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?

-Financing
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.

-Platforms
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.

-Security
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?

-Training
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.

-Services
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.

-Rentals
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.

-Telepresence
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.

Monday, February 29, 2016

Early Indications February 2016: B2B e-Commerce update

While there has been substantial attention paid to consumer e-commerce (Black Friday numbers, for example), the state of both practice and understanding for online business-to-business commerce is less developed. There are some good reasons for this: B2B prices are often more complex than merely buttons in a shopping cart application, sales representatives still play a role in education and facilitation, and hybrids of call center, online, in-person, and even fax orders are hard to disentangle. For all of these obstacles, however, fundamental questions remain regarding for B2B e-commerce.

A current study attempts to answer some of these questions. Beginning with 100 sites and expanding to 250 later this year, I am collecting basic statistics for web/mobile presences. Preliminary results are in, with the caveat that the short list does not allow industry-level analysis given insufficient sample sizes. 16 industries are included, which means there are some apples (Deloitte) to oranges (Bobcat) to grapes (Dolby) comparisons implicit in the results.

Just glancing at the front pages of these sites, it’s apparent that the customer is not always the primary audience: some sites clearly addressed investors most prominently, while in other cases, recruiting appeared to be a higher priority. My colleague Ralph Oliva asked how often a customer value proposition is evident, and on this admittedly subjective yardstick, only 40% of sites successfully articulated why someone should buy from the company. Finally, commerce was rarely an option, outside the firewall: site logins were common, and I obviously could not see order, tracking, or content registration functionality behind the curtain. The one exception to the lack of e-commerce on B2B sites was branded merchandise: hats, sweatshirts, and jackets, along with die-cast model tractors and such, were widely available.

Social media activity was common: 75 sites linked to Twitter, and 93% of those feeds were current. Oracle displayed a staggering 70 distinct Twitter presences; I did not attempt to analyze the content on each of these relative to the others. Facebook and Twitter often featured identical content, the differences in audience notwithstanding. LinkedIn was also commonly employed, whether for sales or recruiting I did not analyze.

Especially as audiences use mobile devices for more of their access, many sites appear to be outdated. One company had a page copyrighted 1999, with the “Download Internet Explorer” logo still live. PDF product catalogs (sometimes separated into small page groupings, but often a massive single download) were common; web-native and mobile-native catalogs were the decided exception. Data sheets for chemical exposure and other risks were frequently available for download; this seems to be particularly low-hanging fruit to pick. Only 65 of the 100 sites were mobile-friendly, while only 12 offered smartphone apps, some of which were extremely well executed.

In sum, innovation was rare, basic execution (such as site loading time) was often uneven, and navigation often confused rather than enlightened. The good news is that there is so much upside, at so little cost. The bad news is needing to know where to start. When asked to summarize the top areas of opportunity, I can offer 3 Cs.

*Content
Many B2B purchases are complex, such as semiconductors, medical devices, or industrial adhesives for special purposes. In such instances of considered purchases, companies that better inform the customer will be at an advantage. I observed wide variation in the richness and depth of documentation; “contact your representative” was unfortunately the default solution on a large number of sites. The often-absent customer value proposition and/or branding can be considered as another content area.

Opportunities to improve this state of affairs abound. Only 21% of sites sampled offered a corporate blog, for example, a channel that interfaces nicely with social media, with trade shows, and with formal content such as white papers or customer case studies. An even richer opportunity lies with the use of online video. While 85% of sites offered some form of video, gauzy corporate overviews were often the first option. In contract, the really effective uses of video were rare: points of view, such as Corning’s “A Day Made of Glass” (with 25 million YouTube views); precise training and instruction (look at Yaskawa); and head-to-head product comparison (Bobcat stands out here), to name only three. Timken got almost 500,000 views for an instructional video about automobile wheel bearings. Haas Automation has a fine video library in support of their machine tools and associated processes. These are the exceptional few; most companies have substantial room for improvement, at low cost and free distribution (compared to the days of pressing DVDs). Social media, cheap in direct expense, does require dedicated headcount, but most companies in the sample had room for improvement in richness, relevance, and engagement.

*Configurators
While in some cases it makes sense to talk to a live salesperson or technician, there are still many opportunities to provide detailed configuration information and perhaps pricing. Such tools were used effectively at Texas Instruments, MRC Global (in an app), Kennametal, and NXP. There’s no reason they couldn’t be used at more businesses. Some configurators are deployed as lead generation tools rather than as customer information repositories: after doing all the work to select and option a Bobcat tractor, for instance, I had to contact a dealer for the actual price.

*Customer contact
The final C provided many examples of good, bad, and ugly options. A simple example lies with e-mail. According to the Direct Marketing Association, commercial opt-in e-mail generated $36.70 of sales per $1 of investment in 2014; it was the ROI champ, far outpacing Internet search (about $22.00), direct mail catalogs ($7.27), and internet display ads ($19.21). How many sites asked for my e-mail address to send me newsletters, product updates, point of view pieces, or other messages? Only 40 of 100. (Similarly, only 40 of 100 connected trade show information to the online presence.)

Contact information was often presented from the inside out: here’s how we are organized (by geographies, by industries, by dealerships, etc) and it’s up to you the customer to figure us out. A smaller number of sites organized contact by customer tasks: “How can we help you?” is a user-friendly way of organizing different product lines, industry solutions, or support functions from the outside in. These rubrics were, unfortunately, in the minority. Many sites offered multiple navigational paths, often on the same page (which can be good practice): Oracle’s pull-down menus were quite complicated and incredibly information-dense, but seemed to get the job done; Oracle’s direct competitor SAP opted for a very different, leaner user experience model. Simplest of all in this industry was Salesforce.

The other number that jumped out with regard to contact concerned live chat. With millennials often eschewing the telephone as a voice tool, “talk to a rep” often sounds unappealing, and “e-mail us for a quote” may take too long. Given these demographic trends, along with the reality that B2B customer support often occurs on customer premises or on noisy shop floors where voice communication is distracting or impossible, it was surprising to see only 13 of 100 sites offer a chat function.

There were many other surprises (such as how often basic execution failed: broken links, outdated posts, and improper server configurations were not uncommon), and the larger data set will deliver further insights. Some of my potential research questions concern proximity to B2B/C sites, especially Amazon and eBay: are companies that overlap these channels more likely to adopt similar site functionality, or should industrial distributors seek to look as little like Amazon as possible? (I saw both approaches in the sample.) Further work also needs to be done to compare like companies or divisions: how can the B2B universe best be segmented so that insights can cross domains at the same time that differences (in purchase frequency, in service/product hybridization, or in end use of the product) are recognized? Finally, getting insight into what’s behind the firewall would be revealing if it is feasible. Until then, I hope these preliminary results offer food for thought and I will be happy to share the entire presentation of findings upon request.

Saturday, January 30, 2016

Early Indications January 2016: Shocks

The past month has been marked by a series of extraordinary events that would have been completely unforeseen only a year ago, or even in mid-summer. (In June, West Texas Intermediate crude oil futures contracts were selling at $60 a barrel, roughly twice the current price.) While this may be an unusual month, the larger question remains: how can human institutions evolve to better address both sudden and glacial change, in both positive and negative directions? Put another way, if we see what keeps surprising us, maybe we can adapt our practices and assumptions to be surprised less often, less acutely, or both.

Oil is certainly big news. While the dynamics of a global market, controlled by a wide range of political and business players, remain fascinating, “common knowledge” in energy markets shifts dramatically. Recall how recently talk of “peak oil” was common: according to Google Trends, searches for the phrase spiked in August 2005 and, at a slightly lower index, May 2008. After 2011, interest dwindled to baseline noise, and today we wrestle with the problems of sub-$2.00 gasoline. The precise events coming into play right now have complex origins: innovations in drilling technology, geopolitical forces (including bitter national and ethnic rivalries), and national budgets whose planning assumptions have been obliterated. Saudi Arabia, for example, can produce a barrel of oil for about $3 but needs $93 to break even for budget purposes given its economic monoculture; Venezuela needs $149 a barrel to break even, to take the most extreme example. At $30, budgets in many places (including Alaska) are a mess.

Given that oil is such big business in so many parts of the world, considerable expertise is deployed in forecasting. Yet the industry’s record, with regard to both estimates of oil reserves and now prices, is consistently poor. Perhaps the lesson is that complex systems cannot be predicted well, so the best answer might be to shorten planning horizons — a tough call in light of the magnitude of investment and concomitant project lead time required.

The next “shock’ is in some ways predictable: U.S. infrastructure investment has lagged for so long that calamities on bridges, railroads, and water supplies are unfortunately overdue. The particular politics of Flint, Michigan’s mismanagement are also not surprising given the nature of both large, overlapping bureaucracies and the governor’s high priority on municipal budget repair to be performed by unelected “emergency managers.” The competing agendas are difficult: if bondholders lose trust, investment becomes prohibitive. At the same time, the dismissal of known test results and risks, and the human consequences thereof, are criminal: GM stopped using Flint water because it was destroying auto parts while Flint’s citizens had to keep drinking it.

The pattern in Flint is not all that unusual, except in its impact: given the size of federal and state governments, it’s hard to imagine who voters could hold accountable for substandard ports, roads, and airports. Many are in poor repair, but the constituencies are diffuse and/or politically marginal, and so can be ignored. Who can one complain to (or vote out) regarding connections inside Philadelphia’s airport, or Amtrak’s unreliability, or Detroit’s crumbling schools? Conversely, what good came to the Detroit mayor who supported that airport’s modernization? Who is the primary constituency that benefits from New Jersey’s extremely heavy spending on roads ($2 million per state-controlled mile) that are consistently graded as among the nation’s worst (at both the Interstate and local arterial levels)? Rather than planning horizons, the issue here appears to center on accountability. The interconnections of race, poverty, and party politics can also fuel tragedy: decisions were made in Flint that would be unthinkable in more affluent Detroit suburbs. (Another water issue, the one in California, could also amplify class conflicts in the event the El Nino snowpack melts to last summer’s levels in coming years.)

The third shock is a positive one. Google’s DeepMind unit (acquired for $400 million in 2014) announced that it had used machine learning to develop a computer capable of defeating the European champion at Go, the ancient Chinese game of strategy. AlphaGo, DeepMind’s program, will now play a higher-ranked champion in March. If the machine can win, another cognitive milestone will have been achieved with AI, about ten years faster than had been generally predicted. Interestingly, Facebook had previously announced that it had made significant progress at Go in a purely machine tournament, but the Google news swamped the magnitude of Facebook’s achievement.

To their credit, DeepMind’s team published the algorithmic architecture in Nature. Two distinct neural networks are built: one, the “policy network,” limits its scope to a small number of attractive options for each move, while the “value network” rates the proximate choices in the context of 20 moves ahead. It’s likely the technology will be tested outside abstract board games, potentially in climate forecasting, medical diagnostics, and other fields.

In this case, the breakthrough is so unexpected that nobody, including the scientists involved, knows what it means. Even though Deep Blue won at championship chess and Watson won at Jeopardy, neither advancement has translated into wide commercial or humanitarian benefit even though the game wins were in 1997 and 2011 respectively. This is by no means a critique of IBM; rather, turning technology breakthroughs in a specific domain into a more general-purpose tool can in some cases be impossible when it is not merely hard.

Elsewhere, however, giant strides are possible: Velodyne lidar, the spinning sensor atop the first generation Google car, has dropped from $75,000 per unit to a smaller unit costing under $500, with further economies of mass production to come. Even more astoundingly, the cost of human genomic sequencing continues to plummet: the first human DNA sequence cost $2.7 billion, for the entire research program. Shortly after, the cost was about $100,000 as of 2002; today it’s approaching $1,000, outpacing Moore’s law by a factor of thousands (depending on how one calculates) in a 15-year span.

In each of these technological instances, people have yet to invent large markets, business models, or related apparatus (liability law, quality metrics, etc) for these breakthroughs. As the IBM example showed in regard to AI, this is in some ways normal. At the same time, I believe we can create better scaffolding for technology commercialization: patent law reform comes immediately to mind. Erik Brynjolfsson and Andrew McAfee suggest some other ideas in their essential book, The Second Machine Age.

Education is of course a piece of the puzzle, and there’s a lot of discussion regarding STEM courses, including why more people should learn to code. I’ve seen several people make the case that code is already the basis of our loss of privacy, and there will be more deep questions emerging soon: who owns my genomic information? who controls my digital breadcrumbs? should big-data collection be opt-in or opt-out? Yes, knowing _how to_ code can get you a job, but more and more, knowing _about_ code will be essential for making informed choices as a citizen. The widespread lack of understanding of what “net neutrality” actually entails serves as a cautionary tale: few people understand the mechanics of peering, CDNs, and now mobile ad tech so much of the debate misses the core issue, which is lack of competition among Internet service providers. “Broadband industry consolidation” isn’t on anyone’s top-5 agenda in the U.S., yet even comedian John Oliver identified it as the major nut to crack with regard to information access.

In the end, humans will continue to see the future as looking much like the present, driven by psychological patterns we now understand better than ever. As shocks increase in magnitude, for many reasons including climatic ones, and impact, because so many aspects of life and commerce are interconnected, it may be time to rethink some of our approaches to planning for both the normal and the exceptional.