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ttunguz.bsky.social @ttunguz.bsky.social · 10h

This is one of the metrics to track!

tomtunguz.com/is-token-con...

Google's AI token processing reaches 1.3 quadrillion monthly tokens in October 2025, but growth decelerates from 250T to 107T per month. What's driving the slowdown?

tomtunguz.com

ttunguz.bsky.social @ttunguz.bsky.social · 10h

Data center investment is scaling towards $400 billion this year. Meanwhile, incumbents are striking strategic deals in the tens of billions, raising questions about circular financing & demand sustainability.

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

I wasn’t able to find any other comparable time series from neoclouds or hyperscalers to draw broader conclusions. These data points from Google are among the few we can track.

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

- Google combines internal & external AI token processing. The ratio might have changed.
- Google may be driving significant efficiencies with algorithmic improvements, better caching, or other advances that reduce the total amount of tokens.

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

- Google may be limited by data center availability. There may not be enough GPUs to continue to grow at these rates. The company has said it would be capacity constrained through Q4 2025 in earnings calls this year.

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

This raises more questions than it answers. What could be driving the decreased growth? Some hypotheses :

- Google may be rate-limiting AI for free users because of unit economics.

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

Between May & July, Google added 250T tokens per month. In the more recent period, that number fell to 107T tokens per month.

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

In May, Google announced at I/O they were processing 480 trillion monthly tokens across their surfaces. Two months later in July, they announced that number had doubled to 980 trillion. Now, it’s up to 1300 trillion.

The absolute numbers are staggering. But could growth be decelerating?

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ttunguz.bsky.social @ttunguz.bsky.social · 10h

Philip Schmid dropped an astounding figure yesterday about Google’s AI scale : 1,300 trillion tokens per month (1.3 quadrillion - first time I’ve ever used that unit!).

Now that we have three data points on Google’s token processing, we can chart the progress.

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

The record at OpenAI is 7 hours of autonomous execution, 150M tokens, and 15K lines of code refactored with this design pattern. Pretty remarkable even for a senior engineer.

Congratulations, Robot. Keep climbing that ladder.

tomtunguz.com/congratulati...

Congratulations, Robot. You've Been Promoted!

OpenAI's Codex went from intern to senior engineer in 12 months. At 92% adoption & 72% more pull requests, the architect-implementer workflow proves AI has graduated beyond junior-level work.

tomtunguz.com

ttunguz.bsky.social @ttunguz.bsky.social · 1d

These tests can be visual (evaluate screenshots), functional (does the code run), or logical (does the code meet the requirements). Then a third robot reviews for quality & style.

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

In the plan, designing the tests / hurdles that a robot must pass to complete the task is critical. The robot runs the tests, fixes the code, runs the tests again, and repeats until passing.

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

The counterintuitive part? The second robot shouldn’t see the first robot’s context. Fresh discerning digital eyes catch more errors.

CLOSED FEEDBACK LOOPS

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

Ask a robot to write the plan document. You’ll refine your thinking as you review it. The robot manages progress through each step.

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

I wrote about architect-implementer architectures on Monday. The pattern splits work between two separate robots : the first designs the solution, the second executes it.

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

The team shared more. The best design patterns for collaborating with Codex are architect-implementer systems & closed feedback loops.

ARCHITECT-IMPLEMENTER

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

Congratulations, Robot. You’ve been promoted - again! From intern to senior engineer in about a year. Quite the trajectory.

Other data points :

- 92% of technical staff use Codex daily
- those staff generate 72% more pull requests (code submissions) than those who don’t use AI

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ttunguz.bsky.social @ttunguz.bsky.social · 1d

Watching the OpenAI Dev Day videos, I listened as Thibault, engineering lead for Codex, announced “Codex is now a senior engineer.”

AI entered the organization as an intern - uncertain & inexperienced. Over the summer, engineering leaders said treat it like a junior engineer.

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ttunguz.bsky.social @ttunguz.bsky.social · 3d

I look forward to welcoming Chris to Office Hours & sharing his insights with our community!

tomtunguz.com/chris-klayko...

Building Sales Teams in the Age of AI : Office Hours with Chris Klayko of Databricks

Join Databricks SVP Chris Klayko to learn how to scale sales teams from $100M to $1B+ with insights from his 20-year journey at Google Cloud, UiPath & SAP.

tomtunguz.com

ttunguz.bsky.social @ttunguz.bsky.social · 3d

San Francisco | October 15, 5:30–7:30pm | Space is limited

Apply to join here : gatsby.events/theory-ventu.... Submit your questions through the registration form & I’ll weave them into our conversation.

Office Hours: Sales Leadership in the AI Age

gatsby.events

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ttunguz.bsky.social @ttunguz.bsky.social · 3d

- How sales leaders can adapt in a fast-changing, AI-driven landscape
- The evolution from traditional software sales to AI & data platform selling
- Creating sales cultures that attract & retain top talent
- Navigating the shift from product-led to enterprise sales motions

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ttunguz.bsky.social @ttunguz.bsky.social · 3d

During this intimate fireside chat, Chris & I will explore :

- Building & motivating high-performing sales teams in the AI era
- Lessons from scaling organizations from $100M → $1B+ at Databricks, UiPath & Google Cloud

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ttunguz.bsky.social @ttunguz.bsky.social · 3d

On October 15th in San Francisco, Theory Ventures is hosting an exclusive Office Hours session with Chris Klayko, SVP of Sales at Databricks.

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ttunguz.bsky.social @ttunguz.bsky.social · 3d

His remarkable journey includes scaling Google Cloud from tens of millions to a multi-billion dollar business in just four years, driving UiPath’s Americas expansion during its hypergrowth phase, & building SAP’s emerging solutions division.

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ttunguz.bsky.social @ttunguz.bsky.social · 3d

Chris Klayko brings over two decades of sales leadership experience transforming technology companies from promising startups to multi-billion dollar enterprises. As SVP of Sales at Databricks, Chris leads the charge in democratizing data & AI for organizations worldwide.

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