[RaftPeople]

I tend to agree with the article.

A typical example of trying to add a new significant capability involves many meetings (days, weeks, months, etc. )with the business to understand how their work flows between systems X, Y and Z as well as all of the significant exceptions (e.g. we handle subset A this way and subset B that way, but for the final step we blend those groups together, except for subset C which requires special process 97).

Then with that understanding comes the system solutioning across multiple systems that can be a blend of internal system or vendor's system, each with different levels of ability to customize, which pushes the shape of the final solution in different directions.

There is certainly value in speeding up coding, but it's just one piece of the puzzle and today LLM's can't help with gathering the domain information and defining a solution.

[physicsguy]

What I've seen in an AI-forward looking environment is that it's much more common for PM/POs to be knocking up at least a UI prototype now, and experimentation is happening often even before writing the tickets. Similarly when devs are proposing something they often are coming with a couple of prototypes already implemented. Both of those mean decisions are coming a lot quicker.

[IanCal]

I wouldn’t discount the value of moving small tasks away from developers, nor the value of fast cheap prototypes.

Product owners can very quickly get, for many problems, an interactive demo without coding. For lots of problems this can be somewhere from a static html page which shows the interactions to a hacked in feature that lets them actually test if it solves the customer need and try several variations before handing over much more concrete specs of what they want to happen. So much time is lost between getting an idea from someone’s head to code to use to then find out it wasn’t communicated well and then finally that the idea didn’t help anyway and we want it in a different way.

Yes yes I know someone is about to say that now there’s pressure to push the prototype out but that’s an organisational level problem that existed anyway.

And small problems can much faster to solve as well, or even move away from devs. Often people just need some text changed somewhere or html putting together, or some basic code for analysis. They could understand the logic, but the task of writing it from scratch and how to run things may be too much - now you don’t need to prioritise work for a dev to get some sql written and they can spend their time on the larger more software engineering level problems.

[bamieater]

"that’s an organisational level problem that existed anyway"

That's very true to many organizations. One cannot just slap an AI tool on it when you are dealing with fundamental organizational problems in the first place.

"they can spend their time on the larger more software engineering level problems"

For sure, devs still needs to focus on the right type of work and maintain the balance. I built a tool to just do that: https://worktypefocus.com/

[wise0wl]

I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM. A continuing architectural diagram is constructed and graph is updated until all cases are covered and then the LLM writes the code, writes the validations, pushes to CI environments, runs tests, schedules prod deploy (by looking at company event schedule), gets CAB approval, deploys code, tests in prod, and fixes regressions.

I'm not saying this is the correct thing, but companies are implementing it and it is "working". I don't think keeping our head in the sand is helping.

[RaftPeople]

> I've seen proposals for Product Managers to define those conditions themselves by speaking with the LLM.

But the LLM is not aware of how the business works and why, so someone needs to work with the business to extract the information. Typically it's not well documented.

[bizzletk]

> someone needs to work with the business to extract the information. Typically it's not well documented.

LLM extraction of the information from the Product Owner is becoming the way to overcome poorly-documented business context.

Non-technical folk are using things like `/grill-me` [0] to seed the LLM with the long-tail complexities that they didn't know they didn't know they needed to put out.

[0] https://www.aihero.dev/my-grill-me-skill-has-gone-viral

[IanCal]

They can ask, they can do a back and forth and they can write documentation to be used from that point onwards and write it in a common style and structure.

These are language models, being able to talk through something with them and have them extract some information is what they excel at. Given that you’d probably get a halfway decent result with a literal fixed set of questions (an Eliza level docbot) gpt 5.5 is going to nail that as a task.

[ijustlovemath]

is it working though? The main outcome we've seen with companies that drink the AI Kool aid en masse is buggy unstable systems. clearly there's a level of rigor that's being missed for ship velocity