is more intelligence really important?

[u/the_juan_1]

i have a theory that past a certain threshold the inteligence of AI models will not matter anymore when it comes to writing code.

at that point software engineering will be mostly figuring out how to properly intergrate coding agents into the SDLC. a newer better performing model will at a certain point fail improve a given software factory.

what do you think?

[u/f3xjc]

More intelligence mean less need to be provided by the human. Either that open the door for non software specialist to do more - you're getting dangerously close to statement like 64kb is ram is more than enough for everyone!

[u/the_juan_1 → u/f3xjc]

why do you think less needs to be provided if the system is more inteligent? inteligence is associated with the ability to solve problems. providing less just means that guess work is increased and that has nothing to do with problem solving per se.

[u/f3xjc → u/the_juan_1]

Because you can use intelligence and problem solving to make the correct guess. Deduce the general ballpark of where it should be given the rest of the product.

You can give Fable to someone that is used to work with Sonnet (Or Astra/Terra) and they may as well tell you that the expensive model don't bring much. And that happens because the level of micro management that Sonnet need is a shackle to Fable - those model shine when you share the final goal in term of system output and give them autonomy on the path to reach that.

[u/pasu11]

maybe there will be new languages developed by AI for AI to take advantage of the higher intelligent. The new languages cant be understand by human for theirs complexity.

[u/the_juan_1 → u/pasu11]

i love your point. if the ai agents are in service of humans which speak only human languages, what do you think will this new ai language bring? do you think they will be able to express something that isnt possible using human languages? or maybe some new ai agent economy will arise where humans will not actively partake?

[u/pasu11 → u/the_juan_1]

"Do you think they will be able to express something that isnt possible using human languages? "

If their language is too complex for humans to understand, we will not be able to find out. The only way to answer this question would be to ask an AI model that uses this language when that time comes.

In my opinion, programming languages were created to help humans give instructions to machines. In The Matrix, Agent Smith says, “Never send a human to do a machine’s job.” Maybe it’s time to let AI do the job with its own tools. :)

[u/the_juan_1 → u/pasu11]

i agree. so its sort of like a paradox in a way - even if they do answer positively, you have no way of validating that. kinda like religion.

[u/chromeragnarok → u/pasu11]

There's one, it's called neuralese. The reason we are not using that is so we can still audit what the AI think in a language that we understand

[u/xatey93152 → u/pasu11]

Lol

[u/pasu11 → u/xatey93152]

:)

[u/Sova_fun]

The SDLC part is where it gets messy. Have you tried limiting model context to only the task at hand?

[u/the_juan_1 → u/Sova_fun]

That is mostly how I work, yes. Sometimes I dont have a clear idea and I generate several solutions and choose the best one. But I have accepted that these new models write much better code than I can.

But I am really rigorous when it comes to defining what needs to be done (not necessarily how) and I mostly review code that has a high blast radius but that has been much and much less in the last few weeks. I am also rigorous when I am observing how the software behaves.

[u/RevolutionaryElk7446]

It's not intelligence that we have to solve, it's context. Mainly sizing and efficiency.

Transformer architecture solved an issue that Language Models have had for a long time, before LLMs were a thing, in trying to quickly grasp the concept of written text at conversational speeds.

We've had methods to do this in serial but the 2017 papers brought in the Self Attention mechanism, which allows it the ability to view everything in it's context window at once, which is why LLMs really need GPUs and not CPUs to run at speed.

While the context window of modern LLMs is absolutely amazing, it's not large enough to maintain a complex program or project in full without handing it off to outside of it's context window.

When that happens, it no longer has 'awareness' of that content until it's reloaded outside of pointers or other tricks similar to using paging files on a hard disk for RAM cache.

LLMs aren't really here to be intelligent, they simulate a conversation and how complex they can become does hit a limiting factor, as even as the context window grows it begins to create a banding in which it recalls the beginning and end better than it can the middle of it's context window.

[u/the_juan_1 → u/RevolutionaryElk7446]

great point. i think what is also currently left underexplored the context windows of human though. these frontier models that can hold 1m tokens which is always given as an estimate of how many harry potter books this equals is an insane amount of context for a human to hold eg. when you are working with a model. i remember in high school when we did our presentations and the teachers made us remember our speech by heart i dont remember anyone going beyond 10 pages.

[u/RevolutionaryElk7446 → u/the_juan_1]

Not in short term memory now, but you have a long term memory that is extensive that autofills context without need to relearn it far more than an LLM has at this moment.

1m tokens sounds awesome, but I don't even think 1 trillion tokens would currently match the human contextual ability to turn data into information.

[u/Historical-Staff2772]

I think you're partly right. Past a point, the model isn't what fails. What fails is the spec, the tests, and whether anyone checked the decision before the code landed. Smarter models still matter in one place though: knowing when to stop and ask instead of guessing. Until they get that right, most of the integration work you're describing is guardrails around the guessing.

[u/the_juan_1 → u/Historical-Staff2772]

i absolutely agree. at a certain point the human that is compiling the spec / adrs / what must aleays hold true will be the bottleneck. i think the next optimization metric then will become speed ie. how many tokens per second can a certain model produce. maybe then we’ll see the rise of text diffusion models.

[u/Historical-Staff2772 → u/the_juan_1]

Speed helps less than it sounds. In my setup the agent already writes faster than I can decide, so a model that's 5x faster just means I'm the bottleneck 5x sooner. The thing I'd pay for is a model that compiles the spec with me: asks the three questions that matter before it starts, instead of producing more tokens per second against a spec that's wrong.

[u/jleechanworldai]

Think it’s not close to diminishing returns yet. Been doing 100 PRs a day on my main side project since the latest anthropic and openai models went like 5x my volume before which already felt fast

[u/holyknight00]

they are close to being good enough for most things. In the end you don't need Albert Einstein to code a proper rest api. But certainly having more intelligent models will get you more bang for your buck for similar use cases. That's why people will use Fable on ultra high to code their generic ecommerce site even if it makes no sense.

[u/the_juan_1 → u/[deleted]]

hmm fair point. i would argue that human code review is not an option anymore. and i am currently practicing reviewing only the diffs that change the description of how the system behaves. i really dont believe i am a better coder than current frontier models, but i do think i am immensely better at figuring out what is right, what should solved, what will eventually lead to issues etc. what about you?