There was nothing stopping everyone from using continuous delivery today, yet many companies still rely on long cycles, manual testing and handovers. The problem isn't the tooling, it's the people.

I don't understand this part either. At some point we're writing software for people to use, and there has to be someone who comes up with the requirements based on what people want. AI doesn't change this fact.

Is there an equivalent for the JS ecosystem? If not, having Dependabot update dependencies automatically after a cooldown still seems like a better alernative, since you are likely to never update dependencies at all if it's not automatic.

RenovateBot supports a ton of languages, and ime works much better for the npm ecosystem than Dependabot. Especially true if you use an alternative package manager like yarn/pnpm.

Too bad dependabot cooldowns are brain-dead. If you set a cooldown for one week, and your dependency can't get their act together and makes a release daily, it'll start making PRs for the first (oldest) release in the series after a week even though there's nothing cool about the release cadence.

The cooldown is to allow vulnerabilities to be discovered. So auto update on passing tests, which should include an npm audit check.

I use Gemini for personal stuff such as travel planning and research on how to fix something, which product to buy, etc. My company has as Pro subscription so I use that instead of ChatGPT.

It's refreshing to hear people say "We're not really sure" in public, especially from experts.

I agree that AI tools are likely to amplify the importance of quick cycles and continuous delivery.

If tech companies want to show they have a high percentage of LoC being generated by AI, it's likely they are going to encourage developers to use AI to further increase these numbers, at which point is does become a measure of productivity.

So the question is, are these AI tools primarily creating inherent complexity, or is it a significant amount of accidental complexity?

And if AI tools are writing all of the code, does it even matter anymore?

It absolutely does matter. LLMs still have to consumer context and process complexity. The more LoC, the more complexity, the more errors you have and the higher your LLM bills. That's even in the AI maximalist, vibe-code only use case. The reality is that AI will have an easier time working in a well-designed, human-written codebase than one generated by AI, and the problem of AI code output turning into AI coding inputs resulting in the AI choking and on itself and making more errors tends to get worse over time, with human oversight being the key tool to prevent this.

The book Software Engineering at Google makes a distinction between software engineering and programming. The main difference is that software engineering occurs over a longer time span than programming. In this sense, AI tools can make programming faster, but not necessarily software engineering.

So they can potentially work offline and deliver push notifications.

Very few applications actually need push notifications, a large majority is only annoying users with upselling, or user engagement.

For Apple, sure. But Google has been leading efforts to make the PWA experience good. In origin trial right now is the ability for websites to install PWAs: https://github.com/MicrosoftEdge/MSEdgeExplainers/blob/main/....

This would make it much easier to find and install web apps than the current method.