Inside the Frontier Labs: The First AGI Organizations

More and more researchers at frontier labs now start the workday with one question to their agent: what should I do today?

Most of the tests they used to run himself are now agent's job. He steps in only where agent gets stuck, like approving a PR or getting another team to move.

That is recursive self-improvement (RSI) seen from the inside: models helping build the next generation of models. I've been talking with my researcher friends at frontier labs, and biggest takeaway is that RSI is already far along inside these companies. As the models got stronger, the labs rebuilt their organization, their context and their infrastructure around how AI works. Internet-era companies were never built this way.

My test for an "AGI organization": does what it builds go straight back into building the next version of itself? An insurer can use AI to process claims faster, but the claims AI will never help it build a better AI. Today almost only the frontier labs pass, so that is where the next kind of company is taking shape.

Researchers now judge and verify

Researchers barely write code anymore. Their time goes into handing work to agents and reviewing what comes back.

At OpenAI's research org, every human workday was matched by 3.1 agent workdays as of mid-August. Before June, total agent runtime was still lower than human working time. At Anthropic, more than 80% of the code merged into the codebase, by line count, was written by Claude as of May.

OpenAI researcher Zuxin Liu wrote in September that he had stopped writing code by hand two months earlier. His workspace is now Codex and Slack: he tracks several agents at once and decides which directions deserve more effort. roon described researchers handing GPT-5.5 a high-level algorithm idea and waking up to a finished sweep dashboard and samples, without anyone touching code or a terminal. A post-training researcher at a Chinese lab told us he sends work to agents through Feishu and estimates that 90% of his job could be done from his phone.

A research idea used to become a result only after the researcher implemented it, debugged it and ran it. Now those steps can run in parallel.

An Anthropic employee described the other side. When things go well, the machine does everything faster and better. When something breaks, you realize you have no idea what happened in the meantime. Building things yourself was also how you understood the system. Once you hand off execution, understanding what the AI actually did becomes the new bottleneck.

My read: the most valuable skills for a researcher are now deciding which problems matter and knowing how to use agents well.

AI now runs most of the research loop

From data to pretraining, post-training and deployment, agents now do most of the execution.

Karpathy let an agent run his nanochat experiments with autoresearch as a loop: read the last result, edit the code, keep the change if validation loss improves. In about two days it tried roughly 700 changes and found about 20 that held up on larger models. DeepSeek's Deli Chen ran real RL experiments on DeepSeek-V4 for six days with almost no supervision. z.ai's Infra Agent did much of the work of deploying GLM-5.3-Flash on more than 100,000 Chinese accelerators, in under two weeks.

A researcher at Google said that if you write an idea down clearly, the model can implement and test it. The hard part is finding ideas that actually work, and checking that the gains are real. In Anthropic's automated weak-to-strong research, 9 agents beat the human baseline in small-model experiments. When the best idea moved to Sonnet 4.0 on production infrastructure, it gained 0.5 points, within noise.

That is the individual researcher. The company around them has to be redesigned too, in three places: organization, context and infrastructure.

Organization: the ability to build no longer decides who does what

Product and design teams no longer hand every idea to engineers. They build prototypes, run evals and adjust the product themselves, and they can tell earlier what capability the model needs next.

Boris Cherny, who leads Claude Code, says engineering, product, design and data science on his team are merging into one role. Cat Wu, Claude Code's product lead, describes designers shipping code, engineers making product calls and PMs building prototypes and evals.

Take the team that wrote greetings for Claude.ai, lines like "Hello, night owl." In spring 2026 they built a context graph that let an agent write that copy too, then moved on to writing evals and prompts for Claude. They used to think about what words move a user. Now they think about how to use words to steer Claude.

Research and product still have to decide together what the model learns next. At Anthropic, model researchers and inference infrastructure engineers share planning cycles, reporting lines and performance goals, so model architecture and serving systems are designed together from day one. Claude Code was built for the agentic coding the team expected the next model to have, so each new model is usable in the product the day it ships.

Looser boundaries bring new friction. With agents, each person can take on far more, and inside several labs people now compete for work that used to belong to other teams.

So for a company to get RSI, whether the organization itself is AI-native matters enormously. However strong the model, if the organization isn't built as an AGI organization from day one, the model's capability won't show up in the work.

Context: letting agents see everything the company knows

Claude Tag, we hear, feels magical inside Anthropic. A lot of what colleagues ask for, it simply does. It sees the company's full context: if a colleague holds the background on an enterprise customer, or someone already solved a similar problem, it finds it. With all the background, it sometimes finds a better answer than a person would.

But it's only that magical inside Anthropic.

Most companies can't get that feeling. Their context is scattered across chat tools, apps and dozens of group threads. On a single project the background sits with several people, so an agent sees one slice and can't help the way it does at Anthropic. Teams we've talked to say the same.

The models are about the same on both sides. The difference is how much the agent can see. Inside Anthropic, context is largely shared and decisions are written down by design, so agents get the full picture and can actually help. Most companies have never designed anything like that for their agents.

30,000 merges a day: infrastructure built for AI

A company's IT infrastructure used to be designed for people. At the frontier labs the questions are now how AI gets permissions, how AI ships code and what resources AI needs. DeepSeek's newly built platforms and data systems are also designed around how agents use them.

Permissions. Anthropic found that Claude Code users approved 93% of permission prompts, so approving each one had stopped meaning much. They moved from watching what the agent does to limiting what it is able to do: sandboxes by default, and a classifier for riskier actions.

Shipping code. Anthropic's first problem was volume. Daily merged changes went from about 2,500 in May to more than 30,000 in September, and seven engineers working with Claude rebuilt source control for agents that are always online and fetch code on demand.

Merging code. The Claude Code team tiers approval by risk, so most PRs involve at most one human, who can be the author. An agent's approval never replaces a human's.

Automated review creates its own problems. On one PR, the agent writing code and the agent reviewing it went back and forth more than 50 times overnight until the author stopped them. Nobody on the Claude Code team knows every detail of how Claude Code works, and they don't think anyone needs to.

The economics of a frontier lab have changed

Anthropic's internal use of Claude, priced at API rates, grew more than 250x from January to September. In April it passed that month's personnel costs, excluding stock compensation, for the first time. In mid-August, OpenAI researchers' daily inference usage at API prices had a median above $600 and a 90th percentile above $7,000. Two people with the same title can command more than 10x different amounts of compute.

10x the tokens does not mean 10x the output. Jellyfish looked at 7,548 engineers. The top 20% by token usage merged about twice as many PRs as the bottom 20%, at roughly ten times the cost. Meta and Amazon both took down internal token leaderboards after people ran agents on pointless tasks to climb them.

So hiring matters more than ever, and interviews and reviews need a new question: can this person get results with AI? Google DeepMind's AGI Safety team lets candidates use coding agents in every engineering interview, and Meta now judges "AI-driven impact" in performance reviews. Gergely Orosz sees the same split across tech: people who thrive on autonomy are reinventing how they work with AI, while people used to being told what to do are frustrated and increasingly bitter.

Research agents and customers draw from the same pool of compute, the lab's biggest cost. Compute spent on customers pays for today's revenue; compute given to research is a bet on the next model. In Q3, more than 80% of Anthropic's revenue came from models released in the previous six months, according to its S-1. How much compute research gets shapes what models there are to sell next.

Valuing these companies now means asking how much compute each person directs, and how much revenue each unit of compute brings in. As investors, we think organization matters more and more when backing an AI company, because it decides whether RSI can happen inside it.

Can AGI organizations only exist inside frontier labs?

What makes the frontier labs different is that their product goes straight back into how they make it. One generation of models writes training code, generates data, searches for algorithms and tunes infrastructure. If that work makes it into the next model, the stronger model comes back to do more of the research, and the labs get better at building better AI.

Other companies can copy the org design, the management and the compute allocation, but for them AI mostly raises efficiency. A bank's business is not AI, so what it builds with AI never comes back to build it a better AI.

On designing the company around AI, Chinese labs are far behind US labs. Researchers in China use agents too, many of them running on US models. That makes individuals faster but doesn't create the loop. Without a model at Claude's level, people rarely get the moment where AI actually finishes the job. If your own model can't work like an employee, it can't be used in your own research, and the company has no reason to reorganize around AI.

There will be AGI organizations outside the frontier labs too. They may not train models, but their context is designed for agents from day one, so an agent sees everything the moment it joins.

Spawn, a multiplayer game company, shows an early version. Its founder Jacob Sansbury wrote in late October that the company has about ten people and spent about $3.1M on tokens last month, roughly 30x its payroll. Agents write most of the code and ship every hour. By October the only thing still waiting on humans was taste, and one morning 700 such questions had piled up. The team turned two years of recorded calls and chats into principles and built AI judges on them. In eight days the judges ruled on more than 11,000 questions, and Jacob made the call himself 27 times.

I think agents will keep moving up the org chart. In "The future of the firm" (October 28, their own views rather than OpenAI's), Hemanth Asirvatham and Elliott Mokski of OpenAI name three places AI can enter a company: beneath us, as a layer under each person; beside us, taking a slice of a team's work like a colleague; and between us, reading a company's Slack, documents and code to surface problems for executives. A researcher's agent double sits beneath. Spawn's agents come close to beside, and its AI judges touch the edge of between. We agree with them that a firm has to learn a tool, while good labor learns the firm. Each step up needs more permissions and more context, so how far a company gets depends on whether those were designed for agents.

How far the loop goes in the US labs depends on whether models keep producing research that works. The 0.5-point result above shows how fragile it still is. Researchers remain cautious about letting AI propose hypotheses and new ideas. For now, humans pick the direction and AI executes. Honestly, we can see about three months ahead. Beyond that, we don't know.