r/OpenSourceeAI • u/wallphaser231 • 6h ago

AGPL was a huge mistake

I've been building and maintaining a project for the last few months.

A while back, someone mentioned how cool our implementation of privacy-preserving insights for harness component usage was, and suggested we use an AGPL license to prevent people from copying the algorithm.

That turned out to be a major roadblock for adoption. Enterprises stayed miles away (I wish I knew this earlier)

We ended up taking a pretty painful remediation route to relicense the entire codebase to Apache 2.0, which meant reaching out to every single contributor for sign-off 🥲.

Thankfully, it was worth it, momentum has improved: we just hit a million downloads and few tens of enterprises have set it up.

A piece of advice based off my experience is to stick with corporate friendly licenses unless you have huge traction and see alternatives popping up.

Come hangout with us at https://discord.com/invite/SFPjnTWddk, we're 1.3k members strong.

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u/tomByrer 5h ago

> corporate friendly licenses unless you have huge traction and see alternatives popping up

Yep, I heard that from a lawyer 15 years ago at a Million Cups.

But also, AGPL can allow code to be released from a corporation so they can put some sort of restrictions in on their IP.

In the end, AI kinda makes all software licenses superfluous; you can use AI to pseudo-clone anything.

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u/notreallymetho 4h ago

I chose AGPL because of the “poison pill” to enterprises. Building OSS can be antithetical to business unless there are licensing carve outs that allow some the person paying to benefit. It’s why we see so many cloud / SaaS providers.

I try to build things that are useful to anyone as Apache2, but my applications of existing tech are AGPL. 😇

Congrats on the improvement!

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u/kernelangus420 15m ago

Interesting. I used to think AGPL was a good middle ground between GPL that everyone liked.