This article is filled with emotional triggers designed to drive engagement. Even the title. It can be hard to separate those things from objective facts.
Putting an llm in front of it helps me focus on the facts.
There are also too many things to read. My default before llms would have been to ignore this article.
At least now I learned some things (mostly about the Gallup poll which had source data)
I do think some people will outsource critical thinking to llms - but it also helps amplify critical thinking by doing a lot of the filtering and organizing and let me focus on the things i think are important.
> This article is filled with emotional triggers designed to drive engagement. Even the title. It can be hard to separate those things from objective facts.
> Putting an llm in front of it helps me focus on the facts.
This argument reminds me of one of Ted Chiang's short stories about "lookism," which (iirc) was a natural preference for people to prefer people who are attractive. In the story, a new technology was developed that could interact with a person's brain to "turn off" their lookism and instead just consider what a person brings to the table without your brain factoring in your own attraction to them.
I won't spoil the story, but a little arms race develops in the technology to "turn off" natural human reactions to things like attraction, emotion in speech, etc., so that users won't be swayed by them in advertising, political campaigns, anything that could possibly have an agenda. By the end, people using the technology are described as highly autistic – unable to perceive any human emotional context, triggers or attraction – so that they're able to interpret just a person's intent and not be manipulated by the underlying motivations.
It's an interesting story, your use of LLMs to cut out the "emotional triggers" from an article and get just the "objective facts" reminds me of that.
> Putting an llm in front of it helps me focus on the facts.
This used to be a very important skill taught in high school and perfected in university. We have lost something if people cannot focus even for short reads.
And then how do you uncover bias in your chatbot? Do you ask it to analyze its own analysis? For that matter, what about the bias in your prompt, which LLMs tend to accept uncritically? Do your own preconceived opinions bias you against the argument made in the article? Are you using a chatbot to think critically about the article, or to avoid thinking critically about your own beliefs?
> At the same time, 79 percent of those surveyed by Gallup “expressed concern that AI makes people lazier,” and 65 percent said that using chatbots “promotes instant gratification, not real understanding” and prevents people from engaging with ideas in a critical or meaningful way.
Perhaps you should take a cue from these surveyees and do your own thinking.
I actually did this - I plugged The Verge article into Claude and got the following critique of what biases are there:
> The article accurately cites real Gallup data but selectively omits findings that complicate its "backlash" narrative — most notably that curiosity is Gen Z's single most common emotion toward AI, and that daily users remain substantially more hopeful and excited than the aggregate figures suggest. The 79% "laziness" concern and declining hope figures are presented as evidence of generational rejection, when the researchers themselves describe what they found as "deep ambivalence." *In short, the article uses real numbers to tell a cleaner, more oppositional story than the underlying polling actually supports.*
Then I then put that Claude critique back into Claude and asked it to analyze the critique for bias and agendas and got this:
> The critique accurately catches real flaws in The Verge article — particularly the omission of "curiosity" as Gen Z's top emotion and the failure to distinguish between heavy users (who are more positive) and non-users (who drive most of the negativity). However, *the critique has its own directional bias, consistently framing every correction in ways that soften the negative trend, while ignoring data that cuts the other way — like the sharp positivity decline even among daily users, and the near-majority of Gen Z workers who see AI as a net negative in the workplace. *Both pieces are selectively using the same real data to tell opposite stories; the Gallup findings themselves are more nuanced and more negative than the critique allows.*
So according to Claude, Claude is biased in how it describes The Verge as biased.
LLMs are breakthrough technologies. The AI products we have today are SaaS products built by companies doing everything they can to find people who will pay for them. Very, very different things.
> LLMs are breakthrough technologies. The AI products we have today are SaaS products built by companies doing everything they can to find people who will pay for them. Very, very different things.
I'm honestly very impressed. You read these passages multiple times across composing two HN replies and did not, at any point, realize that curiosity is not an inherently positive emotion.
Curiosity is a "desire to know." We badly want to know about things that threaten us. People in 2020 were extremely curious about COVID-19, but that doesn't mean they liked it.
You might say, "well it's open for interpretation. It could be positive curiosity." But why stop there? Interpret: Anxiety is more common than anger, and anger is more common than excitement. Given a sample member who is anxious, angry, not excited, and not hopeful, do you think their curiosity is positively or negatively inflected?
Additionally, I don't know where Claude got the idea that "daily users remain substantially more hopeful and excited than the aggregate figures suggest." That's not in the data set, and a different data set will need to be interpreted separately.
I'm sorry if this sounds harsh, but you've completely failed to engage critically with either the article or with Claude. Claude misread the article and then affirmed its own misreading, and you took that all at face value.
> The cool thing about the current generation of AI tools is how easy it is to uncover bias or an agenda in an article like this.
This is only true if you assume that an AI tool is itself unbiased. I'm not sure how anyone can earnestly believe AI tools are unbiased after Grok's MechaHitler episode [0], unless they just aren't giving it much critical thought.
This is the thinking of someone on the timescales of a single life. If humanity persists another 1000 years on our current trajectory (US/world politics not withstanding), I think nothing is really a fantasy. Rather, it's all possible but maybe just not in our own lifetimes. But it is also terribly difficult for us to plan for tomorrow, let alone for a future where our descendants are at the helm.
I agree, it’s just a failure of imagination. Some folks correctly foresee not being able to continue what we’re doing now in the exact same way in some new context and conclude everything is impossible. Life isn’t this fickle, it’s adapted before and will adapt again. This is why great science fiction is so valuable, as some people are better at imagining new ways of being more than others, and can show the rest of us the possibilities.
The counterargument is a simple opportunity cost calculation:
There will never, ever, ever[1] be a scenario where if you weighed up the options of "expand into some less habitable area of the Earth" versus "expand to Mars", the latter is the better option either 1) financially, or b) quality of life.
Nobody[2] ever picks the dramatically more expensive and dramatically worse option!
Also, people that are desperate enough to even consider living in the least desirable -- but still just barely habitable -- parts of planet Earth are essentially by definition too poor to afford interplanetary travel.
And no, no amount future sci-fi technology can possibly overcome the simple energy costs of this! If someone can afford the hugely energy intensive interplanetary travel, and the up-front investment required to survive incredibly harsh environments, then by definition they could more productively invest that here on Earth! It's the cheaper and better option in every possible way, and always will be.
This will remain true even if it's standing room only on the entire planetary surface -- it'll be cheaper to build levels upwards while digging downwards.
Maybe our atmosphere will become horrifically polluted? Sure, okay, air filters are faaar cheaper than a full vacuum-capable space suit!
Etc, etc, etc...
[1] Okay, fine, maybe in a million years. Whatever ends up preferring Mars at that point will no longer be "human" by any sane classification.
[2] For some values of nobody. There are morons that buy overpriced branded handbags made of literal trash. I doubt idiots like that will make for a successful, self-sustaining colony.
The Madrid Protocol says you can't do anything fun with Antarctica. Can't have a mine, a garbage heap, or a farm. I suppose the world's militaries stand ready to capture any enterprising colonists and destroy their structures.
> If humanity persists another 1000 years on our current trajectory
It's unlikely that we can persist in our current trajectory for another 100 years without catastrophic climate events puttung a stop to all of these endeavours.
52 here, been a full time people manager for about a decade now. Coding manually makes me tired just thinking about it. When I think about embark on a new project my mind goes back to all the times I worked 12 hour days trying to get some basic system to function. I’m too old for that now, my back hurts if I sit too long and occasionally get migraines if I look at a screen too much.
Using AI has been really perfect for me. I can build stuff while I do other things, walk the dog, make lunch, sit on the porch.
Sometimes i realize that my design was flawed and I just delete it all and start again, with no loss aversion.
> Using AI has been really perfect for me. I can build stuff while I do other things, walk the dog, make lunch, sit on the porch.
this resonates with me strongly, while i like coding, and understanding it, I understand my human limitations. I couldn't possibly write by hand the stuff I've been making, in the time I am making it, without a team these past few months. I would be coding literally all day, which while I sometimes enjoy the zoning out process of wiring stuff up, what i really enjoy is exactly what you described.
I enjoy being outside and walking my dog, taking a long shower, and cooking. All of these things are simple tasks with a good bit of repetition, and unlike wiring up some code or whatever, they allow my thoughts to flow, and I can think about where my projects are likely heading and what needs to be done next.
Those moments, even before heavy AI assisted coding, have always been the moments i cherish about software development.
It says this in bold red at the top - "This is a preprint; it has not been peer reviewed by a journal."
I am not a climate scientist - how should I think about this statement? Normally I am looking for some statement that shows a document has been vetted.
For non-specialists, I think the most important view on papers is to not view them as nuggets of truth, but communications of a group of people who are trying to establish truth. No single paper is definitive!
Peer review is an important part of scientific publication, but it's also important for the general public to not view peer review as a full vetting. Peer reviewers look for things like reproducibility of the analysis, suitability of the conclusions given the methods, discussions of the limitations of the data and methods, appropriate statistical tests, correct approval from IRBs if there are humans or animals involved, and things like that. For many journals, the editors are also asking if the results are interesting and significant enough to meet the prestige of the journal.
Peer review misses things like intentional fraud, mistakes in computations, and of course any blind spots that the field has not yet acknowledged (for example, nearly every scientific specialty had to rediscover the important of splitting training and testing datasets for machine learning methods somewhat on their own, as new practitioners adopted new methods quickly and then some papers would slip through at the beginning when reviewers were not yet aware of the necessity of this split...)
Any single paper is not revealed truth, it's a step towards establishing truth, maybe. Science is supposed to be self-correcting, which also necessitates the mistakes that need correction. Climate science is one of the fields that gets the most attention and scrutiny, so a series of papers in that field goes a long ways towards establishing truth, much more so than, say, new MRI technology in psychology.
Sometimes reviewers also look for whether the paper cites enough of their own papers, who is publishing it (regardless of whether the review is supposed to be anonymous or not), whether it clashes with a paper they're about to publish... science is just as full of politics and corruption (if not more) as any other field.
I almost added "place the research into the context of other relevant research" as another way of saying "cite enough of the peer reviewer's papers" but fair enough.
I'm not sure if science has as much corruption as other fields, but it definitely has politics. PIs get to their position without the typical selection process for leadership that happens in most larger orgs, so there's more fragile and explosive personalities than I find in other management/leadership positions.
I'd say that for a non-scientist, you should treat it as a non-event -- a paper that hasn't happened yet.
The climate is not something for which you need daily, weekly, or even monthly updates. Rather, this paper is just one more on top of a gigantic pile of evidence that that climate change is serious, something that we can and should do something about.
If the paper passes muster, you'll hear about it then, though all it'll do is very slightly increase your confidence in something that is already very well confirmed. Or, the paper may not pass review, in which case it doesn't mean anything at all, and you fall back on the existing mountain of evidence.
If the paper had reached the opposite conclusion, that might merit more investigation by you now, since that would potentially be a significant update to your beliefs. And more importantly, it would certainly be presented as if it were a fait accompli, even before peer review.
Instead, you can simply say, "I don't know what this paper means, but I already have a very well-founded understanding of climate change and its significance."
Peer review is still very relevant in climate science. But given it is from well-respected authors, I am more inclined to trust the results at this stage.
> Plain Language Summary
The rise in global temperature has been widely considered to be quite steady for several decades since the 1970s. Recently, however, scientists have started to debate whether global warming has accelerated since then. It is difficult to be sure of that because of natural fluctuations in the warming rate, and so far no statistical significance (meaning 95% certainty) of an acceleration (increase in warming rate) has been demonstrated. In this study we subtract the estimated influence of El Niño events, volcanic eruptions and solar variations from the data, which makes the global temperature curve less variable, and it then shows a statistically significant acceleration of global warming since about the year 2015. Warming proceeding faster is not unexpected by climate models, but it is a cause of concern and shows how insufficient the efforts to slow and eventually stop global warming under the Paris Climate Accord have so far been.
A paper being peer reviewed is a good sign, but I feel like the signal is usually over interpreted.
Peer reviewed does not mean the findings of the paper are established fact or scientific consensus. It does not mean that the findings have been replicated by other scientists. It does not mean that the paper relied on a robust methodology, is free of basic statistical errors, or even free of logical fallacies.
Some of these limitations are due to the limitations of peer review itself. Others are just side effects of the way science works (for example, some ideas start as small, unimpressive experiments that are reported on in papers, and the strength of the findings is gradually developed over time). Obviously sometimes the prestige (or lack thereof) of the journal the paper is in decreases (or increases) some of these issues.
Anyway, peer review is a very noisy channel (IMHO).
For one thing, some of the places which would publish this kind of thing will authorize authors to provide anybody and everybody pre-prints but not the final copy they published.
In principle you could go (pay to†) read the actual final published copy, maybe it's different, but almost always it's basically the same, the text is enough to qualify.
If you go to https://eel.is/c++draft/ you'll find the "Draft" C++ standard, and it has this text:
Note: this is an early draft. It's known to be incomplet and incorrekt, and it has lots of bad formatting.
Nevertheless, the people who wrote your C++ compiler used that "draft" document, because it isn't reasonable to wait a few years for ISO to publish the "real" document which is identical other than lacking that scary text and having a bunch of verbiage about how ISO owns this document and it mustn't be republished.
And you might be thinking "OK, I'm sure those GNU hippies don't pay for a real published copy, but surely the Microsoft Corporation buys their engineers a real one". Nope. Waste of money.
† If you have a relationship with a research institution it might have this or be willing to help you order it from somewhere else at no personal cost.
Pre-prints exists because it can take up to 18 months to get a paper published in a journal or reputable conference. Since lots of people can publish pre-prints[1] what you should think depends on the authors. If they have a record of publishing good research you should think highly of the paper.
[1] - Actually, there are hoops on pre-print repositories, such as arXiv, so not everyone can post there. I guesstimate that 99% of the public has no means of posting on arXiv.
This has convinced many non-programmers that they can program, but the results are consistently disastrous, because it still requires genuine expertise to spot the hallucinations.
I've been programming for 30+ years and now a people manager. Claude Code has enabled me to code again and I'm several times more productive than I ever was as an IC in the 2000s and 2010s. I suspect this person hasn't really tried the most recent generation, it is quite impressive and works very well if you do know what you are doing
If you’ve been programming for 30+ years, you definitely don’t fall under the category of “non-programmers”.
You have decades upon decades of experience on how to approach software development and solve problems. You know the right questions to ask.
The actual non-programmers I see on Reddit are having discussions about topics such as “I don’t believe that technical debt is a real thing” and “how can I go back in time if Claude Code destroyed my code”.
People learning to code always have had those questions and issues though. For example, “git ate my code’ or “I don’t believe in python using white space as a bracket so I’m going to end all my blocks with #endif”
The author headline starts with "LLMs are a failure", hard to take author seriously with such a hyperbole even if second part of headline ("A new AI winter is coming") might be right.
But it can work well even if you don't know what you are doing (or don't look at the impl).
For example, build a TUI or GUI with Claude Code while only giving it feedback on the UX/QA side. I've done it many times despite 20 years of software experience. -- Some stuff just doesn't justify me spending my time credentializing in the impl.
Hallucinations that lead to code that doesn't work just get fixed. Most code I write isn't like "now write an accurate technical essay about hamsters" where hallucinations can sneak through lest I scrutinize it; rather the code would just fail to work and trigger the LLM's feedback loop to fix it when it tries to run/lint/compile/typecheck it.
But the idea that you can only build with LLMs if you have a software engineer copilot isn't true and inches further away from true every month, so it kinda sounds like a convenient lie we tell ourselves as engineers (and understandably so: it's scary).
> Hallucinations that lead to code that doesn't work just get fixed
How about hallucinations that lead to code that doesn't work outside of the specific conditions that happen to be true in your dev environment? Or, even more subtly, hallucinations that lead to code which works but has critical security vulnerabilities?
Replace "hallucination" with "oversight" or "ignorance" and you have the same issue when a human writes the code.
A lot of that will come to the prompter's own foresight much like the vigilance of a beginner developer where they know they are working on a part of the system that is particularly sensitive to get right.
That said, only a subset of software needs an authentication solution or has zero tolerance to some codepath having a bug. Those don't apply to almost all of the apps/TUIs/GUIs I've built over the last few months.
If you have to restrict the domain to those cases for LLMs to be "disastrous", then I'll grant that for this convo.
> A lot of that will come to the prompter's own foresight
And, on the current trend, how on earth are prompters supposed to develop this foresight, this expertise, this knowledge?
Sure, fine, we have them now, in the form of experienced devs, but these people will eventually be lost via attrition, last even faster if companies actually do make good on their threat to replace a team of 10 devs with a team of three prompters (former senior devs).
The short-sightedness of this, the ironic lack of foresight, is troubling. You're talking about shutting off the pipeline that will produce these future prompters.
The only way through, I think, will be if (very big if) the LLMs get so much better at coding (not code-gen) that you won't need a skilled prompter.
I have a journalist friend with 0 coding experience who has used ChatGPT to help them build tools to scrape data for their work. They run the code, report the errors, repeat, until something usable results. An agent would do an even better job. Current LLMs are pretty good at spotting their own hallucinations if they're given the ability to execute code.
The author seems to have a bias. The truth is that we _do not know_ what is going to happen. It's still too early to judge the economic impact of current technology - companies need time to understand how to use this technology. And, research is still making progress. Scaling of the current paradigms (e.g. reasoning RL) could make the technology more useful/reliable. The enormous amount of investment could yield further breakthroughs. Or.. not! Given the uncertainty, one should be both appropriately invested and diversified.
For toy and low effort coding it works fantastic. I can smash out changes and PRs fantastically quick, and they’re mostly correct. However, certain problem domains and tough problems cause it to spin its wheels worse than a junior programmer. Especially if some of the back and forth troubleshooting goes longer than one context compaction. Then it can forget the context of what it’s tried in the past, and goes back to square one (it may know that it tried something, but it won’t know the exact details).
That was true six months ago - the latest versions are much better at memory and adherence, and my senior engineer friends are adopting LLMs quickly for all sorts of advanced development.
Last week I gave antigravity a try, with the latest models and all, it generated subpar code that did the job very quickly for sure, but no one would have ever accepted this code in a PR, it took me 10x more time to clean it up than to have gemini shit it out.
The only thing I learned is that 90% of devs are code monkeys with very low expectations which basically amount to "it compiles and seems to work then it's good enough for me"
..and works very well if you do know what you are doing
That's the issue. AI coding agents are only as good as the dev behind the prompt. It works for you because you have an actual background in software engineering of which coding is just one part of the process. AI coding agents can't save the inexperienced from themselves. It just helps amateurs shoot themselves in the foot faster while convincing them they're a marksman.
UChicago’s strains came after its $10bn endowment — a critical source of revenue — delivered an annualised return of 6.7 per cent over the 10 years to 2024, among the weakest performances of any major US university.
The private university has taken a more conservative investment approach than many peers, with greater exposure to fixed income and less to equities since the global financial crisis in 2008.
“If you look at our audits and rating reports, they’ve consistently noted that we had somewhat less market exposure than our peers,” said Ivan Samstein, UChicago’s chief financial officer. “That led to less aggregate returns over a period of time.”
An aggressive borrowing spree to expand its research capacity also weighed on the university’s financial health. UChicago’s outstanding debt, measured by notes and bonds payable, climbed by about two-thirds in the decade ending 2024, to $6.1bn, as it poured resources into new fields such as molecular engineering and quantum science.
A combination of bad bets and mismanagement. Ah! Well I have a friend who is currently going their for law school, so I shouldn't be celebrating this, it harms them and their career prospects.
paste the verge article text into your favorite AI tool and ask for an analysis.
Make sure to ask it to read the source Gallup data that this article leans on and compare the conclusions drawn.
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