Gemini's Next Frontier: 2.0 Flash, Flash Lite Strategy & Real-Time APIs with Logan K from Google Deepmind
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What is the main topic discussed in this episode?
We sort of released the experimental first iteration of Gemini 2.0 Flash back in December. Today we brought Gemini 2.0 Flash, an updated version of it, um, into production so that developers can actually continue to build with it. We announced pricing, 10 cents per million input tokens, 40 cents per million output tokens, which is I think a huge accomplishment for us to pull that off. We're gonna have the world's best coding model at Google. And I I still believe this deeply. And I think like Pro is going to be that model and a bunch of the reasoning work that we're doing is going to be that model that continues to push the frontier for us. The world needs a platform in which it's hosting all of the sort of publicly available benchmarks and sort of leaderboards and stuff like that.
I find it incredibly difficult to just like navigate and get a snapshot of like how good is this model? There's like 20 random benchmarks here and 50 random ones. ones here, they're all split out over the place and it's just like hard to keep track as a developer.
Logan Kilpatrick from Google DeepMind, product manager of the Gemini API and AI Studio. Welcome back to the Cognitive Revolution.
Thank you for having me, Nathan. I'm excited. I'm hopeful that I'm I'm getting close to the record for the most times on your podcast. So I appreciate you for all right.
I think this might be setting the record at four if I if my count is correct. So yes, congratulations. That's uh rare error and well deserved. So it's launch day.
What did Logan Kilpatrick share about joining DeepMind and the AI restructuring at Google?
We'll get to everything that you've launched and um what we should be thinking about building with it. Quick little detour though before we get there, you're now part of deep mind. So, you know, Google obviously is a vast company and is continuing to I don't know, align, restructure, streamline itself to focus more and more on AI. What's the story from the inside on What it's like to be at Deep Mind now specifically.
Yeah, I I'm super excited about this. So we we've been, you know, I joined Google ten or eleven months ago. Literally from day one, it's been a deep collaboration with DeepMind. Deepmind's gone through all these evolutions over the last few years, transitioning from an organization doing fundamental research to sort of actually productionizing models.
How is Gemini enabling text‑to‑app creation and what are the early success stories?
And then within the last three months with the Gemini app moving over and then AI Studio and the Gemini API is now actually an organization. organization that like end to end does the research, creates models, and then actually brings them to products inside of Google. And I think that's been a a shift for them, but from My personal vantage point, like I think this is the thing that makes the most sense. Like being really close to research, and we already were really close to research through this collaboration we've had, but removing as much friction as possible for us to bring the researchers who like actually know how to bring, in many cases, like get the most capabilities out of the models, like bringing those two things together makes a lot of sense and it's going to be a ton of fun.
So as an external person like who doesn't care about Google reorganization. Which is most of the world, the thing that you'll hopefully see is an acceleration of model progress, but also an acceleration of product progress because we bring these two teams together.
Well, it sure seems from my vantage point on the outside that everything is accelerating and we've had previews of some of the stuff that is now going general availability today over the last few months. And of course there's been just one advance after another from DeepMind and others over the last um few months. Looking back a little bit, what would you say are the customer success stories and or just like coolest apps that you have seen come online that have been built with the Gemini API in recent time?
Yeah, I think the thing that I'm most excited about and and also feels like we have the biggest opportunity here still is around all these like text to app creation softwares.
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Chapters
8 chapters
1
What is the main topic discussed in this episode?
0:00–1:21
2
What did Logan Kilpatrick share about joining DeepMind and the AI restructuring at Google?
1:21–2:07
3
How is Gemini enabling text‑to‑app creation and what are the early success stories?
2:07–6:50
4
What are the new Gemini 2.0 multimodal APIs and real‑time video‑text capabilities?
6:50–10:52
5
Why is long‑context important for reasoning and how is Gemini improving it?
10:52–16:01
6
How are vision‑language models being used for passive monitoring and domain‑specific tasks?
16:01–23:02
7
What’s the difference between Gemini Flash, Flashlight, and the pricing strategy for developers?
23:02–29:12
8
What makes Gemini Pro the frontier model for coding and reasoning workloads?
29:12–53:24
Speakers
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