Inside OpenAI: A Developer's Perspective on the World's Most Watched AI Company
A developer who recently left OpenAI after a year shares candid insights into the culture, pace, and inner workings of the company at the center of the AI revolution.
Recently, a developer who spent a year inside OpenAI shared some fascinating details about life inside what might be the most scrutinized company in the world. Having worked there during one of its most explosive growth periods, their observations offer a rare glimpse behind the curtain of the organization racing to build AGI.
Hypergrowth and the Slack-First Culture
The numbers are staggering: OpenAI tripled in size during this developer's tenure, growing from 1,000 to 3,000 employees in just twelve months. To put that in perspective, they became a top-30% tenured employee simply by surviving a single year at the company.
This kind of hypergrowth breaks everything. Traditional corporate communication structures, reporting hierarchies, product development processes - none of it scales when you're adding 2,000 people in a year. Most leadership teams are doing completely different jobs than they were two years ago, simply because the company underneath them has transformed entirely.
What's particularly striking is how OpenAI has adapted: they've gone all-in on Slack for everything. The developer received around 10 emails during their entire year there. Every conversation, every decision, every announcement happens in Slack channels. For someone coming from a traditional corporate environment, this is either liberating or completely overwhelming, depending on how well you curate your notifications.
This communications approach reflects something deeper about OpenAI's DNA. Unlike traditional companies with rigid hierarchical structures, OpenAI operates from the bottom-up. When the developer first arrived and asked about quarterly roadmaps, the answer was simple: "They don't exist." Good ideas can come from anyone, at any level, at any time. The challenge isn't getting approval for your ideas - it's proving they're worth pursuing.
The Three-Company Race to AGI
Perhaps the most sobering insight is how the developer frames the current AI landscape. In their view, the path to AGI has crystallized into a three-horse race: OpenAI, Anthropic, and Google. Each company is taking a fundamentally different approach based on their organizational DNA.
This isn't about incremental improvements or feature competition - this is about who will first create artificial general intelligence. The stakes couldn't be higher, and everyone inside these organizations knows it. The developer describes a culture where teams closely monitor what's happening at Meta, Google, and Anthropic, knowing that their competitors are doing exactly the same thing.
The pressure of this competition creates an interesting dynamic. On one hand, it drives incredible innovation and speed. On the other hand, it means operating under constant scrutiny from governments, media, and the global tech community. The developer regularly saw news about OpenAI in the press before it was announced internally - a surreal experience that underscores how much attention the company attracts.
Python Monorepos and Azure Reality
On the technical side, the insights reveal both the power and chaos of rapid scaling. OpenAI runs on a massive Python monorepo, with growing services in Rust. This creates a fascinating coding environment where you'll find both sophisticated libraries built by 10-year Google veterans sitting alongside hastily written Jupyter notebooks from newly-minted PhDs. There are no enforced style guides across the organization - a reflection of both the research culture and the speed at which they're moving.
The infrastructure story is equally telling. Everything runs on Azure, but only three services are considered truly reliable: Azure Kubernetes Service, CosmosDB, and BlobStore. Unlike AWS with its mature ecosystem of specialized services, Azure forces OpenAI to build more infrastructure in-house. It's a constraint that probably slows them down in some areas but gives them more control over their entire stack.
But here's the kicker: none of the infrastructure costs matter compared to GPU expenses. The developer shares a mind-bending data point: a single niche feature in the Codex product costs as much to run as an entire successful startup's infrastructure. When your primary constraint is access to the most powerful chips on Earth, everything else becomes a rounding error.
The Codex Sprint: Seven Weeks from Zero to Launch
The most detailed story comes from the Codex launch - a product built from scratch and shipped to millions of users in just seven weeks. This wasn't a small internal tool or limited beta; this was a full-featured coding assistant integrated into ChatGPT and made available to the world.
The pace was brutal. The team worked until 11-12 PM every night, got up at 5:30 AM, and worked weekends. For seven straight weeks. The night before launch, five team members stayed up until 4 AM deploying the system, then returned at 8 AM for the public launch announcement.
Within 53 days of launch, Codex had generated 630,000 public pull requests. That's roughly 78,000 public PRs per engineer on the team. The scale of impact is almost incomprehensible - most developers don't create that many meaningful code changes in their entire careers.
What made this possible wasn't just the technology, but the organizational structure. When the Codex team needed help from experienced ChatGPT engineers, they met with the engineering managers and had two senior developers ready to help the next day. No quarterly planning cycles, no bureaucratic approval processes - just immediate action when something important needed to get done.
The Twitter Influence Loop
One of the more unexpected insights is how much OpenAI pays attention to Twitter. The developer notes that if your tweet about OpenAI goes viral, there's a good chance someone at the company will read it and take it seriously.
This creates an interesting feedback loop. OpenAI is building products for hundreds of millions of users, many of whom express their opinions about AI on social media. The company's leadership stays tuned into these conversations, using them as signals alongside traditional analytics and user research.
It's also a reflection of how seriously OpenAI takes its public perception. Unlike most B2B companies that can operate relatively quietly, every OpenAI product launch becomes a global news event. Every feature update gets analyzed by researchers, journalists, and competitors around the world.
The Meritocracy of Ideas
The best ideas win, regardless of who proposes them. Leaders are promoted primarily based on their ability to generate good ideas and execute them, rather than traditional corporate skills like presentation abilities or political maneuvering.
The developer describes seeing 3-4 different Codex prototypes floating around before the team decided to push for an official launch. This redundancy might seem inefficient, but it ensures that the best approaches bubble up naturally rather than being decided by committee.
Security, Secrecy, and Stakes
The security culture at OpenAI is intense. The developer couldn't tell anyone what they were working on in detail. Different Slack workspaces have varying permission levels. Revenue and burn numbers are closely guarded secrets.
But despite the secrecy, the developer emphasizes that everyone they met was genuinely trying to do the right thing. OpenAI gets significant criticism in the press, partly because it's the most visible of the major AI labs. The company has maintained its commitment to making cutting-edge AI broadly accessible - anyone in the world can use ChatGPT, even without logging in, and most models quickly become available through the API for developers.
The safety work is more substantial than external critics might expect, though it focuses more on practical risks (hate speech, abuse, manipulation) than theoretical ones (intelligence explosion, power-seeking). The developer notes that much of this work isn't published, and suggests OpenAI should do more to communicate their safety efforts publicly.
What This Tells Us About the Future
These insights paint a picture of a company that's still operating more like a research lab than a traditional corporation, despite having 3,000 employees and hundreds of millions of users. The bottom-up culture, the bias toward action, the willingness to change direction quickly - these are the characteristics that allowed OpenAI to build and launch transformative products so quickly.
But they're also characteristics that become harder to maintain as organizations grow. The developer notes that many systems break under hypergrowth: communication, reporting structures, hiring processes. The question is whether OpenAI can maintain its innovative culture while building the operational capabilities needed to compete with tech giants like Google.
As we watch the race to AGI unfold, accounts like this provide crucial context for understanding not just what these companies are building, but how they're building it. The culture, the pace, the technical decisions, the human costs - all of these factors will shape what artificial general intelligence looks like when it finally arrives.
Whether OpenAI, Anthropic, or Google ultimately wins the race to AGI, one thing is clear: the pace of change is only accelerating, and the stakes have never been higher.