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I would be really curious to hear from devs at Databricks what the experience of development is like internally. I work at a small startup with essentially unlimited AI spend budget - the entire point is that I should be turning to it at every opportunity since our human labor is so expensive relative to tokens. So generally it's like:

- Spend most time prioritizing/discussing what to do.

- Once that's agreed, use Fable 5 High + 5.6 Sol XHigh come up with a design + plan. Agree on the high level plan. (Usually this just comes down to choosing where the change belongs on the spectrum between minimal patch <-> full redesign)

- Use Opus 5 or Sol Med to execute

- Auto-fix bugs and CI until green + thermonuclear review skill x3.

- Manual interrogation of change/nits

- Come up with QA plan and have Codex Computer Use execute on it

- Manually spot check the final result (usually a sizable diff, thousands of lines, complete feature E2E, etc)

I probably spend like $80 a day at least but I produce the output of 3 or 4 2022 engineers and probably at better quality. So it's easily worth it. Would I save money by switching to GLM 5.2 and such...perhaps? IDK. At our scale it's not worth the time spent building the eval harness to actually understand the performance tradeoff.

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In our team's experience, the product of agents is generally The Homer (1). It does work, but it's vastly overengineered.

When I personally want tight code, I have to spend a considerable amount of time adjusting it manually:

- It needs to be trimmed down. In my experience, at least one agent I use struggles to produce minimalist designs, and it's very frustrating

- I need to consider whether there are solutions based on higher-level assumptions, that AIs typically miss

- I need to check whether there are off-the-shelf solutions - AIs like to reinvent the wheel

IMO, software production has become a mass-produced commodity in every sense - it's much more expensive to produce software manually, but the quality is not the same.

(1) https://simpsons.fandom.com/wiki/The_Homer

This was more true a few months ago but Fable has improved the situation considerably.

Also just remember - minimalist code looks and feels great but customers do not read your code. I have caught myself many times providing "corrections" to abstractions that were already ~fine, just not perfect. The average SWE costs $200/hr. Careful you don't burn $50 worrying about code that will likely be rewritten or can be better abstracted when that's actually needed.

> The average SWE costs $200/hr

This is a pointless quibble but the hourly rate claim is not true--it's like ~$60 in the USA [0]. Maybe you meant at a specific Org but this is important context when comparing "pricing" between human and AI.

[0] https://www.salaryexpert.com/salary/job/software-developer/u...

$15, where we're going.

Federal minimum wage is $7.25 per hour

How is this not true? Taking a Senior SWE @ ~$200K, even just the base salary cost / 2080 working hours is $100/hr. Fully loaded employer cost + accounting for non-coding time gets you to upper 100s easily.

Even for a junior making $100K, I have a hard time believe their time is worth less than $75/hr or so.

Edit: Fine, "Senior" is not "Average". But naive salary is not the true numerator.

Company time != Pay rate, if you're working somewhere that's publicly traded check out "revenue per employee" metrics sometime.

   I have a hard time believe their time is worth less than $75/hr or so.

In many places in Europe it is.

Western Europe is mostly consultancy, and the rate paid by client is usually higher, and doesn't matter if it's eastern Europe, Portugal or even India.

It is. And the quality is on par with any us eng. People here forget that the big comp packages are a minority even in the US. The cost tho is much higher than just salary.

Of course, the SWEs making that much (over 200k) are not representative of the broader field. That's the point.

Pay hits a ceiling, and that ceiling is moving lower regardless of experience. That has nothing to do with AI, but what the market will bear. Hiring counts of humans must increase no matter what. Moving some of the spend to AI reduces the risk of hiring less qualified employees they might have rejected a decade ago.

Wages at the top end are stagnating to subsidize this. That's undeniable.

I hire contractors for a large enterprise in the US. The going rate is typically $85-$100/hr for a senior dev, depending on specialization. Lead-level maybe $120 for the right skill set.

An MBA's rule of thumb is that a full time employee's hourly cost to a business is at least 1.5x to 2x times their salary depending on employer taxes, benefits, offices, travel, training, hardware, perks, etc.

> The average SWE costs $200/hr.

And this is how I find out I'm woefully underpaid.

Whatever you are making this year as SWE you'll be making less next year if the current trend in improvement of AI coding aids is going to be sustained. Think about it : programmers used to derive a lot of their value from the fact that it was a hard skill to acquire. My kids can now 'vibe code' stuff faster (and better looking) than what I could come up with as the beginnings of a design plan. And then I still need to implement it.

There's a massive difference between your kids vibe coding something and an engineer using AI to implement something. If you're unable to discern the difference, that's something to reflect on :)

It doesn't matter if GP is able to discern the difference, it matters if your CEO is forced to care about the difference.

CEOs will always want someone who knows to implement so we're safe from kids vibe coding their way in but in a short while it becomes AI who knows who is managing less expensive AI.

If he is unable to discern a difference, then a paying customer almost certainly won't.

We're entering a market of lemons in software. It's going to be interesting indeed.

That "cost" includes all the overhead provided by the company: benefits, rent for offices, utilities, equipment, etc. The average SWE is not taking home anything close to that, outside of Silicon Valley and a few other limited areas.

You might be paid $100/hr but it costs the employee $200/hr. It’s worth mentioning because cost to the company is not just your salary, but the taxes, pension and contributions on the other side, plus a variety of other things that the employee doesn’t consider.

Minimalist code is necessary to keep AI agents working well for longer than a month on a system IME. At a certain point, their own machinations overwhelm them and they both slow down, and make worse and worse decisions.

Also, you can probably think about it like this:

"Will I benefit from this code being minimalist before [date]", where [date] is whenever you think the agent will be good enough to come back and make the corrections you would make today.

I'd caution that some corrections become harder to make over time, rather than easier. A bad architecture now can become much harder to fix once other things have grown up around it.

Even as a self-contained unit, you can't step in the same river twice, and on [date] some important details may have seriously faded, both in terms of text that can be mined and also in terms of human "why did we do that" and "what was the reason we did it this way and not that way" etc.

The average SWE makes $400k a year? Are you being serious?

Costs, not makes. That includes employer taxes, benefits, offices, travel, training, hardware, any perks.

It took me a few seconds of deliberating if The Homer was a reference to baseball or "The Odyssey" and then realized there was a footnote

> software production has become a mass-produced commodity in every sense - it's much more expensive to produce software manually, but the quality is not the same.

Agreed.

I believe “The Homer” is a reference to Season 2, Episode 15 of The Simpsons, “Oh Brother, Where Art Thou?”

Homer reunites with his long lost brother, who runs car company Powell Motors. Homer is ultimately tasked by his brother with helping to design a car for the “average man” that ultimately bankrupts the company for being wildly overengineered and costing too much ($82,000 in 1991-money).

Here’s the car: https://simpsons.fandom.com/wiki/The_Homer

This monstrosity cost $82,000?!

https://amphetamem.es/meme?id=the-simpsons_02_15_490&text=Th...

No surprise, LLM companies optimize for waste. More tokens, and more prompts means more revenue. Reminds of Google’s Prabhakar Raghavan story: deliberately making search worse [1]

[1]: https://pluralistic.net/2024/04/24/naming-names/#prabhakar-r...

Or, more likely, it's that concise code requires a much deeper, wholistic, understanding that these models just are capable of yet.

Same with a junior dev. They don't write long form spaghetti because they're trying to write more LOC. They do it because not doing it is hard, literally above their pay grade.

I use LLM every day, but they're still completely awful at architecture. I don't think this clear lack of ability is some conspiracy.

Personal anecdote: I spent a few days hacking on my compiler to remove 1k lines of code (about 15% of total code) while preserving behavior

I was only able to do that after I had solved multiple related problems in different places and started introducing subtle bugs by accident / had difficulty detecting all edge cases

I've noticed whenever I use LLMs they introduce the same kind of thing but at much smaller scales than I would. They often suggest solving the wrong problem when I prompt them to diagnose specific bugs too. Usually opting for a shortcut that introduces its own issues and ironically calling the proper direction "too complex" when it's really not.

> I don't think this clear lack of ability is some conspiracy.

Maybe currently not. But we will never be able to know, as models are undeterministic and benchmarks are kind of scams. When you cannot prove that something gets worse, rest assured companies will to it.

This requires something other than a free market, and for China to not exist. We're not there yet. There's plenty of competition to prevent this, for now, with AI spending being an incredibly hot issue.

Currently the bad quality of architecture and holistic view of models is probably explained by the fact that the models just cannot do it. Free markets and China will make sure of that, kind of. In reality Claude and Codex are better and provide much better tooling, so not much competition from free markets and self-hosted models from China, I think.

Then when competition really settles to monoploy or duopoly, like it always does in bigtech, it is really difficult to prove Anthropic and OpenAI enshittify their models. Same with Opus 4.6, that suddenly was worse when 4.7 came out.

> Currently the bad quality of architecture and holistic view of models is probably explained by the fact that the models just cannot do it. Free markets and China will make sure of that, kind of.

I'm not following. Free markets and China will make sure that models stay bad!? Why didn't these entities already stop the progression we've seen? What has been motivating them all this time that has preventing them from stopping?

Keep in mind this was all science fiction just a few years ago.

Bullshit. You can be cynical, it's fine but this is just nonsense. People have so much demand for coding that there is no need to make it waste tokens. People are eager to implement more features, do more testing, more platforms, more file formats, bla bla. There is absolutely no incentive to waste tokens. They can barely serve the demanded tokens anyway. There is in fact incentive to save tokens, so subsidized subscriptions don't consume as much.

If software has become a mass-produced commodity then seems to me the software business will become a much more finance focused business

You will really have to weigh the cost of making the software against the expected revenue.

Since software is still a winner-takes-all market, the mass-production property of software doesn't really matter.

In such markets, what you produce is either worth nothing or worth millions of dollars. For as long as it's the case that well-constructed code (with or without LLM help) is more likely to be in the latter category, the economics of software don't really change.

Even before LLMs, you could've commissioned a half-assed clone of any app you wanted from a 3rd world consultancy for a few thousand dollars. LLMs are basically Bangalore-as-API.

> Since software is still a winner-takes-all market, the mass-production property of software doesn't really matter.

Fairly sure software dev was always an iceberg. Most software and most software devs aren't working on horizontal software, but on vertical software, in cost centers. Sales for that kind of software don't scale as much.

Most vertical software is just horizontal software glued together

Horizontal open source modules... And making the horizontal modules doesn't employ many people, both because of FOSS and also because you only need so many reusable modules.

When we were making brick houses we needed a few brick factories with thousands of people for million of bricklayers.

I think part of the reason software was winner take most was the difficulty of making software.

I remember hearing a story that in the past movies were so technically difficult to make that any movie that got made had a good chance to be a profitable hit. But as movies got cheaper to make, more movies got made. Nowadays movie studio execs have to really calculate out the audience and expected revenue for any new movie and balance that against the budget and the cost of the studio's failed movies.

I think a similar dynamic may happen in software

That honestly sounds a lot healthier than “just ship what the CTO/Product team wants” with as much hand waving as is necessary to very roughly estimate ROI and then pray it hits with the market. In anything that’s not a startup operating in a new industry, the “old way” is a hard way to run a business

> I think part of the reason software was winner take most was the difficulty of making software.

That might be part of it, but I think it also has to do with the reality of replicating and scaling. Hardware or physical goods simply don’t scale like digital goods. There can be hundreds of knock-off physical products that have lower quality and lower cost but serve 90% of the same purpose, because physical capacity for raw materials, construction, labor, shipping, etc. have scaling limits in each market and economy. Digital goods are just so much easier to replicate and scale, so it often doesn’t make sense to buy software at lower quality and lower price if it doesn’t do most of the job. There are still limits of course, and different from physical goods, but I think this is a key reason why software is seen as winner-take-all.

Yes, but there are other digital goods, like music, movies and books which are not quite as hard to make as software. In those you have a hit driven market dynamic with lots of niches instead of winner take all.

I.e. because software was hard to make and complex to copy you would tend to have "natural monopolies" that were hard to compete with. Who wants to try to build a new desktop OS to compete with Windows? Or a web browser from scratch? Or a new search engine? Etc.

Those and other pieces of software were complex and hard to make. The cost to copy and compete was very high. So one winner took most because that winner was the company who could figure that software out.

But as we can see with Kimi Work and other such things, software is now much easier to copy. Let's say it took $1 billion to make a copycat piece of software with people but now takes $100 million or $10 million with AI. Suddenly a copy and compete tactic makes much more sense than before

For example, with AI it might make financial sense to build a Chinese Native OS instead of Windows. Similarly for Russia, Iran, the EU, and a whole bunch of other places. All of a sudden, Windows might not be the winner take most OS, we might have lots of Operating Systems, with smaller markets and lower profits, which require much more careful financial analysis to stay profitable.

This would be just like Movies, TV Shows, Books or Music. When something works, people relentlessly copy it and different regions put their own spin on the idea. After Iron Man succeeded we had so many super hero movies. Etc. So there is not really a winner take most dynamic in these other digital products. Software may be moving that way

I disagree the worth of software will become a lot less as LLM take hold and more people outside software devs know how to use them properly. I am the default IT manager for our small office as I am the guy most interested in tech and in the last 2-3 years I am getting llm to write python scripts for me to do work that would have cost $100s of dollars. Currently you need to pay $100-1000s for a software with 100s of features while you might actually need 1 or 2 of the features which you can get the llm to write for you.

As the LLM's keep getting better at writing software actual coding skills required will decrease which will result in a huge influx of software crashing prices. You can see this in already in open source projects in the last 6 months to a year Jellyfin had a huge influx of custom clients made by different people for fixing their own itches about the official software.

>Since software is still a winner-takes-all market, the mass-production property of software doesn't really matter.

This could not be further from the truth. It might be true if the software works without supervisors in a B2C scenario where everything is self explanatory and nothing can go wrong, movie streaming services for example, and even these have tons of variants and multiple big players racking up billions in market share. So even there "winner-takes-all" does not apply. It gets even worse in B2B software, as there is SO MUCH software thats commonly used and needs tons of supervising by experienced devs. If we had a "winner-takes-all" market a lot more software would already be dead. And a lot of companies would have way less options when adopting new software for something.

"Winner-takes-all" in the software context is never meant literally. It's more like, an oligarchy of a few winners will emerge out of hundreds of contenders.

It's not like restaurants where for example you can open a "Larry's Pizzeria" and still build a valuable business despite a Domino's nearby.

> software production has become a mass-produced commodity

For who?

The public? The public has never liked buying software at any price.

Businesses? Businesses need higher quality software when it's relevant to their core competencies, so they hire people instead. Buying competing SaaS or depending too much on AI is throwing the baby out with the bathwater.

As a business user, the same thing is true for non-code documents. The biggest exertion is reducing the excessive slop down to concise, clear points.

I largely agree, but when this point is brought up I always feel the need to go one step further:

For the vast majority of software out there, this quality gap simply doesn't matter. Not everything is big tech, needing internet scale. Most software built by developers at non-tech companies are simple line-of-business apps used by a handful to a few dozen or maybe a few hundred people to do a thing a bit better than the Excel spreadsheet that was used before.

And AI has essentially automated the development of this kind of software. Or rather, it has dramatically increased the ability of skilled developers to produce larger amounts of these kinds of systems.

Excel hardest hit.

i don't think people writing this understand how computers work. there is no "Internet scale hard" problem. its reliability, safety, efficiency problems. these problems exist at any scale. ai sucks at it.

> its reliability, safety, efficiency problems. these problems exist at any scale

Respectfully, I disagree. When you have 5 users using an app sandboxed within the corporate firewall that just does some CRUD and basic workflows, these problems you're calling out really just don't matter much. Some do some of the time, but not much.

Obviously not all software falls into this category. But so, so much does. Do you know how many thousands of tiny applications are maintained by big all non-big-tech corporations? I've seen many of them up close -- the code is usually complete garbage (seriously, it's bad; and alas, most was written before the age of AI coding agents), and yet, the business continues to operate and use the software.

> For the vast majority of software out there, this quality gap simply doesn't matter.

You don't mean the majority of paid for software can behave non-deterministically? That's the value add of the human in the loop.

>> In my experience, at least one agent I use struggles to produce minimalist designs, and it's very frustrating

Which ones don't?

I used to play Wing Commander II. It was 30mb. Today, the software to adjust the colored lights on my keyboard requires literally a thousand times as much memory.

IME this works until it does not. This approach works well at the beginning of a greenfield project, but at the same time because it is so easy to add features, you will likely ship something that is way too over engineered. And that complexity will not amortize over next increments and will more likely lead to the entire project being a black box only fully understood by AI. However a more careful use of AI for targeted surgical changes is far more ”productive” in the long term IMO.

Disagree. I operate this way inside a multi-million line legacy codebase.

> I work at a small startup

How does a “small startup” end up with a multi million line “legacy” codebase? Something not mathing

Have you worked at many startups?

No, but not relevant.

What is the point of working at a startup if you’re dealing with millions of lines of legacy code ? Isn’t the whole point of startups to create & innovate with a clean slate and modern tools?

No, actually. The point is to build a profitable business.

How long has your startup been around? I’ve worked at plenty of startups over the past 20 years. Including one that was still calling themselves a startup 10 years out. The org I work at now was a startup before my tech giant employer acquired them. We have a very bloated and very profitable 8 year old codebase that is barely 500k LOC.

I’ve never seen a startup with a multi million line legacy codebase.

They may have forked something

Definitely possible, but up thread they wrote:

>”Have you worked at many startups?”

In response to a question about a legacy codebase at a startup. That implies that they think whatever they are doing is common. And forking a multi million line codebase and heavily developing it isn’t common for startups.

You’re right, very confusing thread

I don't know if that's the whole point, but I agree with the sentiment, why would a startup be working in legacy code and where would that code come from if this is truly the start of something.

OP might just be working at a small software company or for one that broke from a bigger one and is now "startup" like?

Something isn't clear about the size of your codebase here and the level of reliability your customers expect, as a reader of your comments. Clarity there will help.

My observation has been:

- Initial greenfield work by an LLM is fast and very effective with minimal or no human oversight.

- Subsequent work ends up being over engineered and very verbose. Assumptions are made that aren't suited to the problem at hand (for example I find Fable is extremely regex happy where structured data would work much better from a readability perspective.)

- Once code bloats beyond a certain point due to unguided LLM usage, complexity is high enough that only LLMs can operate on the codebase with any economical amount of time.

- Rinse repeat and your code ends up unclear about any state that's not explicitly being tested and verified in QA loops

For some of our products this has been fine, for others it's been problematic. An understanding of your size and reliability requirements will help make the conversation more productive.

> unguided LLM usage

Why aren't you guiding your LLM usage? Is that what I said - to spam it and not guide anything? Or to have a careful workflow where you agree on design and maximize your human judgement/leverage?

> any state that's not explicitly being tested and verified in QA loops

As opposed to before, when engineers perfectly reasoned about code behavior from first principals and QA was unnecessary?

> Why aren't you guiding your LLM usage? Is that what I said - to spam it and not guide anything? Or to have a careful workflow where you agree on design and maximize your human judgement/leverage?

You didn't say anything positively or negatively regarding this so I made an assumption that you were using the LLM relatively unguided (e.g. a bit of oversight, not the kind of thing that heavy code reviews used to involve pre-agents.) Feel free to add clarity on your actual usage loop.

> As opposed to before, when engineers perfectly reasoned about code behavior from first principals and QA was unnecessary?

In my experience, most engineers are quite good at reasoning about code behavior for non-QAed code paths. Obviously things fall through the cracks. But I've been in the ground floor of plenty of Big Techs in their early stages before agents and, yes, a lot of initial development had spotty test coverage and yet most of the engineers had good mental models of what was happening. It used to be a very valuable skill to wrap your head around a torrid piece of code with few or no tests but was nonetheless a core piece of your application. Conversely, agentic development can bring cognitive debt [1].

===

This isn't a fight. We aren't sparring over what's right and wrong. I'm just curious how other people use agents in their work as someone who is also now in a startup that uses LLM agents heavily and has no limitations on spend.

[1]: https://martinfowler.com/fragments/2026-02-09.html

> You didn't say anything positively or negatively regarding this so I made an assumption that you were using the LLM relatively unguided

I feel like this statement betrays your lack of advanced experience coding with LLMs.

OP's elaboration of the steps they are going through (planning, agreeing on plan, getting one LLM to draft execution plan, approving it, then executing with a separate LLM, then reviewing/testing) made it super obvious to me that they are guiding their LLMs quite considerably as part of their work.

Anyone making blanket statements about LLMs producing garbage is just telling on themselves about not having proper SDLC practices in place.

Planning, agreeing on a plan, separating planning and implementation LLM, using separate review LLMs, these are all table stakes. This isn't "guidance" if you're getting paid to write software. If you think "unguided" means "I typed a prompt into claude code and waited yolo" I don't know what to say but, you have a very different idea of what professionals do than I do.

I find for my own work that I need to read the diff the LLM produces then offer feedback on the diff in its own loop before I am satisfied, and this is after all the unattended QA steps through Codex Computer or Claude MCPs happen. Then auto reviewers come in and then reviewers come in. Of course, at our stage, we rarely have this luxury and it's only reserved for the very core of our codebase.

This is still much less guidance than we used to do for code before agents became popular. Even at Series A companies, before agents, we used to socialize tech specs, get buy-in from multiple engineers, create test plans, etc etc.

> Anyone making blanket statements about LLMs producing garbage is just telling on themselves about not having proper SDLC practices in place.

> I feel like this statement betrays your lack of advanced experience coding with LLMs.

Are we in school debate club? I don't know what's going on lol, I'm just curious how people are using LLMs! Is it just that irresistable to take a cheap shot at each other?

> "unguided" means "I typed a prompt into claude code and waited yolo"

Yes, this is literally what that means.

> Are we in school debate club?

Not that I know of but that's the conclusion I drew from your statement.

It's not a cheap shot unless you took it personally?

I suppose I could have said "the fact that OP's explanation of how they work did not lead you to conclude they were in fact guiding their LLM usage quite a bit tells me that perhaps you have not been working with LLMs in any advanced capacity".

For the SDLC comment I admit it was a broader statement (based on observing people generalizing that "LLMs produce bad outputs") and not specifically aimed at you, and I didn't make that clear, so my bad.

> If you think "unguided" means "I typed a prompt into claude code and waited yolo" I don't know what to say but, you have a very different idea of what professionals do than I do.

What exactly does "unguided" mean to you, then?

Not having human input in the loop, i.e. allowing agents to act without guidance. I understand the idea of having agents guide agents, but really how much do we gain when Sol scolds Fable?

> how much do we gain when Sol scolds Fable?

You might be surprised at how much benefit you get from the model council[0] pattern.

The common pattern I've seen is "build with Claude, review with GPT", or vice-versa.

It's been proven to be very effective.

0: https://www.google.com/search?q=model+council+pattern&udm=14

I use it. We have more workstreams than people and so we have to rely on this pattern for most of our new products, but I still find the code doesn't meet my bar. To me it produces passable but not good, maintainable code. Good enough for our experimental products with the cognitive debt associated with heavy agentic development. We take this calculated tradeoff of velocity for cognitive debt and middling quality because we're a startup and we feel that velocity matters more than correctness, but that is just a bet as the nature of startups go.

AI is an accelerate tool for any organizations, management thinks it'll solve their organization issue because it accelerates it. Most often, it accelerates toward a wall.

Design is too expensive, we do agile. QA too expensive, we fire all of them, and claim devops is the now, which allows us to fire the Ops team too, 100% ownership from deisng to ops on devs.

One person with an agent can replace all these teams. Yeah mo profits.

> How does a “small startup” end up with a multi million line “legacy” codebase?

Easy! The output of 6 months ago Opus! Which seemed so wonderful at the time.

This! I don't think folks understand how easy it is to go from greenfield to brownfield with these tools, esp if your organization is only valuing velocity. Meaning your doing full agentic development on large features, barely reviewing any code, and shipping without much refinement. It's insane, but this appears to be the status quo in SF startups.

You'd be surprised. I met a guy last week who was proud to tell me he had vibe coded an almost 2 million line code base. The app did not sound that complicated, so I'm assuming it's full of copy-pasta flavored slop.

Meanwhile I'm so proud that I'm keeping my hobby project under 10k lines and spend time trying to delete code for my own sanity

"startup" and "legacy codebase" are diametrically opposed concepts.

And if you're saying (based on your other comments) that a 6 month window is enough to create a legacy codebase...that indicates a serious lack of experience or understanding as to what a legacy codebase is, or why they exist.

exactly. Usually legacy code forms when people lose context and confidence in parts of the codebase due to staff turnover etc and ppl avoid touching or enhancing those parts for long periods. Six months is a short time to accrue that much tech debt, its enough time where most of the people who created that "legacy" are probably still around. As you said indicates bigger problems.

So basically any LLM codebase of sufficient size is immediately legacy.

Man, so many people in this thread just arguing pointless semantics, making weirdo absolutist (and incorrect) statements.

Accept that other people may ascribe different meanings/interpretations to words than you, and that if your reading of their statement doesn't make sense to you, perhaps you are simply reading it wrong.

Trying to hold someone else to your definition of words suits what purpose exactly? Are you just trying to "win" ?

I agree with your larger point. Though I think it's pretty natural to wonder how the poster ended up with a multi million line codebase.

2M lines of code is 15 people committing ~26k lines of code per year (~100 lines per working day) for 5 years.

15 people is a pretty small startup, what if this is a 50-person startup?

Doesn't seem like that much to me, depending on what you're building and the size of your team.

I’ve worked at many startups and consulted at many more over the last 20 years. I’ve never seen 2 million lines of code happen that fast at a small startup.

15 devs putting out 400k LOC a year into the same codebase is not normal at all. I’ve never seen anything close to that kind of rate of growth across that number of people.

And 50 engineers is not even remotely close to a small startup. I have worked at a startup that had 50 engineers after a few years, but it was a multi billion dollar unicorn.

Yes lol. Of all things people are getting on me for it's the number of LoC x Years In Business of this startup. I don't fucking know, I didn't start the company and I wasn't here for several of those industrious years. Looking now it looks like we have slightly fewer LoC than that, I was counting some of the generated stuff.

But who cares? The point is any codebase over a few years old with lots of customers and a big surface area has lots of code, much of it "legacy" from the standpoint of a guy in 2026.

You are wrong and they are wrong but that is ok.

Sounds like you simply don't know what you're talking about.

This is an ultra cop out. There are standards in language that are not all “left means right for me so you cannot assume when i say right it is right and not left”

This whole thread around loc is depressing. It speaks volumes of some peoples inexperience working on actual legacy code. Legacy code is not just age or size but that the technical foundation is dated in a fundamental way. A giant monolith running on a now defunk framework using a database only one guy in canada knows about.

Case and point in my day job. The org that owns XMM development does not know how to recover a physical bench that is bricked because everyone who knew how has left. So now they just use simulators…

Interestingly, AI figured out some of this pretty easily for me. But the org has the exact same AI as i do. At the same time another org is close to a year into a greenfield rewrite that has been developed via agentic swarms. Absolute trainwreck.

AI doesnt make bad engineers good. Anyone who says they are doing 4 eng work likely would be without ai too. Those that claim otherwise, are the bad engineers.

> There are standards in language that are not all “left means right for me so you cannot assume when i say right it is right and not left”

Yes of course there are words that have very clear meaning, and that's not the case here.

"startup" and "legacy" - used in the context of this discussion - are not words like "left" and "right" where everyone has a universal agreed-upon understanding of their meaning.

Wow how many years of experience with Claude Code and Codex do you have? lol

The job requires 10 years of those technologies ;)

I disagree, the approach works well in a legacy project, since there are structures and standards that already exist, which them model can draw from (if you aren't more explicit about it in AGENTS.md)

This is very close to my workflow but you forgot one important step:

- Suggest a better approach that makes the AI say, “That’s much simpler. And you’re right. My original plan was over-engineered.”

I'm probably between $50-$200/day depending on the day; we also have effectively unlimited budget, though a lot of that is because Azure gives startups $150,000 in credits for 2 years, which we've wired up to a LiteLLM gateway & OpenCode. Without that I think our appetite would be more around $400/month/employee.

A lot of my high costs is because I just throw Sol at everything. If I were more selective and brought in Luna or v4 Flash every once in a while, I think I'd be more like ~$400/month. That's why I'm not aligned with the notion that "tokens are subsidized so that's why people are using so much": its not that I'll have to adjust to using less, its just that I'd need to think before I prompt a bit and be more judicious. I could easily see my raw token counts doubling or tripling in the coming months. I don't think that will change as subsidization subsides; though maybe lab revenue will; intelligence per dollar is getting cheaper every week. Its solely a function of adaptation to process, which takes time.

The productivity gains per token are the single most asymmetrical thing I've ever seen in engineering. The engineers on our team are pretty effective with tokens; easily that 2x-4x output as you're seeing, spending $20-$200/day. Some of our security folks have also started contributing more-and-more code, and they're on the other side: they'll spend hundreds a day running in circles, eventually producing these +/-30k loc pull requests that take ages to get merged and are littered with issues. They weren't writing much code before, so arguably they're more productive by some multiplier greater than 1, but I think the drag on the rest of the team, and potential issues with what they produce, has overall created a net-negative situation. Inversely, some other company functions have produced a few one-off websites for things like sales processes, and those have been a huge win. The asymmetry is wild. There's almost a valley of incoming skill where if you know nothing about code, you'll leverage it well; if you know just a little bit, it makes you super dangerous; if you know a lot, you're the biggest winner. Really difficult situation to navigate.

but I produce the output of 3 or 4 2022 engineers and probably at better quality.

Possibly, but the output of a 2022 engineer is about 1/10th of the output of a 2010 engineer, so it's an extremely low bar.

also - as always with these claims there's no actual product / repo / whatever one could check.

I would love to see what these tools create but outside slop there's never: This works, is in production, here's the code.

Any day now.

It’s crazy how we are like ~2y in this AI revolution and still do not have an answer to this question: can you show us the ROI? Where is the revolutionary software your team of agents created?

Why would it need to be revolutionary? It can be some ordinary thing. Software is mostly ordinary.

I found an interesting project recently. As I was looking through the source something felt off. Turned out to be entirely LLM written. There was duplicated code everywhere, same function defined in dozens of files (same name, same intended behavior) but none of them would produce the same output for an input. Dead code all over the place. Over architected. Useless comments.

It was all generated in the last 4 months, so don’t come at me with the “but did they use a model from the last 6 months” nonsense.

4 months is ancient. Fable and Sol are a different animal.

You said the same thing about whatever model was released 4 months ago. You are a walking meme.

$80 sounds extremely low for what you're describing - are you on API token plans?

I have had some $3,000 token days - even without Fable. I don't see how this is sustainable.

My personal 20x plans get so much usage for so cheap. The consumer subsidies are crazy, but alas I can't use them for work.

$80 is definitely low now that I look at my numbers. but not OOMs low, it's closer to like $200 on heavy days. i don't know how you're doing $3k/day, that's wild. i'm pretty aggressive about compaction and session restarts, and i reserve Fable 5/Sol XHigh for "main thread" orchestration

Dude $3k? Holy heck you should look into K3/Deepseek V4 Flash

Do you have issues with performance at the moment? Right now I tend to find that it produces absolutely terrible design patterns and especially performance. I mean maybe I don't know exactly what area you're looking at but yeah for us we tend to find it's terrible wrt dB/caching/scaling and often any performance improvements it proposes end up actually shooting itself in the foot and being worse than before but it's not very good at testing in an organized way to even notice it made it worse despite repeated prompts to do so I mean if I prompt it to test performance in a handheld structured way (it is very bad at finding out what performance to test and why) before making changes I can usually figure it out but it usually takes insistence on the specifics to really ensure a good solution that will actually fix the problem

Performance is better than ever. It's never been more practical to set up wildly complex synthetic test environments and measure perf wins. Plus the models will find every possible algorithmic/design improvement.

It actually gives me quite an uncanny feeling, bulldozing over years of human optimization work with a newer, "perfect" design. Like bringing an AK-47 back to the middle ages.

I needed to thoroughly test rerankers on my companies rather unique corpus.

Opus and I wrote a parallelized test harness and labeled groundtruth in around 2 hours.

In 2022 that would've likely been all I did for a couple sprints

Yes 100%. This morning I casually prompted Codex to drive the browser to complete extensive performance testing in-situ that would have literally been weeks of work before. Probably in reality it just wouldn't have been done, and performance guarantees would have been attempted up front via more careful design.

In this case the design was also AI generated, and there were limited wins to be found because the design was already superb.

I encounter this regularly and it still feels weird.

That sense that you did something better in a few days than you would have in a month 5 years ago. It's like buying a table saw for wood working.

One crazy thing I think about often is how there are so many correctness and testing harnesses that would have taken weeks to build in the past so we simply never would have. We'd just do our best then wait and see what comes to the surface. This is a huge part of what makes it possible to actually make better software with LLMs in my opinion. It isn't just 'LLM codes better than I ever could' (that's often untrue still) but 'LLM enables me to make assertions about the program to degrees that would have been absurdly impractical in the past'. It's huge

> the models will find every possible algorithmic/design improvement

it's so hard to square such totalizing statements with my day to day experience with fable and sol, (every possible, improvement, really?? they are NOT omniscient) arguing with them/my colleagues' agents that no they have slowed down the system 200x with their terrible change, doing string operations on millions of db rows, trying to get it to understand that I don't care that it's calling it a "cache" if a cache hit is slower than what we had before.

These agents do let you learn codebases quickly, and produce code way faster. I don't look at IDEs all that often. But literally multiple times every single day I catch them doing something stupid.

I don't think its impossible that we could get better performance from the agents. I know ive tried all sorts of workflows and skills, few of which seem to have much effect on the things the models struggle with. I think a big part of it is encoding enough context for large codebases, and providing it with all the tools it needs to make it successful, things to automatically check its work, etc. But that's not automatic, in fact its generally a terrible judge of what it needs or what its bad at

yeah it's true, you do have to guide them. i find that the key is you have to know what's possible. you have to have the instinct for "this really shouldn't be so difficult". my junior SWE coworkers have the same trouble as your coworkers.

but the revolution is it doesn't take that long. in like 15 minutes you can chat with fable and get to the meat of whatever the issue is with repeated questioning. and then it does the solution for you. so it's not magic but it's still like a 100x speedup.

Are you using the SOTA models at very high reasoning during planning? IME that makes a LOT of a difference. I‘d also never let them just rip into the architecture, but always push back and ask for alternatives first. Once the overall plan is nailed, not that much can go wrong. Provided it’s a reasonable change set and not a 20k LOC PR.

fable or sol w/ very high both planning and execution, yeah. I feel the "push back" part is a big part of my job now (on every step, planning, execution, and review) yeah, but that feels pretty incompatible with the sorts of "just let it do what it wants" which other people seem to be claiming

Output of 3 or 4 2022 engineers? Its that your self assessment? Output as in number of lines of code?

Not op, but we are at 2400 total points delivered over seven years. 1000 of those in the last six months. About 2-4 devs over that period, just two the last six months.

Noone is questioning the volume of llm output. My question is whether all those points delivered improved your product and software in any meaningful way, or do you now have 20x more code that noone understands with the same quality of software product?

Yeah it did. We've signed three new customers because of one. Another large feature was an integration that landed more. We rewrote the entire front end to modern stack. Used to be a fifteen year old react and backbone bFrankenstein app.

The output yes, but do you produce the impact and value of 3 engineers? I have seen this workflow being toyed with too, and I find it to produce massively overengineered stuff that actual people don't really wanna use

He only spot checks 1000s loc diffs, so probably has no clue.

Only spending $80 a day on Opus 5/Fable 5/GPT 5.6 Sol feels very low. I'll roll through a couple hundred dollars worth of credits a day with those models, the vast majority of which would be on non-coding tasks, and it's still a huge cost savings over me or my team having to do these things manually, if we'd even be able to do them at all.

But that's also why it's now easy to justify the cost of an Nvidia or Intel inference server with Kimi K3 locked and loaded :)

In my experience, code is a small fraction of the work.

I'm in an infra team and for the last 2 weeks or so I've been trying to understand whether a particular workload will catch fire if a switch is flicked. I'm also new to the team so partly it is me wearing training wheels, familiarizing myself with the telemetry etc, but I will state that I'm not completely lousy at this stuff.

No model in my experience can do anything remotely comparable to the work "what happens to the workload if this switch is flicked" needs. They can't even design a reliable quick experiment to answer what cast should be applied to the binary trace_id in table A for the join to table B to work. They will happily do something idiotic and then conclude that the join does not work.

An AI maximalist on my team put up 2 pull requests with ~120-140 changed files this week. If they spent $200 on tokens to achieve this, we spent $2500-3500 in human salary and opportunity cost reviewing it.

You need to change the way you think about reviews

> Spend most time prioritizing/discussing what to do.

you should probably be doing this discussion work along with Fable 5. It will give good feedback if you're working on the correct things.

> Come up with QA plan and have Codex Computer Use execute on it

QA plan should be part of the above "design + plan", not after it. The implementer needs to be able to fully test before publishing a PR. This is true whether humans or agents are writing the code.

> Manually spot check the final result (usually a sizable diff, thousands of lines, complete feature E2E, etc)

unfortunately this is not really scalable with amount of code agents can produce, so you need independent (fresh context) agent reviewers to help. Ideally they only escalate to a human when really stuck.

> I probably spend like $80 a day at least

at a small startup you should be on the $200/month plan(s).

Is the revenue up by 2x or 4x?

My experience is that your description works for a certain time, since you're knowledgeable of the codebase and can guide it. But after too many iterations with not hand-holding the llm, it quickly gets unwieldy.

I'm also at a startup. My workflow is similar but I have Fable 5 xhigh drive the whole thing: it gets Codex CLI installed in its environment with an API key, and it's instructed to delegate ~everything to Codex and review its work, especially for code quality/conciseness. Fable delegates to Sol or Luna (fast mode) xhigh/max depending on the task - I think Luna xhigh on fast mode is basically a Pareto improvement over Sol medium.

Interested to dive deeper on the upfront design discussion. Have you found these to more often then not translate into the real product.

In my experience at the begining of the full agentic coding loop in our company we were more hands on with the codebase and had better judgement over the plans. Now it is quite often that the inital plan after executed needs more refinement and that made the plan review somewhat obsolete for us.

> essentially unlimited AI spend budget

> I probably spend like $80 a day

This doesn’t sound like “unlimited”, I spend more than this out of pocket per day and I have a strict budget.

It's a fair point, it's not truly unlimited and I do wonder how that would change my workflow. I can definitely imagine if I was inside Anthropic or OAI with unlimited "fast" tokens, you would be more tempted to hand over even more of this process. I completely understand why they talk about "graph engineering" and such, my entire workflow above could be a graph and I could try to increase my leverage even further. Realistically though I am bounded by product decision making, not code output right now.

You have an unlimited budget, and you only spend $80/day? I’m up to $3k/week, and still expanding.

Does it not end. Like is there not a point with all this speedups and infinite intelligence that your software system is essentially done.

Interesting. I would probably start with the QA plan first, or at least before implementation (and perhaps even before design.)

>Auto-fix bugs and CI until green + thermonuclear review skill x3.

Gotta love this loop, I have it running while I'm asleep all the time.

How much does it cost you?

Practically free with any of the subscription plans.

for complex open ended coding tasks better models are better

(and mid-to-long-term, often also short-term end up cheaper than weaker models)

this might change soon if we are reaching a certain capability threshold

but right now that's still the case

unless you are working on throw-away trivial stuff where iteration speed and trying many speculative things might give you an edge

I was just about to say, how could routing possibly be worth it at the risk that the work output is sub par?

do the same across 20 terminals (as you should) and now you are up to $1.6k/day. would that give you output or 60-80 engineers? not a chance, right?

no one’s AI spent will be in question working a single terminal with carefully planned out and executed process you do

Do you have tips for generating clean productive output per dollar?

in my humble experience it boils down to mastery.

Are you at least conversational in the subject matter? You're gonna have a good time just by paying attention and adjusting your workflow. If you're getting a lot of back and forth with it, its asking a lot of planning type questions, stop, step back, rethink the whole feature, and start again from the beginning with everything more fleshed out.

If you are in a brand new field, there's no way to bridge that divide. The issue is you don't know what is good or bad, or whether what you have learned is good or bad. You're in a sports car and you don't know how to drive much less what's track and what's field.

You can spend a lot of effort getting good at prompting towards writing tests and E2E tests to at least verify your app does what you expect it to, regardless of experience.

I appreciate this non-judgmental description of what it's like to approach a topic/technology from a newcomer's perspective. Thanks!

This is a great point and I agree. My own productivity varies based on what part of the codebase I'm working on. If it's "been in there before" and I know the right questions to ask, I can one-shot a good design/improvement. If I'm spending 20-30 minutes asking Fable to "draw a diagram so I can understand" - probably less so. But notably, I CAN get there in a fraction of the time it would have taken before. You can general personalized onboarding docs to ~anything.

Keep the decision-making and execution separate. Use the high IQ models to chat about the design and make them drive subagents to do the actual work. "Chat" style threads are actually quite cheap. Where it gets expensive is having Fable 5 output thousands of lines of implementation where 95% of it was already overdetermined and there were only a few important judgement calls.

I actually have no doubt that I could replace my Opus 5 Low/Medium subagent profiles with Grok 4.5/GLM 5.2/Deepseek v4 Flash and perf would probably be pretty similar.

On top of that - highly recommend adding accurate cost counters to your statusline. You can't improve what you don't measure! (Or even have any intuition about).

> I probably spend like $80

mate, if youre not using subscription then youre spending waaaaaaay more. the plan itself with fable/sol will most likely have already cost more then $80 -- ime thats more like 500-2k/day of usage.

most harnesses let you see the usage in the status bar, i encourage you to enable it

> I probably spend like $80 a day

Wait what? I don't understand these numbers. I spend $1k/day

Your story about being told to use AI for everything I was expecting you to be well over that

Makes me wonder the kind of startup this peron is working for where slop is encouraged

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