Sunday, October 02, 2016

Early Indications September 2016: Welcome to Dystopia?

If you’re an author with a knack for conjuring up nightmare scenarios, these are in fact the best of times: George R. R. Martin (Game of Thrones) and Suzanne Collins (The Hunger Games) are absolutely rolling in money and fame. There’s something going on when bleak futures capture national mindshare in books, TV shows, and movies. Look at 1963: with far fewer options, mass audiences converged on distraction. Shows such as Petticoat Junction, Candid Camera, and My Favorite Martian made the top ten, all lagging The Beverly Hillbillies and Bonanza. In 1968, with riots in the streets and the assassinations of Robert Kennedy and Martin Luther King calling the American ideal into question, Bonanza hung in at #3, behind Gomer Pyle U.S.M.C. and Rowan and Martin’s Laugh-in. That Stanley Kubrick was able to confront the madness of nuclear war with the brilliant black comedy of Dr. Strangelove (1964) helps prove the point: dystopias have historically been uncommon cultural touchstones; now they’re everywhere.

Could it be that these cultural artifacts capture our zeitgeist? Whether in the Dark Knight Batman films, The Wire, Breaking Bad, or The Sopranos, our most popular entertainments of the past 15 years present a pretty bleak vision, diametrically opposed to new deals, new frontiers, or “Don’t Stop Thinking About Tomorrow” campaign songs. Along the dystopian line of thinking, it’s easy to find evidence that the world is heading in a very bad direction. A gloomy sample:

*Ocean levels are rising faster than predicted, but the local effects in New York, Miami, the Netherlands, and Bangladesh will all vary considerably. Millions of people will be displaced; where will they go? Norfolk Naval Base will lose acres of land, how fast nobody knows.

*What appears to be the single largest distributed denial of service (DDoS) cyber-attack was mounted earlier this month using at least 150,000 compromised cameras and other poorly secured Internet of Things (IoT) devices. It’s quite possible our cars, garage-door openers, thermostats, and personal devices can be turned against us.

*Guns kill a lot of people in the U.S. Exactly how many is hard to determine, in part because the gun lobby discourages public health officials from calculating statistics. But whether it is suicides (20,000 a year, or 2/3 of all gun deaths), mass shootings, police violence against citizens, or the average of 82 shootings per week in Chicago alone, the numbers are depressing but apparently acceptable, given the lack of action. One statistic provides food for thought. Despite its wide error range, a Harvard study released earlier this month estimated (a key word) that 7.7 million people (3% of U.S. adults) own half the country’s guns. These “super-owners” collect between 8 and 140 firearms apiece.

*Globally, millions of people are being lifted out of poverty, but in the U.S., tens of millions of middle-class people find their fates stuck or, increasingly, declining. Whether from plant closures, downsizing, inadequate skills, offshoring, or automation’s various effects, people can’t get ahead the way previous generations did. For many, complex reasons, class conflict is showing itself in various ways: racial tensions, protests in places like San Francisco where homelessness and extreme wealth collide, and anti-trade sentiment. Immigration and refugees are super-sensitive issues from Turkey to British Columbia.

*At the same time that Colorado and Washington state are finding benefits of legal marijuana, recreational drugs are killing people. In addition to the violence in Chicago noted above, some of which is drug-related, the toll of opioid drugs is shocking. Especially when heroin is cut with fentanyl, overdoses are swamping local EMS and other responders. Columbus saw 27 in 24 hours, while Cincinnati had to cope with 174 heroin overdoses in 6 days. Huntington, WV had calls for 27 overdoses in under four hours last month. Prescription oxycontin was likely a tragic gateway drug in many of these cases. “Just say no” and a “war” on drugs clearly didn’t work; what’s next?

*On the ethical drug front, meanwhile, we live in scary times. Antibiotic-resistant “superbugs” are making hospitalization in any country a frightening proposition. As of 2013, 58,000 babies died of antibiotic-resistant infections in India alone, and in a global age of travel, those bacterial strains are moving elsewhere. An estimated 23,000 people died in the US last year from antibiotic-resistant infections, and just this past May, the CDC reported that a Pennsylvania woman who had not recently traveled out of the country tested positive for the mcr-1 strain of E. coli. This variant resists colistin, widely regarded as the “last resort” antibiotic, though the woman in question _did_ respond to other treatments. Still, the CDC’s language is sobering: “The CDC and federal partners have been hunting for this gene in the U.S. ever since its emergence in China in 2015. . . . The presence of the mcr-1 gene, however, and its ability to share its colistin resistance with other bacteria such as CRE raise the risk that pan-resistant bacteria could develop.”

None of these problems have easy answers; some don’t even have hugely difficult answers. Zeroing in on the technology-related world (thus leaving aside climate change, gun violence, and drug issues for the moment), I see four nasty paradoxes that, taken together, might explain some of how we arrived at a juncture where dystopian fantasies might resonate.

1) Automation brings leisure and productivity; robotics threatens to make many job categories obsolete. From radiologists to truck drivers to equity analysts, jobs in every sector are threatened with extinction.The task of making sure technologies of artificial muscle and cognition have widely rather than narrowly shared benefits runs counter to many management truisms regarding shareholder value, return on investment, and optimization.

2) We live in a time of unprecedented connection as most adults on the planet have access to a phone and will soon get smartphones; interpersonal dynamics are often characterized by savagery (at worst) or distractedness. (Google “Palmer Luckey” for a case in point.) Inside families and similar relationships, meanwhile, the psychologist Sherry Turtle argues persuasively that we are failing each other, and especially our kids, when we interact too much with screens and too little with flesh-and-blood humans.

3) The World Wide Web brought vast stores of the world’s cultural and scientific knowledge to masses of people; a frightening amount of public debate is now “post-factual,” with conspiracy theories and plain old ignorance gaining large audiences. Climate science, GMO crops, and vaccinations are but three examples. The assumptions behind the Web have too often failed: access to knowledge by itself cannot counter fads (hello Justin Bieber), longstanding ignorance, or intolerance. Compare the traffic to YouTube or Facebook with that to the Library of Congress, Internet Archive, or even Wikipedia. At some level, maybe people don’t like eating their intellectual vegetables; junk food is too hard to resist.

4) Billions of sensors, smartphones, and cloud computing virtual machines enable an increasingly real-time world, where information flows faster and wider every year; historical context is lacking for many public assertions and private opinions. In September, a Republican party official claimed there was no racism before 2008. For years, only a minority of people have been able to identify in which century the American revolution or Civil War occurred. Nuanced views of Reconstruction or the Gilded Age, hugely formative of and relevant to today, are difficult to find.

Together, these paradoxes add up to a truly dystopian vision at odds with what seemed inevitable just a few years ago. It’s difficult to be optimistic, but to close I’ll suggest some reasons why solutions are so difficult.

*The digital world doesn’t respect traditional organizational boundaries. Examples abound: Russia is said to be meddling in the US election cycle. Certainly the superpowers have influenced local elections in the past, but the thought of major media outlets and voting machines being compromised by a global adversary calls the whole notion of sovereignty into question. Whether it’s in regard to spam, child porn, copyright, compromised hardware at the chipset level, digital privacy, or the handling of video and music streaming, the global, borderless nature of the mobile/digital platforms calls basic facts of jurisdiction, evidence, and recourse into question.

*At the same time that “where” needs to be redefined, so too we must confront what work is. Who does what, how much they are paid or otherwise valued, how they learn the job, what happens when jobs or entire labor markets disappear — none of our current answers can be assumed to hold stable 10 years from now. Education, unemployment and disability benefits, collective bargaining, workplace health and safety (does sitting really “kill” you?), pensions, internships, retirement, job-hunting, and corporate education and training will all assume new shapes. Some of this will be messy; I can’t see anyone getting it all right the first time.

*Technologies of communication and transportation have usually been a double-edged sword. Trade brings benefits to many parties, but smallpox, influenza, and the AIDS virus all crossed oceans on new modes of transport. Given the essentially free, multimedia, borderless nature of digital communications, what equivalent maladies will be given broad distribution, and what will be their consequences?

*In a pluralistic world, what can serve as a moral compass for an individual, a group, a nation, a continent? The teachings of Muhammad, Jesus, Yahweh, Confucius, and the Buddha all have served to guide people over the centuries, but so too have they justified crimes against humanity. We live in a connected world where religious conflict becomes more likely than in eras with less physical mobility. Given global communications and mobility, how is coexistence possible, given increases in both fundamentalism and secularism in many places, and the ongoing tendency for the major religions to splinter internally, often violently? In a post-factual world, people try to claim their own beliefs, but without sufficiently binding notions of a common identity, purpose, or ideology, we are left less with states of free-thinkers than with new sources of conflict — and fewer resources for building group identity.

To be sure, there are many hopeful signals, and plenty of today’s entertainment is mindless diversion not unlike the television hits of the 1960s. That dystopias can find audiences may be more a function of the multitude of distribution options than of national mood. In any event, I do believe the challenges we confront will test moral resolve, institutional flexibility, and intellectual creativity unlike ever before. It may be that meta-questions are in order: rather than asking how we solve internet security or rising ocean levels, we (a tricky word all on its own) need to ask, what are the political forms, grounds of legitimacy, and resources of the institutions we will design to address these new challenges.

Wednesday, August 31, 2016

Early Indications August 2016: The Next Car

About 125 years ago, when the internal combustion engine supplanted equine power for personal mobility, there was much talk regarding “horseless carriages,” defining the future in terms of the past. We are at much the same juncture today: as electric autonomous vehicles come closer and closer to mass-market availability, much of the conversation starts with what we know human drivers do: “How will self-driving cars avoid bicyclists? How will self-driving cars merge in construction zones? How will self-driving cars make left turns across oncoming traffic with solar glare?” All of these questions must be answered, of course, but I believe it’s not too early to ask what we want of the _next_ car, the one(s) with a largely new set of constraints and capabilities. That is, given a clean-sheet design, what are some questions we might ask? Here are three among many.

1) How do we balance autonomy with “mesh transportation”?

By definition, a driver in a car is largely autonomous and disconnected from the cognition of those around; “what was he thinking?” is a common complaint while observing other drivers. The person at the wheel can follow or ignore traffic laws, brake suddenly or gradually, act with awareness of other vehicles or possess limited situational awareness. There are many consequences of this autonomy: cars have long been associated with personal “freedom,” traffic flows in an annoying and predictable accordion pattern in congested stretches of highway, and of course accidents happen when driver A somehow surprises driver B.

Once driverless vehicles constitute some critical mass of traffic, however, that assumption of autonomy can be challenged. My current frame of reference is a mesh wireless network, a potentially peer-to-peer ad hoc configuration of cars both interacting with vehicles close to them and serving as repeaters for less proximate “nodes.” A simple scenario started my thinking: what if a truck stuck in traffic wanted to see the sensor feed from the car at the front of the pack? Already, Samsung has shown a heavy truck with an LCD display on the rear showing the view out the windshield. Once my vehicle can “see” the sensors n cars ahead, what else can happen?

Although sensor-driven autonomous vehicles are getting substantial attention from Uber, Google, and Tesla, the notions of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are also gaining mindshare. Already emergency vehicles can trigger stoplights at some intersections (green for the ambulance, red for the cross-streets), it doesn’t require an enormous leap of imagination to get to cars addressing and listening to the signaling infrastructure: why train a self-driving car to understand (or memorize) speed-limit signs when the information could be available via wireless beacons? The appeal of V2V is obvious, as per the “what was he thinking?“ scenario above: if vehicle A can signal intent with more lead time, more consistently, to more of the relevant peers and bystanders, the safer matters should become.

For years, European companies have been trialing road trains: a lead heavy truck invites close followers (who hand control off to a wi-fi network) to ride in its wake. The tighter following distances improve fuel economy and free drivers to attend to less mundane chores. Now what happens when self-driving vehicles can self-assemble? If 8 or 10 cars and trucks all enter I-95 from around Princeton going northbound, what if they form a road train until the first of the vehicles comes to its exit? And what if there’s a mechanical connection, like those magnets on wooden train sets? Road utilization improves, fuel economy improves, and the lack of human drivers means nobody is bothered by the unappealing view of the vehicle ahead.

Given the currently lousy security of Internet of Things things in general, and of wireless car systems in particular, for this mesh-vehicle future to be safe, there will need to be massive strides in, and probably complete rethinking of, security practices. This means clearly understanding the trade-offs that are being asked and granted. The security domain thus echoes the larger debate that will emerge as to what vehicles can and cannot, should and should not, tell and learn in interaction with other vehicles and the world.

2) What happens when cars don’t project personal identity?

I know something of my readership. You’ve driven stealth M3s, E-types, Z06 Vettes, and of course 911s. This is not mass-market car-dom, but the extreme proves my point. In many cases, vehicles are designed as much as a projection of the owner’s psyche as they are for road performance. Compare a Suburban to a minivan, or a new pickup truck to a 30-year-old version. Absent towing capacity, the functional performance may be similar but the features, price (I didn’t say cost: GM builds massive profit into that Suburban), and enhancement of one’s personal brand are very different.

Auto executives have already begun publicly worrying about vehicles designed and sold only as appliances: if people buy transportation as a service rather than as a product, the design remit changes. As a rider, I certainly prefer the S-class Mercedes taxis common in Europe to the cramped Priuses (Prii?) I get in US cities. Do I insist on a certain type of car to haul me around, especially when nobody will see it in my driveway either way? No way. Once autonomous vehicles are optimized for whatever we decide to optimize (please make the London taxi one of the blueprints . . .), conspicuous consumption will fall far down the list, particularly for mass-market cars and trucks. (Read more here and here.)

3) What happens if safety is reset to a higher priority?

There’s a famous assignment given to engineering students: design a protective enclosure such that the egg inside it can survive a fall of a specified height. I thought of it immediately when I saw this effort by an MIT team to understand safety trade-offs to be encoded in autonomous vehicle algorithms.

Why the egg drop? All of the Moral Machine scenarios embed numerous assumptions; I wanted to challenge those, not take them as given. The passenger cocoon could be one such: when we design, license, and support different vehicle designs, what do we want optimized?  Should pedestrian safety be a higher priority than what happens to a car’s passengers? Why or why not? To give an example, Google has a patent on a flypaper-like technology to snag people on car hoods after they’ve been struck. How will we set the weights of protection for passengers, cargo, pedestrians, bicyclists, driveway shrubbery, and other features of the driving environment? How much will this be done by markets, how much self-policing will we see, and how much government regulation will be imposed?

If safety is a higher priority than it is today, why have windows at all? Boeing has suggested replacing airplane windows with display screens, so why not equip cars the same way? At the same time, if passengers are cosseted in metal tank-like vehicles, what unintended consequences could there be for driving algorithms, bystanders, and even fast-food restaurants?

Just as interstate highways, McDonald's, and suburban sprawl could not have been foreseen by Daimler, Ford, and Durant, we will see parallel discontinuities in the coming century. Railroad ownership, to take one example, went from being the source of massive wealth and prestige (as with Stanford or Vanderbilt) to a joke (Conrail) in a relative short time. Entirely new ancillary services and industries emerged, and may now recede: will anyone become a billionaire owning parking lots in the next 30 years?

A key question relates, as it always does, to the speed of the transition. From an engineering standpoint, designing a world of only autonomous vehicles would be relatively easy; similarly, we know how to build roads, vehicles, and venues for cars with people driving them. The mixed zone, to borrow a term from the Olympics, is what we currently face, and it’s one hard problem on top of another:

What are the business models? (Google is struggling with this as I write, having replaced a respected-but-departed roboticist with an AirBnB executive to run the car project.) Who bears liability? Who pays to upgrade the infrastructure, whether to fill potholes or install beacons in road signs? What signaling conventions can carry over and what new ones need to be designed; can the camera of a self-driving truck reliably see a turn signal or is a radio message more appropriate? Where will trusted suppliers come from (Panasonic, a partner in Tesla’s battery factory, is emerging as a big player in the global automotive ecosystem, for example)? What about car loan companies, service bays, and other businesses whose mission will be redefined? How will gas stations as real estate, and oil companies as businesses, be forced to evolve (charging a battery takes a lot longer than filling a tank: how many 20-pump gas stations will become 20-plug chargers? Can local electric grids handle such concentration?) Which brands will win and lose? Why? How will human-driven cars fade into antiquity? Will valets, for example, be kept on for ceremonial value at high-end destinations? Which internal-combustion vehicles will have the longest carriers in the new era, and which will be most prized by collectors? What happens to the freed-up parking and other infrastructure? Does congestion pricing a) disappear or b) enter a new phase of complexity? What happens to municipal revenues dependent on traffic tickets? How will domestic architecture evolve without the need for the same size and type of garages? Where will housing be located relative to work? The list goes on, but suffice it to say, Detroit and its extended network is in for the shock of a lifetime.

Postscript: As I was wrapping up this newsletter, tech analyst Benedict Evans of Andreesen Horowitz posted a series of Tweets asking many of these same questions.

Saturday, July 30, 2016

Early Indications July 2016: The Beer Issue

While a focus on beer in mid-summer is timely, the economics of the industry are fascinating rear-round (hopefully even to non-drinkers). The big growth is happening in the craft segment, where sales are up about 15% in an overall declining market. Big brewers have responded predictably, buying such brands as Blue Point, Shock Top, and Goose Island. And, oh yes, the world’s two biggest brewers look like they will merge, minus some divestitures. I’ll leave the details of AB InBev/SABMiller to the bankers and financial reporters; it’s a pretty complex deal. Apart from that, what is the current state of play in the U.S., why are craft brews thriving, and what might the future hold?

The current state of play in the U.S. beer market can be summarized as big and light. 7 of the top 10 sellers are light beers, led by Bud Light with sales of $2 billion annually. The second 10, smaller in revenue by more than a factor of 10, is much more interesting, led by Blue Moon (always owned by Miller Coors but branded like a craft beer) and including Pennsylvania’s Yuengling, Samuel Adams, Sierra Nevada, and Leinenkugel out of Wisconsin. Considering that the light beers in the top 20 add up to $5.5 billion in sales, $140 million for Yuengling or $80 million for Sierra Nevada might seem like a literal drop in the bucket.

The relevant number for a Goose Island or a New Belgium Fat Tire, however, is not necessarily sales, but growth. The number of U.S. craft breweries surged from 284 in 1990 to more than 1,500 in 2000, then past 2,000 companies in 2011 and nearly 3,500 in 2015. Imports are fairly stable, running about 35 million barrels from 2004 to 2014, but the 24 million barrels of craft beer sold in 2015 represented a 13% annual increase in quantity and a 16% increase in dollar volume over 2014. There's a loose craft-industry effort to hit a collective 20% market share by 2020; it's not an impossible target.

Just about any locale can now have some microbrewery presence; there are four (plus one in the process of being reopened) in our county alone. Skill is necessary, as is capital: those 100 barrel stainless steel tanks you might see in your local brew pub list for about $35,000 apiece. The U.S. craft phenomenon is entering middle age: Sierra Nevada was founded in 1979, when Jimmy Carter deregulated the industry; Boston Beer Company (Samuel Adams) followed five years later. Thus some founders are finding ways to cash out and/or generate capital for expansion. One path is an employee stock ownership plan, where employees buy out some percentage of the business. Another is private equity, while still other firms sell to an acquiring brewer, often from outside the U.S.: recent deals included major acquisitions by a Belgian company and a Spanish brewer as well.

Indeed, the global nature of the beer industry is a topic unto itself. Given the high shipping costs and perishability of the product, local production can be a competitive advantage (especially if the primary facility might be threatened by drought). Licensing agreements are common: Australia’s Fosters Lager is owned by SABMiller (headquartered in London) and brewed for the North American market in Toronto at a Molson facility. Sierra Nevada added capacity to its California operation with a vast facility in western North Carolina, as did Colorado brewers Oskar Blues and New Belgium at a smaller scale. California-based Lagunitas opened a Chicago taproom in a former steel mill. Now that the two global giants — AB InBev (headquartered in Belgium and owning Anheuser-Busch) and SAB Miller — are set to merge, there will be more waves of change that could affect most any country on earth at some point.

Returning to the local impact of craft brewing, I haven't seen anything connecting brewing to the alleged U.S. manufacturing revival, but maybe it should be included. The Atlantic’s James Fallows posited a list of factors that predict a town will be in good shape after he traversed the continent in his small plane for three years of research.

“#11: Craft Breweries. A city on the way back will have one or more craft breweries, and probably some small distilleries too. . . . A town that has craft breweries also has a certain kind of entrepreneur, and a critical mass of mainly young (except for me) customers. You may think I’m joking, but just try to find an exception.”

Jeff Alworth, in All About Beer magazine, expands Fallows’ observation from correlation to causation: he argues that craft brewing drives economic development, and his logic is compelling:

“Breweries are industrial operations, and they’re expensive. Beer is a mass beverage, and even making it on a brewpub scale means you have to have quite a bit of space for the brewhouse, fermentation, and storage. All that equipment costs a lot, and real estate does, too. When you’re spending a quarter- or half-million dollars on equipment, you can’t afford expensive commercial space. So breweries end up on the fringes, in bad parts of town where the rent is cheap. That alone is the first step of revitalization.

But breweries aren’t like the average industrial plant. They are people magnets, bringing folks in who are curious to try a pint of locally made IPA. In fairly short order, breweries can create little pockets of prosperity in cities that can (and often do) radiate out into the neighborhood. Pretty soon, other businesses see the bustle and consider moving in, too. It doesn’t hurt that breweries often find run-down parts of towns that have great buildings. Once a brewery moves in and refurbishes an old building, it reveals the innate promise of adjacent buildings to prospective renters.
. . .
But the effect may even be stronger in smaller communities. Little towns are often underserved with regard to cool places to hang out. When they open up shop, they provide much-needed social hubs. That the rent is cheaper there than in big cities gives these breweries a competitive boost, to boot—and we have seen many small towns (like Petaluma, California; Kalamazoo, Michigan; and Milton, Delaware) spawn outsized breweries. And whether they’re in small towns or cities, breweries serve an important community-building function. They’re not only a nice place to spend an evening, but serve as venues for events like meetings, weddings, and even children’s birthday parties.” 

The consumer appeal of craft beers has many facets, but a few of these include the following:

-Craft brewers eschew light beers, the nearly-clear light lagers that dominate the U.S. sales charts. Instead, nearly every craft brewer needs an India Pale Ale to prove its mettle. IPAs can have up to twice the alcohol content of an American “lite,” the taste is rated by bitterness units from the hop content, and they cost more to brew, driven by lower volumes and more expensive ingredients (bought in smaller quantities). Thus the craft movement represents a strong case for government deregulation: consumer welfare has improved with increased choice and availability. If only airline deregulation had so many positive outcomes.

-Craft brewers often have a strong experience component, whether in tours, tastings, or just ambience. The Guinness tour in Dublin is highly orchestrated (thanks to the deep pockets of Diageo, the parent company) but still pretty engaging. I’ve heard good things about Troegs in Harrisburg, Yuengling in Pottsville, PA, Rogue in Portland, and Harpoon in Boston. Sierra Nevada offers a range of tours, including deep-dive sessions for true beer geeks.

-That experience component can be enhanced and/or amplified by social media. Dogfish Head is a leader in this regard. Advertising spend has never tightly correlated to improved sales, as Schlitz proved (to its detriment) in the 1970s, so social media's low cost, responsiveness, and intimacy make it a great tool for the job.

-The craft movement represents a cyclical return to regionalism. In 1900, and even in 1950, there were no dominant national brands. Schlitz, Falstaff, Ballantine, Schaefer, Hamm’s, Olympia, and Stroh’s all thrived until AB began its surge in the 1960s, Coors expanded beyond being a mountain-region favorite, and then Miller enjoyed first-mover advantage in the light beer category it invented in the 1970s. Yuengling is a special case (being the first U.S. brewery, it’s not really a craft), and still doesn’t have wide distribution. Regionalism aside, there is worldwide interest in craft brewing, so growth prospects remain strong: San Diego's Stone Brewing just opened a facility in Munich, and there's no reason to think other beers won't follow them overseas.

-Experimentation is rampant, and rewarded by the market segment. Seasonal brews are a common offering from crafts but not the majors, further emphasizing uniqueness rather than standardization. In the last week alone, I've seen beers featuring habanero peppers, passionfruit, grapefruit, rye wheat, and coffee in my routine travels. Oskar Blues has produced at least 10 variations of its flagship Dale's Pale Ale, according to Beer Advocate; the 17 versions of its Scotch Ale include one flavored with chocolate and marshmallow.

-Beer is being treated more and more like wine, with more “varietals,” ratings, competitions, food pairing guides, and so on. Label art can be low-budget, exquisite, or weirdly idiosyncratic. Different glasses are recommended for different brews, the same as wine.

-The home brewing movement connects the hobbyist and the professional in ways that the majors cannot; I’ve never heard a home brewer try to recreate Natural Light in his or her basement. As of 2014, sales of home-brew starter kits had been growing at about 20% year/year. That’s positive for microbreweries in many ways.

While researching this piece I found a report from the Federal Trade Commission from 1978 (on the eve of deregulation, I imagine not coincidentally). The author -- one Charles Keithahn -- was incredibly far-sighted, predicting that San Francisco’s then-tiny Steam would be the start of something: that very brewer was crucial in the birth of Sierra Nevada, now the archetypal craft brewer.

“And a number of the smaller companies will probably be able to survive for one or more of the following reasons: local loyalty, exceptional knowledge and responsiveness to local tastes and conditions, low transport costs and low advertising costs associated with serving a small market, excise tax breaks, . . .  or finding a special niche in the market. A few examples might include Latrobe, Pickett of Dubuque, Iowa, Spoetzl (Shiner) of Texas and, at least at last report, the Nation's smallest brewer, Steam Beer of San Francisco.”

Is there any other example where industry consolidation has spawned a counter-movement of variety, experimentation, and market enthusiasm? Medical devices, automobiles, airplanes, retail, energy, and tech don't really suggest any comparable examples -- especially if you look at the community-building aspect. All told, beer is good in more ways than the obvious ones. Happy summer.

Wednesday, June 22, 2016

Early Indications June 2016 What do we make of Artificial Intelligence?

For context, an old joke:

Q: What’s harder than solving the problem of artificial intelligence?

A: Fixing real stupidity.

In many current publications, the technical possibilities, business opportunities, and human implications of artificial intelligence are major news. Here’s just a sample:

-a computer beat a grand master at Go about 10 years before many predicted such an outcome would be possible.

-Google is repositioning itself as an AI company, with serious credibility. IBM is advertising “cognitive computing,” somewhat less convincingly, Watson notwithstanding.

-Venture capital is chasing AI-powered startups in every domain from ad serving to games to medical devices.

-Established players are hiring top talent from each other and from academia: Toyota, Amazon, Uber, and Facebook have made noise, but Google remains the leader in AI brainpower.

-Corporate acquisitions are proceeding apace: just this week Twitter bought a London-based company called Magic Pony, which does image-enhancement, for a reported $150 million. Those kinds of numbers (shared around a team of in this case only 11 PhDs) will continue to attract talent to AI startups all over the world

Despite so much activity, basic answers are hard to come by. What is, and is not, AI? By which definition? What is, and is not possible, for both good and ill? The more one reads, the less clarity emerges: there are many viable typologies, based on problems to be solved, computational approaches, or philosophical grounding. I can recommend the following resources, with the caveat that no consensus emerges on anything important: the whole concept is still being sorted out in both theory and practice.

The Wikipedia entry is worth a look.

Here's a pretty good explainer from The Verge.

The Economist reports on the sizable shift of top research talent away from universities into corporate AI research.

Here’s a New Yorker profile of the Oxford philosopher Nick Bostrom.

Oren Etzioni has a piece on “deep learning” (neural networks at very large scale, best I can make out) in a recent issue of Wired:

Elon Musk called AI humanity’s “biggest existential threat” in 2014.

Frank Chen at Andreessen Horowitz has a very good introductory podcast explaining the recent boom in both activity and progress.

Apple is trying to use AI without intruding into people’s identifiable information using something called differential privacy.

Google’s AI efforts, by contrast to Apple’s, build on the vast amount of information the company’s tools know about people’s habits, web browsing, searches, social network, and more.

Amazon has multiple horses in the AI race, and recently made a high-profile hire.

Despite the substantial ambiguity related to the macro-level abstraction that is AI, several generalizations can be made:

1) Defining AI with any precision is problematic. Vendors including Google (“deep learning”) and IBM (“cognitive computing”) are well served by a certain degree of mystery, while the actual mechanics of algorithm tuning are deeply technical and often tedious. There are live questions over how completely the use of a given algorithm (a Kalman filter, used in both econometrics and missile guidance, or a simulated annealing optimization, used in supply chains and elsewhere, to take two examples) is “doing” AI.

2) AI can work spectacularly well in highly defined domains: ad placement, cerebral games, maps and directions, search term anticipation, and more and more, natural-language processing as in Siri/Cortana/Alexa. Leave the domain, however, and the machine and its learning are lost: don’t ask Google Maps to pick a stock portfolio, or Siri to diagnose prostate cancer. The challenge of “general AI” remains a far-off goal: people are more than the sum of their map-reading, pun-making, and logical generalization abilities.

3) Hardware is a key piece of the recent advances. Computer graphics processors feature a parallel architecture that lends itself to certain kinds of AI problems, and the growth of gaming and other image-intensive applications is fueling better performance on the computing frontiers of machine learning. Google also recently announced a dedicated hardware component, the Tensor Processing Unit, built specifically to handle machine learning problems.

4) “Big data” and AI are not synonymous, but they’re cousins. Part of the the success of new machine learning solutions is the vast increase in the scale of the training data. This is how Google Translate can “learn” a language: with billions of examples, not a grammar, dictionary, or by ear.

5) It’s early days, but one of the most exciting prospects is that humans can learn from AI. Lee Sedol, the Go player, says he is now playing better than before his loss to the Google computer. Whether with recipes (for tire rubber or salad dressing), delivery routes, investment strategies, or even painting, getting inspiration from an algorithm can potentially spur people to do great new things. Shiv Integer is a bot in the 3D printing site Thingiverse, and the random shapes it generates are fanciful, part of an art project. It’s not hard to envision a more targeted effort along the same lines, whether for aircraft parts or toys. I would also bet drug discovery could benefit from a similar AI approach.

6) The AI abstraction is far more culturally potent than the concrete instances. The New Yorker can ask “Will artificial intelligence bring us utopia or destruction?” (in the Bostrom article) but if you insert actual products, the question sounds silly: “Will Google typeahead bring us utopia or destruction?” “Will Anki Overdrive (an AI-enhanced race-car toy) bring us utopia or destruction?” Even when the actual applications are spooky, invasive, and cause for concern, the headline still doesn’t work: ”Will the FBI’s broad expansion of facial recognition technology bring us utopia or destruction?” The term "AI" is vague, sometimes ominous, but the actual instantiations, while sometimes genuinely amazing (“How did a computer figure that out?”), help demystify the potential menace while raising finite questions.

7) Who will train the future generations of researchers and teachers in this vital area? The rapid migration of top robotics/AI professors to Uber, Google, and the like is completely understandable, and not only because of money. Alex Smola just left Carnegie Mellon for Amazon. In his blog post (originally intended for his students and university colleagues), he summarized the appeal: less bureaucracy, more data, more computing power.

          “Our goal as machine learning researchers is to solve deep problems (not just in deep learning) and to ensure that this leads to algorithms that are actually used. At scale. At sophistication. In applications. The number of people I could possibly influence personally through papers and teaching might be 10,000. In Amazon we have 1 million developers using AWS. Likewise, the NSF thinks that a project of 3 engineers is a big grant (and it is very choosy in awarding these grants). At Amazon we will be investing an order of magnitude more resources towards this problem. With data and computers to match this. This is significant leverage. Hence the change.”

It’s hard to see universities offering anything remotely competitive (across all 3 dimensions) except in rare cases. Stanford, MIT, University of Washington, NYU, and Carnegie Mellon (which lost most of an entire lab to Uber) are the schools I know about from afar with major defections; those 4 (absent NYU) are among the top 5 AI programs in the country according to US News, and I wouldn’t feel too comfortable as the department chair at UC-Berkeley (#4).

As in so many other domains (the implications of cheap DNA sequencing; materials science including 3D printing; solar energy efficiency), we are seeing unprecedentedly rapid change, and any linear extrapolations to predict 2025 or even 2020 would be foolish. Perhaps the only sound generalization regarding AI is that it is giving us strong reinforcement to become accustomed to a world of extreme, and often troubling, volatility. Far from the domain of machine learning, for example, a combination of regulations, cheap fracking gas, and better renewable options led the top US coal companies to lose 99% — 99%! — of their market capitalization in only 5 years. Yet other incumbents (including traditional universities) can still look at our world and say, “I’m immune. That can’t happen here.” Helping expand perspectives and teach us flexibility may be one of AI’s greatest contributions, unless human stupidity is too stubborn and wins the day.

Friday, June 10, 2016

Early Indications May 2016: Technology and inevitability

Human nature drives us to look backwards and see a series of developments neatly explaining the current situation: we all exhibit hindsight bias is some form. It’s much harder to look back and recapture the indeterminacy in which life is lived in the present tense. Technological history is particularly prone to this kind of thought and rhetoric: the iPhone was famously (but not universally) mocked upon its introduction, to take but one example; looking for “the next Microsoft” or the “next Google” is another manifestation. The project “singularity” of digital cognition surpassing the human kind builds on this kind of logic. Coming in June, longtime tech observer Kevin Kelly’s new book is called simply The Inevitable.

It’s important to remember, however, that merely inventing (or imagining) a technology is a far cry from getting it into garages, factories, living rooms, or otherwise achieving successful commercialization. The low success rate for university technology transfer offices bears this out: a great molecule, material, or method does not a successful product make, absent entrepreneurship, markets, and other non-technical factors. This month I’ll run through a few technologies, some well-known and visible, others largely forgotten, that failed to achieve market success. I do this less out of nostalgia and more in the interest of tempering some current projections with a reminder that luck, competition, timing, and individual drive and vision still matter.

1) Very Light Jets
Led by Eclipse but also joined by Honda, Embraer, and others, the late 1990s stand as the height of the promise of a small, cheap (under $1 million new) aircraft that could both lower the barrier to personal jet ownership and fuel the rise of short-hop air taxi services. Eclipse shipped far later than promised at more than twice the projected cost, and performance problems were numerous: tires needed frequent replacement, the windscreens cracked, fire extinguishers leaked corrosive chemicals into sensitive components, the computerized “glass cockpit” failed to perform, and so on. A few air taxi firms went live (such as DayJet), but failed in the 2008 financial crisis, as did Eclipse. Honda, meanwhile, is prone to showing HondaJets in company advertising, but as of last December, had delivered a total of one plane to a paying customer. Sale prices are in the $4 million and up range — more than a used Hawker or similar mainstream business jet.

2) Flying cars
However intuitive the appeal, flying cars remain a niche market occupied primarily by mad-scientist visionaries rather than established production teams and facilities. The latest attempt, the Aeromobil, is claimed to be ready for market in 2017. The video is pretty impressive. Much like VLJs, flying cars have failed as much for economic reasons as technical ones. Building such a complex vehicle is not cheap, and safety considerations raise the product’s cost in multiple ways: FAA certification, spare parts management, expensive short-run production, and insurance factor into the actual operational expenses. Some of these expenses are out of the control of the aforementioned visionary (and in the Eclipse case, Bert Rutan has throughly impressive credentials), while other business challenges, including marketing, are common in tech-driven startups: who will buy this and what problem does it solve for a critical mass of real people?

3) ATT Picturephone

Here is ATT’s website, verbatim:

"The first Picturephone test system, built in 1956, was crude - it transmitted an image only once every two seconds. But by 1964 a complete experimental system, the "Mod 1," had been developed. To test it, the public was invited to place calls between special exhibits at Disneyland and the New York World's Fair. In both locations, visitors were carefully interviewed afterward by a market research agency.

People, it turned out, didn't like Picturephone. The equipment was too bulky, the controls too unfriendly, and the picture too small. But the Bell System* was convinced that Picturephone was viable. Trials went on for six more years. In 1970, commercial Picturephone service debuted in downtown Pittsburgh and AT&T executives confidently predicted that a million Picturephone sets would be in use by 1980.

What happened? Despite its improvements, Picturephone was still big, expensive, and uncomfortably intrusive. It was only two decades later, with improvements in speed, resolution, miniaturization, and the incorporation of Picturephone into another piece of desktop equipment, the computer, that the promise of a personal video communication system was realized."

*I’m sure the story of exactly _who_ in the Bell System drove this $500 million boondoggle is fascinating, if heavily revised.

4) Voice recognition software
Bill Gates is very smart, and obviously has connected some pretty important dots (as in the Internet pivot Microsoft executed in the late 1990s). On voice recognition, however, “just around the corner” has yet to come to pass. His predictions began in earnest with his 1995 book The Road Ahead, and in numerous speeches since then (well into the 2000s), he doubled down. Even now, in the age of Siri/Alexa/Cortana, however, natural-language processing is a very different beast compared to replacing a keyboard and mouse with talking. Compare two statements to see the difference: “What is the temperature?” vs “highlight ‘voice recognition software’ and make it bold face.”

5) Nuclear civilian ships
President Dwight Eisenhower wanted both Americans and citizens of other nations to temper their fears of military atomic and nuclear weapons by encouraging peacetime uses (his “Atoms for Peace speech” was delivered in 1953). The NS Savannah, a nuclear cargo ship, was intended to be a proof of concept, and it remains a handsome vessel a half-century on. The ship toured many ports of call for publicity and drew good crowds. Reaction was mixed, and the fear of both nuclear accidents and waste leaking into the oceans proved prescient as the US vessel and, later, a Japanese civilian ship both experienced losses of radioactive water. Although operational costs are low, the high up-front investment and, more critically, unpredictable decommissioning and disposal costs presented unacceptable risks to funding agencies or banks. Despite 700 military nuclear vessels becoming standard pieces of national arsenals, nuclear civilian craft have never caught on (with the exception of a few Russian icebreakers). A great BBC story on the Savannah (now moored in Baltimore) can be found here.

6) 3DO gaming console
After the 1980s, in which Sony’s Betamax format lost out to Matsushita’s VHS, consumers remained wary of adopting a technology in the midst of a format war. The lesson has been learned and relearned in the succeeding decades. In the early 1990s, Trip Hawkins (who founded Electronic Arts) helped found a new kind of console company, one based on licensing rather than manufacturing. The effort attracted considerable attention, but numerous problems doomed the effort. Sony and Nintendo can subsidize the cost of their hardware with software royalty streams; this is a basic element of platform economics as seen in printer cartridges and other examples. The 3DO manufacturers lacked this financial capability, so a high selling price was one problem. In another basic of platform economics, software and hardware must be available in tandem, and there was only one game — Crash ’n Burn, ironically enough — available at US product launch. In Japan, a later launch helped enable better reception as six game titles were available, but within a year the platform had become known for its support for pornographic titles, so general adoption lagged. 3DO clearly had some technically attractive elements (some of which were never included in the Nintendo 64 and Sony Playstation that followed) but the superior technology failed to compensate for market headwinds.

7) Elcaset
Unless you’re an audio enthusiast of a certain age, this is deep trivia. Sony introduced this magnetic tape format in 1977, and it was clearly technically superior to the audio cassette that had become entrenched by this time. The tape was twice as wide, and moved twice as fast, improving the signal/noise ratio and allowing for more information to be recorded, thus increasing fidelity. Like a VHS deck, tape handling was done outside the plastic shell, improving performance further. Unfortunately, the added performance came at a cost, and few consumers saw any reason to embrace the odd new format, which was supported by TEAC, Technics (Matsushita), and JVC as well. Also, no pre-recorded titles were available: this was the time when “home taping is killing music” — the 1980s UK anti-cassette campaign was later dusted off for the Internet age by the Norwegian recording industry association — and label execs were of two minds with regard to cassettes. In a curious twist I only recently learned about, Sony sent the remaining inventory of players and tapes to Finland after a distributor there won the wholesale auction, where many of the machines, well-made as they were, continued to work well for decades afterward.

This somewhat random collection of technologies holds very few generalizations. Having high-ranking executive sponsorship — up to and including the President of the United States — failed to compensate for deep fears and uncertain cost projections. Some failures came from corporate labs, others from entrepreneurs. Platform economics prove to be critical, whether for hardware and software, spare parts and airfields, or communications technologies. In the end, the only true generalization might be that markets are fickle, and there’s very little technology that is truly inevitable in its adoption.

Friday, April 29, 2016

Early Indications April 2016: Tesla Thoughts

In the absence of tech IPOs, must-have new apps, or killer demos, we are in a period of waiting:

  • will Uber continue to scale, win its legal battles, and develop a self-driving ride-share car?
  • will the rapid decline of so many “unicorn” company valuations chill the funding side of the cycle as Theranos et al become cautionary tales?
  • will Apple rebound from the quarter where it failed to set a revenue record, whether on iPad adoption, the watch, or growth in emerging economies?
  • will Facebook ever hit a wall past which privacy concerns, a saturated user base, and generationality slow its growth of ad revenue?

Amid all of this wait-and-see, one big shock has hit the tech world, and it’s more in the realm of bits (and electrons) than atoms: Tesla took $1,000 deposits for roughly 400,000 Model 3 sedans — in under a month. For scale, BMW sold 100,000 3-series cars in the U.S. in 2015, a 6% drop from 2014. Tesla’s name of its car is no accident: BMW is the standard for the mid-size sport sedan, and Tesla likely wants to do to that benchmark what the Model S did to Mercedes S-class, BMW 7-series, and Audi A8 sales: torpedo them. All of a sudden, Tesla is shaking up the automotive world, and every time I investigate, another interesting tidbit comes up.

First, the model 3 sales might not be the biggest news. Solar power is about to get cost-competitive in some climates (without subsidies), given new advances in sun-tracking technology for the arrays. One big drawback is the night-time, obviously, so battery power is one key way for solar to make sense. Tesla’s energy business unit is on track to sell 168.5 megawatt-hours of energy storage applications to SolarCity (another Tesla unit), according to GTM Research. That number is six times what Tesla sold SolarCity just last year, and a 60% increase on the entire industry output for 2015.  In addition, the 85kw battery in the model S is massive — just how big, I didn’t realize until I read that it can power the average household for 3 1/2 days. What does that do to electric company projections, to household disaster recovery, to our thinking about what charges what in the family garage? Tesla is remaking the auto industry, but power generation could be affected pretty radically as well.

Second, Tesla is learning the realities of manufacturing quality control, vendor management, and other “boring” supply-chain tasks that are tripping up the company. Some examples: Reuters reports that Tesla spent more than $1,000 per car on repairs, and set aside about $2,000 more per car for future repairs, on cars sold in 2015. Daimler (a more apt comparison than GM or Ford, given the average selling pice) spent $970 per vehicle but set aside only $1294. This approach appears to be well justified: Tesla has missed ship dates, suffered from repeated quality issues, and is trying to rewrite the industry rule book with over-the-air software updates.

The Model X SUV (the one with the funky gull-wing doors) is getting blasted by online forum reviewers, at the Wall Street Journal, and from Consumer Reports (which loved the Model S). Even CEO Elon Musk said earlier this year, "I'm not sure anyone should have built or designed this car, because it's so difficult to make." Doors won't open (or open correctly), the heater is chilly, and the touch-screen freezes, among other issues. Some of this is a reflection of making something as complex as an automobile, now with more software than ever before. Musk tried to point out a bright side in one presentation, noting that only 6 out of 8,000 parts for one car were in short supply — but most of the time, a single part shortage can stop production entirely.

Third, Tesla is taking a bold tack on self-driving. Their cars on the road are minimally instrumented (in that they lack Lidar), but are recording driving data at a prodigious clip: one estimate claims that Tesla “learns” (in AI terms) as much in one or two days as Google has from all of its cumulative driving experience. Thus if Google sees one deer strike per hundred thousand miles, let’s guesstimate, then Google has a base of 12 or 15 deer strikes whereas Tesla has hundreds or even thousands. Every Tesla has a cellular data connection for the software updates, but that link also harvests driving data from owners who do not opt out of being guinea pigs. Thus the Model 3 could offer stronger autopilot capability than anyone else in the market when it appears. (See this for more)

Fourth, the Model 3 buyers could face a nasty surprise if they are late in the queue. Specifically, U.S. government subsidies of $7500 for electric cars expire after 200,000 units have been sold. If U.S. sales of the Model 3 are 50% of the total, using round numbers, the subsidy (which can be augmented with state incentives in a given locality) will run out early in that 400,000 run: sometime in 2018. Thus buyers who came late to the party might pay the sticker $35,000 base price rather than $27,500 (or less in some states). In reality, Musk reported, the typical option package for the first weekend brought the total average selling price up to $42,000 or so.

Fifth, the big question is of course, can Tesla meet demand? The Model S began as a 15-20 units/week exercise in 2012 before hitting 1,000/week in 2015. Assuming early growing pains, but a faster ramp, 400,000 is a big leap, from 50,000 a year to something close to 3 or 4 times that, in less time than the Model S took to get to scale volumes. The good news is that the Model X complexity costs were lessons well learned, and the Model 3 has the potential to be the “best of all worlds” assuming a) battery production at the Tesla/Panasonic factory in Nevada comes on line as predicted, b) the same engineering that delivers “stunningly graceful” ride quality in the Models X and S can be scaled down to meet the price point (in part from a steel rather than aluminum body, most likely), and c) the factory processes, vendor management, and warranty issues can be contained.

For those who ask, no I did not reserve a Model 3: range anxiety in the middle of nowhere is real (the nearest Supercharger is more than an hour away, and there are none in the places I tend to drive for vacation).

Finally, the wonders of Quora continue to amaze me. There, I learned about the Model S as a “green” car: most electricity is not carbon-free, obviously, but how much does power source matter? If we use a Toyota Prius as a benchmark (19 metric tons of CO2 per year), the Model S wins only if it’s on a clean-running grid, such as the California mix of fuels/methods (11 metric tons) or if one can connect to wind (at which point emissions fall below 1 metric ton). A coal-fired diet for the Tesla’s electricity results in a 34-metric-ton CO2 toll, nearly twice that of the Prius.

Given Apple’s share price slide and the apparent saturation of its main markets, along with the heavy cross-pollination of engineers who have worked at both companies, should Apple buy Tesla? Apple’s supply-chain and marketing expertise and its capacity for major capital expenditures make it a seemingly attractive suitor. In addition, Tesla CEO Elon Musk may be too busy with Mars mission plans in his SpaceX capacity to get deeply engaged in the much less interesting earth-bound business issues such as those at Tesla: quality control, procurement analysis, lobbying for company-owned dealerships, etc.

I’m partial to another scenario, however: Apple could team up (somehow) with BMW, a company with a viable electric compact already in the market in the i3 at $44,000 MSRP. The two brands share customer bases, design aesthetics, and profit margins. Apple CEO Tim Cook is reported to have visited the i3 assembly line, which in itself is pretty amazing (see here), as is Tesla’s, to be sure. Tesla has a nice injection of working capital from those deposits, but also a tall order in the need to execute a step-function increase in production, engineering, and after-sales service on an entirely new scale. Cook’s expertise is in supply chains, and he likely understands better than most the risks facing Tesla at this juncture.

However it plays out, cars are suddenly “cool” again, for entirely new reasons, in part because the global smartphone market appears to be saturating. Wherever one sits in the tech industry (except at Amazon Web Services, apparently), there seems to be concern and caution rather than the unbounded-horizon talk to which we’ve grown accustomed (Intel just laid off more than 10,000 employees, to take only one example). Seeing who emerges from the recalibration will be fascinating indeed.

Thursday, March 31, 2016

Early Indications March 2016: Robotics Business Models

For all the engineering successes of robots in the past few years, it’s unclear how the various sub-fields will make money. Past business models appear to be of limited use, so after a recap of the current conundrum, I will speculate on some options.

First, the successes. The Boston Dynamics military robots can run incredibly fast, traverse uneven terrain, and maintain their balance in many circumstances. Last fall Amazon renamed its acquired company Kiva as Amazon Robotics, is hiring aggressively, and serves reference customers in the supply chains of such firms as The Gap, Walgreens, and Crate & Barrel. Self-driving cars are becoming real, and improving, far faster than anyone could have predicted: Tesla made the Autopilot feature (an enhanced cruise control, essentially) a software download in 2015; no hardware retrofits were needed. Drones comprise an essential, if controversial, facet of U.S. foreign policy.

Those engineering successes, however, have not yet translated to revenue. Amazon appears to be in investment mode, with LinkedIn postings mentioning a “new robotics platform” that could involve machine vision. Google/Alphabet is reportedly selling Boston Dynamics, but the future of the other companies acquired at about the same time is less clear within the Alphabet/X division of labor. Google’s commitment to self-driving cars looks to be extensive, but the revenue model — ads? licensing? OEM? a platform play? — remains undisclosed, or undiscovered.

Who might buy Boston Dynamics? Its founder, former MIT professor Marc Raibert, is one of the world’s leading authorities on “legged-ness” (balance and locomotion). The firm has earned multiple DARPA contract wins. The company’s robots (especially the towering humanoid Atlas) can be frightening, the notion of robotic warriors can scare some people, and the economics of potentially being a defense contractor, with long procurement and decades-long product support cycles, don’t work for most start-ups. Amazon has the deep pockets, and potentially the culture (and Amazon Robotics is already located outside Boston); it has been frequently mentioned as a logical landing spot, but the fit of humanoid robots in Amazon’s supply chain isn’t obvious. Toyota announced a massive robotics initiative lead by Gil Pratt (formerly at the DARPA Robotics Challenge, which featured the Atlas as a shared platform) and James Kuffner (formerly the robotics head at Google). Both men know Boston Dynamics well. Another bet was placed by Rich Smith of The Motley Fool: General Dynamics builds land-based weapon systems (tanks) already, knows the procurement process, and could scale production of military robots relatively easily.

None of these three companies would surprise me. Some less likely suitors: the military side of iRobot (which is spinning out of its now-consumer-oriented parent, also based in Boston); GE (making big bets on 3D printing, the Internet of Things, and advanced manufacturing, and with a military clientele); Boeing/Lockheed Martin/Northrup Grumman. I don’t know that they could write the necessary check, but the nonprofit SRI might be appropriate for a pre-revenue technology shop with potential government/defense clients.

Apart from Google, other companies exhibit a lack of certainty. Daimler’s CEO said last year that his firm did not want to play Foxconn to anyone’s Apple. Uber then reportedly placed an order for 100,000 S-class Mercedes sedans, some of which could be self-driving. Terms were not announced, but for those doing the math at home, that’s $10 billion at current sticker prices, not counting any autonomous add-ons. (Daimler’s Freightliner unit is leading the way in self-driving semi trucks, so the autonomous speculation is not far-fetched.) As far as business models for robotics goes, clearly one play is selling pickaxes to miners.

What might be some other robotics business models?

Just as GE Capital, GMAC, and other firms carved out niches providing financing for capital expenditures, there will be a role for companies that can finance robotics purchases, package the loans for Wall Street, and service the accounts on all sides of the transaction. There may be sub-specialties: hospital robots, amusement-park animatronics, and robotic materials-inspectors (for airplanes for example) might each require different sorts of business expertise in the financial realm.

IBM dominated mainframe computing, Unix had its day in the midrange, Windows enjoyed near-monopoly status in the 1990s, and now Android and iOS control much of the world smartphone installed base. Will there be a similar software system for robotics? Possibly, but the heterogeneity of uses and contexts may mean that no one market is big enough to spawn a predominant operating system. At the same time, certain vendors could control crucial IP, much like Qualcomm did and does on the mobile phone. Maybe one company cracks machine vision particularly elegantly and becomes the Qualcomm or Intel Inside of that one subsystem. Traction in mobile robots, batteries, and privacy audits could each end up branded by a vendor that supplies the final machine-assembler.

Who could become the trusted third party, much as some websites announce (TRUSTe et al), that monitors data collection and handling, payments, identity management, and other functions that will become necessary? Google had a major trust issue with Glass in its consumer incarnation; is there some player that could restore confidence as facial recognition, to give only one example, runs the risk of becoming abused, then discarded, and eventually rejected in the market?

Numerous universities, starting with UC-Berkeley and joined recently by MIT, have added targeted training in various facets of data and analytics. The same will hold true here. From trade schools to PhD programs, there will be demand for robot assemblers, installers/systems integrators, repairers, programmers, designers, and more. “Original” education as well as retraining will be required.

-Component suppliers
Unlike computers that sit on a desktop or even reside in a pocket or purse, computers that move about in the physical world require more sensors, actuators, robustness/ruggedizing, and batteries.  Obviously some components will come from the usual suspects: batteries from Panasonic; cameras from Sony, Omnivision, or Toshiba; design by Huge, Frog, or IDEO; etc. Other specialized components such as hydraulics, treads/wheels, and casing could present new opportunities for smaller, more nimble entrants.

As care-bots improve, a visiting nurse service might fold robotic care into an existing contract: a given individual might receive x hours of skilled nursing care per month, y hours of semi-skilled nursing, and z hours of robotic assistance, whether in moving, play, or monitoring. Similar robotic components could become part of security services such as Pinkerton or Securitas, safety inspections in mines or oil drilling, or repairs in places that humans can’t easily reach.

Robots are particularly suited for dull, dangerous, or dirty jobs. Temp-agency firms such as Kelly hire out people to do some of these, so it would be a natural extension of the current business to rent out robots to do nasty things like clean chemical tanks, sort packages for UPS at peak times, or clean out student apartments before move-in in August. (There’s already a germ-killing robot used in hospital bathrooms — why not deploy it in dorms?)

-Hobbyists and gadget-lovers
The personal computer took hold among a subculture that merged political idealism with what’s now known as “maker” culture. Certainly there are plenty of people creating 3D printed artifacts, building robots, experimenting with drones, and even programming self-driving cars (Google “GeoHot car”). The Jibo social robot wants to be a core part of the family interactions (albeit of early adopters) rather than a science-fair project. Absent an obvious home-entertainment adoption pathway as of now (and robotic toys like the Aibo could be the breakthrough device), in what room(s) will a robot reside in a human dwelling? If it’s the workshop, that’s a market, but nothing like the demand for PCs or smartphones. (The entire U.S. power tool market is roughly $10 billion.) That may be the initial play: recall that one of the proposed use cases for a personal computer in the 1980s was as a recipe repository, but that hardly turned out to be the PC’s killer app.

-Testing and certification
As robots and self-driving cars move more and more freely among human populations, the potential for injury will require inspections and licenses closer to those for private aircraft than for cars or boats. Given the stakes in liability cases, the certifying authority will be a major step in market adoption.

The long history of telephones as a less immersive communications technology suggests that people will readily pay for the ability to “be” in more than one place at a time. Suitable Technology’s Beam robots (made famous by Edward Snowdon at TED) could be used for remote visits to a project team, supplier factory, or art museum. Paying by the visit/meeting, or the minute, may be the win; selling the robots may become a secondary consideration if demand pulls said teams, factories, or museums to offer remote visitation.

That’s a brief list; I trust readers will spot many opportunities that I missed. More tellingly, companies (some of which are household names) will soon be reporting results from robotic businesses, and market forces will put pressure on those business models from both customer and capital perspectives. In other words, Alphabet’s sorting-out of its investments portends a much broader testing of the new regime’s various configurations; picking winners will be slightly easier once we get a sense of what the contenders look like.