Back to Blog

Biases: The Biggest Force Holding Back Startup Investors

By Christopher Steiner  •  Oct 26, 2016

Hundreds of investors reviewed pitches from Airbnb, Instacart, and Uber when these companies were nascent startups and chose, ignominiously, to pass. But even great investors can miss big opportunities. It’s part of the profession. Investors can misread the market, misjudge the founders or simply believe an opportunity is overpriced. But often an investor will pass because of biases he or she has developed through experience, from the 'wisdom' of the crowd, or simply from a lack of knowledge.

Biases pervade every industry, even tech, and they can limit startup investors’ potential and their ability to find outliers. Many things that some investors hold to be truths aren't truths at all; they're circumstances that have arisen out of chance.

Startup investing isn't physics; there are few absolutes or laws. Markets change, models adapt, paradigms topple. So applying hard and fast rules can be dangerous when the whole point of startup investing is to find the outlier that can hypothetically grow 1,000x or 10,000x, especially when some of those rules are likely borne out of misguided biases.

Venture capitalists often rely on forms of pattern recognition, which, while they may prove effective in short runs, can dig an investor deeper into existing biases, and a hole. "Pattern recognition stems from a fear of investing in people or spaces that are new or unknown," says Minal Hasan, Managing Partner at K2 Global, a Silicon Valley venture firm. "So then you can lose out on some of the most innovative and game-changing technologies because they’re either unfamiliar to you, or because they don't fit a neat mold."

Humans have built societies and systems upon foundations of cognitive biases that can often run our lives. There's even a classified effect called 'bias blind spot,' where a person might easily recognize the effect of biases on others and their decisions, but the same person is unable to recognize biases within his or her own psyche and how they affect his or her life and choices. Biases can be thought of as a kind of short-run evolution. They're not part of biology, but their effects can be reinforced across hundreds of years and passed down from one generation to another.

It's unsurprising, then, that investor behavior, whether it's in the stock market or within the ranks of angel investors and VCs, can continue to coalesce around the same patterns, even when those patterns don't lead to better outcomes or gains. Investors tend to make their decisions with a mix of thought modes that author and Nobel Prize-winning economist Daniel Kahneman refers to as System 1 and System 2 in his book Thinking, Fast and Slow.

System 1 calculus tends to be quick and instinctive, whereas System 2 methods are built around logic, time and deliberate reflection. Investors may think they've built their processes exclusively around System 2, but System 1, heavily affected by our own cognitive biases, plays into all of our decisions. The key to being a better startup investor is to try and examine opportunities within the construct of Kahneman's System 2. Entrepreneur Elon Musk of PayPal, Tesla, and SpaceX fame, prefers to think through problems from first principles; this is a similar type of unbiased intellectual rigor that Kahneman espouses.

Within the realm of startup investing, some of the acute investor biases we cover in this piece include:

  • Anchoring Bias
  • Experience & Clustering Bias
  • Education Bias
  • Race & Gender Bias
  • Geographic Bias
  • Confirmation Bias

Anchoring Bias

Humans' are regularly influenced by the first relationship or impression formed by a person, place or thing. A tourist who has a tough start to a trip in Berlin might never offer high praise of the city, even though his or her subsequent three days were interesting and enjoyable. Investors are typically no different.

If an angel investor observes some overt JavaScript bugs in a founder's demo, she or he may never be able to shake the idea that this team can't cut it from a technical execution standpoint, even though the company may be in the top 1% within the discipline. The investor’s impressions of that company get 'anchored' in that one demo snafu, possibly preventing the investor from seeing all of the other qualities in the team and the product.

Anchoring may be the most irrational of biases, as it can take on strange forms from seemingly unrelated subjects. Dan Ariely, the author of Predictably Irrational and a professor of behavioral economics at Duke, demonstrated this with an experiment at MIT where, in front of a room full of students, he auctioned off all matters of bric-a-brac, from trackballs to bottles of wine. Before students wrote down their bids for each item, however, Ariely instructed them to write down the last two digits of their social security number on a piece of paper.

Demonstrating the power of anchoring, students whose social security numbers ended in higher digits, like an 67 versus a 15, bid proportionately more for each item. And this all from a number that clearly has nothing to do with the subject at hand, the item at bid. This is why furniture stores often have wildly high list prices along with comically large sales. Humans assign a value to the pieces based on that list price, even though they may be made up.

The best investors will constantly search their viewpoints for traces of this effect. Anchoring needn't come from a number. It could be that the CTO of the startup came from Google, a fact that may attract certain investors to the company even though the product may be average or poor. Some investors can't get away from that initial impression engendered by that association with Google—an effect that’s also related to the next bias.

Experience & Clustering Bias

Investors give special weight to founders with a particular company on their resumes. Even if the founder's experience at the company is utterly tangential to their startup, many investors cling to experience at a company such as Apple, Facebook or Google as a sign of greatness. Considering the companies together employ 160,000 people, investors may want to rethink this kind of barometer.

Investors assume there's a clustering of success around these kinds of companies. But it's not the rate of success that's higher, in most cases, it's simply that these companies are the alphas in the Valley and that their employees receive more chances to be funded, says Dan Shanley, CEO and co-founder of Notion, which creates data visualization tools.

"The reality is that many founders jump around from name brand to name brand spending only 8 months at each to load up their resume hasn't absorbed much of the culture, process, or execution experience at any," Shanley says.

It's true, however, that investors have to evaluate founders’ ability to execute on something, and some of those inputs will likely come from past achievements. Reference checks are good, obviously, but they can only be weighted so much. Investors need to limit how much value they place on founders' experiences that, in fact, have very little to do with how their product and company will turn out.

Yes, it's true that an ex-engineer from Apple will have an easier time finding funding, and angel investors frequently prefer to get in on startups that seem popular—which is yet another bias often referred to as “herd mentality” in startup investing—but those facts don’t appreciably increase the odds of a startup becoming a unicorn.

Education Bias

This is another variation of clustering, but it's one of the most prevalent biases not only within startup investing, but within hiring and society in general.

David Scher, startup investor and head of data and development at LD Micro, an index that tracks U.S. microcap stocks, says he sees investors regularly judge founders based on the school they attended. "It is like a filter for a lot of investors: 'If she can get into Stanford's Computer Science program, than she must be exceptional on some level, even if it is only academic.'"

While raw intelligence and test-taking aptitude can't hurt an entrepreneur, a school isn't as strong of a signal that most investors take it to be. Yes, it's true that Stanford, for one, has produced an outsized number of successful tech entrepreneurs during the last decade, but it's also true that Stanford has far more ping-pong balls in the machine, as the school is tightly integrated with Silicon Valley and its campus is down the street from the offices of most of the largest venture capital firms in the country.

Sam Altman, Managing Director of Research at Y Combinator, and the man who replaced Paul Graham as Y Combinator's president at one point, doesn't think Stanford, for all its chances, has overperformed as a source of entrepreneurial talent at Y Combinator. “To my chagrin,” Altman told the New York Times, “Stanford has not had a really great track record.”

That statement might surprise many people who associate Stanford with outsized tech success, but Altman has overseen investments across hundreds of startups, many dozens of them founded by Stanford engineers. If any entity has the data to make such a statement with clarity, it's Y Combinator. It's not that Stanford doesn't produce great entrepreneurs; it does. But the school likely produces successes at a similar rate to other schools.

The same effect is present with other schools, of course. Pitchbook did a study that tracked which universities' graduates (undergrad) snagged the most funding from venture capitalists from 2010 to 2015. Stanford grads attracted 44% more capital than the No. 2 school, fellow Bay Area campus UC Berkeley, which was followed by MIT and Harvard, both in Boston, which, before New York's rise, was the clear No. 2 tech hub in the country. This effect is related to the next bias.

Geographic Bias

This bias is a sub-category of clustering bias, but it's a big one. Not being aware of it can cost investors opportunities to find great companies whose funding rounds may be far less competitive than those for startups in the Bay Area and New York.

Some investors prefer to invest locally because they want the option to easily sit down and talk with founders. That's understandable. But for investors who don't have that preference or are willing to invest elsewhere, there's no reason that geography should play a prominent role in investment decisions. Exceptions, of course, exist for startups that might be selling to a specific market. It would be hard, for instance, to believe that a fashion tech startup in Kansas could compete with one in New York, which is the center of the fashion world.

But for markets that don't have an overwhelming hub location, investors would be wise to consider all comers. Erich Jacobs, CEO of OnKöl, which makes a hardware product that connects elderly living on their own with family members and caregivers, says that being based in Milwaukee slowed down his fundraising, as investors saw his Wisconsin location as being a weakness. He considers it a strength, however, arguing that his personnel costs are two-thirds lower, in some cases, when compared with those in major tech hubs.

Capital Midwest, a VC firm out of Milwaukee backed by the hedgefund kingpin David Einhorn, wasn't blinded by the same kind of location bias, and has become OnKöl's largest investor.

"This herd mentality drives us absolutely nuts, as the VCs are acting against their own self-interest," Jacobs says.

Companies from the Bay Area obviously profit from this—we calculated that a Bay Area startup is 8x more likely to be funded compared with the average rate across the rest of the United States. But as Howard Marks, the billionaire founder of Activision and StartEngine, points out, “Startups in the Bay Area are often priced at twice the valuation of similar companies in Los Angeles.”

Some founders still perceive there to be a bias against startups not based in the United States, but that effect has surely been ebbing to some degree, as international startups collected 42% of VC money placed in 2015, compared with just 6% 10 years ago.

Some non-U.S. locations fare better than others with investors. Nathan Lustig, who runs Magma Partners, a seed stage fund, often invests in Latin American startups that do business in the United States. But he still feels that, in seeking out these investments, his firm is in the minority, which gives them an advantage for now. "Many investors in our portfolio companies who we have talked to are less comfortable with a startup having operations in Latin America, compared with split teams in Europe, India or Asia," he says. "We see it as a big opportunity that is holding back a lot of investors."

Even Canadian companies can be shunned by investors. Michael Litt, CEO of Vidyard, a video platform for marketing and sales with built-in analytics that is based in Ontario, noticed it right away when he tried to raise following his company's exit from Y Combinator in 2011. To mitigate the issue, Litt simply didn't talk about where Vidyard would be based, and instead focused on the product. Vidyard has now raised $60 million and has prospered in its northern perch.

The geography bias extends into how some companies run their offices. There has been an expectation with many investors that companies need to be in one place, with a tight central office where employees spend most of their lives, in order to reach outsized success. But this is a bias built on subjective observations rather than data.

Mark Faggiano, the founder and CEO of TaxJar, which makes software for automated sales tax collection and filing, says investors constantly can't get past his choice of having what he calls a distributed team. "On more than one occasion we'd be thinking that the pitch was going well until the topic of distributed team would inevitably come up," he says. "We'd spend an inordinate amount of time justifying why we didn't believe we needed to include renting a high-price office space in our forecast."

TaxJar did fill its angel and seed rounds, but it took the several extra months to do so because investors were wary of how its team was located.

There is evidence that attitudes are changing on this topic. Gitlab, which maintains a popular set of open-source code repository software along with some proprietary tools, just raised a $20 million Series B—and its 100+ employees are spread across six continents who largely work from their homes or own offices. Coinbase, which has raised $120 million from Andreessen Horowitz, the NYSE and others, also seeks out engineers all over the world.

Race bias and gender bias

These biases require no explanation, and few investors would admit to being affected by them, of course. But it's there: 87% of all VC-backed founders are white, and 83% of VC-backed founding teams are all white, according to CB Insights data. Whites only make up 63% of the U.S. population, however.

Gender bias has been well documented throughout the tech industry, but it's especially acute when it comes to funded founders. Females only comprise 8% of the U.S. pool of funded founders. Even more striking, 85% of funded startups don't have a female in leadership, and less than 3% of them have a female CEO.

There are movements afoot to mitigate this, of course, some with great momentum. The makeup of batches at Y Combinator, always a bellwether, has changed dramatically during the last five years. In the summer of 2011, my batch, you could count the number of founding teams with a female on one hand (out of 70 companies). Now teams with women comprise more than a quarter of even larger batches.

These numbers can't all be blamed on investors. Racial and gender biases permeate education systems, among other things, and can lead students into or away from specific interests, majors and careers. The good news, however, is that the trends on both of these fronts point to changing tides.

Confirmation bias  

This well-documented effect manifests in people seizing on evidence that confirms their particular view on a subject while also ignoring or underweighting contradictory evidence. This bias can be particularly troublesome for investors who hold strident theses on particular spaces.

Some theses can prove spot-on, of course, but that affirmation often doesn't come for years. Investors who develop a thesis for a new and emerging technology may hang on companies and pitches that reinforce their own thesis, while discounting the chances of companies who challenge or run against it.

It's fine to have theses when investing, of course. But investors shouldn't assume they're unassailable. Evidence or trends that contradict deeply-held views have to be considered with the same rigor as those that may affirm them.

Investors need to examine markets and technologies from every angle and from vantages not their own, says Nelson Chu, founder and CEO of Tritan Collective, a New York product think tank that collaborates with big companies and startups. "What could appear to be the greatest thing that some portion of the population is obsessed over might not even make a dent in the grand scheme of what’s going on in the world."

Paul Martino, managing partner at Bullpen Capital, has built his firm specifically to look for startups who haven't drawn the investment their product merits due to common biases held by investors, including the category of company, its geography, and its founders' backgrounds.

"We have many examples from our portfolio that show what investors blinded by biases are missing," Martino says, citing FanDuel, which has danced at the blurry edges of sports gambling, SpotHero, which forged into the tough-to-crack parking industry, and Namely, an HR startup that has taken on enterprise incumbents.

Martino tries to focus on data whenever possible, as the raw numbers help subvert biases that exist even in his and his partners' minds.


Picking startup teams to back requires good data, understanding trends and, perhaps most important, being able to theorize, ahead of the rest of the pack, on where markets will go in the future.

For instance, Sequoia Capital stepped away from the prevailing wisdom in Silicon Valley when it invested in Instacart. After other companies had failed spectacularly in related ventures, most notably Webvan, in which Sequoia was a major investor, most thought that grocery couldn’t be hacked: too many SKUs, too little margin, too many weird products—like produce—to parse.

Sequoia looked past its own confirmation bias and the dogma of Webvan, and was able to see the gig economy wave coalescing with that of big data, and the trend of delivery services like GrubHub changing how people thought about ordering anything. Had the firm been held back by its own confirmation bias or by the prevalent biases of Silicon Valley in general, it would never have made the investment.

Angel and startup investors should heed the lesson. Applying a template to every investment, while it may be comforting, can also be detrimental to a portfolio. Just as starting a new paradigm requires vision and creativity, so too does investing in that paradigm.