Why your AI strategy shouldn’t start with AI: Steve King, Dragonfly AI Co-Founder and CEO

That's a great question.

When you think about the words artificial intelligence, and you take a moment to think about each word, like artificial intelligence, of course, it's quite difficult for people to adopt it.

Because it's, you know, what you're doing is saying, I'm going to do something which we normally do ourselves.

And there are great people back here.

who do this, and I'm going to do it artificially.

I'm going to do it in a way.

Now, there are huge advantages to that, like getting humans to do the more high-level processes while the artificial intelligence does the lower processes.

But when you step back and think about it, you go, wow, this is going to be a real push to adopt because it doesn't sound very natural, does it? It doesn't sound very real.

And I think that's always been the problem.

You know, I think AI has a distribution problem.

You know, it's hard to get it into places and get it used and get it in because it's got a bad reputation and it's just not natural.

You know, if you have visionaries who can see ways that things can be done better, you know, but in order to get work, they have to prove that to people.

They have to really, you know, it's a lot of work.

We do the work to make sure these systems are adopted.

And that, for me, is the hardest thing for a business is adoption of the idea or not coming up with the idea, not having smart people who understand the possibilities, but like really adopting is a very artificial thing to do into your business.

I like that.

I like that you mentioned, because it has had some bad press, like when people talk about it now and, you know, the term AI slop.

And I think a lot of large language models, obviously... take over people's view or thinking of what AI is.

I also don't think Hollywood did us any favors either, because let's be honest, those AIs and those robots were always out to get us rather than anything else.

But I think that's an important thing, because one thing that strikes me is that people increasingly talk about AI as if it is one thing, if it is just one model, if you like.

But obviously, in reality, I know we've spoken before, but there are very different approaches sitting underneath the all-encompassing label of AI.

I know that Black Swan and Dragonfly were both built around prediction, but not necessarily in the same way that people think about today's generative AI systems.

People automatically think of large language models and chat GPT.

But how do you think about the different types of AI? And more importantly, why does that distinction matter?

Yeah, this is deep in the AI stack now.