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Will OpenAI’s Codex Replace Human Programmers?
No, but centaurs might
Earlier this month, Elon Musk’s artificial intelligence company OpenAI released Codex, a new system that automatically writes software code using only simple prompts written in plain language. Codex is based on GPT-3, a revolutionary deep learning platform that OpenAI trained on nearly all publicly available written text produced by humanity through 2019.
As an early Beta tester, I’ve had extensive opportunities to put both GPT-3 and Codex through their paces. The most frequent question I’m asked about Codex is “Will this replace human programmers?” With world powers like the United States investing billions into training new software developers, it’s natural to worry that all the effort and money could be for naught.
If you’re a software developer yourself — or your company has spent tons of money hiring them — you can breathe easy. Codex won’t replace human developers any time soon — though it may make them far more powerful, efficient, and focused.
Why isn’t Codex an existential threat to human developers? Years ago, I worked with a high-level (and highly compensated) data scientist and software developer from a major American consulting firm on a government database project. Our task was to understand how a state agency was using its database to assign grants to organizations, and then to advise the agency on how to improve the database.
When I first started working with my developer colleague, I had a lot of preconceived ideas about how he’d spend his time. I imagined he’d be hunched over his laptop all day, tapping out code in R or cooking up brilliant formulas in Mathematica to help us better understand our client’s database. I pictured Beautiful-Mind-style frantic scribbling on windows, regression analyses, and lots of time spent in front of a screen, writing thousands of lines of Python code.
Instead, my colleague started the engagement by sitting down with the client and spending several days understanding their grantmaking process. This led to meetings with individual staff members, stakeholders, the agency’s constituents, and more. Only after several months of this kind of work did he finally sit down to analyze the agency’s data, using R and various graphing libraries…
