This is obviously the case to me, but I think HN is very anti-AI.
I genuinely don't believe that they sat down in a board room and said "yeah lets specifically release this now before an IPO so we can juice it!" They haven't even announced an IPO date. So is every blog on capabilities before that date just "pumping up the value of the stock before the IPO?"
I find this version unlikely, since companies very rarely genuinely believe what they preach in PR campaigns. It's always some sales and marketting dudes and gals trying to polish up something as something more than it is. Which is very annoying. We can now choose between Anthropic being the one exception to this, while having huuuuge incentive to hype up their product, or we just write it off as more marketting fluff.
I would be very surprised if this is an actual thought-out PR strategy. I am far more inclined to believe that their employees are just bought-in to the future where AI is genuinely transformative.
Whether they are right of wrong is another matter, but their claims also don’t seem too far out of the realm of possibility to me.
Coding agents have fundamentally changed my day-to-day job. In the last year, my work has shifted from me writing all of my code, to me writing very little code and spending most of my time on understanding problems better and setting direction, and reviewing, verifying, and polishing the output of coding agents. It has been quite a drastic change.
It is not that outlandish to suggest that coding agents could continue to improve at such a drastic rate over the next year. And the implications of that could be quite large! Even just the implications of more white-collar workers adopting tools like Cowork seems potentially very large, with tools that already exist today. It seems sensible to at least consider this as a possibility.
Dario is no John hammond though. That'd be altman. He actually has the discipline and background as an ai scientist to tell what the potential failure modes are. You're right, he might still be just hyping things up, but generally i'd give more benefit of doubts to anthropic. Precisely because Dario was a scientist and I'd stand by it. People who get their phd in science already self-select, or proven at least to be made of different stuff.
Likewise, people don't as easily blame ilya for 'hyping things up' when he said these things.
Also talk about incentives, there are also incentives to lower their valuation. If you wanna be vigilant against social engineering i'd be wary of that too.
These are moot anyway though cause the article isnt even making any super strong claim. If you read it it's no big deal
If they actually have concerns they can communicate them directly and privately. There are less than 10 companies, in only 2 countries, with advanced enough AI programs to qualify for this type of concern. And Anthropic has the phone numbers for all of them.
Companies do tons of communication and work directly, without press releases or blog posts. If a statement is released publicly, it is done for a PR purpose.
Maybe my bar for what constitutes a breakthrough is lower than other people's, but all of these seem like breakthroughs to me:
NLP as a field saw huge shifts. NLP tasks that used to be complex and inaccurate can now be setup very easily and quickly using structured outputs from LLMs, often with greater accuracy.
A small charity I help with has now been able to build their own website to manage their day-to-day operations. It saves them a lot of time, and it was vibe-coded using Manus. I don't think people appreciate how much room there is left for bespoke software to have big impacts on small organisations that can't afford to hire developers. The cost for software like the one they made has gone from 10s of thousands of dollars to $10/month and volunteer hours.
My brother has recently been setting up Cowork to do an automatic review of contracts before human review, and he said it is far more diligent than people when it comes to routine things to check. This is another huge breakthrough for not just efficiency, but the quality of work.
I really don't think we can discount AI finding bugs and vulnerabilities. If you care about code quality and keep up review standard, LLMs can help you write more robust software. AI has found a huge number of bugs for me before they hit production, including potential out-of-bounds memory accesses and segfaults.
ChatGPT has 1 billion MAU. People are now getting life advice, financial advice, and mental health help from chatbots at a scale and cost that no human support network could match.
Also, they have done a good job shutting down the psychotic behavior you could get from 4o era models. If there are remaining issues like that they ought to fix them too.
> ChatGPT has 1 billion MAU. People are now getting life advice, financial advice, and mental health help from chatbots at a scale and cost that no human support network could match.
You can claim the use of AI is unethical, or the work as derivative, but AI being used as a tool in no way precludes something from being art. It is thought provoking and challenging, it seems like textbook art to me, and it’s clearly struck a chord here. There is no “minimum effort” required for something to be art.
I personally found the contrast with the original “They’re made out of meat” to be really interesting. I don’t care that AI was used during its creation at all.
It seems to me that since the advent of image generators, art has been firmly defined by artists to mean that it was made by a human. But there might be a spectrum of human involvement where the less a human is involved the less it's art.
What happens too often during these discussions is that someone who writes "make me a cool image" gets conflated with someone used ai to fixup a small rock in their natural landscape drawing. (two extreme ends)
One problem though, is that we don't really know how much the supposed human author was involved in the piece. Now that it's becoming hard to judge, people against ai art can proudly change their opinion on on a piece once they learn that it was made by ai. I've come to think this is somewhat respectable, like you see a video of some extraordinary event (before ai) and then you learn that it was fake, just for views or something.
But on top of all this, there are different ways to "consume" art. Artists may think more about who the artist is as a person and what they felt when they made the piece, while non-artists may just enjoy the piece for what it is, detached from the artist. These two perspectives clash a lot.
Enterprises are paying API prices, which are ~9x the price of the plan for the same usage. A lot of people on the plans are not maxing them out either.
I don’t remember ever hearing Dario or Sam recommend replacing people. Rather they say that smaller groups of people can do more work, so hiring will slow because small teams can do more.
The only times when people talk about actual full replacement of people is always when they are talking about some “future AGI” that is far more capable than the tools we have today.
Or, tokens are more like energy and prices will drop over time until they reach some equilibrium.
The big labs are actively moving into the application layer, where they’ll have more pricing power. Maybe that layer will end up with a Mac (Anthropic) vs Windows (OpenAI) vs Linux (open-source) dynamic as well if they can create a moat. But so far it’s pretty easy to move between providers.
You don’t need to write code by hand to learn from iterations and experiments. I run more experiments and try out more different solutions than I ever could before, and that leads to better decisions. I still read all the code that gets shipped, and don’t want to give that up, but the idea that all craft and learning is lost when you don’t is a bit silly. The craft/learning just moves.
The entire mistake you are making is comparing using AI to skimming textbooks, or taking shortcuts. Your entire premise is wrong.
People who care about craft will care about the quality of what they produce whether they use AI or not.
The code I ship now is better tested and better thought through now than before I used AI because I can do a lot more. That extra time goes into additional experiments, jumping down more rabbit holes, and trying out ideas I previously couldn’t due to time constraints. It’s freeing to be able to spend more time to improve quality because the ROI on time spent experimenting has gone up dramatically.
a) I cannot effectively review more than 2000 lines of code a day. The LLMs can produce much more than that.
b) Even if I accepted my reading throughput limitations as the cost of being in the loop, reading is not enough to keep cognitive debt in check: my skills will atrophy if I do not participate in the writing ("What I cannot create I cannot understand").
So, to me, it seems like we, humans, either have to come up with higher (and deterministic) abstractions than code to communicate with LLMs or resign ourselves to letting the LLM guess what we want from English and then banging on the output to see if it sort of works. This later state of affairs seems to be what the current trend is and I find that absolutely revolting.
I think the distinction is that for experiments and prototypes the behaviour of the final system is what we are trying to design. We can experiment and see the tradeoffs and explore the design space before committing to a direction. And then we can sit down and produce the final code to a quality we are happy with. If you are serious about this process, there is no way you are producing 1000s of lines of code a day, unless it is trivial boilerplate.
In terms of higher-level abstractions, I agree this is one particularly treacherous rung on the ladder of abstractions. Previous abstractions like compilers or garbage collectors have at least had more structure/rules to rely upon. I don't know exactly how that will look but I don't think we will solely be relying on banging on the output, we will also be spot-checking the source code, using profilers or other tools to inspect the behaviour of systems, and asking the agent to explain the architectural decisions made. I'm not sure exactly how this will look, but I do believe that people who care will still find ways to do good work.
My agentic workflow probably differs somewhat from the majority of others here, but I can positively guarantee you that both the quality and quantity of my output is significantly higher than it has ever been, in my 20-something years of writing code. And at least 90÷ of the code I've written this year was output by an LLM. You can keep sticking your head in the sand, in the end it will only be to your own detriment.
Well you have obviously already made up your mind, so have fun with your confirmation bias. We'll all be over here having a good time, getting more work done. Feel free to come over when you put down your grudge.
Imo the biggest issue with these no-code architects has been that you could become one without ever having coded at any noteworthy level of skill (which meant most of them were like this).
In my experience, in a lot of organizations, a lot of people either lacked the ability or the willingness to achieve any level of technical competence.
Many of these people played the management game, and even if they started out as devs (very mediocre ones at best), they quickly transitioned out from the trenches and started producing vague technical guidance that usually did nothing to address the problems at hand, but could be endlessly recycled to any scenario.
This is an unpopular take, but when I was in undergrad maths in an old-school two-semester courses with one exam (exercises + oral) to cover it at the end, I was able to get to 60-80% score on exercises when I did just theory as prep.
I couldn't get exercises done where there were tricks/shortcuts which are learned by doing a lot of exercises, but for many, these are still the same tricks/shortcuts used in proofs.
This was indeed rare among students, but let's not discount that there are people who _can_ learn from well systemized material and then apply that in practice. Everyone does this to an extent or everyone would have to learn from the basics.
The problem with SW design is that it is not well systemized, and we still have at least two strong opposing currents (agile/iterative vs waterfall/pre-designed).
It still surprises me how effective the /simplify skill is.
I’ve also had some great results with a /reflect skill that asks the agent to look at the work in the broader context of the project. But those are the only two skills I use regularly that aren’t specific to our company, codebase, or tools.
Some engineers I work with have had less than desirable results with /simplify but it overall seems to work! I used to use some of the humanlayer subagents but they haven't been updated in several months
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