>Good software is about creating longer term value and takes consistent skill & vision to execute.

>Those software engineers who focus on this big picture thinking are going to be more valuable than ever.

Not to rain on our hopes, but AI can give us some options and we can pick the best. I think this eliminates all middle level positions. Newbies are low cost and make decisions that are low stakes. The most senior or seniors can make 30 major decisions per day when AI lays them out.

I own a software shop and my hires have been: Interns and people with the specific skill of my industry(Mechanical engineers).

2 years ago, I hired experienced programmers. Now I turn my mechanical engineers into programmers.

Why can’t LLMs understand the big picture? I mean, a lot of companies have most of their information available in a digital form at this point, so it could be consumed by the LLM.

I think if anything, we have a better chance in the little picture: you can go to lunch with your engineering coworkers or talk to somebody on the factory floor and get insights that will never touch the computers.

Giant systems of constraints, optimizing many-dimensional user metrics: eventually we will hit the wall where it is easier to add RAM to machines than humans.

Yes! "Does an AI know how to do that? Does a coding assistant know that an app is really a giant collection of details?"

There are just so many small decisions that add up to a consistent vision for a piece of software. It doesn't seem like LLMs are going to be able to meaningfully contribute to that in the near future.

I tried vibecoding my own workout tracker, but there were so many small details to think through that it was frustrating. I gave up and found an app that is clearly made by a team of experienced, thoughtful people and AI can't replicate the sheer thoughtfulness of every decision that was made to create this app. The inputs for reps/sets, algorithms for adjusting effort on the fly, an exercise library with clear videos and explanations; there's just no way to replicate that without people who have been trainers and sport scientists for decades.

LLMs can help increase the speed that these details turn in to something tangible, but you definitely can't "skip all that crap and just jump to the end and get on with it."

Greg mentions discipline and vision as determinants of successful software, which is correct but I think he misses another aspect of vision: the ability to attract and crystallize a community around their project. Arguably, most successful softwares thrive in the long term because they have a team of people that inspire each other, fill in with complementary talents, and provide continuity.

>LLMs can't be strategic because they do not understand the big picture

While I do tend to believe you, what evidence based data do you have to prove this is true?

> LLMs can't be strategic because they do not understand the big picture -- that the real work of good software is balancing a hundred different constraints in a way that produces the optimal result for the humans who use it.

There’s good reason to think that they could understand the big picture just fine, even today, except that they’re currently severely constrained by what we choose, or have time, to tell them. They can already easily give a much more comprehensive survey of suitable options for solving a given problem than most humans can.

If they had more direct access to the information we have access to, that we currently grudgingly dole out to them in dribs and drabs, they would be much more capable.