Dan Milstein | Photo: The Lean Startup Conference/Jakub Mosur and Erin Lubin
We all know that hard work and good luck are key to startups’ success. But what if that’s not true?
What if all startups have people who work hard? What if a bit of serendipity is fairly common? Let’s make it concrete: Have you worked at—or run—a startup where people were deeply committed and worked long hours, yet the company failed?
In his talk at the 2013 Lean Startup Conference, Dan Milstein explored what does make a difference for startups: Information. It’s worth real money, he emphasized, and the way to make more money is to more quickly gather information that helps you figure out the right things to work on.
This mindset is so critical, in fact, that you should be afraid of working on the wrong things. Dan:
If hard work and luck are important, but they don’t seem to really distinguish the startups that succeed from the ones that fail, then the choices of what we’re working on must be critical. What you choose to work on is actually your biggest lever, with a huge differential effect. You should be very, very scared of working on the wrong things. In fact, you should be terrified. I would say you should be so terrified that you actually don’t work. If you’re not sure that what you’re working on is the most valuable thing to your startup, you should stop working. I tell people this and they think I’m exaggerating, but I’m not. You should only work if what you’re working on is the most valuable thing.
Dan gives examples and does the math to show why working on the wrong thing is devastating for a startup. He also talks about the kind of information you want to gather at a startup: the kind that answers the riskiest or most uncertain questions. He explained: “You actually don’t get much information when you already know something; you get a lot when you’re uncertain. And then, what information is valuable depends on what decision you’re making.”
As you may have noticed in your own startup, identifying your biggest risk can be hard. Dan points out that it’s harder than you think, because risk shifts constantly. He tells this story about a software product, for hospitals, that used a public data set. Before selling or building it, the company’s biggest risk was that nobody would buy it. So the startup created a demo, and one hospital signed a $10-million contract for the product before it truly existed:
That’s great, you did the right thing. So now your sales team is out there trying to repeat that and sell the second one, and you’ve got a bunch of engineers now building that thing. And I want you to imagine something. I want you to imagine a junior developer, someone on the team, bright guy but young–guy or girl. And some morning—it’s a Thursday morning—and they were given a job of taking the demo app and turning it into a real production system. And they’re working with this public data set, and they discover, to their surprise, that it’s not as comprehensive as everyone thought it was. It worked well for the demo, but for the actual hospital, it’s actually not going to work. The whole product that the company has sold is actually not going to succeed the way they’ve done it. They have to do it some other way. In the moment after this person makes this discovery, the biggest risk for the startup has changed. The biggest risk is no longer: Can we repeat this sale? The biggest risk is: Can we actually build the thing that we promised in the first sale that we thought we could build, but we just discovered we were wrong?
If the biggest risk has changed, the thing you should be doing to gather the most information has changed. Because the way you gather the most information is by going after the biggest risk. Therefore, the thing that’s going to get you the most information—and therefore, the most money—has changed. So, as long as the company is still doing what it was doing before that discovery was made, they’re doing the wrong thing. And one way to look at this is that, in order for your company to move fast (the entire organization), the thing that will limit them in how fast they can move and how fast they can make money is how fast they can respond to the changing nature of risk. Because it’s only by going after the biggest risk do you make the most money, and because risks are changing all the time, the entire organization has to be able to change direction. And this, really, nobody gets this.
Learn more about identifying risk, gathering information, and making money by watching or listening to Dan’s 20-minute talk, embedded below. We’ve also included the full, unedited transcript at the end of the post.
When have you realized your biggest risk had changed? Let us know in the comments. – Eds
Dan Milstein is a co-founder at Hut 8 Labs, a software consulting shop in Boston. He’s worked as a programmer, architect, team lead and product owner at a variety of startups over the last 15 years. He is fascinated by the interactions between complex systems and the humans who build and maintain those systems. He’s recently written on How To Survive a Ground-Up Rewrite Without Losing Your Sanity, and Coding, Fast and Slow: Developers and the Psychology of Overconfidence. Follow him on Twitter.
Mercedes Kraus is Startup Managing Editor for The How.
Opportunity cost. Given more than one choice of things to do and limited resources, opportunity costs are the potential benefits you give up in the choices you don’t explore. For example, let’s say you have a customer who asks you to build a highly specialized product for them, even though you don’t generally do extensive custom work. If you take the project, you’ll get money from the customer and perhaps some intangible things like a stronger relationship. But because you don’t have unlimited time and people, taking the project means you’ll give up the opportunity to build something else—perhaps a product that you could sell to many customers. If this sounds like every decision you make has an opportunity cost, you’re right on. Opportunity cost is a central idea in business—and it’s why the value of information in making decisions is so great. We found that these examples from Inc., while a little stiff, help put the term in a wider context.
CRUD app. CRUD is short for create, read, update, and delete: the four basic functions of database applications. It’s the simplest, dumbest kind of app an engineer can make.
Chained risks. A sequence of interconnected risks, where the first risk suggests that other risks will arise. In the talk, Dan mentions an essential risk chain of startups: 1. Can we build it? (this question is often framed as technical or product risk); if so, 2. will they buy it? (often framed as customer or market risk).
Degree of surprise. We only get information when there’s uncertainty and risk; so, the less you know—and therefore the more surprised you are by new information—the more you are learning.
Information theory / Claude Shannon. A branch of applied math, electrical engineering, and computer science. The foundational ideas of information theory were developed by Shannon in order to examine the communication, compression, and storage of data. We like this profile of Shannon in Scientific American. For geeks, this paper [PDF] on the wider context of information theory in the digital age, goes deep.
Series A funding. The first round of major investment, usually $2 – $10 million, that a startup receives (it may not be the first investment, however; seed funding is generally the first money—sometimes the founding team’s own—used to get a startup just off the ground). The name itself refers to the Series A Preferred Stock that investors receive in exchange for buying in; subsequent rounds are referred to as Series B, Series C, and so forth. Venture capitalists (VCs) are generally the investors, though in a round of funding, several firms often invest, and sometimes individuals participate, too. Over at Entrepreneur, they’ve got a good picture of the whole funding timeline; the process doesn’t always look exactly like that, but it’ll give you a sense of how things can go.
Valuation. For startups, this is a kind of appraisal that assesses the company’s financial value, usually based on potential growth rather than current profits or assets. For example, if your company has started selling a service for $100 per year, and you have 100 initial customers, it’s likely worth a lot more to investors than the $10,000 you’ve taken in. If they believe you can gain many more customers rapidly, investors might project your future value in the millions, reflecting your company’s potential, and buy shares on that basis. For more background on valuations, check out this clear post from VC Brad Feld, this useful piece from Founders and Funders (though maybe skip the hectic infographic at the top), and this straightforward discussion from Investopedia.