Worldview as a Competitive Advantage

Recently I’ve been thinking about what makes a strong competitive advantage in a world where individuals and firms are increasingly leveraged and specialized. The top several dozen venture investors basically monopolize the industry. (About 20 firms — 3% of the venture universe — earn 95% of the returns.)

The common value-add or differentiating dimensions are a great network, positive signaling effects, insightful operating experience, or the willingness to invest at the highest price. But it’s not clear that the majority of investors move the needle.

These are all useful from the perspective of founders. The tough part as an investor is that there are few unfilled niches. You’ll run into tough competition on all of the above features. So what’s left? From your perspective, does value-add even matter that much when VCs still pass on many amazing deals?

I think that worldview is the most underrated and robust advantages you can have over other investors.

An interesting thread in politics and psychology these past few years has explored how people change their minds. Example: ideas from Jonathan Haidt’s The Righteous Mind and Arnold Kling’s The Three Languages of Politics have made their way into the mainstream. The core insight here is that people rarely change their minds. Opinions on different issues aren’t individual beliefs based on separate analyses — they’re products of a single underlying worldview. If an idea doesn’t fit your worldview, you’re unlikely to accept it on its merit rather than break up an otherwise consistent and unified understanding of how things work.

This makes VC hard because the best deals are, almost by definition, outliers that don’t easily fit into any worldview. So having a worldview that’s more accommodating to a wide variety of ideas should let investors make the right picks which is historically very hard (see Airbnb and Robinhood examples linked above.)

Perhaps we should focus less on building a network or thought leadership or industry experience, and more on learning worldview-expanding (or, ideally, -shattering) ideas. Less of entrepreneur Eric Reis or How To Win Friends and Influence People, more of polymath Robin Hanson or The Sovereign Individual.

That’s the core point I want to make. As a quick and currently-relevant example, different investors seem to view Tesla through different lenses. Many tech investors I follow hold a big-picture worldview more in tune with what works for VCs. If the macro-trend of electric vehicles is there and the team is right, it’s worth betting on. Other investors, often public equities and bonds people, take a more grounded approach. Tesla is likely structurally unprofitable compared to other manufacturers that are quietly waiting for the right timing, plus their financials are comparable to GM right before it went bankrupt in 2008. One portfolio manager I know called the Tesla bond issue one of the worst he’s seen in recent memory. Luckily for Tesla, more optimistic investors controlled enough capital to purchase the issue anyway.

Of course these are total oversimplifications of each stance, but the point I’m making remains. I think investors make decisions on a case-by-case basis, but with significant bias from worldview. Or maybe worldview-veto is a more accurate descriptor of what’s going on.

There are a number of other interesting threads here such as the problem of demonstrating that you have the right subjective worldview to actually have an edge. Networks, operating experience, etc, are inherently more provable. (This relates to the Thiel contrarian philosophy: if others buy-in to your perspective, it’s not an edge.)

Another question is whether all this matters. You can’t go to an LP when fundraising and just say “I make the best decisions because I have a better wholistic model for how the world works.” The other value-add metrics also act as a positive signal. I named this blog Heuristically Speaking for a reason!

Thanks to Nathan Ju for reading a draft. Subscribe to not miss any future posts!

Degrees of Freedom

I often talk to companies that come up with complex plans involving a technical challenge, going to market, then scaling and fending off competitors. They want to do X to leverage Y, then finally become Z when the time is just right. I suspect this stems from (incorrectly) feeling like the problem they’re solving isn’t innovative enough. Perhaps we glorify different strengths of successful businesses (the product obsession of Apple, the scale of FB, the community of Airbnb, etc) and forget that no company can possibly combine all of those virtues into one perfect startup. Or maybe investors build thought leadership by constantly talking about bleeding-edge buzzwords and founders accidentally make that their default set-point.

Think back to the best startups of the past decade. They all follow fairly simple plans which essentially bet on one core thesis: taxis that you can hail with app. A site where people can rent out a room in their house. Let teens send pictures that disappear. None of these companies had a multi-phase plan from the start. They didn’t solve particularly novel engineering challenges, at least until they were already super successful and hitting scale issues. There was only one singular problem to focus all attention on. And it often sounded like a somewhat lame problem, even though it ultimately wasn’t.

When evaluating startups, I often use a mental model I call degrees-of-freedom. Count how many things need to go right for the company to survive. Each additional degree of freedom makes execution exponentially harder and more complex. (I use that word exponential in the literal mathematical sense, not in the metaphorical sense!)

If you’re dealing with regulatory uncertainty, and building on a new decentralized protocol, and introducing a new business model, sure, you’re doing something innovative. But too many things have to go right. Too many things are simply out of your control.

My advice to these ambitious founders is: don’t feel like you need to do something impressive on every front. Pick one idea to bet the company on, and don’t lose sight of it. Use simple foolproof execution strategies for everything else. Reduce the number of degrees of freedom.

Let’s all get more excited about “simple” startups!

Buying IBM

IBM is one of my favorite sales and marketing case studies. As the saying goes: nobody ever got fired for buying IBM. I had always appreciated the sales hustle, but thinking more about the “Buying IBM Effect,” I began seeing the same dynamic elsewhere:

Huge fund sizes, for example, can hurt VC returns. Deploying large amounts of capital is difficult if you’re only seeing a limited number of worthy startups. You have to choose between investing more capital per company and investing in a larger number of startups. Many investors chose the latter option. This explains some quirks of highly-saturated funding ecosystems like Stanford’s. I’ve seen companies there raise ~1mm with nothing more than an idea and a decent-but-nothing-special team. That’s not to say this strategy is impossible to pull off, but in every case I’ve seen, it’d be crazy to invest on team alone. Nobody ever lost an LP because they invested in Stanford startups, so excess capital is deployed there.

As a manager it can be too risky to hire a brilliant-yet-under-credentialed or not-well-rounded candidate. The upside is simply making the hire and your boss not realizing that you made a tough yet successful decision. The downside is that the candidate, while exceptional in one dimension, can’t cut it some other respect and drops the ball. Then you’re on the hook for bringing them on the team. Unless you’re fortunate enough to work in a managerial environment that understands the risks and rewards of making such a hire, it’s not worth it to take the risk of making the hire. Nobody ever got fired for hiring a mediocre candidate with the right background on paper. But exceptional is often what the organization needs.

Wealth managers want to provide clients with reasonable returns. Although active management and picking individual assets lets you attempt to beat the market, you’ll lose more clients by underperforming than you’ll gain by over-performing. So wealth managers usually defer to index funds which stick to average returns. (In my opinion this is the best strategy anyway, but the point I’m making is that wealth managers are forced into the passive/conservative strategy.) Nobody ever withdrew from a retirement fund because their returns were just fine.

The Buying IBM Effect is just a symptom: asymmetry between the upside and downside is the real problem.

In environments you can control, install a system that judges decisions on the swing instead of the hit. (My deepest apologies for the platitude.)

In environments you can’t control, design your product or pitch to cap the downside. Risk aversion is often an ulterior motive that you won’t uncover directly through conversation. It’s important to anticipate the structural biases of anybody you’re interfacing with and account for their internal decision making considerations.

 

Getting Positive Feedback

I’ve noticed that founders often optimize for finding the most positive feedback possible. That’s directionally a good move and it aligns well with Paul Graham’s idea that it’s “better to make a few users love you than a lot ambivalent.” The benefits of positive feedback in conversations with potential customers are clear: you validate your product, build up a network of fans, and get to iterate towards what specific dimension of your product drives the most value.

But once in a while I run into early-stage founders who have nothing but positive feedback from all parties or have a hard time naming objections and complaints. You’d think that’s great news, but it always makes me a little uncomfortable as an investor. It usually indicates that the founder is exaggerating demand or they’re not executing their validation correctly. If literally everything you hear is positive, it probably indicates that users aren’t being totally honest in their feedback or you’re not pushing hard enough to establish an initial cult following. Here are two things to keep in mind as you talk to users:

Remove bias

Users won’t want to hurt your feelings. Make sure you present your startup as someone else’s idea. Or as a product already on the market which you happen to be researching. While users will have no problem criticizing an fintech product, they’ll be too nice to healthcare products or other “socially positive” businesses. Take every precaution to make sure the feedback your getting will match up with users’ revealed preferences (true needs) when the product comes to market.

Take what you can get

If people say they love your product, then make your ask larger and larger until you’re out of slack. In the early days of Stripe, the Collison brothers would ask people whether or not they would use Stripe. If the user said they would, the Collison brothers didn’t stop there. They’d respond with “great, give me your laptop and I’ll get you up and running.”

If you’re simply interviewing people, ask them to sign up for the beta waitlist. If that still works, ask them to refer you their friends as well for your waitlist. If that still works, ask them to pre-pay for a discount when the product launches. This will not only tell you how badly people actually want your business, it’ll build up your customer base! This is also super useful for investors.

 

Psychology in Product and Sales

I’m experimenting with a new blog post format. Often times I’ll read a multi-paragraph essay and feel frustrated because it could have been condensed into a series of bullet points. So that’s what I’ve made here. Let me know what you think, hopefully the concepts will be intuitive and this bullet-style list will enumerate relevant ideas and examples. This is a list of principles of psychology in product and sales. (I’ve been reading Robert Cialdini and Daniel Kahneman recently!)


  • Signaling
    • Doubling the price on jewelry signals quality, so people will buy more of the same good if it’s priced higher. This is the opposite of what you’d expect.
  • Reciprocation 
    • “Take this thing, no-strings-attached” creates a feeling of debt and favor.
    • Hare Krishnas greatly increased their fundraising efforts by handing out roses for free at airports.
    • Putting a sticky note in a mailed survey request will greatly increase response volume/quality. Response is even better if the note is handwritten.
  • Concession
    • Related to anchoring, people often feel bad or indebted for not being able to fulfill a request.
    • Salespeople start with a big ask for making a purchase but plan on it failing, then say something like okay would you at least be able to give me referrals to three friends who would find this product useful?”
  • Commitment 
    • Having people say they’re in support of something ahead of time (even days or longer) makes a future ask much more successful.
    • Canonical example is political campaigns asking people days before an election will you vote?” and people tend to overcommit and say yes. Then when election times comes, they’ll actually vote to stay true to their word.
    • Once someone goes to the bathroom in a new house or says they’ll buy a car, they’ve already made a decision in their head.
      • Salespeople know this, and will look for signs of mental commitment before jacking up prices.
  • Group initiation 
    • Soldiers go through bootcamp, frat boys haze, and Catholics baptize. Initiation builds critical bonds, and the more intensive/costly the initiation is, the stronger the effect.
    • Products like Stack Exchange make you take steps (earn some amount of reputation, in this case) before becoming a part of the community and having full access to the product.
  • Publicity effect
    • If somebody makes a statement publicly, they’ll think the statement is true even if they’d otherwise rationally find it to be false. Sales tactic would be to get someone to say they have a need for the product out loud.
    • Corollary: be reluctant to publicly share works in progress which would create biases for yourself.
    • If you can get a user to somehow indicate that they use your product (to other people, online, or by having some sort of public profile,) they’re much less likely to churn.
  • Internal vs external beliefs
    • Canonical example: experiment where kids were left in a room with a bunch of lame toys, and one cool robot toy. They are told not to play with the robot, then the experimenter leaves the room.
      • Kids played with the robot if they were told it was wrong and they’d be punished (even though they couldn’t be caught since they were alone in the room)
      • Kids didn’t play with the robot if they were simply told it was wrong
      • People can blame bad external rules for behavior, but it there’s no punishment they would have to do something only a Bad Person™ would do.
    • This backs the socially positive slant that companies like Patagonia or Lyft build their value props on.
  • Inner circles
    • This is related to the group initiation topic. Being in an Inner Circle makes the product much more sticky and drives engagement from users within it.
      • This is particularly important in products where a small group of power users greatly influence the direction and quality of the product.
    • Examples: Reddit’s gilded club, Quora’s Top Writers
    • Inner Circles can come in many layers.
      • Some startups have tried create multi-functional social platforms (meeting new people, messaging friends, etc)
      • But people use these layers to clearly define the relationship: coworkers use LinkedIn, friends/acquaintances use FB Messenger or GroupMe, and close friends use phone numbers/iMessage. This removes ambiguity and says “we’re friends because we use this medium reserved for friends of only this type”
  • Risk aversion
    • People hate losses more than they like gains.
    • “This offer is only open for a limited time!”
    • “The special edition only has 100 copies”
    • “Thanks for joining, here are 50 in-game coins to get started!” (you’d give up this arbitrary freebie if you stopped playing the game)
  • Moral-threat vs consequence-threat
    • People don’t mind taking risks if the expected cost of the consequence is low.
    • But not imposing any punishment shifts the act to a social-signalling/moral burden (rather than a financial one) which has much higher intangible costs and an unlimited downside.
    • Canonical example: a daycare had lots of late child pickups so they started charging $5 each time that happened. Parents were late more often since they had an easy out to their lateness which was simply paying the five bucks.
  • Having an excuse 
    • 6-8% of Gerber baby food is consumed by people who aren’t babies. Gerber actually tried marketing a product specifically for seniors but it failed. People didn’t want to admit they needed that sort of food, so they stuck with the baby product (plausible deniability — lots of seniors have grandkids!)
    • Most hookup apps market themselves as dating apps. While many users are actually focused on dating, nobody wants to tell others they’re only looking for hookups.
  • Anchoring
    • This effect is pretty well known.
    • I was chatting with a guy in SF who was asking for donations for a hip-hop related community org. He challenged me to donate $100 which was crazy, and I ended up donating $10 which in hindsight was twice what I’d otherwise choose to donate.
  • Self consistency
    • People have a need to be self-consistent in their beliefs and actions.
    • The question “why do you want this job?” is also a sales tactic. The candidate will be forced to articulate good reasons out of politeness – and the desire for internal consistency will make them believe these reasons. (source)
    • Unethical example: if you conduct a fake survey about lifestyle, people will hype up and inflate their lifestyle to create a compelling narrative about themself. If you follow that with an expensive ask that would validate that lifestyle, they’ll often go along to not sound self-contradictory.
      • Wouldn’t make sense to say yeah I travel all the time, but this packaged travel money-saving deal isn’t something I want.”
  • Social proof and social pressure
    • Tip jars are seeded” to give the appearance that many other people tip. 
    • Some products with FB login will show you that your friends use it too.
    • Google glass became associated with glasshole” nerds, but Snap Spectacles marketed with attractive and well-rounded models from the start.
    • “Endless chain” where you make a sale, then go to their friend and say your friend John recommended this for you.” This makes it turning down your friend instead of turning down the salesman.
  • Liking
    • Being attractive, personal and cultural similarity, giving compliments, contact & co-operation, conditioning, and association with positive ideas all make people much more open to trying a product or buying something.
    • GitHub’s Octocat is a friendly and fun mascot which users like and build an attachment to
  • Authority
    • This one is obvious. Companies plug high-profile clients whenever possible.
    • Twitter has the blue checkmark to make users feel like they’re getting higher quality information from those people through the platform.
  • Scarcity
    • Robinhood’s famous growth hack where you needed to refer people to move up a spot in the waiting list. Access to the early product was scarce.
    • New coke vs old coke
      • In the 80s, Coca-Cola tried changing the Coke recipe because it had done better in blind taste tests with consumers. But people rejected the New Coke because the Old Coke was then scarce and people wanted to keep what they knew
    • Much stronger to say you’re losing X per month” instead of you can save X per month”
  • FOMO and security
    • Uber guaranteeing people an arrival time increases number of rides since people feel the security associated with having an upper bound.
    • GroupMe SMS’d people who didn’t have the app. This made them feel like their friends were on the app but they weren’t. (Houseparty makes it easy to inspire FOMO with SMS too).

Simpson’s Paradox and Thinking Rationally in Venture Capital

Decision making in venture capital relies heavily on probabilistic thinking and difficult-to-compare historical data. The heuristics are too rough and the feedback loops are too long. Most of the time correlation does not imply causation. You can’t distinguish “A causes B,” “B causes A,” and “C causes both A and B.”

You can get around the correlations vs causation problem by treating startup success as a function of independent variables (see Leo Polovets’ great post on this). Since most investors assess risk through empirical data and qualitative measures learned through pattern recognition, human biases can easily influence decision making.

Here’s my favorite example which is pulled from Michael Nielsen’s excellent post:

Suppose you’re suffering from kidney stones and go to see your doctor. The doctor tells you two treatments are available, treatment A and treatment B. You ask which treatment works better, and the doctor says “Well, a study found that treatment A has a higher probability of success than treatment B.”

You start to say “I’ll take treatment A, thanks!”, when your doctor interrupts: “But the same study also looked to see which treatment worked better, depending on whether patients had large kidney stones or small kidney stones.” You say “Well, do I have large kidney stones or small kidney stones”? As you speak the doctor interrupts again, looking sheepish, and says “Actually, it doesn’t matter. You see, they found that treatment B has a higher probability of success than treatment A, regardless of whether you have large or small kidney stones.”

Take a second to wonder: how is that possible? I was initially stumped, and a couple brilliant friends of mine couldn’t think of a concrete explanation off the top of their heads. It turns out that this result came from a legitimate real-life study. The sample sizes of the different groups were not controlled:

Okay, that makes sense. But the point is that empiricism can easily fail when you treat complex problems as a set of independent variables.

VC is pretty famous for fitting power law distributions and having skewed samples sizes. Replacing large/small kidney stones with a startup-relevant category and Treatment A/B with something a startup is doing, you’ll have a massively uneven set of data points to draw in — this is precisely what opens the door to Simpson’s Paradox.

The question then becomes: what are the most important cases of Simpson’s Paradox in VC? Perhaps large founding teams, or “distracted teams” consisting of university professors fit the bill. There are few examples of this, especially compared to the number of standard 2-3 cofounders we’re used to, so the statistical waters are muddied.

Tomasz Tunguz wrote that this type of thinking can also be applied to finding market opportunities: (In 2013 no less — ahead of the game!)

The Berkeley example reminds me of the SpaceX’s formation story Elon Musk shared at the D conference this year. Musk implicitly knew launching satellites into space would be expensive. After all, NASA’s annual budget is about $19B. But when Musk and his team analyzed each cost component of a space launch, they found that less than 10% of the costs were the rocket and the fuel and the launch equipment. This meant Musk could conceivably reduce the costs of space shipping by 80%.

While it’s not a true statistical example of Simpson’s Paradox, the point is the same. The market held a worldview based on aggregate data. But Musk recognized the aggregate space costs didn’t tell the true story. By digging deeper, he and his team found a lurking explanatory variable and an opportunity to disrupt the industry.

I think everyone should read about the common statistical paradoxes and fallacies. An obvious followup post would cover something like Bayes’ Rule in VC. Only one in five doctors correctly answer the linked Probability 101 question related to cancer rates (!!!) and I bet this many investors fall into similar traps.

Analyzing Venture Opportunities Part 1: The Product and Market

I spend a lot of time talking about business opportunities through my work with Contrary. I’ve noticed that many first-time founders forget to cover certain topics in meetings and pitches. If you’ve been thinking about a startup for a long time, non-obvious ideas become can so ingrained in your head that it’s hard to articulate the assumptions you’re making. This is a list of things that VCs consider when analyzing a venture opportunity’s product and market—make sure to touch on each when talking with a VC.

  • Why now? Think about where this product/market is on the S-Curve. Company should have recently become possible (but not prevalent) because of a new market trend or tech innovation. Unfilled niches are short lived but being too early is very costly.
  • What’s the initial niche? No valuable market is entirely unfilled. There must be some specific niche that can be won over. It’s important that the company provides something 10x better than existing products/services. (Side-note: the degree to which the startup has to be better is directly related to how much they have to change existing customer behavior). Example: Amazon originally focused just on books and made the customer experience convenient and low-price in a way that bookstores fundamentally couldn’t match.
  • Can you grow from that initial niche? Remember that the initial niche is just part of the plan to solve a bigger problem in a bigger market. Example: Amazon used its bookstore cash, workforce, technology, and processes to expand into other retail markets.
  • Is the product/service defensible? There should be something that prevents competition from changing their product or using their resources to create a new product. This is often legal (patents), social (network effects), economies of scale, informational (data that’s valuable across different products), or strategic (example: Facebook struggles to take on Snapchat partly because everything FB contradicts Snap’s core privacy values).
  • What metrics and KPIs will show that you’re growing? Metrics are necessary to make sure growth is on track, and execution should be focused on improving the most important metrics. (How did the company decide which metrics to focus on?)
  • What de-risks your assumptions and bets? Assumptions are pretty much the entire foundation of an early-stage startup’s game plan. Being able to quickly prove or disprove assumptions will give founders a more clear picture of reality.
  • How are you going to make money? There should be some sort of exit strategy or long-term profitability goals. Is the revenue stream recurring, network/data based, etc?
  • Why are competitors doing X and not Y? There should be some analysis of how competitors’ strategy and execution interacts with available market opportunities (related to Peter Thiel’s Secrets — things you know but no one else does).
  • How is the market growing? Both growth rate and change in growth rate are important for a founder to know.
  • What are your current bottlenecks / resource constraints? This ties in to your roadmap and execution strategy. Have you thought deeply about what’s important to get done and what can wait?
  • What have you learned from users and how has that informed your decisions? It’s important to understand what users need and how you can better serve them. Do you look at new users by cohort? Have you segmented users based on any patterns?

Note that every answer to these questions does NOT have to be perfect. Part of analyzing a business is finding the flaws (there’s always at least one) and thinking about how it can be overcome or compensated for. Don’t sweat it too much if you can’t find a great answer to some of these questions.

Part 2: Thinking About People is now available!