@rohanpaul_ai: MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality. They…

MIT study. Code volume surges by 300%, but output increases by only 30%: The AI dividend meets an awkward reality.

They studied 100,000+ GitHub developers and find that AI coding agents massively increase code production, but much less of that work becomes shipped software.

Autonomous AI coding agents raised commits by 180%, but releases rose only 30%.

The paper’s main idea is that software production has weak links, so faster code writing does not help as much when humans still need to review, connect, test, package, and ship the work.

The authors also check app marketplaces and find more new apps, but no increase in total usage, which means more software appeared without clear evidence that users adopted more software.

The marketplace evidence points the same way: more new apps appeared, but total usage did not rise.

The authors compare more than 100,000 GitHub developers before and after they start using 3 generations of AI coding tools, from autocomplete to more independent coding agents.

Autocomplete raised commits by 40%, interactive coding agents raised them by 140%, and autonomous coding agents raised them by 180%.

The 180% commit gain shrank to 50% for the number of projects and 30% for actual releases.

The estimated “elasticity of substitution” is 0.25 i.e. for every big improvement in AI’s usefulness, only a small amount of human work can be replaced.

Because AI can write code faster, but humans are still needed to decide what to build, check if the code works, connect it with the rest of the product, fix messy edge cases, and actually ship it.

papers .ssrn.com/sol3/papers.cfm?abstract_id=6859839

The top 1% of U.S. AI firms are now spending about $7,500 per employee each month on AI.

Source: econlab .substack.com/p/how-much-does-it-cost-to-be-ai-pilled