Jenny Blackburn (3:32)

Yeah, it has been a really exciting time to be at Google, a really exciting time to be in ux. It is amazing to think about everything that has happened since we initially launched Bard a year ago in March. So yeah, some of the things that I would call out that are different, you know, we initially launched Bard as this chatbot that let users talk directly with Google's best AI models. And over time we've been really making it more and more of this personal AI assistant that can help you across every part of your life, whether it's school, work, personal life, to get things done and to create. And then recently this year we launched Gemini Live, which is the first really conversational experience that lets people have this really free flowing conversation with Gemini. I think it's the start of a lot more exciting multimodal experiences to come.

Eli Woolard (4:30)

One thing that's on a lot of our minds as designers and creative folks is how is this going to change our work? How is it going to change our workflows? We've actually been doing a bunch of workshops with teams, even teams working on these products at Google and other places. And it seems like one of the things that's sort of a necessary thing to progress with a tool is just to play around with it. And that's not always like the case that people have time, even if they're working on the tools, they don't have time to play. But I'm curious how you think about this in kind of a two pronged way. One is how do you encourage your team to play with the tools and how do you see it gradually changing people's workflows?

Jenny Blackburn (5:09)

Yeah, I think for my team it's essential that we be playing with these tools and understanding how these models will respond. Because the things that we're building really are about helping people directly connect with the model in order to get help and to get things done. And so you can't do cool, great work designing for that if you don't really deeply understand, you know, the way it works and the way it feels. You know, in a lot of ways the model is the material that we are Designing with. And the more you understand the material, the more you can innovate with it. So we use it throughout the day and, you know, maybe one of the best ways to fit it in, if you will, is to use it for your own work. And so, you know, we try to be really creative and share across our team when we come up with new ways to leverage Gemini to help us get our own jobs done. Great example is generating use cases at the beginning of the project. Gemini is a great collaborator in helping come up with a whole bunch of use cases for how people might try to use the product. And then touching on your second part of your question, which is around, I think, how UX will evolve, I feel like we are living that. I often tell my team, you are inventing sort of the next version of UX Jobs, and I think there's even new UX Jobs being formed right now. A great example is defining what is a great response from a user perspective for a given use case. And so that's an area where we've been investing a lot. And we have team members who have backgrounds in things like conversation, design, linguistics, content design and strategy. Those backgrounds are really, really relevant. But the ways of crafting those experiences are evolving sort of all around us. So it's been really fun and inspiring to see how the team has been adapting and bringing that skill set to this new technology.

Eli Woolard (7:14)

So one of the things that I've personally been experiencing with these tools is this sort of extension of our capabilities as a creative person or person making products or prototypes, in that I'm not a developer. I have dabbled in the past a bit, but now, using tools like Gemini and ChatGPT, I can very quickly and iteratively code up a little app or prototype. And I'm wondering how your team thinks about this. The sort of blurring between the boundaries of who's a designer, who's a developer, and, you know, on the other side of things, many developers are getting access to tools that very quickly let them build out a UI generatively. So, yeah, curious how you think about that, how you're positioning your team to take advantage of this situation.

Jenny Blackburn (7:55)

Yeah, I think one of the things that I am the most excited about with this technology is the way that it enables people to create in new sort of form factors that previously were only available with a lot of training, you know, for example, coding and creating images. And so I think when it comes to sort of like that very human thing of having an idea and bringing that idea to life through creation, it's really powerful. I would differentiate between sort of the identity of someone as a designer or someone as a coder and the ability to create outputs that are images or coding. In fact, I would say in my team, yes, you can use these models to create creative outputs. But a lot of the amazing end work comes with that collaboration with the designer who has, you know, the human sensibility, the ability to creative direct, the ability to sort of deeply think. And that person, in conjunction with this new tool, creates these amazing outputs. And so I think it does give people the ability to do more. And it in some ways democratizes creation. And that deep expertise that people have continues to be really important as well in that equation.

Aaron Walter (9:21)

It's so fun to be a designer and alive in these times where there's a big technological transformation, because as you said, you get to play a hand in reshaping and thinking about what the future would look like with these products. We've been designing design systems and buttons and form fields and so forth for a long time. It's kind of the same stuff over and over again, just baked in a million different ways. And in the past year and a half, we've seen the user interface for how we interact with AI evolve. You know, it started out as kind of a prompt, which I can't help but recall the first time I used a computer and it was dos and I was, you know, had to know what to type in there to get the thing out that I wanted. It puts the burden on the user's shoulders versus a graphical user interface where you can recognize rather than recall and so forth. What have you seen in the evolution of UI design for AI in the past year or so?

Jenny Blackburn (10:26)

Yeah, I would say we're still in the very early days, and I think what UI looks like today and what it looks like in a year or five years is going to change as we learn. It's interesting you bring up recognition over recall, because I think that's something that we think about a lot in these experiences and, and how to make them intuitive. You know, one of the challenges is that these models are so capable in many ways, they're actually more capable than you even expect as a designer. And so the risk is that you actually add too much UI that sort of limits the value that the model can provide that would come if you just facilitated a direct conversation between the user and the model. And so we're always trying to balance adding UI that can help users be more successful and understand how to use the product. So an example of that, the very early examples of these products were really based on sort of messaging UIs, and that was a shorthand for users to understand that this was a back and forth conversation. There's a lot of mental model paradigms that come with messaging that were helpful for people to grok, oh, I'm going to type something in and then it's going to respond and then I can respond to that thing. So just the basic layout of the page was actually really helpful for people as they move forward. And now we're moving into a space where people can do that out loud through speaking and get into this back and forth. So I think you will see us. You know, we are already looking at places where we can add UI to help users, but we're also really intentional about being very light touch so that people can have this like really powerful interaction directly as well.

Aaron Walter (12:15)

I just want to dig into this a little bit more because I think most folks like the first prompt. There's an art to writing a good prompt and explaining yourself well. But, you know, I think a lot of people are kind of adapting and figuring that out, including, you know, my kids, Eli's kids. We just like, this is what we do at bedtime. We just start talking to AI models and making images and stories and stuff. It's the second, third, fourth, fifth, sixth iteration of. I want you to make this for me. Those following iterations where the UI feels very like it's not figured out yet. We haven't figured out how to tweak. What you gave me was 60% there, it's 40%. And the thread is often lost as you push further and further into an image, a piece of copy, whatever it is. How do you guys think about how the UI might evolve or expand as you go deeper into a query?

Jenny Blackburn (13:13)

Yeah, let me give you a couple examples in the UI that kind of gives hints at, I think, what we're experimenting with right now, at least for our advanced Gemini subscribers. We have a feature that's called gems. And gems are basically a way that users can create like a single purpose or a focused purpose version of Gemini that does something really particular for them. And then they do a little bit of setup work, and then every time they come back, they benefit from that setup work because that thing always behaves in that way. And one of the things that we were finding, you know, these are a little bit more advanced use cases. You know, it's something like, I want to create a gem that's going to help me draft my social media content that I'm going to promote next week. And what we were seeing is that in that prompting, people weren't really sure, like you're saying, how to get the most leverage out of this thing. And they would write pretty short prompts. And what we would find is that as they got longer and more detailed, they'd actually have a better experience. And so we created this little just in time affordance. When a user starts to type in their prompt for a gem, looks like kind of a little magic wand. And if they tap on that, it expands the prompt to be much longer and more detailed. It doesn't submit it. So the person has the ability then to look at that expanded prompt. And it does two things. One, in that direct example, they can engage with it and make it better. But there's also this learning that happens as you get exposed to different ways of prompting that sort of expands the approaches that you take the next time. And so that's a light tool that we've given people that we've gotten really positive feedback about. They also can undo it if they don't like it. So that's one, I think, starting with the user's intent, not trying to get in their way. But then once they kind of get started figuring out how do we aid and sort of expand from that initial thing that they're trying to do? We also do within a generative response. So if you're using Gemini to write an essay or draft a poem for you, we added a little capability where the user can, on the website, highlight a portion of that text and give discrete feedback, like, make this shorter, make this longer, make this more creative. And I think that directionally is something that will increasingly be helpful. The ability for the user to almost direct manipulate parts of the response and give like, targeted feedback rather than always having to give feedback about the entirety of the whole thing.

Aaron Walter (15:52)

That's interesting. I like the idea that you train the model and the model trains you back, you know, giving you the language to communicate. That's great.

Jenny Blackburn (15:59)

Yeah, it's like any, you know, conversation. As you go into the conversation, you start to learn about what topics work well. And a conversational partner that you have, if you will.

Eli Woolard (16:09)

Jenny, I'm curious. There's a bunch of experimental tools that Google has been working on, things like Notebook LLM, where you can do research, summaries. But probably most interestingly, and something that sort of caught fire, at least in the tech world, is this sort of podcast generator component where it will create this deep dive podcast. And it sounds very human. There's mistakes and ums and odds, but that just kind of makes you connect to it more. And I made a short deep dive for my dad, who's not been in very good health, and it just, it really lifted his spirits, like just to hear his own story in the voice of these two characters. And I'm curious how you fold these experiments back into products like Gemini, where you discover like, oh, this is a really good way to make this feel more human or other learnings like that.

Jenny Blackburn (16:57)

Yeah, I think there's so much opportunity for learning right now and so it's really awesome to be at a place where there are this diversity of experiments happening all the time. And on Gemini we really look at what can we learn. I think with NotebookLM, there's a couple things that I think are really cool about that experience.

Aaron Walter (17:18)

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