Why We Need New AI Benchmarks, Which Industries Survive AI, and Recursive Learning Timelines | #218

Matt Fitzpatrick (0:00)

Most of the public focus to date has been on the large public benchmarks for things like coding.

SPEAKER_2 (0:05)

I think the problem is though, Matt Fitzpatrick, CEO of Invisible Technologies.

Matt Fitzpatrick (0:10)

We're an infrastructure company. What that infrastructure allows us to do is what I would call hyper-personalized software at scale.

Alexander Wissner-Gross (0:17)

It sounds like your position is we need thousands of new narrow benchmarks to capture maybe every labor category, every industry vertical.

Peter Diamandis (0:26)

That is an interesting second part of this, which is we're going to see the largest disruption ever in 2026 from companies that don't make this change.

Matt Fitzpatrick (0:37)

There are many sectors where the structure of what the industry does is going to change. If you think about knowledge work, the production of large amounts of documentation, where these these technologies are very disruptive. I think the question is which parts of your business can really change your AI.

Peter Diamandis (0:52)

What are you seeing most companies get wrong on their mission to implement AI?

Matt Fitzpatrick (0:59)

You've got two kind of different challenges. One is...

Dave Blundin (1:05)

Now that's the Moonshot, ladies and gentlemen.

Peter Diamandis (1:10)

Everybody welcome to Moonshots. In today's episode, we're going to be discussing why all companies need to become AI companies in 2026 How they do that, what happens if they don't. We'll discuss whether or not big legacy companies can even make such a dramatic change and how they can best do it. We're going to be going over some fun and meaningful AI use cases. So listen up. I think there are ones that are going to sort of get you excited about what you can do. And we'll dive into some predictions from our guest for 2026 Today, joining the Moonshot mates is a friend of the pod, Matt Fitzpatrick, who for more than a decade was at McKinsey, rising to the position of global head of Quantum Black Labs. I love that name, Quantum Black. It's so cool. Leading the firm's AI software development, R&D and global AI products a year ago, Matt joined as the CEO of Invisible Technologies, a company started by a brilliant friend of mine, Francis Pedraza. For those of you who don't know Invisible, the company is a modular AI software platform that uses AI training and provides AI training for most of the large language model providers out there, building custom workflows and agents for enterprise.

They anchor their work in creating clean data and human-in-the-loop delivery to ensure measurable business results. Matt, welcome. Good to have you here.

Dave Blundin (2:32)

Hey, Matt.

Matt Fitzpatrick (2:33)

Thank you for having me.

Peter Diamandis (2:34)

We got DB2, AWG, Salim, almost happy holidays, guys. It feels like we're on this pod every other day. I think we should just move into a large sort of podcast house.

Salim Ismail (2:46)

We will have fully documented the singularity.

Dave Blundin (2:50)

Well, I'm really looking forward to hearing from Matt because on Thursday, we have to do our predictions for next year. Matt is going to give us a ton of insight today. One of my predictions out of the gate is that enterprises are going to move super stupidly slowly compared to AI capabilities. Matt is the world leading expert on the intersection between AI and enterprise. I cannot wait for this.

Peter Diamandis (3:13)

You can't cheat this way, Dave, and sort of use Matt's predictions as yours.

Dave Blundin (3:17)

I can't?

Peter Diamandis (3:18)

No. He won't be there. Everybody listening to this pod will know that. All right. Well, take good notes, nothing else. You guys ready to jump in? Matt, I'm going to kick it off with a question, sort of a broad question for you. And here it is. So within the past year, we've heard from every company out there and every CEO that we're going to be pivoting to become an AI company. Salim, in the last pod, you said something like, we're going to see the largest disruption ever in 2026 from companies that don't make this change. And I think, Alex, the term you used is they're going to be cooked if they don't.

Alexander Wissner-Gross (3:59)

I said knowledge work is cooked, not knowledge workers, not companies, knowledge work as we currently know it.

Peter Diamandis (4:05)

Aha, okay. So you don't think that companies are going to be cooked that don't make the transition to AI?

Alexander Wissner-Gross (4:11)

I think we're going to see many more companies over time and many more smaller companies as well.

Peter Diamandis (4:16)

Okay, well, we're going to dive into that.

Salim Ismail (4:19)