Chapter 4. The role of Silicon Valley in creating the industrial internet
A new kind of hardware alpha-geek will approach those areas of the industrial internet where the challenges are principally software challenges. Cheap, easy-to-program microcontrollers; powerful open-source software; and the support of hardware collectives and innovation labs[41] make it possible for enthusiasts and minimally-funded entrepreneurs to create sophisticated projects of the sort that would have been available only to well-funded electrical engineers just a few years ago — anything from autonomous cars to small-scale industrial robots.
In the same way that expertise in software isn’t necessary to create a successful Web app, expertise barriers will fall in software-machine interfaces, opening innovation to a big, broad, smart community.
Neil Gershenfeld, director of the Center for Bits and Atoms at MIT, compares the development of the amateur hardware movement to the development of the computer from mainframe to minicomputer to hobbyist computer and then to the ubiquitous personal computer. “We’re precisely at the transition from the minicomputer to the hobbyist computer,” he told a conference audience recently. He foresees a worldwide system of fabrication labs that produce physical objects locally, but are linked globally by information networks, enabling expertise to quickly dissimilate.
In complex, critical systems, clients will continue to demand the involvement of experienced industrial firms even while they ask for new, software-driven approaches to managing their physical systems. Industrial firms will need to cultivate technological pipelines that identify promising new ideas from Silicon Valley and package them alongside their trusted approaches. Large, trusted enterprise IT firms are starting to enter the industrial internet market as they recognize that many specialized mechanical functions can be replaced by software.
But the job of these firms will increasingly be one of laying foundations — creating platforms on which others can build applications and connect nodes of intelligence. These will handle critical functions and protect against dangerous behavior by other applications, as we already see in automotive platforms.
The industrial internet will make machine controls easier to develop in isolation from machines and easier to apply remotely. It’s apparent, then, that markets in controls will arise separately from the markets in their corresponding machines. Makers of machines might reasonably worry that value will move from machines to software controls, leaving them with commodity manufacturing businesses (think of the corner case in which consumers buy a car from an automaker and then run practically all of its electronic services from their phones). Collaboration between machine makers and control makers is crucial, and the quality with which machines accommodate and respond to intelligent controls will become a key differentiator.
Silicon Valley and industry adapting to each other
Nathan Oostendorp thought he’d chosen a good name for his new startup: “Ingenuitas,” derived from the Latin for “freely born” — appropriate, he thought, for a company that would be built on his own commitment to open-source software.
But Oostendorp, earlier a co-founder of Slashdot, was aiming to bring modern computer vision systems to heavy industry, where the Latinate name didn’t resonate. At his second meeting with a salty former auto executive who would become an advisor to his company, Oostendorp says, “I told him we were going to call the company Ingenuitas, and he immediately said, ‘bronchitis, gingivitis, inginitis. Your company is a disease.’”
And so Sight Machine[42] got its name — one so natural to Michigan’s manufacturers that, says CEO and co-founder Jon Sobel, visitors often say “I spent the afternoon down at Sight” in the same way they might say “down at Anderson” to refer to a tool-and-die shop called Anderson Machine.
It was the first of several steps the company took to find cultural alignment with its clients — the demanding engineers who run giant factories that produce things like automotive bolts. The entire staff of the company, which is based in Ann Arbor, Mich., has Midwestern roots, and many of its eight employees have worked in the automotive industry. Sight Machine’s founders quickly realized that they needed to sell their software as a simple, effective, and modular solution and downplay the stack of open-source and proprietary software, developed by young programmers working late hours, that might make tech observers take notice. They even made aesthetic adaptations, filling a prototype camera mount with pennies to make it feel heftier to industrial engineers used to heavy-duty equipment.
Heavy industry and the software community will both need to adapt their approaches and cultures in order to make the most of the industrial internet.
The technology industry can easily overreach when it begins to think of everything as a generic software problem. Physical-world data from machines tends to be dirty, and it’s often buried in layers of arcane institutional data structures. (One airline-servicing company found that its first client had 140 tail numbers in its database — but only 114 planes.) Processes in established industries are often the result of decades (or centuries) of trial and error, and in many cases they’re ossified by restrictive labor agreements and by delicate relationships with regulatory bureaucracies.
The demands of the industrial world mean that some of Silicon Valley’s habits developed over years of introducing new services to consumers will need to change. Industrial systems can tolerate downtime only at enormous cost, and their administrators are only willing to install new services if they’ve been thoroughly proven. “I’ve bought systems from startups,” says an industrial engineer who works on fruit-juice processes. “They asked us to report bugs — they should be paying us for that service! Can you imagine running an industrial process on beta software?”
Many of the successful software firms that I spoke with operate as a blend of software startup — drawing bright developers from any background — and industrial firm with specialized engineers. The former bring the agility and innovation that’s driving the industrial internet’s transformation; the latter bring the credibility that these firms need in order to develop business with more conservative industrial companies.
As Sight Machine found, startups need to show industrial firms that they’re serious in terms that their customers will understand. Dan Zimmerle, from the power-systems lab at Colorado State University, says he’s approached at least once a month by a self-styled entrepreneur bearing a design for a perpetual-motion machine. “The skepticism of the industrial buyer is in some sense well-founded,” he observes drily.
Industry, too, will need to change its approach in order to take full advantage of the industrial internet, perhaps by changing incentive structures in order to reward mid-level plant managers for controlling costs as well as for keeping systems running smoothly. As in much of information technology, the reward for saving money is small and incremental, and the punishment for a new system breaking is somewhat more dramatic.
Some real-time applications of machine learning, in particular, can sound informal and can spook industrial managers with their implicit promises to learn from mistakes. Managers would, of course, prefer to avoid mistakes altogether, but machine learning has enormous promise in holding down labor costs, speeding output, and enabling flexibility. When capital budgets and responsibility for smooth operations sit with different people, the right level of risk-taking is unlikely to emerge.
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