The guardian in the machine | Wayfound’s Tatyana Mamut

**Andrew** (0:05)
I'm happy and incredibly excited to kick off a really fun episode today, welcoming back somebody that I adore and that I've worked with a lot.
And one of my favorite thinkers in the tech space, and that's Tatyana Mamut. And when she was on the show last year, we recorded an episode called The People Pleaser in the Machine, which I still think is one of the most fascinating and important conversations we had last year. It's all about how AI sycophancy and psychological traps of how we build and use models and how we evaluate performance out of them. And it really unwind my whole idea around how we can apply those insights to the rest of the industry and actually manage and govern autonomous agents at scale. And I just feel like Tatyana, since you've been on the show, a lot of things you've said have just come so true, and the prophecies of them just become so bigger. You've seen agents become more proliferated everywhere. There's a deeper understanding of what agents are and what they're capable of and that's both exciting and terrifying. And so Tatyana, she's the founder and CEO of Wayfound AI.
She's currently the leading voice champion, the guardian agent, and the central layer of supervision that ensures that AI workforce is actually aligned with our business goals. So Tatyana, welcome back to the show.

**Tatyana Mamut** (1:18)
Thanks, I'm so happy to be here.

**Andrew** (1:21)
So excited to have you. And I just wanted to start by jumping in about kind of the current of the land with models and their providers and the top tier performance foundation models, right? Because when you and I talked last year, the models were in a totally different place than they are now. I would say the ecosystem has evolved a lot in terms of their capabilities. And we've seen like also as well, the market share of how people would use different model providers start to shift, especially in the last few months. Like, what do you think about how this shift and the stickiness of AI platforms is an interesting indicator about where this market is going? But also, what does that say about the underlying capabilities of the models themselves?

**Tatyana Mamut** (2:03)
Yeah, I mean, I think that one of the things that obviously we all know is the capabilities have increased dramatically in the last year. When we talked the last time, models weren't really even capable of doing simple math. They weren't able to do some very simple enumeration like the strawberry thing, ours and strawberry.
A lot of those capabilities have been tackled because they were known issues, right? There were no problems, there were known spaces, and most importantly, the reward functions were very clear to know binarily whether the answer was correct or incorrect, right? So what we've seen, I think, across the board and where I think the models are consistent in their evolution is that the capabilities that can be assessed via a very simple binary reward function, correct or incorrect, a lot of those things have advanced very, very quickly. This is, I think, one of the reasons why coding agents are so powerful is because when an agent writes code, it either compiles and works or it doesn't. It's a very simple binary reward function to really assess whether the agent performed an action well, achieved its goal in the proper way or not. There are many, many, many places though in the world where we do not have binary reward functions, right? Where the assessment of whether the AI agent performed well or not has a lot more with values, principles, subjective assessment. And here we saw the models really diverge based on which audiences they were going after and what kind of capabilities they were creating. So the most, I think, obvious one is kind of the OpenAI versus Anthropic divergence because those are the two models we probably use all the time and they look very different from one another. So the multimodality, the sycophancy, the people pleasing, the emphasis on engagement, frankly, and OpenAI was really a focus toward the consumer markets and the consumer markets based on a business model strategy for potentially putting ads in the platform, which is all about more engagement, right? More people, more time spent, an app, more engagement, kind of like the Facebook model, frankly. Whereas Anthropic was going for more of a business use case where it's not multimodal as much.
I mean, Claude does produce images if you ask it to render the code that it's on.

**Andrew** (4:35)
But no one's asking Claude to do that.

**Tatyana Mamut** (4:38)
I do, I do. I actually do ask Claude. You just gave me code. Can you please render this into some image?