Dear friends,
AI’s usefulness in a wide variety of applications creates many opportunities for entrepreneurship. In this letter, I’d like to share what might be a counter-intuitive best practice that I’ve learned from leading AI Fund, a venture studio that has built dozens of startups with extraordinary entrepreneurs. When it comes to building AI applications, we strongly prefer to work on a concrete idea, meaning a specific product envisioned in enough detail that we can build it for a specific target user.
Some design philosophies say you shouldn’t envision a specific product from the start. Instead, they recommend starting with a problem to be solved and then carefully studying the market before you devise a concrete solution. There’s a reason for this: The more concrete or precise your product specification, the more likely it is to be off-target. However, I find that having something specific to execute toward lets you go much faster and discover and fix problems more rapidly along the way. If the idea turns out to be flawed, rapid execution will let you discover the flaws sooner, and this knowledge and experience will help you switch to a different concrete idea.
One test of concreteness is whether you’ve specified the idea in enough detail that a product/engineering team could build an initial prototype. For example, “AI for livestock farming” is not concrete; it’s vague. If you were to ask an engineer to build this, they would have a hard time knowing what to build. Similarly, “AI for livestock tracking in farming” is still vague. There are so many approaches to this that most reasonable engineers wouldn’t know what to build. But “Apply face recognition to cows so as to recognize individual cows and monitor their movement on a farm” is specific enough that a good engineer could quickly choose from the available options (for example, what algorithm to try first, what camera resolution to use, and so on) to let us relatively efficiently assess:
- Technical feasibility: For example, do face recognition algorithms developed for human faces work for cows? (It turns out that they do!)
- Business feasibility: Does the idea add enough value to be worth building? (Talking to farmers might quickly reveal that solutions like RFID are easier and cheaper.)
Articulating a concrete idea — which is more likely than a vague idea to be wrong — takes more courage. The more specific an idea, the more likely it is to be a bit off, especially in the details. The general area of AI for livestock farming seems promising, and surely there will be good ways to apply AI for livestock. In contrast, specifying a concrete idea, which is much easier to invalidate, is scary.
The benefit is that the clarity of a specific product vision lets a team execute much faster. One strong predictor of how likely a startup is to succeed is the speed with which it can get stuff done. This is why founders with clarity of vision tend to be desired; clarity helps drive a team in a specific direction. Of course, the vision has to be a good one, and there’s always a risk of efficiently building something that no one wants to buy! But a startup is unlikely to succeed if it meanders for too long without forming a clear, concrete vision.
Building toward something concrete — if you can do so in a responsible way that doesn’t harm others — lets you get critical feedback more efficiently and, if necessary, switch directions sooner. (See my letter on when it’s better to go with a “Ready, Fire, Aim” approach to projects.) One factor that favors this approach is the low cost of experimenting and iterating. This is increasingly the case for many AI applications, but perhaps less so for deep-tech AI projects.
I realize that this advice runs counter to common practice in design thinking, which warns against leaping to a solution too quickly, and instead advocates spending time understanding end-users, deeply understanding their problems, and brainstorming a wide range of solutions. If you’re starting without any ideas, then such an extended process can be a good way to develop good ideas. Further, keeping ideas open-ended can be good for curiosity-driven research, where investing to pursue deep tech with only a vague direction in mind can pay huge dividends over the long term.
If you are thinking about starting a new AI project, consider whether you can come up with a concrete vision to execute toward. Even if the initial vision turns out not to be quite right, rapid iteration will let you discover this sooner, and the learnings will let you switch to a different concrete idea.
Through working with many large corporations, AI Fund has developed best practices for identifying concrete ideas relevant to a business. I’ll share more on this in a later letter.
Keep learning!
Andrew