Bret Taylor
speaker
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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You know, if you think about designing a website or designing a marketing campaign, you can have quite a bit of control over it. You can sort of enumerate all the different permutations that your customers might see.
You know, an AI agent, in addition to your consumers being able to say whatever they choose to, the agent, the more you give it agency, the more it will have empathy and feel delightful, but the less control you'll have over it. So the really interesting discussion we have with our customers is,
If you want your agent to have a ton of personality and a ton of empathy, you probably need to turn the knob up on agency. But with that comes risk. You can turn the knob all the way down to zero, which, by the way, our platform supports for the high-risk cases. There's some cases where you don't want a ton of creativity or non-determinism. But in that case, your agent might sound more robotic.
You might sort of regress back to the chatbots of a few years ago. So we don't come in necessarily with a prescriptive view on what's right for a particular customer workflow or a particular brand, but it's a really interesting discussion.
And I think that just like the concept of a user experience designer was a new category of job as the web took off, it wasn't just the domain of box software to design user interfaces. We think that there's a role of an agent engineer who builds these agents on their platform. We think there's a role of an AI architect
who's a customer experience leader, whose job is to do the conversation design and shape the behavior of these agents. And we're essentially building in products and tools for these different new types of jobs that we think are just as meaningful as UI designer or web developer. And I think that's really exciting. But it's also creating this natural tension at our customers.
And I mean tension not in the personal way, but just actual intellectual tension, which is How much agency do we want to afford our AI? And making the guardrails more narrow makes the agent slightly less delightful, but making them more broad reduces control. And that's such an interesting discussion to have with brands.
This is the interesting thing about modern large language models and what I think the industry has come to call generative AI is I think it violates most of the rules we have in our head about computers. You know, computers are designed to be reliable. You click this button, the same thing happens every time you click it. They're designed to be databases.
They're not designed to be creative, right? They're designed to give you facts, follow the rules that we have really, really fast. And just think about software engineering, the craft of software engineering. There's entire methodologies now about how to get
increasingly reliable software, which involves using source control like GitHub and using immutable binaries so that you can roll back and have the same behavior you had yesterday if something goes wrong. We've essentially spent decades trying to make things deterministic, repeatable, reliable.
And now you make this new piece of software that is slow, somewhat expensive, extremely creative, and fairly non-deterministic. You're like, blows people's minds. And I think that as a consequence, people are modeling AI through the lens of how do we make it as deterministic as software was two years ago? I'm not sure that's the right model.
I actually think the thought exercise you did is, okay, let's assume that our salespeople or our call center agents occasionally go off script. How do we deal with that? there probably are operational mechanisms at your company to deal with those situations. Okay, why don't you just use the same mechanisms to deal with the AI as well?
And actually thinking of stop putting AI software in the bucket of computers and that rule set and how you deal with it to try to get to five nines of repeatability. and say, okay, this is actually going to be a really creative, really impactful, much lower cost solution. It will do some things that are incorrect some of the time.
How do we deal with that eventuality rather than try to fully prevent it, which right now is almost impossible.
I really do believe for most of the problems in AI, there are AI solutions to those problems as well. For a lot of content you're looking at, it would be interesting to put it into things like ChatGPT and ask it, is this real? How should I determine if it's real? And you might get some good advice.
As we think about information, veracity, authenticity, my hope is that you end up with the sort of white hat and the black hat. And the white hat teams in this, just like in the world of cybersecurity, will give us all the Iron Man suits we need to be successful and trust or distrust the information that we see.
So I think just like with all of these things, that's why I mentioned that that outlook worm, you know, I think
as these technologies get developed, you know, you end up with collectively are learning about the ramifications of these technologies, which is why I believe in responsible iterative deployment of AI, because I think it's very hard to, in an ivory tower, predict all of the first and second order effects.
But then it is an imperative for there's an industry that we develop technologies and mediations and to, you know, for these different downsides of the technology. But I feel confident we can. I think All the great AI minds are trying to think about how this benefits humanity.
And, you know, for every problem, there's a great entrepreneur or technologist or researcher who I think will come up with ways of meeting that challenge.
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