# Argon usage reached 25 trillion tokens in 72 hours

Markdown copy of https://realalpha.app/s/argon-usage-reached-25-trillion-tokens-in-72-hours/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Sundar Pichai](https://realalpha.app/builders/sundar-pichai/) · Sundar Pichai is CEO of Google and Alphabet.
- Host: [Alex Kantrowitz](https://realalpha.app/people/alex-kantrowitz/) · Founder, Big Technology · Former BuzzFeed News senior tech reporter and author of Always Day One.
- Show: Big Technology Podcast
- Episode: [Sundar Pichai: Path To Recursive Self-Improvement, Compute Allocation Dilemma, Future AI Interfaces](https://realalpha.app/v/F3ItSTZNtT0/), Oct 20, 2026
- Moment: 1:03 to 3:54 of the episode (3 min), playing on https://realalpha.app/s/argon-usage-reached-25-trillion-tokens-in-72-hours/; the episode from there: https://realalpha.app/v/F3ItSTZNtT0/?t=63

## Quote

> “in the first 72 hours since we launched, it's almost up to **25 trillion tokens** used”
> — [Sundar Pichai](https://realalpha.app/builders/sundar-pichai/) at [2:37](https://realalpha.app/v/F3ItSTZNtT0/?t=157) · Big Technology Podcast

## Takeaways

1. Argon approached 25 trillion tokens in its first 72 hours.
2. That was about three times Gemini 3.0's first-72-hour usage.
3. Google was capacity constrained while serving the model.

## About this segment

Sundar Pichai says Argon used nearly 25 trillion tokens in its first 72 hours, about three times Gemini 3.0's launch. Google was already capacity constrained while serving it.

From “Sundar Pichai: Path To Recursive Self-Improvement, Compute Allocation Dilemma, Future AI Interfaces”, Oct 20, 2026. [Watch the full episode](https://realalpha.app/v/F3ItSTZNtT0/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[1:10](https://realalpha.app/v/F3ItSTZNtT0/?t=70) Sundar, great to see you. Welcome to the show. Thanks, Alex. Great to be here. So Google is back on the frontier with Gemini 4 Argon. In plain speak, that means it is a world leading model. It's benchmark breaking. It beats OpenAI and Anthropics public models on a variety of benchmarks, including on coding, math, science, and knowledge work. Now, the conventional wisdom had been that Google had basically given up on the frontier, that it was going to make these fast models that were good enough and use them in its products, and it wasn't pursuing frontier AI anymore. But that was wrong. I mean, very wrong. Look, I think we are so committed to being at the frontier. I think it's in our DNA. We won't do it any other way.

[1:59](https://realalpha.app/v/F3ItSTZNtT0/?t=119) We've been thinking about the AI frontier for well over a decade now. We definitely, the frontier is very dynamic. It's very competitive. It's very exciting. I couldn't be more pleased with how our partners come together. We took our time with it. You know, we made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. But I think across the board, be it for agentic work, knowledge work, coding, creative tasks, I think Argonne is, you know, one of the really good frontier models.

[2:43](https://realalpha.app/v/F3ItSTZNtT0/?t=163) And in the first 72 hours since we launched, it's almost up to 25 trillion tokens used, which is about 3x what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. So excited, but there are always areas, both the frontier is just last. it's very dynamic and you have to keep evolving the models and there are areas which we can see we need to improve as well so we'll be at work on all of that as well Which areas?

[3:21](https://realalpha.app/v/F3ItSTZNtT0/?t=201) Look, on any given model Oregon will be across the board better at everything if you just look even two months back but the space is so evolving but even on coding for example, there are areas, pockets, where we know we need to do better and there's clear feedback and so we'll make it better. Let me at least make the argument for why