SKILNER

It amazes me when I see discussions like this that a significant number of people really and truly believe that they can predict the future about something as wild and volatile as AI.

I asked ChatGPT to evaluate how many comments here seemed to indicate certainty about the future - roughly 15-20%.

One of my favorite quotes from a French philosopher: Trust those who seek the truth, doubt those who find it.

Thanks for providing some actual facts.

So many people flailing around to justify their feelings rather than thinking rationally.

It is time to give up arguments without evidence.

Disappointing - not a very insightful list

If you've ever undertaken the task of documenting entire workflows, then you know that you quickly put up the white flag at the word "entire".

When you actually talk to people about what they do there are often many, many nuances, micro-events, micro-decisions and micro-actions in their work. This is why it can take days/weeks/months to completely train a new person for a job.

This level of detail is barely documented - anywhere. There is a huge amount of information buried in workflows that AI has barely had access to for training. A lot of this is more in the realm of world models, rather than LLMs.

So imagine trying to use AI to improve these workflows it knows so little about. Then imagine AI trying to reinvent them across an organization.

We find these use cases where AI provides great value - totally true - but these barely scratch the surface of what goes on.

This strikes me as a very solid methodology for improving the results of all AI coding tools. I hope Anthropic, etc take this up.

Rather than converging on optimal code (Occam's Razor for both maintainability and performance) they are just spewing code all over the scene. I've noticed that myself, of course, but this technique helps to magnify and highlight the problem areas.

It makes you wonder how much training material was/is available for code optimization relative to training material for just coding to meet functional requirements. And therefore, what's the relative weight of optimizing code baked into the LLMs.

"According to the study, AI still struggles with several crucial facets of coding: sweeping scopes involving huge codebases, the extended context lengths of millions of lines of code, higher levels of logical complexity, and long-horizon or long-term planning about the structure and design of code to maintain code quality."

uhhh, not sure even the best people or teams are very good at this either. Condemning AI for not being capable of something we're not capable of, ok...

“If it takes longer to explain to the system all the things you want to do and all the details of what you want to do, then all you have is just programming by another name.”

This is called the specification process, which hopefully is already occurring today.

There's so much self-serving bias in articles like this, as well as the comments on HN, Reddit, etc. It's good to critique AI, but that self-serving line is frequently crossed by many people.

>> It reeks of cope.

haha, well said, I've got to remember that one. HN is a smelly place when it comes to AI coping.

That's an excellent job of teaching - thank you!

You get to keep your job instead of being replaced by someone willing to use the latest tools.

It's a good idea to think of a converse situation, but this is a bad example. The constraint was not about technology but about budget, perceived benefits and political will.

Let's just imagine we're critiquing cars or planes in about 1910. They're so flawed. No one can say with certainty whether or how they will improve.

Side note of interest, from Wikipedia: "Flying Machines Which Do Not Fly" is an editorial published in the New York Times on October 9, 1903. The article incorrectly predicted it would take one to ten million years for humanity to develop an operating flying machine.

There's a lot of resistance to AI amongst the people in this discussion, which is probably to be expected.

A chunk of the objections indicate people trying to shoehorn in their old way of thinking and working.

I think you have to experiment and develop some new approaches to remove the friction and get the benefit.

Is the UX of the name any indication of the UX of the product?