You can easily develop with models like GLM 5.1 and Kimi k2.6 at a fraction of the cost of GPT 5.5 or Opus 4.7. Requests often cost just a few cents.
Open-source models have caught up tremendously recently. Those who can’t or don’t want to invest a lot of money can already develop with Kimi and GLM without any problems. We don’t have to wait another year for that.
Tried deepseek 4 w/ CC yesterday, and was watch my usage eke up by only 0.01 at a time while doing plenty of high-token-count tasks. I understand it's currently at a discount, but even after that expires the same-quality output will be available at a fraction of the cost of the expensive models.
From experience, the same level of usage would have left me stranded on my CC 5 hr limit within an hour.
There were some difficulties with tool calls, in particular with replacing tab-indented strings - but taking no steps to mitigate that (which meant the model had to figure it out every time I cleared context) only cost relatively few extra tokens -- and it still came in well under 4.6, nevermind 4.7. And of course, I can add instructions to prevent churning on those issues.
I have no reason to go back to anthropic models with these results.
Sure, but there will always be some monstrosities like Mythos that'll pwn all software written by local models in 0.01 seconds, thus forcing people/companies to use the most advanced paid models to keep up and stay unpwned for 1 second longer.
It’ll be priced slightly higher than the cost to actually run. But it’s still not clear what the real cost of the big models is. They seem very subsidised, but by how much?
It remains an unproven hypothesis. The revenue of the top 2-3 labs is still growing nearly exponentially, which is the ultimate piece of data that settles the question empirically for now. Benchmark scores aren't really proof. Benchmaxxing is possible, for example. Only revenue numbers (and gross margins) count.
The ultimate piece is not revenue but profit. At some point these enormous investments will have to be earned back. Good luck with that when open weight models are also continuously improving, have cheap providers and for many are already very usable.
The other point to make is that companies are starting to worry about the risks of externally hosted models.
This is at multiple levels if you have a remote API call as a key part of your workflow/software system.
1. Price risk - might be affordable today - but what about tomorrow?
2. Geopolitical risk - your access might be a victim of geopolitics ( seems much more likely that it used to be ).
3. Model stability/change management - you've got something working at the API get's 'upgraded' and your thing no longer works.
If you are running on open weight models - you are potentially fully in control - ( even if you pay somebody to host - you'd expected there to be multiple hosting options - with the ultimate fallback of being able to host yourself ).
You cutoff a generation of juniors from employment and learning , the seniors are gone and it's all harnesses and AI systems.
I'm not all gloom and doom but the treatment of junior engineers is something I think we will either regret or rejoice. Either will have a spur of creative people doing their own independent thing or we'll have lost a generation of great engineers.
The problem of "instant legacy" systems: something that's vibe coded and reached unmaintainable by either the AI or humans, but is also now indispensable because users are relying on it.
Some of that is already there .. but the users generally have nowhere else to go and ineffective pushback. "Enterprise software" has been awful for decades, things like Lotus Notes and SAP. Everyone hates Windows; everyone continues to use Windows.
Users don't currently trust software. Look at what we've done to them - can you blame them?
The consumer space is about extracting every ounce of personal data possible.
The b2b space is about "maximizing customer value" - that is, not maximizing the value of your product to the customer, but maximizing the value of the customer to your business. Lock them in and lock them down, make your product "sticky" so they can't leave without immense cost.
There will always be competition. For every company negatively impacting customer experience and their own ability to compete, there will be others happy to step in and take advantage of that.
If you fire all your SWEs they won't sit around twiddling their thumbs waiting for an AI collapse, they'll career shift. Maybe to an unemployment line and/or homelessness, maybe to something else productive, but either way they'll lose SWE skills.
If you close down all the SWE junior positions you'll strongly discourage young people training in the field. They'll do something else.
Then if you want to go back, who will you hire for it?
They are large language models. Not automated development machines. They hallucinate.
The goal post has not shifted since 2023 or so. Make an LLM that doesn't blatantly disregard knowledge it has, instructions it has been giving, over and over, and you win. If trillions of USD of investment can't do it, I'd be curious to see what can.
There are definitely automated dev systems, of which an LLM is a part. The remaining part may be called a 'harness' or whatever. The quality of the generated software is another matter.
If the AI is not good enough, then don't fire the devs. If/when the devs are no longer needed, I don't see why the need would return later, that was my point.
A harness like Claude Code does not turn an LLM into a software developer.
If that was the case companies could just have their project managers managing Claude Code instead of developers, and they would immediately realize that using Claude Code to develop software is just as complex and geeky as it ever was - nothing changed in that regard.
A harness and a bunch of skills is just the new "think step by step" prompting technique. Don't just let the LLM rip and write a bunch of code, but try to get it to think before coding, avoid things like churning the code base for no reason, and generally try to prompt it to behave more like a developer not an LLM. Except it still is an LLM.
A coding agent is really not much different to a chat "agent" in this regard. You've got the base LLM then a system prompt trying to steer it to behave in a certain way, always suggest "next step", keep to a consistent persona, etc. None of this actually makes the LLM any smarter or turns it into a brilliant conversationalist, anymore than the coding agent giving the LLM a system prompt magically turns it into a software developer.
If you prefer staying in denial, be my guest. But I've seen multiple instances of fully functioning software created by people who don't even know what code is. Maybe these creators are now developers, in a sense. But no SWE's were needed.
Sure, and I could buy a model rocket engine, strap it to a stick and launch it hundreds of feet into the air. Would that make me a rocket scientist? Next step Mars?
If you don't appreciate the difference between what an LLM or a coding agent can do, vs what a human can do, then I can't help you.
How do you reconcile these ideas with the fact that cheap open weight models are only slightly behind the state of the art?
If anything, I would bet that next year you could get today’s flagship performance for significantly cheaper via an open-weights model.
You can easily develop with models like GLM 5.1 and Kimi k2.6 at a fraction of the cost of GPT 5.5 or Opus 4.7. Requests often cost just a few cents.
Open-source models have caught up tremendously recently. Those who can’t or don’t want to invest a lot of money can already develop with Kimi and GLM without any problems. We don’t have to wait another year for that.
Tried deepseek 4 w/ CC yesterday, and was watch my usage eke up by only 0.01 at a time while doing plenty of high-token-count tasks. I understand it's currently at a discount, but even after that expires the same-quality output will be available at a fraction of the cost of the expensive models.
From experience, the same level of usage would have left me stranded on my CC 5 hr limit within an hour.
There were some difficulties with tool calls, in particular with replacing tab-indented strings - but taking no steps to mitigate that (which meant the model had to figure it out every time I cleared context) only cost relatively few extra tokens -- and it still came in well under 4.6, nevermind 4.7. And of course, I can add instructions to prevent churning on those issues.
I have no reason to go back to anthropic models with these results.
"No moat" indeed.
By that time, the hypebeasts will be explaining how worthless the models of today always were.
And there’s some truth to it.
I expect tomorrow’s models will be so much more capable that we will happily pay more.
But if not, we will still likely get today’s capabilities or more for cheap.
I don’t see a realistic scenario in which the AI genie is going back into the bottle because of affordability.
It seems like wishful thinking by people who dislike the new paradigm in software engineering.
Sure, but there will always be some monstrosities like Mythos that'll pwn all software written by local models in 0.01 seconds, thus forcing people/companies to use the most advanced paid models to keep up and stay unpwned for 1 second longer.
(Timeframes are hyperbolical).
There is no moat. https://newsletter.semianalysis.com/p/google-we-have-no-moat...
It’ll be priced slightly higher than the cost to actually run. But it’s still not clear what the real cost of the big models is. They seem very subsidised, but by how much?
The article is from 2023, I’m wondering if things mentioned still stand true today, can someone pls let me know.
It's much truer today. You can say that article is extremely insightful, as it predicted today's open weighted models scenario 2 years earlier.
It remains an unproven hypothesis. The revenue of the top 2-3 labs is still growing nearly exponentially, which is the ultimate piece of data that settles the question empirically for now. Benchmark scores aren't really proof. Benchmaxxing is possible, for example. Only revenue numbers (and gross margins) count.
The ultimate piece is not revenue but profit. At some point these enormous investments will have to be earned back. Good luck with that when open weight models are also continuously improving, have cheap providers and for many are already very usable.
The other point to make is that companies are starting to worry about the risks of externally hosted models.
This is at multiple levels if you have a remote API call as a key part of your workflow/software system.
1. Price risk - might be affordable today - but what about tomorrow?
2. Geopolitical risk - your access might be a victim of geopolitics ( seems much more likely that it used to be ).
3. Model stability/change management - you've got something working at the API get's 'upgraded' and your thing no longer works.
If you are running on open weight models - you are potentially fully in control - ( even if you pay somebody to host - you'd expected there to be multiple hosting options - with the ultimate fallback of being able to host yourself ).
What will close the way back?
You cutoff a generation of juniors from employment and learning , the seniors are gone and it's all harnesses and AI systems.
I'm not all gloom and doom but the treatment of junior engineers is something I think we will either regret or rejoice. Either will have a spur of creative people doing their own independent thing or we'll have lost a generation of great engineers.
This is not happening at least for 25 years, is what seniors I trust tell me.
I'd say closer to 10-15 but... I'm not sure the point you're making. Is it okay because it's 25 years in the future?
If we try hard enough, we can destroy the planet before we get there, I guess? 25 years is not a long time.
Today junior assembly language programmer are all gone, too.
And that’s going to cause serious issues when people like Linus die and nobody knows how to make operating systems anymore.
We’ve been coasting along on a single generation who have ruled with iron fists.
Yes and that’s why I can charge premium rates for debugging. Most people cannot read a stack trace anymore.
The problem of "instant legacy" systems: something that's vibe coded and reached unmaintainable by either the AI or humans, but is also now indispensable because users are relying on it.
I'm curious if this will cause a drop in quality that will lead users to generally lose trust in software.
Some of that is already there .. but the users generally have nowhere else to go and ineffective pushback. "Enterprise software" has been awful for decades, things like Lotus Notes and SAP. Everyone hates Windows; everyone continues to use Windows.
Users don't currently trust software. Look at what we've done to them - can you blame them?
The consumer space is about extracting every ounce of personal data possible.
The b2b space is about "maximizing customer value" - that is, not maximizing the value of your product to the customer, but maximizing the value of the customer to your business. Lock them in and lock them down, make your product "sticky" so they can't leave without immense cost.
There will always be competition. For every company negatively impacting customer experience and their own ability to compete, there will be others happy to step in and take advantage of that.
See Windows 11
Hey you can just rewrite (or should we say regenerate) it. Second system has never been cheaper!
Brain drain.
If you fire all your SWEs they won't sit around twiddling their thumbs waiting for an AI collapse, they'll career shift. Maybe to an unemployment line and/or homelessness, maybe to something else productive, but either way they'll lose SWE skills.
If you close down all the SWE junior positions you'll strongly discourage young people training in the field. They'll do something else.
Then if you want to go back, who will you hire for it?
I agree with you, but it's a case of the tradegy of the commons. One single company cannot make a meaningful dent even with your insight.
Why would anyone want to go back? It seems likely that the automated dev systems will just keep improving and get faster, cheaper, stronger.
> automated dev systems
They are large language models. Not automated development machines. They hallucinate.
The goal post has not shifted since 2023 or so. Make an LLM that doesn't blatantly disregard knowledge it has, instructions it has been giving, over and over, and you win. If trillions of USD of investment can't do it, I'd be curious to see what can.
There are definitely automated dev systems, of which an LLM is a part. The remaining part may be called a 'harness' or whatever. The quality of the generated software is another matter.
If the AI is not good enough, then don't fire the devs. If/when the devs are no longer needed, I don't see why the need would return later, that was my point.
A harness like Claude Code does not turn an LLM into a software developer.
If that was the case companies could just have their project managers managing Claude Code instead of developers, and they would immediately realize that using Claude Code to develop software is just as complex and geeky as it ever was - nothing changed in that regard.
A harness and a bunch of skills is just the new "think step by step" prompting technique. Don't just let the LLM rip and write a bunch of code, but try to get it to think before coding, avoid things like churning the code base for no reason, and generally try to prompt it to behave more like a developer not an LLM. Except it still is an LLM.
A coding agent is really not much different to a chat "agent" in this regard. You've got the base LLM then a system prompt trying to steer it to behave in a certain way, always suggest "next step", keep to a consistent persona, etc. None of this actually makes the LLM any smarter or turns it into a brilliant conversationalist, anymore than the coding agent giving the LLM a system prompt magically turns it into a software developer.
If you prefer staying in denial, be my guest. But I've seen multiple instances of fully functioning software created by people who don't even know what code is. Maybe these creators are now developers, in a sense. But no SWE's were needed.
Sure, and I could buy a model rocket engine, strap it to a stick and launch it hundreds of feet into the air. Would that make me a rocket scientist? Next step Mars?
If you don't appreciate the difference between what an LLM or a coding agent can do, vs what a human can do, then I can't help you.
Most of the time, a hundred feet is enough. 30 years ago, if you needed an aerial photo shot, you needed a helicopter pilot. Today, you just don't.
Lack of developers, if juniors don't get hired they will move onto other industries.
Company brain drain, knowledge leaves with your seniors if you decide to get rid of them, or they just leave due to the conditions AI creates.
I don't know if the above comes to fruition, there's a lot of questions that only time will answer. But those are my first thoughts.
Time. In a few years there might be no old-school way to develop anymore. Everything will be built around AI.
And blockchain, don't forget blockchain.
Even the programming languages will be made for AI.
All code that could be written by humans, has been written. Henceforth, the rest will be generated.