EP 457: Gemini 2.0 – Google's Logan Kilpatrick gives inside scoop on Gemini updates

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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 an expanded it's 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, uh, you can learn a lot from our guest. 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 is Jordan Wilson and welcome to Everyday AI. This is for you. It is your daily livestream podcast and free daily newsletter helping us all not just learn AI, but how we can learn, 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 chitchat from me. I'm excited to have on another Chicago guy. Love to see it. All right, please, livestream audience, help me welcome Logan Kilpatrick, Senior Product manager at Google DeepMind. Mind Logan, thank you so much for joining the Everyday AI Show.

Jordan, thank you for having me. And for the, 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, it's always funny right, when people are like, yeah, yeah, I'll do the podcast and I'm like, oh, it's this time. Yeah. So you 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 OpenAI 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. So currently lead product for Google AI Studio, one of the co leads 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 or people and got to see that scale out to the crazy, 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, 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 OpenAI, who, 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 we're, we're. There's so much going on. Like 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 and in perhaps other domains that is just like less applicable to me on a daily basis. So I can filter some of the noise out. But it is, 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 AIs hit a wall and like that was the narrative in the media and like there was, you know, there's 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, OpenAI's 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 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 that's literally happening today. The Gemini 2.0 model 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 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, 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 was super positive. Developers have been getting pinged every day being like, hey, we love this model, we want to go and build with it. 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 a 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 the high scale production workloads where the use case is very cost sensitive and doesn't need to have super, super high accuracy. You can imagine a bunch of basic examples for this model like email summarization or you're trying to summarize a bunch of different web pages or something like that for putting together a report, things like that, that the model doesn't need to be super capable. On the other end of the spectrum, we released Gemini 2.0 Pro experimental. This is the first officially branded 2.0 Pro model. It is the predecessor of the Gemini 1206 model, if folks were familiar with that, which was our previous strongest model 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 it's like far and away 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 doesn't actually. And you can see some of the metrics if you're watching this conversation on somewhere where there's video, but the metrics are 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, 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, 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 like, it's this like 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, benched model in the world in terms of, you know, ELO scores which I think are important. You know, it's, which 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 multimodality 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 building a model for the multimodal era. And at that point I think we had the research direction to enable this to happen. But the models at that point were not fully multimodal input output. We're now actually at that point Gemini 2.0 is the delivering on the mission of actually making the models fully multimodal. So being able to take in text, audio, video, images. And now actually, and this is still in 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, and I was responding to some tweet threads yesterday because we released our Imagen 3 model, which is a sort of separate domain specific image generation model. And folks were asking, hey, you know, we're not, they were saying we're not super interested in Imagine 3. We really want this native multimodal capability. And, and really there's this interesting, there's this interesting trade off between these different types of models. Like the imagine domain specific image generation models are really good at generating these beautiful 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 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 were to show 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 OpenAI's operator has, 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 and how does that change the human's role in all of this?

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Yeah, no, that's a great question. So for folks 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. We released Project Jewels, which is a coding agent and you can have it living in your GitHub and helping you solve issues and writing code and doing pull requests for you, which was really cool. We released Project Mariner which allows you to basically have the model co exist with you in your Chrome browser and actually you can talk to it and have it take actions on your behalf and you can sit there with the model and 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 jupyter 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, 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. Um, the thing that's most interesting to me is how intertwined all these different pieces are. So I think like, if you think about, you know, why do we not already have large scale at deployments of, of agents? Like why, why do I not have 100 agents? Like I'm, 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. I'm 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, 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, I am excited because I think the, the direction, at least on like, 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 work well. But yeah, it's. 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 Floyd Flash thinking, and although 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. I'm not currently, you know, orchestrating hundreds of 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 agentic 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, you know, 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, 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 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 one time as a startup cost, but, 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 me. Like I, if I have to be in the driver's seat of that, of that agent experience, 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 for 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 startup startups and like entrepreneurs and like, you know, even the large companies, like, just need time to make a really great product experience. And I think we're going to see those in, in 2025 and like, Mariner is hopefully sort of the early work to make that happen. Operator is hopefully the early work to make that happen for OpenAI and, 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 PO 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 for my podcast and it's incredible for that, but it's still coding for me, honestly, like, I think, 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 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 most pertinent to me. But yeah, I'm also curious for you, what is the top three tools other than Deep Research and NotebookLM that you're working with?

Yeah, those, those two I can't live without.

Right.

Obviously, I, I've been loving OpenAI's version of deep Research that just came out as well. I, I like using those. I like using tools in tandem. Right. And, 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, I, I, I love just anything Deep Research, anything agentic. 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 gonna, I'm gonna do? I'm gonna go into Stream Real Time. I'm going to share my screen inside Google AI Studio, and I'm just going to quickly build 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 a background in 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. I 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 is 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 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 aren't software engineers. 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. This is actually something we think a lot about for AI Studio as well is how do we remove this barrier to people who want to build. 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 make it accessible to the rest of the world.

You know, speaking of building software, you know, I, I know a lot of, you know, entrepreneurs, startup people are, are probably listening in and you know, I think over the last two years it's become increasingly both easier to, to build a company. Right. I think there I, I read like 4 million developers or something like that are using Gemini. Right? Something like that. Crazy. So what advice do you have for, 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 OpenAI can, you know, have one new feature update and kind of, you know, kill that moat or sink 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 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 to today, the cost per million tokens of Gemini 2.0 flash, $0.10 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, that gets me so excited. Like, the reason I love my job is because I think we get to build stuff for people who, who want to, 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 OpenAI? Not, not in this context, really. Like, I think if you're going after, like, if you're trying to compete with OpenAI at being a chat app or you're trying to compete with Google at being searched, like, I think that's a different situation. And 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, and the like, last really quick curve is like the time, like how quickly some of these new AI startups are like scaling to 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 or 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, 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 what 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, I've spent a bunch of time using the OpenAI 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 dabbled with a bunch of the anthropic models and then built a bunch of stuff with OpenAI models and go to AI tools. I'm in AI studio all day obviously, because it's, it's part of my job. But the Gemini app, I think deep research, I, 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 is 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 it. 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 need it 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, 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. 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 everydayai.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.

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