This scenario seems very different because nobody gave the person any material non-public information at all, they simply deduced it from their experience participating in the trial. This feels similar to the question of "can a passenger on the Boeing jet with the door plug that blew out trade on that information" to which the answer appears to be yes.
Misappropriation theory is the following:
> The misappropriation theory of insider trading is a form of insider trading where an individual trades stock in a corporation, with whom they are unaffiliated, on the basis of material non-public information they obtained through a breach of a fiduciary duty owed to the source of the information
The important part, which you're right was unclear in my comment is that the recipient of the information must have a fiduciary relationship with the source of the information, even if they do not have one with the company in question at all. That's the distinction.
1. The misappropriation theory of insider trading covers anyone who trades on material non public information sourced through a trusted relationship regardless of any fiduciary duty to the company. For example, if I tell my personal attorney a non public fact about the company I work at, and they trade on that information, they absolutely can be found guilty of insider trading despite having no relationship to the company at hand.
2. Congress is explicitly covered by insider trading law, which was affirmed in the STOCK Act of 2012. The fact that they’re rarely indicted has more to do with the legal and political challenges associated with doing so, not the legality of the act.
Fair, but I'd like to clarify that in my comment I had asked specifically for any sources indicating that the PFA limits were put in place by the prior administration, since you had made the claim:
> Trump's EPA created these PFAS rules
Your response was what I perceived to be a snarky comment that if only I had bothered to look, I'd have found the evidence, followed by a link that didn't say what was suggested.
> Look how many people on this thread actually believe that the Trump administration is literally trying to poison them. That's not crazy to you?
The claims made all over the place are insane to me. Yes, I doubt the Trump administration is actually trying to kill me. The world is not as polarizing and extreme as people on the internet want to make it sound like it is. Most people are far more docile in the real world, but the collective hive of the internet exacerbates tension. I have no clue what side of the political aisle you're on, but my guess is we probably agree about more things than we disagree about, if we could detach bullshit labels from it all.
But FWIW, the allegation that I wasn't bothering to learn or see if I'm wrong just raised tension further. I was genuinely trying to determine if the claim was true, the evidence I had found suggested it wasn't, and it seems like it in fact wasn't quite true, but perhaps that wasn't the point you were trying to make anyway.
All fine. My hope is that we can all turn down the tension and hostility a level or two. Might be the only hope we have.
> If you'd truly like to learn if you're wrong, it's recommended to seek information that disproves your hypothesis rather than proves it. Both this and the previous article I shared were very easy to find and within the first 2 or 3 results.
Firstly, this is a completely unnecessary comment. My searches were specifically regarding finding the enactment of specific PFA limits. I will acknowledge to not spending that much time looking at it, as you claimed to already have a source and I was curious to see what it was.
But to the point, this document does not outline or set limits on PFAS in drinking water. It's an action plan for measuring and creating limits, but does not itself enforce anything. In fact, every subsequent search I've done has shown that the 2024 Final Rule was the first point at which any limits were put into action.
Quoting directly, the document states that one of the steps being taken is:
> Initiating steps to evaluate the need for a maximum contaminant level (MCL) for perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS);
In other words, it outlines a plan for the research that is used to 1) determine if MCL should be set, and 2) what, if any, it should be set to. Notably, it does it not itself set that limit or come to a conclusion about what it should be.
Further, this research appears to be a continuation of research released in 2016 [1], which was the first time that a guideline (but not a mandate) was set. This would, of course, be prior to Trump's first administration. This is suggested in the document itself, where it outlines that this document is part of a series of actions beginning in 2015/2016, as well as callouts to specific research in the 2016 article linked below.
So the facts seem to show that:
1) The first guideline was set in 2016. It was not a law at this time.
2) Research continued to identify next steps for setting a standard, which were codified and shared in the 2019 article you linked
3) The 2024 Final Rule put a MCL into action for PFAS.
Take from that chain of events what you will, but the initial accusations of "political bias" seem unfounded here.
It seems like the PFAS rules were set in prior administrations [1]. In fact, even in the article you've linked above, the text states:
> retaining its maximum contaminant levels for PFOA and PFOS but pulling back on its use of a hazard index and regulatory determinations for additional PFAS
Key word being "retaining," indicating the maximum contaminant levels were already in place prior to the change mentioned here. Putting aside allegations of "political bias," can you point to a source which clearly indicates the PFA limits were put in place by the current administration? Would like to learn if I'm wrong.
You could have quoted the beginning of the sentence, where the point was about this specific case, and how in this particular case, a gun clearly allowed an assassination that would have been challenging to pull off with a knife.
That is not a way as saying killing someone with a knife is impossible. It's a way of saying that guns allow you to kill people in ways and distances that knives do not.
Yeah I'm not suggesting the same process could apply in the US, I'm just trying to aggressively refute the point that guns are not the problem (or, at least, a major component of it). We need to be creative about solutions, but people have to want to find a solution to be creative about them, and right now many do not.
This narrative isn't helpful. Even in this specific case, it's extremely unlikely anyone would have been able to get close enough to him with a knife to kill him without someone noticing.
Guns allow you to kill 1) multiple people, 2) from a distance, and 3) with nobody aware of the imminent threat.
Of course other weapons can also be used to harm people. Of course no solution is perfect. But it's absolutely incorrect to say "the problem isn't so much the tools." The tools undeniably and irrefutably play a role in every study that has ever been conducted on this topic.
See here for the impact of Australia's gun buyback program, which saw zero mass shootings in a decade after their removal, after 13 mass shootings in the 18 years prior the removal, as well as an accelerated decline in firearm deaths and suicides:
https://injuryprevention.bmj.com/content/12/6/365
I'll take a shot at rationale for this perspective, which is similar to a peer comment:
The tech is undoubtedly impressive, and I'm sure has a ton of headroom to grow (although I have no direct knowledge of this, but I'd take you at your word, because I'm sure it's true).
But at least my perception of the idea that this is a "bubble" presently is rooted in the businesses that are created using the technology. Tons of money spent to power AI agents to conduct tasks that would be 99% less expensive to conduct via a simple API call, or because the actual unstructured work is 2 or 3 levels higher in the value chain, and given enough time, there will be new vertically integrated companies that use AI to solve the problem at the root and eliminate the need for entire categories of companies at the level below.
In other words: the root of the bubble (to me) is not that the value will never be realized, but that many (if not most) of this crop of companies, given the amount of time the workflows and technology have had to take hold in organizations, will almost certainly not be able to survive long enough to be the ones to realize it.
This also seems to be why folks draw comparison to the dot com bubble, because it was quite similar. The tech was undoubtedly world changing. But the world needed time to adapt, and most of those companies no longer exist, even though many of the problems were solved a decade later by a new startup who achieved incredible scale.
To be fair, if those people are right, then NVIDIA's stock price (and revenue) is part of that bubble, so its not really evidence that this isn't a bubble.
Time will tell if they're right or not. But it wouldn't be the first time it has happened.
Both things can be true. The tech can be transformative, and the current valuations and burn rate can be wholly unsustainable in the short term. This is exactly what happened in the dotcom era.
Whether or not this one is the same is impossible to know until after it happens. But there are credible arguments to both sides.
Even if we accept as a premise that these models are doing "smart retrieval" and not "reasoning" (neither of which are being defined here, nor do I think we can tell from this tweet even if they were), it doesn't really change the impact.
There are many industries for which the vast majority of work done is closer to what I think you mean by "smart retrieval" than what I think you mean by "reasoning." Adult primary care and pediatrics, finance, law, veterinary medicine, software engineering, etc. At least half, if not upwards of 80% of the work in each of these fields is effectively pattern matching to a known set of protocols. They absolutely deal in novel problems as well, but it's not the majority of their work.
Philosophically it might be interesting to ask what "reasoning" means, and how we can assess if the LLMs are doing it. But, practically, the impacts to society will be felt even if all they are doing is retrieval.
It is absolutely possible for the unit economics of a product to be profitable and for the parent company to be losing money. In fact, it's extremely common when the company is bullish on their own future and thus they invest heavily in marketing and R&D to continue their growth. This is what I understood GP to mean.
Whether it's true for any of the mainstream LLM companies or not is anyone's guess, since their financials are either private or don't separate out LLM inference as a line item.
This, similar to your other comment, is unrelated to my comment.
This is about determining if AI can be a equivalent or better (defined as: achieving equal or better clinical outcomes) therapist than a human. That is a question that can be studied and answered.
Whether artificial intelligence accurately models human intelligence, or whether an airplane is "smarter" than a bird, are entirely separate questions that can perhaps serve to explain _why/how_ the AI can (or can't) achieve better results than the thing we're comparing against, but not whether it does or does not. Those questions are perhaps unanswerable based on today's knowledge. But they're not prerequisites.
I'm not exactly sure how this relates to my comment above. An analysis of an airline crash and a study are not the same thing.
As it relates to study design, controlling for set and setting are part of the methodology. For example, most drug studies are double-blinded so that neither patients nor clinicians are aware of whether the patient is getting the drug or not, to reduce or eliminate any placebo effect (i.e. to control for the "set"/mental state of those involved in the study).
There are certainly some cases in which it's effectively impossible to control for these factors (i.e. psychedelics). That's not what's really being discussed here, though.
An airline crash is an n of 1 incident, and not the same as a designed study.
This is an interesting take. By this perspective, it's essentially impossible to ever gauge the efficacy of AI in doing anything, because the people who will know how to measure the quality of that thing are also the people who will be displaced by showing the AI can do that thing. In fact, you could probably argue that every study ever is worthless, because studies are generally performed by people who know the subject matter and it's basically impossible to be unbiased on a topic if you're also highly knowledgable about said topic.
In reality, what matters is the methodology of the study. If the study's methodology is sound, and its results can be reproduced by others, then it is generally considered to be a good study. That's the whole reason we publish methodologies and results: so others can critique and verify. If you think this study is bad, explain why. The whole document is there for you to review.
Sorry, I should have clarified, but no this is not ChatGPT's self assessment.
I am suggesting that today's best in class models (Gemini 2.5 Pro and o3, for example), when given the same context that a physician has access to (labs, prior notes, medication history, diagnosis history, etc), and given an appropriate eval loop, can achieve similar diagnostic accuracy.
I am not suggesting that patients turn to ChatGPT for medical diagnosis, or that these tools are made available to patients to self diagnose, or that physicians can or should be replaced by an LLM.
But there absolutely is a role for an LLM to play in diagnostic workflows to support physicians and care teams.
Misappropriation theory is the following: > The misappropriation theory of insider trading is a form of insider trading where an individual trades stock in a corporation, with whom they are unaffiliated, on the basis of material non-public information they obtained through a breach of a fiduciary duty owed to the source of the information
The important part, which you're right was unclear in my comment is that the recipient of the information must have a fiduciary relationship with the source of the information, even if they do not have one with the company in question at all. That's the distinction.