Smart Audio is Here to Stay: Some Takeaways from NPR’s Smart Audio Report

NPR and Edison Research have been putting together The Smart Audio Report. The study, presented at CES in January, gives a good look into how quickly smart speakers like Alexa and Google Home are entering the home:

  • It’s growing fast: 16% of Americans have a smart speaker − 128% growth since January 2017
  • Usage is growing over time: 84% use their speaker the same amount or more than the first month they owned it
  • They’re becoming embedded into people’s lives: 65% say that they would not like to go back to life without their smart speaker

The most interesting chart is a breakdown of the most frequently used activities by the time of day.

I haven’t done many of these things but I look forward to finding out more about them!

Fun with Patents OR The Possible Future of Amazon Alexa and Google Home

In the article Hey, Alexa, What Can You Hear? And What Will You Do With It?, The New York Times delved into some of the patents that Amazon and Google have filed for the future of their voice assistants (Amazon Alexa and Google Home). The article focused on privacy concerns by the group Consumer Watchdog that may or may not have understood what a patent is. The stuff that really freaked people out was the Amazon patent that focused on an “always on” capability where the assistants are always listening to the discussions around them.

It’s an interesting idea to use the conversations in the room to develop a better understanding of them; however, the language used clearly doesn’t take privacy into account. The patent was filed more as a future idea rather than something with all the kinks figured out.  But I can understand why some phrases from the patent Keyword Determinations From Conversational Data upset people. To paraphrase:

In at least some embodiments, a computing device such as a smart phone or tablet computer can actively listen to audio data for a user, such as may be monitored during a phone call or recorded when a user is within a detectable distance of the device. In other embodiments, voice and/or facial recognition, or another such process, can be used to identify a source of a particular portion of audio content.

I thought some of the other patents might provide a window into how Amazon and Google viewed the future. My favorite one was titled Monitoring And Reporting Household Activities In The Smart Home According To A Household Policy and was written by Tony Fadell, founder of Nest and one of the fathers of iPod.

This patent talks about various different ways to make a home “smart.” Today having a smart home means being able to control various devices, but what if you could set a goal (or policy in the words of the patent) and the smart home would partner with you to achieve it. To paraphrase the language of the patent it is:

A method for household policy implementation in a smart home, comprising: monitoring the household, analyzing household activities, taking actions and reporting the information. This system can help a family achieve goals such as how much screen time is used by family members, how often the household eats together and whether mischief might be occurring.

Ignoring the obvious privacy issues, there were some interesting things here. As a father, this was really interesting because it thought of the way to install parental controls over my entire smart home.

Let’s start with the overall partnership model. As the parent, I get to define a goal and the house will help me achieve it. How will this work? Let’s look at the example of tracking screen time. I’m kind of excited about a future where I can say “Limit my kids to 30 minutes of screen time.”

First, we need to monitor screen time. We need to understand who is in the room and what they’re watching.

Then we need to define our goals.

Finally, we take an action based on whether the goal is met or not.

Other factors may come into play. For example, if the child has been grounded they may lose their TV time.

Also, just because this was pretty funny, I have to include the patent’s “mischief detector” that detects mischief by  (again paraphrasing):

listening for low-level audio signatures (e.g., whispering or silence), while the occupants are active (e.g., moving or performing other actions). Based upon the detection of these low-level audio signatures combined with active monitored occupants, the system may infer that mischief (e.g., activities that should not be occurring) is occurring. Additionally, contextual information such as occupancy location may be used to exclude an inference of mischief. For example, when children are near a liquor cabinet or are in their parents’ bedroom alone, the system may infer that mischief is likely to be occurring.

While I probably won’t be using the mischief tracker any time in the future, the idea of setting goals for the household, and letting Amazon and Google help, is quite appealing.

What Does a Hotel Brand Stand for? How Airbnb Changed the Game

I was recently on an airplane with a hotel entrepreneur. His family had immigrated to the US about 20 years ago and they decided to enter the hotel industry. Being new entrants to hospitality, they started with lower quality airport motels (e.g., Econolodge) and gradually moved up to more premium hotels (e.g., Marriott).

I had always assumed that a hotel with a better brand made more money for the owner. I was surprised to learn that this wasn’t necessarily true. Premium hotels are priced higher but these higher prices are eaten up by higher costs in service, staffing and quality of amenities (e.g., beds).

However, it’s generally easier to run a premium hotel. For example, the guests are better behaved, despite their bad rap for being overbearing and demanding perfect levels of service.  Guests at economy hotels bring bad behavior to a whole new level.  My new friend told me about having to break up fights between guests or calming down a customer who was threatening one of his front office staff with racist remarks. People are much more likely to treat the hotel property poorly and even break things in an economy hotel. This leads to additional costs.

One of the worst problems in economy hotels was bedbugs — and not the way you’d think. Customers who already have bedbug problems at home would check into his hotel. Then they would smuggle in some of their bedbug-infested linens into the hotel room. Then they’d check out and wait a few days for the bedbugs to entrench themselves in the hotel room. Then they’d sue the hotel and say that their house became infested with bedbugs because of the hotel. So now the hotel has a room with bedbugs and a lawsuit to deal with.

But stuff like that doesn’t happen at a Mariott (at least I hope it doesn’t). Hotels with premium brands set expectations on the customer experience —price, quality and customer behavior. Put another way, the brand provides a level of trust to the traveler that they will have a good experience.

So what does this have to do with Airbnb? For years, staying at a hotel was the only way that a traveler could trust that they would get a good experience. So when Airbnb came along, most people rejected the idea. In fact, Airbnb was rejected for seed funding by the first seven investors that they approached. I remember hearing that Airbnb was a combination of the two worst ideas in Silicon Valley:

  1. Staying in the home of a stranger
  2. Renting out your spare room to a stranger

In his excellent site Stratechery, Ben Thompson talks about how Airbnb (and others) changed the game. It starts off with something called the Law of Conservation of Attractive Profits that Clayton Christianson wrote about in his book The Innovator’s Solution.

In short, there are commodity suppliers and integrated suppliers in the value chain. The integrated suppliers are the ones that make the big profits. In the original PC business, IBM was the integrated supplier, with its brand and its proprietary components, and everyone else was a commodity supplier. But it’s possible to change the game and commoditize others in the value chain take the profits for yourself. This is what Microsoft did to IBM. Who thought that the OS provider could commoditize the hardware provider — but they did.

Graphical Depiction of the Law of Conservation of Attractive Profits from Stratechery.com

Now let’s look at Airbnb. Travelers have the same needs that they’ve always had. They want a place to stay that’s comfortable and safe that’s somewhere close to the activities that they want to do. So how can a supplier deliver a great experience to the traveler? Before Airbnb, hotels needed to own the whole building (or have a franchisee own it). They would deliver a consistent experience by having a set of corporate standards that represented the brand. So a traveler knew exactly what to expect when they went to a Mariott Courtyard.

However, with Airbnb, the company can set expectations for the traveler during the booking and reservation process. Instead of focusing on broad standards like bed type, free breakfasts, and free Wi-Fi, Airbnb can focus on individual customer experiences for each room that’s rented out. This lets Airbnb commoditize (sometimes called modularizing) the experience of each individual room and still maintain a consistent Airbnb experience. It also let’s Airbnb source from much smaller and diverse suppliers who have extra rooms. So Airbnb becomes the most important player in the experience and therefore the most valuable component in the value chain.

How Airbnb Altered the Hospitality Value Chain, Allowing It to Take Outsized Profits from Stratechery.com

To learn more about how this all works check out Ben Thompson’s writing on Aggregator Theory at Stratechery.com.

Reader Question: Don’t Chaos Monkeys Slow Things Down?

Today’s reader question comes from Marc about my article on Chaos Monkeys on the Simian Army.

I can’t speak for your mother-in-law, but I find this fascinating. Do the testing of problems actually slow down the system, much like as if I were changing a flat tire every week?

— Marc 

What A good question Marc! It’s a question that many people have but rarely ask. This type of testing does slow down the system a little, but the benefits outweigh the costs. It’s like asking “Doesn’t sleeping 8 hours a day make you less efficient? Wouldn’t you be more efficient if you worked the whole 24 hours?”

The key is that failure is baked into this model. Think about if you have 1000 wheels on the car instead of 4. Now each of these wheels is rated to be replaced every 3 years. So each day you can expect about one tire to go flat a day. But that’s on average.

One wheel breaking every day is a pretty easy thing to recover from. But if you have 1000 tires, some crazy things could happen that are very hard to predict ahead of time. What happens if multiple tires go out? What happens if three tires go out that are next to each other? What happens if the front right tire and the front left tire go out at the same time?

It’s much more complicated in Netflix’s case because you have many different types of systems that are interdependent. That’s why Netflix tests all these different contingencies. Yes, there’s a slight overhead in doing this but it allows you to ensure that the system is robust. Also, Netflix wants to make sure that if any single component fails, the system degrades gracefully. For example, if the recommendations system goes down, Netflix should display generic recommendations like new movies or fan favorites and everything else should work fine.

What we’re really talking about is humility in our ability to design a system perfectly up front. In order to run a system at 100% optimal efficiency, you’d need to be able to predict everything that could go wrong and also what may unexpectedly change in the future. For a long time, people have worked hard at making this planning process better. However, trying to make the planning process perfect starts to take more and more time and cuts into the efficiency of the project. Also, and this may be obvious, it’s impossible to plan perfectly for the future.

This is why most software development is moving from a traditional “waterfall” design to a more “agile” design. People used to think that you should build software like you build a building. This was called “waterfall” because you start at the top with your strategy and that design flows down all the way into execution. You make highly detailed plans and then take years to build it. However, we’ve realized over the years that we can solve most key business problems without building the whole software project — we just build the parts that matter. Also, people can start using the software before it’s done — which lets us revise the plans on a regular basis as we see how it’s used. We’re accepting that we don’t know the total plan. We have an idea of where we want to go in five years, but we only know what we’re building over the next few months.

But why does agile work? Isn’t it more efficient to do all the planning first and then build it? Yes. But not in a way you think. The waterfall method is more efficient for software; however, it’s not what’s best to get the job done. When people start a project,  they don’t actually know everything that the product needs to do up front. They wish they could know 100% of the system in the beginning but they never actually do that. Think about trying to design a user interface five years ago. Would you even have considered building a voice interface like Amazon’s Alexa? Of course not. So if you built your 5-year-product roadmap, it wouldn’t have even considered a voice interface.

This idea of breaking projects down into small parts goes beyond software. Bent Flyvbjerg (researcher in project planning with a super awesome name) has found that larger projects are more likely to have cost overruns. However, it’s not necessarily about the size of the project itself, it’s much more related to the size of the segment of the project. Public works projects like building a bridge or a dam, which can only be built in one large chunk, are more likely to have cost overruns than a road, which can be built in small segments.

The Mother-in-Law’s Guide to Chaos Engineering

In this post, I’m trying to take something technical and make it (mostly) readable for my mother-in-law. Enjoy!

One big trend, especially for internet companies like Facebook, Google and Netflix, is not to have one massive computer anymore. This is an oversimplification but computers used to be one big expensive box. The faster the computer you needed, the more money you spent. But eventually, the computers became too expensive to possibly meet the needs of today’s internet companies. So Netflix (and others) started stitching together these large supercomputers out of many smaller and cheaper computers by connecting them in these clever ways.

The benefits of doing this are pretty amazing because they allow you to get this supercomputer that can do incredible things that are very low cost. The problem is with each of the smaller computers. Because they’re so cheap, they can fail at any time. This means that Netflix has computers failing constantly. But customers don’t see this happening. So how does Netflix get this to work?

Netflix needs to make sure that of all its computers and systems are resilient. Using a car metaphor, Netflix is always able to swap out a spare tire if one gets a flat. On their blog, Netflix explains how they test this tire changing/computer failing problem:

Imagine getting a flat tire. Even if you have a spare tire in your trunk, do you know if it is inflated? Do you have the tools to change it? And, most importantly, do you remember how to do it right? One way to make sure you can deal with a flat tire on the freeway, in the rain, in the middle of the night is to poke a hole in your tire once a week in your driveway on a Sunday afternoon and go through the drill of replacing it. This is expensive and time-consuming in the real world, but can be (almost) free and automated in the cloud.

This was our philosophy when we built Chaos Monkey, a tool that randomly disables our production instances to make sure we can survive this common type of failure without any customer impact. The name comes from the idea of unleashing a wild monkey with a weapon in your data center (or cloud region) to randomly shoot down instances and chew through cables — all the while we continue serving our customers without interruption. By running Chaos Monkey in the middle of a business day, in a carefully monitored environment with engineers standing by to address any problems, we can still learn the lessons about the weaknesses of our system, and build automatic recovery mechanisms to deal with them. So next time an instance fails at 3 am on a Sunday, we won’t even notice.

In addition to Chaos Monkey, Netflix has a number of other members of the Simian Army. The Netflix descriptions of these fellows is a bit technical:

Latency Monkey induces artificial delays in our RESTful client-server communication layer to simulate service degradation and measures if upstream services respond appropriately. In addition, by making very large delays, we can simulate a node or even an entire service downtime (and test our ability to survive it) without physically bringing these instances down. This can be particularly useful when testing the fault-tolerance of a new service by simulating the failure of its dependencies, without making these dependencies unavailable to the rest of the system.

Chaos Gorilla is similar to Chaos Monkey, but simulates an outage of an entire Amazon availability zone. We want to verify that our services automatically re-balance to the functional availability zones without user-visible impact or manual intervention.

Iatrogenics OR When Doing Nothing Might Be the Best Alternative

i·at·ro·gen·ic /īˌatrəˈjenik/
Relating to illness caused by medical
examination or treatment.
— Google Definitions

I learned about the word iatrogenic when reading the book Writing to Learn by William Zinsser. The book, written in 1984, used the following passage as an example of medical writing. It talks about the link between medical prescriptions and opium addiction:

The medical profession has a long record of treating patients with useless or harmful relatives, often in clinical settings of complete mutual confidence. Iatrogenic diseases, complications and injury have been, in fact, common in the history of medicine. Only look upon addiction to certain dispensed drugs as one variation among the occasional effects of drug therapy.

I thought, “What an interesting new word!” as did Zinsser who also had to look it up. Then I came across Nicholas Nassim Taleb’s book Antifragile and found that he also fell in love with the word and expanded the idea into a class of issues that he called iatrogenics that went beyond medicine.

Iatrogenics are different from malpractice. Malpractice is doing an operation wrong. Iatrogenics is about doing a treatment correctly but it still having harmful side effects. When doctors ignore these side effects, they are far more likely to use all the tools at their disposal, like drugs or surgery,  whether or not it’s a good idea in the long term.

Let’s look at a recent example. The New York Times recently published Heart Stents Are Useless for Most Stable Patients. They’re Still Widely Used. While they have no medical benefit, putting in a stent makes both doctors and patients feel like they are doing something — that they are in control. And, from both points of view, “they seem to work,” even though they don’t work any better than a placebo.

So what’s the harm in that? Everyone’s happy aren’t they? Well no, they’re not. Doctors are performing an operation that does no better than a placebo so there’s no upside. However, there’s a significant downside in the complications from the operation.

Or take another example from a cruise I went on. Cruises offer Wi-Fi on the ship with tiny data limits (50MB for the whole trip). This is so small that just opening my phone will go over this limit. So a cruise director offered, “Give me your phone and I’ll make it work on the boat.” So I gave him the phone and he starting turning off these data hogging applications. A few months later I realized that one of the things he turned off was my iCloud backup. So the decision that the cruise director made, without telling me, was to give me very limited internet functionality on the boat while turning off my critical backup capability.

Another way of looking at iatrogenics is overvaluing of short term gains vs. long term risks. Take the example of Thalidomide, the poster child for drug overuse. Thalidomide was a sedative that was prescribed around 1960. While it helped women with morning sickness (a relatively minor problem) it caused tens of thousands of serious birth defects.

Indulge me with one more example. When George Washington had left the presidency he’d taken ill. His treatment was the standard for the day — bleeding. However, taking 5 to 7 pounds of blood from Washington’s body is now widely believed to accelerate his death. Bleeding stayed around for a while after that. It was still recommended by leading doctors as late as 1909.

Taleb tells one story of how this problem goes beyond medicine and into finance:

One day in 2003, Alex Berenson, a New York Times journalist, came into my office with the secret risk reports of Fannie Mae, given to him by a defector. It was the kind of report getting into the guts of the methodology for risk calculation that only an insider can see—Fannie Mae made its own risk calculations and disclosed what it wanted to whomever it wanted, the public or someone else. But only a defector could show us the guts to see how the risk was calculated.

We looked at the report: simply, a move upward in an economic variable led to massive losses, a move downward (in the opposite direction), to small profits. Further moves upward led to even larger additional losses and further moves downward to even smaller profits.

At its core, this was what caused the financial crisis. It was people adding more and more risk for smaller and smaller gains. They failed to look at the downside risks which kept growing larger and larger because they couldn’t imagine that they would occur.

Oddly enough, people don’t get in trouble for doing this. There’s a general sense that the people causing the problems were doing the best they could. The idea of “this is the best modern medicine (or modern finance) has — even if it doesn’t work” is well accepted. This is true even when the procedure is successful but the patient died or the economy collapsed.

A lot of this happens because the people making the decisions don’t have skin in the game. They get the upside benefits without being exposed to the downside risk. Taleb mentions that when Roman engineers built a bridge, they were required to sleep under it. Then, if the bridge fell down, the engineers would feel the pain (or death in this case) of the people who were hurt by the bridge.

So what can you do about all this? Try to get your doctor to put a little skin in the game. The next time you have an important medical decision to make, don’t ask your doctor for her medical opinion, ask her what she would do if she were in your place. This changes her mindset from a “disinterested professional” to someone with a personal stake in the game. You might get a very different answer.

Read this along with my story on back pain.

Prospect Theory in Real Life OR How Losing Feels Bad More than Winning Feels Good

I’m going to do a magic trick with a number. I’m going to take a number 1700 and by doing nothing more than raising and lowering it, I’m to show how the interpretation of the number can dramatically change.  Let’s see how that can happen and then I’ll explain how that works.

When my wife was pregnant with our second son, we had a test for Downs Syndrome. This test had three parts:

  1. A “Nucal” sonogram that measured some key ratios. This was the most important test and sets the baseline.
  2. A blood test that measured blood proteins in the mother.
  3. A test of “soft markers” that refined the initial estimates based on other sonogram features.

So we had the initial test. The chance of an issue was 1 in 1700.

“Is that good?” We asked the doctor. “It sounds good to us.”

“Well, in order to be certain, you’d need to have an amniocentesis which has a 1 in 400 chance of serious problems,” said the doctor.

So 1 in 1700 is pretty darn good. Then we got the blood test back. The numbers were even better. Our chances now were 1 in 6800. That was 4 times better than we’d had before!

So we’d finished 2 or the 3 tests. Then, things got tough. We went in for a sonogram and the technician stopped at one point and said, “I need to get the doctor.” That’s never a good sign.

When the doctor came back he said, “Well, your child had 2 soft markers for Downs.”

“What does that mean?” we asked.

“Well, it means that your child has a higher chance of having Downs Syndrome. Maybe you should see a genetic counselor,” he said.

“Before we go down that route, how does this really alter our chances?” we asked.

“Well, we’re not really sure. One soft marker could double the chance of having Downs Syndrome. So 2 soft markers might increase the chance by as much as 4 times but it’s probably less than that,” he said.

“So you’re saying our chances are back to 1 in 1700.”

“Yes.”

See. Magic.

How did this happen? Behavioral Economics has an answer. In contrast to typical economic theory, Behavioral Economics looks at situations and sees how people really react — not how they would react in theory. The situation above is an example of Prospect Theory — the finding that losing something causes about twice as much pain as the pleasure you get from gaining something. So gaining and then losing the same amount still feels like a net loss.

“Saving Money” by Paying More for Netflix

In an earlier piece, I talked about how NetFlix’s move to a subscription model. Why was this subscription model so important?  It made me think of some research that I did on how people think about money about 10 years ago.

One of the big findings was that people have good and bad ways to spend money. For instance, spending money on the house and paying it off every month is a good thing. Having credit card debt every month is a bad thing. But you can get people to make credit cards bills into good payments when you show them how many airline miles they have “earned.” Notice how credit card companies use the word “earned” as opposed to “purchased.”

Getting back to NetFlix, I remember talking to a middle class couple about their finances about 10 years ago. We were in suburban Chicago and sitting on their back porch on a warm spring day. We talked about how they were saving money. They started with normal things like eating out less and spending less money on clothes. But then they said, “We have Netflix so we’re saving money that way too..”

So I asked, “Do you watch a lot of movies?” figuring that they had calculated the cost of renting from Blockbuster vs. their Netflix subscription. This was before streaming. You got one DVD at a time but you could exchange it as many times as you wanted over a month.

“No. Not really,” they said. “But having Netflix means that we don’t have to rent DVDs anymore so that saving money.”

That always stuck with me because I bet they paid a lot more for their Netflix subscription than they ever paid renting videos at Blockbuster. But to them, this was saving money.

The Liars Paradox OR Today Is Not Opposite Day

Today my son Blake started telling me that “Today is Opposite Day!” and then said things like “I love doing my homework. Just kidding. It’s opposite day!”

I told him that he couldn’t possibly be telling me that today is opposite day. If it were opposite day and he was telling me it was opposite day, then it wouldn’t be opposite day. And if it’s not opposite day and he told me that it was opposite day, it would be opposite day. It’s a cycle that never ends. Formally the sentence, “This is opposite day” is neither true nor false and therefore is undefined.

The Infinite Circle of Opposite Day

This, of course, prompted his friend Gabe to try to explain it all to me. “It’s complicated,” he said, “you see, if we say it’s opposite day then we would say that it’s not opposite day to mean that it really is opposite day.” But I didn’t find this line of argument compelling.

Blake tried a different tack, “We can say that Wednesday is opposite day.”

“Yes,” I said, “but you can’t say that on Wednesday.”

This is an ancient logical paradox called the Liar’s Paradox which often takes the form of “I am a liar” or “This sentence is false.” Because the sentence is self-referential and negative.

I figure it’s never to early to teach the kids about logic and paradox. It also makes Blake be more specific about opposite day. The inherent problem with opposite day is that kids randomly choose which items are opposite and which are not (e.g., the sentence “It’s opposite day” is not negated). Now he needs to say “If it were opposite day, I’d say that I love doing my homework.”

Game Theory for Parents

When I was in business school I had a wonderful teacher Adam Brandenberger who wrote a book called Co-Opetition. The book is chock full of lessons on how to apply mathematical game theory to business.

In the book, I learned how to fairly divide things between two companies. But it also works for dividing things between my two kids without them getting upset (formally called Envy Free division). If you have kids, you know that this is a non-trivial problem. Let’s use the example of a cupcake. The most obvious thing is to split the cupcake in half and distribute the two equal pieces to the children. Of course, because you can never cut the cake directly in half, one of the kids is going to complain of unfairness.

The book explains a better way called I cut, you choose . One child (normally the older one who’s better with a knife) makes the cut and the other child gets to choose the one he prefers. This forces the cutter to create two pieces that are as close to as equal as possible because he knows that he’s going to get the piece that’s second best.

This worked well and inspired me to try other systemic solutions to child problems. Here’s the way I solve the problem of two kids sharing an iPhone (or iPad) when watching a movie. Normally the child who’s holding the iPhone will slowly and unconsciously move the phone closer to him, ignoring his sibling. Eventually, the phone gets so far away that I hear, “Hey, I can’t see the phone!”

I’ve been able to solve this problem by having each child have one hand on the phone. Instead of one child controlling the phone, they are sharing control of the phone. This imperceptible pulling between the two children tends to leave the phone nicely spaced between them. You’d think you’d have constant fighting between the two kids — and you do! But the fights are so small that neither kid noticies.