The Myth of the Open Plan Office

Is there anyone who still believes that open plan office layouts increase productivity? Article after article, study after study has made it clear that the massive distraction they create outweighs any benefits from “collaboration” or “spontaneous interaction.” It’s almost a running joke at this point in the tech world:

And yet some of the smartest companies in the world keep building them, over the objections of their own employees. What’s going on?

The obvious answer is cost: office space is expensive, so companies are always trying to reduce the space per employee, and the collaboration/creativity/community stuff is just spin. But even that story only takes you so far, because at some point the lower productivity hurts the company as much as (or more than!) the employee. It’s crazy for a big tech company to save $10k/year on rent by seating a $300k/year programmer on a trading desk where they need to wear headphones all day.

So on some level, there must be people at the top who still believe the myth. In his recent book Deep Work, Cal Newport dissects two of the post-war stories of highly productive collaboration that have driven it: Building 20 at MIT, and Bell Labs in New Jersey.

Neither building offered anything resembling a modern open office plan. They were instead constructed using the standard layout of private offices connected to shared hallways. Their creative mojo had more to do with the fact that these offices shared a small number of long connecting spaces — forcing researchers to interact whenever they needed to travel from one location to another.

Originally, open plan offices were about surveillance as much as cost:

The first modern office was the Larkin Administration Building, which opened in 1906 in New York. Designed by Frank Lloyd Wright, it was based on an open-plan factory, with a giant atrium and very few walls.

“He was interested in creating big cathedral spaces for aesthetic reasons,” says Jeremy Myerson, professor of design at the Royal College of Art.

“But actually it was very useful for managers because no walls meant they could supervise workers in this open space and keep an eye on them. It was all about control.”

Every time I’m in a WeWork or similar coworking office, they seem to be getting more and more subdivided: they’re discovering that members will almost always pay a premium for a tiny glass-walled interior closet compared to a seat in an open bullpen. Which shows you that the closer the decision making is to the actual occupants, the higher the premium becomes on privacy. And if you visit even the newest and best-amenitized corporate open plan offices, you’ll find huge waiting lists for the conference rooms, people running in and out to take phone calls, and those giant headphones everywhere. (A friend at one of these told me their colleagues were starting to replace their Bose noise-cancelling headphones with the industrial ones worn by lumberjacks.)

So many of the changes in the typical office over the last few decades are about trading easy-to-measure cost reductions for diffuse, hard-to-measure costs in time and attention; think of the way that software has eliminated so much admin staffing by making everyone into their own assistant, spending hours logging expenses, contacts and meetings on a dozen different websites. Each of these trade-offs individually is so small and hard to measure (sometimes there are real efficiency gains involved) that you can see why they keep happening, even if they’re collectively a mistake. But this one is just too big to be explained that way.

This is especially true at those big tech companies with more cash than they know what to do with. In these cases an open plan office may be a kind of costly signaling device (like a peacock’s tail) or a way to keep employees distracted from the fine print on their stock options. But more likely it’s just another irrational management trend in an industry that prides itself on rationality. Hopefully we’re finally getting close to a tipping point.

The Problem with Prediction Markets

Prediction markets — websites where you can bet on politics — are rising in prominence. This election season, their “odds” are often quoted in news stories alongside poll averages.

Of course, these odds aren’t worth much if there aren’t many people betting, and there aren’t. This article by Philip Wallach is an excellent survey of the landscape, and as he points out, the US election has 50 times more betting volume at Betfair (a UK sports book) than at PredictIt, the largest current prediction market.

Social scientists have been infatuated with the idea of prediction markets for decades, but gambling is first and foremost a habit, and the problem with these markets is that there’s not enough to bet on to get people in the habit of coming back. The political world simply doesn’t offer frequent enough events that enough people care about.

These two variables — frequency and interest — can explain the popularity of sports betting and day trading, which are the two most widespread forms of gambling that prediction markets could emulate. You probably care a lot about how your favorite team or fantasy players perform, and they play often; meanwhile you may not care as much where the price of gold or Apple stock is in an hour, but the feedback is even more frequent (and traders are highly vulnerable to conspiracy theories about market manipulation, because they want to care).

Meanwhile, you may care who wins the election, but if it’s weeks or months away, what’s going to keep you coming back to the site? Suppose you came to PredictIt in June convinced that Clinton would win, saw that she was “trading” at 66% (1/2 or -200) odds, and bet $100 — so you’d win about $50 if she won the election. Now you come back during her post-convention poll bounce and she’s at 75%; what do you do? You could cash out with a $15 profit, but then what? You could have made $50. Or you could add to your bet at worse odds. Neither is particularly tempting. My guess is you’ll do nothing, and pretty soon you’ll stop visiting the site as frequently.

One way to describe that problem is that the site is trying to get you to think about sports-like betting in a financial market-like framework. But even compared to sports, the idea of waiting months for your bet to settle is frustrating. Most of the betting on sports happens much closer to the event. (According to this chart, 50% of the money bet on an average soccer match is wagered within the preceding two days.)

PredictIt has gotten a lot of things right in terms of basic market structure and fees that previous prediction markets like Intrade got wrong, but I’m not sure how they can solve this basic problem.

One solution would be to mix in political contracts with sports, like they do at Betfair, or with financial markets, as Intrade tried to do at one point. (“Will the Dow close above x?”) But regulators in the US are not about to allow either of those things. Even these political markets require an exemption from the CFTC to operate.

What they’re doing instead at PredictIt is creating lots of arbitrary questions to bet on, so there’s always something new and always something settling soon — like the exact poll average on some random date:


There’s not much trading volume on these questions, because, not to put too fine a point on it, no one cares about them. All you’ve got are the would-be bookies trading with each other.

Let’s go back to our Clinton example for a moment. If you were a perfect rational actor in an economics textbook, you’d go in both times with your exact estimate of her odds of winning, having mentally updated it for events like the convention, and judge the expected value or rate of return of your bet vs. alternative uses of your capital given what the market’s offering, so you’d wind up transacting more frequently before the event itself. You’d also think about the arbitrage opportunities created by closely related markets (will Clinton win, will the Democratic candidate win, will a female candidate win). But very few people really think that way, and for many it’s stressful and unpleasant to be forced to do so.

I’m reminded of another site called Stickk, also started by social scientists, where you can make “commitment contracts” with yourself to quit a bad habit, and attach monetary penalties: for example, you might commit to quit smoking by year end or pay the site $500. Tyler Cowen was skeptical:

I’ve long predicted this won’t work; one group of potential customers doesn’t really want to change, the other group is unwilling to give up control.  It’s not exaggerating to say that human nature is on the line here, and that if I am wrong this is probably the most important idea you will ever encounter.

Prediction markets are running into an analagous problem, and it’s a very tough problem. Even if the novelty of the idea gets people to try it, how do you get them to keep coming back?

Remember those two key factors: interest and frequency. If you wanted to play to interest, you’d let people bet on more non-political subjects. For some of these there are too many people with advance information (Oscar winners, reality show outcomes, who will die on Game of Thrones) and the market could end up spoiling the surprise. For others, like the outcome of high profile trials, there may be considerations of taste or privacy. But from a moral perspective, it’s probably a lot better than focusing on frequency, like lotteries or casinos, and trying to get people hooked on betting on the weather or traffic or some other quasi-random pattern.

I fear that those of us who want to see prediction markets succeed (and I do) have not been focused enough on the core problem: we’re asking people to bet on a subject that 90% of us only care about (in the US) every four years, if even then. Without changing that underlying proposition, I suspect that the current interest in prediction markets, under the klieg lights of a particularly insane election season, is as high as it will ever be.

Graphing Calculators and Competition

This two-year old Washington Post article describes an odd situation that seems to persist today: Texas Instruments is still selling their TI-84 graphing calculator for about $100, though it likely costs only $15-20 to manufacture and they haven’t updated the hardware in over a decade. Phones and tablets can now do all the same things, of course, but they’re not allowed on tests, and I guess you don’t want everyone’s phone out in class either.

At a time when startups are targeting every non-tech business for “disruption,” why has a tech product like this managed to hold on to such a lucrative monopoly? The article describes a cycle in which teachers require the TI models they’re familiar with, parents have to buy them whatever the price, and TI puts a bit of those excess profits back into teacher training and support to make sure they’re still the standard.

No question that’s a big advantage, but it doesn’t sound insurmountable. The competing model mentioned in the article, from Casio, is $80 and running a distant second in sales; they claim they can’t crack that “TI ecosystem,” but my guess is the price difference just isn’t big enough. Shouldn’t someone be able to do this for $40 or $50? Or create a $10 app that locks your phone/tablet in a way that can be monitored or later audited, to eliminate distractions or cheating? (Remember, the TI calculators aren’t perfect in that regard either, since they have persistent memory that can be used for games or cheat sheets, and even the firmware can apparently be overwritten.)

It also reminded me of the RPN calculators made by HP that are still used somewhat in finance, science and engineering. If you’re going to rip someone off, a bond trader is obviously a less sympathetic target than a ninth-grader, but in any case they seem to have maintained the same inflated pricing outside the K-12 education ecosystem.

My first thought was “this is perfect for a big crowdfunding campaign.” A graphing calculator seems like exactly the kind of old-timey hardware device that a small team could design and produce without any proprietary technology, and there’s a social/education angle to the story that should help the campaign spread online. But a quick search on Kickstarter and Indiegogo shows no such projects, and after a little more thought, it’s easy to see why not. Crowdsourced products are infamous for shipping late, and a calculator that doesn’t show up at the beginning of the semester is useless. At the same time, you couldn’t give yourself too much of a time cushion, since your target audience may not even be thinking about this before back-to-school season. And these customers — adolescents and their parents — will be hard to reach anyway, since they straddle the current demographic for Kickstarter and the social networks where the campaigns are promoted.

Why not a venture-backed device startup? It’s probably not a big enough opportunity. According to the article, about 1.6 million calculators are sold per year, and that number has been slowly shrinking as more and more classrooms get school-issued iPads or other new alternatives. It’s still a great business for TI when they’ve got 90% of the market at $100 apiece, but if even if you took half their market share at, say, a $40 price point, your margins would be much lower, you’d need to spend a ton on marketing and support to get there, and when you do, no one’s going to put a tech multiple on a shrinking business.

What about a more developed secondary market? The article doesn’t say what, if anything, TI has done to suppress sales of used models. They’re all over eBay, with almost 10,000 sold in the last couple months (though this may be the seasonal back-to-school peak). That’s still not much compared to 1.6 million total units, though. Here’s one online storefront that sells them refurbished for $83, and another for $84. Again, not the level of discount that would indicate real competition.

Does this represent a gap in the funding market, an opportunity for a bootstrapped lifestyle business or an investor like IndieVC that isn’t built around big exits? What’s the minimum scale for a project like this to pay off? In any case, I’m sure there are a few other examples of obsolete hardware that’s still making 50%+ profit margins, but this seems like a particularly egregious one.

Restaurant Rankings vs. Ratings

This idea seems so obvious to me that I’m sure someone has tried it, but I can’t find any good examples. In a nutshell, it’s an app for user ratings of restaurants that’s based on ranking them in categories, rather than rating them individually on a star or point scale.

So for example, you might have your personal lists of the best burgers, the best Italian food, the best steak, the best vegetarian restaurants, etc. You’d only rank categories where you’ve tried at least two restaurants, of course, and when you try a new one you could add it to your list. So, for example, my list for burgers in NYC might start as follows:

  1. Paul’s
  2. J.G. Melon
  3. Donovan’s
  4. Molly’s
  5. DuMont

Actually, what I’d really have is a ranking of the best burgers I’ve had anywhere, and we could still filter them down to NYC if necessary. But you get the idea.

Now, once we’ve got enough users, we can start aggregating those rankings to present a single ranked list in each category. And this has some major advantages over existing ratings sites:

  • It’s closer to how people already think about restaurants. Everyone has running debates with their friends about their top five burgers, BBQ, or whatever else. Everyone asks “is it better than ___?” No one asks “is it three stars or three and a half?” But sites like Yelp and TripAdvisor ask their users to convert those relative opinions into absolute ones, only to aggregate them back into relative lists (“10 Best Mexican Restaurants in Los Angeles”) — and a lot of useful information is lost in that process.
  • Relative ratings are more useful than absolute ones in this context, because they remove a lot of ambiguity. Is a three-star rating for a Mexican restaurant in New York really the same as a three-star rating for one in California, where the standard for Mexican food is higher? It probably depends on the user. But this way anyone who’s had Mexican food in both places is ranking every restaurant on the same list. (In fact, if you wanted to convert the relative rankings back into star ratings, you’d be more accurate than if users just entered star ratings to begin with — but again, why would you?)
  • It’s faster, more fun and more social than ratings — which means you’d get a different and broader pool of reviewers. Rankings feel like much more of a “curation” experience, more of an expression of your individual identity or tastes. And they still allow for a certain “power user” dynamic without allowing them to be quite as dominant.
  • It doesn’t emphasize rants and raves. Think about how many reviews on Yelp are motivated more by anger or affection for the establishment than any pure desire to inform or help the reader. It means that even with the power users, you only get their input on the best and worst restaurants — these reviews are not a very sensitive instrument for distinguishing one three-star restaurant from another.

This would also be a relatively easy project to get off the ground; you could start with a hundred restaurants in 5-10 categories in a single big city, and a relatively small user base. Even a couple hundred frequent restaurant-goers would provide meaningful data. Then you publish those lists on your site, spend a little money on advertising/promotion and see if anyone cares about them.

I think a lot of people would. There’s a certain appeal to the crowdsourcing and mystery-shopping aspects — which the old Zagat guides really tapped into — but the information would also be more reliable and actionable than anything else available. If a new Szechuan restaurant (my current obsession) opened in New York, I’d honestly be more interested in where it landed in this app’s ranking after a month or two — even if that meant only 5-10 people had visited and ranked it — than in a review in the NY Times or on some food blog.

The algorithm would take a little work, but it also doesn’t have to be as complicated with a small user base in a single city as it would be later — I imagine that cross-city comparisons and aging of reviews would both add some complexity down the road. But that would be a nice problem to have.

Is there a business model? The (alleged) Yelp approach of extorting restaurants to buy overpriced advertising so you’ll remove/underweight their bad reviews doesn’t really work. It would be pretty unpleasant for a restaurant to be ranked last in their category, but unless you’re actively trying to exploit that, you’d probably be showing just the top ten anyway in the aggregate lists — who cares about last place vs. next-to-last?

Could you charge users instead, making this an exclusive/insider thing where you need an existing user’s invitation to join, and pay a small monthly fee to participate? Maybe you could still display your own lists to non-members, but you couldn’t show them the aggregate rankings. (You might waive the fee for any month where someone ranks at least x new restaurants, though you don’t want to give people an incentive to lie and rank a restaurant they haven’t actually visited.)

If you wanted a larger user base, another option is for your ratings to be a loss leader for reservations (looks like Opentable has an affiliate program) or some other commission product, as they are at TripAdvisor and most online travel portals. Even as a completely free service that just plugs into major ad networks, you’d probably at least pay the bills while building up a valuable database.

Now, what else would it work for besides restaurants? It’s hard to think of anything, and maybe that’s why no one has tried this. The startup machine is very good at applying cookie-cutter business models to different categories (so for example, I’m sure there have been a few “Rotten Tomatoes for restaurants” attempts) but ratings are an area where no two categories really work the same way, and most of the other obvious ones have less of a clear advantage for rankings over ratings. There are too many variables on which to rate a hotel, and it would be hard to categorize them. Same with music, books and movies. And with consumer products, most people don’t try enough different ones to rank them; could you tell me your top five printers, cars or vacuum cleaners with the same confidence as your top five pizza places? Better to rely on professional reviews there.

By contrast, there’s one clear variable on which to rate restaurants — quality of the food — and 90% of them fall neatly into a type of cuisine. You don’t need 100% coverage either; if you leave out some fusion or vague “New American” restaurants because they don’t fit into a clear category, so what? There are still a million other review sites that are all over them. And while it’s true that people rate restaurants on other things like service, atmosphere, delivery time, etc, that’s often just as much of a bug as a feature, because sensitivity to these things varies widely and it corrupts the ratings. You could still have people enter text comments on these rankings, and excerpt them like Zagat reviews for the master lists — but they would be more like the brief comments on Instagram posts or FourSquare check-ins, and less like the long, performative stories that seem to rise to the top among Yelp reviews.

The main point is that people care about rankings much more viscerally than ratings. It’s why clickbait sites publish so many arbitrary ranked lists; they know they’ll get lots of comments and shares just from people arguing with them. And I guarantee you some NYC readers have not even made it this far because they were so disgusted by my top five burger choices above. But unlike most exercises in list-making, with restaurants there’s a lot of useful, structured information embedded there. Why not take advantage of it?

The Airbnb Building

By Jeangagnon (Own work) [CC BY-SA 3.0 (], via Wikimedia Commons

There are really two separate businesses on Airbnb and other hosting platforms. Let’s call them “amateur” and “professional”:

  • Amateur: People renting out homes that they live in themselves at least part of the time (either a spare room while they’re home or the whole place while they’re not home)
  • Professional: People renting out homes that they never occupy themselves, for short periods rather than the standard monthly/annual lease

They service much of the same demand, but on the supply side they’ll be governed by different rules and economics in the long run. Let’s look past the current city-by-city political fights and even Airbnb itself (because these businesses will persist whatever happens to one particular company) and think about how the underlying rules and consumer behavior are likely to shake out.

There are two systems of rules in play here, which we might call public and private. The public rules are municipal laws and regulations. The private rules are a matter of contracts – leases, condo/HOA bylaws, insurance policies – and will start to draw more focus once the public rules have been sorted out.

In large US cities, it’s often the case that both types of hosting are technically illegal, but these laws are rarely and inconsistently enforced. Over the next few years, most large cities will settle on a new set of rules with more enforcement behind them, with San Francisco’s recent Prop F debate as one of the first major rounds in this process. I think a reasonable bet is that it shakes out as follows, with one or two cities passing comprehensive legislation and others copying and tweaking it:

  • “Amateur” hosting will be explicitly allowed, with
    • a limit on rental days per year, probably 30-90
    • new transaction taxes (though less than hotel/tourism taxes)
    • new safety requirements (again not as strict as hotels)
    • some type of public registry for hosts, to enforce the above
  • “Professional” hosting will be more or less disallowed, or at least regulated much more harshly

Now, it’s obvious that Airbnb, or any platform that combines both types of host, has an interest in blurring this distinction. Most of their hosts are amateurs, but a professional is worth far more in commissions, because they run higher occupancies and often list multiple units. So Airbnb does everything they can to highlight the more sympathetic amateurs, while pushing for regulation that’s friendly to the less sympathetic professionals.

Ultimately, most voters and public officials will see the difference, and at that point there’s no real public interest or powerful constituency to stand behind the professionals. And the hotel industry, which lobbies against both businesses, is also smart enough to know that the pros are a far greater threat to them than the amateurs and eventually exploit the same wedge.

We won’t argue about the fate of professional hosts, which is really a more interesting question for Airbnb investors than the rest of us. If I’m wrong and that business keeps growing, it’s really nothing new, just an update of the European-style “holiday flats” businesses that have been around since long before the internet.

What’s new, and much more of a social change, is the widespread adoption of “amateur” hosting, and what I want to talk about is how those private, contractual problems – the landlord/neighbor problems – will shake out once the regulatory groundwork has been laid.

These problems are particularly annoying for urban apartment dwellers, and will get a lot worse once those public registries are in place. After all, even if your city allows you up to sixty days of short-term sublets, your landlord can still tear up your lease if they catch you – or if you’re a condo or co-op owner, your neighbors can complain (not unreasonably) that they didn’t buy into a hotel and don’t want strangers around, and get the building rules changed.

So here’s my idea: an “Airbnb building” which explicitly allows short-term hosting and manages it through the building itself.

It could work for a rental or condo building. Either way, when you know you’ll be out of town for a night or more, you’d simply tell the doormen or staff, or enter it into a web calendar, and they’d list it on Airbnb for those nights. If someone books it, they’d arrange for the keys, cleaning and so on. And then you split the extra rent – so for a three night booking at $200/night (after Airbnb’s commission), the building would take in $600 and give you $300.

This may sound familiar — it’s more or less how condo hotels and many other vacation properties are managed. Although in those cases the usage is reversed; the owner is typically there only a few weeks a year, and most of the time the unit’s in the rental pool. In our example, the primary resident is still there most of the time.

This approach neatly solves the landlord/neighbor problem, because the landlord’s getting their cut and the neighbors all know what they signed up for. Most people are still not comfortable renting out their homes to strangers, but you’d expect the people who are to self-select into buildings like this as soon as they start to become available. A typical rental building turns over as much as half of its tenant base every year, so it wouldn’t take long to convert all the leases to allow this; with a condo you might have to sell it that way from construction.

There are obvious time efficiencies and economies of scale in having the building management – who already have photos, floorplans, spare keys, and so on – manage the whole process, as opposed to individual hosts.

And as a traveler, I would hugely prefer to stay in a building like this. I wouldn’t have to worry about whether the neighbors and staff know I’m a paid guest, and the level of trust is far higher. Right now I’d probably never stay somewhere without previous positive ratings, but if I was booking with the building rather than an individual, then just a few positive ratings for any units in the building would make me a lot more willing.

Finally, what if your half of the Airbnb rent was passed on as a reduction or partial rebate of your base rent or condo maintenance fees, rather than additional taxable income? That’s how many amateur hosts think of it already, and the tax savings could be substantial.

It doesn’t solve the insurance problems (liability for theft, damages, injury, etc), and I don’t know enough about that business to speculate, but more professional and centralized management certainly can’t hurt in sorting those out.

Why hasn’t Airbnb set up a building like this themselves, as a kind of prototype to show how well their concept can work? Maybe they don’t want large property management companies to get any ideas. After all, they might get tired of paying those high commissions and just start their own website. In a way, Airbnb benefits from having its hosts operate in an uncomfortable gray area with their landlords and neighbors, because it prevents them from working together openly to gain negotiating power with the platform.

I get the feeling that some newer condo buildings with a high proportion of investment buyers are drifting towards my model already, on the professional side; maybe those buyers had initially planned to rent out their units in standard one-year terms, but they’re realizing they can make much more by listing them on a short-term rental platform and tipping the staff enough to look the other way.

But again, this isn’t meant to be an argument for or against Airbnb, HomeAway or any other particular platform. It’s more about starting from the bottom up: what do hosts and guests want, and what systems and structures can address that in the most efficient way? In the end, the companies that adapt themselves to those structures will succeed. The model I’m describing certainly doesn’t work everywhere, but for serviced buildings in large urban markets (which are already a substantial share of inventory on these sites) it seems like a much better approach. I wonder who’ll be the first to try it.

Hospitality Networks:
The Couchsurfing Concept

logo2 (CS) is a social network whose members host each other when travelling, show their visitors around, and organize various meetups and social events. One way to describe it is a free, community-oriented version of AirBnB, though CS came first.

Several years ago, the site went through a somewhat dodgy conversion from volunteer-built non-profit to for-profit startup, taking in $22 million in venture capital funding. It’s been a rocky road, and it’s spawned or rejuvenated a wave of smaller sites who are trying to replace it.

None of these has really achieved much scale yet, and despite its problems, CS still has over 90% of the active user base for this kind of network. But they also have at least 90% of the total payroll and overhead, so their lack of a revenue model is ominous. They’re already on their third CEO since the transition, and last year they fired almost half of their staff; if they burn through the rest of their cash and can’t raise more, it’s unclear what will happen to the site and its member database.

The alternative is to find a sustainable way to generate revenue from this kind of community without eroding its core values, and that’s mainly what I’ll be writing about here. But there are a few other basic problems with the “free hospitality network” concept that we also need to think about. Even if this is your first exposure to the idea, they may have already occurred to you:

1. More “surfers” than hosts: far more people are willing to accept a free place to stay than offer it.

2. Geographic imbalances: everyone wants to visit the same big cities and other tourist destinations. So even if every member was willing to host as much as they travel, there would still be far more surfers than hosts in these places.

3. Safety and trust: a network in which strangers stay in each other’s homes will be a magnet for thieves, sexual predators, con artists, and lower-grade creeps of all kinds.

These are fairly obvious points, and they’re all readily observable problems with CS, so it’s odd how many online commentators have tiptoed around them. The first two especially have received almost no attention, and as far as I can tell, CS has never done anything to address those imbalances. How much they’ve done to address the safety issue is a controversial subject, but it clearly hasn’t been enough.

So what we’re looking for is an operating model that addresses these issues while generating some revenue. It doesn’t necessarily have to be a for-profit business, of course — it could be a self-sustaining non-profit, like a club or membership organization. And even if it is a business, it may not be a venture capital-sized opportunity. There are lots of viable network or marketplace type services that just don’t work at that scale.

What’s the scale of CS today? Well, they’ve got ten million members worldwide, but many of those profiles are stale. From my recent correspondence with the company and a little sampling with their search filters, I’d estimate that their current active member count is between 500,000 and 2 million, depending on your definition of “active.”

The company tells me they counted 24 million hosted nights in 2014, and that’s probably about ten million “stays,” assuming an average length of 2-3 nights. Which suggests that the average active member is very active, hosting at least five times a year — and maybe more, given that some of them are “surfers” who don’t host at all.

At the other end of the spectrum you’ve got AirBnB, which was founded five years later and is currently running at a pace of 37 million nights booked annually  — not that different in scale, actually. But they raised their last round of financing at a $10 billion valuation, and are reportedly raising the next one at $20 billion less than a year later.

You already know the difference, of course: at AirBnB, those nights are financial transactions on which they take a commission. But why can’t CS do the same? On one level, you might think that a guest willing to pay $10 to AirBnB and another $90 to the host would rather pay $10 to CS and nothing to the host, right? But as social psychologists know, when you introduce a little bit of money into a transaction, you change the nature of it completely. The host starts to feel exploited and the guest starts to feel entitled. There’s still an element of generosity and mutual trust, but the context is very different.

The investors in CS, as well as the founders of the more startup-oriented competing projects, are trying to find a middle ground between the legacy CS model and the AirBnB model, one that can be a successful business without turning the hosting process into an overtly commercial transaction, and reach some demographics that AirBnB doesn’t.

Some of the others have no interest in a startup track or maximizing revenue and user count — they just want to create a sustainable community in a transparent, non-profit framework.

And some may be trying to have their cake and eat it too, combining the member goodwill and positive publicity of the second approach with the potential payoff of the first.

But on some level they’re all facing the same initial problem: how can you generate enough recurring revenue to at least keep the lights on? And ideally this would be done in a way that doesn’t just re-create the “old” CS, but actually improves on it by tackling some of those unresolved supply/demand imbalances and safety issues.

In the next post, I’ll walk through a dozen competing projects and try to estimate their current scale and progress toward these goals. After that, we can start to focus on what’s working, what isn’t, and what to try next — both for the existing networks and any potential new ones.


Wearable Tech: Design Doesn’t Matter


Wearable technology isn’t fashionable, and that problem can’t be solved with better design.

I don’t think the first half of that statement is controversial. Wearable tech may be somewhat cool among a certain narrow demographic, but even then it’s not exactly a fashion statement.

But why can’t design help? Because the problem isn’t what these devices look like, it’s what they do. The reasons people wear them — productivity, health, fitness, even connectivity — are just not compatible with the aesthetics of fashion.

I’m the furthest thing from a fashion expert, but I think this is obvious enough that it doesn’t take an expert to see it. Most of these goals are a form of self-improvement, and self-improvement is almost the opposite of fashion. The essence of fashion is to project confidence, self-assurance and natural beauty — to look like you don’t need to exercise, be more productive, monitor your standing/sitting time or posture or UV exposure, be notified of every like on your Facebook page, or anything else with even a whiff of ambition or insecurity.

Don’t believe me? How many designer clothing ads have you seen that feature models wearing a Bluetooth headset, or a Blackberry belt clip, or even a Fitbit wristband? If the technology isn’t part of what’s being promoted, it’s not an accessory that any photographer wants to add.

Even if it works with a certain outfit, no one who’s really fashion-conscious is going to wear the same accessory day after day with different outfits. It’s as simple as that. Dressing it up with different watch straps or colors just makes it even more obvious. And if you only wear your device on certain days, or only to the gym, it defeats the purpose of much of the long-term monitoring that was the reason you bought it in the first place, and makes it harder to justify the high cost.

If you really want people to wear your gadget all day, every day, your design focus should be to make it less visible and easier to hide. For the manufacturers, hope never dies that people will want to display their devices and provide free advertising, but somehow it never seems to work out that way. Take a look at one of the newest ones, the Lumo Lift posture tracker, which can either be clipped to a bra strap/undershirt, or secured magnetically with a visible metal square that comes in different colors. Any guesses which option more people will choose?

Actually, I predict one of the first popular third party accessories for the Apple Watch will be a clip or case that replaces the strap entirely and makes it easier to carry the device somewhere other than your wrist.

Fashion can embrace certain forms of technology, but it takes a long time, generally long enough for it to be charmingly obsolete. Watches are one example: mechanical watches are still classier than (far more accurate) quartz watches, almost a century after the quartz technology was invented. And they’re covered with marine pressure gauges and other extras that most wearers wouldn’t know how to read even if they needed to. Technology is stylish in inverse proportion to its utility.

Of course, there’s still a market for unfashionable wearable tech. There are millions of Bluetooth headsets sold every year. But they’ll never be as widespread as iPhones, and I think the adoption of the Apple Watch — along with Google Glass and every other visible wearable device — will hit the same ceiling. The first breakthrough wearable that becomes ubiquitous will be one that can be concealed.

What’s Driving the Quantified Self Movement?


I’m an organizer of the current Quantified Self meetup group in Berlin. I attended the global QS conference in Amsterdam earlier this month. And when people ask me what it’s all about, I still have trouble giving them a clear answer.

Judging from the group’s website, I’m not alone. Our slogan is “self knowledge through numbers,” and the About page calls us “an international collaboration of users and makers of self-tracking tools.” That’s already a bit confusing, because quantification and tracking are two different things. They often overlap, but they often don’t.

For example, stepping onto a scale, taking a written test, running a timed race, getting your DNA sequenced — all these things will give you “self knowledge through numbers,” even if you never repeat or track them. And on the other side, much of the discussion within QS centers around self-tracking practices that are not particularly quantitative, like photo lifelogging, mood tracking or even an online dream journal. One recent survey of QS practices even included Foursquare.

In a sense, I suppose every human activity is “quantifiable” with timestamps and map coordinates and subjective rating scales, and similarly you could argue that anything you quantify even once is being “tracked,” but in both cases that seems like a stretch. If we define QS practices so broadly as to include everything that ever produces structured data about human beings, we haven’t really defined them at all.

Maybe it’s the “self” part, as subject rather than object? It’s not just that your “self” is being quantified, it’s that you’re the one doing it, or the one controlling it, or the primary audience for the data. That’s a little better, but it’s still a pretty broad definition.

Even our critics seem confused about what we’re up to. In this article, Mike Elgan mocks QSers as frivolous gadget nerds, but in this recent post he gushes about “lifelogging” devices, which are a big part of QS.

So maybe there’s no good top-down description that separates “QSers” from the rest of the population. But our attendees are hardly a random sample of the cities where we meet — nor even a random sample of the tech scene, with which we overlap heavily. So we should be able to say something more about who they are.

What I want to do here is list some of the main trends that are driving interest in QS, without endorsing or criticizing any of them. They each have positive and negative aspects that are worth exploring further. But I haven’t seen anyone just lay them all out in one place, and I think that’s a good way to start.

1. Smartphones and Social Media

This is the most obvious category, right? Throw in digital photography too. Now that we’re capturing and saving huge amounts of data about our daily lives — data that advertisers and governments are keenly interested in — it’s natural that some of us would be paying more attention to that data ourselves.


2. Better (and sexier) technology

When competitive swimmers were tracking their practice lap times with a stopwatch, taking their pulse manually, and writing it all down on a clipboard, not many amateurs wanted to imitate that routine. Now that they’re building digital goggles with a heads-up display that shows their heart rate in real time, the gadget appeal for the average fitness swimmer is obviously a lot higher.

It doesn’t exactly take Don Draper to see that it’s easier to sell cyborg goggles than clipboards, but it’s also easy to see why serious athletes want to see their heart rate while training instead of afterwards. So this seems like a good example of a tool that works across the professional-amateur spectrum: it offers an edge in training for the most serious swimmers, and a motivational aid for the least serious, with many more falling somewhere in between.

The most visible example of this trend is the latest generation of pedometers, like the FitBit or Nike Fuelband, which are always on and upload your data to websites where you can view it in slick graphs and share it with other users.


3. The rise of behavioral psychology

Over the last fifty years, old-fashioned psychoanalysis — Freud, Jung, Adler, Lacan — has increasingly been replaced by behavioral techniques, particularly the “cognitive behavioral” methods developed by Aaron Beck (above) and Albert Ellis in the 1950s and ‘60s. These involve a more scientific approach than the old “lie down and tell me about your childhood” stereotype, and a lot more recurring measurement of “negative self-talk” and other symptoms. And most of this tracking and journaling is essentially assigned as homework for the patient to do on their own.

Whether and when these methods are more effective than psychoanalysis or psychopharmacology is a whole other debate. But whatever you think of them, behavioral methods have been taking over, and like so many concepts from psychoanalysis, they’re seeping into popular culture and awareness. Behaviorism is also deeply imbedded in the more recent positive psychology movement, whose influence is all over emotion-tracking tools and other QS products — and it’s been a major contributor to…

4. The systemization of self-help

We’re a long way from Dale Carnegie’s How to Win Friends and Influence People (1936) or even Stephen Covey’s Seven Habits of Highly Successful People (1989). If you want to make it as a motivational, fitness, diet or productivity guru today — at least the kind that’s taken seriously by the young and tech-savvy — then you need to provide more than just collected advice. You need a system. Specifically, you need to define your audience’s problems in the context of a system that they can then optimize. And most of the time, this involves quite a bit of self-tracking.

I’m not sure what the tipping point was — maybe Getting Things Done (2002), or Crossfit (~2000)? — but at this point, systematized self-help is everywhere you look, from Tim Ferriss to Lifehacker. And it overlaps very heavily with QS. At our meetings in Berlin, I’d say Ferriss’s books are responsible for more new attendees than any other single cause.

5. Health care reform

The last few years of policy debate in the US have prompted a widespread recognition of the diminishing returns to medical research and treatment throughout the developed world. We’re spending more and more to achieve smaller and smaller gains. So a lot of attention (and money) has turned to “lifestyle” factors. If we can get people to drink less, smoke less, eat less, exercise more, take their maintenance medications, and so on — thereby keeping them out of the doctor’s office in the first place — we can obviously make much bigger gains in population health at a lower cost than by treating them once they’re already sick.

Easier said than done, of course. Some approaches involve top-down policies, like New York’s already-infamous beverage size regulations. But others involve getting people to track their behavior on an individual level. And if you can develop a good tool for doing this, it’ll be increasingly possible to get private and public insurance companies to reimburse it for their members, like they already do sometimes for gym memberships. And that makes entrepreneurs’ and venture capitalists’ ears perk up, because insurance reimbursement is a tidal wave of revenue that can be very sticky for any provider that captures a little bit of it.

6. Techno-futurism

This includes  singularitarians, transhumanists, “radical life extension” advocates, Bitcoin enthusiasts, and many more. They trace their roots to computer science, cognitive science, genetics, science fiction, libertarian philosophy, artificial intelligence research, the human potential movement of the ‘60s, and countless other sources.

I don’t mean to imply that these movements agree with each other about everything, and I don’t mean “techno-futurism” as a skeptical term necessarily; it’s just the only umbrella term I can think of. In any case, these groups are definitely overrepresented within QS compared to the population at large, probably even more than they’re overrepresented in tech. For some, self-tracking technology is just a means to an end, but for others it seems to have real ideological significance in itself. And the natural enemy of the techno-futurist is…

7. Humanist skepticism

These are the social scientists, philosophers and other academics who worry about the ethical and societal implications of self-tracking technology, and technological values replacing human ones:

Like the previous group, this one is hard to generalize about. They’re not all academics, of course; some are writers like Evgeny Morozov and technologists like Jaron Lanier. And some walk a pretty fine line between supporting the new technology and worrying about it — as Peter Kramer did in the ‘90s with antidepressants, for example.

The techno-futurists point out that these kinds of critics have surfaced around every new technical or scientific advance in the past — which is true, of course, but that doesn’t mean they’ve always been wrong. And this kind of critical evaluation of technology is an important part of QS. I don’t think we always do as good a job of integrating it as we could, but that’s a subject for another post.


I think that covers most of the relevant trends, but I’m sure I’ve missed a few. And some of the most interesting people I’ve met in QS are the ones who don’t quite fit into any of these buckets, and are pursuing their own idiosyncratic visions.

Anyway, I’m sure that some readers have bristled at my simplistic, unfair description of whatever subgroup or trend they identify with themselves. And I have been a little simplistic. It’s a lot of ground to cover, and I’m hardly an expert in any of these individual subjects. But hopefully this is a good starting point to give outsiders a better idea of what QS is all about.

A Better Version of Dope Wars


“I used to do drugs. I still do, but I used to, too.”
Mitch Hedberg

Dope Wars (or Drug Wars) is a text-based computer game that’s been around for about thirty years, rewritten countless times for every platform from DOS to graphing calculators to iPhones. You play an independent drug dealer in a big city (usually New York) who travels around buying and selling various illegal drugs. The market prices vary widely from day to day and from one neighborhood to the next, so you can make large trading profits if you watch them closely, but you have to keep an eye out for the police, muggers and other business risks.

It wouldn’t have been around this long if it wasn’t fun and addictive — in fact, it incorporates a lot of the elements from my “What Makes Games Addictive?” post — but there are some basic improvements to the gameplay that (as far as I know) have never been made. Instead, every major redesign has tried to change it into something that it’s not. Zynga’s short-lived version tried to turn it into a typically scammy “social” Zynga game, a kind of R-rated Farmville. No surprise that didn’t work. Others have tried to change the subject to something more innocuous than drugs. But none of the ones that I’ve seen have addressed the core gameplay.

If you’ve never played the game, the rest of this post won’t make much sense to you, so you should take a break and go try it out. Amazingly there doesn’t seem to be a decent web-based version anymore, but if you have an Android phone, this and this are both free and pretty good. For iOS, where the censors won’t allow the original merchandise, there’s this one (free) with prescription drugs, and this one ($1) with zombies.

OK, had a chance to try it out? Here are some of the problems I see, and how they could be fixed:

1. Too much randomness. Some randomness is always good, but too much gets frustrating. It’s realistic that muggings and police stings happen on a fairly random basis, but should you really have to wait for the right roll of the dice to buy a new coat with more pockets? Why not have a “shop” that sells equipment in a fixed location, just like the bank and the loan shark?

2. Too much spread in prices for different drugs. As your bankroll grows, it quickly makes sense to deal only in heroin and cocaine, which means that the prices and “events” for all the other drugs are irrelevant. I get that you can carry a lot more dollar value in cocaine than in Quaaludes, but this is a case where realism should be sacrificed for better gameplay.

3. Too easy to compound money. The bank is completely safe and pays extremely high interest rates, so very often the optimal strategy is to leave most of your money there, especially when you reach the point where you can only carry a small % of your net worth in merchandise anyway. The interest rate should be reduced or eliminated, and your account should occasionally be subject to “freezing” and confiscation. Meanwhile, there should be more ways to reinvest your money in the business. You can only have so many “pockets” I guess, but after that why not buy a car, or a van? Or recruit employees?

I think there’s a real opportunity for someone to make a slick, professional, updated version of Dope Wars for iOS and/or Android — one that tries to improve the core gameplay in ways like these, rather than slathering extra graphics and social features onto a game where they just get in the way. I’d pay for an app like that, and I bet a lot of others would too, and it wouldn’t be very hard to build.

And there are lessons here for anyone trying to implement “gamification” to increase engagement with their own app; the first thing you have to do is understand which game elements are engaging and why, and the best way to do that is to look at the simplest, most stripped-down “games” — like Dope Wars or the math game I described in that previous post — where those elements are most exposed. “Gamifying” often seems to mean just bubbly childlike graphics, arbitrary “achievements,” high scores lists and other “game-like” elements. This is a cargo cult approach to the process, and it’s not surprising that it doesn’t usually work.

UPDATE: Some follow-up comments here.