Videos CoEIs6Xm8m8
Open Source Is Dead. Long Live Open Source. — Saoud Rizwan, Cline
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Transcript
130 cues· 2,729 words· 15,352 chars
- 0:12 Hi, I am Saud, founder of Klein.
- 0:19 I started Klein as an open source project a few years ago.
- 0:24 Some of you might know it as the first ever coding agent back before the Cloud Mac subscription and the Codex subscriptions when people had to pay for each and every API request.
- 0:35 which got extremely expensive.
- 0:38 This was before prompt caching became a thing.
- 0:40 And so there were people that paid hundreds of dollars a day using Klein.
- 0:47 But for a lot of people, it was their first AGI moment.
- 0:50 It was the first time they saw LLMs be able to do their jobs end to end.
- 0:55 And they got hooked.
- 0:57 And I don't think Client would have been as successful as it is if it wasn't open source because it allowed these developers to inspector code and trust it and connect to any API so they could be comfortable with spending so much money on it and know that they weren't getting screwed over.
- 1:18 And we were the first to add things like custom rules and plan mode.
- 1:22 And a lot of that came from talking to and learning from this really incredible open source community we had around the project.
- 1:28 And so, you know, having spent most of my life building open source, it's really heartbreaking to see just like the broader open source community wither and die over the last two years because of how AI has fundamentally changed everything about software development.
- 1:46 GitHub is effectively an archive of slop PRs and issues and security reports, where the sense of community before has turned into this deep skepticism and distrust of each other's responsible use of these tools.
- 2:03 Because AI coding can be extremely dangerous to a project, and everyone's kind of had to learn that on the fly, but especially open source projects that rely on trusting third parties.
- 2:15 I wanted to share some examples of how open source has been dealing with AI.
- 2:22 This is the code of conduct for Zig, which is the language that powers Bunn.
- 2:30 They essentially ban all use of AI.
- 2:31 You can't use it on pull requests or issues or even comments.
- 2:37 The reason for this is that, to them, the core Zig team
- 2:43 They value contributors more than they do the contributions.
- 2:47 And so the primary goal for reviewing PRs and things isn't to add new code, but it's to help grow new contributors who can become trusted over time.
- 2:55 And AI assistance completely breaks that.
- 3:00 This is a post from the CEO of Curl, who says that his project is effectively being DDoSed by AI-generated bug reports, and they're even considering shutting down their bug bounty program for the first time in decades.
- 3:18 And this is TL Draw.
- 3:22 automatically just closing all pull requests, whether they're AI-generated or not.
- 3:27 And it's gone so bad that GitHub added a feature to disable third-party pull requests altogether, which is really sad, because pull requests were the thing that made GitHub what it is today, and we're probably gonna see a lot of big open-source projects opt into this.
- 3:45 And so when I say that open source is dead, I mean some parts of it, like the community.
- 3:52 It's just not worth cultivating anymore, especially because building software is so cheap.
- 3:58 And also the risk of supply chain attacks.
- 4:01 I'm sure you've seen all the reports of things getting compromised.
- 4:05 It's become more dangerous than ever to depend on third-party software, where it takes a single compromise and a massive chain of contributors to get pwned.
- 4:14 So just as an example, LiteLLM is a Python package.
- 4:20 It gets like three and a half million downloads a day.
- 4:23 They were compromised for three hours where attackers used a GitHub app that they used to steal their PyPI publishing tokens and publish a compromised version of the package that would install a credential harvester that would steal your API keys, your SSH keys, your crypto keys,
- 4:44 and also install a backdoor that lets them do remote command execution.
- 4:49 And the only reason this was even caught as quickly as it was was just pure luck.
- 4:54 Because the malware had a bug in it where it would cause cursor to crash if you ran the light LLM MCP server.
- 5:02 And a security researcher noticed that and
- 5:06 was able to figure it out, but if this had been out any longer, it would have caused catastrophic damage, especially because a lot of the people using LightLLM are the enterprise customers and developers that have their own internal gateways.
- 5:23 But despite all of this, I believe there are some parts of open source that are sticking around and becoming more important, like allowing others to use your thing freely in the public domain and build on top of.
- 5:36 And those parts about it are gonna become more important than ever, particularly with open weights models because of the economic impact.
- 5:43 And so to help explain why, I wanna look at what's happening with inference spend right now.
- 5:48 So this is a report from
- 5:51 An anonymous report from a CFO at an unnamed company where they accidentally spent $500 million on Claude in a single month because they didn't set the usage limits on their thousands of employees on their anthropic dashboard.
- 6:06 This is another report by Uber CTO where after they rolled Cloud out to their organization, 95% of their engineers were using it, 70% of their committed code came from Cloud, and their monthly spend per user was up to $2,000, and they said they used their entire 2026 budget in just four months.
- 6:27 And the crazy part is that the AI labs are losing money too.
- 6:32 This is a chart from Semi Analysis where they ran experiments with Cloud, Code, and Codex subscriptions where they would give them long horizon coding tasks until they exhausted their weekly limits.
- 6:42 And they found that a $200 plan for Cloud would give them about $8,000 worth of API usage and a $200 subscription to Codex would give them about $14,000 worth of API usage.
- 6:56 So I think the strategy is pretty obvious.
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Chapters
- 0:00 Introduction to Cline and the early open source coding agent era.
- 1:30 The decline of the open source community and the rise of AI-driven distrust.
- 2:22 How projects like Zig, curl, and tldraw are responding to AI-generated noise.
- 3:45 Systemic risks: The litellm supply chain compromise example.
- 5:22 The economic case for the survival of "open weights" models.
- 5:49 Real-world impact: Corporate AI spending and infrastructure lock-in.
- 8:50 Comparing model intelligence vs. system-level AI verification (GLM vs. Opus).
- 10:57 The Open Compute precedent: How open standards commoditize the industry.
- 12:36 Future projections for AI inference costs and hardware capacity.
- 14:22 A call to action for American labs regarding open weights models.
- 16:01 Cline's shift to an open weights subscription model.