Eric Nehrlich, Unrepentant Generalist

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.

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?

Amazon link

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.

Last week, the Computer History Museum hosted a panel on “The Anthropology of Innovation”. I had to attend since I’m a fan of anthropology, I’m fascinated by corporate culture and how it leads to goals like innovation, and the panel featured Genevieve Bell of Intel, who Jofish and Janet interned with in Portland one summer. I discovered once I got there that the panel also featured George Kembel, a co-founder of the Stanford d-school, which is an institution I admire. I wasn’t as impressed with the third panelist, Laura Tyson, despite her impressive resume including being Clinton’s Chief Economic Advisor

This post is mostly a transcription of my scribbled notes so I have a searchable way of referring to them in the future. It will be even less coherent than my normal posts, as my notes are mostly quotes I found interesting. C’est la vie.

Gillian Tett, the moderator, is an anthropology major turned journalist. She started off the evening with a few remarks on her observations of innovation in society and in companies:

• Every society has a cognitive map.

• “The blank spaces are important.” I think this referred to the idea that the things we don’t talk about provide clues to the assumptions that are taken for granted and could be fertile areas for questioning.
• Companies are organized by silos – increasingly interconnected but also increasingly fragmented
• Innovation is about silo busting.

George Kembel then spoke from his perspective as a former entrepreneur turned educator.

• He said innovation is “thinking freely in the presence of constraints”. The constraints are important as they bound the problem and create the opportunity for innovation. No constraints means you could do anything.

• To answer the question of “how do you innovate?”, he observed that design thinking is not just about designing products – you can be creative in everything you do, whether it’s designing business models, new processes, etc.
• He thinks of d.school as a “school crossing” where they can integrate different points of views, existing outside of the traditional “schools” of engineering, science, arts, etc.
• He mentioned that when they started, they were looking for faculty support for their interdisciplinary school, and they had expected the young up-and-coming professors to be their advocates. But those younger professors were all trying to establish themselves in their field and earn tenure, so they couldn’t take risks by going outside their field. Surprisingly, it ended up being the long-established tenured professors who were more willing to take the risks of crossing between fields. Interesting observation of incentives and constraints there.
• Pay more attention to people, not technology.
• When you’re not sure of what to do, try lots of experiments.
• On the topic of how the d.school encourages innovation, he said that the focus is on the student as an innovator – it’s getting the person to innovate, not about creating a process of innovation. Teaching students to break barriers, to find new ways of looking at the problem, that’s where the innovation will come from. The teacher does not have all the answers, but is more of a coach and facilitator. I like the human-centered approach, which recognizes that each person is dealing with unique situations, so no standard process will work for all of those situations, but teaching the person techniques will allow them to address their own individual situation.
• One suggestion was for students to get a “shared experience of the user whose life they wanted to make better”, as “the biggest barriers to innovation are our own biases and assumptions.” A great story here – the man leading the GE MRI division was really proud of the great technology he had built that saved lives. After going to the d.school, he realized he had never seen an MRI machine in a hospital, so he visited his local hospital. He saw the machine and it was glorious and a shining beacon of technology. And then he saw the little kid who was the next patient, who shrieked in terror at this ginormous scary instrument and sobbed and wouldn’t let go of her parents. And he realized that technology wasn’t the only factor to consider. After some more work, he developed a program with the hospitals where they turned going to the MRI into a camping adventure, with camp counselors instead of nurses, and with the MRI machine decorated as a tent for them to hide in. This program, while it was better for the kids, also improved his bottom-line instrument throughput, as the kids were eager to get in and didn’t hold up the process. Nifty story to demonstrate how a user-focused approach can lead to breakthroughs in how you perceive a problem.
• To innovate, you must be “willing to invite discomfort into your life” as you realize your biases and assumptions might be wrong. “Don’t just accept the problem as it’s framed.”
• “Our experts are our liabilities”
• On K-12 education, he said the question isn’t how to teach innovation, it’s how to preserve the creativity of kids – they have it, we just have to not crush it out of them.

Genevieve Bell’s comments:

• She started with the great story of how when she was hired at Intel, she asked her manager what she was supposed to study. Her manager said “Women.” Genevieve said “Um, women? You mean, all 3.2 billion of them?” “Yes, we don’t think we understand women.” “Okay….anything else?” “ROW” “What does ROW mean?” “Rest of World.” “So….World in this case means?” “The US” “Oh, okay, so everybody on the planet outside the US, plus women. No problem!”

• One of her rallying cries is “That may be your world view, but it’s not everybody’s”
• She said one of the reasons she was successful was “sheer stubbornness”, and that “people measure me by my being difficult”. One such story was where she told Paul Otellini, the CEO of Intel, that he was just wrong at a meeting. She could feel everybody around her internally gasping at her audacity, but Otellini asked her why, and she provided him with her data and supporting arguments and changed his mind. Yay anthropology!

I submitted a question that was selected by the panel moderator which was that in an increasingly specialized world where companies are looking for a specific skill set, and with innovation depending on busting silos, where does the generalist fit in? Genevieve had a great response, which was that a “generalist” adds value if they can “curate the conversation from multiple points of view”. She suggested that I was limiting myself by calling myself a generalist, and needed to re-brand and re-imagine my role to create an specialization that companies would value (e.g. “curator”). George said something similar, where he recommended thinking of myself as an integrator, not as a person outside of specialization. Another point he brought up when I approached him after the panel was that the idea of being T-shaped, with both a broad awareness and a deep area of specialization, is somewhat outmoded – we actually need more people who can integrate different viewpoints by having a certain level of depth in multiple fields, rather than just a shallow awareness in several and a deep expertise in one.

The final discussion was interesting, where an audience member asked about how to apply these ideas to health care. Laura suggested taking George’s viewpoint of focusing on the patient, and re-centering everything in the business around the patient. Instead of having specializations where each doctor was only responsible for their area leading to patients getting passed all around the hospital from doctor to doctor, re-design the whole process around making the patient experience better. George expanded upon that by suggesting that we don’t think of patients as sick, but as healthy people who are temporarily un-well, and thinking of medicine as the process to accelerate them back to their normal selves as quickly as possible.

Genevieve then blew my mind by asking if we could take a similar approach to government, where we put the citizen in the middle and organize the government around enabling the citizen. She didn’t exactly know what that would mean, and it depends on the idea that citizens embrace their role as representing their country. People would have to go beyond thinking of themselves as tax-payers who get services from their government (police, army, social security, etc), towards being citizens who embrace their role as representing the government. It was an interesting thought-experiment and a great way to end the night.

Nifty ideas all around. Fun thought-provoking evening, and I’ll have to think more about my generalist branding given the feedback from the panel.

I’ve started occasionally listening to Rationally Speaking podcast, a production of the New York City Skeptics. What’s funny is that part of the reason I listen to it is that I get into arguments (in my head) with the hosts of the show, who are dedicated to the idea that rationality will lead people to better lives. One of the hosts, Julia Galef, has even started a Center for Applied Rationality to teach people to think more rationally and thereby improve themselves.

And I get that. Heck, I wrote a post eight years ago where I say that “one of the greatest problems facing this country right now is the lack of critical thinking skills. People don’t know how to evaluate information.” And I still believe that critical thinking is an important skill that more people should develop.

But I also have learned to recognize the limits of rationality in driving consensus and agreement. In particular, if two people are starting with a different set of assumptions, no amount of rationality will get them to an agreement – they are living in two different worlds. A mathematical analogy would be the difference between Euclidean and non-Euclidean geometry – the system of proof is the same, but you get different results if you change Euclid’s parallel postulate.

I’ve observed many instances of this over the years, where two people are trying to convince each other with logical, rational arguments, and are unable to do so because they don’t realize they are starting with differing assumptions. They are muttering “Why can’t you be rational about this?” but they need a common starting point or set of axioms before the rules of rationality can help. I’ve touched on this before in regard to the multiplicity of goals one can design a system for and how a religious system is optimizing for different goals than a rationalist system. That doesn’t mean things are hopeless when assumptions differ: Getting to Yes is all about working to shift people’s assumptions so that an agreement can be found.

I also think that rationality is often ineffective, despite being “right”. George Lakoff suggested in 2004 that the Democrats were making that fatal mistake – that “if you just tell people the facts, that should be enough – the truth shall set you free. All people are fully rational, so if you tell them the truth, they should reach the right conclusions. That, of course, has been a disaster. ” All the rational and well-constructed arguments in the world are not as effective as a well-constructed story in getting people to change their behavior, as described in Made to Stick.

So while I applaud those who champion the cause of rationality and critical thinking, like the Rationally Speaking podcast hosts, I think that reason is not enough (hence my mental sniping back at the podcast when I listen – to be fair, I’m setting them up as a straw man in this post). Even in a rational world, people will have different assumptions necessitating a discussion of what is truly axiomatic. And for all of our striving to be rational, it’s much more effective to convince people with a story than with a rational argument, because the story resets their assumptions and encourages their brain to fill in the blanks in a different way.

To tie this back to yesterday’s post on Principled Leadership, a fully rational approach to running a company would be having a strict hierarchy and process, with the reasons for each optimized decision laid out for employees. What I like about leaders modeling guiding principles is that it demonstrates a way of driving a common set of assumptions across the company, and providing stories that people can use to drive their own behavior. It may not be strictly “rational” or optimal, but it may be more effective.

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drive their own behavior: I heard a story at one training on purchase orders where Patrick rejected a purchase order for $2,000 because it hadn’t been taken out to bid and there was no way for him to know that Google was getting a competitive price. Let me tell you that every time I looked at a purchase order after that, I paid attention to whether Google was getting a good deal, no matter how small the purchase.

I like thinking about how to scale a company without making it feel like a big company. The standard way to scale a company is to use hierarchy and process to manage the larger scale – big decisions get passed up the chain to an appropriate decision maker, and little decisions are handled by a process that has been standardized.

But I have always disliked this approach, as it removes the initiative of smart and independent thinkers at all levels of the organization. Why hire smart people if you won’t let them think for themselves? I have long been fascinated by different management structures or bossless companies that trust their employees to make the right decisions. None of these organizations have been proven to scale past a few hundred people, though.

So how can we build companies that give autonomy to small teams while scaling up to thousands of people? Or to put a finer point on it, how can Google really be run like a startup?

As I watch the leaders at Google, I’ve realized that part of the answer is that the leaders tend to be consistent and principled. I work in the finance department, and Patrick Pichette, our CFO, is a master of this. He tends to ask the same set of questions:

• How much investment will you need?

• What does Google get out of that investment? (Is it revenue, cost savings, user growth?)
• What do I have to believe? (what assumptions are you making to model the returns on the investment?)
• How will you measure success? (what metrics will you show me in three months to demonstrate you’re on the right track?)

That’s it. He’s so consistent that I feel like I can project his voice into a meeting, and so I have become an extension of him within the org. I understand the principles he uses to make decisions and can therefore give others a good sense of how he’ll react before they go into a meeting. I can also anticipate the questions he is likely to ask, and make sure that my team has good answers beforehand so we’re not scrambling afterwards.

What’s interesting about this to me from an organizational design perspective is that Google is not depending on process or hierarchy to guide me. The thing that keeps the finance org aligned is a consistent set of principles modeled by the CFO, who then trusts employees to use their judgment in applying those principles. That is an organizational model that can scale.

And when I thought about the rest of Google, I realized that’s a lot of what makes it work – each of us Googlers has an internal model of key decision makers (Larry Page, Patrick Pichette, Nikesh Arora, Susan Wojcicki, Jeff Huber, etc.) and has a good idea of how each of them would react to a proposal. That enables us to make decisions without having to pass them up the hierarchy and without having an explicit process, and still be confident that the decisions will be consistent with the corporate direction.

I think this is how a lot of other great companies have worked. Whether it’s Walt Disney or Steve Jobs or Herb Kelleher at Southwest, their people knew how their leaders would react and could act more independently because of that knowledge. It’s not quite a cult of personality, because that would imply that the followers have no independent thought and are only doing what they’re told. It’s more like teaching a team a playbook and letting them figure out how to apply it in their particular situation. I also read once that the military does a good job of this, making sure that everybody knows the overall strategic goals for an engagement, such that if the situation changes, they don’t blindly follow their orders to “Take that hill!” if it doesn’t help with the overall strategic goal (some Googling reveals this is called Commander’s Intent).

I like this idea of scaling by trusting your people to do the right thing while not hog-tying them with a process. It lets them adapt the leaders’ guiding principles to their individual situations such that they have autonomy, but without having the organization dissolve into chaos. It does have the challenge that it won’t work if you have bad people in the org, but my guess is that pretty much no management tactics work if you have bad people – they will circumvent your process.

Anyway. I’ve been mulling this over for a while, and figured it’d be a good topic for my first blog post in nearly a year. Work has actually been calm for a couple weeks, so I have finally overcome my activation energy to post. I need to make the time to do it more often, as I have dozens of post ideas floating around, and I like myself better when I’m writing regularly.

One particular issue I’ve been thinking about with startup vs. big company culture (and that is referred to in a comment on my last post as well as comments over on Facebook) is how to encourage useful failure – failure where you learn something and then apply what you learned to improve next time.

This sort of grit to struggle through failure (what Seth Godin calls “The Dip”) to find the next level of success is rare in a big company. As is typical for me these days, I would argue this is an issue of incentive alignment.

At a startup, walking away from a failure means quitting and finding a new job, whereas pushing through to find the bigger success (what Marc Andreesen has called product-market fit) has the potential for tremendous upside in the form of stock options. The risks are higher, but it’s worth it.

Big companies and their annual performance reviews tend to reward piling up little successes rather than long struggles with a big success at the end. Sticking with a project that isn’t working can lead to a bad performance rating, so people look for a quick transfer to a different project where they can ride on somebody else’s coattails to success and keep their ratings up. Those that do stick around and try to turn a failing project around rarely benefit from the upside if they succeed – maybe they get one good rating that doesn’t make up for the previous poor ones.

At an organizational level, it’s also easier for the big company to walk away from a “failure” because the company has other projects and revenue streams. As The Only Sustainable Edge points out, companies that only do one thing (e.g. startups) are driven to be the best in the world at it because they have nothing else to fall back on. That lack of a safety net drives further achievement than they would achieve if they could give up more easily.

Another perspective comes from this description of successful startups from Glenn Kelman (CEO of Redfin): “They weren’t afraid of failure, and they didn’t “pivot” when faced with their first setback”. And sometimes by having the grit to stick with a project that they were initially doing for their own passion without regard for commercial potential, they found a way to inordinate success.

How can we instill that kind of grit and passion into a big company? I can think of a few cases where a strong leader has bet the company on a change of direction (e.g. Bill Gates’s Internet memo, Steve Jobs turning Apple into a consumer electronics company, Jeff Bezos mandating that Amazon transform its infrastructure into a service-oriented architecture, Larry Page trying to focus Google on social), but this can also backfire (e.g. Elop’s “oil platform” memo). And these cases are more about a top-down change in direction rather than creating a new culture.

On the topic of encouraging useful failure, I could see some ways of trying to design an incentive system that would encourage people in that direction. Unfortunately, I think the people who work at a big company would rarely agree to such an incentive system. And in my experience, the people who would like such a system will try to do the right thing regardless of the incentive system.

So to re-state the question in a different way – is it possible to create more “startup” people who are willing to take chances and struggle through failure? I wonder if it would involve a re-design of our education system – the US education system is designed to reward people who follow directions and respond to incremental incentives (aka grades), and punishes those who fail even intermittently. Could any incentive system be powerful enough to overcome a lifetime of cultural conditioning?

Hard questions. I don’t have any answers. And, obviously, a lot of digressions. But I’ll keep exploring these sorts of topics over the upcoming weeks. Let me know if you have any thoughts.

Since Larry Page became Google’s CEO again in April, his focus has been on “making a company of more than 24,000 employees act like a startup“. And because of my interest in mapping out organizational space and understanding the different ways in which people can organize themselves, I’ve been trying to figure out what, exactly, differentiates a startup culture from a big company culture.

My current theory is that the difference is in incentive alignment. At a startup, it is difficult to be individually successful while doing the wrong thing for the company, because if the company fails, everybody is out of a job. At a big company, though, fiefdoms can develop, where within a fief, people can get promoted for improving the position of that group despite being obstructionist to the rest of the company. This is often what people disdainfully refer to as corporate politics.

This reminds me of Mancur Olson’s book Power and Prosperity, where he describes how it makes economic sense for special interest groups to subvert democracy in harmful ways – if they represent 1% of the population and push for an action that will benefit them while hurting the overall democracy, they reap 100% of the benefit but only feel 1% of the pain. A similar dynamic is at work for groups within big companies, where they push for their own agenda even when it might hurt the overall company’s position.

This difference in incentives drives many of the differences in behaviors between startups and big companies. At a startup, nobody says “That’s not my job” when asked to do something that’s critical to the company’s success, because they won’t have a job if the company isn’t successful. At a startup, people have an understanding of what drives the company’s success and re-prioritize on the fly if necessary if market conditions are changing. Everybody is invested both economically and personally in the startup’s success, and that drives a unity of purpose that overrides individual agendas.

At a big company, people want to avoid risks and perpetuate the status quo, because creeping up the corporate ladder is the safer path. It’s easier to say no than yes, leading to the big-company phenomenon where every new project has to be signed off on by 10 different departments (legal, finance, security, PR, marketing, sales, engineering, etc), creating 10 opportunities for “No” without having a single person that can say “Yes” and have it stick. It’s possible to get promoted and get paid more without doing anything to benefit the company, if you are advancing your group’s agenda and hitting your individual targets even if the targets are no longer meaningful.

So what does it mean to have a big company with a startup culture? Part of it is figuring out how to get everybody at the company aligned on what the priorities of the company are (this is incredibly difficult at a sprawling company like Google), and rewarding them appropriately. Part of it is to encourage appropriate risk-taking – rewarding those who said “Yes” when it was the right thing to do even if the project failed. Part of it is creating a more risky environment in general – the people who are attracted to safe big companies with a well-defined ladder are not the people that will function well in a startup culture where things are changing fast. And I’m sure there is lots more that I haven’t figured out yet.

I’m fascinated to be part of Larry Page’s Google experiment on creating a big company with a startup culture. I’m not sure it’s possible without addressing the questions of incentive alignment and risk I raise here, but I want for it to be possible. The scale of projects that can be done at a big company are mind-boggling, but I also miss the free-wheeling all-for-one-and-one-for-all culture at the startups I’ve been at in the past. I will be watching closely and looking for opportunities to help with this culture shift as somebody who has startup experience and is interested in these sorts of culture questions.

Amazon link

Matt Taibbi is angry. He is a Rolling Stone columnist who spent the last several years covering the financial crisis, and as an outside observer, is far more negative about the finance industry than anybody associated with it. Griftopia is a collection of columns and other research put together as a striking condemnation of what has happened to America in the last twenty years.

Taibbi’s main thesis is that the finance industry has, rather than produce real value, chosen to exploit value created by others by creating financial instruments. He digs into the mortgage crisis, the commodities bubble, urban privatization and health care and shows how these are all different facets of the same attitude – make a quick buck for yourself, and damn the long-term consequences. I don’t know if all of Taibbi’s allegations are accurate, but he strings his observations together into a compelling story of a country headed into oblivion, because we are letting these jerks get away with it.

Here’s Taibbi’s description of the bubble economy:

Imagine the whole economy has turned into a casino. Investors are betting on oil futures, subprime mortgages, and Internet stocks, hoping for a quick score. In this scenario the major brokerages and investment banks play the role of the house. Just like real casinos, they always win in the end – regardless of which investments succeed or fail, they always take their cut in the form of fees and interest. Also just like real casinos, they only make more money as the number of gamblers increases: the more you play, the more they make. And even if the speculative bubbles themselves have all the inherent value of a royal flush, the money the house takes out is real. … Bettors chase imaginary riches, while the house turns those dreams into real mansions.

Now imagine that every time the bubble bursts and the gamblers all go belly-up, the house is allowed to borrow giant piles of money from the state for next to nothing. The casino then in turn lends out all that money at the door to its recently busted customers, who flock back to the tables to lose their shirts all over again. The cycle quickly repeats itself, only this time the gambles is in even worse shape than before; now he’s not only lost his own money, he’s lost his money and he owes the house for what he’s borrowed.

Taibbi shows how this played out in the subprime mortgage crisis, but also in several other areas:

• Commodities trading used to be about hedging risk, where a corn farmer could lock in a guaranteed price at market. The government used to enforce position limits, to ensure that “the trading on the commodities markets would be dominated by the physical hedgers”. However, in the 80s and 90s, the government issued exemptions to those position limits to several banks like Goldman Sachs, leading to 2008, when “80 percent of the activity on the commodity exchanges was speculative”. Instead of creating and maintaining real value from real crops, the commodities market became just another casino. This played into the oil price craziness of 2008, which exacerbated the slide into recession.

• He also tells the story of how Chicago leased its parking meters for 75 years to an Abu Dhabi coalition for a lump-sum payment to cover a budget deficit – they essentially securitized the parking meter income stream. The downside was that the new lessors immediately raised prices and extended the meter schedule to start making a lot more money than originally projected in the lump sum payment, and left Chicago in worse shape than when it started.

Taibbi uses several more examples to demonstrate that Wall Street is a parasite getting fat by sucking profit out of others. It’s a short-term attitude that destroys value, rather than create value by producing goods and services. He ends the book by describing Goldman Sachs as a “vampire squid”, entwined with every aspect of the American economy and sucking value out of all of it without creating any value itself.

Griftopia is a withering tirade against what Wall Street has done to the American economy, and how the government and we, the people, have allowed it. It’s a quick read, and I recommend it for a different perspective on the recent financial crises than what is reported in more typical news channels.

I was having a conversation with a friend the other day about what we thrived on in a job, and it was interesting to see how our perspectives differed. She talked about the thrill of fixing a problem, of figuring out what was happening, and designing a process or system to solve the problem forever. I talked about how I love the challenge of understanding how all the different parts of a system fit together and figuring out what actually matters. The conversation was a good reminder for me of how important it is to have the right mix of people to get things done in an organization.

I’ve been thinking about this recently as I start a new role at Google where I am trying to articulate to my new team the value that I bring. My strength is as a systems analyst – understanding all of the different parts of a complex system, seeing how they inter-relate, and being able to describe the levers that drive the whole system. This applies whether the system is conversation, corporate culture, or the intricacies of Google’s revenue. I believe that my ability to both understand the big picture as well as the details allows me to extract insights that other people could not from just one level. And I am driven to keep on poking at the system until I feel I understand which stimuli will provoke which responses. The collection of observations on this blog over the years is a reflection of my drive to understand.

However, I struggle in taking the understanding I develop and doing something about it. I can understand how the system is put together and where the friction in the system is, but not how to fix those things. Part of understanding the whole system is understanding why different design decisions were made in the construction of that system, and that understanding sometimes makes it difficult for me to envision a different way of doing things that would solve the issues I identify.

My friend is more pragmatic as she is more interested in fixing important things that are broken. She has worked in a couple different industries, and in each case, it was more about identifying the systemic things wrong with her company, and figuring out how to make them work better by instituting a new process or a new system element. She also has a good understanding of systems, as she wouldn’t be able to fix things effectively if she didn’t. But for her, it’s the fixing that matters, not the understanding.

I think both roles have value to an organization. And a particularly good combination is to pair an understander with a fixer so that the system insights that the understander develops can be fed to the fixer. An understander without a fixer identifies problems but those problems linger since nothing is being put in place to counter them. A fixer without an understander is sometimes fixing symptoms rather than the underlying problems that are driving problems in the system. Together, though, they can be a truly powerful force.

P.S. There are a few other themes inspired here that I’m going to set aside for a future post:

• Good managers understand the strengths and motivations of their people such that they can (a) keep their people happy by giving them the types of problems that interest them and (b) combine their people in ways that complement each other.
• The “fixer” trait fascinates me because I don’t have it. I know many people who see something wrong in the world and are not satisfied until it is corrected (most hackers are like this). I figure out what’s wrong and then work around it, because changing myself is easier than changing the world. But I’m working to develop this trait.
• There is probably a Myers-Briggs or other personality trait that I am describing here – if you happen to know what archetypes I’m describing, please share in the comments.