[aspenmartin]

I think this sounds like a true yet short sighted take. Keep in mind these features are immature but they exist to obtain a flywheel and corner the market. I don’t know why but people seem to consistently miss two points and their implications

- performance is continuing to increase incredibly quickly, even if you rightfully don’t trust a particular evaluation. Scaling laws like chinchilla and RL scaling laws (both training and test time)

- coding is a verifiable domain

The second one is most important. Agent quality is NOT limited by human code in the training set, this code is simply used for efficiency: it gets you to a good starting point for RL.

Claiming that things will not reach superhuman performance, INCLUDING all end to end tasks: understanding a vague business objective poorly articulated, architecting a system, building it out, testing it, maintaining it, fixing bugs, adding features, refactoring, etc. is what requires the burden of proof because we literally can predict performance (albeit it has a complicated relationship with benchmarks and real world performance).

Yes definitely, error rates are too high so far for this to be totally trusted end to end but the error rates are improving consistently, and this is what explains the METR time horizon benchmark.

[sobellian]

Scaling laws vs combinatorial explosion, who wins? In personal experience claude does exceedingly well on mundane code (do a migration, add a field, wire up this UI) and quite poorly on code that has likely never been written (even if it is logically simple for a human). The question is whether this is a quantitative or qualitative barrier.

Of course it's still valuable. A real app has plenty of mundane code despite our field's best efforts.

[aspenmartin]

Combinatorial explosion? What do you mean? Again, your experiences are true, but they are improving with each release. The error rate on tasks continues to go down, even novel tasks (as far as we can measure them). Again this is where verifiable domains come in -- whatever problems you can specify the model will improve on them, and this improvement will result in better generalization, and improvements on unseen tasks. This is what I mean by taking your observations of today, ignoring the rate of progress that got us here and the known scaling laws, and then just asserting there will be some fundamental limitation. My point is while this idea may be common, it is not at all supported by literature and the mathematics.

[sobellian]

The space of programs is incomprehensibly massive. Searching for a program that does what you need is a particularly difficult search problem. In the general case you can't solve search, there's no free lunch. Even scaling laws must bow to NFL. But depending on the type of search problem some heuristics can do well. We know human brains have a heuristic that can program (maybe not particularly well, but passably). To evaluate these agents we can only look at it experimentally, there is no sense in which they are mathematically destined to eventually program well.

How good are these types of algorithms at generalization? Are they learning how to code; or are they learning how to code migrations, then learning how to code caches, then learning how to code a command line arg parser, etc?

Verifiable domains are interesting. It is unquestionably why agents have come first for coding. But if you've played with claude you may have experienced it short-circuiting failing tests, cheating tests with code that does not generalize, writing meaningless tests, and at long last if you turn it away from all of these it may say something like "honest answer - this feature is really difficult and we should consider a compromise."

[aspenmartin]

So what do you think the difference is between humans and an agent in this respect? What makes you think this has any relevance to the problem? everything is combinatorially explosive: the combination of words that we can string into sentences and essays is also combinatorially explosive and yet LLMs and humans have no problem with it. It's just the wrong frame of thinking for what's going on. These systems are obtaining higher and higher levels of abstractions because that is the most efficient thing for them to do to gain performance. That's what reasoning looks like: compositions of higher level abstractions. What you say may be true but I don't see how this is relevant.

"There is no sense in which they are mathematically destined to eventually program well"

- Yes there is and this belies and ignorance of the literature and how things work

- Again: RL has been around forever. Scaling laws have held empirically up to the largest scales we've tested. There are known RL scaling laws for both training and test time. It's ludicrous to state there is "no sense" in this, on the contrary, the burden of proof of this is squarely on yourself because this has already been studied and indeed is the primary reason why we're able to secure the eye-popping funding: contrary to popular HN belief, a trillion dollars of CapEx spend is based on rational evidence-based decision making.

> "How good are these types of algorithms at generalization"

There is a tremendously large literature and history of this. ULMFiT, BERT ==> NLP task generalization; https://arxiv.org/abs/2206.07682 ==> emergent capabilities, https://transformer-circuits.pub/2022/in-context-learning-an... ==> demonstrated circuits for in context learning as a mechanism for generalization, https://arxiv.org/abs/2408.10914 + https://arxiv.org/html/2409.04556v1 ==> code training produces downstream performance improvements on other tasks

> Verifiable domains are interesting. It is unquestionably why agents have come first for coding. But if you've played with claude you may have experienced it short-circuiting failing tests, cheating tests with code that does not generalize, writing meaningless tests, and at long last if you turn it away from all of these it may say something like "honest answer - this feature is really difficult and we should consider a compromise."

You say this and ignore my entire argument: you are right about all of your observations, yet

- Opus 4.6 compared to Sonnet 3.x is clearly more generalizable and less prone to these mistakes

- Verifiable domain performance SCALES, we have no reason to expect that this scaling will stop and our recursive improvement loop will die off. Verifiable domains mean that we are in alphago land, we're learning by doing and not by mimicking human data or memorizing a training set.

[sobellian]

Hey man, it sounds like you're getting frustrated. I'm not ignoring anything; let's have a reasonable discussion without calling each other ignorant. I don't dispute the value of these tools nor that they're improving. But the no free lunch theorem is inexorable so the question is where this improvement breaks down - before or beyond human performance on programming problems specifically.

What difference do I think there is between humans and an agent? They use different heuristics, clearly. Different heuristics are valuable on different search problems. It's really that simple.

To be clear, I'm not calling either superior. I use agents every day. But I have noticed that claude, a SOTA model, makes basic logic errors. Isn't that interesting? It has access to the complete compendium of human knowledge and can code all sorts of things in seconds that require my trawling through endless documentation. But sometimes it forgets that to do dirty tracking on a pure function's output, it needs to dirty-track the function's inputs.

It's interesting that you mention AlphaGo. I was also very fascinated with it. There was recent research that the same algorithm cannot learn Nim: https://arstechnica.com/ai/2026/03/figuring-out-why-ais-get-.... Isn't that food for thought?

[aspenmartin]

What is unreasonable? I am saying the claims you are making are completely contradicted by the literature. I am calling you ignorant in the technical sense, not dumb or unintelligent, and I don't mean this as an insult. I am completely ignorant of many things, we all are.

I am saying you are absolutely right that Opus 4.6 is both SOTA and also colossally terrible in even surprisingly mundane contexts. But that is just not relevant to the argument you are making which is that there is some fundamental limitation. There is of course always a fundamental limitation to everything, but what we're getting at is where that fundamental limitation is and we are not yet even beginning to see it. Combinatorics here is the wrong lens to look at this, because it's not doing a search over the full combinatoric space, as is the case with us. There are plenty of efficient search "heuristics" as you call them.

> They use different heuristics, clearly.

what is the evidence for this? I don't see that as true, take for instance: https://www.nature.com/articles/s42256-025-01072-0

> It's interesting that you mention AlphaGo. I was also very fascinated with it. There was recent research that the same algorithm cannot learn Nim: https://arstechnica.com/ai/2026/03/figuring-out-why-ais-get-.... Isn't that food for thought?

It's a long known problem with RL in a particular regime and isn't relevant to coding agents. Things like Nim are a small, adversarially structured task family and it's not representative of language / coding / real-world tasks. Nim is almost the worst possible case, the optimal optimal policy is a brittle, discontinuous function.

Alphago is pure RL from scratch, this is quite challenging, inefficient, and unstable, and why we dont do that with LLMs, we pretrain them first. RL is not used to discover invariants (aspects of the problem that don't change when surface details change) from scratch in coding agents as they are in this example. Pretraining takes care of that and RL is used for refinement, so a completely different scenario where RL is well suited.

[troupo]

> So what do you think the difference is between humans and an agent in this respect?

Humans learn.

Agents regurgitate training data (and quality training data is increasingly hard to come by).

Moreover, humans learn (somewhat) intangible aspects: human expectations, contracts, business requirements, laws, user case studies etc.

> Verifiable domain performance SCALES, we have no reason to expect that this scaling will stop.

Yes, yes we have reasons to expect that. And even if growth continues, a nearly flat logarithmic scale is just as useless as no growth at all.

For a year now all the amazing "breakthrough" models have been showing little progress (comparatively). To the point that all providers have been mercilessly cheating with their graphs and benchmarks.

[aspenmartin]

> Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.

I'm just going to ask that you read any of my other comments, this is not at all how coding agents work and seems to be the most common misunderstanding of HN users generally. It's tiring to refute it. RL in verifiable domains does not work like this.

> Humans learn.

Sigh, so do LLMs, in context.

> Moreover, humans learn (somewhat) intangible aspects: human expectations, contracts, business requirements, laws, user case studies etc.

Literally benchmarks on this all over the place, I'm sure you follow them.

> Yes, yes we have reasons to expect that. And even if growth continues, a nearly flat logarithmic scale is just as useless as no growth at all.

and yet its not logarithmic? Consider data flywheel, consistent algorithmic improvements, synthetic data [basically: rejection sampling from a teacher model with a lot of test-time compute + high temperature],

> For a year now all the amazing "breakthrough" models have been showing little progress (comparatively). To the point that all providers have been mercilessly cheating with their graphs and benchmarks.

Benchmaxxing is for sure a real thing, not to mention even honest benchmarking is very difficult to do, but considering "all of the AI companies are just faking the performance data" to be the "story" is tremendously wrong. Consider AIME performance on 2025 (uncontaminated data), the fact that companies have a _deep incentive_ to genuinely improve their models (and then of course market it as hard as possible, thats a given). People will experiment with different models, and no benchmaxxing is going to fool people for very long.

If you think Opus 4.6 compared to Sonnet 3.x is "little progress" I think we're beyond the point of logical argument.

[nprateem]

But the issue isn't coding, it's doing the right thing. I don't see anywhere in your plan some way of staying aligned to core business strategy, forethought, etc.

The number of devs will reduce but there will still be large activities that can't be farmed out without an overall strategy

[aspenmartin]

Why do you think this is a problem? Reasoning is constantly improving, it has ample access to humans to gather more business context, it has access to the same industry data and other signals that humans do, and it can get any data necessary. It has Zoom meeting notes, I mean why do people think there's somehow a fundamental limit beyond coding?

The other thing you're missing here is generalizability. Better coding performance (which is verifiable and not limited by human data quality) generalizes performance on other benchmarks. This is a long known phenomenon.

[skydhash]

> Why do you think this is a problem?

Because it cannot do it?

Every investment has a date where there should be a return on that investment. If there’s no date, it’s a donation of resources (or a waste depending on perspective).

You may be OK with continuing to try to make things work. But others aren’t and have decided to invest their finite resources somewhere else.

[aspenmartin]

> Because it cannot do it?

Ah ok so you didn't really read my comment, what is your counter argument? Models are just fundamentally incapable of understanding business context? They are demonstrably already capable of this to a large extent.

> Every investment has a date where there should be a return on that investment. If there’s no date, it’s a donation of resources (or a waste depending on perspective).

what are you implying here? This convo now turns into the "AI is not profitable and this is a house of cards" theme? That's ok, we can ignore every other business model like say Uber running at a loss to capture what is ultimately an absolutely insane TAM. Little ol' Uber accumuluated ~33B in losses over 14 years, and you're right they tanked and collapsed like a dying star...oh wait...hmm interesting I just looked at their market cap and it's 141 Billion.

> You may be OK with continuing to try to make things work. But others aren’t and have decided to invest their finite resources somewhere else.

I truly love that. If you want to code as a hobby that is fantastic, and we can go ahead and see in 2 years how your comment ages.

[skydhash]

> They are demonstrably already capable of this to a large extent.

I’d very like to see such demonstration. Where someone hands over a department to an agent and let it makes decisions.

> This convo now turns into the "AI is not profitable and this is a house of cards" theme?

Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.

[aspenmartin]

> I’d very like to see such demonstration. Where someone hands over a department to an agent and let it makes decisions.

That's your bar for understanding business context? I thought we were talking about what you actually said which is: understanding business context. If I brainstorm about a feature it will be able to pull the compendium of knowledge for the business (reports, previous launches, infrastructure, an understanding of the problem space, industry, company strategy). That's business context.

> Where did I say that? I didn’t even mention money, just the broader resource term. A lot of business are mostly running experiments if the current set of tooling can match the marketing (or the hype). They’re not building datacenters or running AI labs. Such experiments can’t run forever.

I misunderstood you then, I wasn't sure what point you were trying to make. Is your point "companies are trying to cajole Claude to do X and it doesn't work and hasn't for the last year so they are giving up"? If so I think that is a wonderful opportunity for people that understand the nuance of these systems and the concept of timing.

[jwpapi]

@skydhash I think aspenmartin is ragebaiting he can’t be for real.

[embedding-shape]

> - coding is a verifiable domain

You're missing the point though. "1 + 1" vs "one.add(1)" might both be "passable" and correct, but it's missing the forest for the trees, how do you know which one is "long-term the right choice, given what we know?", which is the engineering part of building software, and less about "coding" which tends to be the easy part.

How do you evaluate, score and/or benchmark something like that? Currently, I don't think we have any methodologies for this, probably because it's pretty subjective in the end. That's where the "creative" parts of software engineering becomes more important, and it's also way harder to verify.

[jorl17]

While I agree we don't have any methodologies for this, it's also true that we can just "fail" more often.

Code is effectively becoming cheap, which means even bad design decisions can be overturned without prohibitive costs.

I wouldn't be surprised if in a couple of years we see several projects that approach the problem of tech debt like this:

1. Instruct AI to write tens of thousands of tests by using available information, documentation, requirements, meeting transcripts, etc. These tests MUST include performance AND availability related tests (along with other "quality attribute" concerns) 2. Have humans verify (to the best of their ability) that the tests are correct -- step likely optional 3. Ask another AI to re-implement the project while matching the tests

It sounds insane, but...not so insane if you think we will soon have models better than Opus 4.6. And given the things I've personally done with it, I find it less insane as the days go by.

I do agree with the original poster who said that software is moving in this direction, where super fast iteration happens and non-developers can get features to at least be a demo in front of them fast. I think it clearly is and am working internally to make this a reality. You submit a feature request and eventually a live demo is ready for you, deployed in isolation at some internal server, proxied appropriately if you need a URL, and ready for you to give feedback and have the AI iterate on it. Works for the kind of projects we have, and, though I get it might be trickier for much larger systems, I'm sure everyone will find a way.

For now, we still need engineers to help drive many decisions, and I think that'll still be the case.These days all I do when "coding" is talking (via TTS) with Opus 4.6 and iterating on several plans until we get the right one, and I can't wait to see how much better this workflow will be with smarter and faster models.

I'm personally trying to adapt everything in our company to have agents work with our code in the most frictionless way we can think of.

Nonetheless, I do think engineers with a product inclination are better off than those who are mostly all about coding and building systems. To me, it has never felt so magical to build a product, and I'm loving it.

[embedding-shape]

> Code is effectively becoming cheap, which means even bad design decisions can be overturned without prohibitive costs.

I'm sorry, but only someone who never maintained software long-term would say something like this. The further along you are in development, the magnitude of costs related to changing that increases, maybe even exponentially.

Correct the design before you even wrote code, might be 100x cheaper (or even 1000x) than changing that design 2 years later, after you've stored TBs of data in some format because of that decision, and lots of other parts of the company/product/project depends on those choices you made earlier.

You can't just pile on code on top of code, say "code is cheap" and hope for the best, it's just not feasible to run a project long-term that way, and I think if you had the experience of maintaining something long-term, you'd realize how this sounds.

The easiest part of "software engineering" is "writing code", and today "writing code" is even easier. But the hardest parts, actually designing, thinking and maintaining, remains the same as before, although some parts are easier, others are harder.

Don't get me wrong, I'm on the "agentic coding" train as much as everyone else, probably haven't written/edited a code by myself for a year at this point, but it's important to be realistic about what it actually takes to produce "worthwhile software", not just slop out patchy and hacky code.

[wyre]

I've never maintained software long-term so i could be wrong, but I interpret "code is cheap" to mean that you can have coding agents refactor or rewrite the project from scratch around the design correction. I don't think 'code is cheap' ever should be interpreted to mean ship hacky code.

I think using agents to prototype code and design will be a big thing. Have the agent write out what you want, come back with what works and what doesn't, write a new spec, toss out the old code and and have a fresh agent start again. Spec-driven development is the new hotness, but we know that the best spec is code, have the agent write the spec in code, rewrite the spec in natural language, then iterate.

[aspenmartin]

because it has business context and better reasoning, and can ask humans for clarification and take direction.

You don't need to benchmark this, although it's important. We have clear scaling laws on true statistical performance that is monotonically related to any notion of what performance means.

I do benchmarks for a living and can attest: benchmarks are bad, but it doesn't matter for the point I'm trying to make.

[embedding-shape]

I feel like you're missing the initial context of this conversation (no pun intended):

> Like for example a trusted user makes feedback -> feedback gets curated into a ticket by an AI agent, then turned into a PR by an Agent, then reviewed by an Agent, before being deployed by an Agent.

Once you add "humans for clarifications and take direction" then yeah, things can be useful, but that's far away from the non-human-involvment-loop earlier described in this thread, which is what people are pushing back against.

Of course, involving people makes things better, that's the entire point here, and that by removing the human, you won't get as good results. Going back to benchmarks, obviously involving humans aren't possible here, so again we're back to being unable to score these processes at all.

[aspenmartin]

I'm confused on the scenario here. There is human in the loop, it's the feedback part...there is business context, it is either seeded or maintained by the human and expanded by the agent. The agent can make inferences about the world, especially when embodiment + better multimodal interaction is rolled out [embodiment taking longer].

Benchmarks ==> it's absolutely not a given that humans can't be involved in the loop of performance measurement. Why would that be the case?

[troupo]

> because it has business context

It doesn't because it doesn't learn. Every time you run it, it's a new dawn with no knowledge of your business or your business context

> better reasoning

It doesn't have better reasoning beyond very localized decisions.

> and can ask humans for clarification and take direction.

And yet it doesn't, no matter how many .md file you throw at it, at crucial places in code.

> We have clear scaling laws on true statistical performance that is monotonically related to any notion of what performance means.

This is just a bunch of words stringed together, isn't it?

[skydhash]

Almost every task that people are tackling agents on, it’s either not worth doing, can be done better with scripts and software, or require human oversight (that negates all the advantages.

[aspenmartin]

I assume this is a troll because it's just so far removed from reality there's not much to say. "Almost every task" -- I'm sure you have great data to back this up. "It's not worth doing" well sure if you want to put your head in the sand and ignore even what systems today can do let alone the improvement trajectory. "can be done better with scripts and software" .... not sure if you realize this but agents write scripts and software. "or require human oversight (that negates all the advantages." it certainly does not; human oversight vs actual humans implementing the code is pretty dramatically more efficient and productive.

[aspenmartin]

> It doesn't because it doesn't learn. Every time you run it, it's a new dawn with no knowledge of your business or your business context

It does learn in context. And lack of continuous learning is temporary, that is a quirk of the current stack, expect this to change rather quickly. Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able to grok codebases or do internal searches effectively and this will only improve.

> It doesn't have better reasoning beyond very localized decisions.

Do you have any basis for this claim? It contradicts a large amount of direct evidence and measurement and theory.

> This is just a bunch of words stringed together, isn't it?

Maybe to yourself? Chinchilla scaling laws and RL scaling laws are measured very accurately based on next token test loss (Chinchilla). This scales very predictably. It is related to downstream performance, but that relationship is noisy but clearly monotonic

[troupo]

> It does learn in context

It quite literally doesn't.

It also doesn't help that every new context is a new dawn with no knowledge if things past.

> Also still not relevant, consider that agentic systems can be hierarchical and that they have no trouble being able

A bunch of Memento guys directing a bunch of other Memento guys don't make a robust system, or a system that learns, or a system that maintains and retains things like business context.

> and this will only improve.

We've heard this mantra for quite some time now.

> Do you have any basis for this claim?

Oh. Just the fact that in every single coding session even on a small 20kloc codebase I need to spend time cleaning up large amounts of duplicated code, undo quite a few wrong assumptions, and correct the agent when it goes on wild tangents and goose hunts.

> Maybe to yourself? Chinchilla scaling laws a

yap yap yap. The result is anything but your rosy description of these amazing reasoning learning systems that handle business context.

[aspenmartin]

> It quite literally doesn't.

Awesome you've backed this up with real literature. Let's just include this for now to easily refute your argument which I don't know where it comes from: https://transformer-circuits.pub/2022/in-context-learning-an...

> It also doesn't help that every new context is a new dawn with no knowledge if things past.

Absolutely true that it doesn't help but: agents like Claude have access to older sessions, they can grok impressive amounts of data via tool use, they can compose agents into hierarchical systems that effectively have much larger context lengths at the expense of cost and coordination which needs improvement. Again this is a temporary and already partially solved limitation

> A bunch of Memento guys directing a bunch of other Memento guys don't make a robust system, or a system that learns, or a system that maintains and retains things like business context.

I think you are not understanding: hierarchical agents have long term memory maintained by higher level agents in the hierarchy, it's the whole point. It's annoying to reset model context, but yet you have a knowledge base of the business context persisted and it can grok it...

> We've heard this mantra for quite some time now.

yes you have, and it has held true and will continue to hold true. Have you read the literature on scaling laws? Do you follow benchmark progression? Do you know how RL works? If you do I don't think you will have this opinion.

> yap yap yap. The result is anything but your rosy description of these amazing reasoning learning systems that handle business context.

Well that's fine to call an entire body of literature "yap" but don't pretend like you have some intelligible argument, I don't see you backing up any argument you have here with any evidence, unlike the multitude of sources I have provided to you.

Do you argue things have not improved in the last year with reasoning systems? If so I would really love to hear the evidence for this.