261–270 of 374 posts
If "Era of Scaling" means "era of rapid and predictable performance improvements that easily attract investors", it sounds a lot like "AI summer". So... is "Era of Research" a euphemism for "AI winter"?
Bug bounty will be replaced by research bounty.
Earlier quoted context omitted.
If all frontier LLM labs agreed to a truce and stopped training to save on cost, LLMs would be immensely profitable now.
That isn't what I've seen: https://www.wheresyoured.at/oai_docs/
[1]: https://martinalderson.com/posts/are-openai-and-anthropic-re..., https://github.com/deepseek-ai/open-infra-index/blob/main/20...
[2]: https://www.snellman.net/blog/archive/2025-06-02-llms-are-ch...
> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things about the models right now. As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workf…
Another limitation that I see right now is that for "economic impact" you want the things to have initiative and some agency, and there is well-justified hesitancy in providing that even where possible. Having a bunch of smart developers that are not allowed to do anything on their own and have to be prompted for every single action is not too advantageous if everyone is human, either ;)
So is the translation endless scaling has stopped being as effective?
I'll be convinced LLMs are a reasonable approach to AI when an LLM can give reasonable answers after being trained with approximately the same books and classes in school that I was once I completed my college education.
> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things about the models right now. As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workf…
You integrate, you build the product, you win, you don’t need to understand anything in terms of academic disciplines, you need the connections and the business smarts. In the end the majority of the population will be much more familiar with the terms ChatGPT and Copilot than with the names behind it, even if the academic behemoths such as Ilya and Andrej, who are quite prominent in their public appearance.
For the major population, I believe it all began with search over knowledge graphs. Wikipedia presented a dynamic and vibrant corpus. Some NLP began to become more prominent. With OCR, more and more printed works had begun to get digitalized. The corpus had been growing. With opening the gates of scientific publishers, the quality might have also improved. All of it was part of the grunt work to make today’s LLMs capable. The growth of the Cloud DCs and compute advancements have been making deep nets more and more feasible. This is just an arbitrary observation on the surface of the pieces that fell into place. And LLMs are likely just another composite piece for something bigger yet to come.
To me, that’s the fascination of how scientific theory and business applications live in symbiosis.
> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things about the models right now. As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workf…
Earlier quoted context omitted.
They are doing "the same thing" only from the point of view of function, which only makes sense from the point of view of the thing utilizing this function (e.g. a clerical worker that needs to add numbers quickly). Otherwise, if "the parts are all different, and the construction isn't even remotely similar", how can the thing they're doing be "the same"? More importantly, how is it possible to make useful inferences…
The more you try to look into the LLM internals, the more similarities you find. Humanlike concepts, language-invariant circuits, abstract thinking, world models. Mechanistic interpretability is struggling, of course. But what it found in the last 5 years is still enough to dispel a lot of the "LLMs are merely X" and "LLMs can't Y" myths - if you are up to date on the relevant research. It's not just the outputs. The…
However, if you can point us to some specific reading on mechanistic interpretability that you think is relevant here, I would definitely appreciate it.
Earlier quoted context omitted.
Another limitation that I see right now is that for "economic impact" you want the things to have initiative and some agency, and there is well-justified hesitancy in providing that even where possible. Having a bunch of smart developers that are not allowed to do anything on their own and have to be prompted for every single action is not too advantageous if everyone is human, either ;)
Screw driver doesnt have agency but it certainly helps me get tasks done faster. AIs don't need agency to accelerate a ton of work
But a screw-driving assistant is more useful if he drives in screws on his own than if you have to prompt his every action. I'm not saying that a "dumb" assistant does not help at all.
> When do you expect that impact? I think the models seem smarter than their economic impact would imply. > Yeah. This is one of the very confusing things about the models right now. As someone who's been integrating "AI" and algorithms into people's workflows for twenty years, the answer is actually simple. It takes time to figure out how exactly to use these tools, and integrate them into existing tooling and workf…
The assumption here is that employees are already tuned so be efficient, so if you help them complete tasks more quickly then productivity improves. A slightly cynical alternate hypothesis could be that employees are generally already massively over-provisioned, because an individual leader's organisational power is proportional to the number of people working under them.
If most workers are already spending most of their time doing busy-work to pad the day, then reducing the amount of time spent on actual work won't change the overall output levels.
Earlier quoted context omitted.
My definition of intelligence is the capability to process and formalize a deterministic action from given inputs as transferable entity/medium. In other words knowing how to manipulate the world directly and indirectly via deterministic actions and known inputs and teach others via various mediums. As example, you can be very intelligent at software programming, but socially very dumb (for example unable to socially…
> My definition of intelligence is the capability to process and formalize a deterministic action from given inputs as transferable entity/medium. I don't think that's a good definition because many deterministic processes - including those at the core of important problems, such as those pertaining to the economy - are highly non-linear and we don't necessarily think that "more intelligence" is what's needed to simu…
I do understand proofs as formalized deterministic action for given inputs and processing as the solving of various proofs.
> Formalising a process, i.e. inferring the rules from observation through induction, may also be dependent on available computational resources.
Induction is only one way to construct a process and there are various informal processes (social norms etc). It is true, that the overall process depends on various things like available data points and resources.
> I don't have one except for "an overall quality of the mental processes humans present more than other animals".
How would your formalize the process of self-reflection and believing in completely made-up stories of humans often used as example that distinguishes animals from humans? It is hard to make a clear distinction in language and math, since we mostly do not understand animal language and math or other well observable behavior (based on that).