> have the LLM focus and summarize a slice of the system when they need to know about it or ask for it. But.... That's not knowledge? That's just search a la google? And thus seems to be subject to the same problems where the quality of your search dictates the quality of your knowledge that you surface at any given time?

This is the crux. Take pause, because hidden here is a sea-change in our fundamental trust model of software...

There is no longer anyone to turn to if this system breaks by, eg, having a critical bug that no one can figure out. No causal model in anyone's head that can be appealed to, to slowly and methodically debug. By design, the system is too large for that, and everything hinges on Claude itself being able to find and understand the right slice of the system in any situation. As long as that very new invariant holds, everything will be all right. If it fails catastrophically, there is no recovery. You are the owner of a recovered alien spacecraft with tech you have no hope of understanding.

The new trust model is: We trust Claude to always understand the system enough to debug anything or make any reasonable change we could want. The risk might be worth the reward. But it's worth explicitly laying out the deal we're making.

Tbf, it's worth pointing out that many large legacy systems already work like that (sort of), with "Claude" replaced by "whoever happens to be maintaining it this year".

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patrickdavey 2 hours ago | parent | next [-]

"Tbf, it's worth pointing that many large legacy systems already work like that (sort of), with "Claude" replaced by "whoever happens to be maintaining it this year".

The real difference as I see it is that in the human maintained old style system, the human would take things slowly and ensure they understand any changes. Claude let loose will just say "yes, I know how to do this" and bork the system, because it doesn't have a concept of humbleness in the face of complexity!

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daveguy 2 hours ago | parent [-]

Not only that, when you have a human designed and implemented system you know it's at least coherent enough that someone understood it at some point well enough to take responsibility for it. No such assurance with the llms.

	▲	SR2Z an hour ago \| parent \| next [-]
		I am not sure that "someone understood it well enough to take responsibility" means what you think it means. I have definitely come across old systems which have had addons bolted on to them in so many places that the overall behavior of the system is unpredictable and only mostly works.
	▲	csallen an hour ago \| parent \| prev \| next [-]
		But is this really a problem? If you have testing and you have version control via Git, you can roll back changes and investigate them. I also don't even know if it's really true. Sure, maybe it's not coherent enough that a person understands it, but if an LLM wrote it, you can be ~99% certain (in my experience) that that LLM will understand it, and usually in less than the time it took to write it. And unlike humans, the LLMs don't die and take their knowledge with them, or change companies, or get ornery when you ask them to explain. Also, in my experience, Claude doesn't really do what the parent is saying and dive head-first into complex problems and bork your code. It is fairly humble in the face of complexity. It constantly recommends breaking down larger problems into smaller ones that are more easy to grok. But most importantly, anyone who's a decently skilled software engineer and who's using LLMs to write code is going to be thoughtful. Especially when they're writing code that's important. Unless you're an AI researcher pushing the bounds of what LLMs can do, which let's face it, the vast majority of programmers are not, it's almost always the case that you'll have your LLM writing intelligible code that you can understand and have it explain easily enough.
	▲	antihipocrat an hour ago \| parent \| prev [-]
		I'd like that to be true, but many software service startups were stitching together node packages and selling the result long before LLMs became useful.

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peab 2 hours ago | parent | prev [-]

most large software companies are already too large for anyone to have a complete mental model of.

The idea that someone at say, Google, will be able to fit even half of the system architecture in their heads is not realistic. It also hinges on being able to find and understand the right slice of the system in any situation.

But we have all sorts of guardrails to make that easy - i.e logs, traces, etc

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jonahx 2 hours ago | parent [-]

Right, that's what I alluded to in my last sentence.

It is true that this gamble has existed in the form you mention for many years. Still, I sense something new here, both in scale and in the attitude of trust. Also, I find those massive, incomprehensible human-built systems frightening too.

	▲	dasil003 an hour ago \| parent [-]
		Having worked continuously this decade on one massively complex consumer facing app that interacts heavily with real-world scenarios, I can say that the problems of cohesion existed well before the agentic revolution. Tons of features built by myopic teams simply because they couldn't understand enough of the system (both online and offline, meat-space interactions) to make good design decisions. AI actually helps here by being able to perform deeper reading and analysis than any human can do. But then the risk is that it can also produce way more random non-cohesive features. So far I kind of feel like it nets out to the same bottleneck being good judgment from decision makers. And you might say, no, leadership will just put more and more pressure to add more complexity just because we can and it makes it appear that we are moving fast. But when it comes to real-world apps, there is a backstop which is that things that are too complex and illegible end up not being fit-for-purpose for human problems. Claude Code and other general purpose AI tools are not like this, because they are literally trying to optimize it for every single kind of knowledge work with zero constraints on use case or target audience other than chasing the next juiciest one they can with essentially unlimited resource. 99% of software is not like this, and when the dust settles I suspect software won't be anywhere near as different as the doomers or the maxxers are predicting right now.