Bret Taylor

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184 appearances 1 recordings 1 series first heard Oct 2024 last heard Oct 2024

Bret Taylor’s voice in public audio — every appearance, attributed to the second.

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: Bret Taylor: The AI Bubble and What Happens Now | How the Cost of Chips and Models Will Change in AI | Will Companies Build Their Own Software | Why Pre-Training is for Morons | Leaderships Lessons from Mark Zuckerberg · 2 Oct 2024 podcast

And I always go back to the early app store days and the early apps being such skeuomorphic apps like flashlights. And then you have the mobile native experiences like WhatsApp, DoorDash, Uber, Instacart. It took one generation for those things to really exist. I have a sense that it will take a little while to see agent-native consumer experiences and agent-native devices.

And the hard part about particularly consumer electronics is you kind of need the consumer experiences to lead a little bit to have the market available. So it might take a while, but it certainly seems in the cards now in ways it wasn't before.

I think WhatsApp is very well situated. If you look at the usage of WhatsApp in places like Brazil and India, approximating this already, but I think, you know, large language models and agents like the ones we built at CIRA open the door to sort of much more full featured experiences. But I also think the same is true as most mobile platforms.

You know, I think that, you know, when you install an app on this, it's probably going to be an app and an agent in the future. Like when we work with our customers, you know, we want to enable them take their AI agent and whatever form factor becomes a dominant consumer experience, you should be able to install your agent in that experience.

I'll describe a technology problem, and then I'll describe the human problem that was harder than I expected around it. So generative AI is very creative, but inherently non-deterministic. It's very hard to create determinism, the same inputs creating the same outputs, in particular because if you think about the breadth of human language, it's just inherently less precise than most.

And then similarly, if you afford AI the ability to reason, you know, sort of by definition, you can't enumerate all the possible outcomes from there. So when you're building industrial grade agents, you know, for businesses that have real business rules they need to follow, we like to say software is going from the age of rules to the age of goals and guardrails.

And the hard challenge there is how do you enable businesses to express their goals and guardrails effectively?

When I think of rules, just imagine you're a retail website. You probably have a menu at the top left and you click it and it has the ability to sort of filter down all the items that you sell. Men, women, shoes, socks, pants, that type of thing. You probably experience this. You've essentially enumerated the rules by which people engage with your site. Here's the categories.

Here's what you can click. You could probably have someone actually click through all possible pages on your site and verify that they look correct if you wanted to. Now imagine you put an AI agent on your site. It's a free form text box. People can type whatever they want. If you explicitly enumerate all the things the agent can say, it's going to feel like a robot.

And that's essentially what chatbots from like three or four years ago felt like. And actually, in fact, many of them had almost like the multiple choice options available to you because they couldn't figure out how to express that universe, nor did they have the natural language understanding to create a meaningful experience.

So with an agent, you want to enable the AI to have agency and creativity to actually understand and really comprehend what the customer's problem is. But then once you go to, say, a let's just say you're a streaming service and you want to use your agent to process cancellations.

So when someone wants to cancel their account, probably the thing you should do is ask why you might want to offer a discount. And if the person doesn't still wants to cancel, you might want to cancel their subscription.

The goal might be to process the cancellation and the goal, and you probably want to afford the AI agent some creativity on how to present those discounts to really do some discovery like a good salesperson was on like what value you hope to get from the streaming service, you know, things like that. And then eventually you want to cancel.

Within that, there's lots of areas where you want to afford the AI agency and creativity, just like a really good salesperson would have that conversation with you. And in an empathetic, not pushy way, just try to figure out if there's a way to retain you as a customer. And that's nuanced, right? Empathetic, not pushy. That's where you need to get a lot of agency.

But you don't want the AI to go off script.

Yeah. Or even worse, there was an airline that had a chatbot that hallucinated a bereavement policy. Someone had a death in the family and the chatbot's like, the ticket's on us. I won't name the brand on your podcast. But it was like, it was a pretty bad thing. So you don't want the ad to have so much agency that in the extreme case, it hallucinates.

And in the case that you mentioned, you don't want the ad to just basically represent your brand poorly as well. So essentially, when you're making an AI-mediated customer experience, like a conversational agent, you need to really be able to declare both the goals of what the AI is supposed to do and the guardrails, which could be around language and brand.

It could be a tone, how pushy you want to be, how forceful. And then similarly, like, here's the offers that are available, things like that. So that's the technical problem that we solve at Xero, and I think fairly novel, like in a novel way.

I think that as AI improves, you'll see these agents adopted for increasingly more mission-critical systems. So I think the adoption curve rationally starts with relatively low-risk interactions and then progresses from there. But our customers already are using it for revenue generation, sales, subscription churn management for subscription services, things like that.

So, you know, I think that as companies develop confidence in their agents, they can go to sort of increasingly higher risk areas. But this is actually sort of getting to the challenge where we started this conversation is it's a very different design problem than traditional consumer design problems.

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