Wednesday, February 26, 2014

Early Indications February 2014: Who will win in an Internet of Things?

As usual, Silicon Valley is in love with tech-y acronyms that sometimes do not translate into wider conversations involving everyday people. We’ve discussed “big data” in a recent newsletter, “cloud computing” has been around for a long time and still confuses people, and now, various firms are pointing toward a future of massively connected devices, controllers, sensors, and objects. Cisco went for the whole enchilada in its branding, opting for “the Internet of Everything.” IBM tended toward cerebral globality with its “smarter planet” branding. GE is focusing on its target markets by trying to build an “industrial Internet.” My preferred term, the Internet of Things (IoT), raises many challenging questions — but they’re worth pondering.

It’s commonly said that “there are more things than people connected to the Internet,” or some variant of that assertion. The current number is 8 to 10 billion “things,” which of course outstrips the population of planet Earth. But what is a “thing”? Out of that total — call it 9 billion for the sake of argument — probably 2 billion are smartphones and tablets. Add in routers, switches, access points, military gear, and PCs, and we’re probably in the 5-6 million range, total. That means that “things” such as garage-door openers, motor controllers, and webcams probably add up to 2-3 billion — about the same as the number of people who use the Internet on a regular basis, out of a global population of 7 billion. (For detailed numbers see here and here.)

This ratio will change rapidly. Given the inevitability of massive growth in this area, many big players, including some surprising ones, are beginning to fight for mindshare, patent portfolios, and other predecessors to market leverage. I will list the major players I’ve found in alphabetical order, then conclude with some summary thoughts.

Chip companies (ARM, Freescale, Intel, Qualcomm, Texas Instruments)
The magnitude of the opportunity is offset by the low price points required for true ubiquity. The business model should, in theory, be friendlier to an embedded-systems vendor like Freescale than to the makers of IP-heavy, super-powerful microprocessors such as those made by Qualcomm and Intel (unless we get data-aggregation appliances, dumber than a PC but capable of managing lots of sensors, in which case a set-top-box might be a useful guidepost). If a company can capture a key choke point with a patent “moat,” as Broadcom and Qualcomm did for previous generations of networking, this could be a key development.
Overall grade: lots of ways to win, but unlikely that any of these players dominate huge markets

Cisco: CEO John Chambers told a tech conference earlier this month that he projects the IoT to be a $19 TRILLION profit market in the next few years. (For scale, the US GDP is about $15 trillion, with about a fifth of that health care in some form or fashion; total global economic activity amounts to about $72 trillion.) Given such a massive addressable market, Cisco is bundling cloud, collaboration, data analytics, mobility, and security offerings into a go-to-market plan with an emphasis on lowering operational costs, raising business performance, and doing it all fast. This IoT strategy blends strengths in Cisco’s legacy businesses with an anticipation of new market needs.
Overall grade: Given history, the developing hardware stack, and their brainpower, Cisco must be taken very seriously

GE: If you look at how many pieces of capital equipment GE sells (from oil drilling gear, to locomotives, to generators, to MRI machines, to jet engines), and if you look at the profit margins on new sales vs after-market service, it makes complete sense that GE is working to instrument much of its installed base of Big Things That Break. Whether it’s preventive maintenance, or pre-empting a service call by a competitor, or designing more robust products for future release, the Industrial Internet is a smart approach to blending sensors, pervasive networking, and advanced analytics.
Overall grade: in GE's market strongholds, getting to IoT first could be a multi-billion-dollar win.

Google: Given the scale and speed of CEO Larry Page’s recent deals, assessing Google’s IoT future is a guessing game. There are huge building blocks already in place:

-Google mapped millions of wi-fi access points and has a well-regarded GIS competency (even if the privacy stance attached to said capability is less beloved).
-With the experience of owning a hardware company, if only briefly, and by owning the #1 global smartphone platform as measured by units, and by running a massive internal global network, and by trying their hand at municipal fiber, Google has extensive and relevant expertise in the transport layer.
-As possibility the biggest and best machine learning company anywhere, Google knows what to do with large numbers of sensor streams, possessing both the algorithmic know-how and computational infrastructure to handle heretofore unheard-of data problems.
-With the Nest purchase, Google gets more data streams as well as user experience expertise for IoT products in the home.
-The self-driving car effort has been a large-scale proving ground for sensor integration and actuator mechanics, with advanced vehicle dynamics and GIS/GPS thrown in.
-Perhaps less noticed than the Nest deal, Google’s recent agreement with Foxconn to develop an operating system for manufacturing robots potentially puts Google into closer competition with Amazon’s supply-chain automation investments, most notably Kiva.

Overall grade: Too big, too secretive, and too data-intensive to ignore. A possible 800-lb gorilla in a nascent market.
Software companies (SAS, Oracle, IBM, Cloudera, etc)
Obviously there is sensemaking involved in sensor-world and the rapidity of how the "big data" industry is generating multiple layers of toolsets suggests that there will be both incumbent and startup data management and analysis firms with a chance to win very big. One possible clue comes in the massive number of web tracking and analytics forms: running Ghostery in my browser has been a highly educational exercise as I've learned how many websites run how many beacons, widgets, and cookies. Will intense speciation be the rule in the IoT as well?
Overall grade: way too early to predict

Storage companies (EMC, Western Digital, Amazon)
Obviously there will be plumbing to build and maintain, and selling pickaxes to miners has been good business for centuries. The question of discontinuity looms large: will this wave of computing look similar enough to what we’re doing now that current leaders can make the transition, or will there be major shifts in approach, creating space for new configurations (and providers) of the storage layer?
Overall grade: Someone will win, but there’s no telling who

Wolfram: This was a surprise to me, but their reasoning makes all kinds of sense:

"But in the end our goal is not just to deal with information about devices, but actually be able to connect to the devices, and get data from them—and then do all sorts of things with that data. But first—at least if we expect to do a good job—we must have a good way to represent all the kinds of data that can come out of a device. And, as it turns out, we have a great solution for this coming: WDF, the Wolfram Data Framework. In a sense, what WDF does is to take everything we’ve learned about representing data and the world from Wolfram|Alpha, and make it available to use on data from anywhere."

An initial project is to “curate” a list of IoT things, and that can be found here.  The list began at a couple thousand items, from Nike Fuelbands to a wi-fi slow cooker to lots of industrial products. Just building an authoritative list, in rigorous Wolfram-style calculable form, is a real achievement. (Recall that Yahoo began as a human-powered directory of the World Wide Web, before Google’s automated crawl proved more adequate to the vastness of the exercise.)

But Wolfram wants to own a piece of the stack, getting its standard embedded on Raspberry Pi chips for starters. The WDF jumpstarts efforts to share simple things like positional data or physical properties (whether torque or temperature or vibrational frequency). Because the vast variety of sensors lack an interconnection/handshake standard like USB, Wolfram definitely helps fill a gap. The question is, what’s the long-term play, and I don’t have the engineering/CS credentials to even speculate.

Overall grade: Asking some pertinent questions, armed with some powerful credentials. Cannot be ignored.

A more general question emerges: where do the following conceptual categories each start and finish? One problem is that these new generations of networked sensing, analysis, and action are deeply intertwined with each other:

*cloud computing
*Internet of Things
*artificial intelligence
*big data/data analytics
*social graphs

Let me be clear: I am in no way suggesting these are all the same. Rather, as Erik Brynjolfsson and Andrew McAfee suggest in their book The Second Machine Age (just released), recombinations of a small number of core computational technologies can yield effectively infinite variations. Instagram, which they use as an example, combined social networking, broadband wireless, tagging, photo manipulation software, and smartphone cameras, none of which were new, into an innovative and successful service.

The same goes for the Internet of Things, as sensors (in and outside of wireless devices) generate large volumes of data, stored in cloud architectures and processed using AI and possibly crowdsourcing, that control remote robotic actuators. What does one _call_ such a complex system? I posit that one of the biggest obstacles to be overcome will be not the difficult matters of bandwidth, or privacy, or algorithmic elegance, but the cognitive limitations of existing labels and categories, whether for funding, regulation, or invention. One great thing about the name “smartphones”: designers, marketers, regulators, and customers let go of the preconceptions of the word “computer” even though 95% of what most of us do with them is computational rather than telephonic.

I read the other day that women now outnumber men at UC-Berkeley in intro computer science courses, and it’s developments like that that give me confidence that we will solve these challenges with more diverse perspectives and approaches. The sooner we can free up imagination and stop being confined by rigid, archaic, and/or obtuse definitions of what these tools can do (and how they do it), the faster we can solve real problems rather than worry about what journal to publish in, how it should be taxed and regulated, or which market vertical the company should be ranked among. I think this is telling: among the early leaders in this space are a computer science-driven media company with essentially no competitors, a computer science-driven retailer that's not really a retailer and competes with seemingly everybody, and a "company" wrapped around a solitary mathematical genius. There's a lesson there somewhere about the kind of freedom from labels that it will take to compete on this new frontier.