Also, I have the impression that LLMs bring some gains or benefits for individuals but not relevant enough at the organization level.

We have a person who wants, effectively, a formatted report generated on demand from four sources. The current interface is four different programs, all of which were written by different groups inside the corp, but they also all draw from the same or similar databases. There's a unified login, but each interface has its own permissions.

The company brings in an AI initiative and soon enough drops all security restrictions for the AI's access to the databases. The new formatted report gets generated through the use of a few tens of thousands of tokens each time, and about 5% of the time synthesizes non-existent data.

A competent DBA and application programmer could have spent a week doing the same thing, producing a program which would do the job faster, cheaper (at run-time), secure and in a way which could be extended and debugged.

But DBA and application programmer time is expensive up-front and the execs are gung-ho about the stock-price now that they are hip and trendy.

    > ...but that doesn’t mean it’s generating the correct code.

Before, when there was the notion that "building is expensive", product teams would think things through, do user interviews up-front, actually do discovery around the customer + business context + underlying human process being facilitated with software.

This has shortened the cycle to first working prototype, but I'd guess that in the longer scale, it extends the time to final product because more time is wasted shifting the deliverable and experience on the user during this process of discovery versus nailing most of the product experience in big, stable chunks through design.

At the end of the day, there is a hidden cost to fast iterative shifts on the fundamental design of the software intended for humans to use and for which humans are responsible for operation. First is the cost on the end users who have to stop, provide feedback, and then retrain on each cycle. Second is that such compounding complexities in the underlying implementation as product learns requirements and vibe-codes the solution creates a system that becomes very challenging for humans to operationalize and maintain.

Ultimately, I think the bookends of the software development process are being neglected (as author points out) to the detriment of both the end users and the teams that end up supporting the software. I do wonder if we're entering an "Ikea era" of software where we should just treat everything as disposable artifacts instead.

However, while the engineering team successfully fast tracked development, UAT, and production testing largely thanks to AI other departments only began digging deeper into the project toward the end of April. To be fair, they do use AI in their workflows to some extent, but they haven't adapted their processes to keep pace with engineering's increased productivity.

In my opinion, this lag is mostly because many employees in those departments are older and hesitant to change their routines. While I understand that resistance to change is a natural human trait, what comes to my mind is this beautiful German adage, "Wer nicht mit der Zeit geht, geht mit der Zeit" which loosely translates to, "Who doesn't change with time is left behind by time"

The proper implementation and design still take time, but still faster in systems with a lot of available resources online.

I get most value from them when I'm asking it to either fill in the blanks of something already half implemented or when I need some feature in a given context/language that only exists in other languages

Everything is OK, but the size of Gantt chart should be expanded.