Sources and Methods #45: Rowing with Bruce Smith
Bruce Smith 101:
Company website: Hydrow.com
Show Notes:
2:32 - If you play the piano, which I do, if like math, or you like repetitive motion, there’s something really really compelling about the rowing motion.
Within five years of arriving in Chicago, we’d built five boathouses.
4:00 - [What is the best way to get nothing done?] I have a tremendous nack for tanking things quickly, and it involves me telling them how great my idea is. I’m an expert on that. I still do it, despite having my head beat in a whole bunch of times. So, the best way to not get anything done is to not listen. It took me many, many many iterations to learn that the best way to get stuff done is to ask people questions about what they think about your idea. One or two sentences, and then ‘what do you think?’ is probably the best thing you can do.
Presenting the facts and skipping the discussion is probably the worst way to get anything done.
11:10 - I think that being a founder definitely requires some element of delusion and grandiosity. If you don’t have that, if you don’t believe on some level that you’re right about something, then you absolutely cannot be a founder. It’s just extremely stressful. You have to be willing to jump off a cliff without any kind of net. If there was a net there, than 18 people would’ve already jumped. So you really have to believe in something. To me, that’s the most valuable thing. And the people I like being around are people who believe in something to that degree. People that are actually willing to go to the edge.
There’s an upside and a downside. If you’re crazy enough to believe in something that, that often means that you do not see reality very well. I think that really great founders are people who can survive that cognitive dissonance between believing something that is not there yet and has no evidence, so they have faith in their ability to see something that other people can’t see. And then also the ability to take in the reality of the situation and understand that there are real gaps that you have to explain to people and walk them through. And be able to see the gaps in your own idea and your own faith. It’s a crazy tension.
15:52 - Joseph Conrad talked about The Work. Sailing a ship across the ocean is incredibly tedious, and likened it to a sewing machine, just keep working on the machine.
16:46 - That’s one of the things about success - you have the euphoria, you have the terror. But you also have to be able to grind. And rowers grind.
18:56 - Rowing brings people together. There’s really good brain science that shows that people who do things together, like synchronous motion, build trust.
19:35 - The more time you spend with your screen, the more time you spend isolated, the worse you feel. What’s the best thing that I could do as a human being to help other human beings feel better about themselves? That was the motivation for the company. If someone could tell me something else that would build more trust.
21:09 - I think a lot about the model of the tragedy of the commons. How do you get people to make decisions that are not in their personal best interest in the short run, but are in everybody’s - including their own - best interest in the long run? What I see developing in society is this horrible nexus of concerns, where the tragedy of the commons is actually coming to hit us in our daily lives, so we’re not able to make decisions in favor of the environment. We’re not able to make decisions in favor of public education. I think it’s because people feel more alienated and more separate from their fellow human beings. We have to do something.
I think it’s not just about the activity of the mind. It’s the mind-body connection.
25:41 - There are three kinds of competition: positive-sum, equal-sum, and negative-sum competition. In one kind of analysis, you could say that football or hockey or lacrosse are negative-sum competitions. Two teams enter the field - the only way for us to determine a winner is for one team to make the other team lose, and the team that wins has to physically hurt themselves to take that win.
With a positive-sum sport like rowing, or track and field, or swimming, you can put as many teams as you want on the field. In rowing, there are six lanes, so in the Olympics six teams go down the field. All six of those crews can have a personal record in the final race. One person still wins, but everyone has come to the table and may have produced their personal record. Everyone leaves with a better record.
That’s the kind of competition that we want to foster. Where people understand competition not as something that is negative and destructive, that involves taking something away from the other person or group, but something that lifts everybody up. By everybody bringing their best effort to the table, everybody gets better.
That was the competitive model before two world wars - rowing used to be the most popular sport in the United States. Tens of thousands of people would watch rowing races.
31:26 - This is really four different companies - the software, the hardware, the content, and the marketing, all have pretty different agendas and would like to spend our money differently. But it’s also a great moment for creativity. It is unbelievably satisfying to have all these facets of human life reflected in one place. We all come together and argue all day long in order to get to the end goal.
38:20 - I haven’t raised a single penny from a cold call. And I haven’t hired anybody without being introduced to them through an acquaintance for a friend. I call it the Quality Mafia. You find one really great person, and hold onto them like Grim Death, and give them whatever they need to come with you on your journey. And once you find that one great person, then they know about 20 or 30 really great people. And so you put out the call to those 20 or 30 people - “You need X? Oh, I know someone who used to do that.” And you keep being honest and open with people.
42:10 - Fast, Cheap, and Good. Pick two. We chose Fast and Good.
42:58 [On Workflow] - I use email. I star emails that need responses, and my goal is to keep the starred list around 10.
I have a huge amount of respect for work that happens face to face. If you’re working face to face with your direct reports, things go a lot better. I don’t know how that scales, but I think we can handle it for this type of company, we don’t anticipate growing beyond a few hundred people.
We use Slack internally, because that’s fun. I hate powerpoints, we only use them when absolutely necessary. The power of a clear, well-constructed sentence clarifies everyone’s thinking and ensures communication is rock solid.
I draw clear distinctions between kinds of meetings. There are Decision Meetings, and those should never take more than half an hour. If it’s more than half an hour, then you missed the point of the meeting. There’s not enough information, you’re chasing your tail, and you should not have that meeting.
If you don’t know what kind of decision you’re trying to make, you need a different kind of meeting. I call those Making Meetings. It’s not my idea. Basically, an hour is a minimum, and 2.5 to 3 hours if you’re mapping stuff out. Those are different things. If you confuse these two kinds of meetings, you waste everyone’s time.
I’m very skeptical of making very good decisions. Trying to make a decision is better than trying to make a really good decision.
48:54 [Advice on starting a company] Start early, do it often. It’s really really fun. Don’t try to make money, do something you believe in. The money part of it is so beyond irrelevant if you’re trying to effect some kind of positive change in the world. Then, once you get that straight, money will flow from an idea. If you don’t have a good idea, you won’t get any money, so don’t worry. Put emphasis on values and live those values.
That said, I love making money. Money is time, and money is freedom. It’s not like it’s not a goal, it’s a secondary goal. First goal: value. Second goal: money.
52:15 - I think a lot about Dostoevsky, and the University of Chicago, and people who were suffering after the war. Suffering a lot. If you work at the University of Chicago, you are surrounded by this violent, poor neighborhood. And yet, they produce the greatest number of Nobel Prize winners. You’re Fyodor Dostoevsky, and you can’t write what you want, but you create the greatest novels of all time, because you’re under this extreme pressure. Those are just two anecdotes and I have no idea if they hold over a broader spectrum. But it seems to me that creativity comes out of some level of discomfort. Cognitive dissonance, pain, and something that happens in peoples’ lives that produces creativity.
55:15 [On living a full life] - I will be completely didactic on this. If you want to be a complete human being, there are two things that you have to do. You have to read John Milton’s Paradise Lost, and a short biography of John Milton.
Then, you must read Anna Karenina. I read it every year. It is a complete compendium to all intellectual responses, human responses, emotional responses, to the existential challenges that we face as human beings. It’s a bit like the Bible - it is a complete story. It catalogues how you can respond to life.
Books Discussed in the show:
Sources and Methods #44: Deep Learning with fast.ai's Jeremy Howard
Jeremy Howard 101:
Jeremy on Twitter: JeremyPHoward
Free online programme / MOOC (“Practical Deep Learning for Coders”) at: fast.ai
“The wonderful and terrifying implications of computers that can learn” (YouTube)
Show Notes:
5:55 - My entire education is one degree in philosophy.
7:30 - Joined McKinsey at 18 with extremely basic knowledge.
12:19 - At Fast.ai our target audience really is people who have interesting and useful problems, and have a feeling that using AI might be a useful way to do that, that maybe don’t have a background in machine learning. It’s the people I came across in my career who were working in extremely diverse industries and roles and geographies, who are smart and passionate and working on interesting and important problems but don’t have any particular background in computer science or math. There’s a snobbish-ness in machine learning, that most people in it have extremely homogeneous backgrounds, young, white, male, who have studied computer science at a handful of universities in America or Europe.
David Perkins at Harvard, and his learning theory of the ‘Whole Game.’
18:10 - For some reason, the STEM field on the whole have gotten away with shoddy, slack teaching methods, where we expect the students to do the work of sticking with it for 10 years and putting it all together.
20:02 - We’ve discovered that the most practical component in AI is transfer learning. Taking a model that someone else has created and fine tuning it for your task. It turns out that this is the most important thing by far for actually getting AI to work in the real world. Apply and transfer learning effectively.
I think many people teach a list or a menu of things that they know, rather than really getting to student learning.
22:41 - Each year, we try to get to a point where the course covers twice as much as the previous year, with half as much code, with twice the accuracy at twice the speed. So far, we’ve been successful at doing that three years running.
28:48 - I think that will be one of the two most important skills over the next decade or two - the idea of how to work as a domain expert to provide appropriate data to a machine learning system and to interpret the results of those things in a way appropriate to your work. If you don’t know how to do it, you’re going to be totally obsolete.
31:09 - Back in the early days of the commercial internet, being an internet expert was extremely useful and you could have a job as an internet expert and be in a company of internet experts, and sell yourself as an internet expert company. Today, very few people do that, because on the whole the internet is what it is, and there’s a relatively few number of people who need such a level of expertise that they can go in and change the way your router operates and such. I think we’re going to see the same thing with AI.
39:08 - I started learning Chinese not because I had any interest in Chinese, but because I was such a bad language learner in highschool. I did six months of French, I got 28% and I quit. When I wanted to dig into machine learning, I thought one of the things that might be better to understand was human learning, so I used myself as a subject. A hopeless subject. If I can come up with a way that even I can learn a language, that would be great. And to make sure that was challenging enough, I tried to pick the hardest language I could. So according to according to CIA guidelines, Arabic and Chinese are the hardest languages for people to pick up. Then I spent three months studying learning theory, and language learning theory, and then software to help me with that process.
It turns out that even I can learn Chinese. After a year of this - by no means a full time thing, an hour or two a day - I went to China to a top language learning program and based on the results of my exam got placed with all these language PhDs, and I thought wow. Studying smart is important. It’s all about how you do it.
Spaced repetition is such an easy thing that anyone can do, for free, you can start using it.
[Jeremy’s amazing Anki talk]
If you’re not using Anki, you’re many orders of magnitude less likely to remember a piece of vocab. So you come away like I did, thinking you can’t learn a language. But once you learn vocab, the rest is really not that hard. Don’t try to learn grammar, just spend all your time reading.
45:04 - If you’re not spending a significant portion of your early learning, learning how to learn, then you’re going to be at a disadvantage to those that did for that entire learning journey. Spending 12 years at school learning things, but nobody ever thought you how to learn, is the dumbest things I’ve ever heard.
Coursera’s most popular course is Learning How To Learn.
Exercise is the other most important thing.
49:03 - My third superpower is taking notes. Exceptional people take a lot of notes. Less exceptional people assume they’re going to remember.
50:19 - Taking notes in class is kind of a waste of time. I don’t really see the point of going to class most of the time honestly, it’s probably being videotaped.
52:54 - Learn Python if you’re interested in data science, deep learning.
54:22 - I think there are two critical skills going forward, pick one. One is knowing how to use machine learning. And the other is knowing how to interact with and care for human beings. Because the latter one can’t be replaced by AI. The former one will gradually replace everything.