Everyday AI Podcast – An AI and ChatGPT Podcast - EP 457: Gemini 2.0 – Google's Logan Kilpatrick gives inside scoop on Gemini updates

Episode Date: February 7, 2025

One of the smartest leaders in AI is taking us to Gemini school. Google just released its highly anticipated Gemini 2.0 updates. Logan Kilpatrick is the Senior Product Manager at Google DeepMind and is widely considered one of the leading voices in AI development. What better way to learn about Google’s groundbreaking model update than straight from the source? Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Ask Jordan and Logan questions on Google AIUpcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: [e-mail] with Jordan on LinkedInTopics Covered in This Episode:1. Google’s Gemini 2.0 Update2. Rapid Progress in AI and LLM Capabilities3. Multimodal Features of Gemini 2.04. Google's Agentic AI Projects5. Future of Work & Personal ProductivityTimestamps:00:00 Exciting Gemini AI Update05:02 Relentless AI Model Progress08:42 Small Model Success Drives Frontier Bridging11:59 Advancements in Image Generation Models14:55 "Agent Development Experimental Releases"16:43 Advancing AI Reasoning Models21:32 AI's Impact on Software Engineering24:24 Future Shift: Developers to AI Builders28:25 Empowering Builders in AI Economy30:57 Proactive Task Management App VisionSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Start Here ▶️Not sure where to start when it comes to AI? Start with our Start Here Series. You can listen to the first drop -- Episode 691 -- or get free access to our Inner Cricle community and all episodes: StartHereSeries.com Also, here's a link to the entire series on a Spotify playlist. 

This is the Everyday AI Show, the everyday podcast where we simplify AI and bring its power to your fingertips. Listen daily for practical advice to boost your career, business, and everyday life. Meet Firefly AI Assistant, now live in Adobe Firefly, the all-in-one creative AI studio. Just describe what you want to create and the assistant handles the rest, orchestrating multi-step workflows across Photoshop, Premiere Express, and more in one conversational interface. You direct the outcome. The assistant accelerates execution. If you follow AI developments at all, it has been an extremely exciting couple of months.

And I think that excitement even peaked even more in the past couple of days as Google just released and expanded its Gemini 2.0 family of models. And I think that there's a lot of aspects of Gemini 2.0 that a lot of people are overlooking. And I think it can fundamentally change how we work. So we're going to be talking about that and a lot more today on everyday AI. And I'm extremely excited for today's guest because let me just say this. If you're not following AI every single day, you can learn a lot from our guests. I think he is probably one of the smartest people in AI and has really shifted how the world works, whether you all realize it or not just with his work background.

So thank you for tuning in. Maybe it's your first time. what's going on. My name's Jordan Wilson and welcome to Everyday AI. This is for you. It is your daily live stream podcast and free daily newsletter, helping us all not just learn AI, but how we can leverage all of these new updates, new model upgrades, how we can use this to actually grow our companies and our careers. It doesn't make sense to just know what's going on. You have to be able to put hands at keyboard or voice, right, voice in microphone and be able to actually grow something. So that's what this is all about. And your next best friend, if this is your first time here is our website,

your everyday AI.com. There, you can sign up for our free daily newsletter. We're going to be recapping today's conversation and a whole lot more. But while you're there, we've talked to hundreds of AI experts around the globe. You can go listen to it all for free there. So make sure you go check that out. And if you are looking for the normal AI news, that's going to be in the newsletter. Normally we go over in the beginning, but I want to make sure to squeeze every single minute we can with our guests. So enough chit-chat from me. I'm excited to have on. Another Chicago guy. Love to see it. All right, please. Livesream audience, help me welcome. Logan Kilpatrick, senior product manager at Google DeepMind. Logan, thank you so much for joining

the Everyday AI show. Jordan, thank you for having me in for the overly generous intro and for getting me out of bed at 7.30 in the morning. So I'm excited for this conversation. Yeah, it's always funny, right? When people are like, yeah, yeah, I'll do the podcast. Then I'm like, oh, it's this time. Yeah. So definitely coffee in hand. But you know what, I'll say this, Logan, I don't think it was overly generous. So people that maybe don't know your background, right? You started, you know, before this, you were at Open AI and now you're leading a lot of, you know, AI projects at Google. So before we jump into the new 2.0 updates, what's new, what's shiny, what's helpful. Can you just quickly tell everyone about your background for those that don't know?

Yeah, yeah, yeah. So currently lead product for Google AI Studio, one of the co-leets for the Gemini API, we're really focused on how do we enable developers to be successful building with Gemini. Before this, led developer relations at OpenAI was there, joined when it was a small startup. People and got to see that scale out to the crazy company that it is today. And then before that was actually doing another startup in machine learning and deep learning for digital pathology. Before that was a machine learning engineer and a software engineer. So I've sort of done the whole spectrum of work from training models to writing

software to building products. And I love this stuff. It feels like this is the, in hindsight, all of that experience has been like the perfect thing to help me be successful in this moment. But yeah, it's been a ton of fun so far. All right. Before we get into all the new updates, I just want to get your take on just the pace of AI right now.

I think especially over the last two months, right? I've been doing this every day for more than two years. The last two months for me have been so. hard to keep up with, even though I do it every day. So between, you know, we've seen a lot of new stuff from your former employer at Open AI, Google, I mean, between what you announced in December, end of January this week, it's been nuts. Where are we at right now with the state of AI in large language models and capabilities? And, you know, is it hard for you to keep up too, or is it just me? Yeah, that's a great question. I feel like you and I are both in the same boat that

there's so much going on. It depends on, like, what you're trying to keep up with. I feel like I have in the sort of developer world, there's a substrate of stuff that I care a lot about and there's a lot of stuff that's happening in perhaps other domains that is just less applicable to me on a daily basis so I can filter some of the noise out. But it is interesting.

You talk about this pace of innovation, and it's funny how the narrative, like the broader narrative shifts from like, you know, two months ago everyone was talking about AI's hit a wall and like that was the narrative in the media and like there was no more progress to be had on the model side. And then like you look at,

what has turned out to be like a bunch of actually incredibly substantive model progress in the last two months. And it's like it is, yeah, it's interesting to see that. And I think the takeaway for me is like the model progress is not going to stop anytime soon. And actually I think there's going to be a lot of product level progress. And if you look at like some of the new agent products that people have been putting out recently and like Mariner, opening eyes operator, et cetera, et cetera, it's this product experience that's like actually powering the sort of like in combination with a frontier model, this new state of the art experience that wasn't possible before. I think there's, we're going to just see more and more of that as people, as the model capability starts to

really unlock a bunch of new use cases that weren't possible. And like that's literally happening today. Like a like the Gemini 2.0 model like might have been the thing that unlocked a bunch of the use cases that you as a consumer or developer wanted to have it do and it couldn't do before. and like now it just works, which is crazy. Yeah, it is, it is crazy in some of the capabilities. So let's jump in and, hey, live stream audience. If you have a question for Logan, let's get it in now. But Logan, like walk us through what's new in Gemini 2.0.

And maybe, you know, for our audience, if you don't follow it every day, don't worry if it's confusing because there's always so many developments because, you know, we had some Gemini 2.0, you know, back in December. But now we have a whole new wave of updates. Logan, what's new and what does it mean for how we work? Yeah, so this moment for this week was really an expansion of the Gemini model family. So we released the initial experimental version of 2.0 flash back in December. That was sort of the initial moment.

The reception of super positive developers have been getting pinged every day being like, hey, we love this model. We want to go and build a fit. This is the thing that's going to enable our business, which has been super exciting. So we've been pushing really hard on how do we make the 2.0 flash model ready for production? The model is actually, it's an upgraded variant of that model, so it's a further improved version of that model. But we didn't stop there. So there's two other additional new models.

The Flashlight model, which is a smaller, lower cost variant of the main 2.0 Flash model. And this is really for like the high scale, you know, production workloads where you're, the use case is very cost sensitive and doesn't need to have like super, super high accuracy. You can imagine a bunch of basic examples for this model, like, you know, email summarization, or, you know, you're trying to summarize a bunch of different web pages or something like that for putting together a report, things like that, that, you know, the model doesn't need to be super capable. And on the other end of the spectrum, we released Gemini 2.0 Pro experimental. So this is the first sort of officially branded 2.0 Pro model. It is the predecessor of the Gemini 1206 model of folks.

familiar with that, which was our previous strongest model. And it's, again, an updated variant of that model. It's a further improved version of that model, which really excels like the main use case that 2.0 pro excels at is like, I think coding is like the thing that is like far in a way better at. And I'll make one other quick comment, which is, you know, for folks who have been following some of the discourse around some of the new models, I've seen a lot of reactions from folks being like, you know, the 2.0 Pro model like doesn't actually, and you can see some of the metrics if you're watching this conversation on Summer Brothers video, but the metrics are, you know, 3%, 4%, 5% better between Flash and Pro. And I think there was questions from the community being

like, hey, why is that the gap right now? And I think there's two things that are true. The first thing that's true is to me, this is like a success story of small models. Like there's been all this work and innovation that's happened of like, how do we take the frontier capabilities, bring them into smaller models so that like at scale and at a low cost, developers can put this stuff into production. So I think we've been successful doing that. And to me, that's why there's sort of this continually shrinking gap between like the total frontier and some of the smaller size models.

But separately, like, I don't think, you know, if you're not someone who thinks about like metrics or like looks at evals and like really understands what's happening. happening, there is this non-linear amount of additional work, but also capabilities that come as you like continue to move up the frontier. So like moving from, you know, moving from, you know, 80% to 85% could be like, could be this like, not only is it this massive amount of work, but also like the order of magnitude of use cases that like that 5% increase unlocks is actually pretty crazy relative to the previous 80%. So it's this exponentially difficult curve that you have to go up.

So lots of interesting stuff around that and happy to chat more about it. Yeah. And yeah, the benchmarks are obviously very impressive. Gemini, you know, it has been kind of this back and forth battle over the last couple of months. But I think right now it's it is the highest, you know, bench model in the world in terms of, you know, Elo scores, which I think are important, you know, it's what should do, you know, real world humans, prefer. But, you know, I want to get into some of the maybe overlooked aspects of Gemini 2.0 because Logan, like, I know AI, you know, runs so fast, but it is the only true multimodal large language

model out there right now, right, where you can input video, right? You don't have to just input a photo or text. Can you talk a little bit about some of the improved multi-modality capabilities of this 2.0 model? Yeah. No, 100%. So I think when we initially put out Gemini back at the end of 2023 now at this point. The sort of title was, you know, building a model for the multimodal era. And at that point, it was, I think, like, we had the research direction to enable this to happen.

But the models at that point were not, like, fully multimodal input output. We're now actually at that point. So, like, Gemini 2.0 is sort of the delivering on the mission of, like, actually making the models fully multimodal. So being able to take in text, audio, video, images, And now actually, and this is still an early access, but rolling out to more folks broadly, hopefully soon, the model can actually output audio and images as well. And I think this next step is super important.

And the example that I was responding to some tweet threads yesterday because we released our Imagine 3 model, which is a sort of separate domain-specific image generation model. And folks were asking, hey, they were saying, we're not super interested in Imagine 3. We really want this native multimodal capability. And really there's this interesting, there's this interesting tradeoff between these different types of models. Like the imagine domain specific image generation models are really good at generating these beautiful, like sort of artistic, picture perfect images. And if you look at what the Gemini models are able to do,

it's like really they benefit from all this world knowledge. And if you think about like lots of image recognition task where there's like a lot of complexity and you're maybe looking at a, I'm looking at my kitchen table right now and there's like lots of objects sitting on it and you have to understand the relationship between these objects and understand physics and understand like bounding boxes and all this stuff like really complex and you can't do those tasks unless you have the baked in world knowledge of a large language model and again those like domain specific image models don't do that. So there's all, I'm really excited about this because there's all of these interesting use cases which are now

sort of just going to work out of the box because of how good these models are. And also for Flash specifically because of how like cost effective it is for developers. It's yeah, it's getting, you know, if you would have shown me these benchmarks like two years ago, I'd be like, no, not possible. Right. It's not possible to be that fast, that cheap and that powerful. Yet here we are. But, you know, Logan, I want to talk a little bit.

You know, we kind of just talked about multimodality, which I think was, you know, all the buzzword, you know, maybe two years ago. but now the conversation has really centered around agentic capabilities. And obviously, you know, Google has had a lot of different, you know, kind of announcements in the space. And, you know, I think Open AI's operator has caught a lot of people's attention so far. I'm personally excited to see Mariner. Can you tell us what that is and maybe even your thoughts on agentic work in the future?

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Lightroom Express, and more to help bring your ideas to life. You can also get started with creative skills, a growing library of pre-built workflows for common creative tasks, like batch editing photos, creating mood boards, portrait retouching, and creating social variations. Every step the assistant takes is visible so you can refine, redirect, or take over at any time. You stay in the driver's seat as the creative director. Adobe Firefly AI assistant now in public beta. See it today at firefly.adopi.com. Yeah. No, that's a great question. So for folks who who didn't see when we did the 2.0 flash release back in December, we also released a bunch of sort of experimental research around the direction we were going with agents. So we released

Project Jules, which is a coding agent, and you can sort of have it living in your GitHub and helping you solve issues and writing code and doing polar questions. for you, which was really cool. We released Project Mariner, which allows you to basically have the model, you know, co-exist with you in your Chrome browser and, like, actually sort of, you know, you can talk to it and have it take actions on your behalf and you can sit there with a model and sort of it does work for you in some cases. And then we also released a data science agent inside of CoLab, which is a sort of Jupiter notebook, if folks are familiar with that, and you can do sort of data science tasks.

And I think all three of these things were sort of validation of the sort of fundamental research that was being done, which is how do we enable the next step in this journey, which is being able to build agents. And I think there's a lot of different ways that people have tried to build agents and will build agents in the future. The thing that's most interesting to me is how intertwined all these different pieces that are. So I think like if you think about, you know, why do we not already have large scale

at deployments of agents. Like, why do I not have 100 agents? Like, I'm, I feel like I'm living on the cutting edge of AI and like, I don't have 100 agents doing my bidding every day, you know, showing up to podcasts to talk for me, sending all my emails. Like, I don't have that today. And if you think about, like, why don't you have that today? I think it's just been historically that a lot of the fundamental capabilities weren't

there. Like for multimodal, for example, like, the way I interact with the world is in a multimodal capacity. Like, I see things. I hear things. you know, there's some processing video all the time. And that just historically hasn't been good enough. Like, what else hasn't been good enough? The models can't actually reason.

Like, they kind of just have to, you know, it'd be like if you were forced to not think anymore and you had to just like give your gut take at every single question or every single interaction that you've had. And I think we're sort of getting that. We're solving that problem now with our reasoning model. So I think the sort of the dominoes are falling in order to make this agentic workflow actually happen, which I'm excited about because. I feel like if you look two years ago at sort of what was being promised to the world with this technology,

like it really is all those agent workflows. It really is like, hey, I have this thing and the model is going to go and do work for me. And it sort of removes that burden from my life. I don't feel like we're fully there yet. But I am excited because I think the direction, at least on the capability side from a model perspective is like very clear. Like we're going to get there. I think people need to build the products around it to make it to work well. but yeah, this is going to be a great year for agents.

Yeah. And you know, you kind of mentioned reasoning and thinking models. You know, I was playing right after the announcement. I was playing with Flash thinking. And although it didn't get every single thing, right, I was actually less worried about that. And I was more taken aback and more impressed just by its level of reasoning. And kind of looking at that chain of thought.

But, you know, you kind of mentioned Logan like, hey, like I'm not currently, you know, orchestrating hundreds of eight. agents myself and you live on the cutting edge. So I'm wondering maybe why or what are the hurdles that we have to clear because we have power even just at one company, Google, right? We have extremely powerful multimodal models. We have agenic capabilities and we have very capable reasoning models. So it's like all the individual pieces are there. So maybe what are those hurdles and kind of what has to be done to get over those until we do have that more quote unquote future version of work. Yeah, no, this is a great question. And I actually had a conversation with

someone yesterday trying to dig into what is the answer to this to this question. I think maybe some of this is personal preference, but part of what I think has been missing is the models really being proactive in a bunch of use cases. And I think there's two layers to this. The first is like what I want is not to have to instruct the model in a bunch of cases like, hey, go do this work for me. I think that maybe happens like one time as like a startup cost. But then what I really want is the model proactively, you know, for the email use case,

going through my email every day, looking for, hey, here are some maybe high value things that I missed or that I didn't have time to look at and like compiling that into a report. And then actually pushing that information to meet. Like I, if I have to be in the driver's seat of that, of that agent experience, it's like at least in my personal life, like it's not going to happen. Like I don't have enough time. There's too much other stuff going on.

Like what I need is the model to sort of take the burden of like following up with me and making this happen, sort of what you would expect like a really good assistant to be able to do more you. And if you just look at like what are the products that people have built so far using this technology, it isn't that experience. And I think this is like one of the big, the big gaps that still exists. And I think there's like parallels to what has happened in the last two years with AI. going into production and like, you know, in a lot of demo use cases and a lot of like really simple chat use cases. And then it's like kind of people are sitting there thinking, you know, was this

really all worth it, all this effort and stuff like that? I think a lot of this is like the product experience needs to be better. And I think the models and the technology and the scaffolding is finally good enough to enable this to happen. But like it just takes time to build great products. I think is the simple answer to this. And like startups and like entrepreneurs and like, you know, even the large companies like just need time to make a really great profit experience. And I think we're going to see those in in, in, in 2025. And like, Mariner is hopefully sort of the early work to make that happen. And operator is hopefully the early work to make that happen for Open AI and for others. So I think we're going to see more of it, which I'm excited about. So one, one thing that I love asking people who

are building the AI that we all use is, you know, I think there's a lot to be said on, you know, learning how they eat their own dog food, right? Like right now, I can't do anything without using Google deep research, without using Notebook LM, right? Even this very podcast, every podcast I do, right? I use those two tools every single time. What are some of even, whether it's your own internal Google tools, but, you know, what are some of those kind of AI tools or processes that you can now

not live without that maybe three years ago weren't even on your radar? Yeah, I think for the thing that I use AI for probably, most successfully. I do the deep research stuff from my podcast, and it's incredible for that. But it's still coding for me, honestly. I think that's just the use case that provides the most value. And I think it's also the one that's like fundamentally changed the way I operate. Like I think I was a software engineer. So like I lived the life of like I am writing this code from scratch and solving this problem from zero by myself, which is just crazy to think about. And I think now when I was having this debate with folks internally this week actually about like what what is our interview process actually like for some of these like future jobs and like should we be, you know, how much do we want to actually check that a software engineer should be like doing this stuff from scratch versus like using an AI tool.

And it's like a really interesting like philosophical debate as we're like in the middle of this fundamental shift for how software is created of like should we be sort of going for people who are. are really good at using AI tools and like know how to like get the most out of them but are still like good at software engineering should we go for people who can just like do it all from scratch but maybe like don't use the AI tool so it's this really weird situation but I think I'm in AI studio all day I'm using code assist which is our sort of coding extension I'm in cursor all day using the latest Gemini models those are probably the things that are are most are most pertinent to me but yeah I'm also curious for you, like what are the, what is the, like, top three tools other than deep research and notebook LM that you're that you're working with? Yeah, those two I can't live without, right?

Obviously, I've been loving open AI's version of deep research that just came out as well. I like using those. I like using tools in tandem, right? And I look forward to the day when there's just one system that, you know, you can use all these different tools and features. But, you know, I love just anything deep research, anything agented. right, you know, it's my workflow. I use way too many AI tools every single day, right? So I'm actually trying to use less and kind of build more. But even another example of that, right?

Like I was using Google's in AI Studio, the stream real time. I was running into an issue. And I'm like, you know, I'm using deep research to try to troubleshoot this issue. And I'm like, no, you know what? I'm going to do. I'm going to go into stream real time. I'm going to share my screen inside Google AI studio. And I'm just going to quickly build.

in a Chrome extension, right? You mentioned like, you know, coding and development. So I'm curious, you know, that's something I did. I never thought I would be building Chrome extensions, but it takes me like minutes now. How do you see the future as someone with the background and development? How do you see that future? Is it going to be where, you know, people like that are just going to be spinning up their

own apps on the fly? Yeah. I do fundamentally believe this. I think if you look at how like the number of people who we would consider like software developers today, I think that's going to transition to like AI builders or software creators where the artifact that is actually being created in that process is code. How much of that code is being written by the person who's actually creating it. I think it's going to continue to go down over time.

I do think like there'll be some, you know, people who know how the software works behind the scenes are going to have like this, you know, competitive advantage essentially. Like if you can go in and troubleshoot and like I've used a lot of the AI tools that like help you go for. from like text idea to like working app. And like sometimes things break and like being able to know and like look at code and understand what's happening is incredibly useful. But I do think like that is the new frontier to me. The new frontier is like letting and enabling

every single person in the world to be able to make their idea come to fruition. And I had a bunch of with friends in Chicago actually like people who have really interesting ideas that they want to see come into the world and these folks, like aren't software engineers. Like it's actually really hard if you're not using one of these AI tools to bring any of your ideas to life.

So I'm excited. And like this is actually something we think a lot about for AI Studio as well as like, how do we remove this barrier to people who want to build? So I'm excited. Hopefully we'll have more to share around that soon and keep building experiences that remove the barrier for people to create software and actually like make it accessible to the rest of the world. You know, speaking of building software,

You know, I know a lot of, you know, entrepreneurs, startup people are probably listening in. And, you know, I think over the last two years, it's become increasingly both easier to build a company, right? I think there, I read like four million developers or something like that are using Gemini, right? Something like that crazy. So what advice do you have for those people in a time where it's technically easier than ever to validate an idea or bring something to market? Yet at the same time, Moat is so hard because a company. like a Google or a Microsoft or an open AI can have one new feature update and kind of, you know, kill that moat or sync a startup. How should, you know, entrepreneurs be building and,

you know, maybe how does Gemini help in that? Yeah. No, this is a great question. I think there's like, and we'd probably need like a 30 minute conversation just about this to do the deep dive. But I think the really quick lens that I look at this through is like, what is the scale of company you want to build. I think there's like really interesting problems to be solved that would create a lot of value for yourself and perhaps like a small business. And like you could make a bunch of money doing that. And like you don't need to be venture scale. You don't need to raise money. I think the like really interesting thread of the AI moment is there's sort of these a bunch of intersection intersecting intersecting curves. And like one of them is the cost of building with AI and using AI has gone down like

99.9% in the last two years. So if you look at like the cost per million tokens of GPT4 at like $30 per million tokens of Gemini 2.0 flash, 10 cents per million tokens like a massive cost reduction while the capabilities have actually improved. Like the models are better and can do more for you. And at the same time as that curve is going down, you look at what is the consumer willingness to pay for AI technology and the awareness of AI. And it's like actually consumers are willing to pay a lot more if you can actually provide them value. And more and more people are realizing, hey, these things can actually do stuff for me. So this is like this for people building stuff, it's this beautiful situation where like your costs are going down. You have more users showing up and your users are willing to pay more money.

And to me, like, that gets me so excited. Like the reason I love my job is because I think we get to build stuff for people who want to build. We're building for builders. And I think that I don't think that this is going to change, this like willingness to invest in AI while the cost continues to go down for people building. And that just means like the value creation is going to builders. I think to hit on the point of like, you know, are you going to be disrupted by Google or Open AI, like not in this context, really. Like, I think if you're going after, like, if you're trying to compete with Open AI

at being a chat app or you're trying to compete with Google at being searched, like, I think that's a different situation. I'm like, I have this very specific problem I'm trying to solve. And like, it's not one of those two, like, very, very horizontal problems. I think there's just like this massive amount of value to be created. And like, it's never been a better time in the history of the world. And the like last really quick curve is like the time. the time, like how quickly some of these new AI startups are like scaling and like getting

profitable and monetization is crazy to me. Like I just saw a chart yesterday that cursor is like I think the fastest company, one of the fastest companies now to reach like 100 million ARR, MMR, MMR, whatever the number is for them, which is just crazy to think about. And like it's, it's this like massive breakout success that's enabling builders to go and build stuff, which is so cool. All right. So we have to land this plane here in a second, but hoping we can get rapid fire questions here. So real quick, a question from Lincoln, LinkedIn, someone asking outside of Google models, what are some of your favorite LLMs? Also, what are some of your go-to AI tools that you use regularly? Yeah, yeah, yeah. That's a great question.

I mean, I've spent a bunch of time using the open AI models. I think the anthropic models are great for coding. I think those are probably the three. I use Gemini the most, and then I've dappled with a bunch of the anthropic models and then built a bunch of stuff with open AI models. And go to AI tools. I'm in AI studio all day,

obviously because it's part of my job. But the Gemini app, I think deep research, it's rolling out in iOS, which I'm super excited about next week. And I'm waiting for that to happen because I don't, I'm always on the go and have my ideas. and it's hard to be strung to a desktop.

So I'm excited about that. Oh, can't wait for that one. All right. Last one. Pedro from LinkedIn asking, Logan, what will agents be doing for you in quarter four? So by the end of the year, right? You said we're not there yet, but what are agents going to be doing for you by the end of the year? Yeah, this proactive stuff.

Like the product experience I want as I wake up in the morning, I have an app that shows me the list of all these potential tasks that the agent wants to go and execute for me. I sort of review and see, yep, I want this to be done. nope, I don't think this is worthwhile for you to do or change the plan or do whatever. Like, that's the product experience I want. And it's like material, like actually high value work, not just like, you know, AI that's auto drafting a response to a spam email that I got. Like I don't need that product experience. I needed to like know that, oh, no, this is actually like a conversation with Jordan about AI or,

you know, whatever it is, like something that's actually high value. And I think we're going to get there, which is exciting. All right. We covered a ton in today's episode. Logan, can't thank you enough for coming on to share with our everyday audience. So thank you so much. Last takeaway. What's the most important thing people need to know about Gemini 2.0?

It's available today. I think you can go start building with it the best price per performance of any model on Earth, which I'm happy about. Love to see it. All right, Logan, thank you so much for joining the Everyday AI show. We really appreciate it. We covered a lot, everyone. And we're going to be recapping it in today's newsletter. Logan talked about a lot.

All those links are going to be in there. So please go to your everyday AI.com. Sign up for that free daily newsletter. Thank you for listening. Thank you for tuning in. We'll see you back for more Everyday AI. Thanks, y'all. Meet Firefly AI assistant.

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