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Its more and more clear to me that AI is a force multiplier for small teams and hobby workflows, but seems to have diminishing returns for larger teams.

melvinroest 6mo ago
How so? Could you give some specific examples?
- hogehoge51 6mo ago
  My experience moving between startup/SME/corp: Smaller teams have more agency to move and usually team members with broader responsibility and understanding of the systems. Also possibly closer to stakeholders, so are already involved in specification creation and know where automation can add value. Add an AI agent and they can pick and choose where they can be most effective at a system level. Bigger teams have clear boundaries that stop agency - blockers due to cross team dependencies, potentially no idea what stakeholders want, just piecemeal incremental change of a bigger system specified by someone else. If all they can do is automate that limited scope it's really just like faster typing.
  - melvinroest 6mo ago
    Ah, I feel what you're saying. Yea that makes total sense.
  - hirsin 6mo ago
    That's the success case. In the failure case you have emboldened, pressured teams jumping in to make a "quick fix" or "that feature we needed" in a codebase for a team they've never heard of, and leaders cheering it on in the name of progress. Not every company is going to see those boundaries and stakeholders as features, and they'll be under pressure to "mitigate those blockers to execution". That's where the cognitive debt skyrockets.
  - JohnBooty 6mo ago
    I agree with the agency aspect. But... as team size grows, LLMs can be more valuable in other ways. Larger teams typically have larger codebases to comprehend, more users, more bug reports to triage, etc. It's SO much easier to get up to speed on a big existing codebase now.
- skylanh 6mo ago
  Small teams prioritize expertise and agency. Large teams prioritize service resilience and depth of coverage.

melvinroest 6mo ago

How so? Could you give some specific examples?

hogehoge51 6mo ago
My experience moving between startup/SME/corp: Smaller teams have more agency to move and usually team members with broader responsibility and understanding of the systems. Also possibly closer to stakeholders, so are already involved in specification creation and know where automation can add value. Add an AI agent and they can pick and choose where they can be most effective at a system level. Bigger teams have clear boundaries that stop agency - blockers due to cross team dependencies, potentially no idea what stakeholders want, just piecemeal incremental change of a bigger system specified by someone else. If all they can do is automate that limited scope it's really just like faster typing.
- melvinroest 6mo ago
  Ah, I feel what you're saying. Yea that makes total sense.
- hirsin 6mo ago
  That's the success case. In the failure case you have emboldened, pressured teams jumping in to make a "quick fix" or "that feature we needed" in a codebase for a team they've never heard of, and leaders cheering it on in the name of progress. Not every company is going to see those boundaries and stakeholders as features, and they'll be under pressure to "mitigate those blockers to execution". That's where the cognitive debt skyrockets.
- JohnBooty 6mo ago
  I agree with the agency aspect. But... as team size grows, LLMs can be more valuable in other ways. Larger teams typically have larger codebases to comprehend, more users, more bug reports to triage, etc. It's SO much easier to get up to speed on a big existing codebase now.
skylanh 6mo ago
Small teams prioritize expertise and agency. Large teams prioritize service resilience and depth of coverage.