An idea that's beginning to solidify for me is that AI tools make software development harder.

It's harder because they dramatically raise the bar for what's possible to do. An individual developer can take on significantly more challenging projects now, because the ultimate constraint has always been time and AI can help you get more done in the time available.

But the stuff you can get done with that time is a whole lot harder. You have to understand lots more things, and get radically outside your pre-AI comfort zone.

It used to be acceptable to spend several days refactoring a codebase, or figuring out how to ship a small feature because it's in a part of the system you hadn't worked in before or involved learning a new library in order to build it.

Coding agents mean you can climb those curves a whole lot faster, but you still need to climb them - and the volume of information coming your way is much higher.

If you're worried about non-technical vibe coders taking your job, the correct response is to be much better at building software than those vibe coders. That means you need more skill, more ambition, and more experience. It's hard!

Taste, architecture, new innovations. These are all streams of tokens which are subject to the same scaling laws as code, language, and basic classification.

We are going to see a new generation of models which effectively “solves” these problems for most businesses. Likely within the next two years- then we’ll talk about some other problems which limit adoption.

LLMs are an additional tool to add to your arsenal. They are not omnipotent and need care, just like any other tool.

My best effort, so far, at an analogy is a modern drill driver compared to a screw driver/brace and bit/etc:

You can get some remarkable results in a very short time compared to the "old school" gear.

You can get some "amazing" anecdotes eg "I screwed down an entire floor at 16" x 1" c/c within an hour instead of an entire day and I took loads of fag breaks" (I could have used a nail gun instead in half the time but I'll never raise that floor easily in the future, and probably done at twice the cost)

I have several on prem LLMs and access to the rest and I'm pretty sure I'll be extending my analogy to ... brand, eventually.

What I do not expect to be doing is looking for a new job. A drill driver is not a carpenter/site labourer/useful without a person!

Remember the OOP Hype 20 years ago? I'm still cleaning shit up from then in our codebase when everyone used patterns after skimming through the GoF book without even knowing why .... My prediction is in 20 years I will clean up the shit that was co-authored by Claude ...

https://mastodon.gamedev.place/@JeremiahFieldhaven/116654345...

It all feels to me similar to how spectators or laypeople judge pro sports.

Don’t quote me on this, just trying to make a point:

They’ll say you need perfect symmetry to do well in sports, which is highly correlated to development stability in the womb; higher symmetry = perfect development.

Then after some years, news will come: Bruce Lee’s one leg is shorter than the other by a significant amount, and Usain Bolt has a similar asymmetrical development.

Then they’ll flip-flop around their initial argument by claiming that they are outliers so the general rule need not apply.

brother just build what you find interesting and it may work :)

Haven’t you heard? If you don’t adapt now you’ll be left behind, never to be able to work again! Copilot? That’s so last year. Agentic engineering? You’re already late!

> Until AI is basically in a stable, predictable, state of improvement or stagnation, these takes will continue to be pointless and most likely completely wrong.

Little thing to keep in mind about AI: a technology is only called AI while it doesn’t work yet. Once it works reliable, we give it a proper name and something else becomes AI.

I think it's more: AI assisted engineering is a new skill people are trying to develop and we're on this collective experimentation process, working out how to use AI for engineering with varying degrees of success.

If that's true, any statements defining what is necessary to do should be ignored in that context. I'm still interested in hearing about what people tried, what results they think they saw, and then trying to apply those findings to my own processes.

Which is to say I don't think the pontificating is pointless, but as statements of Real Truth, I agree they're likely wrong. We're too early in the game.

Overall I agree with this, though I do think that there will be a trend to hoard/keep-secret domain knowledge by professions. Like plumbers will try and make it a trade-secret or protected intellectual property how to change a pipe fitting.

I'd like to believe that stable state ends in a pair-programming structure, with a systems thinker/engineer and a domain expert.

Someone needs to spot when a linked list is better than a map. And the other needs to spot when clinical trial coding should happen before claims, audits, or patient outreach.

>How much pontificating needs to be done before people acknowledge nobody has any idea what to do with AI on an individual level?

if nobody has any idea what to do, talking about it is the right approach

I write software that makes money and AI helps me write software that makes more money.

> nobody has any idea what to do with AI on an individual level?

I appreciate the frustration, but some of us are actually successfully using these tools.

Several things can be true at once.

All of those things matter. One needs to be able to judge the solution in order to make a judgement if it is fine, or not. Why yes, and why no. No matter who you export the typing process to. LLMs are just tools speeding up the typing process.

So far AI has been a (genuinely) massive improvement for...

Search

It's reading my requests more clearly than (for example) Google's search input ever did, and it's got (some) understanding of how close the result (or fragments of results) are to what I want.

I can ask it about things I know about, and it can answer with strategies I hadn't thought of.

HOWEVER - I still need to understand the results AND AI can overreach - it can say (figuratively) "Oh you are searching for Event handling, therefore I will write a orchestration saga" - which, if I am not across, can get us both in trouble.

Further, we KNOW that AI has no (real) understanding of the responses - it's just token adjacency - and it fails basic logic tests

Current AI is just awesome natural language processing, but it's still got a ways to go to where I would say "It can replace people"

Edit: LLMs demonstrate (almost perfectly) the difference between correlation and causality. LLMs identify correlative patterns, but the job still needs (us) to make the causative judgments.

I think what matters is.... the person being intelligent. I do not mean this as an us vs them thing, but a LOT of companies have some not very smart people in and running them. It's never about exact skills or roles.

My observation on AI is that some frankly less intelligent folks think they don't need smart people any more because AI makes them smarter. I disagree.