Archive for the ‘thoughts’ Category

How is your memory indexed?

Tuesday, May 13th, 2014

My Facebook friends have heard me complain a few times that I have apparently exceeded my brain’s capacity to keep track of people. At Google, I have worked with hundreds of people, and it’s entirely embarrassing when one of them sees me at lunch or elsewhere on the Google campus and says “Hi Eric!” and I completely blank on their name. I recognize them, and I know we worked together, but I don’t remember any of the details. A couple weeks ago, somebody waved me down in the cafeteria, and I had lunch with him, and talked for 30 minutes without me being able to remember his name or what specifically we had worked on together.

The funny bit is that once I got back to my desk and looked up his name, all of that information came flooding back in. And that’s how it works for me in general – when another Googler says hi and I can’t place them, a glance at their name badge will trigger the memory cascade of how we know each other. As far as I can tell, my memory is indexed on people’s names, not on what they look like, so I can’t look up information on them with their face, but only with their name. I was talking about this with a friend yesterday, and he thought it was the weirdest thing ever because he is a visual thinker, and faces are what trigger all the memories for him.

The analogy to a database is clear – in a database, there are many fields in each record, but one of them is generally marked as the “primary key”, which the database will index on and optimize lookups for. If you try to look up a record by a different field, it will be much slower and more inefficient.

So I’m curious how other people feel their memories are indexed. Are you a visual thinker and seeing a person triggers all the memories you have associated with them? Am I the only text-based thinker?

P.S. Based on the On Intelligence theory of pattern-recognition, I wonder if my memory indexing on text/names is because most of my information gathering as a child was by reading, rather than by learning from my peers. I definitely think in terms of text and ideas, and that’s part of why I have a blog – text hyperlinking is a perfect fit for how my brain works. I also wonder if that’s why I don’t get Instagram – maybe Instagram is the equivalent of blogging for a visual thinker – it matches how their brain works.

Expertise as exception handling

Thursday, April 24th, 2014

A few months ago, I wrote a post claiming that expertise was doing difficult tasks consistently and Rif challenged me on that. And I’ve been thinking about it over the past few months and have another model I’m going to throw out there: expertise as exception handling.

One example of this is my experience as both a skier and a snowboarder. I am an expert skier, having skied on and off since I was a kid, and an intermediate snowboarder, having picked it up a couple years ago. I spent most of the winter skiing in anticipation of skiing trips to Japan and Baldface. After I got back from Baldface, I got on the snowboard for the first time in a year, and it was interesting to see how my mindset changed. On my skis, I am confident I can handle any terrain and conditions, even people cutting me off while I’m speeding down the hill. On the snowboard, I can comfortably go down any groomed run, no matter how steep, but as soon as the conditions are uneven (e.g. moguls) or off-piste or if people around me do something unexpected, I freak out because I don’t know how to adjust quickly.

Another example comes from volleyball, where a previous post noted how better teammates put me in a better position to succeed. If I’m given a good set, I can hit it down. If a ball is spiked right to me, I can dig it. However, if the ball is a little off, I’m not as reliable. Meanwhile, the expert players can take a ball hit out of their reach, and if they can get one knuckle on the ball, they’ll pop it up perfectly to their partner. And if the set is five feet off the net, they find a way to hit it down anyway.

A last example comes from bridge. I’m an intermediate bridge player at best, but I am subscribed to the bridge players list at Google. The discussions on that list are often around rare hands, where there’s no standard play or bid to cover the situation. The experts on the list debate about how to handle such situations, and many of them have arcane bidding systems to cover all sorts of unusual hands. These are situations I could never figure out how to handle with my basic understanding and the standard bidding system, but they have played enough to figure out how to handle these corner cases.

In all of these examples, the difference between the intermediate player and the expert is that the expert can handle a wider variety of rare situations. The intermediate may be almost as good as the expert the majority of the time, but in unusual situations, the greater experience of the expert allows them to do something when the intermediate is frozen by uncertainty.

This also explains why mere repetition is not enough to acquire expertise. Mastery requires deliberate practice, where one is continually and deliberately testing the edge of one’s ability. By setting up artificial practice situations which don’t come up normally, one gains the ability to handle these exceptional situations whereas repeating the standard situations would not help. It was described once to me as the difference between ten years of experience, and the same year of experience ten times.

I don’t think my advice changes from my last post on expertise, which suggested that deliberate practice was how to gain consistency, but I like this model better. Expertise is learning about how to handle anything that an activity can throw at you, and do it with confidence because you’ve seen it all before. This is also consistent with Gary Klein’s Recognition-Primed Decision Model. Build up your intuition and expertise by getting oneself into more difficult and rare situations so that you can handle them better in the future.

Maximizing collisionability

Wednesday, April 23rd, 2014

Last night, Tony Hsieh of zappos.com spoke at the Long Now on the topic of Helping Revitalize a City. He described Downtown Project, which is the company he designed to create a thriving community (tech, art, fashion, family) in downtown Las Vegas.

As he discussed the project, he brought up a great concept that I want to apply to my own life, which is the idea of “collisionable hours”. The idea is that a community is built by maximizing the number of times that community members might run into each other: at the bar, at a cafe, on the street, etc. Suburbs have low collisionable hours since residents go into their garage, hop in their car, drive to the store, drive straight back home, and therefore spend little time in public spaces where they might have unplanned collisions. City neighborhoods can have high collisionable hours, when everybody is out walking around to get their errands done (shades of Jane Jacobs). So for the Downtown Project, they now evaluate projects based on maximizing collisionable hours.

This made me think about maximizing my own personal collisionability. How do I put myself into more situations where I have unplanned interactions that can spur new thoughts? I made some effort at that in my Year of Yes last year by going to new conferences and trying new things, but how can I build on that? One thing I’ve started is a monthly SF Salon, which is a great excuse for me to see SF friends and talk about ideas. I also need to start posting more here, as I think that increases the chance of random people on the Internet finding me and interacting with my ideas.

That points out a difference I have with Tony Hsieh – he’s focused on unplanned physical interactions, which I think are valuable and interesting (I depend on them for parts of my job at Google). But I’m interested in building a community of ideas as well, which I think can be done online. We’ll see if I make progress on that.

Two other vignettes that I think are relevant to the collisionability discussion:

  • When LBJ first got to Washington as a congressional aide, all the aides stayed in a communal dorm. He would get up in the morning, go to the communal bathroom, shower, brush his teeth, shave, and chat with others. Then he’d go back to his room, wait 5 minutes, and go back to the bathroom and do it again. He would do this 4-5 times a morning, which is how he met everybody and started his path to becoming the most influential person in Washington. That’s maximizing collisionability!

  • At the YxYY conference last year, we had a great session on “Finding the Others” inspired by this Timothy Leary quote:
    Admit it. You aren’t like them. You’re not even close. You may occasionally dress yourself up as one of them, watch the same mindless television shows as they do, maybe even eat the same fast food sometimes. But it seems that the more you try to fit in, the more you feel like an outsider, watching the “normal people” as they go about their automatic existences. For every time you say club passwords like “Have a nice day” and “Weather’s awful today, eh?”, you yearn inside to say forbidden things like “Tell me something that makes you cry” or “What do you think deja vu is for?”. Face it, you even want to talk to that girl in the elevator. But what if that girl in the elevator (and the balding man who walks past your cubicle at work) are thinking the same thing? Who knows what you might learn from taking a chance on conversation with a stranger? Everyone carries a piece of the puzzle. Nobody comes into your life by mere coincidence. Trust your instincts. Do the unexpected. Find the others…

    What we concluded was that finding the others required putting oneself out there into the world and increasing your “surface area” to make it easier for the others to find you. In other words, increasing your collisionability.

Anyway, I liked the concept, and plan to use it more in my own life. And if anybody reading this has ideas or people that I should be colliding with, please put me in touch!

Don’t act like a special snowflake

Wednesday, March 12th, 2014

Disclaimer: This post is not about you. Please do not take offense. It is an exaggerated amalgam of people for the sake of making a point.

Over the past several months, I have talked to a number of people looking for jobs or practicing company pitches. And what I see is people getting frustrated that their audience doesn’t see why they are special, and not being given the opportunity to show that they are special. They do not understand that it is their responsibility to put in the work of framing themselves to make their special-ness easily visible to their audience. I am repeating points from my advice on writing a resume, but the general principle is that your audience does not have much time. You have to make it instantly clear to them why they should care about you and not move on to the next candidate.

When I hear people complaining about how much work it is to hand craft each resume or interview or pitch, I feel like they are acting like they are a special snowflake and the world should marvel in their presence. They are putting the onus on their audience to dig deeply and spend their time to figure out why this person is a special snowflake. And sadly, the world does not work that way – the people you are trying to impress are busy, and so you have to make it easy for them to understand why they should work with you.

What I think they should do instead is to spend the time to really understand their audience and what problems they have. After that, they can craft a resume or pitch to explain quickly and concisely how they (or their product) solves that problem. And to be clear, I am not advocating lying or exaggerating – I am talking about being selective in what is presented to the audience so that their focus is only on the attributes relevant to this interaction.

Engineers hate this advice. They feel that the truth is the truth, and framing is lying, and this is all annoying MBA nonsense. And I get that – it took me a long time (and the work of George Lakoff) to convince myself that framing mattered and was a meaningful activity. But as I get older and crankier, I don’t have a lot of patience for people who waste my time. If I’m interviewing you, and you insist on giving me a laundry list of your accomplishments with no consideration of showing how you can help me, it’s not a good sign. I value those that demonstrate they have thought about what I’m looking for and how they can help.

I also think this is a completely transferable skill. No matter what your job is, you will have to go to other people and ask them for help or resources. And when you do that, if you can demonstrate that you understand their criteria for making decisions and frame your request in those terms, I guarantee that you will be more successful in your pitches.

One last piece of advice – people’s attention is given iteratively. With a good pitch, you have 30 seconds to get their attention that there is a problem worth solving. A good problem in the first 30 seconds earns you another 2 minutes to convince them that your product can solve the problem. Once you’re past that, then you can get into the details of the investment and the company. But start with the hook and get their attention immediately, or you will never keep their attention long enough to talk about the rest of your material. Get to the point quickly – if they ask for more clarifying details, you’ve caught their attention, which is the first step.

A similar process applies to a job search – a resume is not to get you a job. A resume is to catch the eye of the hiring manager and get you a phone interview. Your goal in a phone interview is to get an in-person interview. Once you’re at the in-person interview stage, then your goal is to get a job. But if you try to get the job with the resume, you’re skipping too many steps.

So the next time you’re doing a pitch, whether it is for a job or for investment, take the time to think about your audience and how your pitch is quickly demonstrating to them that you can help them out. Good luck!

P.S. I really enjoy critiquing pitches so if you want to brainstorm on your pitch with me, please contact me.

P.P.S. There is one exception to the “special snowflake” rule – if you have enough of a personal brand, the goal of your audience is to spend time with you, so you don’t have to figure out what they’re looking for. I once went to a Seth Godin seminar where he asked everybody to describe their superpower that made them special (I said mine was interdisciplinary storytelling). When we finished, somebody asked him what his superpower was, and he said “At this point, my superpower is that I’m Seth Godin.” In other words, he had a big enough audience that just being himself was enough (and it sure was, since dozens of us had paid for a three-day seminar with him). But he spent a long time building up his audience to get to that point, and he did that by filling a need – I think Seth’s superpower was writing clearly and thoughtfully about the consequences of the Internet on marketing.

Inequality, Globalization and Technology

Tuesday, November 26th, 2013

There has been a discussion about economic inequality on an email list I’m on. It started with a link to this CNN summary of “must reads” on inequality, and has continued over a few other threads the past couple months. I’ve written a few thoughts in those threads, and thought I would assemble them here to see what others have to say.

My main theory is that the rise in inequality is linked to the rise in globalization. Capitalism (theoretically) links income to the impact that a company has. When that company can operate globally instead of locally, they (a) can make more money because they are having a bigger impact with more customers and (b) are out-competing local providers who are losing their customers. So the successful companies get richer while the local providers get driven out of business, which increases inequality. Within the US, you can see this in Amazon and Walmart rising to dominance at the expense of local shopping.

This dynamic of greater competition from globalization is also playing out in the labor market. A manual laborer is now competing with billions of people worldwide, and with that greater supply of people, demand has gone down and thus the price for their labor has gone down. At the same time, highly skilled software engineers can now be employed worldwide, and with companies from around the world bidding for their services, incomes for those engineers have gone up. Again, globalization is increasing inequality. The best in the world at their professions, whether they are a CEO, athlete, or engineer, are reaping the benefits of their expertise while those without similar skills are losing out.

One other effect that is happening is that technology is enabling more work to be done by less people. For instance, the graph at http://inequality.is/expensive shows average productivity going up without wages going up, with the implicit statement that these should go up in parallel. But suppose an engineer creates a machine that increases productivity by 10x, such that one person can now do what 10 people did before on the factory floor. 9 people are now out of a job, and if the engineer gets paid, say, 50% of that difference, the difference between average productivity and wages will have increased while also increasing inequality. A similar story is in play when a technology company is willing to give away a product for free (e.g. “Android may be the greatest legal destruction of wealth in history”).

If those inequality trends are linked to globalization and technology, as I posit, then I don’t see how efforts to decrease inequality by mandating pay caps (e.g. this Swiss law proposing that executives can make no more than 12x their lowest-paid employees) are going to work without also breaking the market economy. Under such caps, somebody is not getting paid “fairly” – either the executives or engineers are not getting paid enough, or the janitor is getting overpaid.

“Fair” has two meanings here, which is also part of the confusion. I am using “fair” in the sense of “people should earn what they are paid”, and so people who have more impact should get paid more, and that people who aren’t contributing don’t deserve anything (shades of the Protestant work ethic). But many liberals believe that it is unfair for one person to earn orders of magnitude than another because we are all humans and all interconnected. (thanks to Jonathan Haidt’s book The Righteous Mind for identifying this interesting split on the meaning of “fair”)

So is the rise in inequality intrinsically a bad thing? Many liberals, using the second definition of fair in the previous paragraph, take the stance of “Inequality is growing. That is unfair. QED.” I disagree and believe that inequality is “fair” in and of itself, as it is a reflection of the greater impact that people and companies can have in a world of rising globalization and increasingly powerful technology. But that’s not the whole story.

One thing that came out of the email discussion is that the effects of inequality serve to perpetuate inequality. In particular, highly concentrated wealth uses the existing system to reinforce itself. There are two ways in which this happens:

  • Money buys access in our political system, such that those who make a lot more money can change the rules to preserve their advantage in a de facto plutocracy.

  • Money buys access to education. For children, parents need money to afford private schools or to afford houses in great school districts. For college students, the best universities cost an absurd amount of money, but I still think those elite universities are worth it. So having more money gives a huge advantage to the next generation also, making it more difficult for people outside the wealthy class to get a “fair” chance to break into that class.

These are both problems that need to be addressed, but I don’t think that the problem is inequality per se. A cap on how much people can earn does not feel right to me, as it is attacking inequality directly, when I think it is these effects that are the problem. And trying to cap how much people or companies can earn via laws feels like it will be a game of Whack-a-Mole, as people will find loopholes in the laws, and other countries (e.g. tax havens) will write their own laws to attract such people and companies.

For political access, I’d prefer to address that with Lawrence Lessig’s efforts to attack the money problem in politics directly. Let’s figure out how to get money out of politics and out of campaigns, and then it won’t matter as much if companies make a lot of money. They will have the same access everybody else does

For educational access, we need to break the connection between money and education. I don’t know if the right answer is better public schools (particularly preschools), funding public schools from the state level rather than the district level (to break the connection between property taxes and school quality), or just finding ways to pay our teachers more so that our best students can make a good living by teaching (I know several people who loved teaching but eventually give it up, because they can get paid so much more as an engineer). It’s a tough problem, but it’s the right problem to solve – not how to stop inequality from happening.

What do you think? Is rising inequality a problem in and of itself? Or is it in the effects of inequality perpetuating itself through political and educational access? And what should we do about it?

Consistency

Wednesday, October 30th, 2013

As I mentioned in my previous post, I’ve gotten back into playing volleyball this summer at Google, and have been really enjoying it. The sand court on Google’s main campus is in regular use, and I’ll occasionally stop by and watch to pick up some pointers. One Wednesday at lunch, I was watching the really good game with players that are probably AA-rated (one notch below the top beach players). One of my coworkers sat down next to me and asked me what was the difference between me and them.

He asked “Can you hit it like that?” and I said “Sometimes” and he asked “Can you make a defensive play like that?” and I said “Sometimes”, and so he didn’t understand why I said those guys were at a whole different level than I was.

Of course, the difference is that I said “Sometimes”. When I go up for a hit, I can hit it down hard 10-20% of the time – these guys were doing it 90% of the time. When my opponent spikes the ball down, I can dive for it and return a playable ball maybe 5-10% of the time – these guys were doing it 30% of the time. So watching them is amazing because one of them will go up and pound the ball past the blocker, and the defender will have read the hit and pop it up, and it’ll go back and forth a few times. Whereas when I play, half the time I screw up the initial pass and get a weak hit at best.

A similar situation arose when I was playing in a pickup ultimate frisbee game last week. I had the best game I’ve had all year – I was jumping over two defenders to catch the disc, I was throwing hucks for scores, etc. The frustrating thing is that I know I can play like that, but it is rare when I can put it all together – at the previous week’s game, I couldn’t get open and missed several throws.

And that’s the difference between the intermediate player and the advanced player: consistency. Anybody athletic can have an occasional great play, but being a great player means being able to make that play every single time. I am stuck at a certain level in these sports where I have moments of greatness, but am mediocre most of the time, because I can’t consistently play at that higher level.

To take the next step requires the patience of deliberate practice. It’s not just about doing the activity more – it’s about breaking the activity down to its component parts and practicing each of those parts so much that they become second nature. So rather than just playing volleyball, I need to do drills to practice passing over and over again, and then drills on setting, hitting and digging, to focus on each individual skill. But that requires effort and organization, so I just play instead and have to therefore be content with my current level of inconsistency.

As usual, this doesn’t just apply to sports. Every activity requires practice to embed it deeply into our unconscious expertise, whether it’s cooking or project managing or data analysis. If you have to use your limited conscious bandwidth to examine something, it will take too long. So if you’re stuck at a level of inconsistency, are you willing to take a step back and practice individual skills assiduously? If not, you’re going to be inconsistent for a lot longer.

P.S. I’ve written about this topic before, in posts on what it takes to achieve mastery and the importance of coaching and feedback in improving and getting results, but I decided to write about it again, given my recent inconsistent play in sports.

Being a good teammate

Wednesday, September 4th, 2013

As those of you who follow me on Facebook know, I have gotten back into playing volleyball this summer, specifically sand doubles volleyball. I have been playing with a variety of folks on the main Google court. We typically get 4 people together to play, and then rotate through teammates, so everybody plays with everybody else over the course of 3 games.

There have been a few times when I have jumped in with the advanced players because they needed a fourth. And whenever I play with them, I feel totally bad-ass because I play so well – I hit better and set better than I ever do in the intermediate games where I normally play.

It was only recently that I realized that my higher skill level when playing with better teammates wasn’t necessarily me stepping up my game.

Both teammates are typically involved in every point in doubles volleyball, with one teammate passing the ball to the other, who sets it up for the first teammate to hit the ball over. The teammates have to work together to be successful (and I love being part of a team).

So what happens when I play with a better teammate? If I pass off target, they can recover and still give me a great set to hit – it’s placed perfectly for me to swing away. And when I am setting, my set doesn’t have to be perfect for them to be successful in hitting the ball down. When I was playing better, I was really just being made to look better by my teammate, because they could do more with my bad plays than my intermediate teammates normally could (which was in their interest because it made the team more successful).

And I realized that what is true on the volleyball court is also applicable to being a good teammate in general:

  • A good teammate sets you up with the inputs you need to be successful.
  • A good teammate takes your input and figures out how to apply it to make everybody look good.
  • A good teammate does both of those things without you realizing that you’re being helped so that you think you’re more badass than you necessarily are.

So I learned something about team building and management out on the court – that means volleyball counts as work, right?

P.S. I have now completed the restoration of blog posts from the Wayback Machine, and think I’ve gotten all of them. Unfortunately, I don’t have any way to recover comments, so I apologize to those of you that added insightful comments. I will be backing up more frequently (including after this post is up), especially since Textdrive has given me no indication that they are alive other than the server still being up (no email, no Twitter, no answering of service tickets).

Big Data isn’t the answer

Friday, November 16th, 2012

I was talking to somebody last week who had recently moved to San Francisco, and she randomly interjected Big Data into the conversation. She said she’d learned that’s what you do in SF – Big Data is a buzzword that can be used at any time on any topic. I found this amusing, because Big Data is becoming almost messianic – all of our problems will be solved once we have Big Data! Everybody should become a statistician or economist or data analyst!

My response? Don’t believe the hype.

The trends are unmistakable – humans are creating and capturing more data than ever before. IBM estimates that 90% of the data in the world today was created in the last two years. And the tools we have to sift through data are becoming ever more powerful, with open-source packages like Hadoop for map/reduce, and R for statistical computing.

This flood of data and the tools to analyze it are creating market opportunities for businesses and career opportunities for individuals – the story of Target identifying a pregnant teenager from her purchases is a founding myth of Big Data. And some credit Obama winning the presidency to data analysis. So why am I skeptical?

There is a belief that if we could only quantify everything, we would be in control. The management saying is “You can’t manage what you don’t measure.” So if we have more data, and can measure everything, we should be able to manage everything! Except that the world is not that simple. Just because something has been quantified doesn’t mean that it is good or meaningful data – how the data is collected can introduce biases or trends that render it useless for making decisions. And just because an analysis gives a numerical output does not make it into useful knowledge or wisdom.

What I am seeing in the rush to Big Data is the urge to quantify things before understanding them. Recording 600 metrics and tossing them all into a database creates a ton of data, and analysts can spend weeks or months looking through the data. But is that really driving value for an organization? Similarly, I’ve seen situations where an analyst uses a standard ARIMA model to forecast a trend with confidence (because it’s data-driven!), and later being surprised that the forecast is wrong because they never really understood the underlying data. Another example is when a consultant creates a 500-line Excel spreadsheet, where every possible variable is quantified and every change ripples through the spreadsheet… but of those 500 lines, 490 are assumptions, so it’s impossible to tell which variables really matter.

Another potential peril is when analysts start their work with a preconceived notion of the result they want to get. With Big Data, you have enough data to support almost any conclusion if you slice the data in the right way. One of my favorite stories about the perils of data analysis came from my time as an intern at CERN – a grad student was looking for a particular energy resonance from the L3 detector data, and displayed this beautiful graph showing that resonance. Dr. Sam Ting, Nobel Prize winner, smelled a rat – the result looked _too_ clean. He told the grad student to show the data with all of the filters removed, and the raw data showed nothing but noise. The student had applied the filters to show what he wanted to see. Note that I’ve seen similar things happen at Google – as a coworker commented to me recently, if even Google (and MIT grad students) can’t consistently get data analysis right, can anybody?

I worry that the quantification of the world in the form of Big Data is being seen by businesspeople as an end in itself, rather than as the tool it is. Like any tool, data analysis can be used well by those who have trained in its use, or it can be used poorly and cause damage by those without experience. Understanding data is hard. It takes time and effort, and while a well-constructed tool can accelerate that process, it doesn’t replace the need to sit and work with the data to understand its quirks and characteristics. After really understanding the data, you may discover that only 3 metrics out of 600 really matter, and so you don’t need Big Data to run your organization – just a dashboard with the 3 things that matter.

Big Data isn’t a silver bullet that will fix everything with your organization. It is a powerful tool that can help you better understand what is going on, but only if you spend the time to use it properly. Just because your analysts create output that is quantitative doesn’t mean it’s right. Trust, but verify. Use your judgment and all of your tools including walking around to figure out what to do, because in the end, you are the one responsible, no matter what the data says.

Career development in the 21st century

Tuesday, October 30th, 2012

It’s performance review time at Google, and that means that I am reassuring the young’uns in their mid-20s that it’s all going to be fine. They have been at the top of their class their entire life, they got into the best colleges, and they plan on continuing to ace every test they’re given. And so they come to me to ask me whether they are on the right track with their career development. I have given out the same advice to several people in their mid-20s recently, so I figured I may as well share it here as well. And that advice is: Chill!

It is a ridiculous concern to be worried about being on the right career path in your mid-20s. Your career is not like the Milton Bradley Game of Life, where everybody is on the same path, and it’s a race as to who gets there first. It’s about figuring out the right path for you to achieve the results that you desire. I suppose I should stop here to caveat that I have an unconventional career path that influences my viewpoint, but so far it seems to be working for me – I keep finding interesting jobs and convincing companies to hire me.

Here is the situation in the 21st century:

  • The world is changing faster and faster. World-famous companies are appearing and disappearing in years, if not months. The old model of going to work for a company and retiring 40 years later is not realistic in this fast-changing world.

  • More possibilities for careers exist than ever before. We are not constrained in our choice of professions by what exists in the world – we can create our own professions by combining existing skill sets in new ways.
  • Because the world is changing faster and because the possibilities are growing exponentially, the one thing you can guarantee is that the skills you have today are not the skills you will need in ten years. Developing an aptitude and zest for learning will be key to staying relevant.

Unfortunately, most career development advice is still based in 20th century thinking.

  • Work your way up the career ladder.

  • Do what you have to do to get promoted, jumping through the hoops.
  • Visualize your dream job in ten years, and develop the skills to have that job.

These all assume a static world, and I just don’t think that’s a reasonable assumption. My last four jobs (three at Google) didn’t exist before I took them, so there was no way I could have planned to get those jobs in advance – they existed on no career ladder. And I think that’s the direction we’re heading, where more ad hoc positions will be created to bridge gaps in the state of things.

So how should a college grad face this new world? What does it mean to develop your career in the 21st century?

  • Always be learning (ABL?). Learning is the key to keeping up with a rapidly changing world, so developing that skill at every opportunity will put you in the best position to succeed.

  • Also, make sure that you are learning useful skills – skills at managing people, building influence, and domain-specific knowledge are transferable skills that you can keep in your toolbox, skills on how to play the politics at a dysfunctional company are not transferable.
  • When you’re in a job, solve problems and build relationships. These are often related – you don’t build relationships by schmoozing – solving people’s problems is what earns you respect and ensures that you are remembered.

So pick jobs where you are learning useful skills, and where you can have an impact and build relationships. If you do that repeatedly, you will find new positions and careers being created for you, rather than trying to climb over other people on a ladder that others built.

One other point – people early in their careers often worry that a given position is the wrong choice and that it will put them “behind”. I tell them that there are no wrong choices at that stage, as they will learn something from every position they take. Comparing themselves to peers who may be “advancing” faster is not useful since careers are no longer comparable given the multiplicity of options – we are each creating our own path.

So, yeah. Keep learning, get things done, build relationships, and then learn how to package together your unique skill set to find or create your next opportunity. That’s my advice – what do you think?

The Idea Factory, by Jon Gertner

Sunday, October 7th, 2012

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In light of my last post on the Anthropology of Innovation, it was apropos that I was just finishing The Idea Factory, by Jon Gertner, a history of Bell Labs and its impact on 20th century innovation. I actually also saw Gertner at the Computer History Museum in March, but had to wait a few months for the book to get through the hold queue at the library.

The book itself is a well-told story of the creation of Bell Labs and its rise to pre-eminence, mostly focusing on the 1930s to the 1950s. Bell Labs started off as just an R&D lab for AT&T, developing technology and materials to better enable phone calls. But in the 1930s, the director started to expand its mandate by hiring physicists and chemists to do basic research. During World War II, the lab was essential to the war effort, contributing to radar among other projects, and after the war, they were given the freedom to work on whatever they wanted. This led to the invention of the transistor in 1947, but also the conception of information theory by Claude Shannon in 1948 – two amazing leaps forward in how we think about the world.

The most interesting part of the book to me was how Mervin Kelly engineered a culture of innovation at Bell Labs that will probably never be rivaled. The idea that an industry lab would do research leading to thirteen Nobel Prizes is inconceivable today. To be fair, AT&T had a government-granted monopoly, so they could do research that wouldn’t pay off for 20 years, and know that they would still be in business to benefit. And to keep on earning that monopoly, they had to demonstrate that their research was contributing to the basic good of humanity – I was surprised to learn that they were required to license out all of their innovations for low cost, including the transistor. So that combination of monopoly-protected resources and a requirement to do good was a key factor in enabling Bell Labs to go beyond any other lab.

But Bell Labs wouldn’t be Bell Labs if it was just a monopoly-driven research lab. Kelly designed Murray Hill, the New Jersey home of Bell Labs, to be a building where people had to run into each other going back and forth. This is now pretty common (e.g. at Google, they have a micro-kitchen on every floor to encourage such congregation), but at the time was very unusual. He then populated that building with the smartest people he could find, regardless of their field of expertise – physics, chemistry, mathematics, materials science, electronics – every field was relevant to something AT&T was doing (e.g. going from the vacuum tube to the transistor required inputs from all of those fields). Another aside: the scope of what they had to create included all sorts of things I wouldn’t have thought of but were an essential part of building for the long term – the book talked about burying wood logs in swamps to see how they would hold up to 20 years of service as a telephone pole, or designing materials that could handle seawater so they could insulate their underwater cables.

Kelly also instituted a culture where these bright minds could work on what they wanted, and ask anybody anything – so if you wanted to ask Shockley (the inventor of the transistor) a question about semiconductor physics, you just went and did it. This created a cross-pollination of ideas, where you might have a cockamamie idea, but could go ask the world expert on it, who was just down the hall, and that might lead you together to think of a more reasonable idea, so you’d stroll down the hall some more to talk to an engineer who could build a prototype. And the challenges of running a nationwide communication network meant that there were always new problems to think about. This combination of challenges and bright minds and the need to turn ideas into real products led to an enormous number of breakthroughs.

It’s interesting that nearly a century later, the same principles are still at the forefront of creating innovation. The Anthropology of Innovation panel talked about breaking down silos between fields, and about focusing on the user (Bell Labs was always grounded by the mission of delivering the best possible service to somebody making a phone call). The principles are straightforward, but it’s hard to really apply them, and so it’s impressive to read about an institution that did so and was pre-eminent as a result for decades. I don’t think such a lab could exist today (again, the monopoly-protected revenue stream was a key component), but it’s an inspiring example of how to take those principles and create a beacon of innovation.

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