In 2025, venture capital can’t pretend everything is fine any more – Pivot to AI

The Future of Software Development is Software Developers – Codemanship’s Blog

The hard part of computer programming isn’t expressing what we want the machine to do in code. The hard part is turning human thinking – with all its wooliness and ambiguity and contradictions – into computational thinking that is logically precise and unambiguous, and that can then be expressed formally in the syntax of a programming language.

That was the hard part when programmers were punching holes in cards. It was the hard part when they were typing COBOL code. It was the hard part when they were bringing Visual Basic GUIs to life (presumably to track the killer’s IP address). And it’s the hard part when they’re prompting language models to predict plausible-looking Python.

The hard part has always been – and likely will continue to be for many years to come – knowing exactly what to ask for.

The Colonization of Confidence., Sightless Scribbles

I love the small web, the clean web. I hate tech bloat.

And LLMs are the ultimate bloat.

So much truth in one story:

They built a machine to gentrify the English language.

They have built a machine that weaponizes mediocrity and sells it as perfection.

They are strip-mining your confidence to sell you back a synthetic version of it.

AI CEO – Replace Your Boss Before They Replace You

Delivering total nonsense, with complete confidence.

The Jeopardy Phenomenon – Chris Coyier

AI has the Jeopardy Phenomenon too.

If you use it to generate code that is outside your expertise, you are likely to think it’s all well and good, especially if it seems to work at first pop. But if you’re intimately familiar with the technology or the code around the code it’s generating, there is a good chance you’ll be like hey! that’s not quite right!

Not just code. I’m astounded by the cognitive dissonance displayed by people who say “I asked an LLM about {topic I’m familiar with}, and here’s all the things it got wrong” who then proceed to say “It was really useful when I asked an LLM for advice on {topic I’m not familiar with, hence why I’m asking an LLM for advice}.”

Like, if you know that the results are super dodgy for your own area of expertise, why would you think they’d be any better for, I don’t know, restaurant recommendations in a city you’ve never been to?

The only winning move is not to play

My mind boggles at the thought of using a generative tool based on a large language model to do any kind of qualatitive user research, so every single thing that Gregg says here makes complete sense to me.