- How To Build a Defensible A.I. Startup
How To Build a Defensible A.I. Startup
And Not Get Incidentally Killed By OpenAI
With all the excitement around the recent OpenAI Dev Day event (whereby they launched a slew of new capabilities), one of the questions that has been floating around the Internet is:
How many startups did OpenAI kill during that event?
In other words, what startups are no longer necessary/relevant given what OpenAI launched.
Let’s make one thing clear: I’m pretty sure Sam Altman (founder/CEO of OpenAI) doesn’t wake up in the morning and ask himself “What AI startups can I kill today?”.
The fact that happens is somewhat incidental.
But, for the entrepreneurs running those startups, that brings little solace.
How does one build a defensible A.I. startup?
Let’s take a step back. The way you build a defensible A.I. startup is the way you build any kind of defensible startup. A.I. startups are interesting now because of the new opportunities that generative A.I. has unlocked — and because the speed at which things are happening.
Also, recognize that this question of defensibility is not a binary one. It’s not that one startup is defensible and another is not. It’s a spectrum of defensibility. And, there are different factors that will increase the defensibility.
Factor 1: Creating Enough Customer Value
The first thing to recognize: The most common threat your startup has of being rendered irrelevant is not that OpenAI (or someone else) renders it irrelevant because of a new launch. The most common cause of death is that your startup wasn’t relevant enough in the first place because it didn’t create enough customer value. Note: Chances are, you’re creating some customer value (no entrepreneur intentionally starts a company that doesn’t create customer value). The issue is that you have to create enough customer value to overcome the natural inertia of the market. In other words, the energy of your startup has to overcome the friction people have to go through to even consider your product.
Factor 2: Doing Hard, Helpful Things
To increase defensibility, you need to do things that are relatively hard to do which makes your product more helpful to customers. The reason they need to be hard things is that otherwise others can readily do it and you have a hard time differentiating.
This is usually where the new crop of A.I. startups often fail. Often, they are described as “thin wrappers around the GPT APIs”.
Here’s an example: You build an A.I. powered tool that helps the recruiting department write effective job postings. A couple of years ago, that would have seemed magical because nobody had really figured out how to understand natural language — or to write in natural language. But now, with the popularity of ChatGPT (and the GPT APIs more broadly), this is a widely available technology. So, the hard thing you were doing is no longer hard. In fact, it’s easy enough that customers can reasonably just use ChatGPT to get a decent approximation of what they need. Perhaps not as good as yours, with its fancy prompt-engineering, but often good enough.
So, to increase your defensibility, you need to do things of value that are going to be hard for others to do for some period of time. If all you’re effectively doing is supplying GPT a really fancy prompt, that’s unlikely to be enough if your market opportunity is large enough.
There’s an important point embedded in there: The more customer value you are creating, the more economic opportunity your startup has. And, the more opportunity there is, the more competition that opportunity will draw. So, you’re trying to balance doing something interesting enough to be valuable, but have a counter-balancing difficulty that will limit the number of competitors that also attempt it.
This one’s worth breaking down, in terms of the types of hard things you can do.
You have access to a proprietary asset (like data) that others don’t have easy access to. In our “write job postings” example, perhaps you have a corpus of thousands of job postings including some outcome scores (as to how well they did). You could use this data to create better job postings. Others don’t have ready access to this data. Note: The asset doesn’t have to be data. It could be prior code that you can leverage. It could be hard-to-gain partnerships with suppliers. Anything that’s valuable and hard.
You have efficient access to customers. This is an oft-ignored one. If you have built channels to reach customers in efficient ways, that’s an advantage. Even if others build a similar (or even slightly better) product, you can still have defensibility if you have a way of more easily gaining users/customers. Specifically in the A.I. world, often access to users/customers provides a related advantage: more data. It’s possible to create a virtuous loop whereby the more users of your product, the better the product gets (because of feedback loops) thereby making it easier to get even more users. That’s often hard for others to replicate.
You have a network effect. A simple and common example is if you’re creating a marketplace of some sort. These are often hard to do — but that’s precisely what makes them so valuable, because once you have a strong network effect in place, it’s really hard for others to replicate or displace it.
Ok, so let’s dig in a bit specifically to A.I. startups. Some additional thoughts:
Building foundational LLMs is exceptionally hard (and expensive). So, there’s an opportunity to create some defensibility there. But the challenge is that what is hard today may be rendered not-so-hard tomorrow either through open source (which is moving at a torrid pace) or a new release by one of the existing LLM providers (like OpenAI).
If you’re building dev tools that make it easier for developers to leverage generative A.I., you are at the risk of the LLM providers themselves building that into their offering. They have every incentive to make their platform easy and useful. They’re not intentionally trying to kill you, they’re just trying to better serve the developers on their platform.
If you’re building a “ChatGPT for [x]”, it’s important to be mindful of whether the differentiation you have on top of ChatGPT is really enough. Especially now that OpenAI is launching custom “GPTs” with their own app/GPT store.
To close out, let’s summarize.
Here’s what I’d ask myself:
How valuable is what I’m doing?
What makes this hard for others to do?
Could this get dramatically easier because of what others launch or how the industry evolves?
If this does get easier, do I have a “Plan B” to create differentiated value?
Note: An early version of this post appeared in my personal blog (still a work in progress). Decided to update it and post here because it’s a better fit for this newsletter.