behind it may very well end up in its back seat. Uninsured and underinsured coverage, comprehensive coverage, coverage for property damage and liability coverage will be needed for a long, long time.

See also: Connected Vehicles Can Improve Claims  

In the meantime, auto insurers will have the opportunity to work out the liability paths with the autonomous auto manufacturers. And auto insurers will have the time to adjust their product mixes and financials. Underwriters and claims personnel will adjust processes and practices. Believe me, I am not suggesting that change won’t happen. It will. And some of the changes will be painful from several perspectives. But with all the various factors potentially reining in the spread of autonomous vehicles — cost, regulators, consumer wariness — insurers will adjust. At least the insurers that believe in innovation and data and analytics will adjust — and flourish.

What people are not focusing on is the rapid change in the products and services that are coming from the convergence of emerging technologies — and this is happening right now. Take a quick perusal of the topics in SMA’s Next-Gen Innovation Community, and some examples come to light:

  • To diagnose a rare genetic disorder, a physician combined an exomes analysis with a facial comparison using an app called Face2Gene, which was developed by the same programmers who taught Facebook to find your face in your friends’ photos.
  • To reduce manufacturing times and production periods, Adidas will use 3D printing or additive manufacturing methods as the core technology of its factory that produces sneakers.
  • The use of “cobots” — collaborative robots that perform tasks alongside humans — is rapidly gaining popularity, and it is the fastest-growing segment of the U.S. robotics industry.
  • The global semiconductor industry is pushing to develop new chip designs, materials and manufacturing processes. One reason is the widening use of the artificial-intelligence technique known as deep learning in products — both consumer and manufacturing products.

Additional examples could consume volumes of space. But what does this convergence mean for the insurance industry? It means unknown risk and unknown liability.

Unlike the autonomous vehicle world, where underwriters and claims adjusters understand the general risk and liability landscape, the convergence of emerging technology is generating significant numbers of unknowns. Relationships between the unknown risks and liabilities will be the most challenging.

The insurance industry should not be as concerned about the impact of autonomous vehicles as it should be about what is happening now in the rapidly changing world of products and services because of the effects of emerging technologies. Underwriters and claims personnel must be supported by sophisticated data and analytics capabilities using AI, machine learning and cognitive computing (pick your favorite advanced tool!)  because risk and liability will not be in the same forms as are currently familiar to insurance professionals.

See also: Autonomous Car Tech Reaches Mid-Market  

There are a lot of reasons that insurers will not look the same in five to seven years. Core modernization, digital capabilities and, yes, autonomous vehicles will generate change. But the halls of successful insurers will not be empty. They will be filled with technical experts in product liability, D&O, E&O, cyber and medical malpractice — to name but a few product lines that will have the ability to rapidly respond to a risk and liability landscape that didn’t exist  in their wildest imaginations two to three years ago.

Insurers that are not preparing themselves for this eventuality will fail.