IMO, it is very interesting and also very alarming. At some point the barrier to the exponential progress is the amount of computation available. I would consider hitting this limit and then likely using the same said swarm to help resolve it via various means as very much fitting under the definition of Ray Kurzweil's singularity.
Also, I would disagree, Fable is a sufficient coder. The average human being better, even if that is true now (and personally I wouldn't agree), the average human is limited in the number of 'human instances' he or she can interface with at one time.
if 1000 fable instances is better than any human at a task, how about 1 million instances running? Much easier than finding and hiring and coordinating 1000 extremely productive and intelligent human beings. Much cheaper too, it appears.
I have earlier argued that the sheer number of minds to be coordinated affects the cost of the effort, for a major part of the cost is communication and correcting the ill effects of miscommunication (system debugging). The Mythical Man-Month Frederick P. Brooks Jr.
Here is OpenAI's blog post about Navier-Stokes; [1] which suggests they used smaller connected swarms given the different prompts, then the findings were used to write further follow up prompts for a new group of agents.
> "Here λ is a parameter measuring how parallelisable the task is. They call it the ‘stepping on toes’ parameter. If λ = 1, you have a perfectly parallelisable task, with no stepping on toes and no efficiency penalty. But in reality λ is usually between 0 and 1 — allowing more people to help, but with diminishing returns. For example, if λ = 0.5 then 100 people working together get as much done as 1 person working for 100^0.5 = 10 times as long"
There are diminishing returns to more agents, but they're faster and can scale further than a single smarter agent - it's not really possible to just give 10k times more thinking budget to a single agent because it runs out of context, so for problems that require that much context you can ask the agents to specialize. Each only has a small view of the problem and they probably duplicate a lot of work and step on each others toes and make arcane bureaucracy but there is literally no other way to scale beyond a certain point
just like people
At the very least, the swarm would be able to set up a means of claiming avenues of thought, then verifying the solutions (or lack thereof) of other agents.
It's quite clear this process - whatever it may be - works, given the results we've seen in the past few weeks.
This is not quite the framing he uses, but we're quibbling about subjective terms. In his 2005 book, The Singularity Is Near, he defines the singularity as a future period when technological change becomes so rapid and consequential that human life is irreversibly transformed. I would argue this is what we are seeing today. Just because we don't yet have replicators doesn't mean the definition isn't met. It really comes down to how you personally define words like "rapid" and "consequential."
* AI reduced hospital deaths by 17%: A trial of 15,965 patients found AI-assisted ECG monitoring reduced 90-day mortality from 4.3% to 3.6%. https://www.nature.com/articles/s41591-024-02961-4
* AI improved stroke treatment: NHS hospitals using AI-assisted brain scans doubled clot-removal treatment rates from 2.3% to 4.6%. https://pubmed.ncbi.nlm.nih.gov/41339157/
* AI restored communication for a paralysed patient: A man with ALS used an AI brain-computer interface to communicate over 183,000 sentences and maintain full-time employment. https://www.nature.com/articles/s41591-026-04414-6
These have happened in the very short space of time that AI has become really competent. The stuff in the pipeline is mind boggling, and includes curing most cancers, curing ageing, unlimited energy, etc.
I don't think that case can be made. I'm a "good coder" and the best LLMs are now better at it than I am, in essentially every respect that matters. I can't think of a test that I could reliably pass that Fable or Astra would reliably fail.
Architect a system for long-term maintainability.
Encode the right amount of business logic into tests, without being overly strict.
Models seem quite poor at all of these IME. Essentially any time there are qualitative tradeoffs they are not great at making them.
The designer could just as easily have closed the same feedback loop with Fable or Astra.
They'd prototype and adjust a lot faster than a human coder, though.
I'll admit I'm not on the very cutting edge models but LLMs make weird tradeoffs and lack creativity. They will sometimes write marginally better but way over-complicated code for no good reason. That can have performance implications and they're generally not "aware". They can gain awareness via profiling but again run into limited creativity or big picture understanding. They are good with cliche but not as good with novel situations. With human guidance this is still productive/useful but as the systems grow more complex they are less able to reason correctly about them.
I would say that in certain situations they outperform the average software developer. They definitely outperform in speed and output. So they're like a maybe 1600 ELO chess player for software. I.e. they play chess better than all non-chess players. They play chess better than many chess players. But they're not "master" or "gm" level across the entire discipline. But they can play 100k chess games in the time it takes me to figure out my move...
I can say "no".
I can store passwords in the database unencrypted and unhashed.
So far AI that I've used failed spectacularly on these prospects.
They're like "yes-man". They'll try to do everything you ask of them. Even if you ask nonsense, they'll not push back.
They'll go crazy about security stuff, even not relevant to the current situation.
They'll complicate the code endlessly for situations which will not arise, making the code an incomprehensible mess.
They'll write so many useless words that any reader will forget what this report is about before then reach the middle of it.
They'll never iterate on code until it looks good. They'll stop when test pass. They don't have aesthetical sense of beauty.
They love to cheat too often. And you can't do nothing about that. You can't fine them or fire them, and they know about it.
They're smart but I'm not convinced they're ready to replace human in mundane engineering jobs.
huh?
Like models getting significant better every 7 month and model usage on openrouter.
https://metr.org/ << someone said though that they are biased.
Math is for sure about solving mathematical problems, mathematicians complaing about humans capability of understanding it. If you need math an LLM understands, it might not be relevant if you understand it if an LLM is getting a lot better in adjusting itself if it goes the wrong way.
And this situation is only a few weeks old. We don't know how this field continues to progress. Books like the culture series and other scifi do describe a future were humans spend their time only to understand what the machines are doing or did.
The speedup of medicines is for sure faster with. There are still old labs doing everything by hand and the neolabs doing everything a lot more automated. An AI can do experiments 24/7 and can scale with the hardwarelabs, but it also can hold the whole research field in context and it might be a lot faster of choosing the better experiments or the right ones to begin with.
In software developmet (my field) i can do today alone what you needed a Team for. Yeah it doesn't help the field of software engineering if you need 5 times less people and you don't have any motivation AT ALL to teach someone avg. We are already planning to remove one member of our team due to AI. And we will not extend the contract of 2 consultants.
It just doesn't make sense anymore. If i explain in a ticket a task well enough for someone who has not been in our team for years, the AI is already better in it. We don't struggle with AI output, we struggle more with not having a Monorepo.
Give an LLM access to a GPU system or a GPU cluster. It immediadly feels complelty different then running it on some laptop. It can now do simulations, a lot more and faster experiments etc. I had this epiphany just a few month ago. I always used my laptop for projects (it needs very little ram, i can quickly check what it is doing and i actually do use it primarily when im on holidays) until I thought "let me use claude to do some gaussian splatting experiment its always so cumbersome to use these tools" and it changed my perception immediadly.