Reflexivity In A.I. Startup Markets and the latest industry news

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 behaviour 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.

Example.

Prediction of future demands of the customer better than your competitors, add things customers are not asking for, but would in the future. Position yourself as a leader, and your actions will influence customer behaviour 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, there are a lot of platforms, which entrepreneurs built in hopes other entrepreneurs could figure out the real use cases, and there have been lots of marginal products (existing product adding machine learning to make it slightly better). And then 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.

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.

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.


In other news

Just How Shallow Is The Artificial Intelligence Talent Pool? — A look at one of the most critical topics in A.I. at the moment — that there aren’t enough people to work on it.

Can Computers Learn Like Humans? — A good general piece about the challenges of deep learning.

After Settling With Uber, Waymo Faces Bigger Challenges — A look at Waymo post settlement and what it takes to be successful going forward.

This AI tailor startup failed its beta test — customers sent in a photo of their favorite shirt so that it could be measured and replicated, but the fit was off.

Researchers have discovered a way to use machine learning algorithms to analyze and enhance human memory using electrodes implanted in the brain.

In this piece in the Harvard Business Review, the author says that our reliance on so many intelligent, connected devices makes us vulnerable to global-scale shutdowns. He says that the industry needs to borrow a page from the natural disasters playbook in how we deal with the risks posed by AI.


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