George Soros is a big proponent of a concept called “reflexivity.” It deals with circular relationships between cause and effect, and I think it is a very important concept in the development of new products in new markets. The early attempts at products, the various technologies that rise to the top in an early market influence how potential customers, entrepreneurs, and investors see that market going forward.
I see too many entrepreneurs look at problems linearly, as if there is a static issue that impacts the world and, they solve it, then build a company around it. It never works that way. Buyer behavior changes all the time, along with UI expectations, technology options, pricing expectations, and pretty much everything else. Rule #1 in business is that the world is dynamic.
A Reflexivity Example
I will give you a very personal example from my last startup. We sold to enterprise customers, and SOC2 was a relatively new standard. Our main competitor announced their SOC2 certification and, at our next executive management meeting we discussed it and said “this is dumb, no customer is asking for this, the few customers we spoke to said it wasn’t that important, our competitor wasted their time, we are not wasting money on a SOC2 audit.”
Then that competitor proceeded to kick our ass for 6 months until we got one. Why? Because on every sales call now they educated the prospects on the value and importance of SOC2, and so customers suddenly began asking us something they weren’t asking before. In other words, our main competitor didn’t beat us by “listening to customers” better or “responding to customer requests” better or whatever other pithy aphorism you hear about how to build a company. They predicted the future demands of the customer better than us. They added things customers weren’t asking for, but would in the future. They positioned themselves as leaders, and their actions influenced customer behavior and customer requests. This is a form of reflexivity.
How This Applies to A.I.
A.I. is a field where early products have sometimes been difficult to build, and everyone has been unsure of what the “killer apps” will be. As a result, we’ve seen lots of platforms, which entrepreneurs built in hopes other entrepreneurs could figure out the real use cases, and we’ve seen lots of marginal products (existing product adding machine learning to make it slightly better). And we’ve seen a few real use cases like self-driving cars and better predictive analytics. But it still feels like something is missing.
I think what is missing is clear market demand for “intelligence” built into everything we use. What I mean is, everyone can nod their heads and say they want smarter software and appliances and whatever, but, when push comes to shove no one agrees on exactly what that should look like. In many markets you can determine customer needs by simply talking to customers but, as we build intelligence into things, it’s different.
To be successful in these markets entrepreneurs need to embrace product reflexivity. They need to accept the idea that customer development in brand new markets is a circular, partially self-referential process. It starts with understanding some potential needs of some potential customers, and then showing them ideas to solve their needs but, also suggesting other applications of the same technology set. Unfortunately it’s also a more ambiguous and uncertain process than more direct forms of market entry.
I was in graduate school during web bubble 1.0, so, I didn’t work directly in the space, but it seems to me the web 1.0 space went through a similar process. What is possible? What is useful? What is actually likely? The difference this time around is that A.I. as an industry has a very different set of properties and structure. The A.I. industry is driven as much by new data sets as it is by new technologies. Plus there is a flywheel effect around data acquisition, learning, and algorithm performance where they strengthen and reinforce each other in ways that build defensibility. Your success isn’t just a product of your approach to the problem you are solving, it’s also a product of the data you have access to and the new things it enables.
But all of this leads to a conclusion that is possibly counterintuitive for entrepreneurs and investors, which is, your reflexivity process should circle around the data sets you have more than anything else. People always ask “what problem are you solving?” And that’s important to answer eventually. But new problems are arising all the time. You have to reason from first principles and move where the market is going.
So if you are starting an A.I. company, you have to show customers vision just as much as you ask them about their problems. Customers don’t understand yet what these new technologies are capable of. And the process is reflexive because what you (and other) early startups do, impacts how customers perceive the early market and thus how they see the problems they have and the potential solutions A.I. can provide. In other words — it’s more complicated than before, but the payoffs could be bigger, so it is still worth pursuing.
The most common prospect we get at Talla says something like “my boss said we need to use more A.I. so, I am trying to figure out where.” These prospects aren’t looking for our particular product. They have no particular problem to solve. They are looking to understand what A.I. can do and where it can have an impact. That is a very different market dynamic than what we saw in the SaaS cycle of startups.
But the good news is, if you are an early A.I. entrepreneur, you can use reflexivity to your advantage. You can educate customers in new ways that highlight problems they didn’t know they had, new problems they are about to have, and new things that A.I. enables that they haven’t thought of.
I have 40+ early stage A.I. investments. If you are an entrepreneur who isn’t solving an existing problem, but instead is looking at a future to enable something amazing that wasn’t possible before, I’d love to chat.
