Will AI Automate Coding? OpenAI Codex Lead Alexander Embiricos Explains

TL;DR

AI will automate specific coding tasks, but OpenAI Codex product lead Alexander Embiricos expects more builders, not fewer. He compares the shift to engineers moving from assembly to higher-level languages, which enabled much more code and increased demand for software engineers. He also predicts more full-stack roles as the talent stack compresses. Read on for his views on engineers, product managers, and AI-assisted building.

Transcript

You still need software engineers today. You still need designers. I'm a PM. Do you need PMs? You know, you can have some fun jokes about that. I don't think you need them. Today, joining us in the hot seat, we have Alexander and Bericos, product lead for codeex at OpenAI. This is an incredible discussion. Time to get the notebook out. For me, the ... Read More

Key Insights

• Coding is one of the first domains where LLMs became genuinely capable, according to OpenAI Codex product lead Alexander Embiricos, though he argues 'coding is automated' is a heavy, imprecise claim worth unpacking rather than accepting at face value.
• Automation historically expands demand rather than eliminating jobs. When engineers stopped writing assembly and moved to higher-level languages, they wrote far more code, which created greater demand for code and required many more software engineers overall.
• The word 'computer' originally referred to humans doing manual tabulation, reportedly at Bletchley Park decoding the Enigma, and early spreadsheet software mimicked offices of desks arranged in a grid, showing specific tasks get automated while output demand explodes.
• The talent stack is compressing: engineers are becoming more full-stack rather than split into separate backend and front-end roles, so teams still need builders but fewer narrowly specialized positions than a few years ago.
• Product manager is an explicitly undefined role whose goal is adapting to whatever the team or business needs, so its functions can be absorbed by a strong engineering lead or product-minded designer until the team grows very large.
• Human typing speed and validation work, not model compute or architecture, is described as the key bottleneck to AGI, because people cannot prompt AI often enough to capture its full potential value.
• AI should assist tens of thousands of times per day rather than the tens of times most users currently reach, but people are too lazy to type that many prompts and too uncreative to imagine every way AI could help.
• The ideal future removes prompting effort entirely: AI should connect to your context and chime in helpfully without requiring users to learn special prompting techniques, similar to packaged tools like Claude for legal or Claude for Excel.
• "I think for sure I would agree that coding is one of the first domains where LLMs are really good." (2:21)
• "We were just able to write much more code and then as a result actually there was much more demand for code and there were many more software engineers required." (2:38)
• "And so I definitely think we'll have many more builders." (3:44)
• "You know, you still need software engineers today." (3:50)
• "I kind of think of the role as like actually explicitly undefined and your goal is just to adapt to whatever the team or business needs." (4:40)

Questions & Answers

Q: Will AI automate coding?

Alexander Embiricos says coding is one of the first domains where large language models are really good, but he questions what it means to call coding automated. He argues that specific tasks can be automated without eliminating the broader work of building software.

Q: Will AI replace software engineers?

Embiricos does not expect AI to remove the need for software engineers. He says teams still need software engineers and predicts that there will be many more builders, even as the specific tasks they perform change.

Q: Why does Alexander Embiricos compare AI coding to higher-level programming languages?

He notes that moving away from assembly to higher-level languages did not cause people to declare coding automated. Instead, engineers could write much more code, demand for code increased, and many more software engineers were required.

Q: Will there be more or fewer engineers in 5 years?

Embiricos expects many more builders in 5 years. His reasoning is that automating manual tasks has historically produced an explosion in demand for their output, requiring more people even as the work changes.

Q: What does compression of the talent stack mean?

Compression of the talent stack means that previously separate specialties are becoming more integrated. Embiricos says engineering work on the Codex team is now much more full-stack, with less separation between backend and front-end engineers than a few years ago.

Q: Will teams still need software engineers and designers?

Yes, Embiricos explicitly says teams still need software engineers and designers today. He expects people to continue building, although their roles may become broader as the talent stack compresses.

Q: Why does the OpenAI Codex lead say product managers may not be needed?

Embiricos calls this his fun joke, then explains that the product manager role is incredibly hard to define. He views it as explicitly undefined, with the goal of adapting to whatever the team or business needs.

Q: What does a product manager do according to Alexander Embiricos?

He says a product manager can step back while others build quickly, look around corners, and help determine what to do next. The role also adapts to the changing needs of the team or business.

Summary & Key Takeaways

• Alexander Embiricos, product lead for Codex at OpenAI, agrees coding is one of the first domains where LLMs are strong but pushes back on 'coding is automated' as an oversimplification, comparing it to abandoning assembly language for higher-level languages that expanded rather than eliminated engineering work.

• He predicts more builders in five years, not fewer, citing how automation of manual tasks historically explodes demand for output. The talent stack is compressing toward full-stack engineers, and he jokingly argues product managers, whose role is inherently undefined, may not be strictly necessary on smaller teams.

• The key bottleneck to AGI is human typing speed and validation, not compute. Embiricos wants AI helping people effortlessly tens of thousands of times daily, connected to their context, so benefiting from AGI requires no special prompting skill, and he favors building tools for individuals over top-down enterprise automation.