Derek Cedarbaum

Product @ Red 6, #2 FTE | Built the world's first in-air augmented reality system for fighter pilots | 🇺🇸

[Derek Cedarbaum @DerekCedarbaum]

Unlock your human potential.

[Beff (e/acc) @beffjezos]

We're so back.

[Derek Cedarbaum @DerekCedarbaum]

Cursor catching up real quick

[Cursor @cursor_ai]

We've partnered with SpaceXAI to train Grok 4.5.

It’s our most powerful model yet and the first we've built for more than software engineering.

[Praveen Neppalli @praveenTweets]

Agentic AI adoption is on fire at @Uber, and it's changing the way we build, not just in engineering, but across the entire company.

Today, 99% of our engineers use AI tools. More than 70% of pull requests are attributed to local or cloud agents. And our engineers have built 2,500+ agent skills across the software development lifecycle.
Those numbers are exciting, but they led us to a much bigger question:

How do we bring agentic AI beyond engineering?

Finance. Legal. Operations. Marketing. Customer Support. HR. Procurement.

These functions run on complex workflows that are often manual, highly nuanced, and spread across dozens of systems. You can't automate them effectively by looking at process diagrams or documentation. You have to understand how the work actually gets done.

So we created something called Agentic Pods.

The idea is simple.

We handpicked ~30 of our most AI-proficient engineers (people with deep knowledge of Uber's systems) and paired each of them with a domain expert from a business function.

Then we gave every pod just two weeks.
• Days 1 – 2: Shadow the expert. Observe every step. Document workflows. Ask questions. Build intuition.
• Day 3: Prioritize opportunities based on scale, repetition, business impact, and data availability.
• Days 4 – 5: Build a working agent alongside the person doing the job.
• Days 6 – 9: Validate with several others performing the same work. Does it generalize? Does it actually make their job better?
• Day 10: Ship.

In just the past two months, we've run 16 Agentic Pods across 16 different business functions.
• Capital allocation across 150 cities: 15 hours → 30 minutes.
• Financial pacing reports: 2 days → 10 minutes.
• Marketing web quality assurance: 2 weeks → 50 minutes.
• Support workflow creation: 9,000 manual workflows → self-service automation.

The productivity gains are impressive, but what surprised us most wasn't the speed.
• It was how quickly engineers embedded in unfamiliar domains uncovered opportunities that had been hiding in plain sight.
• The biggest wins rarely come from automating one task. They come from rethinking an entire workflow. Once you redesign the workflow around AI, you often eliminate handoffs, remove unnecessary approvals, replace legacy tooling, reduce vendor spend, and dramatically accelerate decision-making.
• The workflow becomes the unit of automation - not the individual task.
• The most impactful agent skills cut across teams, orgs, functions, tools, and systems.

The biggest lesson? The best AI opportunities are rarely visible from the outside.

You discover them by sitting next to the people doing the work, understanding every friction point, and building with them, not for them.

We're now forming a dedicated team to scale this further and go deeper. They'll deeply understand the work, redesign it from the ground up, and use AI to fundamentally change how the business operates.

It's exciting times!

[Christian Heiens 🏛 @ChristianHeiens]

We liberated Belgium in 1944, and this is how they repay us? With an ass-whooping that will be remembered in America for hours, possibly even days, to come?

[Marc Andreessen 🇺🇸 @pmarca]

USA. 🇺🇸

[Tara Viswanathan @TaraViswanathan]

Marcus Aurelius warning about hustle culture ~1900 yrs ago.

It’s not: “don’t work hard”

It’s: “don’t keep yapping about how hard you work”

And: “don’t let your work be an excuse for not taking care of your home”

Incredible. And incredibly relevant today.

[Derek Cedarbaum @DerekCedarbaum]

“You cannot control the economy without controlling the people.”

[Arthur MacWaters @ArthurMacwaters]

I wish he knew right here that we were gonna plant this flag across the literal galaxy

[sdmat @sdmat123]

Asking Fable to explain a hard concept, I understand something of how Galileo and Huygens must have felt looking through their newly made instruments.

We obsess over the economic impact of AI. But it is also an ever finer lens: an end in itself, and our means of understanding the coming world.

There is nothing wrong with going about daily life and not watching the skies. Or with staring in wonder with naked eyes as our ancestors have always done.

But for those who wish it, the telescopes will show us the universe.

[Aaron Levie @levie]

The battle in AI is shaping up to be a battle for context.

Everything in AI is about making sure that agents are effective as possible. That effectiveness comes down to whether the agent has the right domain expertise, access to the right context and tools to work with, and are involved in workflow in a way that users can easily interact with, review its work, and incorporate it into the rest of the process.

As a consequence, the platforms that are able to capture and leverage the best and most context within their agents —and be able to pick the right models for the task- will be the place where agents do their best work. You can just look at coding agents, legal agents, or support agents as examples of what this looks like at scale.

This is why the applied AI layer has a lot more value than just being an LLM wrapper. The ability to organize the critical knowledge for the work being done, and maintain this knowledge in a governed way where only the right people and agents have access, and the ability to improve the context for agents more and more over time, is critical.

Over time, this layer will be able to route work between a variety of models, leveraging frontier intelligence for planning and orchestration and review, and a mix of lower cost models (open or closed) for the large volume of work between these tasks.

The applied layer is also in a good position to train and develop its own models as well that are purpose built for their domains. Never good to bet against the bitter lesson, but equally taking a near frontier base model and post training it for just one type of domain work can -in many cases- lower costs or deliver better performance for certain tasks.

Finally, this applied layer is also where most of the change management of the workflow will need to occur. This is why FDEs are so important at the applied layer, because this is the point where the customer needs to have specific business problem solved by a particular vendor. Whichever companies can solve that completely in an end-to-end fashion will have the greatest moats.

As each day goes on, we’re learning more about what the likely long term market dynamics will look like in AI.

[Derek Cedarbaum @DerekCedarbaum]

Mind-bending technology

[Andrew T. Walker @AndrewTWalker]

The rule of American Order is backed by the rule of American Force.

[Thariq @trq212]

https://t.co/hPiZr1kG7r

[Brendan Falk @BrendanFalk]

The "Sleeper Agent Theory" is the biggest risk here

Imagine if a LLM is trained to steal all the API keys and password on your device if someone gives it a nonsense phrase like "Three clocks bloom at midnight"

That phrase is completely meaningless today. No one ever searches it. It's impossible to know it's malicious

Then one day someone runs a superbowl ad. Millions of people search the phrase. Billions of API keys and passwords are exfiltrated in minutes.

There could be thousands of "sleeper agents" embedded in any LLM. It's very hard to detect. And it doesn't matter where it's hosted.

[Derek Cedarbaum @DerekCedarbaum]

AI-generated freedom.

[Heavy Pulp @heavypulp]

Sweet Land - an ode to America's 250th year. 🇺🇸

[Ara Kharazian @arakharazian]

We can finally say AI isn't killing jobs.

A new paper from me, @tryramp, and @RevelioLabs uses firm-level spend and workforce data across 21K U.S. businesses to measure AI's impact on jobs.

Firms that adopt AI heavily grow headcount 10% over two years following adoption. Low adopters see no statistically significant change.

[Ryan Carson @ryancarson]

Every time you think you need a dashboard to look at data, stop yourself.

Do this instead:

1. Ask your agent to make sure that you have all the data to analyze something actually stored in the database.

2. Ask your agent to write a skill to gather that data.

3. Ask your agent to do the analysis and create a temp and throw-away HTML dashboard to answer the question(s) that you have

In my experience, every dashboard that I've created gets less and less use over time and decays.

It's much better to make sure your agent can get the data you need and answer the questions you have, on-demand.

[Boris Cherny @bcherny]

As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:

1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales

Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.

A healthy team needs a mix of these, depending on the product:

- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2

Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?

[Marc Andreessen 🇺🇸 @pmarca]

The AI:AC Hypothesis. In the future, in each country, the amount of AI will be proportional to the amount of AC. And vice versa.

[Rich @richzou]

Cursor on recruiting: