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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 · 2d

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 · 2d

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 · 2d

- 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 · 2d

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 · 2d

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 · 2d

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 · 2d

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

The self-help aisle promised transformation through commitment. AI promises transformation through experimentation.

tomtunguz.com/ai-enables-i...

From Books to Prompts: The Instant Future of Productivity

Early adopters are testing productivity systems in hours instead of weeks. The signals suggest AI prompts will eventually subsume the $30 book market.

tomtunguz.com

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

Wednesday : hybrid workflow (chai latte—wait , is this even coffee?). By Thursday , the data shows which saves 3 hours per week.

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

We can now test productivity systems like trying coffee drinks. Monday : architect/implementer (cappuccino—structured , classic). Tuesday : traditional single-session (flat white—simpler , maybe better?)...

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

The pattern emerging : instant implementation.

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

Third, over the weekend I read Jesse Vincent’s architect/implementer workflow via Simon Willison’s write-up. The approach splits AI coding into two sessions: one architect to design, one implementer to execute. No weeks of trial & error to understand the nuances. Read, implement, test, done.

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

Second, when I discovered Google’s Agent Development Kit research on tool design patterns, I asked AI to redesign my automation tools following those principles. The result : a 41% reduction in AI operation costs, implemented while I was answering emails in another browser tab.

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

Previously: read the paper (2 days), understand the methodology (1 day), code the implementation (3 days). Now: point AI at the repository, get a working agent by lunch.

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

First, Paper2Agent transforms research papers into working code agents in 30 minutes to 3 hours. Ask it to implement AlphaGenome’s genomic analysis from a published paper & it processes the code, builds the environment, runs the tutorials.

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

But something new is emerging. We’re discovering we can redesign workflows instantly with AI prompts instead of studying methodologies for weeks.

Three recent experiments hint at what’s coming.

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