A long review of Phil Town’s 2006 Rule One Investing

Who’s the author and do we care?

Phil Town is the author of two books, 2006’s Rule #1 and Payback Time

Town’s origin story is that casting about after serving in Vietnam as part of the Green Berets, he worked as a river guide in the Grand Canyon, where an older investor he met offered to teach him the ways of the stock market.[1] He then claims to have turned $1,000 into $1,450,000 in five years and became a full-time investor[2].

Now, he’s got a popular podcast where he’s teaching his daughter Danielle Town (who is so great as a foil and voice of the listener), and wants you to attend his seminars and subscribe to his stock research tools from him.

Should I read this or not?

Totally worth reading if you don’t already have a valuation strategy or process you’re into, with the caveat that theres’s some confusing additional material that might make you scratch your head.

If you do, or you’re more generally familiar with value investing (why it can work, particularly), there may not be a lot here.

The meat of the book: how to find good businesses on sale

Distilled, Rule One tells us to invest in good stocks that are 50% off. He’s got the “Four Ms” for you

  • Meaning
  • Moat
  • Management
  • Margin of Safety

And then walks you through each one. Some are easy to understand: for “Moat” he talks about competitive advantages you can count on over time. For “Management” you’re looking for some key metrics, and there are good explanations of why you want to look at each one.

“Meaning” isn’t quite the right term (and to his credit, on his podcast Town’s joked about how the neccessities of book marketing forced the names so there would be a catchy “Four Ms”) — but here, do you understand what the business is doing, and would you buy the whole business?

Where the real meat is for people will be in how you value and price stocks, looking for the discount. And here’s where a lot of math comes in, and I respect that: this isn’t Greenblatt’s “Magic Formula” where you just rank all the stocks. For every stock, Town walks through how to estimate the future value of the stock, and compare it to alternatives, like investing in U.S. Treasury bills, or against expectations.

I dug this a lot, but I also had to break out the spreadsheets, and it’s way more of a pain to get all the data together (this as much a problem with the web in 2017 though). Handy solution though — you can just subscribe to his website and get all those numbers in one place.

You end up with what Town calls the “sticker price” for a stock, the current fair value for a company given expectations for its growth. The explanation for the concept is excellent — that price should be a starting point, and you want a sale.

Running those numbers was an eye-opener for me and taught me a lot about how to think about stocks as both being a company’s value in the present but an expectation of future growth as well. For instance, I’d been griping that one company seemed wildly under-valued based on their price/earnings ratio. When I ran through the process here, I understood that it was priced perfectly reasonably.

Once you can calculate the sticker price, you want stocks that are at least 50% off. For this to be true, either the company is deeply out of fashion or something has gone horribly wrong and the stock’s being beaten up over bad news. As I write this, the CAPE (Cyclically Adjusted PE Ratio) is at 30.62, which is historically very high, and you’re not going to find many stocks on sale for half off.

Going off the rails: add technical analysis

Chapter 12 is “The Three Tools” and here Town talks about “red light” and “green light” indicators for whether to buy a stock. Here we diverge entirely from the value of a stock and get into some entirely different territory, and where I think the book loses people. Buying quality businesses on sale is simple, and while it takes a little work, it makes sense as you’re doing it.

In Chapter 12, suddenly we’re not just looking at business, we’re now trying to figure out what everyone else is doing, and things get way harder to understand.

The three tools:

  1. Moving average convergence divergence. As a measure of whether there’s “pressure” pushing the stock up or down
  2. Stochastics. Measures if something is “overbought” or “oversold” — and the explanation of this one is not great
  3. Moving average.

I’m going to say up front I didn’t get this chapter at all. I feel like I’m missing the point, so this will be a bit more uncertain.

This chapter confuses the issue by . For instance, you’re supposed to look at Stochastics for crossings of the 14-day and 5-day lines. For one, most places you look you don’t get a ‘buy’ and a ‘sell’ line as the book describes them, you get a “%k” and “%d” and it’s confusing. When I got through the instructions for setting one up using Town’s settings for time frames, I looked up some stocks, and it was a coin flip on whether the stock went in any discernable direction.

I don’t know if I was just unlucky or if I’m even using the tools right. My point is that if you’ve gone through the book and you’re working with pretty clear, understandable numbers (earnings per share is pretty much earnings per share), this shift to difficult to understand technical indicators is confusing and exaperating.

Moreover, there’s no convincing why here. The explanation of what we’re doing in this chapter doesn’t make it clear why it’s important to pay attention to it. It seems like the fact that you’ve found a company that’s on sale for 50% off should far outweigh whether others are getting into or out of the stock at the moment.

I was left thinking there are important signals I shoud pay attention to in addition to basic valuation, but unsure how I’d do that.

This is also where the book falls down on the cover promise of “successful investing in only 15 minutes a week!” Even if you’re only monitoring the few stocks you’ve bought, using these three tools once a day to see if you should bail on a stock, you’ve burned your 15 minutes squinting at whether one line crossed another.

It all feels like Town, as a long-time experienced investor familiar with tools like these, was unable to explain these or their importance in a way that made sense to those without the same experience, which is unfortunate because I got a lot out of the valuation sections.

And from there, much good is undone

Chapter 13 then walks through a possible example as a couple considers buying The Cheesecake Factory, Inc. The valuation all makes sense, and they buy. A chart of spectacular returns is shown — CAKE goes from $18.90 to ~$36 in two years, an 90% gain. Nice. The couple’s example $20,000 is now $38,000. Except… here’s what Town writes:

By getting in just below $19, and then moving in out 11 times in two years with the big guys, and by adding $500 a month they were saving, by July 2005, CAKE gives Doug and Susan a nice compounded rate of return of 56% and their $20,000 is now worth $78,000

He’s claiming twice that return rate.

Nope. Nope nope nope nope. Just… not true. At all.

1) Is the regular additional contribution counted as part of the return? It’s unclear. But why mention it otherwise? You can’t have them put more cash in and count that as what it’s worth now. I could have an $100 investment that’s totally flat and add $1m and wow, I beat the stock market over two years because now I have $124. This is, at best, confusing. At worst, he’s counting over $10,000 and associated gains in value as part of the return that shouldn’t be there.
2) They moved in and out 11 times? How do we know that? Does this assume that they perfectly timed each of those? When were those 11 moves? What did the technical indicators say at the time?
3) They’re going to get creamed on short-term taxes. The investor who held for more than a year pays way less.

Investor A: bought at the same time and held. +$18,000, may be paying zero in taxes
Investor B: bought, as a beginner times moving in and out 11 times, up $58,000, might be $40,000 after taxes.

If you think Town is counting the additional contributions in taxes, we’re down to around $30,000 for Investor B.

It’s not okay that this important calculation is so unclear, and the handling of the additional contributions makes me extremely suspicious. It doesn’t make sense as written, and I don’t understand what the point is. 89% over two years is great! Why confuse things?

This raises a larger question: does this all work? We’re presented with a couple examples of Company A compared to Company B, walked through the Cheesecake Factory example, but beyond those hand-picked examples, all we have to rely on is Town’s accounting of his track record. I’d have felt much better about the whole thing given a wider accounting of his trades, or of studies done on historical data — like we have for Greeblatt’s Magic Formula book.

Put it all together, what’s it mean

I dug reading it, and particularly found it useful in making me do real work putting things together and looking at companies. But I also found the technical tools section confusing and hard to understand, and the example math particularly worrying. It occupies a weird place in an investment bookshelf: I can see recommending it to someone who is interested in Warren Buffet’s investment philosophy and wants to know how to crunch the numbers to find those great companies at attractive prices.

[1] The story, as told, is so perfectly mythic that it’ll probably raise at least one eyebrow if you read it. But whatever.

[2] (I spent a little time looking at this, but besides him raising money in 2013 for Rule One Capital, I couldn’t find anything on early investment proof or his returns as a manager).

[3] Similarly, Town claims early that as a Vietnam veteran, on his last day in uniform he was at the Sea-Tac International airport when someone spat on him and ran away. This… I don’t know. That spitting at veterans happened at all is disputed: there’s a whole book on this, Spitting Image by Lembcke, discussing how no incident like this has ever been documented as part of a larger case that antiwar activists and veterans were allied far more than is now generally believed.

I thought about looking into this in greater detail (particularly, why would he be at Seatac on his last day in uniform, when it seems like he’d have flown into McChord Air Force base?), and stopped. This has been hashed out many other places, and it doesn’t seem relevant. Let’s just grant Town this.

Solving Drobo random unmounting issues

(Writing this up for hopeful discovery of future Drobo owners)

My Drobo started acting up a while ago in an incredibly frustrating way:

  1. The Drobo would sometimes not show up, or not mount, requiring a dance of restarting it, restarting the computer, plugging, unplugging
  2. When it was mounted, you’d get a short while before the Drobo went unresponsive in the middle of an operation, and then it’d unmount (and OS X would throw a warning about dismounting drives improperly)
  3. Sometimes if you left it connected for long enough, it would show up again, hang around for a bit, and then disconnect.

Nothing worked: re-installing software, resets, the “remove drives, reboot the Drobo, wait, turn it off, put the drives back in…” And all the while status-light-wise, and Drobo Dashboard-wise, it reported everything was good

And unhappily, Drobo support costs money, and I’m cheap, so I wasted a ton of time troubleshooting it. As a bonus, their error logging and messaging is either unhelpful or encrypted.

(I feel like if you encrypt your device’s logs, you should offer free support at least for unencrypting the logs and letting the user know what’s up. I’m disappointed in them and will not be purchasing future Drobos. Or recommending them.)

Eventually I pulled each of the drives and checked their SMART status (OK status overall on all drives, though I also pulled the details and one of them had flags, but SMART’s not great (see: Backblaze’s blogs on this). So I cloned them sector-by-sector onto identically-sized drives. The drive with the odd SMART errors (but, again, overall OK status) made some really unsettling noises at a couple points during sustained reads, but the copy went off okay.

Fired it up, and it worked. Drobo came back on, mounted, works fine (for nowwwww….).

I spent some more time hunting around in the Drobo support forums looking for more information, and found someone reporting back on a similar issue said they’d had a drive go bad but the Drobo never reported any issues, and it wasn’t identified until support looked through the encrypted error logs and said “oh, drive number X is going bad, that’s causing your Drobo’s strange behavior.” Clearly, given my success, at least one of my drives was secretly bad and cloning and replacing was the solution

So! May writing this up help at least one future support-stranded Drobo owner: if your Drobo is unmounting randomly, not showing up in the Finder, throwing dismount errors, but the Drobo’s reporting that everything is hunky-dory, and you don’t want to pay for support and you’re willing to take advice from some random fellow owner on the Internet who may not even have the same issue… here’s one approach before you throw your malfunctioning Drobo out the window:

  1. Power it down and pull the drives
  2. Using whatever utility you like, check the high-level SMART status on the drives to see if something’s clearly screwed up
  3. (optional, if they’re all okay) look at the detailed SMART errors and see if any of the drives looks really wonky
  4. If any of them are bad, do a sector-by-sector clone of that drive, swap the clone in, power up the Drobo, see if that works. If yes: yay! If not —
  5. Clone & replace them all, see if that works.

May this work, and may the drive be in good enough shape to successfully clone.

I should also note that as much as I’m annoyed my Drobo was out of support, assuming they would have been able to tell me what was happening and which drive to clone and replace, it would have been worth it to pay for the per-incident to save myself the headache.

Promotions are recognition, not elevation

Or: the importance of good managers and 1-1s

When I was a Program Manager with no Senior title, I went through a period where I didn’t get promoted, not being promoted made me more and more impatient and even resentful, and that in turn prevented me from making progress towards being promoted.

I’ll paraphrase how I started one of my weekly 1-1s with my manager (Brian Keffeler!):

“Wahhhhhh! Why aren’t I a Senior Program Manager? Look at what I’m doing! It’s amazing! Look at these (n) people who are Senior Program Managers and they aren’t working on as big stuff or doing as well! Wah wah wah!”

And Brian, bless him, listened to me until I’d run out of rant and said:

“I’m not going to argue whether you’re doing better than (person) or (person). Set that aside for a second. None of that matters. You’re not going to be promoted because people look at you and think ‘he’s better than a couple of people who already have the title.'”

I thought “Fuck, he’s right.”

He kept on.

“If you want to be a leader, if you want to be promoted because you’re deserving, you need to stop comparing yourself to them. You need to be so good people assume you’re already in that role. You need people to be surprised to find out you’re not a Senior. When title reviews come up, you want everyone in the room to say ‘He’s not already a Senior? What the hell?’ Right? You want your promotion to be a recognition that you’re already successful operating at that level.”

It was one of the moments in my career where the skies parted, sun shone down on me, and trumpets sounded. I knew immediately that not only was he absolutely correct, that if I was ever to be promoted I needed to prove that demonstrating potential wasn’t enough — that I needed to be operating on this next level. But also, and just as importantly, that me being hung up in the petty bullshit of whether I was the best in my pay-grade and whether I was better than some people in the next pay-grade was fucking up my relationships and career, and that I needed to let go of it.

I might have spent years in that destructive spiral, burning myself out generating my own frustration, with a different manager, or if they’d delivered the message at a different time, or in a different manner.

So I went out and did great work, and people started to assume I was already a Senior Program Manager, and then I got promoted.

Brian’s awesome, and I owe him a great debt.

Honesty without obscenity

When I was a Program Manager at Expedia, and Aman Bhutani had just showed up to right the ship through by demonstrating the value of clear leadership, he started a regular “Big Boulders*” meeting with the Program Managers working on the most critical projects, like the giant re-platforming, or new shopping paths, or rethinking the checkout process.

He wanted to get direct feedback on what was going on, unfiltered, and to discover where he could help. We’d show up and give a high-level status using a standardized couple slides showing timelines and dependencies, and if Aman could help by raising an early point of emphasis to another of his peers about a cross-organizational dependency that had historically been trouble, we’d ask.

Aman built trust with us by delivering — if you brought something up that concerned you, and he said he’d go look after it, you could check it off your list of worries.

For us Program Managers, to have his ear and direct engagement was a huge step forward, though dangerous because we didn’t want to report status to him that we hadn’t already talked to our managers about (because at that point we hadn’t entirely recovered from the stabby years). And it was also pressure-filled. Not just because he was there, or because he’d ask amazingly insightful questions you wanted to be prepared for (and to which “I had not thought of that solution, wow” was a perfectly good answer). In front of a peer group of others trusted to deliver the most important projects, you wanted to have your shit together.

Some people didn’t deal with all of this well (each time starting with a forced grin and “It was another great week on the ____ team!”) but in general, Expedia’s Program Manager corps was a lot of no-credit-taking, jump-on-the-grenade, jaded leaders-through-delivering who’d kept at it through some dark years because they believed in the mission, and they’d be honest. But also, still, sometimes you left the door open knowing he’d ask a question, because you didn’t want to volunteer something you were worried about that your boss wasn’t, but it was keeping you up at night**, and you wanted him to know.

After the initial progress, Aman wasn’t satisfied with a true but also wary status report. So at one meeting, he challenged us. He wanted to hear the status with our insights, whatever they might be, into the present and future, no matter how dangerous the truth seemed.

I felt excited that for the first time someone way up the chain was not only recognizing the chain itself distorted and delayed truth, and he wanted to try and bridge that. And because we’d built so much trust, we were safe — it wasn’t a trap.

So off I went.

“We are so fucked,” I started, and I took off from there. “This org is fucking us, this other thing is fucked up, but this team is fucking amazing, totally saved our ass. This thing we bought from a vendor to help is a piece of shit…” I just went the fuck off, running down everything in terms that would have made a stub-toed sailor tell me to calm down.

Aman nodded through the whole thing, entirely even-keeled. When I was done, he said “So first, yes, that’s the transparency I’d like to see.” And then he paused for just a moment and said “But I’d suggest it’s possible for us to get that honesty without the obscenity.”

I felt relieved, and also like I could do better***.

He let that hang out there for a comfortable pause, thanked me, and then we moved to the next person.

It was an important step for me in how I expressed myself, taking this challenge to be concise, and true, and also not angry. Because I realized that while you get some truth in the emotion, you also lose clarity. “Fucked” expresses frustration, but does that express a need or problem to someone who might help you? And for many people, if you’re cursing like crazy, or you’re coming across angry, they’re not going to receive the message at all — and when you’re speaking, sometimes you can’t expect the audience to come to you, and if you want the right outcome, you’ve got to deliver in a way that’s most effective for them.

Afterwards, the Big Boulders meetings got way more raw, without the cursing, and we got to the next level of trust. And that led to things overall improving, and I felt like I’d contributed in some small way to taking that step forward. And taking Aman’s advice, I start trying to consistently hit that level of openness and honesty in all my communication, without the cursing.

DMZ

  • first you figure out the boulders, then you see what rocks you can cram in around them, and then you pour in sand until the container’s full

** if you’re sleeping well, you’re not paying enough attention to your project. It’s why we’re all such coffee fiends

*** the ability to support people while also helping them realize they can — and want to– do better being one of Aman’s super powers

My first job at Expedia, joining a small crack team who all seemed wildly smarter than me* my manager was Tim Besse**. Once I was stuck on a particularly thorny problem over a bit of UX, and he stopped by to help. We brainstormed, we drew all over the whiteboards in my office***, we argued, we revised and we came up with something that solved the tangled issues to everyone’s satisfaction.

Relived, I went to write the whole thing up. Tim, standing back from the whiteboard, shook his head and frowned.

“No,” he said. “This isn’t good enough. We can do better.”

I felt anger, frustration — we’d finally come up with a way out and he wanted to discard it? We both had a long list of other things we needed to figure out. Checking this off and moving on was a huge relief and a victory for everyone.

I looked at him in dismay while he stared at the diagrams. I took a couple deep breaths and let go of the frustration.

“Okay,” I said. “Where do we start?”

We began again. I remember it as taking twice as long before, in wavy boxes with my chicken-scratch handwriting everywhere, we’d found something wildly better in every way.

We looked at each other and smiled. I felt a sense of rightness and satisfaction I hadn’t touched in the previous one.

I’ve carried that with me since: that when you’ve arrived at something that’s good enough, push on it a little. As much as I pride myself on being pragmatic above all else, push on good enough. Does it rattle a little? Is there a little give? Do you feel like there’s a hidden switch that’ll rotate the whole thing?

Take the time. See if you can turn good enough into something amazing. Challenge others to do better.

And believe that when someone says “we can do better” they believe it, and that you can.

Thanks, Tim.

— DMZ

  • In the words of Isaac Jaffe:
    “If you’re dumb, surround yourself with smart people. If you’re smart, surround yourself with smart people who disagree with you.”

** Tim went on to co-found Glassdoor

*** shared. As a Microsoft spin-off, we were all about private & shared offices. It was great! Then they abandoned it and I’ve never since enjoyed such productive work spaces.

Flowcharting cheat sheet

How to go from sketching boxes to producing clear and consistently readable flowcharts, in under 500 words.

My team came across something like this online:

flowchart-no-no

It started a discussion on learning the most basic guidelines for making a good flowchart. I volunteered to write this and share it now in the hopes it’ll help future generations.

Using these will help not only make flowcharts more readable, by being consistent you’ll more easily find errors and things that are unclear in the flow you’re documenting.

Cultural note: this assumes you’re in a language/culture that reads left to right, top to bottom. Adjust as you see fit.

Direction matters

Overall, for chart flow

Reduce effort by flowing as your audience reads: left to right and then down. The chart as a whole all the way down to each object: arrows come in from the left and exit from top/bottom/right.

If you can’t do left-right for the chart (or an object’s connection), top to bottom’s 2nd-best.

dense L to R

Don’t go snake-style:

snake-style

Direction matters in decisions

Yes/No or True/False should go in the same direction each time they’re on the chart. Anything else creates confusion and possibly someone making the wrong choice.

Generally, I’ve found that the positive (“Yes”/”True”) is most easily read if they’re the up in up/down and right in left/right, but as long as you’re consistent it’ll be okay.

Sizing matters

Attempt wherever you can to keep the boxes a consistent size, unless the difference in sizing carries meaning.

Spacing matters

Keep the amount of space between symbols as consistent as you can. If you can, line up things of the same type, like decisions and conclusions, especially if they share something (for instance, they happen at the same time).

Decision boxes

Use them, they help immensely. Two ways to do this.

Recommended: diamond with annotated lines

diamond choice

If possible, put the labels right next to the decision — don’t make people search for what the decision is. They should at the decision point know the answer to the question and be able to immediately know which line to follow.

More readable for some people: diamond with answers. Requires the reader to scan all the landing points for the answer, and making the ‘answers’ obvious might require use of shapes and colors, resulting in more complexity. Still, if you prefer:

decisions as boxes

You will note that this is helped if you’ve already set the viewer’s expectations about which direction is which.

Okay, so let’s see this in practice

Take this:

flowchart-no-no

Applying only the suggestions here and a couple minutes of cleanup, and noting that there’s at least one problem in the flow there that’s concealed by it being a mess:

first pass cleaned up

If I put both of those in front of someone and asked them to follow through the decisions, it’s now much easier to read and figure out what to do.

Good flowing

Let me know if this helped, or if there’s more simple, easy-to-apply guidelines I should include.

DMZ PdM Bookshelf: “Good to Great” by Jim Collins

What I’m doing

As part of our hiring at Simple, there’s a little question at the end:

Please include a cover letter telling us something awesome about you and the projects you’ve worked on, along with the best product management book you’ve ever read, regardless of claimed subject. (You like “Design of Everyday Objects?” So do we.)

We ask because we’re curious about candidates, and we get often get more information in the cover letter than we do in the resume (for instance: why are they in product management? Do they read all of a job listing before applying?)

A surprising proportion don’t have a book. For those who did, I decided I’d read all the books that came up and do a write-up on their place on the Product Management Bookshelf.

Have a suggestion for something I should read? Nope! Gotta apply.

Today’s book tldr

“Good to Great” by Jim Collins

Is it worth reading? Yes but not for the reasons you’re told to read it.

Sarcastic summary: take lessons from several companies about to fall

Well go on then

“Good to Great” is a wonderful example of survivor bias, and a cautionary tale. If you go out and take a successful actor, for instance, you can go into excruciating detail about everything they did (she puts chia seeds in their smoothies!) and attribute their success to those details. But if you don’t look at it the other way — do actors who put chia seeds in their smoothies succeed more often? There may be millions who do and don’t succeed — chia seeds may make it less likely to succeed. Blind imitation doesn’t help.

Here, in selecting companies that seemed destined to rule forever, Collins demonstrates the fallibility of this approach.

He provides eleven examples, and compares them to someone else in their space.

One great company? Fannie Mae!

“For example, the Fannie Mae people were not passionate about the mechanical process of packaging mortgages into market securities. But they were terrifically motivated by the whole idea of helping people of all classes, backgrounds, and races realize the American dream of owning their home.” -p. 110

I’ll suggest that Collins bought into their line, but also that looking back at this, it’s strange to read that and think “perhaps they should have been more passionate about the mechanics of what they’re doing, as the mission relied on it.”

It becomes a cautionary tale that a great mission without sustainable economics is worthless and even dangerous.

“Good to Great” discusses Fannie Mae’s turnaround — seven years later, having brought about a market collapse, the U.S. Government placed them in receivership, at a cost of hundreds of billions of dollars. And they still haven’t spun them back out, because it’s unclear how they’d even do that without crashing the housing market again (and because as housing prices have risen, Fannie Mae’s profitable for the Feds to hold onto, a danger in itself).

This is not isolated. Circuit City went under eight years after the book.

“Let me put it this way: If you had to choose between $1 invested in Circuit City or $1 invested in General Electric on the day that the legendary Jack Welch took over GE in 1981 and held to January 1, 2000, you would have been better off with Circuit City — by six times.” — p. 33

After that through today, you’d have lost ~1/3rd of your GE money and all of your Circuit City money. GE’s market capitalization as I write this is $289 billion dollars.

On the other hand, Bethlehem Steel was gone in two years after the book and Nucor’s up 360%. Wells Fargo took a hit along with Bank of America and then has done wildly better. Kimberly-Clark bought their comparison point.

My point is this approach: asking why the successful are successful, is ultimately limited. You can ask a lucky gambler why they’re so hot at craps and odds are good they’ll have a convincing answer — or at least, one they’re convinced of.

No one at Fannie Mae was examining their success in 2001 and realizing they’d gone wrong. Examining failure, when assumptions are open to question and it’s easier to tease out luck’s role, is in general far more fruitful.

I would bet though that “Great to Gone” would not have sold nearly as well. We look for inspiration, not cautionary tales.

Takeaways for my fellow Product Managers

In hiring consider whether favoring people from high-profile successful companies is potentially survivor bias, and also, whether people from humbler backgrounds might have learned as much or more from their experiences, even failures.

Introducing the DMZ PDM Bookshelf: Horowitz’s “Hard Thing About Hard Things”

What I’m doing

As part of our hiring at Simple, there’s a little question at the end:

Please include a cover letter telling us something awesome about you and the projects you’ve worked on, along with the best product management book you’ve ever read, regardless of claimed subject. (You like “Design of Everyday Objects?” So do we.)

We ask because we’re curious about candidates, and we get often get more information in the cover letter than we do in the resume (for instance: why are they in product management? Do they read all of a job listing before applying?)

A surprising proportion don’t have a book. For those who did, I decided I’d read all the books that came up and do a little write-up on their place on the Product Management Bookshelf.

Have a suggestion for something I should read? Nope! Gotta apply.

Our first book

Today, a leading response in no small part by being massively popular when we opened our listing, Ben Horowitz’s “The Hard Thing About Hard Things”

Is it worth reading? No.

Sarcastic summary: successful wagon-train driver reminisces about how very sore his whip hand got driving those horses to death.

There are some good pieces of advice in here: the compressed “what a Product Manager should do” summary is a succinct and useful description of the job, and there are indeed bits worth reading.

And one must appreciate a business book that isn’t told in fable form.

But it’s not good. The only connecting thread in the book is that Ben Horowitz sure went through some tough times! Over and over, there’s a crisis, but he and his team had to make huge sacrifices!

There’s never a consideration of whether the sacrifices were worth it: he’ll mention the costs his teams took on as evidence of their heroic resolve. And because they came through, it’s all justified. The skies part, options vest, and there’s glory enough for everyone to bask in.

The result is if you see your leadership team reading this and talking about how much they’re inspired by it, you should be wary.

I’ll humbly submit as an outsider, as someone who has not accomplished what he has, that the truly difficult thing would have been to avoid the deathmarches entirely.

And his examples — if reading about one company’s dependence on one customer doesn’t set off all your Product Manager danger senses, you need a vacation.

Your data are racist

Say you’re a university loan administrator. You have one loan and two students, who anonymized seem to you in every way identical: same GPA, same payment history, all that good stuff. You can’t decide. You ask a data-driven startup to determine which one’s the greater risk to default or pay late. You have no idea how they do it, but it comes back —

The answer’s clear: Student A!

Congratulations, you’ve just perpetuated historical racism.

You didn’t know it. The startup didn’t know it: evaluated both students and found Student A’s social networks are stable, and their secondary characteristics are all strongly correlated with future prosperity and loan repayment. Student B scores less well on social network and their high school, home zip code, interests, and demographic data are associated with significantly higher rates of late payments and loan defaults.
From their perspective, they’re telling you — accurately, most likely — which of those two is the better loan risk.

No one at the startup may even know what the factors were. They may have grabbed all the financial data they could get from different sources, tied it to all the demographic data they could find, and set machine learning on the problem, creating a black box that they show is significantly better than a loan officer or risk analyst at guessing who’s going to default. No one

Machine learning goes like this: you feed the machine a sample of, say, 10,000 records, like

Record Foo

  • Zip Code: 98112
  • Married: Yes
  • Age: 24
  • Likes Corgis: Yes
  • Defaulted on a student loan: No
  • Made at least one payment more than 90 days late: No

Record Bar

  • Zip Code: 91121
  • Married: No
  • Age: 34
  • Likes Corgis: No
  • Defaulted on a student loan: Yes
  • Made at least one payment more than 90 days late: Yes

You set the tools on it and it’ll find characteristics and combinations of characteristics that it associates with the outcomes, so that if you hand it a new record: Angela, a 22-year old from Boston, unmarried, doesn’t like Corgis, and your black box says “I’m 95% sure they’ll default.”

It’s the ultimate in finding correlation and assuming causation.

You see how good it is by giving it sets of people where you know the outcome and see what the box predicts.

You don’t even want to know what the characteristics are, because you might dismiss something that turns out to be important (“People who buy riding lawnmowers buy black drip coffee at premium shops? What?”).

Because machine learning is trained up on the past, it means that it’s looking at what people did while being discriminated against, operating at a disadvantage, and so on.

For instance, say you take ZIP Codes as an input to your model. Makes sense, right? That’s a perfectly valid piece of data. It’s a great predictor of future prosperity and wealth. And you can see that people in certain areas are fired more from their jobs, and have a much harder time finding new ones, and so default on payments more often. Is it okay using that as a factor?

Because America spent so long segregating housing, and because those effects continue forward, using ZIP means that given ZIP X I’m 80% certain you’re white. Or 90% if you’re in 98110.

We don’t even have to know, as someone using the model, that someone is black. I just see that people in that zip code predict defaults, or being good on your loan. Or I might not even know that my trained black box loves ZIP Codes.

And if you can use address information to break it down to census tract and census block, you’re even better at making predictions that are about race.

This is true of so many other characteristics. Can I mine your social network and connect you directly to someone who’s been to jail? That’s probably predictive of credit suitability. Oh — black people are ~9 times more likely to be incarcerated.

Are your parents still married? Were they ever married? That’s — oh.

Oh no! You’ve been transported back in time. You’re in London. It’s 1066. William, Duke of Normandy, has just now been crowned. You have a sackful of gold you can loan out. Pretty much everyone wants it, at wildly varying interest rates. Where do you place your bets?

William, right? As a savvy person, you’re vaguely aware that England has a lot of troubles ahead, but generally speaking, you’re betting on those who hold wealth and power to continue to do so.

Good call!

What about say, 500 years later? Same place, 1566. Late-ish Tudor period. You’re putting your money on the Tudors, while probably being really careful to not actually reminding them that they’re Tudors.

Good call!

Betting on the established power structure is always a safe bet. But this means you’re also perpetuating that unjust power structure.

Two people want to start a business. They’re equally skilled. One gets a loan at 10% interest, the other at 3%. Which is more likely to succeed?

Now is the bank even to blame for making that reasonable business decision? After all, some people are worse credit risks that others. Is the bank to disregard a higher profit margin by being realistic about the higher barriers that minorities and women face? Don’t they have a responsibility to their shareholders to look at all the factors?

That’s seductively reasonable. Too see this at scale, look at America’s shameful history of housing discrimination. Blacks were systematically locked out of mortgages and house financing, made to pay extremely high rates without building mortgages, never building equity. At the same time, their white counterparts could buy houses, pay them off, and pass that wealth to their kids. Repeat over generations. Today, about a third of the wealth cap in families, where white families have over $100,000 in assets and minorities almost nothing, comes from the difference in home ownership.

When we evaluate risk based on factors that give us race, or class, we handicap those who have been handicapped generation after generation. We take the crimes of the past and ensure they are enforced forever.

There are things we must do.

First, know that your data will reflect the results of the discriminatory, prejudiced world that produced them. As much as possible, remove or adjust factors that reflect a history of discrimination. Don’t train on prejudice.

Second, know that you must test the application of the model: if you apply your model, are you effectively discriminating against minorities and women? If so, discard the model.

Third, recognize that a neutral, prejudice-free model might seem to test worse against past data than it will in the future, as you do things like make capital cheaper to those who have suffered in the past. Be willing to try and bet on a rosier future.

Citations on wealth disparity:

http://www.demos.org/blog/9/23/14/white-high-school-dropouts-have-more-wealth-black-and-hispanic-college-graduates

http://www.demos.org/publication/racial-wealth-gap-why-policy-matters