Grant Harvey

Like, I mean, I can tell the story and then the person who's being referred to is going to find it very amusing.

We've got someone in Invisible who like, he's someone who came in, he actually came in as a contracted agent, like doing human data work.

And he was excellent and we brought him on full time.

He's now running an AI ML team, engineering team, because he is an unbelievably good prompt engineer and also good at understanding how to use them correctly.

And a colleague of mine who I brought in, who's also a very, very, very good ML engineer, had a theory about this.

And he was like, yeah, this guy, he was a professional League of Legends player for three years.

He got incredibly good at making a huge number of decisions in massive sequence and also very quickly evaluating trade-offs.

Which is all you have to do.

And he's not a trained, he hasn't gone through 20 years of software engineering training, but he understands complex systems and how to handle them in different ways.

And it's a non-traditional skill set, but it's proven incredibly valuable because three years ago, this job didn't exist.

With clarity, as a lifetime Nintendo fanboy, like, I'm not advocating for League of Legends here.

Okay, fair enough, fair enough.

But the general idea, like there are non-traditional skills that are incredibly valuable in this world where you're effectively engaging with a model or an interface on top of a model to try and understand the problem or try and break it down or try and deconstruct it or try and like sequentially map it and do all sorts of complicated things to set up a set of agents using different tools to do some piece of work.

Like that's not sit down, write Python, hello world.

You're like, let's talk about the car.

I'm just like, hey, I'm just chatting on League of Legends.

Okay.

I mean, there's a whole heap of things.

I think thinking it's step one, talk about AI, step two, question mark, step three, magic.

Like, no, it's a very boring set of processes.