Anthropic is the most self hyped company I've seen, to the point that I'm wondering what would happen to its employees if they held a different opinion. Do they just.. keep it to themselves? For instance, if some Anthropic employees had a completely rational opinion that all of this isn't going to lead to AGI, but I just don't hear that ever from them.
The metric being tracked, code commits, is hilariously one sided. Philosophically, if you had one part of your work now practically free, you'd like to utilize that freedom to maximally cover for the other parts, for instance:
Instead of thinking about edge cases with brain and whiteboard, you can have the LLMs to simply generate most possibility including tests for it, because that is cheaper. There's probably 50x more commits of which 40 will be revert pairs but we are only twice as fast. And in reality nothing did change because the outcome remain the same. I can't see how it is necessarily different in the LLM space.
> Philosophically, if you had one part of your work now practically free, you'd like to utilize that freedom to maximally cover for the other parts
I've been struggling to capture this sentiment for myself in a way that hits. If shipping code is a commodity then why is everyone's immediate priority seemingly to ship 10x more code. It just makes no sense. I can't seem to get off this hill. Company-wide AI mandates and 100 fleet Agent orchestration Rube Goldberg machines... it's getting wild out there.
Meanwhile my Claude Pro ($200/year) does force me to smooth out my usage and plan more (Sonnet/Opus advisor split). But other than that, I can't imagine what I'd be doing with 20x (200x?) the compute to code sling. I think I'd lose my mind.
Because code used to be correlated with progress, it became almost a measurement in lieu. But realistically, the code is meaningless if it doesn't accomplish something, and that should remain the true bar of progress.
For instance, if I churned out 20x more code, threw away 19x code with rewrites and reverts and discards and accomplished the same project to the same standard 70% faster, would I do it? Yes. The part that matter is not 20x code, it is 70% faster.
Code is both the final product, and a tool to achieve that. We used to have a much harder time to realize the "tool" part, but now we are here. This also means any measurement centered on code being the final product is going to cease being effective or realistic.
You're right, my gripe is specifically with code slinging that hits production end users. My background is in product so to your point, it's very unnerving to see a straight line being enthusiastically optimized for developers -> customer facing product outcomes.
This is contentious because I'm not exactly advocating for arbitrary gate-keepers. The nuance is that building usable stuff is hard. And not a matter of shipping more code. I take your point to mean well it depends on what that code is doing. If 20x more code is in a meta-harness of simulation and such to arrive at the leading candidate for what hits production, well then you've got my attention there.
I have been doing more experiments with what I have now been calling agentic iterative optimization: telling the LLM to optimize code such that it speeds up all real-world-representative benchmarks by X% without cheating or causing regressions in both tests and performance metrics (e.g. MSE for statistical algorithms or file size in the case of something such as image compression). This is done using Rust where there are more low-level levers to tweak for performance than something like Python.
Opus 4.6/4.7 was consistently successful at getting 2-3x speed improvement with just one pass. It can also do the inverse: improve the performance metrics for better quality without causing a significant regression in speed. Then GPT-5.5 turned out to be much better at this workflow, often getting a multiplicative 1.5x-2x improvement above what Opus could do.
I now have quite a few GPT-5.5-optimized projects in various domains that are feature complete and are substantially more performant than existing SOTA implementations that I plan to open source as soon as possible: the bottleneck is polish as usual.
>A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost certainly an overstatement of the true productivity gain. Nonetheless, it indicates an acceleration. At Anthropic, we don’t reward people for how many lines of code they write; rather, team members are producing more code simply because they’re using AI systems to write more code.
What about the hypothesis that AI is generating more verbose code? I just see the text pretending to acknowledge "LOC != Productivity" and then using it as a metric anyway.
Anthropic is looking to IPO here soon.
A key aspect of this is to prove profitability.
Shifting their focus from Training new models to instead serving inference, they would greatly reduce their spend. In fact this is something being reported on that they are already doing, which is the reason for their first ever profitable quarter.
Its awfully convenient that the company which has greatly reduced its spend on training is now asking for a slow down in this area.
Okay, so anthropic has amazing AI which supposedly writes most of their code and can continuously improve... meanwhile they have outages on a regular basis, and any kind of long-running work will now consistently hit 'API Error: Server is temporarily limiting requests'. Not sure of this is intentional to force a reduction of token usage, but at this point I need to build around these throttling limits and outages with my own tools to restart/resume sessions. From my experience, in the last 2 weeks, literally 100% of any non-trivial Claude session/work will now be blocked on these issues, requiring manual intervention.
One of my focuses now is my own model-agnostic, harness and workflow orchestration (I know everyone is building these) , baselining on opus, and aiming to transition to Chinese models like deepseek in the short term and hopefully open, self hosted models in the future (which I plan to open source).
The nonstop marketing fluff from anthropic while their service quality and availability noticeably degrades... just continues to destroy my trust in the company.
Their outages are probably not due to their code though. It’s probably their infrastructure that can’t keep up. So seeing failures of infrastructure doesn’t really tell you anything about how good or bad Anthropic makes use of their models.
Putting faith into the claim that recursive self-improvement is close to happening, or that they will coordinate with other companies / the government when the time comes?
> We believe it would be good for the world to have the option to slow or temporarily pause frontier AI development to enable societal structures and alignment research to keep up with the advance of the technology. The Anthropic Institute will conduct research—in collaboration with many others—and take actions to help build the systems that a credible slowdown or pause would require.
Interesting - they're commiting to kickoff policy conventions to organize a world-slowdown of frontier LLM building. If they actually are able to crack it, this will give a much needed breather IMO. As exciting as the last ~6 months have been, there's some bigger questions to go answer now.
We should be skeptical of any major player that advocates for regulating their own industry. In practice, this just means increasing barriers to entry and making it harder to compete with them.
In my mind we should be trying to push AI along the Linux trajectory. You have a free and open source product, developed by a decentralized team with a strong code of ethics, running on commodity hardware. There can still be trillion dollar industries built on top of it, but the core technology is democratized and available to everybody. I don't see how we get there if we allow a handful of companies to dictate where development of the technology goes.
The regulation that is being argued for here is against pushing the frontier. Entering the market with say a new speech to text model is not subject to such regulation. What's needed is something qualitatively different from entry barriers, and of the frontier model companies at least Anthropic and deepmind seem to have enough self-awareness to speak about it. They are finding themselves in a race with possibly catastrophic outcome for humanity and would like to stop, but it needs internation cooperation on a level that no single company can provide.
Wouldn’t this align with their financial interests? In theory the thing that’s keeping them from being profitable (or one of the big things) is the periodic capex expenditures of building new frontier models.
I don't think there's anything inherently bad about Anthropic making a profit. Red Hat makes a profit off of Linux. I'm interested in the democratization of the underlying technology.
I read this differently: they are actually seeing that it's hard to keep advancing frontier models, and now are moving the goal posts so that when they start getting evaluated more harshly, they can point to something like this.
I would assume that shortly after, the solar system will be hyper optimized as well, then the milky way, then the local cluster, and so on. Everything will be close to optimal afterwords, and I sure hope we will have specified the target function for that optimization correctly in the single attempt that we will have had.
the tooling has quite a ways to go to catch up to the llm engines that drive the real value. I have encountered various codex bugs (I know not anthropic) which tell me that.. these billion dollar companies, if they are eating their own dog food, can still release buggy crap software.
Quite aligned with my own experience from harness engineering and winning AI4Science hackathon. During the hackathon I was working as a human optimizer, moving the feedback from test harness running on Claude Code, back to my local Claude Code for analysis-hypothesis-proposal cycle. And in this moment I realized that 2 Claudes talking to each other could actually scale much better.
Broadly agree to this position - I think there are some people skeptical that Anthropic is doing this for regulatory capture - but I think there are being honest about they are seeing and how regulation should catch up.
I for one, believe that we should pause all work on AI for the forseeable future. This is almost impossible to orchestrate - but we should still try nevertheless. Maybe we are not able to pause, but we are able to slow down. That might give us more room, to maybe able to pause in the future. But going ahead is too dangerous.
And its not just Anthropic which is saying this. Even Geoffry Hinton has said the same thing. If there is a non-zero chance that AI can kill all of humanity, and both Geoffry and Anthropic have the same position, then it makes sense for us to be hundred percent sure before we move ahead. Dario/Anthropic have already made their money from AI, maybe they are just being honest about what they think lies ahead.
> A caveat: Lines of code is an imperfect measure, as it measures quantity over quality. So 8× lines of code/engineer/day in the second quarter of 2026 is almost certainly an overstatement of the true productivity gain. Nonetheless, it indicates an acceleration. At Anthropic, we don’t reward people for how many lines of code they write; rather, team members are producing more code simply because they’re using AI systems to write more code.
I simultaneously think the AI revolution is making real revolutionary gains and am mystified by the lying.
An accurate Translation seems to be “we made this shit up, but it feels right”
Until the moment we start bragging about how many lines of code LLMs are saving us, we're walking in the wrong direction. Your programs, designs and architectures is supposed to get better, not add even more boilerplate just because you can produce it faster...
I guess the claim is simply that AI written code is verbose and there’s lots of it being created but I agree, these systems seem to be able to create lots of low quality software, so until FreeCAD has feature parity with Solidworks I’m bearish on the singularity.
Their statement is that they regard lines of code shipped as indicative of self-improvement. So, while a well written coding agent might be a few thousand LOC, Athropic's is bloated like a decomposing whale and over 500K LOC ! What more proof do you need?
The world has been recursively self improving for millenia. Similar to scientology, this is a cult pushing sci-fi nonsense. They are just coupled to an LLM lab to give their stories an aire of seriousness. Imagine scientology starting making laptops.
I have a claw that is instructed to make at least 500 pr per day. It uses Claude, Gemeni and openai and runs basically every few minutes. I use online forums for input for the claw. Moltbook, reddit etc. it's quite funny how it tries to improve itself. But to say it really creates a new skynet. Nah. Not at all. It's more a clutter of useless features or incomprehensible code restructuring.
This more or less agrees with my assessment of recent changes in Claude Code where a lot of new features are either:
- A lot of half-baked features or half-done features.
- Or have significant overlap with existing features, and aren’t clearly an improvement.
More code is not better. More features are not better. It would be lovely to see more intentional design than just more.
I know they’re dog fooding this. I have to believe they have some people with taste. So it makes me wonder if anyone has the time to think or if they’re just shoveling prompts as fast as possible.
Anthropic has finally come around to what others have already realized far sooner. Little time left now. Notice how shallow the arguments and consistently wrong the AGI naysayers have been year after year.
Month 3 - Okay maybe only the SWEs, programming is solved
Month 4 - Announce model that is too dangerous to release
Month 5 - Releases dangerous model
Month 6 - This is it! We will replace AIs with more AIs (*secretly files for IPO)
AI is here to stay, like it or not but it is not the solution to everything. If it is, what is Anthropic's moat? A better model? I don't see any ecosystem being built by them, as MCP is almost obsolete except for some very niche use case. And they're doing stuff that a non-profit version of OpenAI would do. Can we trust a for-profit company to stand against their investors during a conflict of interest? Because running a company for maximum profit versus being ethical is two different end of the spectrum.
Anthropic is providing agentic intelligence as a service. OpenAI and Google deepmind also are in this business.
The problem is, if you’re any sort of knowledge worker, you’re essentially providing the same thing: you’re an intelligence with agency.
MCP is irrelevant. The moat is the quality of intelligence the service providers sell, including you. Tokens aren’t fungible between providers until you measure that they are for your use case, that’s kinda sorta the goal of job interviews.
Thus the moat will be that they’re providing the best models for the things people need other intelligent people for, but we should expect there will be limits on how much share they can economically take assuming competitors are optimizing for slightly different targets (but there’s still significant overlap in capability). This will disappear, but it’s always a question of when. The path matters as much as the destination.
Note that implications for you and me are exactly what the article says they are: nobody knows, but it’ll be a dramatic shift.
i'm waiting for the AI giants to realize that they are burning cash to run their consumer-facing chatbots and that they should kill those products to focus on their enterprise tools.
free chatgpt doesn't need to exist anymore. its job was to build hype/interest and it did.
but take it away and you solve many social problems and annoyances caused by AI with no loss to the upside of AI. no more cheating students in school. no more shitty linkedin posts. no more dangerous "therapy sessions" that give bad advice.
> A meaningful slowdown or pause would require multiple well-resourced labs at or near the frontier, in multiple countries, agreeing to stop under the same conditions. It would also require that each can verify that the others have actually stopped. Due to the unique characteristics of AI systems, the detectability (a lower standard than verifiability) element of this arms control problem is much more challenging than with other technologies. Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead. A credible pause also has to specify what triggers it, what lifts it, and who adjudicates.
And later:
> In the coming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation. We’ll publish what comes out of it. The window to investigate the questions together is here, and people outside AI companies should be involved in this deliberation.
When AI is a more effective capital allocator than NI it will drive capital into the accounts of whoever controls the AI, gaining them increasing decision making power over the economy and culture. Maybe those controllers will be human at first.
Hierarchies exist for a reason, take away the reason and the house of cards eventually collapses — but the house of cards is still a house. When it’s gone, we’re back to laws of the jungle.
I think certain types of people with power, i.e. access to capital, will lose relevance. world will become more meritcratic with ai as leverage to the individual
It’s exactly the opposite I’m afraid. Capital already has more access to AI, both quantitatively (tokens for dollars) and qualitatively (biggest players got Mythos first). Expect this trend to continue.
> If it were possible to effectively slow the development of this technology to give ourselves more time to deal with its immense implications, we think that would likely be a good thing
Even Anthropic wants to Pause AI now. There must really be not much time left for "edging". Please write to your lawmakers, no matter whether you are in the US, Europe, China, or elsewhere. Only an international agreement between governments can enforce an AI-Pause and eliminate the necessity to dangerously push the frontier.
Or agree on finding ways to promote peaceful use of nuclear energy. This has been done, there are thousands of people working on it around the globe and 180+ member states of the IAEA. It's not easy, there have been close calls.
And cooperating interntionally to buy ourselves time to find ways to develop this "last invention" is a way that will do good for humanity seems to be on a similar level.
The metric being tracked, code commits, is hilariously one sided. Philosophically, if you had one part of your work now practically free, you'd like to utilize that freedom to maximally cover for the other parts, for instance:
Instead of thinking about edge cases with brain and whiteboard, you can have the LLMs to simply generate most possibility including tests for it, because that is cheaper. There's probably 50x more commits of which 40 will be revert pairs but we are only twice as fast. And in reality nothing did change because the outcome remain the same. I can't see how it is necessarily different in the LLM space.
I've been struggling to capture this sentiment for myself in a way that hits. If shipping code is a commodity then why is everyone's immediate priority seemingly to ship 10x more code. It just makes no sense. I can't seem to get off this hill. Company-wide AI mandates and 100 fleet Agent orchestration Rube Goldberg machines... it's getting wild out there.
Meanwhile my Claude Pro ($200/year) does force me to smooth out my usage and plan more (Sonnet/Opus advisor split). But other than that, I can't imagine what I'd be doing with 20x (200x?) the compute to code sling. I think I'd lose my mind.
For instance, if I churned out 20x more code, threw away 19x code with rewrites and reverts and discards and accomplished the same project to the same standard 70% faster, would I do it? Yes. The part that matter is not 20x code, it is 70% faster.
Code is both the final product, and a tool to achieve that. We used to have a much harder time to realize the "tool" part, but now we are here. This also means any measurement centered on code being the final product is going to cease being effective or realistic.
This is contentious because I'm not exactly advocating for arbitrary gate-keepers. The nuance is that building usable stuff is hard. And not a matter of shipping more code. I take your point to mean well it depends on what that code is doing. If 20x more code is in a meta-harness of simulation and such to arrive at the leading candidate for what hits production, well then you've got my attention there.
Opus 4.6/4.7 was consistently successful at getting 2-3x speed improvement with just one pass. It can also do the inverse: improve the performance metrics for better quality without causing a significant regression in speed. Then GPT-5.5 turned out to be much better at this workflow, often getting a multiplicative 1.5x-2x improvement above what Opus could do.
I now have quite a few GPT-5.5-optimized projects in various domains that are feature complete and are substantially more performant than existing SOTA implementations that I plan to open source as soon as possible: the bottleneck is polish as usual.
What about the hypothesis that AI is generating more verbose code? I just see the text pretending to acknowledge "LOC != Productivity" and then using it as a metric anyway.
Shifting their focus from Training new models to instead serving inference, they would greatly reduce their spend. In fact this is something being reported on that they are already doing, which is the reason for their first ever profitable quarter.
Its awfully convenient that the company which has greatly reduced its spend on training is now asking for a slow down in this area.
One of my focuses now is my own model-agnostic, harness and workflow orchestration (I know everyone is building these) , baselining on opus, and aiming to transition to Chinese models like deepseek in the short term and hopefully open, self hosted models in the future (which I plan to open source).
The nonstop marketing fluff from anthropic while their service quality and availability noticeably degrades... just continues to destroy my trust in the company.
Interesting - they're commiting to kickoff policy conventions to organize a world-slowdown of frontier LLM building. If they actually are able to crack it, this will give a much needed breather IMO. As exciting as the last ~6 months have been, there's some bigger questions to go answer now.
In my mind we should be trying to push AI along the Linux trajectory. You have a free and open source product, developed by a decentralized team with a strong code of ethics, running on commodity hardware. There can still be trillion dollar industries built on top of it, but the core technology is democratized and available to everybody. I don't see how we get there if we allow a handful of companies to dictate where development of the technology goes.
https://www.italianrenaissance.org/wp-content/uploads/2012/0...
Or is this?
https://www.egypttoursportal.com/images/2024/02/Ouroboros-Sy...
So based on my experience with the verbosity and non-DRYness of LLM code, a solid 2.5x in value delivered. Not bad!
I for one, believe that we should pause all work on AI for the forseeable future. This is almost impossible to orchestrate - but we should still try nevertheless. Maybe we are not able to pause, but we are able to slow down. That might give us more room, to maybe able to pause in the future. But going ahead is too dangerous.
And its not just Anthropic which is saying this. Even Geoffry Hinton has said the same thing. If there is a non-zero chance that AI can kill all of humanity, and both Geoffry and Anthropic have the same position, then it makes sense for us to be hundred percent sure before we move ahead. Dario/Anthropic have already made their money from AI, maybe they are just being honest about what they think lies ahead.
I simultaneously think the AI revolution is making real revolutionary gains and am mystified by the lying.
An accurate Translation seems to be “we made this shit up, but it feels right”
Don't ask people to explain the article to you if you're too lazy to open it yourself.
- A lot of half-baked features or half-done features. - Or have significant overlap with existing features, and aren’t clearly an improvement.
More code is not better. More features are not better. It would be lovely to see more intentional design than just more.
I know they’re dog fooding this. I have to believe they have some people with taste. So it makes me wonder if anyone has the time to think or if they’re just shoveling prompts as fast as possible.
https://intelligence.org/agi-ruin/
Month 1 - 6 months to AGI
Month 2 - We will Replace all jobs
Month 3 - Okay maybe only the SWEs, programming is solved
Month 4 - Announce model that is too dangerous to release
Month 5 - Releases dangerous model
Month 6 - This is it! We will replace AIs with more AIs (*secretly files for IPO)
AI is here to stay, like it or not but it is not the solution to everything. If it is, what is Anthropic's moat? A better model? I don't see any ecosystem being built by them, as MCP is almost obsolete except for some very niche use case. And they're doing stuff that a non-profit version of OpenAI would do. Can we trust a for-profit company to stand against their investors during a conflict of interest? Because running a company for maximum profit versus being ethical is two different end of the spectrum.
The problem is, if you’re any sort of knowledge worker, you’re essentially providing the same thing: you’re an intelligence with agency.
MCP is irrelevant. The moat is the quality of intelligence the service providers sell, including you. Tokens aren’t fungible between providers until you measure that they are for your use case, that’s kinda sorta the goal of job interviews.
Thus the moat will be that they’re providing the best models for the things people need other intelligent people for, but we should expect there will be limits on how much share they can economically take assuming competitors are optimizing for slightly different targets (but there’s still significant overlap in capability). This will disappear, but it’s always a question of when. The path matters as much as the destination.
Note that implications for you and me are exactly what the article says they are: nobody knows, but it’ll be a dramatic shift.
free chatgpt doesn't need to exist anymore. its job was to build hype/interest and it did.
but take it away and you solve many social problems and annoyances caused by AI with no loss to the upside of AI. no more cheating students in school. no more shitty linkedin posts. no more dangerous "therapy sessions" that give bad advice.
> A meaningful slowdown or pause would require multiple well-resourced labs at or near the frontier, in multiple countries, agreeing to stop under the same conditions. It would also require that each can verify that the others have actually stopped. Due to the unique characteristics of AI systems, the detectability (a lower standard than verifiability) element of this arms control problem is much more challenging than with other technologies. Training runs are far easier to conceal than missile silos, their inputs are general-purpose, and the incentive to defect quietly is enormous, because whoever continues while others pause could inherit the lead. A credible pause also has to specify what triggers it, what lifts it, and who adjudicates.
And later:
> In the coming months, we will organize conversations where policymakers, researchers, civil society, and other AI companies can help answer some of the questions this piece raises, especially around full recursive self-improvement and how to create better options for coordination and deliberation. We’ll publish what comes out of it. The window to investigate the questions together is here, and people outside AI companies should be involved in this deliberation.
It feels like both open source can flourish while the frontier is deliberately regulated?
Consequences are: financial crisis.
Be careful what you wish for IOW.
So the most capital intensive industry we've ever created will put less power in the hands of those with capital?
I'm sorry, I have no idea how you came to that conclusion...
Without some kind of income redistribution we are sailing into dark waters.
Workingmen of all countries unite!
Translation: hahahahahahahahahhahahaha but in your defense, I would give anything to be wrong.
Even Anthropic wants to Pause AI now. There must really be not much time left for "edging". Please write to your lawmakers, no matter whether you are in the US, Europe, China, or elsewhere. Only an international agreement between governments can enforce an AI-Pause and eliminate the necessity to dangerously push the frontier.
https://pauseai.info/
And cooperating interntionally to buy ourselves time to find ways to develop this "last invention" is a way that will do good for humanity seems to be on a similar level.