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The study looks at forms of AI that have existed for a long time, as well as newer LLMs. From the article: "Their dataset included 41.3 million English-language papers published between 1980 and 2025 in ..."

Earlier studies looked at the effect of web search on research. This is all covered near the beginning of the article.

I'm genuinely curious how you think we should avoid it? If I had to buy a household appliance tomorrow, or a new car, I cannot think of a single company I'd feel safe purchasing from.

There are many beautiful parks and it's wonderful to spend time in them. But at some point you have to return to domesticity, and if you can't live that part of your life without being repeatedly screwed over by greedy corporations that don't even bother to provide a good service for the money you pay them, I think it's understandable people are starting to rage.

What do you do when the expensive options are also junk? Relatively few manufacturers actually focus on quality today.

you're trying to compete with millions of square miles of naturally sun-lit dirt

You're begging the question with this statement. Indoor growing is used when you don't have access to this kind of resource. There are many locations where access to land or suitable conditions is restricted.

CEA has been used profitably for a long time, and the most valuable crops are mushrooms and leafy greens, not exotic or illegal plants.

There currently isn't a effective treatment for varroa that doesn't also kill the hive. It is not a solved problem, and there is certainly room for more research in this space.

I agree with your point, but to play devils advocate: doesn't a competitor arguably need access to these beyond-frontier models to even become an effective competitor to OpenAI and Anthropic?

I'm not sure what you think I'm saying, but in case it needs clarifying, it is not that Apple should have built fabs 3.5 years ago, or today, or at any other point in time.

What I did say is that in a hypothetical world where Apple did invest in their own fabs, they'd have much better access to memory and could be selling machines with vastly more memory than their competitors today. Should they be doing this? I'm making no statement either way.

a very wise man took me to dinner and counseled me to never post mortem my decisions on the outcomes, but only on the decision process

I appreciate this wisdom on one hand, but on the other it's comically misguided. You have no way to evaluate the decision process if the outcomes are not considered. Don't post mortem your decisions in a way that leads to self-blame or paralysis, but certainly post mortem them to see whether the process was suboptimal.

In this case, any board laughing at the idea of investment in memory 3.5 years ago should instead have been looking more closely at likely memory needs over the then-near-future, given the quite obvious upcoming demand for GPU memory. There's no magic bullet and they wouldn't have been able to make perfect decisions regardless, but clearly the process they followed at that time was suboptimal.

Isn't one of the points of the article that memory manufacturers leave demand unmet for their own financial safety? In which case, nobody (including Apple) is paying close to manufacturing costs. There isn't enough memory to go around and prices are extremely inflated.

You're talking about the "best" things Apple could do with their money, in terms of investment returns, but I think that misses the point that Apple literally can't buy enough memory at any price.

A world where Apple has invested in their own fabs, so they can sell devices with drastically more RAM than their competition at entry-level prices.

I think your comment about inventing new words is an interesting one. One of the things that I believe limits our ability to discover new ideas is our ability to describe related concepts. For example, the reason we still can't have clear discussions on consciousness is probably partly due to the fact that the necessary concepts haven't been cemented in language. We need new language before we can describe consciousness.

I would guess LLMs are limited in their ability to be genuinely novel because they are trained on a fixed language. It makes research into the internal languages developed by LLMs during training all the more interesting.

I'm not making a circular argument, nor one based on legality. You explicitly changed your example to use "public domain" content, and ignoring the legal specifics of that it's clear that's a separate category of content. Most people have no ethical issue with remixing or using content that has already done the rounds and delivered most of its immediate value to the creator. This is very different to your earlier examples with books, framed as two contemporary pieces of media competing with each other.

Letting companies train LLMs on the "classics" is very different to training on contemporary media where the creator still depends on it.

If AI turns out to be a bust, ignoring it could become a significant win. One possible outcome of AI adoption is that existing code bases are degraded, and existing programmer capability is allowed to atrophy. In that situation, companies that adopt AI lose out relative to companies that eschew it.

Your examples, as you say, are all public domain. Are all the works we train LLMs on public domain too? Was the original book in my analogy in the public domain? What do you think about training on material that isn't yet in the public domain?

A better analogy would be that you do original research or work and produce a valuable book. Somebody else looks at your work, decides it has value, and reproduces it in a new book under their name. The new book is cheaper, or easier to find, or for whatever reason displaces your original book created through your own research and investment. Now somebody else is profiting off your creativity or work, without payment or even acknowledgement.

I'm not sure how this plays out legally, but it certainly seems unethical

What kind of work are you applying Opus and other LLMs to? I'm quite curious to understand how other people are using these tools.

At the moment neither Opus nor any open weights models seem to be capable of doing complex work, and for less complex work the additional cost of Opus hasn't been worthwhile. This is for reasonably math-heavy computer vision applications.

What LLMs have been useful for is identifying forgotten code that will be affected when planning a change, reviewing changes, and looking up docs/recipes for simple tasks. But Opus doesn't seem necessary for a lot of that.

No one at my company cares about OpenClaw either. We do care that we can be billed unexpectedly (either usage quota immediately being consumed, or being charged additional costs), generally with zero recourse, because a particular set of characters that Anthropic doesn't like appears somewhere in a repo.

This week the characters are "OpenClaw". I won't even try to guess what might lead to erroneous billing next week.

Yes, fully agree. Nonetheless, I suspect violence can be used more effectively and more minimally if it's considered and performed by a group rather than haphazardly by individuals. I recognise that's a very simplistic view.

There's still a meaningful difference between violence wielded by a single individual who feels angry or unheard, and violence wielded by a large representative group who has invested genuine effort in conversation before collectively deciding violence is required.

Tools do author commits in my code bases, for example during a release pipeline. If I had commits being made by Claude I would expect that to be recorded too. It isn't for recording a bill of tools, just to help understand a projects evolution.

Vendramini's theory appears to be built upon some fairly extraordinary ideas that are not supported by actual evidence. In fact some of his claims (e.g. about face shape and skull placement) are directly contradicted by actual evidence. It's probably worth approaching this with some scepticism.

This is rather disingenuous. It can be hard to overcome momentum in research, but most researchers would be giddy with excitement if they could show our (extremely disturbing) forecasts regarding climate change are wrong and that things are much rosier than expected.

I also suspect you would find easy funding from existing climate change deniers. There is no shortage of well-heeled folk in that space.

Do you have a chip on your shoulder regarding research? You're begging the question by stating it is conducted in a "practically religious" way. Ask whether that's true before you question the effect it would have on somebody's behaviour.

There's a case cited in that paper which does suggest something similar:

A report in the lay literature describes the case of Claire Sylvia who reported changes in her personality, preferences, and behaviors following a heart and lung transplant at Yale-New Haven hospital in 1988. Following surgery, Sylvia developed a new taste for green peppers and chicken nuggets, foods she previously disliked. As soon as she was released from the hospital, she promptly headed to a Kentucky Fried Chicken to order chicken nuggets. She later met her donor’s family and inquired about his affinity for green peppers. Their response was, “Are you kidding? He loved them… But what he really loved was chicken nuggets” (p. 184, [9]). Sylvia later discovered that at the time of her donor’s death in a motorcycle accident, a container of chicken nuggets was found under his jacket [9].

I haven't read the whole thing, maybe there's something more relevant as well. That report isn't really about accessing the previous persons "memories" but at least claims she adopted a part of their personality. I'd be skeptical about its accuracy without more such reports, however.

Would you consider that any current LLM is close to passing the Turing test?

If you think there's an LLM that can do so, I'd love to try it out! Even talking to the best models available today, it's disappointingly clear I'm talking to an LLM.

It's quite possible that the models from different companies are clustering together now because we're at a plateau point in model development, and won't see much in terms in further advances until we make the next significant breakthrough.

I don't think this has anything to do with AGI. We aren't at AGI yet. We may be close or we may be a very long way away from AGI. Either way, current models are at a plateau and all the big players have more or less caught up with each other.

Aaron Swartz wanted to provide the public with open access to paywalled journal articles, while Anthropic want to use other people's copyrighted material to train their own private models that they restrict access to via a paywall. It's wild (but unsurprising) that Aaron Swartz was prosecuted under the CFAA for this while Anthropic is allowed to become commercially successful

He did not cause a serious hazmat situation. The authorities decided to evacuate a street, and are responsible for the seriousness of their over-reaction.

The packages were labelled correctly, and blocked at the border, and USPS delivered them anyway. He offered to send them back as soon as he was made aware they weren’t permitted.

The real failure here is at the border, where they were flagged and then let through, followed by the absurd over reaction of the authorities to a situation they’d enabled

The article says “the quantities of material were so small they were safe to eat”

If that’s true, the overreaction and evacuation is higher risk than possession of the elements

You can’t blame Lidden for the overreaction of others

Do you think we can avoid multiple-degree temperature increases with the amount of CO2 (and equivalents) in the atmosphere already?

If not, how do we mitigate those increases without CDR?

What you think the purpose of determining the error of an instrument is, if not to calibrate it?