The Secrets Behind OpenAI's Frequent Shipping

OpenAI has become the tech world's darling, thanks in large part to ChatGPT's rise since its November 2022 launch. But what exactly fuels the blistering pace at which OpenAI ships updates and new features? Digging deeper into this topic I found a whole bunch on interesting stuff that I wanted to summarize in a as short as possible way.

If you want to dig deeper here are the sources that I found the most helpful. For some you have to subscribe and pay, but I do and let's just say I got my money's worth.

Evan Morikawa Twitter Account @EØM

Ivan Mehta on Techcrunch

Peter Yang on Creator Economy

Gergely Orosz on Pragmatic Engineer

Open AI / Research / Index

Lets start...

1. Operating like a startup within a giant:

OpenAI structures its projects, including ChatGPT, like independent startups. This setup fosters a culture of agility and rapid iteration, with small, dedicated teams working in isolated codebases and environments, much like a startup aiming for product-market fit. When the "Applied"-team was formed, they were given their own code repository and a fresh cluster, mimicking the environment of a new startup. This allowed them to iterate quickly without being bogged down by legacy systems.

2. Tight integration with research:

OpenAI blurs the traditional lines between engineering, product, design, and research, forming what they call "DERP" teams. Product teams at most companies consist of “EPD” (engineering, product, design). At OpenAI, each team is a “DERP” unit — Design, Engineering, Research, and Product.

Instead of sitting in their ivory tower, researchers sit with the product team to improve the model and build prototypes. Features like browsing, code interpreter, and more all started as research prototypes. This tight integration means that many product challenges are directly addressed through research, ensuring rapid prototyping and deployment of features. We have seen this approach in the early days of Lockheed and Lockheed Martin Satellite Company (now Lockheed Martin Space ) working with Frederic Tarman RadLab on the Corona 9009 Project. The collaboration between researchers and engineers accelerates the transition from experimental ideas to production-ready features.

3. Long-term thinking and mission focus:

OpenAI's mission drives its long-term strategy. This clear focus helps prioritize projects and maintain a steady stream of innovation. OpenAI’s mission to "develop AGI that benefits all of humanity" is a guiding principle in product discussions. For instance, during decision-making processes, teams often ask which options bring them closer to AGI, helping to maintain focus and prioritize long-term goals. This mission-driven approach has led to sustained innovation and careful alignment of product development with strategic objectives. Just as we see in the limiting KPI at #tesla #SpaceX, which gives us a more practical view: "cost of transportation for one kg mass" referring to Joe Justice JoeDX material.

4. Uncoupled and incremental releases:

#OpenAI avoids "big-bang" releases, instead opting for gradual, incremental rollouts. This approach is exemplified by the phased introduction of #ChatGPT features such as plugins and web browsing, which were first released in beta to gather feedback and ensure safety. This strategy not only facilitates continuous learning and improvement but also aligns with their safety protocols. Gradual rollouts with close monitoring allow for real-world feedback and quick adjustments, ensuring that new features are safe and effective before a full-scale launch.

5. High talent density:

OpenAI's hiring strategy emphasizes high talent density by focusing on senior engineers with significant experience. For example, the Applied Engineering team, which grew from 6 to 130 members, prioritizes hires who were qualified to make immediate, impactful contributions. This focus allows for quick decision-making and rapid development.

6. In-person collaboration:

In-person collaboration plays a crucial role in OpenAI's rapid development cycles. Unlike other hyped companies like NVIDIA The team works from the office three days a week, fostering spontaneous interactions and ad-hoc problem-solving. This setup has been proven for them for maintaining high velocity and effective onboarding as the team rapidly scales. If NVIDIAs strategy is contradictory to this one is still to be shown.

💡Learnings:

OpenAI’s success is built on a foundation of small, focused teams, deep integration of research and product development, and a clear, mission-driven approach. For other tech companies thriving to be innovative, the lesson is clear: cultivate a startup mentality, integrate cross-functional teams, prioritize long-term goals, and maintain high standards for talent. This combination can unlock unprecedented levels of productivity and innovation.

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