A friend of mine got a job at a big company and was shocked to see his colleagues worked just a few productive hours a day. They didn’t seem to care about their work or have relevant expertise. My friend said: “Wow, this company is going under.” Then the company released its quarterly reports and profits rose to an all-time high. The momentum of the company’s brand and relationships was sufficient to propel it forward.

On the flip side, first-time entrepreneurs often fail to realize that when you build something new, no one will care. People won’t use your product, won’t tell people about it, and almost certainly won’t pay for it. (There are exceptions – but these are as rare as winning the lottery). This doesn’t mean you’ll fail. It means you need to be smarter and harder working, and surround yourself with extraordinary people.

The default state of the world is to stay the way it is, which means the default state of a startup is failure.

Facebook relies on an old internet business model: display ads. Display ads generally hurt the user experience, and are also not very efficient at producing revenues. Facebook makes about 1/10th of Google’s revenues even though they have 2x the pageviews. Some estimates put Google’s search revenues per pageviews at 100-200x Facebook’s.

The good news for Facebook is there is a lot of room to target ads more effectively and put ads in more places. The bad news is that, if there is one consistent theme in both online and offline advertising, it’s that ads work dramatically better when consumers have purchasing intent. Google makes the vast majority of their revenues when people search for something to buy or hire. They don’t have to stoke demand – they simply harvest it. When people use Facebook, they are generally socializing with friends. You can put billboards all over a park, and maybe sometimes you’ll happen to convert people from non-purchasing to purchasing intents. But you end up with a cluttered park, and not very effective advertising.

The key question when trying to value Facebook’s stock is: can they find another business model that generates significantly more revenue per user without hurting the user experience? (And can they do that in an increasingly mobile world where display ads have been even less effective.) Perhaps that business model is sponsored feed entries, as Facebook seems to be hoping (along with Twitter and perhaps Tumblr). The jury is still out on that model. Personally, I have trouble seeing how insertions into the feeds aren’t just more prominent display ads. You still have to stoke demand and convert people from non-purchasing to purchasing intents. A more likely outcome is that Facebook uses their assets – a vast number of extremely engaged users, it’s social graph, Facebook Connect – to monetize through another business model. If they do that, the company is probably worth a lot more than the expected $100B IPO valuation. If they don’t, it’s probably worth a lot less.

- Start every morning by skimming through news, blogs, articles, etc. Much of this is tech related. I used to get tech news in the newspaper, then in Google Reader, and now mostly from Twitter. If someone I meet mentions something interesting that was published that I didn’t read, I go back and figure out how I missed it and change who I follow on Twitter so it doesn’t happen again.

- Try to meet with interesting people during the week. The reason being up on tech news is important is so that we can get the most out of the meetings. Often we’ll talk about whatever each of us is working on at the time but it’s also good to have news or blog posts as shared reference points. This makes the meetings more interesting for everyone.

- Try to learn at least one interesting thing each week and then blog about it. Then see how people react in comments, on Twitter etc. I guess some bloggers don’t like comments but for me they are the crucial so that I can get feedback on new hypotheses. Blogging new hypotheses also means a decent portion of your blog posts need to be ignored or ridiculed. Otherwise you are playing it too safe.

1) Public tech companies: Anyone with a basic understanding of finance would have trouble arguing many large public tech companies are trading at “bubble valuations” – e.g. Apple (14 P/E), Google (18 P/E), eBay (16 P/E), Yahoo (17 P/E). You could certainly debate other public tech stock valuations (there are a number of companies that recently IPOd that many reasonable people think are overvalued), but on a market-cap weighted average the tech sector is trading at a very reasonable 17 P/E.

2) Instagram seems to be the case study du jour for people arguing we are in a bubble. Reasonable people could disagree about Instagram’s exit price but in order to argue the price was too high you need to argue that either: 1) Facebook is overvalued at its expected IPO valuation of roughly $100B, 2) it was irrational for Facebook to spend 1% of its market cap to own what many people considered one of Facebook’s biggest threats (including Mark Zuckerberg – who I tend to think knows what is good for Facebook better than pundits).

3) Certain stages of venture valuations do seem on average over-valued, in particular seed-stage valuations and (less obviously) later-stage “momentum valuations.” The high seed-stage valuations are driven by an influx of angel/seed investors (successful entrepreneurs/tech company employees, VC’s with seed funds, non-tech people who are chasing trends). The momentum-stage valuations are driven by a variety of things, including VC’s who want to be associated with marquee startup names, the desire to catch the next Facebook before it gets too big, and the desire of mega-sized VC funds to “put more money to work”.

4) Certain stages – most notably the Series A – seem under valued. Many good companies are having trouble raising Series As and the valuations I’ve seen for the ones who do have been pretty reasonable. Unfortunately, since the financials and valuations of these companies aren’t disclosed, it is very difficult to have a public debate on this topic. But many investors I know are moving from seed to Series A precisely because they agree with this claim.

5) No one can predict macro trends. The bear case includes: something bad happens to the economy (Euro collapses, US enters double dip recession). The warning sign here will be a drop in profits by marquee tech companies.  The bull case includes: economy is ok or improves, and tech continues to eat into other industries (the “software is eating the world” argument). Anyone who claims to know what will happen over the next 3 years at the macro level is blowing hot air. That’s why smart investors continue investing at a regular pace through ups and downs.

6) The argument that sometimes startups get better valuations without revenue is somewhat true. As Josh Koppelman said “There’s nothing like numbers to screw up a good story.” This is driven by the psychology of venture investors who are sometimes able to justify a higher price to “buy the dream” than the same price to “buy the numbers.” This doesn’t mean the investors think they will invest and then get some greater fool to invest in the company again. For instance, at the seed stage, intelligent investors are quite aware that they are buying the dream but will need to have numbers to raise a Series A.

7) No good venture investors invest in companies with the primary strategy being to flip them. This isn’t because they are altruistic – it is because it is a bad strategy. You are much better off investing in companies that have a good chance to build a big business. This creates many more options including the option to sell the company. Acquisitions depend heavily on the whims of acquirers and no good venture investors bet on that.

- Dell thrived when PCs dominated the computer market and Dell was the low cost provider of commodity hardware products. The shift to mobile and tablet computing meant that hardware quality (not price) was once again the primary basis of competition. As a result, Dell’s laser-like focus on cost reduction became a liability.

- The New York Times was, for many decades, one of the few premium channels through which brand and classified advertisers could reach mass consumers. Thus car companies and real estate brokers subsidized foreign reporting and investigative business journalism. The internet provided a vast alternative channel, and the Times became far less relevant. At the same time, the internet provided many new sources for breaking news, editorials etc, hurting the Times on the subscriber side.

- Yahoo didn’t lose because Google out-competed them on search. They lost because they didn’t really care about search – indeed, they outsourced algorithmic search to Alta Vista, Inktomi and then Google itself. The leading portals back in circa 2000 (Yahoo, Excite, Lycos etc) desperately wanted to keep keep users on their site – the buzzword was “stickiness” – but Google knew better and focused on getting users off of Google to other places on the web. Yahoo became just another place to read celebrity gossip and use generic web services.

- Netflix thrived when they could simply ignore the movie companies and rely on the first-sale doctrine to get DVDs. The market shift to streaming video created a new and brutal dependency. They had to go make deals with content companies. Now they are even trying to create their own content to lessen this dependency. They have a brilliant and visionary management team but this is a tough transition to make.

- Sony relied on its Steve-Jobs-like founder, Akio Morita, to repeatedly develop incredibly innovative products (among them: the first transistor radio, the first transistor television, the Walkman, the first video cassette recorder, the compact disc) that seemed to come out of nowhere and create massive new markets. Since he left, the company has floundered and the stock has fallen dramatically.

- Google’s biggest risk isn’t a direct competitor. Startups and incumbents who’ve tried to create better search engines have barely cut into Google’s market share. Google’s primary risk – and they seem to know this – is that they are no longer relevant when people find content through social sites, and where an ever increasing portion of the web is uncrawlable.

Google released their “Dropbox-killer” a few days ago. I don’t know if Dropbox has yet achieved incumbent status, but they certainly seem to be the market leader. They also seem to have a very competent management team. So if history is a guide, Dropbox’s biggest risk isn’t a competitor but irrelevance – if, for example, files become less and less important in a web services world and Dropbox doesn’t adapt accordingly.

1) Issuing of senior securities with multiple liquidation preferences. Example:

Series A: Small investor invests in $1m round, getting 1x straight preferred

Series B: Large investor invests $10m, getting 4x senior straight preferred

Company gets sold for $30m. Management gets $3m carveout, Series B investors get $27m, and Series A investors get zero.

2) Issuing of massive option grant to management along with new financing at a below-market valuation. Example:

Series A: Small investor invests in $1m round, getting 1x straight preferred for 10% of the company.

Company is doing well and is offered a Series B at a significantly higher valuation. Instead, large investor invests $5m at below-market valuation, getting 40% of the company, and simultaneously issues options worth 50% of the company to management.

Result: Series A investors are diluted from 10% to 1% of the company, even though the company was doing well and in a normal financing would have only been slightly diluted.

3) The company is actually multiple entities, with the smaller investor investing in the less valuable entity. Example:

Company has entity 1 and 2. Small investors invest in entity 1 that licenses IP from entity 2. Value of IP increases and entity 2 is sold and eventually cancels entity 1′s license, making entity 1 worthless.

4) Pay-to-play or artificially low downrounds. Example:

Series A: Small investor invests in $1m round, getting 1x straight preferred

Series B: Large investor invests $10m in pay-to-play round (meaning any investor that doesn’t participate has their preferred shares converted to common). Smaller investor doesn’t have the cash to re-invest in Series B, but deeper pocketed investors do.

Company sells for $10m. Series B investors get $10m. Series A investors get nothing.

There are ways to protect against these shenanigans. Protections can be written into the Series A financings documents (pro-rata rights, ability to block senior financings, etc). There are also some legal protections all minority investors are granted under, say, Delaware or California law. But usually even when these protections exist (and they exist far less frequently these days than in the past), smaller investors usually can’t, say, invoke blocking rights by themselves (indeed, it’s often not economically viable for smaller investors to hire lawyers to review every financing document for every company they invest in). Another way smaller investors can protect themselves is to set aside capital amounting to, e.g. 30% of every investment made, in case they need it later for defensive purposes (I do this). But in my experience this is all very complicated and difficult to execute in practice, even when the small investors are “professional” investors. I worry it will be even harder for “amateur” investors to protect themselves.

A lot of startups over outsource. A few years ago, you’d sometimes hear tech startups say they were going to outsource software development. Thankfully, founders have gotten smart about this and it rarely ever happens except as a stopgap. It is still common for startups to hire outside PR firms. If you decide to hire an outside PR firm, that means you don’t care about PR. Just because you are willing to spend some of money on it doesn’t mean you think it’s important. You probably shouldn’t hire an investment banker during an acquisition unless your company is later stage. And you might occasionally use an outside recruiter but the core recruiting activity needs be done by founders.

The excitement over mobile-first apps is justified. Smartphones have unleashed a wave of creativity, resulting in entirely new categories of applications. But to me an even more exciting trend is what people have been calling (for lack of a better phrase) ”offline first, mobile enabled” apps.

For example, Foursquare is primarily about improving your offline experiences (meeting friends and finding new places to go). And it couldn’t exist without smartphones (ok, Dodgeball existed on feature phones but had a fraction of the utility). Similarly, Uber couldn’t exist without smartphones. The Uber apps (one for drivers and one for customers), while essential, are all about enabling for the car service. Square is about making payments more convenient and giving small businesses better analytics. The mobile app is just an enabler.

It seems natural that the first wave of mobile apps would be about improving core smartphone apps (e.g. photo apps) or porting apps from other devices (e.g. games). And there is probably a lot of interesting innovation remaining there. But the really massive opportunity is dreaming up new ways that the little computers loaded with sensors that we carry around with us everywhere can improve our real-world experiences.

What we’ve seen have been evolutionary improvements on the patterns established five years ago. The platforms that have seemed hot in the last couple of years — Tumblr, Instagram, Pinterest — add a bit of design or mobile intelligence to the established ways of thinking.

One thing these critics need to be careful about is that, as Clay Christensen has long argued, many important new inventions start out looking like toys. Twitter (Founder Fund’s headline example of a “trivial” startup) started out looking like a toy but has since transformed the way information is distributed for tens of millions of people. Madrigal dismisses cloud computing as “a rebranding of the Internet” whose only effect has been to make “the lives of some IT managers easier,” overlooking that cloud-based services solve the “third party payer” problem of enterprise sales, thereby completely changing how enterprises adopt new technology.

That said, I generally agree with the sentiment that the startup world is too focused on chasing trends. I don’t think this is the fault of entrepreneurs. I meet entrepreneurs all the time who are working on ideas that seem quite meaningful to me. Some of them are building futuristic new technologies. Some are trying to disintermediate incumbents and thereby restructure large industries. Others are trying to solve stubborn problems in important sectors like education, healthcare, or energy.

The problem I encounter is that many of these “meaningful” startups have trouble raising money from VCs. An entrepreneur working on groundbreaking robot technology recently joked to me that he’d have an easier time raising money if his robots were virtual and existed only on Facebook. He was only partly joking. His startup will require a lot of capital and doesn’t have an obvious near term acquirer. Only a small group of VCs today will even consider such an investment.

One of the most important lessons I’ve learned is that there are only two ways to make useful products out of large data sets. Algorithms that deal with large data sets tend to be accurate at best 80%-90% of the time (an old “joke” about machine learning is that it’s really good at partially solving any problem). Consequently, you either need to accept you’ll have some errors but deploy the system in a fault-tolerant context, or you need to figure out how to get the remaining accuracy through manual labor.

What do I mean by fault-tolerant context? If a search engine shows the most relevant result as the 2nd or 3rd result, users are still pretty happy. The same goes for recommendation systems that show multiple results (e.g. Netflix). Trading systems that hedge funds use are also often fault tolerant: if you make money 80% of the time and lose it 20% of the time, you can still usually have a profitable system.

For fault-intolerant contexts, you need to figure out how to scalably and cost-effectively produce the remaining accuracy through manual labor. When we were building SiteAdvisor, we knew that any inaccuracies would be a big problem: incorrectly rating a website as unsafe hurts the website, and incorrectly rating a website as safe hurts the user. Because we knew automation would only get us 80-90% accuracy, we built 1) systems to estimate confidence levels in our ratings so we would know what to manually review, and 2) a workflow system so that our staff, an offshore team we hired, and users could flag or fix inaccuracies.

* My first job was as a programmer at a hedge fund, where we built systems that analyzed large data sets to trade stock options. Later, I cofounded SiteAdvisor where the goal was to build a system to assign security safety ratings to tens of millions of websites. Then I cofounded Hunch, which was acquired by eBay – we are now working on new recommendation technologies for ebay.com and other eBay websites.

The increasing velocity has implications for the valuations of incumbent tech companies. Users have limited time, and while web and app usage are growing, hit startups are growing much faster and therefore gaining adoption, at least in part, at the expense of incumbents. It’s not clear this risk is priced into the valuations of companies like Facebook (P/E expected to be ~100) and Zynga (P/E ~31). In other words, faster velocity should lead to a narrower distribution of valuations from seed to late stages. We’ve seen the seed stage adjust but not the late stage.

The current posture of big VCs seems to be to wait to see what takes off and then chase the winners. Tons of investors tried to invest in Instagram’s A and B rounds, and I’m sure VC interest in Pinterest is intense.

The problem with this model of Series A and B investing is that, in reality, many of the companies with big hits weren’t overnight successes. Pinterest, OMGPOP, Twitter, and Tumblr were around for years before taking off and all benefited greatly from having patient investors. In the current financing environment, a lot of good companies won’t live to get Series As and Bs and big VCs will pay valuations on hits that are priced to perfection.

Increasing velocity is great for users and for the winning companies and investors. But when good companies aren’t getting follow on rounds because they aren’t yet “hockeysticking”, the long term health of the startup ecosystem suffers.

Angry Birds was Rovio’s 52nd game. They spent eight years and almost went bankrupt before finally creating their massive hit. Pinterest is one of the fastest growing websites in history, but struggled for a long time. Pinterest’s CEO recently said that they had “catastrophically small numbers” in their first year after launch, and that if he had listened to popular startup advice he probably would have quit.

Fast company on April 3, the opening of “The dirty little secret of overnight success“:

Angry Birds, the incredibly popular game, was software maker Rovio’s 52nd attempt. They spent eight years and nearly went bankrupt before finally creating their massive hit.

Pinterest is one of the fastest-growing websites in history, but struggled for a long time. Pinterest’s CEO recently said that it had “catastrophically small numbers” in its first year after launch and that if he had listened to popular startup advice he probably would have quit.

No link or attribution.

Update: Thanks to Fast Company for a fast response and changing it to a quote with citation.

As I see it, Facebook had 4 choices:

- Settle. Given their pending IPO, this would have been the easiest route. But, by rewarding Yahoo, settling would have encouraged more frivolous patent lawsuits.

- Defend without countersuing. On the surface this would have been the “principled” stance, but it would have severely weakened their legal position, and therefore would have made it more likely that Yahoo profited from the lawsuit.

- Countersue without signaling any aversion to patent lawsuits.

- Countersue and signal that they are averse to patent lawsuits, which in turn signals that they will drop the lawsuit if Yahoo does. This seems to be what Facebook has done:

“From the outset, we said we would defend ourselves vigorously against Yahoo’s lawsuit,” Ted Ullyot, Facebook’s general counsel, said in a statement. “While we are asserting patent claims of our own, we do so in response to Yahoo’s short-sighted decision to attack one of its partners and prioritize litigation over innovation.” [emphasis added] – NYTimes

Countersuing gives Facebook the best chance of fending off Yahoo’s lawsuit – and therefore not rewarding patent lawsuits. And signaling they are only doing so in response to Yahoo (hence might drop the suit if Yahoo does) keeps them on the right side of innovation.

Since then, enough founders have directly experienced the downside of taking seed money from big VCs that I think it’s safe to say there is no more room for debate. I can think of about 15 founders I’ve spoken to recently who tried or are trying to raise Series As but are seriously hampered by the fact that a big VC invested in the seed round but isn’t participating in the Series A. (I’d love to mention specific companies and firms but it wouldn’t be appropriate for me to do so – I guess I’ll just have to cite Jay Rosen’s “I’m there, let me tell you what I see” principle of reporting).

There are two important nuances to point out here. First, there are big VCs who invest in seed rounds the right way – with the genuine expectation to follow on and the intention to help out during the seed stage (some that I’ve invested with include USV, True, and Spark). One important sign of this is how much they want to invest. If a $300M fund wants to invest $100K, they are buying an option. If they want to invest $500K, they are more likely making an investment.

The second nuance can be counterintuitive: the danger of taking seed money is positively correlated with the reputation of the firm. If a top VC invests in the seed round and then passes on the A, other VCs will have difficulty overlooking that the smartest money that knows the company the best isn’t following on. If the VC isn’t well respected, it is easier for other VCs to second guess them.

I’m not revisiting this issue to criticize big VCs. A healthy startup environment requires smart, ethical investors at all stages. But I don’t think these big VC seed programs benefit anyone. And there are enough angry entrepreneurs out there that I expect the message will get through.

One particularly effective version of freemium is: “give away the diagnostic, sell the remedy.” The best known example of this is anti-virus companies that give away free virus scans but charge for virus removers. In fact, this tactic works so well for anti-virus that it almost seems coercive (and has indeed been abused, for example, by “anti-spyware” software that deliberately conflates cookies and viruses). But, in general, giving away a diagnostic seems like a reasonable way to demonstrate the effectiveness of a product while still being able to sell valuable additional features.

Selling the remedy has become increasingly popular with B2B companies. For example, a friend recently wanted to ensure that his company’s (non-spam) e-mails weren’t getting blocked by spam filters, so he contacted an “email delivery optimization” company. They ran a free test and reported that his emails weren’t getting filtered. Two months later they called back and said “uh oh, your emails are getting filtered.” Sure enough his open rates had dropped and his anecdotal tests confirmed that his emails were being inaccurately labelled as spam. Because of the free diagnostic, he had confidence in the company’s technology, and was willing to pay them to fix his problem. And the email optimization company had spent almost nothing to acquire a new customer.

You tend to hear about startups when they are successful but not when they are struggling. This creates a systematically distorted perception that companies succeed overnight. Almost always, when you learn the backstory, you find that behind every “overnight success” is a story of entrepreneurs toiling away for years, with very few people except themselves and perhaps a few friends, users, and investors supporting them.

Startups are hard, but they can also go from difficult to great incredibly quickly. You just need to survive long enough and keep going so you can create your 52nd game.

This method worked for a while but then mysteriously stopped working. Since the features the computer identified were embedded in complicated mathematical equations, no one could figure out what it was really doing and therefore why it stopped working. Eventually someone realized that in the training set, all of the images with tanks were taken on a cloudy day, and all the images without tanks were taken on a sunny day. The algorithms had fixated on the most obvious pattern – the color of the sky. When the algorithm was tested on new photos where the weather varied, it was completely flummoxed.

It is commonly said that good startup investors develop “pattern recognition” that allows them to identify great entrepreneurs and companies. If you look at the hugely successful startups of the last decade, the founders have many similarities that are easy to observe. When they started, many were male, young, unmarried, computer programmers, dropouts of elite universities, etc. As a result, a lot of investors look for founders with these characteristics. But without an understanding of the deeper reasons these founders succeeded, these observable characteristics could just as well be the color of the sky and not the tanks.

At the level of individual investors, pattern recognition can lead to bad investments and missed opportunities. In the context of markets, it can cause companies and sectors with the “right patterns” to be overvalued, and ones with the “wrong patterns” to be undervalued. In the broader cultural context, it can cause large groups of talented entrepreneurs to be denied access to capital.

The classic scientific method provides a better model for investing. Scientists observe data, notice patterns, develop hypotheses, and then test those hypotheses. Pattern recognition is only a step along the way to developing hypotheses about the underlying cause.

Perhaps dropping out of college shows a strong level of commitment. Knowing computer science was probably a necessary condition for starting a tech company in the past, but no longer is. Being young could mean you are inexperienced enough to pursue bold ideas that more experienced people would consider crazy. I am just speculating – I don’t know why these characteristics are common among past successful founders. But the mere repetition of patterns shouldn’t be satisfactory to anyone who wants to understand and predict the success of startups.

- Don’t be afraid to ask what the rules are. Is this on or off the record? If they are writing an article about your company, do they require exclusivity? What is the angle of the story?

- Don’t use a PR firm unless you are so successful that you need someone to help you manage inbound press interest. Most journalists, when talking candidly, will tell you they’d vastly prefer getting an email from the founder of a startup than a PR firm. If you’re Bill Gates, it is understandable that you have someone reaching out for you. If you are a small startup, having a PR rep contact a journalist says “I’m not competent enough to reach you” or “I don’t respect your time enough to reach out directly.”

- Treat journalists with respect. Tech/business journalists often interact with rich and powerful people, some of whom treat them disrespectfully. Like entrepreneurs, journalists are usually interesting people with diverse interests. You’ll probably like them if you talk to them and might even become friends.

- Unless you’re a super hot startup, the existence of your company is not a news story. Exclusives of launches, financings and acquisitions are usually news stories. Trend stories that you are part of could be a news story. Relating your startup or data your startup generates to something already newsworthy (journalists call this “pegging”) can dramatically increase your chances of getting covered.

- Whether you like it or not, the press will put your company into a category, and might run “horserace” stories comparing how the companies in your category are doing. The best you can do here is to try to choose which category you’ll be put into. Arguing that you have no competitors or are creating a new category is pretty much impossible.

- Try to put yourself in the mindset of the journalist. How will this story get them on Techmeme or featured by their editors? What were their most successful recent stories? Do background research on any reporter before talking and read a bunch his/her articles.

- Don’t just contact reporters when you need them: try to be helpful even when you don’t. Sometimes, I get calls to talk about, say, the state of the venture market or asking for some background on a tech sector that is new to the journalist. My guess is they appreciate this and are more responsive when I contact them about a possible story.

Innovation is an important new product or process, deployed on a large scale and having a significant impact on society and the economy, that can do a job “better, or cheaper, or both.” Regrettably, we now use the term to describe almost anything. It can describe a smartphone app or a social media tool; or it can describe the transistor or the blueprint for a cellphone system. The differences are immense. One type of innovation creates a handful of jobs and modest revenues; another, the type Mr. Kelly and his colleagues at Bell Labs repeatedly sought, creates millions of jobs and a long-lasting platform for society’s wealth and well-being.

The conflation of these different kinds of innovations seems to be leading us toward a belief that small groups of profit-seeking entrepreneurs turning out innovative consumer products are as effective as our innovative forebears. History does not support this belief. The teams at Bell Labs that invented the laser, transistor and solar cell were not seeking profits. They were seeking understanding. Yet in the process they created not only new products but entirely new — and lucrative — industries.

Putting aside the obvious rebuttal that large companies like Intel, Microsoft, Apple and even AT&T were once startups, the author seems to confuse “jobs” with “income streams”. For example, it would be easy to dismiss a website like Craigslist as a “social media tool” that has only created a few dozen jobs for its employees. But in fact it has created billions of dollars of income streams for people buying and selling things on its platform. The internet is increasingly reshaping our economy from one of huge corporations with lots of jobs to huge platforms with lots of income streams.

One side-by-side comparison struck me recently.  Company A is just now raising a seed round. The money they raised will last 12 months (personally, I strongly recommend raising 18 months of runway – if you have the option to do so). Company A was also, in my opinion, not ready to raise money (they needed to work on their plan and team more). Company B raised a seed round about 10 months ago and is now struggling to raise more. Company B had the option to raise more money back then but chose to only raise 12 months runway in order to minimize dilution. Company B also made the mistake of having a large VC invest $100K in the round (a meaningless amount to a large VC). The large VC has since said they won’t support the company (despite the fact that the company made pretty good progress on the business) creating a massive signaling problem.

In the current “frothy” environment, where seed investors are aggressively offering money to entrepreneurs, it is easy for an entrepreneur to think “well, if I’m getting offered money this easily at the seed stage, I’ll get offered money easily later.” In fact, once you take professional investor money, the attitude of investors (both insiders and outsiders) changes dramatically: you’ve gone from planning mode to operations mode. When you do planning, research, experimenting etc. without having raised money, investors think you are prudent (I recently interviewed the Warby Parker founders for TechCrunch and they said they spent 1.5 years planning/researching before they raised money). When you do it with other people’s money, and don’t make what they perceive to be enough progress, the investors can quickly lose faith.

The obvious lesson is well known by experienced entrepreneurs. Don’t raise money until you are ready, and when you do, raise enough to have a good shot at reaching “accretive milestones” so you can raise more money, become profitable, or whatever your goals might be.

Big timing is a huge mistake. Why? 1) big timers vastly underestimate the degree to which senior people trust their junior people, 2) most non-jerks (no matter how successful) interpret big timing to be an insult to their firm and therefore to their senior people, 3) junior people are often far more active and informed than senior people and therefore great people to spend time with.

It would be great to think that in the startup industry, people would realize that today’s junior person could become “big time” tomorrow, and that you should therefore be meritocratic and respectful to everyone. But that’s not my experience.

Here’s my non-technical interpretation of the essence of the P vs NP problem:

Can every answer that can be feasibly verified also be feasibly calculated?

What I am calling “feasible” is what computer scientists call algorithms that can run “polynomial” as opposed to “exponential” time.

There are at least four possible outcomes to the attempts to solve this problem: 1) the current situation continues – no proof of anything is found, 2) P=NP is proved true, 3) P=NP is proved false, 4) it is proved that it’s impossible to prove P=NP to be true or false.

If P=NP were proved true, there would be many serious real-world consequences. All known encryption schemes rely on the fact that prime factors of large numbers are something that can be feasibly verified but not calculated. If P=NP, that means there would also be feasible ways to calculate prime factors, and hence decrypt codes without their private keys. So if someone does prove P=NP, he or she should probably inform authorities before publishing the proof and all hell breaks loose (thanks Matt for this observation – you could also imagine a lot of conspiracy theories about what happens to scientists who try to prove P=NP..!)

Most computer scientists seem to suspect P does not equal NP. MIT computer scientist Scott Aaronson gives informal arguments against P=NP in this entertaining blog post, including this philosophical argument:

If P=NP, then the world would be a profoundly different place than we usually assume it to be. There would be no special value in “creative leaps,” no fundamental gap between solving a problem and recognizing the solution once it’s found. Everyone who could appreciate a symphony would be Mozart; everyone who could follow a step-by-step argument would be Gauss; everyone who could recognize a good investment strategy would be Warren Buffett. It’s possible to put the point in Darwinian terms: if this is the sort of universe we inhabited, why wouldn’t we already have evolved to take advantage of it?

He follows up with a much longer essay (which I found really interesting but ultimately unconvincing) on the philosophical implications of computational complexity (the field of computer science that studies questions like P vs NP).

Building 20 [a scene of incredible innovation at MIT] and brainstorming came into being at almost exactly the same time. In the sixty years since then, if the studies are right, brainstorming has achieved nothing—or, at least, less than would have been achieved by six decades’ worth of brainstormers working quietly on their own. Building 20, though, ranks as one of the most creative environments of all time, a space with an almost uncanny ability to extract the best from people. Among M.I.T. people, it was referred to as “the magical incubator.”

The fatal misconception behind brainstorming is that there is a particular script we should all follow in group interactions. The lesson of Building 20 is that when the composition of the group is right—enough people with different perspectives running into one another in unpredictable ways—the group dynamic will take care of itself. All these errant discussions add up. In fact, they may even be the most essential part of the creative process. Although such conversations will occasionally be unpleasant—not everyone is always in the mood for small talk or criticism—that doesn’t mean that they can be avoided. The most creative spaces are those which hurl us together. It is the human friction that makes the sparks.

I think this underscores one of the main reasons remote early-stage projects often fail. We mistakenly think of brainstorming as something you can do in meetings, and teaching as something you can perform through carefully composed documents or lectures.

I was part of a number of failed remote R&D attempts. The one time it worked was when we decided to abandon meetings, project documents, tracking tools, etc. Instead, we got a high quality speakerphone so everyone could overhear everyone else’s conversations, and we left it on all day, every day. It wasn’t the same as being together in person, but we did manage to get some of the human friction back.

When you depend on a platform for distribution (acquiring and retaining users), you take on different risks. Specifically:

1) Oversaturation. The risk that supply of products on the platform significantly outpaces demand. This seems to have happened recently to the iOS App Store: there are over 500,000 apps and counting, and popularity tends to be highly concentrated, making it very difficult for new apps to get noticed. Oversaturation also happened to Google (organic) results in most query categories in the last 2000′s.

2) Barriers to discovery. The risk that the discovery methods on the platform aren’t meritocratic. iOS apps depend upon appearing in iTunes’ Top 25 lists, leading to a “rich get richer” bias, along with aggressive attempts to game the system. Apple has other app discovery mechanisms like its Featured Apps and Genius features, but those seem to drive far fewer downloads than the top lists. Google search has increasingly been favoring Google’s own products and also seems to heavily favor older, well-entrenched websites, making it very hard for new sites to gain significant SEO traction. Currently, social networks like Twitter and Facebook seem to have the most meritocratic discovery mechanisms, which is one reason so many startups target them for distribution.

3) Throttling. The risk that the platform will throttle distribution or monetization (for apps that rely on paid advertising, throttled monetization also means throttled distribution). Facebook started out letting apps send unfiltered notifications to users’ timelines but then introduced algorithms that heavily filtered them (thereby entrenching the position of leading app makers like Zynga). Facebook also started out letting apps charge users directly, but later changed that policy and imposed a rev-share.

If you are launching a new website or app, you should have a distribution strategy beyond just “people will love it and tell their friends about it”. Your strategy should probably involve at least one major platform. And you should think through the distribution characteristics of the platform and decide if they are a good fit for your product and how best to mitigate the risks.

Finally, it is worth noting that some of the most successful startups grew by making bets on emerging platforms that were not yet saturated and where barriers to discovery were low. Today, the most interesting new platforms are probably Android tablets and emerging social networks like Foursquare and Tumblr. Betting on new platforms means you’ll likely fail if the platform fails, but also dramatically lowers the distribution risks described above.

Of course, for those of us immersed in startup world, creating the next MySpace would be considered a huge success. MySpace was once the most visited website in the US and was acquired for $580M. It flamed out later under its corporate owner, but that happens to a lot of great startups.

Mainstream culture seems to depict startups as either being complete failures where everyone loses their shirts or else huge hits like Facebook. But the reality, as usual, lies in the middle: in 2010, according to Dow Jones, there were 522 venture-backed exits with a combined exit value of $53 billion – implying an average exit price of around $100M.

The best thing about startups is you get to work with great people on interesting projects, and can be successful by conventional metrics, even if no one outside of tech has ever heard of you or what you’ve built. There’s great stuff between failure and Facebook.

Amazon and eBay are the two largest e-commerce companies. As of this writing, Amazon has a market cap of about $87B, trading at a trailing twelve-month P/E of about 139. eBay has a market cap of about $42B, trading at a trailing P/E of about 13. Each company competes with many other companies in many different areas. For example, Amazon competes with Apple on tablets (Kindle vs iPad) and digital media (Amazon’s media store vs iTunes). Ebay’s Paypal unit competes with multiple payment companies, and its marketplaces division competes with other “peer-to-peer” e-commerce sites like Craigslist. But given the potential size of the e-commerce market (not to mention the online-to-offline commerce market), Amazon and eBay’s main competitors are each other. And to understand their large strategic moves (e.g. large acquisitions like GSI and Zappos), it is important to understand their fundamentally opposing strategic outlooks: eBay wants commerce to be more decentralized (around its GSI/Magento partners and eBay marketplaces sellers) and Amazon wants it to be more centralized (around itself).

First, some background. During the dot-com boom, many largest offline brands debated how to best move their businesses online. Some tried to build their own websites from scratch. Others partnered with commerce technology providers. Toys ‘R’ Us took a novel approach and signed a “strategic alliance” to outsource all of their e-commerce operations to Amazon. Over the next few years this relationship soured – apparently Toys ‘R’ Us felt Amazon was competing too directly with them and successfully sued to end the relationship.

The end of the Toys ‘R’ Us – Amazon relationship marked a turning point for a company called GSI Commerce. GSI took an aggressively neutral approach to providing technology and marketing solutions to retailers. Their main appeal over Amazon is that they didn’t compete with their partners (but of course their partners competed with each other). This approach paid off: GSI now powers over 500 large commerce sites, including Toys ‘R’ Us, Adidas, Ralph Lauren, and the commerce sites of all the large sports leagues like the NFL, MLB and NBA.

Last year, eBay paid $2.4B to acquire GSI Commerce. They also acquired a smaller company called Magento that provides e-commerce technologies to smaller retailers. You can think of GSI as the leading commerce platform for the “fat head” of retailers, and Magento as the leader for the long tail.

The key difference between eBay and Amazon isn’t auctions vs. fixed price sales (the majority of eBay sales aren’t auctions anymore). It is that eBay doesn’t take inventory, and prefers to be an intermediary that facilitates peer-to-peer commerce. This strategy wins if e-commerce becomes more decentralized, with the majority of commerce continuing flow through small to medium retailers. In this world, eBay makes money by sending traffic from eBay.com, from fees collected by GSI and Magento, and Paypal transaction fees. In a centralized world, Amazon grows its current 9% e-commerce market share to a much larger percentage, taking advantage of its scale, efficiency, advanced technology, and the convenience of shopping in one place.

One way to view this battle is to think of eBay as a platform a la Windows or Android and Amazon as an end-to-end solution a la Apple computers in the 90s or iOS devices today. Platforms tend to provide greater diversity. In the case of e-commerce, the platform approach could also have a price advantage. As the CEO of TrialPay, Alex Rampell, argues: “Who can beat Amazon on price? The companies whose products are sold on Amazon”. End-to-end solutions like Amazon’s tend to provide greater convenience and a better user experience.

I’m not arguing that one approach is superior to the other. My point is simply that when you understand that the battle is between centralized and decentralized commerce, the strategic moves of the two companies make a lot more sense.

2. I don’t know what the product design motivations are for uploading contacts, but I assume there are legitimate ones. [commenters suggest it is mainly to notify users when their friends join the service].  If this or something similar is the goal, you could probably do it in a way that protects privacy by (convergently?) encrypting the phone numbers on the client side (I’m assuming the useful info is the phone numbers and not the names associated with the phone numbers since the names would be inconsistent across users).

3. Many commentators have suggested that a primary security risk is the fact that the data is transmitted in plain text. Encrypting over the wire is always a good idea but in reality “man-in-the-middle” attacks are extremely rare. I would worry primarily about the far more common cases of 1) someone (insider or outsider) stealing in the company’s database, 2) a government subpoena for the company’s database. The best protection against these risks is encrypting the data in such a way that hackers and the company itself can’t unencrypt it (or to not send the data to the servers in the first place).

A bad outcome from this controversy would be to have companies encrypt sensitive data over the network and then not encrypt it on their servers (the simplest way to do this is to switch to https, a technology that is much more about security theater than security reality). This would make it impossible for 3rd parties (e.g. white-hat hackers) to detect that sensitive data is being sent over the network but would keep the data vulnerable to server side breaches / subpeonas. Unless Apple or someone else steps in, I worry that this is what apps will do next. It is the quickest way to preserve product features and minimize PR risk.

4. I worry that by just adding tons of permission dialogs we are going back to the Microsoft IE/Active X model of security. With lots of permission popups, users get fatigued and confused and just end up clicking “Yes” to everything. And then the security model says: If the user says “yes”, and the app uses “best practices” like https, it can do whatever it wants. We saw how this played out with the spyware/adware epidemic on the web from 2001-2006 and it wasn’t pretty.

Most of the time, important new ideas don’t succeed on the first attempt or even the first ten attempts. But then they do, and it seems to happen suddenly. It’s hard to tell why this is. It’s probably a combination of timing (riding some fundamental shift in technology or culture), and execution (getting the product just right).

An idea getting tried over and over tends to be a positive signal (which is one reason that competition is overrated). It’s very easy when you spend lots of time around startups to get cynical. You could tweet and blog predictions that every new startup will fail and how the ideas are derivative and you’d be right 95% of the time. The hard part – and what matters for founders and investors – is figuring out the right mix of timing and execution to finally get it right.

In my first programming job out of college our bosses told us to entirely rebuild our product. The person in charge of the networking layer decided the best way to do this was to write our own low-level networking toolkit, using some new, relatively untested networking techniques. We also wrote our own versions of core Java libraries (because, it was said, the existing ones weren’t sufficiently thread safe). This decision ended up leading to repeated delays and bugs, and a codebase that most of the other employees didn’t understand. It also made it much harder to train new hires and find replacements for departed employees.

A related issue is what is usually called the “bleeding edge” tendency: the desire to use the shiny & new over the older & battle-tested. Lately, I’ve personally been programming with MongoDB and love it. But I’m also an investor in a startup that made Mongo their main production database, and when their Mongo expert left unexpectedly it took them far longer to find a replacement than it would have to find a MySQL expert.

Great programmers are intensely curious and inventive. They love to improve code and try new things. There will always be bedrock and bleeding edge tendencies within strong engineering teams. The key is to have a great VP Engineering/CTO who can balance those tendencies with the reality that talent, money, and time are scarce, especially in startups.

1. Jobs & the economy. Businesses all over the world need more programmers. Every company I know is hiring engineers (e.g. see this list of NY tech startups). Top programmers can make $100K+ right out of college. Yet there were only about 14,000 computer science (CS) majors last year. Meanwhile about 40,000 people got law degrees even though demand for lawyers has been shrinking. America is suffering from what economists call structural unemployment:  jobs are available but our labor force isn’t trained for those jobs.

2. Programming is a great foundation for a tech/startup career. CS is a great foundation to do other things in tech industry like starting a tech company (although I’d argue that design is an increasingly valuable foundation for web startups). I suspect one of the reasons for the low number of CS majors is people don’t realize all the non-programming opportunities that are opened up by a background in programming.

3. Programming is an important part of being “culturally literate.” Algorithmic thinking is as fundamental a type of thinking as mathematical thinking. For example, Daniel Dennett convincingly argues that the best way to understand Darwin’s theory of evolution is by thinking of it as an algorithm. (I haven’t read it yet but I’m told the premise of Stephen Wolfram’s A New Kind of Science is that algorithmic methods should be applied much more broadly across the sciences). Teaching algorithmic thinking – which is what CS does – should be a core part of a liberal arts education.

4. Programming is a great activity.  Most people who program describe themselves as entering a mental flow state where they are intensely immersed and time seems to fly by. It feels similar to reading a great book. You also feel great afterwards – it is the mental equivalent of going to the gym.

5. Should non-technical people at tech startups learn to code? This is where I disagree with some of the conventional wisdom. Certainly it is worthwhile learning programming, at least for reasons 3 & 4 above. You should realize, however, that to become a good programmer takes thousands of hours of practice. I’d also argue that if you are a non-technical person working at a web company the the first thing you should learn is internet architecture (DNS, http, html, web servers, database, TCP/UDP, IP, etc). Learning some programming is good too, to help relate to technical colleagues. But if your goal is to build a large-scale web service, your time as a non-technical person is better spent recruiting people who have been coding for years.

* Disclosure: I’m an investor via Founder Collective in two companies related to teaching programming:  Codecademy and Hacker School.

It comes down to what each company has promised its users. Facebook promised its users their stuff would be private, which is why users rightfully get pissed when that line blurs. Twitter has promised users, well, that it will stay up, and that is why users rightfully get pissed when the whale is back.

Google has promised its users and the entire tech community, again and again, that it would put their interests first, and that is why Google users, rightfully get pissed when their results are deprecated to try to promote a lesser Google product instead.

It’s all about expectations.

Most websites spend massive amounts of time and money to get any of their pages index and ranked by Google’s search engine. Indeed, there is a entire billion dollar industry (SEO) devoted to helping companies get their content indexed and ranked.

Twitter and Facebook have decided to disallow Google from indexing 99.9% of their content. Twitter won’t let Google index tweets and Facebook won’t let Google index status updates and most other user and brand generated content. In Facebook’s case this makes sense for content that users have designated as non-public. In Twitter’s case, the vast majority of the blocked content is designated by users as public. Furthermore, Twitter’s own search function rarely works for tweets older than a week (from Twitter’s search documentation, they return “6-9 days of Tweets”).

There is a debate going today in the tech world: Facebook and Twitter are upset that Google won’t highly rank the 0.1% of their content they make indexable. Facebook and Twitter even created something they call the “Don’t be evil” toolbar that reranks Google search results the way they’d like them to be ranked. The clear implication is that Google is violating their famous credo and acting “evil”.

The vast majority of websites would dream of having the problem of being able to block Google from 99.9% of their content and have the remaining 0.1% rank at the top of results. What would be best for users – and least “evil” – would be to let all public content get indexed and have Google rank that content “fairly” without favoring their own content. Facebook and Twitter are right about Google’s rankings, but Google is right about Facebook and Twitter blocking public content from being indexed.

Update: after posting this I got a bunch of emails, tweets and comments telling me that Twitter does in fact allow Google to index all their tweets, and that any missing tweets are the fault of Google, not Twitter. A few people suggested that without firehose access Google can’t be expected to index all tweets. At any rate, I think the “Why aren’t all tweets indexed?” issue is more nuanced than I argued above.

Most recent successful tech companies seem to have chosen the former: growing revenues at the expense of margins. Again and again, we see S-1 filings with revenues growing rapidly but profit margins that are low to negative. The same is true for the rumored financials of private companies. I think I understand why they made this choice, but wonder if it was a mistake.

To understand why these companies made this choice, you need to look at their formative stages. Many of them raised money from VC’s at multi-hundred-million to multi-billion dollar valuations, often before the companies were profitable or had even settled on a business model. In most cases, the companies and investors were acting reasonably[2]. But the end results might have been to unwittingly commit themselves to revenue over margin growth.

Why? Money has its own inertia and somehow always seems to get spent. Some of this spending is reasonable and even necessary (infrastructure, defensive expansion to international markets). But then there are harder choices. For example, do you invest heavily in sales and marketing to grow your revenue faster? Do you stay open and try to become a platform and therefore force yourself to experiment with new business models? Or do you become closed to “own the user” and therefore benefit from existing business models like advertising? Fast revenue growth seems to be the best way to justify your valuation. But the next thing you know you have a high cost structure that requires you to raise even more money and grow revenue even faster.

The root cause here is a deeply held belief throughout the business world that exceptional revenue growth is more likely than exceptional margins. For example, if you talk to professional public market investors and analysts you’ll often hear statements like “that’s a low margin industry” – implying that every industry has “natural” profit margins which companies can only defy for short periods of time. This belief is also reflected in public market valuations for recent tech IPOs: companies like Groupon that put revenue over margins command very healthy valuations.

The problem is that this deeply held belief in “revenue exceptionalism” over “margin exceptionalism” is a hangover from the industrial era. Unlike industrial era companies, information businesses tend to be deflationary, shrinking the overall revenue of an industry. They also tend to have network effects (and complementary network effects), making them more defensible and therefore higher margin than non tech businesses. Given this, why do companies continue seeking revenue at the expense of margins? Fred made this same point in his original post, but people didn’t seem to listen.

[1] Companies (like all cash generating assets) are ultimately valued at a multiple of present and projected future profits. The historical average P/E ratio of the DJIA is about 15, meaning that (on average) if a company is generating $100M in profit, it is valued at $1.5B (Fred prefers to use a 10 multiple, perhaps to be conservative?). One way to understand this is to imagine that companies dividend out all their profits every year. If you bought something for $1.5B and it dividended out $100M every year, that would be a 6.6% annual return.

[2] Why are these high-priced financings reasonable? From the company’s perspective: your traffic is growing so fast you need to invest millions of dollars in infrastructure. Meanwhile copycats are popping up in other countries. You don’t know if the financial markets will suddenly dry up. Someone offers you, say, $50M for minimal dilution. Seems like a reasonable hedge. From the investor’s perspective: the history of venture capital shows that almost all the returns are generated from big hits like Amazon, eBay, Facebook and Google. (As Paul Graham once put it: “The difference between a bad VC fund and a great VC fund is one big hit”).

Some of the most interesting startups today are founded on the same premise of maximizing capacity utilization. Being information technology startups, their method for doing so is generally by matching demand for capacity with supply of un-utilized capacity. AirBnB lets people rent out unused space, increasing the utilization of their homes. Uber lets drivers rent out their unused time, increasing the utilization of their cars and labor. Services like TaskRabbit are trying let people fully utilize their “labor capacity”. Over time, services that increase capacity utilization tend to drive prices down (even if, at first, they sometimes have higher prices).

Whenever Southwest would begin servicing a new city, it drove prices down so dramatically that economists began referring to it as the “Southwest Effect“. One particularly remarkable aspect of the Southwest Effect: when Southwest began servicing a city, it would stimulate new business activity – and thus air travel – to such an extent that even Southwest’s less efficient competitors ended up benefiting.

Instagram built a product devoted to a job that users were previously performing improvisationally using multiple products. This is a common pattern for popular software and services. Before Twitter, people shared interesting links through email or “link round-up” blog posts. Tumblr’s short-form blogging/re-blogging was inspired by an “unintended” use of long-form blogging platforms like WordPress. Before Foursquare, power socializers sent out mass text messages with their locations (in fact, Foursquare’s predecessor Dodgeball did exactly that).

New startup ideas are all around you, in the improvised behaviors of people you know. It takes a keen product eye, however, to notice these improvisational behaviors and recognize which ones are worthy of being developed into standalone products.

- The most important thing to understand is what motivates programmers. This is where having been a programmer yourself can be very helpful. In my experience programmers care about 1) working on interesting technical problems, 2) working with other talented people, 3) working in a friendly, creative environment, 4) working on software that ends up getting used by lots of people. Like everyone, compensation matters, but for programmers it is often a “threshold variable”. They want enough to not have to spend time worrying about money, but once an offer passes their minimum compensation threshold they’ll decide based on other factors.

- Software development is a creative activity and needs to be treated as such. Sometimes a programmer can have an idea on, say, the subway that can save weeks of work or add some great new functionality. Business people who don’t understand this make the mistake of emphasizing mechanistic metrics like the number of hours in the office and the number of bugs fixed per week. This is demoralizing and counterproductive. Of course if you are running a company you need to have deadlines, but you can do so while also being very flexible about how people reach them.

It is sometimes helpful to think of recruiting as 3 phases: finding candidates, screening candidates, and convincing candidates to join you.

- Finding means making contact with good candidates. There are no shortcuts here. You need to show up to schools, hackathons, meetups – wherever great programmers hang out. If your existing employees love their jobs they will refer friends. Try to generate inbound contacts by creating buzz around your company. If you have trouble doing that (it’s hard), try simple things like blogging about topics that are interesting to programmers.

- Screening. Great programmers love to program and will have created lots of software that wasn’t for their jobs or school homework. Have candidates meet and (bidirectionally) interview everyone they’ll potentially be working with. If the candidate has enough free time try to do a trial project. There are also more procedural things that can be useful like code tests (although they need to be done in a respectful way and they are more about getting to know how each side thinks than actually testing whether the candidate knows how to program (hopefully you know that by this stage)).

- Convincing them to join you. This is the hardest part. Great programmers have tons of options, including cofounding their own company. The top thing you need to do is convince them what you hopefully already believe (and have been pitching investors, press etc): that your company is doing something important and impactful. The next thing you need to do is convince them that your company is one that values and takes care of employees. The best way to do this is to have a track record of treating people well and offer those past employees as references.

A few things not to do: you will never beat, say, Google on perks or job security so don’t even bother to pitch those. You’ll never beat Wall Street banks or rich big companies on cash salary so don’t pitch that either. You’ll never beat cofounding a company on the equity grant, but you can make a good case that, with the right equity grant, the risk/reward trade off of less equity with you is worth it.

Finally, I’ve long believed that early-stage, funded startups systematically under-grant equity to employees. Programmers shouldn’t have to choose between owning a fraction of a percent of an early-stage funded company and owning 50% of an unfunded company they’ve cofounded. Naval Ravikant recently wrote a great post about this:

Post-traction companies can use the old numbers – you can’t. Your first two engineers? They’re just late founders. Treat them as such. Expect as much.

Making those first engineers “late cofounders” will dramatically increase your chances of recruiting great people. This is a necessary (but not sufficient) condition for getting the recruiting flywheel spinning where great people beget more great people.

* As someone who personally programmed for 20 years including about 10 years professionally, I preferred to call myself a “programmer.” Some people prefer other words like “hacker” “developer”, “engineer” etc. I think the difference is just uninteresting nomenclature but others seem to disagree.

An internet of people

Making industries “garage ready” for startups

Business development: the goldilocks principle

Some lessons learned

Do you want to sell sugar water or do you want to change the world?

What the NYC startup world needs (and doesn’t need)

Founder/market fit

Best practices for raising a VC round

There are two kinds of people in the world

Apple and the TV industry

Google’s social strategy

MIT is a national treasure

Dropbox and why you should invest in people

SEO is no longer a viable marketing strategy for startups

Selling pickaxes during a gold rush

Predicting the future of the Internet is easy: anything it hasn’t yet dramatically transformed, it will.

For older posts, see the contents page. I haven’t updated this page in a long time but plan to do so soon.

Alcoa, the biggest aluminum company in the country, encountered two problems peculiar to Iceland when, in 2004, it set about erecting its giant smelting plant. The first was the so-called hidden people—or, to put it more plainly, elves—in whom some large number of Icelanders, steeped long and thoroughly in their rich folkloric culture, sincerely believe. Before Alcoa could build its smelter it had to defer to a government expert to scour the enclosed plant site and certify that no elves were on or under it. It was a delicate corporate situation, an Alcoa spokesman told me, because they had to pay hard cash to declare the site elf-free, but, as he put it, “we couldn’t as a company be in a position of acknowledging the existence of hidden people.” The other, more serious problem was the Icelandic male: he took more safety risks than aluminum workers in other nations did. “In manufacturing,” says the Alcoa spokesman, “you want people who follow the rules and fall in line. You don’t want them to be heroes. You don’t want them to try to fix something it’s not their job to fix, because they might blow up the place.” The Icelandic male had a propensity to try to fix something it wasn’t his job to fix.

Back away from the Icelandic economy and you can’t help but notice something really strange about it: the people have cultivated themselves to the point where they are unsuited for the work available to them. All these exquisitely schooled, sophisticated people, each and every one of whom feels special, are presented with two mainly horrible ways to earn a living: trawler fishing and aluminum smelting. There are, of course, a few jobs in Iceland that any refined, educated person might like to do. Certifying the nonexistence of elves, for instance. (“This will take at least six months—it can be very tricky.”) But not nearly so many as the place needs, given its talent for turning cod into PhDs. At the dawn of the twenty-first century, Icelanders were still waiting for some task more suited to their filigreed minds to turn up inside their economy so they might do it.

Enter investment banking.

It’s a short book – just 5 chapters covering Iceland, Ireland, Germany, Greece, and California. What’s particular fascinating is how each place had a wildly different reaction to the credit glut.

- Great for Boston. Fairly or not, Boston is often typecast as an infrastructure, B2B, hardware, and biotech town. Between Tripadvisor and Kayak, Boston now has at least two very important consumer internet companies.

- Big win for the “golden age of SEO”.  By which I’m referring to roughly 2001-2008 when “demand” for content (people typing in search queries) far outpaced supply (good content). Companies like Yelp and TripAdvisor (along with Wikipedia, IMDB, etc) grew huge during this period, almost entirely through SEO. They did this by getting highly defensible flywheels spinning where more content meant more SEO which meant more users which meant more content. It is now far more difficult to grow a startup primarily through SEO. Almost all monetizable search categories have vast excesses of SEOd content. Moreover, Google is creating their own content (e.g. Google Places) which, at least at times, they have favored in their search results.

- The user experience should improve. MG Siegler and others have criticized TripAdvisor for an excess of ads. I don’t disagree with MG, but I also think this is largely the result of the broken online ad attribution system that punishes intent generators and rewards intent harvestors. Travel reviews are for users at the beginning of the travel research process (which on average takes weeks), but all CPA and CPC ad programs pay only for the last click which usually means when users are purchasing tickets or making reservations. Hence review sites are forced to saturate their website real estate with purchasing widgets and display ads. Hopefully as online ad attribution improves this will no longer be necessary.

- It’s weird how little coverage this IPO got and how the financial press missed the interesting stories. TripAdvisor ended the day at ~$3.5B in market cap, making it the second most valuable East Coast consumer internet company (after Priceline). Every story I saw focused on the share price drop over the day. The fact that the price dropped from its opening price simply means the bankers mispriced the stock and therefore insiders didn’t get the sweetheart deal they thought they were getting.

Update: I interviewed the CEO/founder of TripAdvisor on TechCrunch yesterday. Topics include the company’s origins, relationship with Google, SOPA, and advice to fledgling entrepreneurs.

A fast-food restaurant chain wanted to improve its milkshake sales. The company started by segmenting its market both by product (milkshakes) and by demographics (a marketer’s profile of a typical milkshake drinker). Next, the marketing department asked people who fit the demographic to list the characteristics of an ideal milkshake (thick, thin, chunky, smooth, fruity, chocolaty, etc.). The would-be customers answered as honestly as they could, and the company responded to the feedback. But alas, milkshake sales did not improve.

The company then enlisted the help of one of Christensen’s fellow researchers, who approached the situation by trying to deduce the “job” that customers were “hiring” a milkshake to do. First, he spent a full day in one of the chain’s restaurants, carefully documenting who was buying milkshakes, when they bought them, and whether they drank them on the premises. He discovered that 40 percent of the milkshakes were purchased first thing in the morning, by commuters who ordered them to go.

The next morning, he returned to the restaurant and interviewed customers who left with milkshake in hand, asking them what job they had hired the milkshake to do. “Most of them, it turned out, bought [the milkshake] to do a similar job,” he writes. “They faced a long, boring commute and needed something to keep that extra hand busy and to make the commute more interesting. They weren’t yet hungry, but knew that they’d be hungry by 10 a.m.; they wanted to consume something now that would stave off hunger until noon. And they faced constraints: They were in a hurry, they were wearing work clothes, and they had (at most) one free hand.”

The milkshake was hired in lieu of a bagel or doughnut because it was relatively tidy and appetite-quenching, and because trying to suck a thick liquid through a thin straw gave customers something to do with their boring commute. Understanding the job to be done, the company could then respond by creating a morning milkshake that was even thicker (to last through a long commute) and more interesting (with chunks of fruit) than its predecessor. The chain could also respond to a separate job that customers needed milkshakes to do: serve as a special treat for young children—without making the parents wait a half hour as the children tried to work the milkshake through a straw. In that case, a different, thinner milkshake was in order.

There are at least three obvious ways to apply this concept: 1) when searching for startup ideas, think about jobs people want done that they can’t currently get done, 2) when thinking about how to fix or improve your product, understand why existing users are hiring your product (or should be hiring your product) and try to improve those experiences, 3) when analyzing markets, segment companies by the jobs they are hired for. Sometimes products that might appear similar (e.g. two photo sharing apps) are actually hired for very different purposes, and are therefore misclassified as competitors.

There’s actually 2 levels of trust here. The first is knowing and trusting the person you’re buying from. And if you don’t know who they are, then you must move on to the second level of trust, which is do you know and trust the platform the person is using.

The ability to have “second order trust” is one of many reasons the internet has made so many institutions obsolete. Take the SEC’s role in policing private companies that market themselves to potential investors. This was sensible consumer protection back when the government was arguably the only organization that had the means and incentives to identify fraudulent investment schemes. But today we have many examples of websites that’ve built mechanisms for reliably tracking the reputations of individuals and organizations. This means the SEC could – in theory – make the unit of regulation platforms instead of investors and startups (something the crowdfunding bill being considered by Congress seems to do at least in part), which in turn could unleash a new wave of innovation among crowdfunding platforms and crowdfunded startups.

When a trend like this emerges, it’s always interesting to ask “why now?” For example, for almost a decade, entrepreneurs tried to create video sharing services like YouTube, but only succeeded when certain key dependencies – broadband, digital video cameras, a version of Flash that “just worked” – became widespread.

I asked Roelof Botha the “why now” question regarding web-based marketplaces. He said something I thought was really interesting: marketplaces depend on trust, and trust requires knowing the reputation of a prospective counterparty. Today, for the first time, you can get background information on almost any prospective counterparty by searching Google, Facebook etc. Or put more simply: we finally have an internet of people.

I’ve been blogging more lately which explains why Dec 2011 is tracking to be up near 2M page views (although frankly that number seems high to me- I wonder if somehow they are counting each page view multiple times – maybe due to the way WordPress works?)

As if we needed another reminder of how wrong Compete data is here is their chart:

Not even directionally correct.  Yeah they show UVs and not pageviews but I don’t see any reason those would have gotten decoupled.

(I had cdixon.org tagged with Quantcast for a while but removed it a few weeks ago –  their chart when cdixon.org was tagged makes more sense (as you’d expect)).

I blog just for fun / hobby, so don’t really care about these stats. But it’s interesting to see the (in)correctness of these popular analytics services.

You can count on there being a whole host of impinging forces that will affect the dynamic of decision-making on any issue at a big company.

The consensus building process, trade-offs, quids pro quo, politics, rivalries, arguments, mentorships, revenge for past wrongs, turf-building, engineering groups, product managers, product marketers, sales, corporate marketing, finance, HR, legal, channels, business development, the strategy team, the international divisions, investors, Wall Street analysts, industry analysts, good press, bad press, press articles being written that you don’t know about, customers, prospects, lost sales, prospects on the fence, partners, this quarter’s sales numbers, this quarter’s margins, the bond rating, the planning meeting that happened last week, the planning meeting that got cancelled this week, bonus programs, people joining the company, people leaving the company, people getting fired by the company, people getting promoted, people getting sidelined, people getting demoted, who’s sleeping with whom, which dinner party the CEO went to last night, the guy who prepares the Powerpoint presentation for the staff meeting accidentally putting your startup’s name in too small a font to be read from the back of the conference room…

Man, I wish Marc still blogged.  (ht saul lieberman)

These CEOs are worrying too much. VCs know what they are doing and almost always invest with a financial instrument – preferred shares – that protects them even when the valuation is very high. Preferred shares behave like a stock on the upside and a bond on the downside.  The only way investors actually lose money is if the company is sold for less than the amount of money raised (which is generally significantly lower than the valuation).

Here is what the payout function looks like for common stock (for example, what you get when you buy stocks in public markets):

And here is what the payout function looks like for preferred shares:

So, to take a concrete example, Dropbox reportedly raised their last financing at a $4B valuation*. If you think of this as a public market valuation of common stock, you might think this means the VCs are betting $4B is the “fair value” of the company, and will lose money if Dropbox’s exit price ends up being less than $4B.  But in reality, assuming the standard preferred structure, the last round investors’ payout is as follows :

Scenario 1: Dropbox exits for greater than $4B ==> investors get a positive return (specifically, exit price divided by $4B)

Scenario 2: Dropbox exits for between $257M (total money raised) and $4B ==> investors get their money back (possibly more if there is a preferred dividend)

Scenario 3: Dropbox exits for less than $257M ==> investors lose money

If reports are true that Dropbox is profitable and generating >$100M in revenue, then scenario 3 – the money losing scenario – is extremely unlikely.

Will investors be thrilled with scenario 2?  No, but they are pros who understand the risks they are taking.

Going back to the entrepreneur’s perspective, in what sense is a high valuation “setting the bar high”?  In the preferred share payout model, there are two “bars”:  money raised and valuation.  I don’t see any reason why entrepreneurs shouldn’t be as aggressive as possible on valuation, especially if they are confident they won’t end up in scenario 3.

An important point to keep in mind is that, in order to maintain flexibility, entrepreneurs shouldn’t give new investors the ability to block an exit or new financings. Investors can get this block in one of two ways – explicit blocking rights (under the “control provisions” section of a VC term sheet) or by controlling the board of directors. These are negotiable terms and startups with momentum should be very careful about giving them away.

* Note that I have no connection to Dropbox so am just assuming standard deal structure and basing numbers on public reports. I am making various simplifying assumptions such as not distinguishing between pre-money and post-money valuation.

I think the most common form of tech acquisition is a variant of the [business acquisition], in which the acquirer wants to inject forward motion into the organization. It’s far more difficult for a public company to rally around a launch into what might seem like a small sector… it just doesn’t seem worthy of the biggest brains and bravest folks, so it gets shunted aside.

On the other hand, once a smart tech company acquires a smaller company with momentum, it gives the company permission to drive, perfect, polish and grow that business. I’d argue that this what actually happened with YouTube.

The logic underlying organizational momentum acquisitions can be found in Clay Christensen’s disruptive technology theory. Smart CEOs of large companies realize how hard it is to shift internal momentum away from developing sustaining technologies. As a way to avoid this trap, Christensen recommends that large companies set up internal startups that are as organizationally separate as possible. But, as Seth points out, acquiring startups with momentum is another way to get the same result.

- Talent. When the acquirer just wants the team (generally just engineers and sometimes designers). As a rule of thumb, these acquisitions are priced at approximately $1M/engineer.

- Tech: When the acquirer wants the technology along with the team. Generally the prices for these acquisitions are significantly higher than talent acquisitions. Sometimes they are even in the hundreds of millions of dollars for fairly small teams (e.g. Siri). The calculation the acquirer uses to price tech acquisitions is usually “buy vs build”. An important component in this calculation is not just the actual cost to build the technology but the opportunity cost of the time it would take them to do so.

- Business: When the company is either bought on a financial basis (the acquisition is “accretive”) or bought based on non-financial but highly defensible assets (Google buying YouTube which had minimal revenue at the time but a huge network of producers and consumers of video).

As large companies mature they move from doing just talent acquisitions to doing talent and tech acquisitions to eventually doing all three types of acquisitions. Usually it takes a startup beating the large company in an important area for the large company to realize the necessity of business acquisitions. For example, Google seemed to dramatically change its attitude when YouTube crushed Google Video. Eventually every large company has a moment like this.

If I understand correctly, “garage-ready” essentially means separating design from manufacturing, i.e. “creativity-intensive” processes from capital-intensive ones. This may be an inevitable result of industry maturation and specialization, but there is a downside to it, at least for the so called “developed” nations. The result of differential costs for commodity labor, the fungibility and liquidity of capital, and the ease of transmitting both human and machine-readable information across arbitrary distances, means that capital-intensive processes – i.e. making things – migrate to locations with lower total cost of operations (which, Germany excepted, tend to be locations with lower labor costs). Another way of saying this is that nothing is fabless; the foundry is merely outsourced and moved to a cheaper location. This reality is great for the creative class and for the lower cost locations, but it’s less happy for the residents of the higher class locations that are not so lucky to be part of the creative class.

I’m not ready to draw the conclusion that this is the cause of the economic inequality in the US and malaise across Europe and Japan, but there definitely appears to be some correlation. Again, I don’t know if these results of the “garagification” of an industry can be reversed or mitigated in the name of societal stability, but if anyone can find a way to do it it would be the creative class. Unfortunately, because techies and entrepreneurs are solidly part of the creative class and perhaps even *the* primary beneficiaries of the separation of design and manufacturing, we generally avoid acknowledging or discussing the negative aspects of this trend.

Note that I said “reversed or mitigated.” Trying to reverse or stop these trends is probably a quixotic goal, but perhaps mitigation is in fact possible. For example, is it possible to create a country in which the entire labor force is “creative”? I myself have trouble seeing how such a possibility could be made real, but I’d like to see more intellectuals and entrepreneurs spend some brainpower on the question.

It is true that new technologies often lead, in the short term, to lower wages and fewer jobs. Craigslist, for example, has about 30 employees yet, by replacing the classified ad industry, eliminated many thousands of jobs (local newspaper reporters, classified ad salespeople, etc). The same could be said for almost every popular website.

On the flip side, new technologies have driven down prices (Walmart and Amazon), led to massive increases in information productivity (Google and Wikipedia), and created new income sources (eBay and Craigslist). Greater productivity and lower prices at least partly compensate for part-time jobs and lower wages.

Jordan is right that these are questions we – the technology community – should spend more time discussing.

I remember first observing this when I worked at Bessemer. For example, there was a startup that supplied services to video websites. For years, the company soldiered along, barely growing. Then, suddenly, YouTube blew up and this company took off along with it.

As a founder, these exogenous shocks are out of your control, but you can 1) understand what exogenous shocks you depend on, 2) try to guess when those shocks will hit, 3) manage your runway so you survive long enough for them to hit.

More adventurous entrepreneurs might argue 18 months is too conservative. It’s true that following the 18 month rule can be extra dilutive. At SiteAdvisor, we raised our Series A about three months before we were acquired. So we gave up equity for cash that we never spent. But in retrospect, given what we knew at the time, I think it was the right decision.

The question of when to raise money is one of the few times that entrepreneurs and early-stage investors have somewhat divergent economic interests. If you control a large investment fund, you always have the option to extend a company’s runway. The entrepreneur doesn’t have this option. I’ve even heard some entrepreneurs whisper about Machiavellian VCs who deliberately try to get you to the end of your runway so they can negotiate harder. I think this is a bit of a conspiracy theory. Almost all VCs I know care primarily about the success of their companies and not about extracting every last point of equity.