[HN Gopher] 2025 AI Index Report
___________________________________________________________________
2025 AI Index Report
Author : INGELRII
Score : 151 points
Date : 2025-04-10 15:13 UTC (1 days ago)
(HTM) web link (hai.stanford.edu)
(TXT) w3m dump (hai.stanford.edu)
| Signez wrote:
| Surprised not to see a whole chapter on the environment impact.
| It's quite a big talking point around here (Europe, France) to
| discredit AI usage, along with the usual ethics issues about art
| theft, job destruction, making it easier to generate
| disinformation and working conditions of AI trainers in low-
| income countries.
|
| (Disclaimer: I am not an anti-AI guy -- I am just listing the
| common talking points I see in my feeds.)
| simonw wrote:
| Yeah, it would be really useful to see a high quality report
| like this that addresses that issue.
|
| My _strong_ intuition at the moment is that the environmental
| impact is greatly exaggerated.
|
| The energy cost of executing prompts has dropped enormously
| over the past two years - something that's reflected in this
| report when it says "Driven by increasingly capable small
| models, the inference cost for a system performing at the level
| of GPT-3.5 dropped over 280-fold between November 2022 and
| October 2024". I wrote a bit about that here:
| https://simonwillison.net/2024/Dec/31/llms-in-2024/#the-envi...
|
| We still don't have great numbers on training costs for most of
| the larger labs, which are likely extremely high.
|
| Llama 3.3 70B cost "39.3M GPU hours of computation on H100-80GB
| (TDP of 700W) type hardware" which they calculated as 11,390
| tons CO2eq. I tried to compare that to fully loaded passenger
| jet flights between London and New York and got a number of
| between 28 and 56 flights, but I then completely lost
| confidence in my ability to credibly run those calculations
| because I don't understand nearly enough about how CO2eq is
| calculated in different industries.
|
| The "LLMs are an environmental catastrophe" messaging has
| become so firmly ingrained in our culture that I think it would
| benefit the AI labs themselves enormously if they were more
| transparent about the actual numbers.
| tmpz22 wrote:
| If I were an AI advocate I'd push the environmental angle to
| distract from IP and other (IMO bigger and immediate
| concerns) like DOGE using AI to audit government agencies and
| messages, or AI generated discourse driving every modern
| social platform.
|
| I think the biggest mistake liberals make (I am one) is that
| they expect disinformation to come _against_ their beliefs
| when the most power disinformation comes _bundled_ with their
| beliefs in the form of misdirection, exaggeration, or other
| subterfuge.
| dleeftink wrote:
| How is that a mistake? Isn't that the exact purpose of
| propaganda?
| __loam wrote:
| The biggest mistake liberals have made is thinking leaving
| the markets to their own devices wouldn't lead to an
| accumulation of wealth so egregious that the nation
| collapses into fascism as the wealthy use their power to
| dismantle the rule of law.
| achierius wrote:
| You imagine that this is a mistake, but it wouldn't be
| the first time that liberals went hand-in-hand with
| fascism to protect their capital.
| __loam wrote:
| The mistake is not understanding the inevitability.
| mentalgear wrote:
| To assess the env impact, I think we need to look a bit
| further:
|
| While the single query might have become more efficient, we
| would also have to relate this to the increased volume of
| overall queries. E.g in the last few years, how many more
| users, and queries per user were requested.
|
| My feeling is that it's Jevons paradox all over.
| fc417fc802 wrote:
| The training costs are amortized over inference. More
| lifetime queries means better efficiency.
|
| Individual inferences are extremely low impact.
| Additionally it will be almost impossible to assess the net
| effect due to the complexity of the downstream
| interactions.
|
| At 40M 700W GPU hours 160 million queries gets you 175Wh
| per query. That's less than the energy required to boil a
| pot of pasta. This is merely an upper bound - it's near
| certain that many times more queries will be run over the
| life of the model.
| signatoremo wrote:
| LLM usage increase may be offset by the decrease of search
| or other use of phone/computer.
|
| Can you quantify how much less driving resulted from the
| increase of LLM usage? I doubt you can.
| pera wrote:
| > _Global AI data center power demand could reach 68 GW by
| 2027 and 327 GW by 2030, compared with total global data
| center capacity of just 88 GW in 2022._
|
| "AI's Power Requirements Under Exponential Growth", Jan 28,
| 2025:
|
| https://www.rand.org/pubs/research_reports/RRA3572-1.html
|
| As a point of reference: The current demand in the UK is 31.2
| GW (https://grid.iamkate.com/)
| mbs159 wrote:
| > ... I then completely lost confidence in my ability to
| credibly run those calculations because I don't understand
| nearly enough about how CO2eq is calculated in different
| industries.
|
| There is a lot of heated debate on the "correct" methodology
| for calculating CO2e in different industries. I calculate it
| in my job and I have to update the formulas and variables
| very often. Don't beat yourself over it. :)
| Lerc wrote:
| Every time I have seen it mentioned, it has been rolled into
| data center usage.
|
| Is there any separate analysis on AI resource usage?
|
| For a few years now it has been frequently reported that
| building and running renewable energy is cheaper than running
| fossil fuel electricity generation.
|
| I know some fossil fuel plants run to earn the subsidies that
| incentivised their construction. Is the main driver for fossil
| fuel electricity generation now mainly bureaucratic? If not why
| is it persisting? Were we misinformed as to the capability of
| renewables?
| Taek wrote:
| There's a couple of things at play here (renewable energy is
| my industry).
|
| 1. Renewable energy, especially solar, is cheaper
| *sometimes*. How much sunlight is there in that area? The
| difference between New Mexico and Illinois for example is
| almost a factor of 2. That is a massive factor. Other key
| factors include cost of labor, and (often underestimated)
| beautacratic red tape. For example, in India it takes about 6
| weeks to go from "I'll spend $70 million on a solar farm" to
| having a fully functional 10 MW solar farm. In the US, you'll
| need something like 30% more money, and it'll take 9-18
| months. In some parts of Europe, it might take 4-5 years and
| cost double to triple.
|
| All of those things matter a lot.
|
| 2. For the most part, capex is the dominant factor in the
| cost of energy. In the case of fossil fuels, we've already
| spent the capex, so while it's more expensive over a period
| of 20 years to keep using coal, if you are just trying to
| make the budget crunch for 2025 and 2026 it might make sense
| to stay on fossil fuels even if renewable energy is
| technically "cheaper".
|
| 3. Energy is just a hard problem to solve. Grid integrations,
| regulatory permission, regulatory capture, monopolies, base
| load versus peak power, duck curves, etc etc. If you have
| something that's working (fossil fuels), it might be
| difficult to justify switching to something that you don't
| know how it will work.
|
| Solar is becoming dominant very quickly. Give it a little bit
| of time, and you'll see more and more people switching to
| solar over fossil fuels.
| davis wrote:
| Just curious: where do you work given it is your industry?
| Lerc wrote:
| I guess for things like training AI, they can go where the
| power is generated which would favour dropping them right
| next to a solar farm located for the best output.
|
| Despite their name I imagine the transportation costs of
| weights would be quite low.
|
| Thank you for your reply by the way, I like being able to
| ask why something is so rather than adding another
| uninformed opinion to the thread.
| iinnPP wrote:
| I want to take the opportunity here to introduce a rather
| overlooked problem with AI: Palantir and anything like it.
|
| Where certain uses equate to significant jumps in power of
| manipulation.
|
| That's not to pick on Palantir, it's just a class of software
| that enables AI for usecases that are quite scary.
|
| It's not as if similar software isn't used by other countries
| for the same use cases employed by the US military.
|
| Given this path, I doubt the environment will be the focus,
| again.
| simonw wrote:
| Is that really overlooked? I've been seeing (very justified)
| concerns about the use of AI and machine learning for
| surveillance for over a decade.
|
| It was even the subject of a popular network TV show (Person
| of Interest) with 103 episodes from 2011-2016.
| fc417fc802 wrote:
| The topic as a whole isn't overlooked but I think the
| societal impact is understated even by Hollywood. When
| every security camera is networked and has a mind of its
| own things get really weird and that's before we consider
| the likes of Boston Dynamics.
|
| A robotic police officer on every corner isn't at all far
| fetched at that point.
| StopDisinfo910 wrote:
| > Surprised not to see a whole chapter on the environment
| impact.
|
| Is it? I don't think I have ever seen it really brought up
| anywhere it would matter.
|
| It would be quite rich in a country where energy production is
| pretty much carbon neutral but in character from EELV I guess.
| andai wrote:
| There's a very brief section estimating CO2 impact and a chart
| at the end of Chapter 1:
|
| https://hai.stanford.edu/ai-index/2025-ai-index-report/resea...
|
| A few more charts in the PDF (pp. 48-51)
|
| https://hai-production.s3.amazonaws.com/files/hai_ai-index-r...
| simonw wrote:
| Page 71 to 74 cover environmental impact and energy usage - so
| not a whole chapter but it is there.
| calvinmorrison wrote:
| whats the lifetime environmental impact of hiring one decent
| human being who is capable enough assist with work. Well a lot,
| you gotta do 25 years with 30 kids to get one useful person.
|
| You get to upgrade them, kill them off, have them on demand
| simonw wrote:
| I saw a fun comparison a while back (which I now cannot find)
| of the amount of CO2 it takes to train a leading LLM compared
| to the amount of CO2 it takes to fly every attendee of the
| NeurIPS AI conference (13,000+ people) to and from the event.
| danielbln wrote:
| Well don't let us hanging.
| simonw wrote:
| "(which I now cannot find)"
| mrdependable wrote:
| I always see these reports about how much better AI is than
| humans now, but I can't even get it to help me with pretty
| mundane problem solving. Yesterday I gave Claude a file with a
| few hundred lines of code, what the input should be, and told it
| where the problem was. I tried until I ran out of credits and it
| still could not work backwards to tell me where things were going
| wrong. In the end I just did it myself and it turned out to be a
| pretty obvious problem.
|
| The strange part with these LLMs is that they get weirdly hung up
| on things. I try to direct them away from a certain type of
| output and somehow they keep going back to it. It's like the same
| problem I have with Google where if I try to modify my search to
| be more specific, it just ignores what it doesn't like about my
| query and gives me the same output.
| simonw wrote:
| LLMs are difficult to use. Anyone who tells you otherwise is
| being misleading.
| __loam wrote:
| "Hey these tools are kind of disappointing"
|
| "You just need to learn to use them right"
|
| Ad infinitum as we continue to get middling results from the
| most overhyped piece of technology of all time.
| simonw wrote:
| That's why I try not to hype it.
| mvdtnz wrote:
| You're the biggest hype merchant for this technology on
| this entire website. Please.
| simonw wrote:
| I've been banging the drum about how unintuitive and
| difficult this stuff is for over a year now:
| https://simonwillison.net/2025/Mar/11/using-llms-for-
| code/
|
| I'm one of the loudest voices about the so-far unsolved
| security problems inherent in this space:
| https://simonwillison.net/tags/prompt-injection/ (94
| posts)
|
| I also have 149 posts about the ethics of it:
| https://simonwillison.net/tags/ai-ethics/ - including one
| of the first high profile projects to explore the issue
| around copyrighted data used in training sets:
| https://simonwillison.net/2022/Sep/5/laion-aesthetics-
| weekno...
|
| One of the reasons I do the "pelican riding a bicycle"
| thing is that it's a great way to deflate the hype around
| these tools - the supposedly best LLM in the world still
| draws a pelican that looks like it was done by a five
| year old! https://simonwillison.net/tags/pelican-riding-
| a-bicycle/
|
| If you want AI hype there are a thousand places on the
| internet you can go to get it. I try not to be one of
| them.
| __loam wrote:
| The prompt injection articles you wrote really early in
| the tech cycle were really good and I appreciated them at
| the time.
| andai wrote:
| Could a five year old do it in XML (SVG)? Could an
| artist? In one shot?
| maleldil wrote:
| It's true that simonw writes a lot about LLMs, but I find
| his content to be mostly factual. Much of it is positive,
| but that doesn't mean it's hype.
| JohnKemeny wrote:
| Uh... You don't do anything _but_ hype them.
|
| I literally don't know who anyone on HN are except you
| and dang, and you're the one that constantly writes these
| ads for your LLM database product.
| simonw wrote:
| I think you and I must have different definitions of the
| word "hype".
|
| To me, it means LinkedIn influencers screaming "AGI is
| coming!", "It's so over", "Programming as a career is
| dead" etc.
|
| Or implying that LLMs are flawless technology that can
| and should be used to solve every problem.
|
| To hype something is to provide a dishonest impression of
| how great it is without ever admitting its weaknesses.
| That's what I try to avoid doing with LLMs.
| bluefirebrand wrote:
| > without ever admitting its weaknesses
|
| I don't think this part is necessary
|
| "To hype something is to provide a dishonest impression
| of how great it is" is accurate.
|
| Marketing hype is all about "provide a dishonest
| impression of how great it is". Putting the weaknesses in
| fine print doesn't change the hype
|
| Anyways I don't mean to pile on but I agree with some of
| the other posters here. An awful lot of extremely pro-AI
| posts that I've noticed have your name on them
|
| I don't think you are as critical of the tech as you
| think you are.
|
| Take that for what you will
| tzumaoli wrote:
| also "They will get better in no time"
| simonw wrote:
| That one's provably correct. Try comparing 2023-era
| GPT-3.5 with 2025's best models.
| xboxnolifes wrote:
| It's not provably correct if the comment is made toward
| 2025 models.
| simonw wrote:
| Gemini 2.5 came out just over two weeks ago (25th March)
| and is a very significant improvement on Gemini 2.0 (5th
| February), according to a bunch of benchmarks but also
| the all-important vibes.
| torginus wrote:
| LLMs are a casino. They're probabilistic models which might
| come up with incredible solutions at a drop of a hat, then
| turn around and fumble even the most trivial stuff - I've
| had this same experience from GPT3.5 to the latest and
| greatest models.
|
| They come up with something amazing once, and then never
| again, leading me to believe, it's operator error, not pure
| dumb luck or slight prompt wording that lead me to be
| humbled once, and then tear my hair out in frustration the
| next time.
|
| Granted, newer models tend to do more hitting than missing,
| but it's still far from a certainty that it'll spit out
| something good.
| pants2 wrote:
| In my experience, most people who say "Hey these tools are
| kind of disappointing" either refuse to provide a
| reproducible example of how it falls short, or if they do,
| it's clear that they're not using the tool correctly.
| __loam wrote:
| Ad infinitum
| sksxihve wrote:
| I'd love to see a reproducible example of these tools
| producing something that is exceptional. Or a clear
| reproducible example of using them the right way.
|
| I've used them some (sorry I didn't make detailed notes
| about my usage, probably used them wrong) but pretty much
| there are always subtle bugs that if I didn't know better
| I would have overlooked.
|
| I don't doubt people find them useful, personally I'd
| rather spend my time learning about things that interest
| me instead of spending money learning how to prompt a
| machine to do something I can do myself that I also enjoy
| doing.
|
| I think a lot of the disagreements on hn about this tech
| is that both sides are mostly on the extremes of either
| "it doesn't work and at and is pointless" or "it's
| amazing and makes me 100x more productive" and not much
| discussion about the mid-ground of it works for some
| stuff and knowing what stuff it works well on makes it
| useful but it won't solve all your problems.
| doug_durham wrote:
| Why are you setting the bar at "exceptional". If it means
| that you can write your git commit messages more quickly
| and with fewer errors then that's all the payoff most
| orgs need to make them worthwhile.
| mickael-kerjean wrote:
| The latest example for me was trying to generate a
| thumbnail of a PSD in plain C and figure out the layers
| in there as I was lazy to read the specs, with the
| objective to bundle it as a wasm and execute it on a
| browser, it never got to extract a thumbnail from a given
| PSD, it's very confident at making stuff but it never got
| anywhere despite spending a couple hours on it which
| would have been better spend reading specs and existing
| code on that topic
| input_sh wrote:
| How are we supposed to give a reproducible example with a
| non-deterministic tool?
| TeMPOraL wrote:
| No, it's just you and yours.
|
| IDK, maybe there's a secret conspiracy of major LLM
| providers to split users into two groups, one that gets the
| good models, and the other that gets the bad models, and
| ensure each user is assigned to the same bucket at every
| provider.
|
| Surely it's more likely that you and me got put into
| different buckets by the Deep LLM Cartel I just described,
| than it is for you to be holding the tool wrong.
| slig wrote:
| Was that on 3.7 Sonnet? I feel it's a lot worse than 3.5. If
| you can, try again but on Gemini 2.5.
| avandekleut wrote:
| I'm glad I'm not the only one that has found 3.5 to be better
| than 3.7.
| johnisgood wrote:
| When did 3.7 come out? I might have had the same
| experience. I think I have been using 3.5 with success, but
| I cannot remember exactly. I may have not used 3.7 for
| coding (as I had a couple of months break).
| simonw wrote:
| 3.7 came out on 24th February. My notes from that
| release:
| https://simonwillison.net/2025/Feb/24/claude-37-sonnet-
| and-c... and https://simonwillison.net/2025/Feb/25/llm-
| anthropic-014/
| johnisgood wrote:
| I will have to check, but apparently I have been using
| 3.5 with success, then. I will give 3.7 a try later, I
| hope it is really not that much worse, or is it? :(
| mrdependable wrote:
| This was 3.7. I did give Gemini a shot for a bit but it
| couldn't do it either and the output didn't look quite as
| nice. Also, I paid for a year of Claude so kind of feel stuck
| using it now.
|
| Maybe I will give 3.5 a shot next time though.
| namaria wrote:
| It's overfitting.
|
| Some people say they find LLMs very helpful for coding, some
| people say they are incredibly bad.
|
| I often see people wondering if the some coding task is
| performed well or not because of availability of code examples
| in the training data. It's way worse than that. It's
| overfitting to diffs it was trained on.
|
| "In other words, the model learns to predict plausible changes
| to code from examples of changes made to code by human
| programmers."
|
| https://arxiv.org/abs/2206.08896
| simonw wrote:
| ... which explains why some models are better at code than
| others. The best coding models (like Claude 3.7 Sonnet) are
| likely that good because Anthropic spent an extraordinary
| amount of effort cultivating a really good training set for
| them.
|
| I get the impression one of the most effective tricks is to
| load your training set up with as much code as possible that
| has comprehensive automated tests that pass already.
| torginus wrote:
| I've often experienced that I had what I thought an obscure
| and very intellectually challenging coding problem, and
| after prompting the LLM, it basically one-shotted it.
|
| I've been profoundly humbled by the the experience, but
| then it occurred to me that what I thought to be an unique
| problem has been solved by quite a few people before and
| the model had plenty of references to pull from.
| zifpanachr23 wrote:
| Do you have any examples?
| suddenlybananas wrote:
| He got an LLM to finally solve fizzbuzz for him.
| torginus wrote:
| Yeah for the positive example, I described the syntax of
| a domain-specific-language, and the AI basically one-
| shotted the parsing rules, that only needed minor fixes.
|
| For a counterexample, working on any part of a codebase
| that's 100% application specific business logic, with our
| custom abstractions, the AI is usually so lost that it's
| basically not even worth using it, as the chances of
| writing correct and usable code is next to zero.
| namaria wrote:
| > ... which explains why some models are better at code
| than others.
|
| No. It explains why models seem better at code in given
| situations. When your prompt mapped to diffs in the
| training data that are useful to you they seem great.
| simonw wrote:
| I've been writing code with LLM assistance for over two
| years now and I've had plenty of situations where I am
| 100% confident the thing I am doing has never been done
| by anyone else before.
|
| I've tried things like searching all of the public code
| on GitHub for every possible keyword relevant to my
| problem.
|
| ... or I'm writing code against libraries which didn't
| exist when the models were trained.
|
| The idea that models can only write code if they've seen
| code that does the exact same thing in the past is
| uninformed in my opinion.
| namaria wrote:
| > The idea that models can only write code if they've
| seen code that does the exact same thing in the past is
| deeply uninformed in my opinion.
|
| This is a conceited interpretation of what I said.
| xboxnolifes wrote:
| If this isn't what you meant, then what did you mean? To
| me, it's exactly how I read what you said.
| namaria wrote:
| I am sorry but that's nonsense.
|
| I quoted the paper "Evolution through Large Models"
| written in collaboration between OpenAI and Anthropic
| researchers
|
| "In other words, the model learns to predict plausible
| changes to code from examples of changes made to code by
| human programmers."
|
| https://arxiv.org/pdf/2206.08896
|
| > The idea that models can only write code if they've
| seen code that does the exact same thing in the past
|
| How do you get "code that does the exact same thing" from
| "predicting plausible changes?"
| simonw wrote:
| That paper describes an experimental diff-focused
| approach from 2022. It's not clear to me how relevant it
| is to the way models like Claude 3.7 Sonnet (thinking)
| and o3-mini work today.
| namaria wrote:
| If do not you think past research by OpenAI and Anthropic
| on how to use LLMs to generate code is relevant to how
| Anthropic LLMs generate code 3 years later I really don't
| think it is possible to have a reasonable conversation
| about this topic with you.
| simonw wrote:
| Can we be sure that research became part of their
| mainline model development process as opposed to being an
| interesting side-quest?
|
| Are Gemini and DeepSeek and Llama and other strong coding
| models using the same ideas?
|
| Llama and DeepSeek are at least slightly more open about
| their training processes so there might be clues in their
| papers (that's a lot of stuff to crunch through though).
| fergal_reid wrote:
| Strongly agree.
|
| This seems to be very hard for people to accept, per the
| other comments here.
|
| Until recently I was willing to accept an argument that
| perhaps LLMs had mostly learned the patterns; e.g. to
| maybe believe 'well there aren't that many really
| different leetcode questions'.
|
| But with recent models (eg sonnet-3.7-thinking) they are
| operating well on such large and novel chunks of code
| that the idea they've seen everything in the training
| set, or even, like, a close structural match, is becoming
| ridiculous.
| namaria wrote:
| All due respect to Simon but I would love to see some of
| that groundbreaking code that the LLMs are coming up
| with.
|
| I am sure that the functionalities implemented are novel
| but do you really think the training data cannot possibly
| have had the patterns being used to deliver these
| features, really? How is it that in the past few months
| or years people suddenly found the opportunity and
| motivation to write code that cannot possibly be in any
| way shape or form represented by patterns in the diffs
| that have been pushed in the past 30 years?
| simonw wrote:
| When I said "the thing I am doing has never been done by
| anyone else before" I didn't necessarily mean
| groundbreaking pushes-the-edge-of-computer-science stuff
| - I meant more pedestrian things like "nobody has ever
| published Python code to condense and uncondense JSON
| using this new format I just invented today":
| https://github.com/simonw/condense-json
|
| I'm not claiming LLMs can invent new computer science.
| I'm saying it's not accurate to say "they can only
| produce code that's almost identical to what's in their
| training data".
| namaria wrote:
| > "they can only produce code that's almost identical to
| what's in their training data"
|
| Again, you're misinterpreting in a way that seems like
| you are reacting to the perception that someone attacked
| some of your core beliefs rather than considering what I
| am saying and conversing about that.
|
| I never even used the words "exact same thing" or "almost
| identical". Not even synonyms. I just said overfitting
| and quoted from an OpenAI/Anthropic paper that said
| "predict plausible changes to code from examples of
| changes"
|
| Think about that. Don't react, think. Why do you equate
| overfitting and plausibility prediction with "exact" and
| "identical". It very obviously is not what I said.
|
| What I am getting at is that a cannon will kill the
| mosquito. But drawing a fly swatter in the cannonball and
| saying the plastic ones are obsolete now would be in bad
| faith. No need to say to someone pointing that out that
| they are claiming that the cannon can only fire on
| mosquitoes that have been swatted before.
| simonw wrote:
| I don't think I understood your point then. I matched it
| with the common "LLMs can only produce code that's
| similar to what they've seen before" argument.
|
| Reading back, you said:
|
| > I often see people wondering if the some coding task is
| performed well or not because of availability of code
| examples in the training data. It's way worse than that.
| It's overfitting to diffs it was trained on.
|
| I'll be honest: I don't understand what you mean by
| "overfitting to diffs it was trained on" there.
|
| Maybe I don't understand what "overfitting" means in this
| context?
|
| (I'm afraid I didn't understand your cannon / fly swatter
| analogy either.)
| namaria wrote:
| It's overkill. The models do not capture knowledge about
| coding. They overfit to the dataset. When one distills
| data into a useful model the model can be used to predict
| future behavior of the system.
|
| That is the premise of LLM-as-AI. By training these
| models on enough data, knowledge of the world is
| purported as having been captured, creating something
| useful that can be leveraged to process new input and get
| a prediction of the trajectory of the system in some
| phase space.
|
| But this, I argue, is not the case. The models merely
| overfit to the training data. Hence the variable results
| perceived by people. When their intentions and prompt fit
| to the data in the training, the model appears to give
| good output. But the situation and prompt do not, the
| models do no "reason" about it and "infer" anything. It
| fails. It gives you gibberish or go in circles, or worse
| if there is some "agentic" arrangement if fails to
| terminate and burns tokens until you intervene.
|
| It's overkill. And I am pointing out it is overkill. It's
| not a clever system for creating code for any given
| situation. It overfits to training data set. And your
| response is to claim that my argument is something else,
| not that it's overkill but that it can only kill dead
| things. I never said that. I see it's more than capable
| of spitting out useful code even if that exact same code
| is not in the training dataset. But it is just automating
| the process of going through google, docs and stack
| overflow and assembling something for you. You might be
| good at searching and lucky and it is just what you need.
| You might not be so used to using the right keywords or
| just be using some uncommon language, or in a domain that
| happens to not be well represented and then it feels less
| useful. But instead of just coming up short as search,
| the model overkills and wastes your time and god knows
| how much subsidized energy and compute. Lucky you if
| you're not burning tokens on some agentic monstosity.
| simonw wrote:
| If that's the case, it turns out that what I want is a
| system that's "overfitted to the dataset" on code, since
| I'm getting incredibly useful results for code out of it.
|
| (I'm not personally interested in the whole AGI thing.)
| namaria wrote:
| Good man I never said anything about AGI. Why do you keep
| responding to things I never said?
|
| This whole exchange was you having knee-jerk reactions to
| things you imagined I said. It has been incredibly
| frustrating. And at the end you shrug and say "eh it's
| useful to _me_ "??
|
| I am talking about this because of deceitfulness,
| resource efficiency, societal implications of technology.
| simonw wrote:
| "That is the premise of LLM-as-AI" - I assumed that was
| an AGI reference. My definition of AGI is pretty much
| "hyped AI". What did you mean by "LLM-as-AI"?
|
| In my own writing I don't even use the term "AI" very
| often because its meaning is so vague.
|
| You're right to call me out on this: I did, in this
| earlier comment -
| https://news.ycombinator.com/item?id=43644662#43647037 -
| commit the sin of responding to something you hadn't
| actually said.
|
| (Worse than that, I said "... is uninformed in my
| opinion" which was rude because I was saying that about a
| strawman argument.)
|
| I did that thing where I saw an excuse to bang on one of
| my pet peeves (people saying "LLMs can't create new code
| if it's not already in their training data") and jumped
| at the opportunity.
|
| I've tried to continue the rest of the conversation in
| good faith though. I'm sorry if it didn't come across
| that way.
| mdp2021 wrote:
| > _My definition of AGI is pretty much_
|
| Simon, intelligence exists (and unintelligence exists).
| When you write <<I'm not claiming LLMs can invent new
| computer science>>, you imply intelligence exists.
|
| We can implement it. And it is somehow urgent, because
| intelligence is very desirable wealth - there is definite
| scarcity. It is even more urgent after the recent hype
| has made some people perversely confused about the idea
| of intelligence.
|
| We can and must go well beyond the current state.
| fergal_reid wrote:
| You are correct that variable results could be a symptom
| of a failure to generalise well beyond the training set.
|
| Such failure could happen if the models were overfit, or
| for other reasons. I don't think 'overfit', which is
| pretty well defined, is exactly the word you mean to use
| here.
|
| However, I respectfully disagree with your claim. I think
| they are generalising well beyond the training dataset
| (though not as far beyond as say a good programmer would
| - at least not yet). I further think they are learning
| semantically.
|
| Can't prove it in a comment except to say that there's
| simply no way they'd be able to successfully manipulate
| such large pieces of code, using English language
| instructions, it they weren't great at generalisation and
| ok at understanding semantics.
| namaria wrote:
| I understand your position. But I think you're
| underestimating just how much training data is used and
| how much information can be encoded in hundreds of
| billions of parameters.
|
| But this is the crux of the disagreement. I think the
| models overfit to the training data hence the fluctuating
| behavior. And you think they show generalization and
| semantic understanding. Which yeah they apparently do.
| But the failure modes in my opinion show that they don't
| and would be explained by overfitting.
| mdp2021 wrote:
| > _overfitting_
|
| Are you sure it's not just a matter of being halfwitted?
| lispisok wrote:
| The PR articles and astroturfing will continue until investors
| get satisfactory returns on their many billions dumped into
| these things.
| simonw wrote:
| They released the data for this report as a bunch of CSV files in
| a Google Drive, so I converted those into a SQLite database for
| exploration with Datasette Lite:
| https://lite.datasette.io/?url=https://static.simonwillison....
|
| Here's the most interesting table, illustrating examples of bias
| in different models
| https://lite.datasette.io/?url=https://static.simonwillison....
| colesantiago wrote:
| It's great to see that there will be new jobs when AI usage in
| businesses skyrockets.
| ausbah wrote:
| honestly hope that LLMs end up creating mountains of
| unsustainable tech debt across these companies so devs have
| some job security
| andai wrote:
| Note that this is an overview, each chapter has its own page, and
| even those are overviews, each chapter comes as a separate PDF.
|
| The full report PDF is 456 pages.
| mentalgear wrote:
| "AI performance on demanding benchmarks continues to improve."
|
| My feeling is that more AI models are fine-tuned on these
| prestigious benchmarks.
| trott wrote:
| Regarding point number 11 (AlphaFold3 vs Vina, Gnina, etc.), see
| my rebuttal here (I'm the author of Vina):
| https://olegtrott.substack.com/p/are-alphafolds-new-results-...
|
| Gnina is Vina with its results re-scored by a NN, so the exact
| same concerns apply.
|
| I'm very optimistic about AI, for the record. It's just that in
| this particular case, the comparison was flawed. It's the old
| regurgitation vs generalization confusion: We need a method that
| generalizes to completely novel drug candidates, but the
| evaluation was done on a dataset that tends to be repetitive.
| joe_the_user wrote:
| I recall Stanford's past AI Reports being substantial and
| critical some years ago. This seems like a compilation of many
| small press releases into one large press release ("Key take
| away: AI continues to get bigger, better and faster"). The
| problem is that AI went from universities to companies and the
| publications of the various companies themselves then went from
| research papers to press releases/white papers (I remember
| OpenAI's supposed technical specification of GPT-something as a
| watershed, in that actually involved no useful information but
| just touted statistics who context the reader didn't know).
| janpmz wrote:
| What I'm certain of is that the standard of living will increase.
| Because we can do more effective work in the same time. This
| means more output and things will become cheaper. What I'm not
| sure of, is where this effect will show in the stock market.
| soulofmischief wrote:
| Standard of living for who? Productivity has not scaled
| appropriately with wages since the industrial revolution.
| janpmz wrote:
| For almost everyone I think. Since the industrial revoultion
| we have availability of cheap electricity, cheap lighting, an
| abundance of food and clothing etc. How the wages developed
| is something I don't know.
| elevatortrim wrote:
| This is assuming most white collar economically productive work
| is currently utilised to improve standard of lives and is a
| bottleneck which is at best questionable.
| vander_elst wrote:
| Meta question, why does the website try to make it more difficult
| to open the images in a new tab? usually if I want to do that, I
| right click and then select "open image in a new tab". Here I had
| to go through some loops to do it. Additionally, if you just copy
| the URL you get to a image that's just noise and that seems to be
| by design. I still can access the original image though and
| download it from AWS S3 (https://hai-
| production.s3.amazonaws.com/images/fig_1e.png). So the question,
| why all the loops, just to scare off non-technical users?
| andai wrote:
| The whole thing is over-engineered, could have been a few lines
| of HTML. They just made it harder to use and navigate,
| unfortunately.
| dartharva wrote:
| > In the U.S., 81% of K-12 CS teachers say AI should be part of
| foundational CS education, but less than half feel equipped to
| teach it.
|
| I'm curious, what exactly do they mean when they say they should
| teach AI in K-12?
| janalsncm wrote:
| > The U.S. still leads in producing top AI models--but China is
| closing the performance gap.
|
| Most researchers that I know do not think about things in this
| lens. They think about building cool things with smart people,
| and if those people happen to be Chinese or French or Canadian it
| doesn't matter.
|
| Most people do not want a war (hot or cold) with the world's only
| manufacturing superpower. It feels like we have been incepted
| into thinking it's inevitable. It's not.
|
| In the other hand, if in some nationalistic AI race with China
| the US decides to get serious about R&D on this front, it will be
| good for me. I don't want it though.
___________________________________________________________________
(page generated 2025-04-11 23:02 UTC)