6 Game-Changing Ways to Use Google Gemini AI Tools in 2025

Hey everyone, I am excited. We have a very, very special guest. Logan Kilpatrick from Google DeepMind is here and he's going to break down the aspects of Google Gemini AI that people aren't using that they should be going to talk about where the real value is, going to talk about their brand new image generation API, their new reasoning models, deep research, so much. If you were trying to learn about Google AI, this is the show for you. Let's get to today's episode.

Logan, really excited to have you on the podcast. Big follower of your on Twitter for a long time or X for a long time. Excited about your move to Google. Have been following that as well. But let's maybe start not at the start for you, but maybe at the start for some of us. You started following along your journey with being an early employee over at OpenAI like one of the most transformative companies there is. You changed, you went to Google. Google's pace is also I think expediated. Like they have some really killer launches. But what are maybe a couple of differences that you've seen between OpenAI and how OpenAI are approaching AI in general and what Google's approach is?

I think there's a lot of factors at play. I think people perceive Google as a company very differently than people perceive OpenAI as a company. OpenAI also has a very different product offering. Google has many, many, many products across many, many different domains. And there's just a lot of downstream impacts of the beautiful thing for OpenAI is they have a clean slate in many ways. So people like either haven't formed a prior about, you know, some specific angle of what they're doing or they just have like an open, like even something very tactically like they have an open namespace. Like they can call their products anything, they can use whatever URL they want, you know, et cetera, et cetera because like there's nothing conflicting with that. They don't need to worry about the sort of crossover between these different products. One of the things that Google really benefits from is like the breadth of all the work that's happening so on, on our team's now a part of Google DeepMind. On one end of the spectrum we have, you know, alpha fold and sort of protein folding and Nobel Prize, you know, science happening on the other end of the spectrum there's, you know, image generation models and there's weather models and there's Gemini and there's, there's this huge breadth of different stuff that's happening from an AI perspective and DeepMind is really the only place in the world where that depth is actually happening. You can look at all the other labs and you can see the models that they're creating and like those are the models that are being created at other labs is like one pillar of what DeepMind is doing. And I think the really exciting thing to me is like, if you buy into the idea that this like multimodal, multi capability enabled model is going to be the thing that enables humans to do all the things that we want to do, like there's only one place in which it's possible that that's going to be created and it's, and it's inside of DeepMind because of this breadth of all the work that we're doing. And we see this actually happening in practice with, you know, the cr. Pollination of research from AlphaFold to again to weather models to alpha proof, which is our math, our math model and like how all that actually trickles back into the mainline Gemini model that is available to consumers and is available to developers. So I'm super excited about that. I'm excited for us to like lean more into that story and lean more into that, that advantage that, that DeepMind has. I feel like we miss telling the magic of why this technology is so important when you sort of don't go the authentic route. And there's like so many of these, like really. And I like feel these so deeply inside of me. These like really authentic, interesting AI stories that again, that only Google can tell and like, but the only way to land that message is in a really authentic way. And there's just like so many different angles of this tension to reconcile. And I just don't think that OpenAI is an example. Like they don't have to deal with this because they're just not the size of a company where like they have all those different dimensions of tension right now.

One of the advantages Google has is that like the breadth of all of their tools and being able to just like seamlessly integrate Gemini and even if the story isn't there, like when I opened up like Google Maps and I looked at a place and Gemini was now on the place listings and I could just ask Gemini anything about that place. I was like, holy cow, this is incredible. I could just ask it, oh, I'm going to this place in New York, how long? If I get there at 10am, how long am I going to have to wait in line? Right? Basic things like that, that would have been impossible or I would have read like 30 Reddit threads to find out the answer is now like a 10 second answer which I think is the nature of having a big platform with lots of different products is that you can just integrate that into those wow moments. It's almost the story is getting told by like the user just discovering those features too.

I think again one of those advantages from the breadth perspective is like when, when DeepMind builds Gemini, it's not just like we're not building it for a chat app. Like if you think about like where Gemini is actually being integrated, it's like across some of the largest product suites that touch the most users in the entire world. And like that has a very different set of constraints and Gemini is powering search. Gemini's in YouTube and like these are like billion, billion billion user products that, that like all of these very nuanced characteristics matter a lot of. And actually we've seen like lots of really, really great examples of, you know, the requirements for a model to be really good for search actually leading to like something that's really great from a developer perspective. Like we've, we've seen this with a few of the last 1.5 series of models where you know, the search team needed something and it ended up being like a really great trade off of capabilities that developers also ended up wanting those things. And it's, it's cool to have those levels of sort of internal engagement from these teams.

What are some of the AI use cases today like that you can actually just go and start using Google Gemini for today that you think are like wildly underestimated or underused by the average consumer of AI, the person who maybe isn't in the details and in the weeds day in, day out.

In December we launched Deep Research. So we launched sort of the world's first iteration of Deep Research, which if folks haven't used it, is essentially a research assistant. And you can put in whatever your query is and the model will go off and search. And in the context of our Deep Research visit, you know, thousands, potentially of different websites to answer the question. You can sort of, I think that artifact that like simple product artifact of like showing you the number of websites that the model browsed through in order to get you the answer is the thing that just like makes that product experience work for me. I'm like, I would in 0% of any of the research I've ever done in my life have I looked at more than 10 websites. So the fact that the model went out and looked at a thousand websites just gives me a lot of confidence in the model and I think this is one of the biggest challenge for AI products, which is in many cases AI products are asking the user to basically do all this upfront work in order to provide them value. And I think Deep Research is this great example of the model and the technology just doing the heavy lifting for you. And you as a user get to just ask your silly question or your serious question and then the model goes and finds it. I love Deep Research. I think lots of really interesting use cases, very underutilized today and it's now powered by our line of reasoning models which is awesome.

Hey everyone, if you are enjoying our conversation with Logan and this has got you thinking about Google Gemini and how you can level up your AI game, we've got you covered. We just dropped the Go to guide that shows you exactly how you can use Gemini and NotebookLM to become an expert in your field at lightning speed. I'm talking about slashing research time in half while actually improving your content and strategy. We'll give you the strategies you can steal so that you can instantly implement these into your research workflow. You can get it right now scan the code or grab the link at the description below. Now back to the show. Far and away the number one thing, if nobody's really using AI for much than just some random questions, they should do use Deep Research. Deep Research is incredibly powerful and the amazing thing about Google Gemini, and you correct me if I'm wrong, is that like Deep Research is a free feature. I don't think there's any hard limits on Deep research yet. And really the biggest thing there is you then rolled out these reasoning models which makes it think through the ability of what sources and what follow up questions to ask around those sources. Far, far better than it was just a couple of months ago.

So you, you used to be able to use to go down to this dropdown and you would pick whatever model you want to use. Now you can actually just click it here. So if you want to have Deep Research. When do I use this Deep Research? Like what's the, what's the perfect way to delineate between Flash, which is also incredibly great at a model. Now I need the Deep Research. Plus you mentioned something really important which is now you've integrated reasoning into that maybe just explain what we mean to users that we've integrated. You've integrated reason into this Deep Research?

Yeah, I, I think that basically the land is like your everyday questions which are like very, you know, if it's a simple question, just use 2.0 flash it's going to be very quick. It'll get you an answer, like, almost, almost instantly. If you need to sort of compile, like, truly, like I, I, maybe to maybe not poke fun at the name, but like, if you really do need something that is not surface level, like, if you're looking for like, you know, who won the Cubs game yesterday, you know, you don't need deep research for that. If you're trying to understand, like, why the Cubs. And I'm, I'm based in Chicago, so I'm using, and I'm not even a Cubs fan either, so I don't know why I'm using the Cubs example. If you're, if you're trying to understand why the Cubs, you know, built the, whatever the ivory wall is around the back of the field and like, what the technique that they use to build that and who the people were who worked on it and like, what type of permitting they needed in order to put that together. Like that level of depth in the question that you have, there's, there's no product that can do that besides Deep Research. And I think the reasoning models is the, is the sort of key enabler of this. And we initially launched Deep Research back in December with Gemini 1.5 Pro and like, it was really powerful. But a lot of the techniques being used by 1.5 Pro were actually like, trying to get it to do what a reasoning model actually does, which is be able to sort of have this essentially this like, inner monologue of, you know, thinking through different pieces of a question, like actually reflecting back on the initial answer that it's given and like trying different versions of this. And you can sort of think about how we as humans think through this process. By default, AI models just kind of spit out an answer as quickly as they can. Is basically the way that models are trained today. And the thinking models are trained to like, don't actually spit out the answer as quickly as you can, but iteratively go through this process, try a bunch of different things, make sure that you're sort of covering the breadth and depth of what a user might be asking for, and it actually leads to some, like, pretty substantively different outcomes. So if folks have tried stuff before with AI and you're like, ah, the models are just dumb and don't, don't really have the ability to do these things, try the new sort of generation of reasoning models. I think there's a lot of use cases that just were not possible before that like all of a sudden just work.

Today I am Very convinced that most humans do not realize what, like, deep research especially is capable of. Like, for example, like, I was wanting to get rid of, like, a tree in my yard, and I was like, I don't know what the permitting process is. I don't know what you would need to do. It did all of that, told me exactly what I could and couldn't do, what the rules are. And I literally just said I wanted to remove a tree and gave it my address and it did everything else. Right. And it's like, people would just wouldn't think that it could do things like that. Like, I had to estimate an entire construction project. It's like, well, all right, somebody's giving me this estimate. What does deep research say? And it's like, it's incredible how detailed it is. And its ability to go through complex documents and frame things in simple ways is really, really good now.

Yeah. And I think it's wild to just reflect on that. This is like the V0 of the product. Like, truly, like, this is the. This is the bare bones version of what deep research can possibly be. And like, today, it's essentially just using search and there's more. And I actually think at the time of this going out, because it's rolling out today, the ability for deep research to be combined with audio overviews from NotebookLM is also rolling out. So now you can sort of take that deep research experience that you have.

Let's do that.

And then, yeah, actually just click a button, turn the entire thing into an audio overview, and then you have a podcast about. And you're learning about the permitting process in whatever city you are, and you.

Can ask it questions and interrupt and, like, interrupt that podcast and be like, well, I'm not. I don't understand what you mean here.

Yeah. So I think this whole, the whole experience and the way in which humans sort of get this type of information is changing, which is just, I think, a good thing because I think about, like, my own. I just wouldn't do it, to be honest. Or, like, I would.

Correct. Exactly.

Or I would pay, like, you end up paying, like, this, like, market inefficient price because you're like, well, I'm not willing to do the research because this is boring, not interesting to me. So, like, therefore, I'm gonna get gouged by somebody and they're gonna charge me $20,000 to take a tree out of my, my backyard or something crazy like that.

But it's also not even the price, right? It's the time, like, to get to that same outcome would have taken me weeks. It's like I can just accomplish so much more in my life. And I don't think any human realizes, like the rate of progress you can now make.

It's a combination of there's things you would never have done because you didn't have the time or money and now you can do and just how quickly you can do them. And one of the ones I just, I didn't, I just don't want to gloss over because you mentioned something that is actually one of my favorite workflows for Deep Research. We did an entire video of it, which is Deep research into Notebook and then turn it into audio, create podcasts and ask podcast questions. That's actually how I've been learning about call options, which is this is the worst time to start doing call options. But that's a whole other thing that kept an eye on.

No financial advice.

No financial advice. But that's one of the things we've been playing around with. But when you say that's being integrated, do you mean I can just go to Google Gemini? Like you're integrating an LLM notebook into Gemini or how is Deep Research still going to exist separately or are they going to be combined? How is that integrated together?

Yeah, I think for today's launch it is when you do a Deep Research query inside of the Gemini app, you'll see an, you'll see an option when the Deep Research process is done to convert the Deep Research into an audio overview. And I actually think, I think there's maybe like a direct way to also just like take the Deep Research artifact and go straight into NotebookLM. If you want to like leave and do more of the. Because there's a. It's not, it's like a subset of the features. So like actually the like interruption piece wouldn't be included in this. It's just like a single shot. If you want the like full Notebook LM experience of everything, you would, you would go back in a Notebook lm. But it is cool to just like be able to have that, that single shot version of the, of the overview created for you.

Why don't you give us a little, give the users a little bit of your ways of using Notebook Alum, because that's actually a favorite tool of this show. And so I don't think I, again, I don't think enough people even know that tool or are using it enough.

Yeah, I think for folks who haven't tried Notebook, you can think about, you can think about Notebook LM Sort of as the knowledge assistant that you might have, like Imagine or a learning assistant or a tutor that you might have or honestly, just like a way to bring content to life is another version of this. Like Josh Woodward, who leads the Google Labs team has this idea of sort of like infinite repurposing of content. And I think Notebook Alum is like a really great example of this. You can take Imagine you have something really boring like a onboarding manual to set up a vacuum cleaner. And for whatever reason you're one of the 10 people who actually reads the onboarding manual and you have to read it, but you're, you're really bored and you don't actually want to go through the whole thing. You could take a PDF version of that, you know, 150 page onboarding manual that says all the different gizmos and gadgets and things that your product does, stick it in a notebook LM and really quickly generate like a summary learning guide. A principally like one of the things that folks are most excited about, a podcast level conversation with like really sort of witty and smart sort of nuance in the conversation about whatever the content is that you're uploading. And it now has features like you can actually interject mid conversation and say things like, you know, hey, this is actually really boring, spice things up a little bit or you know, I don't understand this point that you were just making. Can you, can you sort of re articulate what this is? And one of the most common flows that I actually see folks who I work with and I don't, I work remotely so I don't commute to the office. Uh, even though I, I probably should go to the office every once in a while folks like take a bunch of like work documents, they put them into, into NotebookLM, create audio reviews and then they like listen to them on the drive.

So this one here is exactly what you said. It's like a, a strategic knowledge assistant that's specific to one of the cross functional pods that I run here. And one of the cool things I can do is like add all the documentation. So I had all like the meeting transcripts at all the docs that have been created that month, that week. And then when I'm out walking the dogs I can actually either listen to it or now you have this interactive way of being able to talk to the podcast directly and ask questions. And so it's a pretty cool way where you can actually take knowledge with you with work and actually conversate with it. Like if you're out of the office, if you're talking, if you're driving, if you're walking. I have a, I have knowledge assistants trained on every single project and I can always ask them questions. And so it's like you have a project assistant slash executive assistant for every project. And as I said, I wasn't lying. Like all, like all the. This is like one example where I had Deep research teach me about like we'll talk about call options and then try to pick out like five call options that are underpriced and wide beliefs that things are underpriced using third party sources. And what I say is like use the most trusted third party sources to come up with that hypothesis. And then I had a whole conversation with it in this interactive mode when I loaded the podcast and then had a whole conversation. Conversation lasted like 15, 20 minutes all around. Its prediction on why meta was a good call option that Kip will tell you that was actually, that was done four weeks ago and actually it was pretty right because I think Kip has already done that trade. So pretty, pretty powerful.

One of the threads we're going here is that, and I do think this is true, is that the Google suite of AI products that you all have rolled out I think are the best to help humans learn. Because I'm about to pull a deep cut out of one of my favorite Google tools that nobody talks about because we've talked about deep research, we've talked about Notebook lm. I love Learn About, I love learn about, which is basically this. If the audience is watching here, you can basically just decide you want to learn about something, like how do I make pasta from scratch? For example? And what I love about Learn about, it's like much more of like where deep research is like a deep dive on a topic to gain perspective and then you can kind of ask follow up questions. On NotebookLM, this is much more like almost like a structured learning process and course and it creates this amazing like composable web experience over here and then that you can break down and dive into different components. And basically it solves a lot of the empty box problem of like, hey, I don't know anything about this thing. I don't even know what to ask. And it's prompting me a bunch of different aspects about making pasta. I happen to know how to make pasta, but it's like if you don't know when you how to add pasta to boiling water because you've never done it before, like this is very helpful, right? And so I think if you look at Deep Research, NotebookLM and Google learn about like there are three tools that are really masterclasses in helping people learn the real magic.

And the bow of this is like, how can you bring all that together, understand a user's intent, put the right product in front of them?

This is my point.

This is the challenging like and to have, you know, I think at the surface that the sentence that I just said sounds, you know, simple to say when you actually look at it like there are like really like these are the problem, the engineering and product problems of the decade. Like really these are like not a trivial thing to bring together that level of technology. Especially in a world where you're sort of balancing this user context problem which is like if folks talk to AI models all the time, like every time you talk to a new AI model, it has no context of who you are. It doesn't know what you've done before. Which is another thing that, that sort of just landed in the Gemini app, which is the ability to personalize model responses with your Google search history. And the model can really intelligently say like, here's what this user has done before, here's what they're interested in. How can we actually use that to sort of prime the model to give you the right stuff? And I think it's like that level of personalization that's going to enable pulling in the right like for you Kip, it'll know like here's this, here's this, you know, learn about like experience because Kip's been doing a bunch of stuff over here and this other product versus you know, search for you know, call options and bringing that stuff. So there's a whole like spectrum of different use cases. And I think having the personalized context means that you can get the right product surface or you can get the right product experience in front of the right user Persona. Which I think is not something that happens in Today's Like Software 2.0 product suite, which is really like product software is static. It's like predefined for you in a lot of ways.

What you're basically saying is instead of at some point go into this kind of dropdown, if I just like bring it back up, just kind of drop down experience, you will have one assistant that you talk to and in the background the assistant is basically deciphering intent and the assistant can pass you to whatever model it thinks fulfills that intent. And because even in us trying to implement assistance across our go to market, which is like a much more simplistic thing that you all have than you all have to do. Let's say we have a sales assistant that can actually help sell through chat. We have a support assistant that can help do a bunch of support tickets. We need to actually create a upsell assistant so we can actually upgrade customers to different tiers and sell them on those different tiers. And what we have is like these individual assistants. But what the way we decipher intent is somewhat like wherever you are in the go to market we say well this is probably the right assistant but even us we need multi bot orchestration. At some point where you have one assistant, the assistant can say you're trying to buy product for the first time, you have a support question, you are trying, you are an existing customer and you need to upgrade. And that is really complicated. And so I can't even imagine how complicated it is for a Google where the intent is like any single possible.

Thing in the world.

How do you ever decipher that intent? But even when I think about my AI experience with Google, you all have a ton of great products and I would love to end with one of the ones that I think is maybe transformational for this year, the image generation. But they're disparate. Right. Like we, we jumped into NotebookLM, we jumped into Kip, jumped into the Learn product, we jumped into all these different AI models you can choose through to the dropdown. Do you is the plan for Google to like pull them all together at some point so you just have like one AI interface as a knowledge worker, maybe one AI interface as a consumer?

Yeah. This goes back to one of the challenges that Google has as a product or as a company that has lots of product services. I do think more and more the Gemini app is becoming this sort of unified place to get a lot of these experiences and sort of a externalization path for Kip was showing learn about which is one of the Google Labs experiments. I think more and more of those experiences are finding their way into the Gemini app. So I'm hopeful that that trend continues. I'm hopeful that Gemini app ends up being like the single place to get a lot of these experiences. But there really are just like different user journeys and, and like different and like we're like, I think about the like four or five different products that I use lot like all day, every day and like there's different, there's different journeys a part of those products for different users and I think they'll will continue to be like different products. And we have, we didn't talk about AI Studio. But like, AI Studio is another one of these where, like, that's actually how folks access the native image generation capabilities today. And like, I think in the future we're still going to have AI Studio because, like, the user Persona that we care about in AI Studio is a developer who's sort of exploring these models and wants to build something which is very different in a lot of cases than sort of the main experience for users going to the Gemini app who like, are trying to use the Gemini app really as like a, as an assistant on a daily basis. It's like a daily active user product versus AI Studio is really intended as sort of the portal to the developer world for folks interested in Gemini.

Maybe Kep has it up here, maybe talk about the latest image release. I feel like this is one of the. When I think about AI and where we might have been a little wrong, I thought video would be further along than it is today. And I mean, AI's ability to go from text to video. Right. It's still pretty clunky in a lot of cases. It's not like production ready. And then the other one was like, images, text to image. And I felt like the first iteration of that were all these great tools where you could go to image, but for image to be really useful for people, there had to be great editing tools as part of that. And so Google's latest release, I think is Kip and I were playing around with it last week is like, really awesome. And maybe could you just give the context on what that model is and why it's so good for folks who.

Haven'T been following closely. We launched Gemini 2.0 back in December and we showcase these capabilities actually, and we roll them out to a small group of trusted testers to get some initial feedback. And then last week we, we rolled out to every developer the ability to use Gemini's native image generation. And I think the thing that's actually capturing a lot of interest is the native image editing capability. So you can actually, because the model is natively multimodal, you can pass in an image. You can say, hey, update this image to, in the example we're looking at on screen, add a little chocolate drizzle to these croissants or add a strawberry drizzle to these croissants. I've seen a bunch of really cool examples, which I did not think about, of taking black and white images and actually asking the model to colorize those images.

Incredible.

You can bring them back, you can take in two images and you can sort of fuse them together if you had. You know, I saw a funny example of someone posting a bunch of like hot dogs and stuff like that and merging them together into this like comedic image and lots of different random things like this. That is the full spectrum of really useful to really silly. But the model is now like the, the thing that I think is capturing folks attention is if you think about how you would have had to do this workflow, pre native image generation and image editing, it's just hard to do. Like the number of people who can do that in whatever the professional tool is, is pretty limited. You can just do it with like a very, very simple text prompt. So like now the, the opportunity space of people like creating dynamically edited images is now essentially every human on earth is able to do that. Which is just like this is the thing that continues to blow my mind is you get a capability and it goes from. And this is what AI is enabling across so many different domains. It's this thing that only a few people could do and then overnight it ends up being this thing that everyone can do. And I think actually coding is like before image generation is also having this like parallel moment with vibe coding and everything where like before code was an artifact that only, you know, professional developers could create. And now it's like every human on earth could go and create code as an artifact if they want and pretty much get what they were looking for. The same thing with image editing today. Before you had to be, you know, very good at using one of these tools in order to do this stuff. And now it's like every human on earth can do this and it just changed overnight, which is just such a weird, such a weird experience to think about.

Vibe designing. This is the kind of dream, I think for AI in general, or the transformative mission of AI in general as it unlocks creativity somewhat overnight because it allows people now to kind of unleash their creativity and they're no longer hindered by having to learn the tools. And I know that sounds like a lazy way to think about creation, but I don't think it is because I think the creation part is the important part and learning the tools shouldn't hinder that. That part of you like the ability to create things maybe just end on as this visual story. Could you just end on what this product is? Because we discovered this today. I'd love to know like what is the way we should think about. We should think about visual story, which again, these are for people who are following along on YouTube or even on RSS. These are all available in AI Studio and you should really go in and play with because there's a play with that product because there's a bunch of great tools in there.

AI Studio is, again, it's our service for developers intended to bring the models to life in a way that, that ultimately wants to get you to build with them. So here, like, we're, we're trying to. This example that we're looking at, trying to capture developer sort of imagination of what products they could go and build themselves. But principally, AI Studio is not intended to be like the Daily Assistant product. Like, it's a very thin surface on top of the models. We make a bunch of very opinionated decisions to keep the core AI Studio experience the same as the experience you would get in the API. So we don't have a bunch of fancy bells and whistles that you yourself couldn't do as a developer in AI Studio, which makes it limiting as a product. If you want. People are always like, why don't we have deep research in AI Studio? I want that. It's because deep research is something you could possibly build sort of a similar deep research experience using the API, but it's not available to developers today. So we don't want that experience in AI Studio. Yeah. So if you're, if you're someone who wants to build stuff, you, you know, getting your Gemini API key, all that stuff, testing out the latest capabilities of the models is. Happens in AI Studio and we oftentimes bring. It's like the. In our team sits in Google DeepMind right next to the model team physically. So that's. Oftentimes our product is sort of the fastest path to externalize the latest Gemini models, which is a lot of fun and it's, it's cool to be on the frontier and it's cool to see the excitement with, with native image generation.

Kind of like the way ChatGPT was for text. I feel like this is similar for images, really. The first time I've seen the ability to kind of get the image crisp and concise to the way you want it. This was awesome. Logan, we really appreciate you coming on and giving us a deep dive into what it's been like to be part of this kind of AI journey. You're working with two of the most transformational companies there are you or you have. And also just going deep into like, how people can start using the Google tools straight after this episode.

Yeah, this was a ton of fun. Thank you for having me. Hopefully the call options go well and we'll. We'll all be together in a few months.

That's our litmus test to whether AI is truly good or not.

Is.

Is it making us money with call options?

I love it.

Thank you so much, Logan. This was awesome.

Appreciate the time in this data is wrong every freaking time. Have you heard of HubSpot?

HubSpot is a CRM platform where everything is fully integrated.

Well, I can see the client's whole history. Calls, support tickets, emails. And here's a test from three days ago I totally missed. HubSpot grow better.