Some things to expect in 2025

AI‑generated code, understanding, and professionalism

• Many comments latch onto the prediction that a project will discover large amounts of AI-generated code whose “authors” don’t understand it.
• Strong consensus that submitting code you cannot explain is unprofessional, regardless of whether it came from an LLM, StackOverflow, or elsewhere.
• Some argue this can be a fireable offense, especially where security or confidentiality are at stake; others see it as a coaching opportunity for juniors.
• Reviewers say they would not reject code solely for being AI-generated, but they expect the submitter to explain behavior, correctness, and implications.

LLMs vs StackOverflow and learning practices

• Several people note that blindly pasting from StackOverflow has been a long-standing problem; LLMs mainly amplify this.
• Differences highlighted: LLMs can generate larger, integrated chunks of code and adapt to the user’s context, increasing the temptation to skip understanding; SO answers at least have visible peer review and require some integration effort.
• Some see LLMs as excellent for translation, boilerplate, and “rubber-ducking,” while warning they hallucinate APIs, mis-handle edge cases, and are weak on newer or niche libraries.
• Concern about “learning debt”: juniors and students may advance by outsourcing thinking to LLMs, only to hit a wall later when deeper understanding is suddenly required.

Organizational controls: review, tooling, and risk

• Experiences differ widely: some teams have tight CI/static analysis and block “funky” code; others deploy to production within an hour with minimal review.
• Static analysis and quality gates are seen as helpful but not sufficient; they can enforce style and catch trivial issues, but not guarantee design quality.
• Some worry about maintainer burnout from low-quality “drive‑by” LLM PRs in open source.

Open-source funding and Linux ecosystem

• Debate over whether Linux and key libraries are dangerously underfunded.
• One side claims critical software is maintained by “hobbyists” and that large organizations and governments should fund it at scale.
• Others counter that most kernel work is already done by paid professionals, but acknowledge many crucial user‑space tools and libraries remain single‑maintainer, volunteer‑driven and thus risky.

Free/“ethical” LLMs and copyright

• Some want “truly free” models that do not rely on mass, unpaid scraping of copyrighted material; others argue current copyright law is too restrictive and that broad training use should be allowed.
• There is concern that small players lack the legal cover large companies have when training on potentially infringing data.

Security, maintainers, and geopolitical risk

• Single‑maintainer projects are discussed as both a liability (bus factor, coercion risk) and, paradoxically, simpler to trust because there’s one known person to evaluate.
• XZ-style backdoors are expected to recur; some speculate such attacks might be quietly monetized rather than disclosed.
• Geopolitical fragmentation is seen as a growing risk, though there is disagreement on how much it will actually disrupt open source collaboration.

Other technical notes

• Brief mention of Rust-for-Linux continuing despite a high-profile maintainer’s resignation.
• sched_ext is noted as promising, with at least one concrete gaming-related scheduler example.
• Concerns are raised about cloud‑tied hardware being bricked when vendors fail or shut services, reinforcing “you don’t really own it” worries.