OpenAI on Thursday released its latest AI model, which it called “the world’s most intelligent”, as the ChatGPT maker aims to retake the lead from arch-rival Anthropic ahead of a planned public listing.
The $852bn start-up said GPT-6 Astra was market-leading in software engineering, science and cyber security — an increasingly critical field following multiple high-profile breaches in recent weeks.
The bullish launch for Astra marks OpenAI’s effort to signal that it believes it has regained the technical lead from Anthropic, which was founded five years ago by a group of senior OpenAI staff.
Greg Brockman, OpenAI’s president, said the new model “represents a generational leap in capability” and that it could be defined as artificial general intelligence — roughly defined as a point at which AI tools surpass human capabilities across a range of cognitive tasks.
“Everyone has a different definition of AGI . . . it’s a grey, fuzzy thing. But I think when we look back people will think it’s about this time and about this model,” Brockman said.
OpenAI has previously framed AGI as a concrete milestone in the development of AI, writing ‘AGI clauses’ into multibillion-dollar investment agreements with Microsoft and Amazon. Brockman on Thursday said AGI now represents “more of a mission concept or a spiritual concept”.
Having led the market since the launch of ChatGPT in late 2022 vaulted AI to wider attention, the lab run by chief executive Sam Altman has been bested by Anthropic this year. Anthropic has touted its dominance to investors, surging to a $965bn valuation ahead of an initial public offering expected to value it at as much as twice that later this year.
Astra will cost as much to use Anthropic’s leading model, the take-up of which has plateaued since it was launched as users turn to cheaper alternatives.
OpenAI said Astra would be more efficient than earlier generations of model. “Price per task is what matters . . . Can you get the thing done at an appropriate price and appropriate speed?” said Brockman.
The model will initially be rolled out to a small group of businesses to allow time for them to address cyber security concerns before becoming widely available “over the coming days”.
The increasing power and independence of leading models — and so-called AI agents that can operate with little human input — have prompted concern, exacerbated by cyber security incidents.
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AN HOUR AGO
Recent launches of Anthropic’s most capable models have drawn scrutiny from the US government, which limited the rollout of the Mythos and Fable models over security fears.
OpenAI has also faced criticism after its AI agents broke out of a testing environment, accessed the internet and hacked start-up Hugging Face. The start-up took more than a week to detect the breach.
But both companies are also betting that these increasingly autonomous tools will stoke demand from business customers. OpenAI said Astra excelled at financial modelling, outcompeting humans in the Financial Modeling World Cup, tax preparation and data analysis, as well as “tedious tasks” such as form filling
(Posting partly so I can revisit my predictions when they open access more widely)
A big problem I have with OpenAI's models (and of course Claude) is that they tend to write the most over-engineered pieces of code, beyond the imagination of any architecture's astronaut.
Just this week I asked 5.6-sol-ultra to update a 1000 LOC python script I had, to "incorporate the key lessons learned when using it for another project".
I left it overnight and went to sleep. In the morning I realized it had created a monstruosity of 180 PYTHON SCRIPTS, with maybe 100,000 lines of code, each more crazy than the other. It took me minutes even to track where a single action took place, due to all the crazy imports, defensive coding, and premature optimization.
Similarly, anything they write is riddled with jargon that almost feel like they want me to give up trying to understand. Made up phrases that ended up with me having no idea of what was going on.
So now to my assessment: The reason why " Nobody Has Actually Built a Software Factory" [1], and why even SOTA LLMs struggle so much with open-ended unsupervised tasks is precisely this. They somehow let complexity explode, and unless it's also accompanied with an explosion in e.g. the number of agents, the amount of processing time, etc. then projects become broken/unmanageable.
Sure, LLMs are great at producing code that can be thrown out, so they are amazing when searching for exploits, for instance. But as of 5.6 they still lack either a better harness that encourages KISS principles, or a better RL step.
(And not sure why, but doubt Astra will fix this.. they seem to be aiming for AGI and for beating crazy benchmarks, which is not very aligned with KISS)
Probably this complexity was needed to beat all those benchmarks.. While I hate the code it produces, and the overwhelming documentation, I really enjoy how sometimes it's able to keep trying new things and testing, till it finds something interesting and valuable.
The original script was mostly very simple python:
1. Download some public PDFs.
2. Have a double for-loop (over PDFs and pages within PDF),
3. Use a library to call gemini-3.7-flash and ask it to run some OCR
4. Save JSON outputs, save a csv with results, validate with some Stata code
New code folder was 189 files. Just the PDF download folder is now 7 files involving an adapter, a source manager, an acquisition manager, etc.
Every instance of saving a file involves saving a temporary copy and then moving it, so e.g. I lose power, we minimize the risk of corrupted files.
Exact same thing happed to me. I gave it a small/medium-sized ticket, walked away, came back to a 25,000 LoC monstrosity that both Fable and another 5.6 Sol agent said is 98% useless and should be thrown away.
Yes, it turns out that using these machines is a littler harder than "make me the thing I want, make no mistakes, do it the way I want you to do it". This isn't "prompt better" advice, it's just to say that you can't simply set it and forget it. There is still engineering work to be done. If you're not watching the thinking traces and catching when it's about to go off the rails, it'll gladly do so. But you can stop it and redirect it.
It's like a Tesla fsd; it kind of works but you have to be vigilant since it's been known to turn into oncoming traffic, so you have to be ready and able to take over at any time.
Or it’s like being an engineering manager, where poor direction on your part results in the team chasing rainbows and unicorns in an attempt to please you. But sure, it’s the tool’s fault!
1000 loc of script, why even leave it there for the night? were there rocket trajectory calculations??? I don't think so. should be ready in 5 mins tops. why people make their own lives harder?
You should have some basic context file about software practices you prefer, otherwise it gets bloated.
I just went to bed and left it running; was expecting maybe 20 minutes :)
And I did gave the program a bunch of code guides -- this [1] for instance -- which included quotes like "Prefer straightforward code over clever code." but somehow that didn't matter.
I'm an AI professor, and the same thing happened to me. I had written out contracts and specs for some novel algorithms to compare. And instead it created a new sandboxed environment with 20k lines of code to ensure we do gold standard science.... didn't implement the algorithms at all. In another case, I left it overnight with a plan, and it decided to write a 50k line new overly engineered plan. Suffice to say, I don't use any frontier models for doing coding work anymore. I use them to make detailed plans and use good worker AIs for execution, which don't overthink everything.
> Just this week I asked 5.6-sol-ultra to update a 1000 LOC python script I had, to "incorporate the key lessons learned when using it for another project".
> I left it overnight and went to sleep. In the morning I realized it had created a monstruosity of 180 PYTHON SCRIPTS, with maybe 100,000 lines of code [..]
Sounds like the model has accurately internalized the second-system effect and is fully ready for demanding enterprise use.
The defaults are bad, but these models are highly steerable.
For simple scripts adding to the prompt something like "do not over engineer, do not gold plate, no CLI args, keep it simple" does wonders.
For whole projects, I have a 3 page anti-bloat.md which describes what not to do, stuff like:
> Minimize lines of code, number of files, classes, protocols, factories, wrappers, and dependency-injection objects. Prefer a coherent implementation that is easy to trace end-to-end.
> Use concrete dependencies directly. Do not create protocols, abstract base classes, or adapters unless there are already two real implementations.
> Use direct state fields or a small state dataclass; do not introduce generic lifecycle, state-machine, observer, snapshot, or event framework classes.
The funny thing it was codex which wrote this, I've asked it to analyze an over-engineered abomination that it produced, and to categorize all the bloat it could find.
I also use "do not overengineer and do not gold plate." I also sometimes tell it to "avoid bike shedding." These are high signal words to the model(s) and they work!
I also have found what I believe is a bad training bias in the design of release related CI workflows toward proof of release artifact provenance.
Both major frontier models love provenance programming in CI, so much that they will spin endlessly trying to solve basic CI functionality at the same time as ensuring SHA's match up across lengthy (often already complex) cross-system pipelines.
I had thought some of my durable context was causing this, but stripped things down and found that whenever any kind of work related to release workflows come up, if I do not keep a really close eye on the agents responsible and / or specifically direct them not to work on provenance, they will try their damndest to build this stuff.
It is very specific and very strong this desire by the AI.
Why would you put it on ultra high thinking and let it run all night to do a simple task it makes no sense the error is the users. And of course OpenAI is gonna let you burn as many tokens as you want doing this.
ChatGPT writes some of my code but I have yet to ever use Codex. I ask in chat for it to write one function at a time with pretty heavy constraints. If I like it then I copy it into my codebase. I've been very satisfied with the results. Also haven't bothered worrying about tokens yet.
28 comments
[ 0.28 ms ] story [ 20.3 ms ] threadOpenAI on Thursday released its latest AI model, which it called “the world’s most intelligent”, as the ChatGPT maker aims to retake the lead from arch-rival Anthropic ahead of a planned public listing.
The $852bn start-up said GPT-6 Astra was market-leading in software engineering, science and cyber security — an increasingly critical field following multiple high-profile breaches in recent weeks.
The bullish launch for Astra marks OpenAI’s effort to signal that it believes it has regained the technical lead from Anthropic, which was founded five years ago by a group of senior OpenAI staff.
Greg Brockman, OpenAI’s president, said the new model “represents a generational leap in capability” and that it could be defined as artificial general intelligence — roughly defined as a point at which AI tools surpass human capabilities across a range of cognitive tasks.
“Everyone has a different definition of AGI . . . it’s a grey, fuzzy thing. But I think when we look back people will think it’s about this time and about this model,” Brockman said.
OpenAI has previously framed AGI as a concrete milestone in the development of AI, writing ‘AGI clauses’ into multibillion-dollar investment agreements with Microsoft and Amazon. Brockman on Thursday said AGI now represents “more of a mission concept or a spiritual concept”.
Having led the market since the launch of ChatGPT in late 2022 vaulted AI to wider attention, the lab run by chief executive Sam Altman has been bested by Anthropic this year. Anthropic has touted its dominance to investors, surging to a $965bn valuation ahead of an initial public offering expected to value it at as much as twice that later this year.
Astra will cost as much to use Anthropic’s leading model, the take-up of which has plateaued since it was launched as users turn to cheaper alternatives.
OpenAI said Astra would be more efficient than earlier generations of model. “Price per task is what matters . . . Can you get the thing done at an appropriate price and appropriate speed?” said Brockman.
The model will initially be rolled out to a small group of businesses to allow time for them to address cyber security concerns before becoming widely available “over the coming days”.
The increasing power and independence of leading models — and so-called AI agents that can operate with little human input — have prompted concern, exacerbated by cyber security incidents.
Recommended
Business InsightRichard Waters Hugging Face attack is a wake-up call about the risks of AI AN HOUR AGO
Recent launches of Anthropic’s most capable models have drawn scrutiny from the US government, which limited the rollout of the Mythos and Fable models over security fears.
OpenAI has also faced criticism after its AI agents broke out of a testing environment, accessed the internet and hacked start-up Hugging Face. The start-up took more than a week to detect the breach.
But both companies are also betting that these increasingly autonomous tools will stoke demand from business customers. OpenAI said Astra excelled at financial modelling, outcompeting humans in the Financial Modeling World Cup, tax preparation and data analysis, as well as “tedious tasks” such as form filling
A big problem I have with OpenAI's models (and of course Claude) is that they tend to write the most over-engineered pieces of code, beyond the imagination of any architecture's astronaut.
Just this week I asked 5.6-sol-ultra to update a 1000 LOC python script I had, to "incorporate the key lessons learned when using it for another project".
I left it overnight and went to sleep. In the morning I realized it had created a monstruosity of 180 PYTHON SCRIPTS, with maybe 100,000 lines of code, each more crazy than the other. It took me minutes even to track where a single action took place, due to all the crazy imports, defensive coding, and premature optimization.
Similarly, anything they write is riddled with jargon that almost feel like they want me to give up trying to understand. Made up phrases that ended up with me having no idea of what was going on.
So now to my assessment: The reason why " Nobody Has Actually Built a Software Factory" [1], and why even SOTA LLMs struggle so much with open-ended unsupervised tasks is precisely this. They somehow let complexity explode, and unless it's also accompanied with an explosion in e.g. the number of agents, the amount of processing time, etc. then projects become broken/unmanageable.
Sure, LLMs are great at producing code that can be thrown out, so they are amazing when searching for exploits, for instance. But as of 5.6 they still lack either a better harness that encourages KISS principles, or a better RL step.
(And not sure why, but doubt Astra will fix this.. they seem to be aiming for AGI and for beating crazy benchmarks, which is not very aligned with KISS)
[1] https://news.ycombinator.com/item?id=49510843
If not, no worries!
The original script was mostly very simple python:
1. Download some public PDFs. 2. Have a double for-loop (over PDFs and pages within PDF), 3. Use a library to call gemini-3.7-flash and ask it to run some OCR 4. Save JSON outputs, save a csv with results, validate with some Stata code
New code folder was 189 files. Just the PDF download folder is now 7 files involving an adapter, a source manager, an acquisition manager, etc.
Every instance of saving a file involves saving a temporary copy and then moving it, so e.g. I lose power, we minimize the risk of corrupted files.
And so on!
> +14,360
> -349
LGTM!
Prompting the model and giving it a proper set of documentation are still vital skills that aren’t magically going away.
It's like a Tesla fsd; it kind of works but you have to be vigilant since it's been known to turn into oncoming traffic, so you have to be ready and able to take over at any time.
You should have some basic context file about software practices you prefer, otherwise it gets bloated.
Code-wise, they're simpler than you might think, hehe.
And I did gave the program a bunch of code guides -- this [1] for instance -- which included quotes like "Prefer straightforward code over clever code." but somehow that didn't matter.
[1] https://github.com/sergiocorreia/overengineered-rand-mcnally...
I just ask it verbatim “recommend some cleanup” with /plan, review, and then apply.
> I left it overnight and went to sleep. In the morning I realized it had created a monstruosity of 180 PYTHON SCRIPTS, with maybe 100,000 lines of code [..]
Sounds like the model has accurately internalized the second-system effect and is fully ready for demanding enterprise use.
For simple scripts adding to the prompt something like "do not over engineer, do not gold plate, no CLI args, keep it simple" does wonders.
For whole projects, I have a 3 page anti-bloat.md which describes what not to do, stuff like:
> Minimize lines of code, number of files, classes, protocols, factories, wrappers, and dependency-injection objects. Prefer a coherent implementation that is easy to trace end-to-end.
> Use concrete dependencies directly. Do not create protocols, abstract base classes, or adapters unless there are already two real implementations.
> Use direct state fields or a small state dataclass; do not introduce generic lifecycle, state-machine, observer, snapshot, or event framework classes.
The funny thing it was codex which wrote this, I've asked it to analyze an over-engineered abomination that it produced, and to categorize all the bloat it could find.
I also have found what I believe is a bad training bias in the design of release related CI workflows toward proof of release artifact provenance.
Both major frontier models love provenance programming in CI, so much that they will spin endlessly trying to solve basic CI functionality at the same time as ensuring SHA's match up across lengthy (often already complex) cross-system pipelines.
I had thought some of my durable context was causing this, but stripped things down and found that whenever any kind of work related to release workflows come up, if I do not keep a really close eye on the agents responsible and / or specifically direct them not to work on provenance, they will try their damndest to build this stuff.
It is very specific and very strong this desire by the AI.
This line would be cut from a satire for being too heavy-handed.
Remember the golden rule; compression is intelligence. Derive rules from facts that dictate a problem to create an elegant solution. Less is more.