From first-hand experience, for established OSS initiatives it's good for repetitive, high-volume work task like security alerts, fuzzing, duplicate issue detection, PR review, summarizing long threads, and legacy refactoring.
Bad patches are of course bad, but creating confident-looking noise for maintainers who are already stretched thin...now that's not good!
Issue trackers and PRs are definitely getting harder and harder to trust. That said, AI is helping ALOT in OSS, but we definitely need guardrails around provenance, automated issue actions, and sudden changes in a contributor’s behavior.
So AI infrastructure buildout is starting to feel a lot like emergency industrial mobilization...
Also, building rapid temp shells plus nearby gas turbines paints a very different picture than the one conveyed by the "clean-energy" PR around hyperscale data centers.
Very Apple-ish approach to AI catch up: wrap an external tool in a privacy architecture, embed into the OS and productize the orchestration layer.
It will be interesting to see if the Private Cloud Compute + on-device routing can make third-party model capabilities feel like a first-party system without leaking user context to the model provider.
If Apple handles the Google-Apple boundary right, this will be an elegant move on their part, otherwise it will feel like Apple Intelligence with a just a privacy-polished frontend for Gemini.
That's the promise of every new technology. Although there's been massive progress over the past 50+ years, the amount of free time that people have has actually gone DOWN (https://clockify.me/working-hours)..."I want AI to do my laundry and dishes so that I can do art and writing, not for AI to do my art and writing so that I can do my laundry and dishes"...we'll see
A lot of the current AI economics seems to depend on three assumptions being true at once: 1. inference costs fall fast enough 2. usage grows into very large recurring revenue 3. customers don't cut once handed the bill
We should draw a distinction between "AI is valuable" and "AI justifies its current investment levels." There's real productivity value in AI, especially for things like search, boilerplate, tests, refactoring, etc...BUT that doesn't mean every enterprise should let token spend grow without strict telemetry, cost-attribution and outcome-based measurements.
The teams that win here will not be the ones using the Most AI, but the ones that treat it like any other expensive production dependency, which means measuring unit economics, cap runway usage, properly align models with tasks(not just Opus everything), and scale workflows with ROI in mind.