As more and more people look to the startup community to save our nation and our economy, it is only reasonable for things to get way more political. As more former bankers and teachers and rodeo clowns start companies, we’re witnessing the emergence of entrepreneurs as a political bloc.

But we are influenced by a different set of issues than the average partisan — H1B visa reform, patent reform, regulatory streamlining, et cetera — some of which haven’t yet been divided along partisan lines. In fact, it’s extremely difficult to assign the “entrepreneur’s agenda” to a traditional right-left spectrum. But this makes it even more interesting to attempt to arrive at some conclusions about the unique political beliefs of entrepreneurs.

However, some challenges. First, it’s notoriously hard to generalize anything about an entire industry. You can say that auto dealers and doctors tend to be conservative while teachers and scientists tend to be liberal, but technology-focused entrepreneurs are an even more diverse bunch. I’ve met founders who are die-hard socialists and others whose neo-conservative beliefs would make Glenn Beck blush. Second, politics is something that many founders simply don’t discuss in public. It’s tends to have a pretty poor risk-reward ratio.

However, I’ve seen a couple broad, fairly common threads among the political beliefs of startup founders, investors and early employees. I summarize them as “Libertarian Populism”, combining two political philosophies rarely seen together in the wild:

Libertarianism: A strong belief in individual freedom of thought and action. See the broad-based support for patent reform, creative commons licenses, regulatory reform (often de-regulation) and immigration reform.

Populism: The belief in the struggle of the people versus the “elites”, commonly represented by large corporations, specifically Wall Street. This tends to be particularly strong in developer-centric communities like Hacker News — most of the articles I’ve written that have hung on the front page for a while have had a strong populist streak.

But it’s more than just the independent adoption of these beliefs. Rather, libertarianism and populism are combined into a desire for freedom from all institutions, with little distinction between government and large corporations. After all, government and larger companies are both things that can (and do) harm small businesses. In many cases, government and big companies seemingly collaborate to attack startups: modern US patent policy, for instance, gives a massive advantage to enterprises with hoards of cash and lawyers.

Many entrepreneurs — especially the more common, cash-flow-focused entrepreneurs creating lifestyle businesses — consider their actions to be a declaration of independence of sorts from institutionalized corporate America and 9 to 5 drudgery. Yet the government’s rules tend to be written with the assumption that all companies are big companies, and the resulting administrative headache creates an antagonistic relationship between entrepreneurs and governments. Add to that the fact that big corporations — say, a health care provider — have similarly unfriendly rules, and the entirety of mainstream American institutions are thrown into the same bucket. Thus, libertarian populism.

This all has interesting implications. For instance, I’ve lately seen entrepreneurial populism leveraged to attack VCs. Take Chris Dixon, for instance, who has done a remarkable job leveraging his audience’s populist streak to paint VCs as the “other”. In reality, Chris is probably wealthier than most VCs — but he doesn’t have to answer to LPs, who are easy to lump into the government/corporate mob (and often justifiably so).

As our nation’s expectations of the startup community grows, expect the politicization of entrepreneurs to only deepen.

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Every office building in New York City grows by a few percent per year. Each stuffy pre-war tower, art-deco complex and shiny corporate center. They’re all growing, like those Kafkaesque SimTower projects that added a few lone offices to the roof each quarter when the rent came in.

Except they’re not actually growing — just adding more space on paper. All office buildings in New York City add a few percent per year to their official square footage, often in lieu of raising rent. So if you are a startup paying $30 per square foot for 1,000 square feet of office space and need to renew your lease, it is more likely that your landlord will claim that your office has grown — perhaps to 1,050 square feet — than attempt to raise your rent.

This is simply the way the commercial real estate world works, and it’s second nature to people in the business. All buildings grow every year — it’s called “loss factor“. But to someone who isn’t familiar with commercial real estate, the concept is completely absurd and unethical.

What makes it seem particularly wrong? Well, square footage isn’t just some abstract number. It’s a real measurement of area. Burger King can claim that a new line of burgers is “50% tastier” regardless of reality, but they would step over the line if they claimed that these same burgers had 50% less fat unless that were actually true. “Tastiness” is a subjective, abstract concept. “Fat” is not.

Somehow, over the course of the development of the commercial real estate industry, square footage has converted from a concrete unit to an abstract unit in the minds of people in the industry. But to the rest of us, square footage remains a concrete measure of area. When I hear 400 square feet, I think of a room that is 20×20 or 40×10 or perhaps even 4×100. When someone in commercial real estate hears 400 square feet, he or she thinks of a space they can lease for $12,000 per year at $30 per square foot. It doesn’t matter how big the space actually is.

Now that we’re here, I don’t see things getting any better — the Nash equilibrium of the situation is for each player to continue increasing the “size” of their spaces by a few percent per year. If one landlord bucks the trend, they’ll need to correspondingly raise their rent per square foot to stay competitive with other landlords. But if everyone else is smudging the size of their buildings, why should you stop? It just means that anyone who wants office space in New York City has to figure out this bizarre system — or get screwed.

So how does this kind of thing start? First, it’s important to recognize how commercial real estate is leased. A commercial space shares a nasty problem with an airline seat: a product that is not a commodity yet is bought and sold as one. In most searches that brokers perform, they are given a price cap by their clients — say, $35 per square foot. If a space falls above that cap, it simply isn’t shown to the client. Thus, a landlord (like an airline) is heavily incentivized to keep their price as low as possible to get visibility in front of lots of potential tenants. The other shoe can drop once the tenant is in the door. This is all analogous to bag check fees, ticketing fees and the other unpleasantries of 21st century air travel.

Therefore, landlords will try to throw as much as possible into the square footage to lower the all-important “cost per square foot”. Initially, the included items were somewhat reasonable — shared bathrooms, hallways and HVAC. In fact, that’s how loss factor is still explained by most brokers. But then someone decided to include not only hallways, but the elevators. Another landlord responded by including not only the elevators, but the lobby and delivery bay. Pretty soon, it got so complicated that square footage became an utterly abstract, meaningless number in the minds of anyone involved in the industry. The square foot was no longer a concrete measure of area.

I don’t think most people in commercial real estate are unethical. Rather, the industry has adopted unethical practices due to a combination of individually innocuous factors: the characteristics of pricing, selection and demand. The airline industry is also clearly trending in this direction, as is higher education. Once an industry is at the bottom of an ethical slope, it is ripe for disruption by young companies that can sell through an honest, straightforward process.

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There is a certain watershed moment in the evolution of most consumer web entrepreneurs. In this moment, entrepreneurs recognize that they do not understand the way most people think about and interact with the internet. They recognize that if you are going to market to “Normals“, you have to leave your intuition of user behavior on the web behind.

People who are successful at marketing and serving content to Normals recognize that most internet users can’t distinguish between platform and app, content and ad, “good” content and “bad” content. Normals need a different level of clarity than savvier users, and if something isn’t immediately apparent, they’ll leave. Normals type URLs into Google, Microsoft Word or their email search bar. Each Normal is unique, and there are many different categories of Normals, but they all interact with the web in different ways than us (“early adopters”, “techies”, “bleeding edge”, etc), ways that may or may not be predictable to even the most seasoned designers and entrepreneurs.

Take journalism, for instance.

Old-line journalists regularly equate newspapers with journalism and blogs with something lesser and dirtier. In truth, there are a significant number of blogs that do far better journalism than newspapers have ever done. But there are many, many more that are simply content factories that benefit from a deep understanding of Google’s ranking algorithm that smaller (and journalistically superior) players don’t have. Demand Media, for instance, built a massive business by understanding that Normals have difficulty distinguishing quality, well-researched content from content that was generated by an Indian freelancer paid half a penny per word.

So what does any of this have to do with food? Well, the core problem with American food production in the early 20th century was informational: normal people didn’t have sufficient knowledge of their food’s origins and quality and were thus unable to distinguish between “good” food (e.g., meat processed in a clean environment) and “bad” food (e.g., meat processed in filthy sweatshop-slaughterhouses). In an economic environment in which buyers cannot distinguish between high quality and low quality products, all producers will move to solely creating low-quality products. High-quality producers will simply be priced out of the market.

Today’s content production industry is in the midst of that phase shift. As the vast majority of consumers cannot distinguish between good and bad content, mass-produced low-quality content is slowly pushing high-quality, journalistic online content deeper and deeper into niches.

Created in 1906, the FDA set out to solve this informational asymmetry in the food production industry by introducing basic hygiene standards coupled with labeling, inspections and reporting. This is analogous to what Google claims to be doing today for content — decreasing our informational asymmetries around content by calculating the “authority” of various content sources and offering content to users accordingly.

But I don’t think Google is doing a particularly good job. Google’s inability to effectively determine and communicate the value of content is, after all, why Demand Media and its kin can exist as businesses. It is why searches for high-value keywords (“online degrees“, for instance) return a bunch of affiliate honeypots and garbage content size wholly focused on acquiring users via SEO. And if you’ve ever ranked well for a particular search term, you know well that those are low-value users as opposed to users acquired via other channels. Web search has become a dramatically inferior way to discover anything online.

But unlike the FDA, Google is not a government agency. It’s a private company with private competitors. Competitors that may — no, will — eventually unseat Google as the king of search.

So who is going to write The Jungle of content?

Haven’t gotten your pop psychology fix today? Well, OkCupid is pleased to let you know that iPhone users have more sex than users of other smartphones. Their evidence — a handy chart — is below:

OkCupid didn’t provide sample sizes, but let’s assume we’re dealing with their entire userbase — a statistically significant sample.

This is a pretty shocking graph. At age 27, iPhone users report having more than twice as many sexual partners as Android users. Quite an impact for a mobile operating system. So let’s look a bit deeper.

—

First, the heading — “iPhone users have more sex”. There’s nothing in that chart about the amount of sex people have. It’s a chart of the number of sexual partners. Anecdotally, if one removes the celibate — that is, people who have had zero sex and zero sexual partners — I haven’t seen a strong correlation between having a lot of sex and having a lot of sexual partners. In fact, there may be a negative correlation given the likelihood for people who don’t have long-term relationships to go through significant dry spells. That said, I haven’t seen any scientific data on this.

Second, it is important to realize that this is self-reported data on an online dating site. Data on sexual experience is notoriously bad even when subjects don’t clearly stand to gain by inflating the numbers, as they would on OkCupid. But that doesn’t explain why iPhone users claim more sexual partners than Android or Blackberry users, right?

Let’s take a step back. There are some oddities in this data. While I’m aware that the graph doesn’t provide a longitudinal sample, let’s consider it as such for the moment. The average iPhone user:

1) Has had four sexual partners by the time they graduate from high school.

2) Has less than one sexual partner during college

3) Has 2 sexual partners per year from 24 to 27

Those numbers are…odd. Especially given that the Kinsey Institute finds that about 35% of people haven’t had sex at all by age 18. I’m sure there is a long tail of kids who are having a *lot* of partners, but many of those teens would come from poorer and more urban families, not exactly a hot demographic for iPhones. And less than one (new) sexual partner during college? Who are these people?

So they’re probably lying, at least to some extent. But that still doesn’t explain the delta between iPhone users and Android or Blackberry users, which could be attributed to either or a combination of (a) an actual difference in sexual partners or (b) a difference in exaggeration.

–

OkCupid is particularly popular in San Francisco and New York — markets where the iPhone is more or less useless as a phone. It takes a certain type of person to own a really expensive, well-designed smartphone that doesn’t really work as a phone. Specifically, a person who perhaps:

1) Is wealthy enough that money is no (or very little) object.

2) Is extremely concerned about appearances and social status.

3) Is in the technology industry and needs it to keep up with innovation.

Let’s put (3) aside for a moment — while tech innovators are a big chunk of my own community, it’s probably a small percentage of the sample here.

(1) is probably correlated with having more sexual partners, although I can’t find any data on this. Once again, however, it is probably a small percentage of OkCupid’s users and could hardly explain a >2x difference in reported sexual partners by age 27.

(2) is more interesting. While someone who is more fashionable probably has more sexual partners than someone who isn’t, I’m more interested in the meta level — the need to appear fashionable and trendy. Awareness and desire to maintain appearances should be strongly correlated with inflating hard-to-verify information on an online dating profile. Like number of sexual partners, for instance.

In reality, the delta is probably driven by a number of factors. But a big piece of it is just about keeping up appearances — in profiles and phones.

Unqualified lessons are hard to wring from startups. It’s difficult to really understand something without doing it, and no two people who do it have identical experiences. That said, I’ve seen two schools of thought on how to learn from others’ startup experiences:

1) It’s best to learn from success. Stories of failures have no prescriptive advice on how to accomplish something. Stories of success, on the other hand, offer models to copy.

2) It’s best to learn from failure. Success is fairly random, whereas failure usually happens for distinct reasons.

First, anyone thinking of starting a company should take lessons from wherever they’re available — successes, failures or those yet to be determined. But given a choice, the best lessons come from failure.

I’ve stopped thinking of a startup as a discrete success or failure. Rather, a company is a series of small (or big) successes and failures, one side of which eventually overwhelms the other. Some failures are singular and crushing — taking on a bad co-founder, for instance. Others are subtle and can sit under the radar for years, even in companies that are otherwise successful. Poor initial equity distribution or choice of corporate structure, for instance.

That said, most companies aren’t killed by one overwhelmingly bad decision. They’re killed by dozens of bad decisions that pile up and stick the company in an unrecoverable morass. These small, could’ve-gone-better decisions offer the best startup learnings, are the exact lessons covered up by success.

Take one of the greatest entrepreneurial success stories of our generation — Facebook. Clearly, Facebook did some things right. But they also did a whole lot of things wrong, and it’s hard for anyone to determine how much more Facebook could’ve screwed up before they would have doomed their chances to win the social networking wars. It’s entirely reasonable that they could’ve blown any number of decisions and still come out on top.

On the flip side, copying success is tricky. Let’s look at Facebook again. The most difficult thing about copying Facebook’s success (or any company’s success) is not deriving the factors that led to their success, but figuring out the level on which they should be copied. For instance:

Level 1: Facebook was successful, so I should copy what they created: a new social network. Good luck with that one.

Level 2: Facebook was successful, so I should copy their operational or strategic decisions for my own idea. As I pointed out above, it’s not obvious that Facebook made great operational or strategic decisions.

Level 3: Facebook was successful, so I should copy their methodology of thinking about ideas or products. This sounds high-level enough, but how does one execute on it? Does anyone know what goes on inside Zuckerberg’s brain? More practically, are those thoughts relevant to your product as opposed to a walled garden social network?

On the theme of translating theory into practice, I helped create Founders at FAIL, a forum for entrepreneurs to talk about their failures. Think Founders at Work, but all the stories have unhappy endings. I’ll be speaking there (along with GoCrossCampus founder Matthew Brimer) on August 18th.

I made a lot of mistakes while running GoCrossCampus. Lots of people ask me questions that more or less amount to “If you could have done X differently, what would you have done?”

I always answer these questions sincerely. “Oh, I would’ve done A rather than B and C rather than D” or something like that. But in the back of my mind I know that counterfactuals make for odd teachers. That is, the information I have about the specific problem has obviously changed — and the more interesting lessons come from shifts in my decision-making methodology.

These “If you could have done X differently” questions are essentially asking “If you knew back then what you know now, how would the decision you made have changed?” Interesting, yes. But the information that led to the decision — for instance, my beliefs on the marketability of a product or the right way to scale an app — isn’t that useful. Rather, the juicy bits are the framework through which I took the information and produced an actionable plan. Consider the following question:

If you could do it again, how would you structure your financing round?

And answers:

I would’ve clearly taken a priced round rather than a convertible note given the difficulty we had in closing the round.

or

Given the information I had at the time, I would not have done anything differently. That is, even though my information was wrong, my decision-making methodology was sound.

In other cases, my methodology may have been wrong yet my decisions right — something we in the business call “getting lucky”. But those two replies are answering the question on different levels. One is concerning facts — the who, what, when, where and how of the situation at hand. The other is concerning methodology — given the facts at hand, how did I come to a decision?

When evaluating past decisions, too many entrepreneurs focus on the facts rather than the methodology. Methodology is replicable. The mental algorithms I use to convert data into a decision are used over and over again. Data is all too often ephemeral and unrepeatable, and condemning a decision-making methodology that was plagued by bad data is often a quick way to take a step backward (or vice versa, endorsing a poor system that got lucky due to circumstance or poor data.)

Focusing on the methodologies rather than the facts will allow you to see patterns and make better decisions in related but non-identical situations. If I get hit by a car, it would be odd to simply say “Wow, next time I’ll see the car.” Rather, I’m going to make fundamental changes to the way I think about crossing the street — specifically, how I gather information and translate that information into actions. Why should a startup be any different?