We've seen performance optimizations across the board, across use cases. Some folks on the Antigravity team have been using the model to migrate from Fortran to Rust. So, code migrations.
Wait, who's using Fortran in the—
No, they've been taking Fortran code bases.
External Fortran code bases.
External code bases. It's like a museum kind of thing.
Yeah, it's like the world runs on Fortran. But there are still code bases in Fortran externally.
Who here has ever— I have written Fortran.
I have not.
Oh, you have? Nice. Okay. Anybody used punch cards, paper?
No. No? I mean, I've seen them, I haven't really used them, to be honest.
Hey, everyone. How's it going? Welcome back to Release Notes. My name's Logan Kilpatrick. I'm on the Google DeepMind team. Today we're here talking about Gemini 4 Argon. I'm super excited. We've got Josh, Melvin, Sergey, Tulsee, the entire crew spanning product, model, research, everything in between. So, maybe, Josh, maybe you kick us off with the news. Gemini 4 Argon available, today, for people to use.
That's right. It's going to be in the Gemini app, in AI Studio, the Gemini API on cloud, as well as Antigravity. And we can't wait for you to try it. It's got a bunch of leaps. We can unpack it here today.
I love it. Yeah, I think we announced the model benchmark charts. We handed it off to a bunch of folks in the Fairwind program, cyber defenders. And maybe actually, it's worth just starting with what's been going on. We showed a bunch of, we showed a nice, beautiful benchmark chart, two weeks ago roughly. And between now and then, all this stuff, I know we can talk about it from all different angles, product testing, early access research, et cetera?
Yeah, it's been great. So I think, like you said, we announced it about two weeks ago. And since then, we've been doing a few things that have been really great. One is just continuing to test the model. So we've been giving it to a set of cyber defenders and have actually been working with Wiz, which is an internal cybersecurity company, through their Scan For Good program to actually already start identifying and patching vulnerabilities across a bunch of critical systems. We've actually started also testing it with enterprises and customers, so slowly rolling out to start getting some feedback. We've been testing it, of course, internally a ton, and also with some of our researchers who are focusing on different domains, right? So quantum, robotics, science, and that's been really interesting. And then lastly, but really critically, we've been really investing in the safety story. So that doesn't just include our relationship with the US government where we submitted the model for testing, but we've also been improving our own guardrails, right? Really making sure we have the right monitoring setup, the right internal guardrails within the model, external guardrails around the model setup. So that we're actually very comfortable releasing tomorrow or today or yesterday whenever this podcast drops.
Whenever this podcast drops. Sergey, I feel like the internal usage of the models to me is one of the biggest— feels like one of the biggest differences in how we've gone about thinking about what is a great model to bring to the world. And we have 100,000 amazing engineers who are using the model. Varun quoted this earlier when we were catching up with him, 100,000 people internally testing, which I feel like is probably more than we've ever had for any model released before in the past. And so I'm curious how, I feel like you're at the helm of a lot of this internal flywheel and the testing that's happening.
Yeah, I'm very excited about the cadence that we have nowadays. And if you look at even before Gemini 4, when we had Flash 3.6, 3.7, 3.8, we're starting to get onto that RSI treadmill. And now the, you know, our researchers and the model work hand in hand to make the next version of the model. Actually, I just popped in here randomly because Melvin and I were talking about the next version of the model. And, probably by the time folks are viewing this, internally we're already using a couple models ahead. So it's just very exciting to see the progress and the cadence. And I expect it'll only continue to accelerate.
Yeah. As the acceleration continues to happen, almost like the bar for where we bring the model out, or what the launch bar actually is, I feel like continues to change and evolve because it feels dynamic. And so I'm curious, from a research perspective, also Tulsee, maybe from the model product perspective, how, yeah, how are we dealing with that shifting bar and like what is good enough to bring to the world?
One thing about Argon that, I feel really good about is we have extensively tested the model for, more than three, four weeks now internally and all of the signs tell us that it's good. But what I'm excited about is getting it out to a broader set of use cases and a broader set of users across Gemini app, Antigravity, and cloud. So when we're in this fast cadence where we're building the next one, we can actually iterate on that feedback. Right now we're getting bits and pieces but we want more and more people to use it. And we're on this fast three to four week cycle. Every couple of weeks we have candidates every three weeks we can graduate some promising ones. But if we can inject that feedback back into how we design the models, that's going to get us even better.
Yeah. Well, something that's interesting is I'm on a Varun quoting chain right now, and he mentioned earlier that one of the most— other than engineers inside of Google, I feel like one of the other top personas is the cloud sales team using Argon internally. And so it's like this whole knowledge. It's not just great for coding, great for all those use cases, great for science, but also, great for knowledge work. And so I feel like in the Gemini app, a bunch of updates landing. Josh, I don't know if you want to talk through any of those.
Yeah. So, well, one we focused a lot on that hopefully everyone will feel is just writing quality. It has a house style that feels unique. And so part of that started, we've been tracking feedback on this and there's AI slop everywhere, though, load-bearing phrases, to be honest, listification, all this stuff. And so we made a real concerted effort to really try to just stamp that out. So it should feel more concise, more precise. We've worked with a bunch of professional writers, and they've just been marking up the model output. And we turn those into goldens, and we climb on that. And so there's a lot of thought that's gone into that. So yeah, whether you're on the sales side, the legal side, the finance side, even writing code, obviously, it's great at. But hopefully you'll feel that it doesn't feel or sound like another model. It has its own tone. So that's been a big focus for the Gemini app and beyond.
Yeah. What my really quick personal question is, I don't use AI for any writing. And so like, do you think this— I don't know if you felt this for yourself because you do a lot of writing actually internally, externally. And so has it crossed that chasm where you're willing to start letting it be more in the driver's seat? Because I'm like 100% artisanal. I type or write every word. I want my words coming out, also because of all the right to think stuff.
I still write, so, I haven't given up writing, but it's an incredible copy editor.
And so usually I'll write something— actually, I'll dictate something when I'm walking somewhere or in the car, and I'll throw it over to Argon and say sharpen this. What are the three weakest parts of this? What are three ideas I didn't think of, three ideas that might confuse people. It's exceptional at this.
Yeah, and I actually, like, I've been having wrist issues, and so I actually will just ramble at Antigravity and then have it turn that into writing. And I found that, actually usually that's pretty hard to do because it comes out very, dense, clunky, very AI sloppy, and, then it doesn't actually feel that good. But with Argon, I've been able actually to do that a lot more and actually find the artifact reasonable, which then I can then edit. But at least the concept is still from my thinking, but it's more from my verbal input, which I think is actually pretty cool.
I love that. Yeah, any other good internal use cases or examples that folks have seen that have really stuck out as Googlers have used this model at scale? I'm curious.
I think this is probably the first time Sergey can attest to this because he's been pushing us to do that, is having agents in the model building process in a more substantial way. So, we actually have agents monitoring the hero runs. We have agents giving feedback on misconfigurations. We have agents looking at reward scales or finding bugs, patching bugs, agents looking at data and duplicates and stuff like that. I think that's exciting, and we're only going to get better at that kind of thing.
Did you see the data center game that was going around?
Did a simulation of our data centers. And as they're running these training jobs and even down to, like, people having to go and swap GPUs and all those things. I don't know if it just went around the chat. It's amazing. I mean, the kinds of things you can do now with just a prompt and you just come back and there it is.
We put out a call to Googlers to ask them, like, what are the exciting things that they're doing with Argon and what are some of the use cases? And we've been compiling a lot of that as just to better understand how the model is working. And there's been a bunch, like the Chromium team has been using the model pretty aggressively to address some of their work. We've seen Gemma optimizations. We've seen performance optimizations across the board, across use cases. Some folks on the Antigravity team have been using the model to migrate from Fortran to Rust. So code migrations.
Wait, who's using Fortran?
No, they've been taking Fortran code bases.
External Fortran code bases.
It's like a museum thing.
Yeah, it's like— the world runs on Fortran still. There's still code bases in Fortran externally, and so they've been trying to do just like— okay.
Who here has ever— I have written Fortran.
I have not.
Oh, you have? Nice. Okay. Anybody used punch cards? Paper?
No.
No?
No. I mean, I've seen them. I haven't written them, to be honest.
That counts.
I've done BASIC, COBOL.
I've done BASIC.
Oh, yeah. No, BASIC. I've never done COBOL.
Wow.
You got to get back into it. There's a big market. COBOL developers.
For migrations.
Yeah, for migrations.
I think, okay, one other example, actually, Sergey might know this also, is we've had these hardware issues during our hero runs where we would get some bad state in the machines. And we've been trying to get to the bottom of it for a long time. We've learned to live with it. And then one of the engineers sent the problem to Argon and it found an actual issue in the hardware. We took it back to the hardware team in Sunnyvale. They're like, are you bringing us AI slop? We don't want to listen to you. But we actually, went, sat there with them and they actually went back to the provider and found corrupted HBM.
Wow.
So this is pretty good. Now we're able to identify all of the machines that come from that vendor and when we can outcast them from the fleet.
Melvin, you didn't submit this into what are the cool examples.
Sorry.
I should have.
Right now, live.
I should have.
You know, funny story. The first twenty or so servers we bought for Google, it must have been 'ninety-eight, 'ninety-nine maybe. But I just couldn't get the operating system to install on these servers. We'd had like two machines before and we scaled up to twenty and just none of the new ones were working. And I debugged and debugged and debugged and eventually I ran a memory tester and I realized there was an incompatibility between the memory and like the motherboard or whatever. They'd actually given us fancier memory for free, but it didn't work. But I had spent days figuring that out, so thank you, Argon. I guess in today's world I wouldn't have had to do that.
Were those in the Lego server racks back— that was before.
No, okay, the Lego, which was in fact an off-brand Duplo knockoff called Mega Bloks.
