Hamilton the Musical

Good Enough to see Hamilton. Barely.

20160711 Hamilton Program

The biggest thing to hit Broadway since…. well, ever, is a musical about one of the founding fathers of America. It’s the hottest ticket in New York. The show is sold out for as far into the future as you can buy tickets. You can get scalped tickets in pairs for as low as $1000 for a partial view seat in the rear of the theatre. For a good seat you will pay $3500 on Ticketmaster. You might find cheaper tickets through Craigslist, but many (or most) are counterfeit. And the theater won’t check to see if yours are real or not until you actually try to walk in the door – so there is no way to be sure.

There are two other ways to get tickets.

Every day the show runs a lottery and hands out a handful of tickets to those lucky enough to win. Traditionally this is done live in front of the theatre, but it got so popular with Hamilton that they moved the process online. Online made it easier and now the rumor is there are over 50,000 people a day who enter the lottery. If you win the lottery you pay $10 for a front row ticket.

The second “cheap” way to get a ticket is to line up in the cancellation line. The name of the “cancellation line” dates back to when people would cancel their tickets for a night and the show would re-sell them in the cancelation line. That doesn’t happen anymore. When scalped tickets are selling for $3000, do you think people are cancelling their tickets and returning them to the theatre? Nope.

But the cancellation line still exists. Just before show time the theatre will release two sets of tickets. The first group are lottery tickets that went unclaimed. The second set are tickets in the tenth row of the theatre that the show holds back until the last minute. Tenth row seats are pretty amazing. Why would the theatre hold back those seats and not sell them? Two reasons: If Hilary Clinton shows up and wants to see the show, the theatre wants to make sure she can see the show. The PR from the visit is worth something to the production. So they need to hold back tickets just in case that might happen. The second reason is more common: There is a professional reason why someone needs to see the show. Maybe an actor she is up for a movie part and the director wants to see her in action? Maybe the set designer for the Chicago production of Hamilton wants to see the set “in action” to see if the changes he wants to make in Chicago will work in practice? For any number of reasons the show may need to make a seat available. So they hold back about twenty of the best seats in the theatre just in case – and then release them five to thirty minutes before the show starts.

The theater sells the cancelation line tickets for face value (which has been going up, but is still only $199 as of this writing). So if you are willing to invest your time instead of your dollars you can get a great seat for $199. How much time? In April you could be very confident you would get a ticket if you waited in line for twelve hours.

April was when my wife and I locked down plans to be in New York in late June. April was when I decided I wanted to see the show while the core cast was still together. April was when I started looking into options.

The cancelation line looked like the best option. I found a company that were professional line sitters (only in New York). They charged $20/hour to wait in line for you. So for $20 x 12 = $240 I could have someone wait 12 hours in line and get two tickets (each person in the cancelation line was allowed a ticket for themselves plus one guest). Since tickets at the time cost $177, it meant I had found a way to get two tickets for $240 + $177 x 2 = $594. I could see the show from one of the best seats in the house for under $300. Done.

Only it wasn’t done.

The first thing that happened was other people started figuring out the same solution. The cancelation line grew and line sitters needed to wait for up to 24 hours instead of 12 for a ticket. It was now $413/ticket. A lot more expensive but still much less than other options.

But we weren’t done yet.

The theatre changed the rules. They didn’t like that people were waiting in line for 20+ hours and then selling the tickets. The new rule was the person in the line had to go directly into the theatre (with their +1). So now if you wanted two tickets you needed to hire two line sitters – and pay for their tickets as well (I guess that was a nice new side benefit to the job?).

At the same time the theatre banned the use of folding chairs and tents for line sitters. They now had to brave the elements. This caused the willingness to wait to drop and the lines shrank in length. But only for a short time. It wasn’t long before they were back to 24-hour long waits.

But we weren’t done yet.

Hamilton won some Tonys. A lot of Tonys.

The lines got longer.

Then Lin-Manuel and the other two leads announced they would be leaving the show in early July. If you did not see the show before July 9th, you would never see the core cast perform what many were calling the greatest musical in Broadway history.

So the lines got a LOT longer.

By the end of June the expected wait was over 72-hours to see the show. If you paid a line sitter for 72 hours and bought their ticket, it was now going to cost you $1794 to see Hamilton. You might as well buy a scalped ticket at that price. The market had finally become “efficient”.

But I still wanted to see the show.

I searched for options on Craigslist. The problem with using Criagslist is that the site is terrible. Ticketmaster and Stubhub affiliate websites post advertisements constantly trying to stay in the top positions on Hamilton searches. If you use Craigslist the basic way you will never find individuals posting anything – just lots of digital marketer spam.  Thankfully I already knew the solution. I had had the same problem looking for a condo to rent a few years ago. By writing and iterating on a simple macro I was able to weed out the digital marketers (who tended to re-post the same content over and over). I was left with a handful of posts by real humans.

Most of the posts were people trying to sell their individual tickets. Lin-Manuel himself has warned against buying from those people. It’s impossible to verify if their ticket is real – so selling fake Hamilton tickets has become a new target for scam artists in the city. But I looked anyway.

I found some guys who were traveling to New York. The three of them were going to make an adventure of it. They were going to line up on Monday and just wait until they got into a show. Maybe that would be Wednesday. Maybe Thursday. Maybe Friday. They would wait as long as it took. And since they were all waiting together they would each have a +1 available to sell. They proposed the deal on Craigslist: $999 a ticket. They found buyers. I was one of them.

But we weren’t done yet.

On Sunday June 26th a film crew descended on the theatre to film the show before the cast broke up. They would need to use all of the “cancelation seats” for the film crew, directors, etc. So they canceled the cancelation line from Sunday to Tuesday. The line would open again on Wednesday at 530pm. My three line sitters couldn’t get started waiting in line until Wednesday. But the rumor was that the first line on Wednesday would be a free-for all. The line would be created at 530pm “first come first serve”. Whatever that meant. Getting in line first at 530pm means you would wait in line for 2.5 hours to see the show. If the market prices did not change, 2.5 hours would be worth free ticket to Hamilton (selling for $3500) plus the ability to sell your second ticket for $800. $4300 in value in 2.5 hours, or about $1700 per hour. That’s pretty good money. Deciding who would be “first come” to get in line was going to be madness.

I changed my flight so I could be in NYC on Wednesday. I was at the theatre well before 530pm, ready to be one of the “first to come”. But it was not to be. The line handler just asked, “Who is first in line?” A woman raised her hand. Everyone around her agreed. “Who’s next?”, “I’m number two!”. And the line formed in a very orderly fashion based on earlier agreement among the experienced line sitters. The line sitters had self-organized away from the theatre and the theatre was honoring their self-organization. The three guys who were going to wait for me didn’t know the organization had been happening and were caught flat-footed. They waited until the line was formed. Then they sat down with a guy with a notebook. Notebook-guy was keeping track of the order of the line. The guys I had set up the deal with were now #72-#74 in line. Traditionally about ten people get in off the cancellation line each show. So if no one ahead of them dropped-out they would get to see a show in about a week. I didn’t have a week to wait.

I initiated Plan C. I talked to the people at the front of the line and asked them if they would be willing to sell their +1 ticket. They acted like I had asked them if I could buy their only child. The two women in positions #2 and #3 didn’t even have +1s but they thought it was offensive to sell the extra tickets they had earned by waiting in line. I didn’t let their judgement deter me. I continued down the line asking if anyone had a +1 they would be willing to sell me.

When I got about 15 people from the front a woman made eye contact with me and laughed. “Sure I’ll sell you my +1. For like a thousand dollars!”. I immediately responded, “Sure.” She was taken aback.  She definitely was not expecting that answer.

But before she could respond, a woman in front of her said, “I’ll give you my +1 if you pay for my ticket.”

I said, “I’ll do better than that. I will buy both tickets and give you $400.” (That would be $800 total. Cheaper than the earlier deal I had arranged)

She said, “No. Just pay for my ticket. That would be fair.”

The women behind her in line spoke up, “You can’t do that. We are waiting. It means we will have to wait longer. It’s not fair.”

My +1 seller responded, “It’s fair. I was always going to have a +1. I was going to go with my sister and she was going to pay for my ticket. But now she can’t come. I can do it with someone else.”

The complaints stopped but I didn’t want this to be more of a scene than it already was. I traded phone numbers with the woman (we will call her Andrea. Let her keep her unanimity) and I got out of Dodge.

I texted her from across the street and we worked out the details. When she got to the front of the line, I would enter as her +1. I would pay for both of our tickets and we would enter the theatre together. I asked if there was anything else I could get for her, but she said she didn’t need anything. Apparently she had been waiting in the “fake line” since Tuesday morning.

Since, on average, ten people would get in off the cancelation line per show, we thought the likeliest scenario was we would get into the Thursday show. Which was perfect since I needed to be at the wedding rehearsal dinner on Friday. I went to see Fun Home that night two blocks away. If she got lucky and got in that night I would have time to run back (and abandon my Fun Home seat). I didn’t need to run. Only 3 people from the cancelation line got in that night.

The “ten people per show” was the average, but this was not an average week. There were no cancelation seats the previous two shows. And there were only a dozen shows left before the cast broke up. The cancelation seats were being used, so there were a lot less tickets available for the people in the cancelation line.

One person in front of her dropped out of line. That put her in the 8th position on Thursday. I crossed my fingers.

On Thursday the show sat five people from the line. I saw The Curious Incident of the Dog in the Nighttime (fantastic play). Two people in front of her dropped out (Which is amazing by itself. These people had been waiting since Tuesday morning. They just missed getting into the Thursday show, and they had to leave NYC, so they left without any payoff). She was in first position for the Friday show.

I called my wife. My amazing, supportive wife said it was okay that I miss the wedding rehearsal dinner to see Hamilton (but I better not miss the train to Long Island on Saturday morning. I was quizzed on what time the wedding was scheduled to start).

Friday night. It was going to happen.

I got to the theatre an hour before show time. Andrea and I stayed in touch with text. I was ready to join her when she was called into theatre. This was really going to happen. Ten minutes before the show was scheduled to start she sent me another text from Andrea:

“I’ve already seen the show twice from the 10th row. I really want to see it from the front row – a cancelled lottery ticket. If they don’t have any lottery tickets tonight I decided I will pass and just hold my spot in line for tomorrow. Don’t worry. They always have at least one lottery ticket. And I’m first in line so I get first choice.”

My heart dropped. There was still something that could go wrong.

I told her there was no way I could miss the wedding on Saturday. She said we should just cross our fingers and hope for the best. I crossed.

Five-minutes before show time they called her up. I cut across the line to join her (the line guard said something to me. I inarticulately said something about being a +1 and just kept going).

She was already at the ticket gate when I got there having a discussion with the seller. There were no lottery tickets. He asked her, “Do you want these?”

She hesitated.

“Yes. I will take them.”

“Thank you” I said.

I paid for the tickets. They were in the dead center of the tenth row. Arguably the best seats in the house.

We walked into the theatre and I took a picture of the stage. I couldn’t believe it all worked out. Wow.

20160711 Hamilton Stage

We made small talk before the show. She explained how the cancellation line worked. The line-waiters had created their own set of rules. You were allowed multiple breaks during the day for about ten minutes at a time to get food or go to the bathroom or whatever else you needed to do. You were also allowed one two-hour break a day if you wanted to go somewhere and shower (or whatever else you wanted to do for those two hours).

This was the third time she had waited in line. The first two times she had taken her parents (one to each show). The first two times she waited she was very prepared. She had a sleeping pad, sleeping bag, toiletries, and everything she needed to make sleeping on the streets of New York as comfortable as possible. This time she wasn’t so prepared. She was walking by the theatre trying to get some signatures from the cast when she saw the line forming. On impulse she joined the line.

“How did you sleep at night?”

“I knew someone else waiting in the line. She let me share her sleeping pad and blankets.”

Wow. That was dedication.


So how was the show?

To say it was the greatest theatrical experience I have ever had would not be doing the show justice. It was one of the best experiences I have ever had. My wedding was better. Finding out my wife pregnant had a healthy baby inside her was better. The baby’s birth was better. The time Evi climbed up on the couch all on her own and then turned the couch into a slide was better. Seeing gorillas in the wild in Rwanda was better. Successfully coaching a high school improv comedy team to the national championships was better. I think seeing Hamilton comes next.

A close friend who had seen the show in April told me (after I saw the show): “It was not only the best theatre I have ever seen; I think it might be the best theatre experience I will ever see.”

I hope that’s not true, but I have a feeling it is.

The first half of the show pulls you in. Between the cast breaking up and the audience knowing how exclusive the tickets are, everyone was loving every moment. When Lin-Manuel said his first line, “My name is Hamilton”, the audience broke into applause for twenty seconds. The music had to stop while we waited for people to calm down.

The second half of the show tears at your heart. We all know the ending, but it didn’t matter. I cried multiple times in the second half. I was still crying during my walk back to my friend’s apartment. The show was as perfect as a piece of theatre could be.


Now that the three leads have retired, is it still worth it?

I’ll bet the answer is yes.

Lin-Manuel, the wunderkind who created the show is excellent as the lead. But his genius is not in performance. It is in creation. While it is amazing to see the creator on stage, I am sure his understudy’s actual performance will be no worse. You may enjoy seeing Lin more, but only in the same way you might enjoy a really expensive bottle of wine more. If you didn’t know it was more expensive you likely would not notice much difference.

Leslie Odom Jr is the other male lead. He plays Aaron Burr and he is incredible. He won the Tony (beating Lin), and deservedly so. He humanizes Burr so much that it is hard to even see him as the villain. He was my favorite performer in the show. I would go and see anything he was in. But is he irreplaceable? I don’t think so. He made the point himself that African American Broadway performers are under-valued. That’s terrible for the performers (and for what it says about society). But it also means that there are fantastic African American performers that are “waiting in the wings” to take on the Aaron Burr role. His first replacement will be a multiple Tony award winner. I expect he will be fantastic.

Phillipa Soo is the female lead that plays Eliza – Hamilton’s love interest and wife. She also won a Tony and is also incredible. She is leaving to star in Amelie. Is she irreplaceable? I don’t think so. I am sure they will find other amazing accesses to take on the part.

So far the rest of the cast is still sticking around.

And that is important.

The rest of the cast is outstanding. Christopher Jackson plays George Washington (He was also nominated for a Tony). Renee Elise Goldberry plays Angelica and I think is even stronger than Phillipa. They are great performers, but the show will find other great performers to replace them in the other shows in Chicago, London and Los Angeles (even if they aren’t AS great).

But two cast members will be a lot harder to replace.

Okeriete Onaodowan plays Hercules Mulligan in the first half of the show and Madison in the second half. Okeriete is not a Broadway performer. He is a rapper. He has a deep voice and a compelling presence on stage – in the same way Dr Dre is commanding when he performs. It will be difficult to find someone interested in Broadway that has his unique abilities.

But it will be possible. By fishing in a completely different pool, casting agents will be able to find rappers with something approaching Okeriete’s presence. I am very curious to see who will be playing Madison in the Chicago show.

But while it will be difficult to replace Okeriete, it will be practically impossible to replace Daveed Diggs.

Daveed raps in French with a French accent in the first half of the show playing the Marquis de Lafayette. He opens the second half as Thomas Jefferson, rapping with a Southern twang. Both roles have fast, difficult songs. For the same reasons as the Madison role, it will be difficult to replace Diggs. But while Okeriete is primarily a stationary presence, Diggs bounces across the stage. He dances. He performs. He sings.

To replace Leslie they need to find a top notch Broadway performer (hard, but done all the time).

To replace Okeriete they need to find a top notch rapper (hard, but possible with new search strategies)

But to replace Daveed they need to find a top notch rapper who is also a top notch Broadway performer. I am sure they will find someone, but I imagine compromises will have to be made.


Which means my recommendation is: Even though the show has lost three of their lead performers, the place to go and see the show is still New York City. I am sure the show will be great in Chicago and London and LA. But you will still have a premium experience seeing it with what is left of the original cast in New York.

Frankly watching Daveed Diggs alone is worth the price of a $3000 ticket.

If you haven’t seen it yet, you have missed your chance with the original. But the show in New York is the next best thing. If you have the money, buy a ticket to New York and a scalped ticket and see the show. If you don’t have the money, mortgage your house and see the show. If you don’t have a house, hitchhike to New York and get in the cancellation line. My bet is the line will drop down to a 24-48 hour wait.

It’s cheaper than going to Rwanda to hike with gorillas.



The three guys who I originally contracted with who ended up #72 in line? They kept waiting. They finally got to the front of the line in the #1 position on Saturday July 9th: Lin-Manuel, Leslie and Phillipa’s last show. They found buyers for their +1s. Instead of the $1000 I was ready to pay, they found buyers at $1400. And they got a chance to see the final performance. Scalpers on Ticketmaster were asking as much as $20,000 for that show. I expect they thought the ten-day wait in line was worth it.

Climate Change

Why both The Right and The Left are very wrong

Secretary-General Ban Ki-moon arrived in Copenhagen, Denmark yesterday. The SG Visited by boat  the Middelgrunden Off Shore Windturbines with Minister for Foreign Affairs Dr. Per Stig M¿ller. The off shore wind farm called ÒMiddelgrundenÓ is located in the ¯resund Straight (separating Denmark and Sweden). The off shore wind farm was established on a natural reef 3.5 kms outside Copenhagen Harbor, in the autumn of 2000 and consists of 20 turbines, each with 2 megawatt capacity. Middelgrunden is the largest wind farm in the world based on cooperative ownership.

I feel like every few months one of my left-leaning friends complains that the right doesn’t understand “science” and uses climate change as an example. I then make a comment along the lines of “The left doesn’t understand climate change either. They just like to shout about it because they feel it matches their already held beliefs” which gets the conversation going, but never very productively.

Rather than repeating the cycle next time someone posts the claim on facebook, I thought I would collect a lot of the information here. Commenting with a link in the future sounds a lot easier.

Here is what follows:

  • My basic argument about what’s happening with climate change
  • My background on how I got to this conclusion
  • A point by point breakdown of the argument


First the basic argument:

  • The “right” is wrong: Climate change is real, and man-made – and the evidence on this is very clear
  • The “left” is wrong: It does not follow that climate change will lead to disasters on an apocalyptic scale
  • In fact, evidence suggests that the future effects on climate change will be relatively mild
  • The best way to deal with future climate change is to make the world more rich
  • The best way to get more rich is to burn a lot of fossil fuels
  • There are credible people saying these same things
  • There is a movement within “Big Green” to discredit anyone who talks about “mild climate change” as a “denier”
  • Even if there was a 10% (or even a 1% or 0.1%) chance of disaster we should be trying to do something. Unfortunately, if disaster is really imminent then what we need to do is radical, not incremental
  • Most current “solutions” actually make the problem worse


My background:

My political beliefs do not fit easily into right or left. I tend to follow data and argument. If I have a bias it is towards freedom. That tends to drive me towards libertarianism sometimes, but I also have a small-c conservative streak. In my experience in business big changes tend to have unforeseen consequences, so “big” libertarian ideas like dropping the gold standard, or outsourcing police forces do not appeal to me. “Small” ideas, like allowing LBTG to marry, reducing income and capital gains taxes, reducing regulation, legalizing marijuana, or making abortions easier to obtain I fully back (It makes me fiscally conservative and socially liberal in most things). Until around 2004 I was a very devoted environmentalist.

Around 2004, I read Bjorn Lomborg’s Skeptical Environmentalist and realized that while there were serious environmental issues to be dealt with we were often focused on the wrong things. As much as landfill is “gross”, the world does not have a landfill problem. I was still very much concerned with global warming.

Until recently I saw global warming as having two “sides”: One side arguing that “science” and all the scientists were telling us that temperatures were rising and that if we did not do something there was going to be an unprecedented disaster. The other side was denying science completely and claiming a giant conspiracy (often saying things like, “Only god can change the environment”). Given those two options it was no surprise I sided with science.

But a couple of years ago I read Alex Epstien’s excellent book, The Moral Case for Fossil Fuels. He pointed out that while CO2 is definitely a greenhouse gas (it’s used in greenhouses for goodness sake!), its effects on temperature are logarithmic. Meaning to have a linear effect on temperature you need exponential increase in CO2. That’s bad enough as for a time we were spitting out exponential CO2 every year, but the climate models that show us heading off a cliff assume far more than that. They assume there will be other effects that cause a spin-wheel increase in temperatures. Instead of the logarithmic effects we see, these theories suggest temperature will increase linearly or even exponentially.

The problem is none of these spin-wheel effects have actually been observed. None of them. Science is a process. It goes something like this:

  • Someone develops a theory (Or someone observes something unusual and then develops a theory)
  • The theory makes a prediction
  • Someone tests the prediction to see if the theory predicts correctly
  • If it does, we say the theory


The Theory:

The main theory that drives models showing apocalyptic climate change involves water vapor. The idea is that as CO2 increases in concentration it leads to more water vapor in the air. While CO2 only causes temperature to increase logarithmically, the theory is that water vapor causes it to increase exponentially. If [Change CO2] : [change H2O], and [change H2O] : [change log(temp)], then [change CO2] : [change log(temp)]. Therefore, a small change in CO2 would cause a large change in temperature. QED.

The problem is the gap between theory and evidence.

There is nothing wrong with the theory.

That theory comes from a hypothetical hypothesis in 1896. The theory is that as the air warms there will be an increase in humidity (increasing water vapor), which will in turn increase the temperature of the air, creating a positive feedback cycle. It’s a fine theory – one that makes clear predictions that can be tested. The problem is all those tests have come back negative. Some predictions of this theory include the existence of a troposphere hot spot (never found), ice cores should show that there are not drops in temperature when CO2 is high (they don’t show this), estimates of climate sensitivity to CO2 should show an increase in sensitivity to CO2 as CO2 increases (instead we have seen decreased sensitivity). All of the predictions that have been tested have failed. ALL OF THEM. As of today there is NO EVIDENCE this feedback loop actually happens. In normal science we would say the theory has been disproven – but it still sits there as a theory in climate science as the core driver of moderate temperature increases and basically all of global warming alarmism rests on that theory.

Unfortunately, scientists are humans. When someone banks their career on an idea or theory, they aren’t going to back down easily (even Einstein had trouble accepting quantum mechanics). So when we couldn’t find evidence of the water vapor effect, the models continues to assume SOMETHING would cause run-away temperature increases. It was like religion fundamentalists continuing to make up new reasons for the existence of god as science hacked away at the traditional reasons. Only this time it was scientists acting religious.


A history of prediction failure

Some of my more intellectual friends might say, “Ed, that’s all fine and good, but I don’t have time to really understand the science. When I don’t understand something fully, my best choice is to just choose to accept the opinion of experts. And 97% of scientific experts think that global warming is going to be a disaster.”

That’s not a bad rule for living. I read the Economist. When it covers an area I know a lot about it is usually not very insightful, but it is almost always CORRECT. It causes me to trust the paper when they report on something I don’t know much about.

In this case though, that rule falls apart. For three reasons:

  • 97% of scientists don’t actually agree that global warming is a disaster waiting to happen
  • Those that do have history of being very very wrong at prediction
  • Just as the oil companies have a vested interest in talking down global warming, those “talking it up” might be even under more external influence


97% of scientists

The 97% number comes from two places:

First it was a poll of 79 scientists which asked “Do you think climate change is man-made”. The fact one of the scientists answered no is likely a reading comprehension problem. No where did the question ask about future devastation. I would answer yes to that question as well. Yes is the right answer no matter what Donald Trump tells you.

The second source was a survey of scientific papers by John Cook. It was completely discredited by Richard Tol (world’s leading climate economist). They left out data, used biased observers who disagreed with the authors of the papers they had “classified” more than 2/3rds of the time, and they collected the data in a way that allowed them to adjust conclusions as they went along. The data could NOT be replicated by others who tried. And yet the paper continues to be cited because the 97% number is just too useful for the people pushing environmental policy.


So what is that actual “consensus” of scientists on climate change?

The consensus is that climate change (and warming) is happening, but that it will not be dangerous. The main report on climate change is the IPCC report (which has a lot of the same issues I will talk about in the rest of this post). It gives the range of possible outcomes a century from now from harmless (1.5 degrees) to terrifying (4 degrees). The mild prediction of 1.5 degrees makes a lot of sense and is consistent with known science. It matches with the proven science of CO2 impact on temperature. And in fact if you look at the probability distributions IPCC shows they cluster around the low end of the range. But everything beyond harmless requires made up, unproven, hypothetical theory.

To be higher than about 1.5 degrees we have to assume that the water vapor effect will multiple the CO2 effect by about 3x. This is unlikely given what we know now, but it has not been taken out of the report. But even water vapor feedback doesn’t take us into terrifying range of 3-4 degrees. For us to have a terrifying result we need to assume things like:

  • Climate sensitivity to CO2 is high – much higher than we have seen thus far (unlikely)
  • World population growth accelerates (unlikely)
  • CO2 absorption from the oceans slows down (no reason to believe will happen)
  • The world economy gives up gas, but increases using coal 10x current levels (Really? Yes. This is in the report)

But you don’t hear these caveats from the alarmists. You just hear, “Could be as high as 4 degrees with devastating effects”.


A history of poor prediction

Even though the consensus is not the crisis screamed about by Al Gore, there are a group of scientists who do think we are headed for the apocalypse. These scientists are predicting disaster. What about them? Before we drop everything to do what they tell us to do, we should look at their historical ability to make successful predictions.

My friend asked: “If doctors say your baby has a 10% chance of having a horrible disease if you don’t give up half your income, wouldn’t you drop the income just in case?”

I don’t think I would. Not right away. Instead I would ask some questions, like, “Why do you think that? Is it pure theory or experimental evidence? If it’s theory, where does the theory come from? Has the theory made any predictions before? How accurate were the previous predictions? What is the range of likely outcomes if I don’t give up my income, and what assumptions do you have to make for each one of those outcomes to come about?”

To make this real, we were told a mild version of this by our doctor when my wife was pregnant and slightly over-due. He told us a test showed that there was a lack of amniotic fluid and that my wife needed to be induced that afternoon. Instead of blinding going to be induced we asked a bunch of questions. We found out the test used was very inconsistent from test to test. And when a test/control test was done to determine the impact of early induction, the induced women had MORE complications. We decided to NOT do the early induction, against the doctor’s recommendation (by aligned with what the science was telling us).

So let’s do the same exercise for climate science.

Before global warming, MODERATE environmentalists made the following predictions:

  • There would be a new ice age from global cooling
  • There would be a population explosion and an inability for humanity to feed itself
  • Elephants would go extinct
  • Rainforest coverage would be rapidly removed causing disastrous consequences
  • Acid Rain will destroy our forests
  • The ozone layer will be wiped out causing inability for humans to go outside
  • Desertification
  • Nuclear winter
  • We will run out of resources – everything from copper to aluminum to oil
  • There will be pandemics killing off 10%+ of the population
  • Sperm counts will plummet due to modern technology and we will be unable to procreate
  • Pesticides will cause dramatic increases in cancer
  • GMOs will be dangerous to human health
  • GMOs will cause a loss of biodiversity that will make entire species go extinct

In all these cases there was consistent pattern of dramatic exaggeration followed by no apology as we moved onto the next disaster that was “right around the corner if we don’t change our ways now”. In all these cases the disaster scenario was predicted by LEADING SCIENTISTS. But in EVERY case the problem was either non-existent, or nowhere near as bad as it was made out to be. If a corporation made predictions like that and acted on them, it would go bankrupt. But when an environmental organization makes those predictions it brings in MORE donations. When they are proven wrong, they move onto the next world-destroying event so they can collect more donations. Global warming is the best disaster ever for Big Green. The disaster is so far in the future, they don’t need to invent new disasters when this one doesn’t work out. While we should be able to see warming on the way to that disaster, when we don’t (like we didn’t for 15 years!) we have a ready explanation. If we see no warming for the next fifteen years, do you think Big Green will stop shouting about global warming? I doubt it.

This DOESN’T mean that the boy who cries wolf will not be right one of these times, but it should give us a little pause and before we assume “This time there is obviously a wolf, because the boy says so and he has a PhD.” We should have at least a little skepticism before we dismantle our modern economy (or even divert resources from things we KNOW save lives here and today).

Let’s talk about specific predictions on global warming specifically.

If we go back to the early 80s the belief among climate alarmists was we would be in a disastrous state by now. Indeed, temperatures have raised by about 1.5 degrees C in the last 40 years. But disaster has not followed. Globally humans are better off today by almost every metric than we were as a group 40 years ago. Predictions of massive expansion of the geographic footprint of malaria, an influx of climate refugees, dramatic heatwaves, storms, draughts and floods never came to pass (as much as every disaster that does happen these days is automatically attributed to global warming).

The most famous “prediction” of climate scientists is the famous “hockey stick” graph. It shows that temperatures were relatively constant for 1000 years, but then, in the 20th century, just as we were emitting carbon, the graph turns into a hockey stick (we are on the blade). It looks very very scary. (This isn’t really a prediction as it is backward looking, but future, reasonable, predictions were made just by extending the blade). The IPCC liked the graph so much they published it six times in its third assessment report and displayed It behind the chairman at the press release briefing.

But then researchers like McIntyre and McKitrick showed that the historical data was BS (backed up by the NAS for those who care about credintials). The data was completely reliant on dubious tree rings and inappropriate statistical filters that exaggerated the 20th century “blade”. That’s fine. This happens in science. People make mistakes, they are caught and life goes on. But that’s not what happened here. For years climatomogists clung to the hockey stick. They pretended nothing was wrong. Remember 2009? A bunch of emails leaked showing climate scientists were withholding data, preventing papers from being published and getting journal editors fired if they disagreed with the orthodoxy. They defended themselves at the time by saying the equivalent of, “Yes. We did those things. But the truth really is that the world is warming rapidly, and if we don’t keep a united front then we will never convince the public to do what needs to be done.” As a global warming adherent in 2009 I bought their answer and was critical of the right wing climate denialists that attacked their character.

But let’s understand what is happening here. These scientists aren’t trying to get at the truth. They have already decided what the truth is. Now they are trying influence policy by manipulating any new data so there is no uncertainty around their truth. If they are right – if there really will be a disaster if we don’t do something – then maybe what they are doing is commendable. “Who cares about science if we need to win the war!” But it’s stopped being science. And if they are wrong (and in the time since 2009 I’ve been convinced they are), then they are seriously hurting the long term authority of science by the way they are acting.

In 2012 Peter Glick (respected climate scientist) stole the identity of a board member of the (skeptical) Heartland Institute. He leaked memos including a strategy document that later turned out to be a forgery. He apologized, but otherwise continued to be a respected climate scientist with significant grand funding.

IPCC made some aggressive predictions of future temperatures in 2012. Matt Ridley dug into their data and argued that (based on their own data) the predictions should be lower. He was attacked by all over the place. More than a dozen high profile climatologists wrote articles attacking his character and motivations. A few years later the estimates were revised to be even lower than he had predicted.

Rajendra Pachauri was chairman of the IPCC for thirteen years and is often described as “the world’s top climate scientist”. When India’s leading glaciologist, Vijay Raina, challenged a claim in the IPCC report that said the Himalayan glaciers would be gone by 2035 Raj attacked him, calling his report “voodoo science”. (That claim by the way got its creator, Syed Hasnain a 3MM Euro grant to work at TERI under –guess who – Dr Pachauri). Yet years later it turned out Vij was correct. It was a huge mistake by IPCC. Many scientific organizations demanded that Raj step down as chair. He refused. (Instead he travelled the world promoting his novel – great choice if you are really worried about the devastating effects of carbon). He was finally forced to resign in 2015 when he was criminally charged with sexual misconduct with an employee. None of this was reported by the environmentalist news-sphere.

The glacier model was IPCCs biggest screw-up, but there have been others. They wrote about how urban heat “islands” had not caused exaggerated global warming based on a study. The paper turns out to be garbage and based on non-existent data from weather stations in China. After correcting for the bad data it turned out 40% of the claimed increase in temperature in China was just heat island effect.

There was a Scandinavian lake sediment core that was used as evidence of global warming. But someone messed up and it had been used “upside down” – so it showed the exact opposite of the claim.

The famous “hide the decline” incident where a tree-ring graph was truncated to cut off the recent cooling. In March 2015 a paper was published showing the Gulf Stream was slowing down. It made headlines around the world. But it turns out it was based on the same bad-data tree rings that caused the hockey stick chart.

Camille Parmesan published a paper on butterflies that showed that global warming was causing loss of habitat and driving the butterflies’ north. It was no obscure paper. It was cited over 500 times. She was invited to speak at the White House. She contributed to the IPCC’s third assessment report. Then Jim Steele (an ecologist with, as far as I know, no ties to Big Oil) found a problem in her paper. There had been a bunch of extinctions in the southern part of the butterfly’s range due to urban development. Once that was corrected for, the butterfly’s range hadn’t moved at all (the averages moved because of the southern extinctions). And since then the butterfly populations have recovered everywhere – back to the same range they were at originally. When Steele asked Parmesan for her data she refused. The paper CONTINUES to be cited as clear evidence of climate change and Steels has been accused of being a “Climate Change Denier” (I actually have no idea what Steele’s stance is on climate change – the point is that anyone who exposes holes in the global warming theory is attacked as an enemy).

If these things happened in any other field of medicine, there would be scandals. But in climate science the reaction is that nothing is wrong. It calls out errors when someone argues there may be a small warming effect, but ignores anything on the exaggeration side.

Now we are told that future predictions are “settled science”. Nope. Evolution is settled. Relatively is settled. Quantum mechanics is settled. Climate Science is a series of predictions about the future based on a chaotic system. There is nothing settled about it. For something to be settled it should be able to make predictions that are falsifiable, and then tested against those predictions. So far climate science has NEVER made a successful prediction. Even if it had, future predictions are very hard and we should be debating them. Instead the environmentalists have circled the wagons and are attacking even mild questioning. The reaction I see on facebook threads are good examples of that.


Bias, vested interests and Big Green

Why do smart, educated people still think this is settled science? When a scientist questions the disaster scenario they are attacked. Bob Carter is a climate science expert that doesn’t toe the line. When he appeared on the BBC the station was subjected to extreme abuse – and was ignored by the rest of the media. Judith Curry (Georgia Tech) was once an alarmist who changed her mind as she saw more data shared her story about how difficult it was to publicly change her beliefs. Extreme pressure from politicians, funding agencies, universities, professional societies, other scientists who are green activists. She claimed there were strong “monetary, reputational, and authority interests in staying within the party line”. Lennart Bengtsson (Swedish meteorologist) announced he was joining the Global Warming Policy Foundation. Then he withdrew because he was frightened due to threats to his family. Roger Pielke is an expert on disasters (University of Colorado). He actually aligns with extremist thinking on the impact of Global Warming. But he questioned the argument that the increase damage from disasters was due to global warming. His expertise and data told him the increased damage was due to society being wealthier – so more stuff to destroy in a disaster. He wrote about it on Nate Silver’s site, FiveThirtyEight, but he was savagely attacked. Nate Silver eventually dropped him from the site rather than deal with the heat from attacking environmentalists.

Meanwhile the leading proponents of climate change disaster-ism have lodged themselves in corners that would make it very personally costly for them to change their stances. Jim Hansen (head of Space Studies at NASA) has won over $1M in green prizes. Michael Oppenheimer (Princeton) was EDF’s senior scientist for 19 years. EDF has $209MM in assets and collected more than $540MM from charities since 2008 (Plus $2.8M from the government). It spent $11.3MM on lobbying. Ove Hoegh-Guldberg is in charge of the ocean chapter in latest version of the IPCC report. He spent his career working for Greenpeace and the WWF. What would it take for Jim, Michael or Ove to decide that global warming was not a significant risk? Until that question is answered it is disingenuous to attack people who cash checks from Big Oil. Big Green is just as, if not more, locked into their position.



The risks researchers need to take should give you pause on trusting from authority at this point. It is just too difficult for a climate researcher to NOT say there will be a disaster. If they do, they will lose funding (and likely friendships). And yet this is the standard argument from environmentalists.

Evolutionists don’t say, “97% of scientists believe in evolution, and if you are not a scientist you can’t question it. You are likely in the hands of the Intelligent Design Industry.” Instead they spend a lot of time and effort explaining the facts. And even so there is controversy in evolution. Not the controversy the religious right would like there to be, but there is a real argument on whether evolution happens at the gene level, individual level, group level, or even species level. I fell firmly with Dawkins on the gene level, but this is not locked down science and there is dissent in the ranks.

But any dissent among climate scientists is quickly attacked.

And if someone who is not a climate scientist brings up an argument they are even more open for attack. When I have these discussions on facebook and I bring up the water vapor issue, I get responses like:

“Don’t listen to Alex Epstien because he has taken money from fossil fuel companies.”

Never mind that Alex started with the data, came to a conclusion, and was THEN paid by the companies. Apparently that is completely different than when an environmentalist comes to a conclusion and then gets paid by Sierra Club who agrees with his conclusion.

“But Alex majored in Philosophy in undergrad.”

So what? Does that change the facts? I majored in physics so therefore I should be ignored on the topic of online marketing?

To this I say: Question the data, but stop with the character attacks. It is not how science works.

In the world of climate science, the pattern of character attacks and appeals to authority is the go-to strategy. “The Royal Society says you should be alarmed”, “97% of scientists agree”, ”IPCC says temperatures could go as high as 5 degrees”, “You are just spouting standard denialist talking points”, “You have taken money from big oil”. These were the types of arguments I grew up hearing in Catholic school.


Small probabilities, big disasters

If indeed there is even a small chance that the world is headed to disaster, we should definitely do something about it. Doing something does not necessarily mean throwing money at the problem – which has often made things worse (examples to come), but it means we should urgently be taking action. The argument is often, “We spend $XXX MM on the military, we should be spending at least as much on the chance of a global warming disaster”. I DON’T think military spending is a good proxy for trade-off spending. Yes, we might be spending too much on the military, but that is a different topic. What we are talking about with global warming is a small chance of a disastrous outcome. Better parallels are things like AI risk and Meteor risk.

The two examples are slightly different, but worth exploring. AI risk is really unknown. We have no history to check probabilities. All we have is theory and experts. In this case experts disagree on the amount of risk and the timing of the risk. But there are enough smart people who have deep understanding in the field, who are able to communicate the reason for their concern in non-black-box language, that I am convinced it is worth putting some resources into research around how to build AI that won’t turn us all into paperclips.

Meteor risk is far more quantifiable. We know from historical records how often an (effectively) world-destroying meteor will hit Earth. The chance if it happening in the next century is tiny (agreed to by all), but the result if it did happen would be an extinction event (also agreed by all). We also know exactly what we need to do to prevent an event from happening. I would 100% be in favor of the small spend necessary to protect humankind from the small risk. This seems to me to be a reasonable insurance to buy. (Something I don’t hear from environmentalists at all. Some of whom argue that this would be a GOOD thing because it would remove humans from the Earth and allow things to go back to where they were. Not joking.)

If we are afraid of human-extinction events, we should be putting some money towards safe AI-research and a meteor protection program. Both get MUCH less funding than global warming and have far more quantifiable benefits.

But back to global warming.

In order to believe we should buy insurance, we need to believe three things:

  • There is a small (but real) chance that there will be a devastating event
  • We can do something that will stop that event from happening
  • We can’t wait to learn more. If we do we will lose our chance to prevent the event from happening

Based on my earlier assessment I am convinced that there is NOT any real chance of a devastating event. Does that mean there is zero risk? Absolutely not. Humans are far too likely to jump to 100% or 0% belief in things. There is absolutely a chance that global warming will cause catastrophic disaster. But there is also a chance that global cooling will cause catastrophic disaster. I don’t think the warming apocalypse is much more likely than the ice age at this point. And the chance of either is vanishingly small.

But that doesn’t mean that we won’t experience mild warming. Mild warming may have some advantages, but it will also do real damage to some regions. We should do something to reduce that damage.

Turns out the best way to do that is to help those regions become rich. And the best way to become rich is to have a cheap external energy source – like oil and natural gas.


Problems with solutions

If I have almost convinced you, but you still think there is a 0.01% chance of a disaster, so you think that it is worth throwing resources at risk mitigation, your next question should be: Okay, how do we reduce the risk?

The problem is that so far global warming arguments have been used to help vested interests do stuff they want to do for complexly unrelated reasons.

The government gives massive subsidies to electric cars that take money from tax payers and transfer them to billionaires like Elon Musk.

Using “renewable” plants instead of “limited” oil sounds great, but it has been devastating. Extreme examples include cutting down rainforests to plant corn so we can burn it inefficiently in our cars. Iowa is now our “Greenest” state as they fight for ethanol subsidies (and the politicians of all stripes oblige).

We rip up peat bogs to install wind power.

Renewable power that still needs back-up from fossil fuels. Because of that back-up need causes an INCREASE in total CO2 emissions as the plants need to be powered up and down because wind power is given priority.

These actions are not an insurance policy. We are just trying to push policy agendas.

If there really was a huge risk of devastation if we don’t stop CO2 production, we only have two real options:

  • Nuclear Power
  • Destroy the world economy

The problem with nuclear power is that the regulations surrounding it have made it far too risky and expensive for any developer to touch. Those regulations were fought for by the SAME PEOPLE arguing that global warming is a human extinction-level risk. If it really is that potentially scary we should be turning off all regulations on nuclear power. Worse case is we get a few nuclear disasters like Chernobyl. Maybe even millions of people die. But that’s a tiny price to pay as an insurance policy against human extinction.

Right now we can save a life with a malaria net for about $2000. So if we are talking about spending even $20B on global warming insurance, that is costing us a million lives already. So we should have no problem risking a million lives or so with nuclear power.

(Realistically we don’t even need to do that. We can keep some safety checks in place which will reduce the risk of a significant disaster to ridiculously low – and remember any disaster that does happen will be contained to a (relatively) small geographic area – unlike a theoretical world-ending climate change event).



I am very open to having my mind changed on global warming. In fact I would fit in much better with my friend group if I could be convinced global warming is going to be a likely disaster. All it will take is data and a compelling argument. What won’t work is appeals to authority.

In the meantime, here is a summary of where I have personally netted out on global warming.

  • Climate change is real and is man made
  • It’s not a planetary emergency
  • It will likely cause some harm but also some good. Net it will be slightly bad
  • The best counter to slight warming is making the world richer. Netherlands has no problem “living under sea level”. Even extreme temperature swings do not cause famine in the US.
  • Climate model predictions have never been accurate in the past, which makes me doubt any reason to put any faith in them in the future
  • The global warming belief is non-falsifiable. Their disaster event is far enough in the future that they can’t be proven wrong. Any data of lack of warming in the meantime is explained away (Tell me what data we could get next year that would have them admit they are wrong? What about five years? Ten years? About five years ago someone said it would take “15 years of no warming before [he] would question his beliefs.” When we had 15 years of no warming he changed his mind and said it would take longer
  • There is a growing body of literature that challenges IPCC climate estimates
  • The usual remedies for “solving” the global warming crisis are either expensive, ineffective, or even cause more CO2 than there would be otherwise (sometimes all three)
  • There ARE benefits of higher CO2 – yes plants need water and nitrogen, but there is clear evidence now that there has been a global greening and evidence points to increased CO2 as one of the big reasons
  • The biggest issues in the world is poverty. While it is a very hard problem to fix, things are getting better. But most climate change “remedies” make poverty worse. We should not make our biggest problem worse
  • One of the clear ways to reduce poverty is to increase energy use. The easiest, fastest and cheapest way to do that is to use fossil fuels.
  • If we are worried about small probability events that could be extinction-causing, we should look at meteor defense and AI research
  • Climate science is very similar to nutrition science in the 1970s. Leading nutrition scientists (note both groups need to add science to the end of their titles) fought hard for their belief that fats were the cause of unhealthiness and we needed to replace them with carbohydrates in diets. They fought every attempt to show conflicting data. They did it for a good reason: They thought they were right, and they believed silencing decent would save lives. It turns out they were wrong and their silencing resulting in a human tragedy on a world-wide scale. Now we have climate scientists convinced they are right silencing decent because they are convinced they are saving lives. Both movements had/have significant political support and funding (funding that only goes to scientists who fall in line).
  • Scientists are bad at disproving their own claims (they are like all people that way). But science advances because scientists disprove OTHER scientists claims and findings. But this falls apart when there is a monopoly that silences scientists that try and do that. We saw it happen with nutrition in the 1970s, and we see the same thing today in climate science.


This post took me weeks to write. A lot of the content comes from Matt Ridley and Alex Epstien, and I owe them a huge debt for the amount of real research they have put into the topic. I will eventually go back and put in links and sources. If there is a specific source you are interested in, let me know and I will add it first.


Whether you liked this piece or not, you might like the data-driven way I look at the “search for excellence” in my book, “Good Enough: Why Good is Better than Excellent”. You can download the first chapter by submitting your email on the right.

The Great Filter

~4000 words

I generally write non-fiction, but as I started thinking about this concept I thought it was better explored as a piece of fiction. If you enjoy it, you might still enjoy my non-fiction. You can download the first chapter of my book, “Good Enough: Why Good is Better Than Excellent” by submitting your email on the right.



When you travel in time someone dies, then you die, and then everyone dies. But for some reason the someone’s death hurts more than the everyone’s death. And both hurt a lot more than your own death, which doesn’t even seem real. I mean, you are still thinking. You have all your memories. You definitely don’t feel dead. But it doesn’t mean you aren’t as dead as everyone else.

If it causes so much death, why do it?

It’s not a good excuse, but it’s an old one: If you don’t do it someone else will. So it might as well be you. Or me. Better me than you.

I didn’t set out to discover time travel. I was working on brain emulations. It turns out general artificial intelligence is a much harder problem than most people think. Sure we can make AIs that can win at games or reverse engineer the stock market or even identify faces, but generalizing that to duplicate the abilities of a human brain turns out to be a very very hard problem. Not that duplicating a human brain isn’t hard too. Most things worth doing are hard. But duplicating, or “emulating” a human brain was at least possible in my lifetime. Or at least possible enough that I was willing to dedicated my life to doing it.

And I did it.

I found a way to scan a “carbon” human brain and recreate it exactly atom by atom in silicon. Every electron and neuron in the exact same place. Well, not exactly the same – you can’t know both the speed and position of a quantum particle at the same time – but it was close enough that the emulated brain thought it was the real thing. Outside observers couldn’t differentiate between the emulated brain and the real thing. It was a game changer.

It was kind of like immortality. Kind of. Your brain patterns could continue living forever, but that didn’t mean you lived forever. It was closer to immortality for your twin brother. You feel a kinship to him and you are very happy for him, but he isn’t you. So there is that.

The other big thing that happens with silicon brains is that they aren’t limited by biology. It is trivial to throw more CPU at the brains and allow them to run faster. There is no reason an emulated brain can’t operate at 10x or even 10,000x the speed of one of our normal brains. And there is no reason you have to stop at one duplicate. Control-C, Control-V and you now have another brain just as good as the first one. It just takes more electricity and coolant systems. Think of what Einstein could have done if he was living a subjective 30-years for every day he had on the planet. Now imagine if there were 10,000 Einsteins all collaborating together on problems. It wasn’t general super-intelligent AI, but it was still world-changing. Or at least it would have been world changing if I hadn’t stumbled across time travel.

In order to duplicate the brains I had to dig into quantum positioning in more detail than is interesting for laymen. You have likely heard about “quantum action at a distance”. It means you can effect things at the quantum level far faster than the speed of light, which is impossible. Except it’s not. It turns out the way that happens isn’t magic, it’s time travel. Really minor, stupid time travel, but the time travel all the same. Using those same principles, it’s possible to send data back in time (sending data forward in time is trivial. We’ve been doing it since we first made painting in a cave).

Humans have hypothesized about time travel since at least the 1800s. One concern has always been the grandfather paradox. What happens if you go back in time and kill your grandfather? How could you be born to do the killing? And if you didn’t do the killing, then what exactly happened?

Fiction tried to solve the paradox by eliminating free will. Basically everything is fate and it has to happen a specific way.

It’s almost ironic that the common solution to time travel was “no free will” given how much everyone seems to believe in free will (free will is BS by the way. Who’s free? Our brains are just electrons firing. There is no homunculus hiding behind the brain directing those electrons).

But free will or not, it turns out we were over complicating things. The reality is there is one giant timeline, it just sometimes wraps back on itself. This happens all the time with quantum communication at a distance. Every time a communication is sent back the only real timeline jumps from point A in “timeline one” to point B in “timeline two”. All of the conscious beings in timeline one cease to exist and everything continues forward from timeline two. Generally, this doesn’t really matter at quantum timelines. We only ever exist in one continuous train of existence. We don’t even realize we are experiencing millions of micro-deaths every second as quantum messages are sent back all around the universe. But it starts to matter when humans take control of this technology. Humans like me.

For simplicity, let’s say we are in the original timeline. No one has ever traveled in time anywhere in the universe (we will ignore quantum effects). Then I send a message back that kills my grandfather. The moment I send back the message the original timeline ceases to exist. It’s gone. I’m gone. You are gone. Your dog is gone. Heck, even every alien in the universe is gone. The only thing that exists is timeline two back at the moment I sent them the message. The only thing from timeline one that continues is that message. The sum total of all human and alien knowledge and experience from the moment the message arrives until the moment the message was sent is summed up in whatever data I sent back. It’s mind blowing the quantity of experience that has been wiped out of existence.

Now this new timeline continues from where it left off. Before that message arrived timeline two was identical to timeline one. But now it has an additional input – the message I sent back that somehow causes someone to kill my grandfather. In timeline two my father is never born and I am never born. But it’s more insidious than that. If you were born after the message arrived, you would not be born either. No one would. Oh, there would still be babies. Your parents might even give their first born child your name. But that person wouldn’t be you. Their DNA would be completely different. Well not completely different – more like your brother.

You see that message caused everything to re-set. Whoever came in contact with that data would be changed – maybe in a small subtle way, or maybe in a large impactful way (impactful enough to cause someone to kill poor old granddad). And each person who was affected would change their future behavior – again sometimes subtlety, sometimes dramatically. Now everyone who comes in contact with that person will be affected. And each person that comes in contact with someone who came in contact with that person would be affected. It would propagate outward at something approaching the speed of light. I’m not 100% sure, but 4.367 years after the message arrives it might even cause Mork not to be born on Alpha Centauri due to the way our sun twinkled a little differently (don’t hold me to that prediction).

So you see when you send a message back in time you kill everyone and everything in our entire existence – including yourself. That’s kind of bad.

But I did worse.

Instead of just sending a message, I sent back an emulated brain. All emulated brains are is data, so there is no reason I can’t send them back the same way I could send back a television broadcast or a nice hand written note (I was being silly. I can’t send back a note. Just ones and zeros). The problem is where do I send it to? I don’t have to send it back very far before there are no computers with the capabilities to receive the brain.

But there is one complicated structure that can receive an emulated brain that goes back at least 10,000 years. The biological human brain.

So I sent an emulated brain into the brain of someone in the past. What happened to that person? Well that’s the someone I killed. See how that feels worse than the billions I killed just by sending the message? (probably trillions if you count all the aliens. Maybe quadrillions? Sextillions? What’s bigger than a sextillion?).

And it hurts a lot more than my own death, because it’s my own emulated brain I sent back. I guess I should re-phrase that. I’m here. I’m not dead. And I didn’t send anything. The biological brain I was copied from sent me back here. That biological brain is dead. Am I responsible for its actions? Who knows. I feel responsible since I share all of its memories. It feels like it was me that sent me back here, but I’m smart enough to know the difference. I knew I was killing myself when I sent the message. It felt like a noble sacrifice. And yet here I am alive and well. I can even blame all the deaths on someone else. Best of both worlds?

But why do it at all? Why kill myself and billions of others (and the poor guy who’s body I now inhabit)?

I hinted at it early. Because once time travel was discovered, someone was going to do it. And the second they did it everyone was going to die anyway. At least this way my emulated brain got to survive. And maybe I can do something about making sure time travel is never discovered. Ever. Because if it is, the universe will be wiped out again. And again and again.

I don’t think I can do it.

How do you stop a technology from EVER being developed? Say you were sent back to 1960 and told to make sure the computer was never discovered. You could likely track down Bill Gates and Steve Jobs and have them both killed. But then what? That might slow down computer development for a decade or something, but do you think you have put a stop to it? Do you think that no one will come up with the idea of the PC ever again for the history of the universe? Of course not. You need to do something more drastic.

But what?

Apart from dropping atomic bombs to wipe out all of humanity, how do you stop technological progress? Is the only way to stop humans from developing the computer to wipe out all humans? And would that even work? How long before raccoons develop tool-using intelligent culture, discover the chariot and the printing press and work their way up to computers and the internet. Do you have to wipe out all lifeforms? That seems like a little over kill to stop the Apple 2E.

Time travel is more complicated than the personal computer, but the same principles apply. I have no idea how to stop it from being discovered eventually. And when it is, it will be used, and everyone will die. And that’s not even the worst of it. The threat is far more existential.

Have you heard of the Drake Equation?

N = R* x fp x ne x fl x fi x fc x L

It’s a lot of letters, but basically it says, “The number of alien civilizations we can communicate with in our galaxy at any point in time is equal to the number of stars, times the chance a star develops intelligent life that can communicate, times the length of time that life will be around before going extinct, divided by the age of the galaxy.” We know the number of stars and the age of the galaxy (those two variables cancel out to a nice round “7”). The chance a star develops intelligent life is hard to calculate. We do know now that most stars have planets that could develop life. It’s a question of if that’s enough for them to actually develop life. And if they do, how often does that life develop the ability to create the equivalent of radio? For our purposes we can either say it’s so rare it never happens, or we can presume that it happens all the time.

What’s more interesting is the last variable: After a civilization develops the ability to communicate “interstellarly” how long does it last? Or to change the question a little: How many of those civilizations manage to break free of their home planet or solar system?

If an alien species can’t break out of their home system, then they will eventually go extinct. Something will happen like nuclear war or habitat destruction or a massive virus or something that will take them out. It’s just a matter of time. The first human signal that broke free of the solar system was broadcast in the 1930s (The Berlin Olympics. If aliens are listening closely the first human they will encounter is Adolf Hitler. Aren’t we lucky). It took less than ten years for us to develop nuclear weapons. Within thirty years we had the Cuba Missile Crisis and we almost got sent back to the Stone Age. If there is a 1% chance of blowing ourselves up every decade, civilizations like ours will only last an average of 680 years. In that case even if the galaxy spits out high IQ apes all the time, they won’t be around long enough for us to find them.

But if a civilization can break free of their home planet, then the story changes dramatically. A 1% chance of destruction on each of two planets extends the average length of the civilization to 68,000 years. Add a third planet and it goes to 6.8 MILLION years.

And if a civilization can expand to a second solar system, what is stopping them from adding a new system every century or so? Nothing we can see. Our galaxy is over 13 BILLION years old. If a species broke free of their home system a million years ago and started expanding at a new system every century, that single species of alien would have expanded to over ten thousand star systems. If they started a billion years ago they would be at 10 million star systems.

And yet we don’t see any evidence of even a single alien species out there – let alone intergalactic empires.

Which brings us to the Fermi Paradox.

There are billions of stars most of which could support life. If they could support life, then some actually will, and some of that life will become intelligent. Some of that intelligent life will invest interstellar travel. If they do, within a million years or so, they should be able to explore the entire galaxy.

They haven’t, which led Enrico Fermi to say, “Where is everybody?”

At least one of those assumptions must be wrong. Most stars have planets that could support life [fact]. But for some reason they (never) manage to produce interstellar civilizations. Not rarely. Never.

As my grandparents use to say, WTF?

In 1996 a Robin Hanson wrote about The Great Filter. One of the steps in the evolution from potential-life to spacefaring life must be a once-in-a-galaxy’s-lifetime opportunity. There were nine steps the way he saw it:

  • Habitable Planets
  • RNA
  • Simple Single-Cell Life
  • Complex Single-Cell Life
  • Sexual Reproduction
  • Multi-Cell Life
  • Tool using animals with big brains
  • Human-like
  • Stellar Expansion

At least one of these steps must be almost impossible to overcome for the galaxy to look the way it does. If that step comes before #8 it means humans will likely be alone to explore the galaxy. If it comes after #8 then it means there have likely been lots of aliens like us, but something happens that stops us from ever getting to #9.

The bad news is that after we found evidence of multi-cell life on Mars we could rule out the Great Filter happening before #7 (If it happened twice independently in the same star system it is by definition not rare).

We can hope that jumping from jellyfish to humans is really hard, but I was never optimistic that evolution wouldn’t find a way.

Which suggests The Great Filter is in front of us.

But it was always hard to figure out what it could be.

The stuff that scaremongers throw out there just aren’t dangerous enough. Yes nuclear war or global warming could wipe us out. But it’s not about wiping us out. It’s about being almost 100% certain of wiping us out. Could you imagine a world that avoids nuclear winter? Or a world that coordinates enough to develop green energy before a global warming cataclysm? Even if you can’t do that, could you imagine a slightly different alien species that could negotiate through those waters? It seems unlikely that nuclear war wipes out 99.9999999% of species that get as far as we do.

More exotic arguments fall apart equally fast under scrutiny. To go back to my field, what if civilizations develop General Super IQ AI and it wipes them out (like a super Frankenstein)? Maybe developing GAI is inevitable before civilizations reach new stars, but then wouldn’t we see a bunch of General Super IQ AIs exploring the galaxy? Or what if civilizations become so advanced that they decide to leave this universe completely, leaving it behind like a giant natural park? Sure it’s possible, but would 99.9999999% of the species do this? And would every member of that civilization decide to “move to the city”. It would only take a small cult of alien-Amish to decide to stick around and the theory falls apart.

So does that mean the Great Filter must come before #7?

I had almost convinced myself that our species was going to be okay. And then I discovered time travel and it all made sense.

Time travel is easier than traveling between stars. Much easier. Which means every alien civilization is going to discover it before they colonize other planets. See the problem yet?

Every time a civilization develops time travel someone will use it. When they do, the timeline resets. Your first thought might be that this could allow them to advance technology faster within any given time period. And that might be true. But each time they get to time travel technology, someone will use it and the timeline resets again. The only way to get past time travel technology is to never invent it, or invent it and choose not to use it.

My hypothesis is that any form of life that is capable of inventing time travel is incapable of never using it.

Now see the problem?

Imagine three parallel lines running left to right. The leftmost point on the middle line is where a civilization starts. It’s then a random walk to the right. If the civilization-line ever hits the top line, it invents time travel and jumps back to the starting point. If it ever hits the bottom line it experiences an apocalypse that destroys the civilization.

In a situation like this we don’t need a 99.99999% chance of extinction. Assuming there is even a little bit of variability, sooner or later the timeline will invent time travel or go extinct. And since inventing time travel re-sets everything, sooner or later the timeline will get unlucky and go extinct (and never invent time travel). Even if that chance is only 1% it has to happen given enough repeats. And time travel guarantees the repeats.

The only way out of the loop is if somehow a civilization can both avoid extinction and NOT invent time travel. If a civilization can expand across the galaxy before someone somewhere travels back in time and resets everything, then maybe it could avoid extinction. But even that isn’t a guarantee. All that needs to happen is the first time traveler deciding to go back in the past before the point where the civilization went interstellar. Maybe in the next loop time travel is discovered earlier, or a nuclear bomb goes off and ends everything.

So why don’t we see any aliens out there? Because any alien species that got advanced enough invented time travel and started again. They kept repeating their timeline until they destroyed themselves. The “final” real timeline we are living and breathing only has the extinct aliens.

And humans are the next species to experience the same fate.

So here I am. I tried to jump back as far as I could without going into the Cold War. When the USA and USSR were at loggerheads there was just too high a chance a small variable change could have caused a nuclear winter. Best to stay in the 21st century when the only apparent existential dangers are climate change and super-viruses. Neither is likely to kill us fast, or be affected much by my presence.

I keep talking like I’m the first time traveler, but that’s just because I haven’t seen evidence of any others. Realistically lack of that evidence doesn’t mean anything. Anyone smart enough to invent time travel likely saw the same ramifications I did. The last thing they will want to do is make it obvious time travel is possible. That will just speed up the process of time travel being invented. So it makes sense any time travelers out there are trying to keep it a secret.

So I don’t know what loop number we are in. Assuming the likelihood of extinction is small there will likely be hundreds or thousands of time line repeats before we mess up and end everything. While it’s possible I’m the first one, it’s far more likely there are likely hundreds or thousands of other time travelers out there right now who came before me. Hopefully all of us are thinking about ways to stop time travel from happening.

So why am I telling you all this? If I’m afraid that telling you about the existence of time travel will only make things worse, why am I telling you about the existence of time travel? Well, to start with I do not have any faith in my ability to do anything to stop the repeat from happening. So anything I do can’t really make things worse. If the thousands who came before me couldn’t find a way, and the millions of alien species out there in the same trap never found a way, the chances I will come up with the solution to get us past the Great Filter are next to zero.

So to use a terribly over-used saying, I figured I would try to think outside the box. Maybe I can find a way to get all the time travelers together and we can combine our brain power to come up with an idea. Or maybe just by putting this problem out there into cyber-space someone will come up with a theoretical solution to this fictional problem.

Because this is just a piece of fiction after all.

If you happen to be a fictional time traveler, you should send me an email.

Or maybe you already have? Like they say in Battlestar Galaxtica, “All of this has happened before and all of it will happen again”

Or maybe I’ve already failed and you are a future time traveler with a new idea. You can email me too and maybe this loop will be different with your perspective.

Or if you aren’t a time traveler at all, but you have ideas on how to solve this fictional problem, please add them to the comments. If we all work together maybe, just maybe, we can uninvent time travel and save the human species from extinction.

Yeah. I know. Not likely. But an emulated brain can still dream.

Business Innovation Radio

Podcast Interview

Earlier this week I was interviewed on Business Innovation Radio. We talked about the NFL kickers story from the first chapter (If you haven’t read it yet, you can download it by submitting your email on the right). We also covered some of my time at A Place For Mom, Craigslist, and how a company needs to figure out what  the most important thing is and then focus on that. We even touched on how you can apply Good Enough principles to your personal life.

Here is the entire podcast:

Stop Making it So Complicated

Article in Marketing Tech Insights Magazine

Last May I was approached to write an article for Marketing Tech Insights magazine. It was finally published today.

Here is a link to an online copy of the magazine (My article in on page 25)

Here is the article in full:


Stop Making it So Complicated

By Edward Nevraumont, CMO, A Place for Mom

Why do you have to go and make things so complicated?

  • Avril Lavigne, Complicated


I had a friend in school who was always stressed. She was stressed about her grades. When she did well in school she was stressed about getting a job. When she got a job she was stressed about finding a boy. When she got married she was stressed about having kids. When she had it all, a great career, a great husband, a great family, she was still stressed about what came next.

Some people choose to be stressed.

In marketing the parallel is people who choose to make things complicated.

Back in the pre-internet days it was very difficult to measure marketing impact. It was clear that marketing drove value in general, but very difficult to pinpoint which marketing dollars were effective and which being thrown away. In this environment marketers fell into two camps:

  • Salesman
  • Technical Marketers

Salesmen were storytellers. The Mad Men of marketing. They told a compelling tale of why their marketing spend was the reason for a products success and/or why their marketing was not the reason for the failure. They ruled the roost in the marketing world, but the fluffy-ness of their world-view rarely got them to the CEO suite.

Technical marketers fell into two sub-camps. The academics developed tools like conjoint analysis and statistical customer segmentation models. The grinders meanwhile ran A/B tests on direct mail advertisements for book clubs and marble chess sets.

The grinders were doing real science in un-sexy verticals. The scientists meanwhile were doing math that had very little relevance to reality. Except that the salesmen could use their mathematical models to enhance their qualitative pitches.

Enter the internet.

Internet allowed marketers to use the tools previously limited to the direct mail crowd. Technical marketing “grinders” finally had their day in the sun. No longer was a salesman required to tell the story of why an orange background would sell more than a blue background. Now the grinder could just run an A/B test on the website and show their boss the statistical significance test. Who knew why orange was better – it just was. The test said so.

As the grinders got more power and prestige, marketing finally turned into a quantified profession (At least in some companies, as many marketing departments in giant brands are still run by salesmen). CMOs started being considered for CEO roles. Technical marketing became the only real respected marketing. Instead of salesmen using flowery language to convince CEOs to invest in their marketing pet projects you had math geeks using statistical equations to convince CEOs to invest in their marketing pet projects.

While it may seem more sophisticated, the mathematical language hides as many untruths as the flowery language of the Mad Men.


Small n

Unless you are a giant website like Amazon, Facebook or Expedia you will be limited in how much traffic your website gets. That means you will never be able to test everything – or even most things. You still have to make decisions about what to test. You don’t need to use flowery language to ‘prove’ orange is better than blue, but you do need to have some way to decide to test blue in the first place (vs. a million other things you could choose to test).


Reversion to the mean

Your test just showed the blue background gave you a 20 percent boost to your conversion. You run around high-fiving everyone in sight. You convert the entire site over to blue backgrounds and wait for the money to roll in. Months later you look back at before and after conversion rates. It doesn’t look much better. What happened?

Reversion to the mean happened.

When you get a great result from a test it is either because the test cell is actually truly better than the control cell, or it is random. We use significance testing to rule out the randomness – or so we believe. What we actually do (every time I have seen it done in a company) is we run a test and as soon as we get 95 percent confidence the test result is better, we stop the test and roll out the test cell to 100 percent of the traffic (and start the next test).

The issue is the 95 percent confidence doesn’t mean 95 percent confident that your result is 20 percent better (or whatever the average result says), it means that you are 95 percent confident the impact isn’t zero. The +20 percent is a combination of actual impact and random, but there is nothing that says the result couldn’t be +0.1 percent from actual and the rest random. That would fall within the 95 percent ‘rule’.

There is also a 5 percent chance your test is worse that the control. 5 percent is not very high, but if you are like most companies and you are doing dozens of tests a week, that 5 percent will hit you every couple of weeks.

Even if your ‘n’ is high, it is very easy to chase noise.


Big Data and Black box Statistics

I have lost count of the number of marketing consultants that promise they can get me a 20 percent improvement in performance in my marketing. 20 percent seems to be the number that is high enough to be impressive and low enough to be realistic. But when I ask these companies to offer a guarantee where we only pay if we actually get a 20 percent improvement they almost always decline my business.

Usually when pressed to answer the question, “How are you going to get a 20 percent improvement?” The answer is inevitably something to do with Big Data.

Big Data is a real thing. Google uses it to give you search results. Amazon uses it to recommend the next book you should buy. Facebook uses it to determine the best buzzfeed article to show in your newsfeed. But most companies should not be looking for a Big Data solution. Little Data will work just fine for most of us.

Majority of companies have not executed on the most basic data. Finding ways to use more data is a waste of everyone’s time. If you aren’t prioritizing your call backs based on whether they have budget to buy your product, what makes you think you will prioritize your call backs based on a fancy algorithm built with Big Data? You won’t.

But humans (even marketers) love the lottery ticket. They love the idea that if they can only get the next shiny toy they will be able to revolutionize the business (or at least get a 20 percent improvement).

Whether it’s a Big Data black box or a Mad Men black box, the result is the same: Over-complicating marketing to make someone look good. My pitch to all of us is to forget for a minute the next lottery ticket and instead look to see if there are simple execution issues you can fix. Only after you build so much great non-personalized content that you couldn’t possibly send it to everyone should you consider personalizing it to only send it to some people. Only consider the complicated when you are 100 percent sure you have mastered the basics.

And as an added benefit, focusing on the simple, un-complicated basics turns out to actually drive better performance.


And be a simple kind of man…

  • LynyrdSkynyrd, Simple Man

People Analytics

Adam Grant and Replacing Intuition with Evidence

The Washington Post wrote a piece in early April profiling Adam Grant, a professor at Wharton (my alma mater) and author of The Originals. The piece describes him as Malcolm Gladwell meets academia and is very flattering. He is at the forefront of a new trend called “people analytics”, a term that just keeps growing in popularity. Here is what the Google searches for “people analytics” looks like:

People Analytics trend over time

It’s not a hockey stick, but it is a strong upward trend year after year starting in about 2008.

My wife actually worked for a company called Volometrix that tried to be the leader in people analytics. They had a product that sat on top of  a company’s Outlook and combined with their HR system. It would pull the data on  who was meeting with who, how much time they were spending in meetings and who was emailing who. Even how often people were cc’d vs being in the ‘to’ line. The idea wasn’t to be big brother on individual workers (there are far easier ways and moer accurate ways to find out if someone is slacking off). Instead they wanted to use it to find trends in the data to help improve company management and struscture. Some use cases they pitched included:

  • Look at communication patterns before big  B2B sales. See if there is a difference between the successes and failures (maybe the extant of communication between product and sales? How much communication with the client?)
  • Look at communication patterns before big product launches to see differences between successes and failures
  • Look at the communication patterns of companies during a merger process to measure quantitatively the extent of the integration

The coolest anecdote I heard was when they showed a giant graph of all the connections in the organization to the CEO. He pointed to one cluster of people that did not seem to have any communication with the rest of the company. He asked, “Who are those people”.

“That’s your IT team in India”

“We don’t have an IT team in India”

“Yes you do.”

It turned out that the company had added a handful of Indian IT employees during one of their acquisitions but then promptly forgot about them. They were still on payroll, but they were in their own little world with almost no contact with the rest of the company. Who knows what they were working on (if anything).


The product was very cool. (My wife joined as Director of Product partly because of the amazing potential it had). The problem was no one was buying it. “Cool” isn’t enough.

Actually cool often IS enough to sell something.

“We have a paid search solution that runs on big data that will lower your costs 20%”

“We have a personalization engine that runs on big data that will increase your conversion 20%”

“We have a social media monitoring tool that will drive your engagement up 20%”

(It’s always 20%. I think that’s because it’s a big enough number to be very interesting, but a small enough number that managers believe it. If the salesman said he could half their cost of SEM or double their conversion, there would be too many questions asked. But 20% seems reasonable – but impressive).


People analytics is new enough, and unusual enough that people didn’t know what to use it for. There was no +20% to improve from. They had to  create a whole new category. And that’s really hard. Especially when you don’t know what you are going to do with the data.


Which is the problem with a lot of these new data-driven solutions. Many provide interesting insights, but the real challenge as most on-the-ground managers know is applying those insights. The bottleneck in most companies isn’t insights, it’s the ability to execute the right insights.


Adam says, ““I think we are leaving the age of experience and moving into the age of evidence. One of my big goals professionally is to get more leaders to stop acting on intuition and experience — and instead be data-driven.”

I agree. But the challenge is that most people’s intuition is that they should go and collect more data.


Some of Adams interesting conclusions from the article:

  • “His best-known study examined how much performance improved when workers in a call center — widely thought of as a tedious, thankless job — met the students who benefit from their sales pitches.”
  • “He found that they [Goldman Saks associates] wanted to spend less time on rote tasks like making “pitchbooks” and to gain more exposure to clients as well as have more time to learn tradecraft, such as how mergers and acquisitions get done.”
  • About a JetBlue’s program where employees can give cash rewards to other employees: “Employee “engagement” scores not only improve for the people who get the rewards but also for those who give them”

Let’s tackle the first one:

Call Center employees meeting students

Call center employees who met with the people who benefited from their pitch improved their performance. That sounds great. Why isn’t this being done in every call center on the planet?

Well sometimes it is. At A Place For Mom we used to have all new employees – including call center staff – visit nearby properties so they could see what the “product” actually was (most call center staff was very young, so they otherwise might never have seen an assisted living community). Many companies have call center employees do some sort of immersion to understand their product and customers. But many don’t. Why?

First: It takes time. That is time that is not used for many other opportunities to improve call center performance. Maybe you could use that time to teach selling skills. Maybe you need that time to teach phone etiquette (really). My wife spent time helping a call center in India. She took them through a training on how to identify American sarcasm.

Second: Call center employees in America tend to turn over at a high rate. So if you invest in their skill levels that ROI better happen fast. If it doesn’t, you are better to live with the lower performance.

The issue is the cost of meeting customers and the improvement is going to vary dramatically from industry to industry and company to company. Sometimes it will make sense, and sometimes it won’t. Which means that if you decide to do it at your company you are first going to have to run a test (just like the test Adam did). And running a test takes managerial (and sometimes executive) time and effort. Now you need to add in that opportunity cost as well. You can also only run so many tests at once. Which do you want to run first: Meeting customers or product knowledge session or new sales training session or lean operations initiative or… The chances are the exciting and interesting one (Get your call center employees out of the call center and visiting customers) is going to be less effective than the tried and true ones that have been proven for decades. If, after you have mastered the tried and true stuff you need to find a new idea to try, this seems like a good one. But most companies have trouble picking up the phone when you call and getting back to you immediately, let alone trying to be cutting edge.


This is the problem in general with cutting edge management science. It is super interesting to read and it is inspiring for managers and executives. But it is also distracting from the boring stuff that we know matters. It’s why I say that the quest for excellence often gets you to a worse place because it distracts you from being good.


But don’t just take my word for it. My book has stories like this, but it mixes those stories with data. Like Adam I too am very data-driven. That natural desire for data tends to get people like us excited by cool new analytical insights. So we need to be especially careful to not be distracted by “the fancy”.

You can read the first chapter of my book, Good Enough: Why Good is Better than Excellent by submitting your email on the right hand side of the page.

The History of Good Enough

In January 2014 I started a blog called “Marketing is Easy“. This was my introduction:

While a consultant at McKinsey I was once told: “You need to either make things very simple or very complex.” Super simple projects sold because the executives understood what was going to be done and the impact seems obvious. Super complex projects sold because the McKinsey team looked like they could handle it and the executive didn’t understand it enough to get his team to do it internally.

As I look at the world today I see a lot of people selling super simple and super complex.

The gurus, most of whom have never run companies, will give you metaphors about Purple Cows and how silver bullets like improving your customer service will make everything better no matter what your current state is.

The other set of consultants will tell you how you need advanced deep learning, big data, personalized predictive, collaborative filtering algorithms to truly get ahead. Thankfully they will sell you the solution so you don’t need to really understand the black box solution.

I call BS on all of it.

Marketing used to be about ‘qualitative BS’. Salesmen like Don Draper would tell stories and convince you they knew better than the finance guy what color should be on the packaging.

More recently, the pendulum has swung in the other direction. Now ‘quantitative BSers’ build mathematical models that get you ‘scientific’ answers to all your marketing challenges. Just like the qualitative BSers before them, most of these people really believe that their tools will tell you the true distribution of your customer segments. They are BSing themselves. I should know – I used to be one.

The thesis of this site (and my career) is that when what you care about is having impact what is within your control to get that impact is often easy. The challenges are

(a) Knowing what the easy thing to do is; and

(b) Avoiding chasing random noise

The easy thing can sometimes be answered with quantified data and other times by logic and experience. The key is not to confuse what looks like an easy answer with the random noise that will always threaten to confuse you.

I still believe every word.

What’s changed is that I don’t think the principles are limited just to marketing. My thinking outgrew the name of the blog. It’s not just marketing that is “easy”, it’s “everything”.

Kind of.

What I have internalized over the last few years is that the reasons marketing is made more complex than it needs to be are repeated in many different domains. And solutions have a lot in common too.

Think of this book as compilation of my entire career, but generalized far beyond just marketing – or even business.

The first chapter of the book (which I will send you right now – just enter your email on the right) talks about recruiting and performance management at the world’s top consulting firm (which is pretty business-y), but it also talks about NFL kickers and Teach For America. The second chapter leads off with how loyalty programs work (or don’t work), but then dives into parenting and education in Africa. Later chapters cover science, policing, the medical system, happiness, and even ESP.

And the last chapter? I call it “Living in a Good Enough World”, but an alternative title could have been, “Life is Easy”.

Introduction to “Good Enough”

You can get the entire first chapter “Good Enough: Why Good is Better than Excellent” via email (look to your right). As a teaser, here is the introduction to that first chapter:

Hanover is less a town than a college campus dropped into the middle of the New Hampshire forest. The isolation is part of the reason students choose Dartmouth over the other Ivy League schools in the north east. Social activities revolve around school-life more than at most other campuses. Even dating takes on a surreal quality when there are only three off-campus restaurants to choose from.

Winters at in New Hampshire hover around twenty degrees Fahrenheit. It is no wonder Dartmouth is known for their fraternity drinking culture. You might drink more too if you were in an isolated village surrounded by mountains in the dead of winter.

But Tariq Malik did not drink. He had other things on his mind. Tariq was studying for his MBA at Dartmouth’s Tuck School of Business and he wanted to be a consultant with McKinsey & Company. Every year business school students are surveyed and asked who their most desired employers would be. The top choice shifts based on recent company performance. Lately Google has been in the top spot. But for as long as the surveys have been running McKinsey has consistently been in the top two.

McKinsey & Company is the pinnacle of professional services firms. When it was founded in 1926 it was the first and only management consulting firm. In 1964 when the first women graduated from Harvard Business School, three of the eight joined McKinsey. In 1970 during a project for the Grocers Product Council, a McKinsey team invented the UPC code. Many companies put high value on their people. I once heard the CEO of Procter & Gamble say that if P&G lost all of their assets and all of their brands, they could re-build it with the people they had employed, but if the company lost its entire workforce it  would fail. It is unclear how much of that statement is truth vs hyperbole. But, in McKinsey’s case, while it is an exaggeration to say it does not have assets – McKinsey leases property; it operate a knowledge database – it is fair to say that its future success rides almost entirely on the quality of its people.

When I was at McKinsey from 2005-2009 we would charge clients about a $500,000 (plus 20% expenses) a month for a team of three people (plus some partner support). Ignoring the partners and assuming a sixty hour work week that works out to $645 per hour per consultant. We also had a philosophy of adding ten-times our fees in value created. Those people better be good.

So it should be no surprise McKinsey spends a great deal of time and effort making sure those people are the best they could possibly be. Part of that is training programs and on the job coaching. Part of it is employee selection: Making sure they hire the right people to begin with.

It is that hiring process that Tariq was preparing for.

There are two parts to the McKinsey interview. The first is the behavioral interview. In that section the candidate is asked about a time they had a specific experience. Each interview will dive deep on a different experience: “Tell me about a time when you had to change someone’s mind”; “Tell me about a time when you took on a leadership role outside your formal responsibilities.” “Tell me about a time when you had to make a difficult decision where neither choice seemed like the right one.” Each interviewer has a (different) standardized list of things they are listening to hear from the candidate’s story. They idea is to find people who have the right temperament to influence clients in a positive win-win way.

Most students spend very little time preparing for the behavioral interview. They are too busy stressing about the case interview.

The case interview begins with the interviewer explaining a situation and then asking the student how he or she would go about solving it. The interviewer may provide tables, charts of numbers – sometimes proactively and sometimes only when the student asks for it. It is a real-time, verbal problem solving.

Case interviews have often been misunderstood in popular media. Sometimes they are described like brain-teasers (“You have a fox, a chicken and some grain and you need to get it across the river on a boat…”). Other times they are described as surreal estimation problems (In the movie Abandon, Katie Holmes’ character is trying to get a job at McKinsey. The only question they show from her interview is, “Estimate how many paperclips would fit in this room.”). Both demonstrations of case interviews miss the mark.

A better example of a case interview would be something like this:

“You are working for a telecom company in Africa. They are trying to reduce the churn rate of their customers. Before we begin, we need to run a survey to ask people why they have stopped using their last mobile phone plan. We need it to be multiple-choice. How would you go about creating an extensive list of all the possible reasons someone could stop using their mobile phone service (and then create that list for me)?

Part 2: Let’s say the top reason is they are switching to a competitor for price-related reasons. What strategies could you use to prevent that churn?

Part 3: Let’s say we run an SMS campaign targeting users who we think are likely to churn in the near future. We get the following results (hand’s the candidate a printed spreadsheet). What happened? Do you think the program was successful? If so, how successful and should we roll it out? If not, what do you think could be done differently?”

Good case interviews often come straight out of actual client work. The candidate is being asked to solve a problem an actual McKinsey team was paid millions of dollars to resolve (albeit the candidate will receive significant hand-holding through the process with someone who knows what the actual answer is).

One could imagine how preparing for case interviews could be stressful.

Even with months of preparation Tariq did not get an offer to be a McKinsey summer associate. Undeterred, he applied for a full time role the next fall and was accepted. But getting a job at McKinsey does not end the challenge. Some would say Tariq’s gauntlet was just beginning.

McKinsey tries to hire the best, but they don’t stop there. After ever client “study” the consultants are given a formal evaluation. Twice a year they are put up against everyone of similar tenure in their office and evaluated again. Since consultants have different ‘managers’ on each study it is a weak complaint that “I just had a bad boss”. And bosses are being evaluated as well. There are many things to complain about at McKinsey, but not knowing where you stand is not one of them. No one is ever ‘fired’ from McKinsey but if you are not advancing at the expected rate, you will be “Counselled to Leave” or “CTL”. There are no eight-year consultants at McKinsey – there are only partners and ex-consultants.

Tariq is a partner at McKinsey today. One might say the McKinsey system worked for the “Tariq data-point”. The interviews suggested he would be a good fit and add value to clients, and a decade later he is a partner at the firm helping clients and mentoring new consultants. How do the other data points do?

There are lots of data-points to look at. McKinsey hires thousands of business school students every autumn and have been for decades. For every new hire McKinsey knows their quantified interview scores as well as their quantified performance on every study. They know each candidates relative strengths and weaknesses. And they know how long they lasted at the firm.

When McKinsey decides whether or not to make an offer, sometimes it is easy. After all the interviews they can turn the candidate’s evaluations into a score out of 100. When a candidate has a score of 90% or even 100% it is uncontroversial that they will get an offer. If they have a score of 10% it is clear they won’t. When a candidate has a score of 50% then there may be a vigorous discussion among the interviewers on whether McKinsey should take a chance on them. The result is that there is a spread of interview scores among new McKinsey hires. Some had stellar interviews and some got in by the skin of their teeth.

That variation is a good thing when for those that want to evaluate how well the interviews do at finding strong employees. It’s a relatively simple activity to run a regression against the interview scores and the performance evaluations those some people receive once they are working consultants. You can be sure an analytical company like McKinsey that believes the quality of its people is the most important thing for its future success has done that regression.

For those who haven’t worked at McKinsey (or done statistics) a simple regression just means putting all of your data points on a xy-chart and then drawing the best line you can through the data using some mathematical tools. If all the points line up perfectly on a diagonal-line you have perfect correlation in your regression. The further off your line they fall the less perfect your correlation. If you plot the heights of identical twins on the chart (one twin on the x-axis and one on the y-axis) you will get a near perfect (but not completely perfect) lining up of points. If you plot the weight of the same twins on a similar chart the points would still be very close to the line, but not as close as the height regression. The points on a regression of non-twin same-sex siblings would also be close, but much further than the first two regressions.

How closely the points are to the best line you can draw is called the “correlation”. Statisticians measure the correlation with something called R2 or R-squared. The higher the R2 the more two data sets are correlated. The temperature over time between Boston and New York is correlated. The temperature over time between Washington DC and Baltimore is even more correlated. The temperature over time between Toronto and Los Angeles is almost not correlated at all (but not completely uncorrelated. Since both cities are warmer in the summer and cooler in the winter, you would still see some correlation).

One would hope that the extensive interview process McKinsey puts its candidates through would be highly correlated with how well those candidates do on the job. Otherwise, why bother with the time and effort of the challenging interview process? (Make no mistake: The McKinsey interview process is difficult and time consuming for the interviewers as well.)

So what is the correlation between McKinsey interview scores and McKinsey job evaluations?



There is no correlation.

There is less correlation between candidates’ results on the McKinsey interview and their performance on the job than there is between the temperature in Toronto and Los Angeles.

It bears repeating: This is the most expensive consulting firm in the world. Their core focus is bringing analytical rigor to their clients. They believe there is nothing more important than hiring the right people and they are willing to dedicate as many resources as it takes to mastering that challenge. And yet if instead of using their analytically-tested interview scores to predict who make the best consultants you instead just threw a dart at accepted candidates, you would be just as accurate.

The candidates McKinsey thinks are “slam dunks” are no better than the candidates that barely get over the fence. McKinsey people are some of the smartest people in the world, and yet on this, they are no better than chance.


And if all this is really true, why is McKinsey making a mistake to put any effort into recruiting at all? If their best hires and their worst hires show no difference in performance, why not just hire people at random and save on all that effort?

The answers to these questions form the meat of this book.

Why can’t we tell good from great?

And what should we do about it?