> we're now able to accumulate technical debt faster than ever

LLM's just enable you to speed run your way into a legacy code base.

> without even building the institutional knowable needed to keep it sane

Does feature XXX move the needle? Did you gain more business or retain existing business because a feature exits? Has AI tooling helped your product team move the needle? No? Why not?

How easy is it to remove the feature that NO ONE uses is a question no one is asking. How many people got promoted for "removing the most garbage" from the system?

We, as an industry, might need to have a candid conversation about what we're doing and how we do it.

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pixl97 16 hours ago | parent [-]

Remove a feature no one uses:

How do you properly measure this? Do you setup a database that monitors when every feature is used? Is the feature only used once a year for some reason? Is the feature only used on some rare data that is uncommon but still can occur?

And that really applies to something fully in your control. In systems controlled by customers its far harder.

	▲	zer00eyz 15 hours ago \| parent [-]
		> How do you properly measure this? The same way you measure utilization and cost at a customer level. I can name at lest three companies where their biggest clients are also the bulk of their costs - they lift revenue but drag the margins. I can name another couple who spent years marking their products entirely wrong because they simply had no clue how their product was really being used. > Is the feature only used on some rare data that is uncommon but still can occur? This is where the competence of your product team comes into play. We're building features faster with AI but none of it is moving the needle. That has little to nothing to do with code quality, and everything to do with product teams.