Other energy usage figures, air pollution, gas turbines, CO2 emissions etc are fine - but if you complain about water usage I think it risks discrediting the rest of your argument.
(Aside from that I agree with most of this piece, the "AGI" thing is a huge distraction.)
UPDATE an hour after posting this: I may be making an ass of myself here in that I've been arguing in this thread about comparisons between data center usage and agricultural usage of water, but that comparison doesn't hold as data centers often use potable drinking water that wouldn't be used in agriculture or for many other industrial purposes.
I still think the way these numbers are usually presented - as scary large "gallons of water" figures with no additional context to help people understand what that means - is an anti-pattern.
I think much of this may be a reaction to the hype promoted by tech CEOs and media outlets. People are seeing through their lies and exaggerations, and taking positions like "AI/LLMs have no values or uses", then using every argument they hear as a reason why it is bad in a broad sense. For example: Energy and water concerns. That's my best guess about the concern you're braced against.
The comment you're replying to is calling other people AI skeptics.
Your advice has some fine parts to it (and simonw's comment is innocuous in its use of the term), but if we're really going meta, you seem to be engaging in the tribal conflict you're decrying by lecturing an imaginary person rather than the actual context of what you're responding to.
Politics is the set of activities that are associated with making decisions in groups, or other forms of power relations among individuals, such as the distribution of status or resources.
Most municipalities literally do not have enough spare power to service this 1.4 trillion dollar capital rollout as planned on paper. Even if they did, the concurrent inflation of energy costs is about as political as a topic can get.
Economic uncertainty (firings, wage depression) brought on by the promises of AI is about as political as it gets. There's no 'pure world' of 'engineering only' concerns when the primary goals of many of these billionaires is leverage this hype, real and imagined, into reshaping the global economy in their preferred form.
The only people that get to be 'apolitical' are those that have already benefitted the most from the status quo. It's a privilege.
I'd love this but it's impossible to have this discussion with someone who will not touch generative AI tools with a 10 foot pole.
It's not unlike when religious people condemn a book they refuse to read. The merits of the book don't matter, it's symbolic opposition to something broader.
I have no idea why.
I don't think that the correlation is 1, but it seems weirdly high.
Zero obligation to satisfy HN audience; tiny proportion of the populace. But for giggles...
Technical merits: there are none. Look at Karpathy's GPT on Github. Just some boring old statistics. These technologies are built on top of mathematical principles in textbooks printed 70-80 years ago.
The sharding and distribution of work across numerous machines is also a well trodden technical field.
There is no net new discovery.
This is 100% a political ploy on the part of tech CEOs who take advantage of the innumerate/non-technical political class that holds power. That class is bought into the idea that massive leverage over resource markets is a win for them, and they won't be alive to pay the price of the environmental destruction.
It's not "energy and water" concerns, it's survival of the species concerns obfuscated by socio-political obligations to keep calm carry on and debate endlessly, as vain circumlocution is the hallmark of the elders whose education was modeled on people being VHS cassettes of spoken tradition, industrial and political roles.
IMO there is little technical merit to most software. Maps, communication. That's all that's really needed. ZIRP era insanity juiced the field and created a bunch of self-aggrandizing coder bros whose technical achievements are copy-paste old ideas into new syntax and semantics, to obfuscate their origins, to get funded, sell books, book speaking engagements. There is no removing any of this from politics as political machinations gave rise to the dumbest era of human engineering effort ever.
The only AI that has merit is robotics. Taking manual labor of people that are otherwise exploited by bougie first worlders in their office jobs. People who have, again with the help of politicians, externalized their biologies real needs on the bodies of poorer illiterates they don't have to see as the first-world successfully subjugated them and moved operations out of our own backyard.
Source: was in the room 30 years ago, providing feedback to leadership how to wind down local manufacturing and move it all over to China. Powerful political forces did not like the idea of Americans having the skills and knowledge to build computers. It ran afoul of their goals to subjugate and manipulate through financial engineering.
Americans have been intentionally screwed out of learning hands on skills with which they would have political leverage over the status quo.
There is no removing politics from this. The situation we are in now was 100% crafted by politics.
I used to share your view, but what changed my mind was reading Hao's book. I don't have it to hand, but if my memory serves, she writes about a community in Chile opposing Google building a data centre in their city. The city already suffers from drought, and the data centre, acccording to Google's own assessment, would abstract ~169 litres of water a second from local supplies - about the same as the entire city's consumption.
If I also remember correctly, Hao also reported on another town where salt water was being added to municipal drinking water because the drought, exacerbated by local data centres, was so severe.
It is indeed hard to imagine these quantities of water but for me, anything on the order of a town or city's consumption is a lot. Coupled with droughts, it's a problem, in my view.
I really recommend the book.
I do think the whole water issue as pointed out in the book is completely discredited with this call out.
The author Karen Hao acknowledges this:
For decades we've been told we shouldn't develop urban centers because of how it development affects local communities, but really it just benefited another class of elites (anonymous foreign investors), and now housing prices are impoverishing younger generations and driving homelessness.
Obviously that's not a perfect comparison to AI, which isn't as necessary, but I think the anti-growth argument isn't a good one. Democracies need to keep growing or authoritarian states will take over who don't care so much about human rights. (Or, authoritarian governments will take over democracies.)
There needs to be a political movement that's both pro-growth and pro-humanity, that is capable of making hard or disruptive decisions that actually benefits the poor. Maybe that's a fantasy, but again, I think we should find ways to grow sustainably.
Nestle is and has been 10000x worse for global water security than all other companies and countries combined because nobody in the value chain cares about someone else’s aquifer.
It’s a social-economic problem of externalities being ignored , which transcends any narrow technological use case.
What you describe has been true for all exported manufacturing forever.
Golf and datacenters should have to pay for their externalities. And if that means both are uneconomical in arid parts of the country then that's better than bankrupting the public and the environment.
> I asked the farmer if he had noticed any environmental effects from living next to the data centers. The impact on the water supply, he told me, was negligible. "Honestly, we probably use more water than they do," he said. (Training a state-of-the-art A.I. requires less water than is used on a square mile of farmland in a year.) Power is a different story: the farmer said that the local utility was set to hike rates for the third time in three years, with the most recent proposed hike being in the double digits.
The water issue really is a distraction which harms the credibility of people who lean on it. There are plenty of credible reasons to criticize data enters, use those instead!
My perspective from someone who wants to understand this new AI landscape in good faith. The water issue isn't the show stopper it's presented as. It's an externality like you discuss.
And in comparison to other water usage, data centers don't match the doomsday narrative presented. I know when I see it now, I mentally discount or stop reading.
Electricity though seems to be real, at least for the area I'm in. I spent some time with ChatGPT last weekend working to model an apples:apples comparison and my area has seen a +48% increase in electric prices from 2023-2025. I modeled a typical 1,000kWh/month usage to see what that looked like in dollar terms and it's an extra $30-40/month.
Is it data centers? Partly yes, straight from the utility co's mouth: "sharply higher demand projections—driven largely by anticipated data center growth"
With FAANG money, that's immaterial. But for those who aren't, that's just one more thing that costs more today than it did yesterday.
Coming full circle, for me being concerned with AI's actual impact on the world, engaging with the facts and understanding them within competing narratives is helpful.
Key quote:
> If any region has a lot of freshwater and little potable water, the best way to make potable water more available and cheaper is to introduce a new large buyer, which will give the local utility enough revenue to upgrade and expand their treatment facilities. Saying that my data is misleading because Al "only uses valuable potable water" actually gets the issue backwards: adding demand for more potable water in regions with lots of freshwater makes potable water cheaper and more abundant for everyone else per unit.
1. That just because a region doesn’t have enough potable water to support humans and data centers, it also doesn’t have enough potable water to support the humans alone.
2. That the temporary increase in water prices due to the new demand of the data centers will provide enough revenue to upgrade its facilities
3. Even given enough revenue to upgrade its facilities, that the utility will choose to upgrade its facilities and increase demand
4. That the downsides of a temporary increase in water prices while new facilities are built is acceptable and will not cause suffering
5. Even after new facilities are built, that the cost of those facilities will be low enough and the increase in supply large enough that water prices for humans will be lower than they were originally, even with a large and wealthy new buyer on the market.
It doesn’t feel like a very strong argument to me.
Like, if agriculture uses fresh water and data centers use potable water, the important question is how hard it is to convert fresh water into potable water?
The answer seems to be "not very" so the difference is kind of moot
There's a bunch in there but this bit caught my eye in particular:
> The US public water supply uses ~40 billion gallons per day, all of this is potable. Data centers used 50 million gallons per day onsite in 2023. So their potable water usage was 0.13% of the public water supply.
This is more than 4 times more than all data centers in the US combined, counting both cooling and the water used for generating their electricity.
What has more utility: Californian almonds, or all IT infrastructure in the US times 4?
Of course water used up will eventually evaporate, and produce rainfall in the water cycle, but unfortunately at many places "fossil" water is used up, or more water used in an area then the watershed can sustainably support.
This is a constant source of miscommunication about water usage, and that of agriculture also. It is very different to talk about the water needs to raise a cow in eg. Colorado and in Scotish highlands, but this is usually removed from the picture.
The same context should be considered for datacenters.
I think it's bad though to be against growth, for reasons I've described in another comment.
I also think the energy usage stuff is kind of nonsense. If energy usage is a major part of your operating expenses, you're probably going to locate your data center where energy is cheap, and cheap energy is always renewable. I'm sure you can find data centers that run off coal plants or other thermal power, but thermal power costs in the neighborhood of 100¢ per peak watt, while solar cells cost 12¢ per peak watt, so thermal power won't be competitive for very long.
It may be a valid criticism today, but no one will be complaining about AI's environmental impact after those first few plants go live and mass production begins within the next decade. Knock on wood.
I first came across this type of info with the book "How Bad Are Bananas", from Mike Berners-Lee. I really enjoyed it, and I just saw that the new edition even includes stuff like hosting a World Cup, data centers, and space tourism!
It should give a good foundation to start talking about it.
https://andymasley.substack.com/p/the-ai-water-issue-is-fake
Unlike most datacenters, AI datacenters being far away from the user is okay since it takes on the order of seconds to minutes for the code to run and generate a response. So, a few hundred milliseconds of latency is much more tolerable. For this reason, I think that we should pick a small number of ideal locations that have a combination of weather that permits non-sub-ambient cooling and have usable low carbon resources (either hydropower is available and plentiful, or you can build or otherwise access nuclear reactors there), and then put the bulk of this new boom there.
If you pick a place with both population and a cold climate, you could even look into using the data center's waste heat for district heating to get a small new revenue stream and offset some environmental impact.
What prevents data centers from using non-evaporative cooling to keep their water usage low? The water usage argument loses a lot of its relevant in that case.
In europe several power plants get shut down each summer because the heated water from those plants would have significant impact on the local wildlife.
I think you're still good on your original assertion, it seems many/most of the biggest players are using non potable in new facilities and also retrofitting some old ones to avoid potable water as well [1]
I think you'd be good either way: The distinction sounds like an important point until you realize that the cost of turning raw water potable is so vanishly small compared to the cost of these data centers. Some rough estimates place it as less than one single rack of a GB200 NVL72 to build enough-- or more economically, bolster the local existing plants for raw water processing. Even if they had to go to brackish water desalination the cost there looks to be mostly in ongoing electricity costs which amount to ~$3k per day such that their existing power plant build outs for these would easily cover it, or a few such new desalination plants to cover many many data centers.
I'm not unsympathetic to aspects of these overall concerns either, but critics have to do a lot better than concerns that are less hyperbolically expressed as the much less catchy "No AI!... without small and reasonable policies for covering proportional infrastructure cost increases!".
[1] https://datacentremagazine.com/articles/reclaimed-wastewater...
If you look at the original paper they are quite upfront with the difficulty of estimating water use. It’s not public data—in fact it’s usually a closely held trade secret, plus it’s got all kinds of other issues like you don’t know where the training happened, when it happened, what the actual cooling efficiency was, etc. The researchers were pretty clear about these limitations in the actual paper.
Basically, it’s urban legend at this point. When OpenAI’s CEO later said ChatGPT uses ~0.3ml per query, that’s roughly 100x less than the viral claims.
[1] <https://arxiv.org/abs/2304.03271> [2] <https://www.washingtonpost.com/technology/2024/09/18/energy-...> [3] <https://www.seangoedecke.com/water-impact-of-ai>/
People are critical of farmland and golf courses, too. But Farmland at least has more benefit for society, so they are more vocal on how it's used.
So, even if there's no recycling, a data center that is said to consume "millions" rather than tens or hundreds of millions is probably using less than 5 acres of alfalfa in consumption, and in absolute terms, this requires only a swimming-pool or two of water per years. It's trivial.
There's lots of promising lower-consumption cooling options, but seems like we are not yet seeing that in a large fraction of data centers globally.
Only 14% use municipal water systems to draw water. https://www.usga.org/content/dam/usga/pdf/Water%20Resource%2...
That said, here are the relevant numbers from that 2012 article in full:
> Most 18-hole golf facilities utilize surface waters (ponds, lakes) or on-site irrigation wells. Approximately 14 percent of golf facilities use water from a public municipal source and approximately 12 percent use recycled water as a source for irrigation.
> Specific water sources for 18-hole courses as indicated by participants are noted below:
> 52 percent use water from ponds or lakes.
> 46 percent use water from on-site wells.
> 17 percent use water from rivers, streams and creeks.
> 14 percent use water from municipal water systems.
> 12 percent use recycled water for irrigation.
It uses as much water per year as 200 acres of alfalfa in California’s Central Valley. There are around 1M acres of alfalfa growing in California.
2.5MW of data center capacity is roughly equal to 1 acre of irrigated alfalfa in water usage. If you’re pulling fossil aquifer water, open loop evaporative cooling may not be the best idea, but there are plenty of places east of 100 degrees west in the US that have virtually ‘unlimited’ water where cooling towers are a great idea since they almost double the COP of a chilled water system.
People are using these arguments for the simple reason that they demonstrably resonate with average people who live near data centers.
They probably don’t resonate with people who have plenty of income and/or do not compete with data centers locally for resources.
Perhaps this is the point, maybe the political math is that more people than not will assume that using water means it's not available for others, or somehow destroyed, or polluted, or whatever. AFAIK they use it for cooling so it's basically thermal pollution which TBH doesn't trigger me the same way that chemical pollution would. I don't want 80c water sterilizing my local ecosystem, but I would guess that warmer, untreated water could still be used for farming and irrigation. Maybe I'm wrong, so if the water angle is a bigger deal than it seems then some education is in order.
If it was being used for evaporative cooling then the argument would be stronger. But I don't think it is - not least because most data centres don't have massive evaporative cooling towers.
Even then, whether we consider it a bad thing or not depends on the location. If the data centre was located in an area with lots of water, it's not some great loss that it's being evaporated. If it's located in a desert then it obviously is.
To say that it's never an issue is disingenuous.
Additionally one could image a data center built in a place with a surplus of generating capacity. But in most cases, it has a big impact on the local grid or a big impact on air quality if they bring in a bunch of gas turbines.
NYT article gift link where people reported wells ran dry after data centers moved in. : 'From Mexico to Ireland, Fury Mounts Over a Global A.I. Frenzy' https://www.nytimes.com/2025/10/20/technology/ai-data-center...
From https://www.eesi.org/articles/view/data-centers-and-water-co... , I understand there are two types of cooling with water in DCs, open-loop that's simple but water-intensive, and closed-loop that's expensive but efficient.
>> This can be achieved through air cooling using water evaporation, which is an open-loop and more water-intensive method, or through server liquid cooling.
hmm why exactly? mineral content?
When evaluating the economical cost or morality of a thing, (just like when training a machine learning model) the more data you consider the more accurate the result (although just like statistical modelling it is worth to be wary of overfitting).
> An H100 on low-carbon grid is only about 1–2% of one US person’s total daily footprint!
The real culprit is humans after all.
Assumptions you are making:
- AI = transformer ANNs
- People sceptical of transformer ANNs directly leading to AGI within any reasonable period are also sceptical of transformer ANNs directly leading to AGI any time in the far future
This kind of generalisations don't help you as the huge number of comments underneath yours likely shows
As for food production; that might be important? IDK, I am not a silicon "intelligence" so what do I know? Also, I have to "eat". Wouldn't it be a wonderful world if we can just replace ourselves, so that agriculture is unnecessary, and we can devote all that water to AGI.
TIL that the true arc of humanity is to replace itself!
It's not about it being scary, its about it being a gigantic, stupid waste of water, and for what? So that lazy executives and managers can generate their shitty emails they used to have their comms person write for them, so that students can cheat on their homework, or so degens can generate a video of MLK dancing to rap? Because thats the majority of the common usage at this point and creating the demand for all these datacenters. If it was just for us devs and researchers, you wouldn't need this many.
https://www.bbc.com/news/articles/cx2ngz7ep1eo
https://www.theguardian.com/technology/2025/nov/10/data-cent...
https://www.reuters.com/article/technology/feature-in-latin-...
And the final kicker: the human brain runs on like two dozen Watts. An LLM takes a year of running on a few MW to train and several KW to run.
Given this I am not certain we will get to AGI by simulating it in a GPU or TPU. We would need a new hardware paradigm.
We're totally incapable of building an AI that can do anything resembling that. We're still at the phase where robots walking on rough terrain without falling over remains a bit impressive.
I doubt the limitation is that we can't produce enough raw compute to replace a single bee.
Evolution is winning because it's operating at a much lower scale than we are and needs less energy to achieve anything. Coincidentally, our own progress has also been tied to the rate of shrinking of our toys.
I do think we probably need a new hardware approach to get to the human level, but it does seem like it will happen in a relative blink of an eye compared to how long the brain took.
The EDA [0] problem is immune to the bitter lesson. There are certainly specific arrangements of matter that can solve this problem better than a GPU/TPU/CPU can today.
[0] https://en.wikipedia.org/wiki/Electronic_design_automation
Those feature sizes are tiny, yes, but we struggle to put them in a block the size of a human brain and keep it cool enough to be useful (or even make it affordable).
This is simply a scaling problem, eg. thousands of single I/O functions can reproduce the behaviour of a function that takes thousands of inputs and produces thousands of outputs.
Edit: As for the rest of your argument, it's not so clear cut. An LLM can produce a complete essay in a fraction of the time it would take a human. So yes, a human brain only consumes about 20W but it might take a week to produce the same essay that the LLM can produce in a few seconds.
Also, LLMs can process multiple prompts in parallel and share resources across those prompts, so again, the energy use is not directly comparable in the way you've portrayed.
I think it's more than just scaling, you need to understand the functional details to reproduce those functions (assuming those functions are valuable for the end result as opposed to just the way it had to be done given the medium).
An interesting example of this neuron complexity that was published recently:
As rats/mice (can't remember which) are exposed to new stimuli, the axon terminals of a single neuron do not all transmit a signal when there is an action potential, they transmit in a changing pattern after each action potential and ultimately settle into a more consistent pattern of some transmitting and some not.
IMHO: There is interesting mathematical modeling and transformations going on in the brain that is the secret sauce for our intelligence and it is yet to be figured out. It's not just scaling of LLM's, it's finding the right functions.
It's not even that. The architecture(s) behind LLMs are nowhere near close that of a brain. The brain has multiple entry-points for different signals and uses different signaling across different parts. A brain of a rodent is much more complex than LLMs are.
In our lane the only important question to ask is, "Of what value are the tokens these models output?" not "How closely can we emulate an organic bran?"
Regarding the article, I disagree with the thesis that AGI research is a waste. AGI is the moonshot goal. It's what motivated the fairly expensive experiment that produced the GPT models, and we can look at all sorts of other hairbrained goals that ended up making revolutionary changes.
One neuron is ufathomably complex. It‘s offensive to biology to call a cell in a mathematical matrix neuron.
The brain also has plasticity! The connections between neurons change dynamically - an extra level of meta.
I’ve always thought about nature didn’t evolve to use electricity as its primary means of energy. Instead it uses chemistry. It’s quite curious, really.
Like a tiny insect is chemistry powered. It doesn’t need to recharge batteries, it needs to eat and breathe oxygen.
What if our computers started to use biology and chemistry as their primary energy source?
Or will it be the case that in the end using electricity as the primary energy source is more efficient for “human brain scale computation”, it’s just that nature didn’t evolve that way…
[1] https://sciencesensei.com/scientists-created-thinking-brain-...
Like you mention, each individual neuron or synapse includes fully parallel processing capability. With signals conveyed by dozens of different molecules. Each neuron (~86 billion) holds state information in addition to processing. The same is true for each synapse (~600 quadrillion). That is how many ~10 Hz "cores" the human computational system has.
The hubris of the AI community is laughable considering the biological complexity of the human body and brain. If we need anywhere close to the same processing capability, there is no doubt we are multiple massive hardware advances away from AGI.
Read: https://pythonic.ninja/blog/2025-11-15-ev-of-agi-for-western...
A lot of people say that, but no one, not a single person has ever pointed out a fundamental limitation that would prevent an LLM from going all the way.
If LLMs have limits, we are yet to find them.
1) Try to build a neuron-level brain simulator - something that is a far distant possibility, not because of compute, but because we don't have a clear enough idea of how the brain is wired, how neurons work, and what level of fidelity is needed to capture all the aspects of neuron dynamics that are functionally relevant rather than just part of a wetware realization
OR
2) Analyze what the brain is doing, to extent possible given our current incomplete knowledge, and/or reduce the definition of "AGI" to a functional level, then design a functional architecture/implementation, rather than neuron level one, to implement it
The compute demands of these two approaches are massively different. It's like the difference between an electronic circuit simulator that works at gate level vs one that works at functional level.
For time being we have no choice other than following the functional approach, since we just don't know enough to build an accurate brain simulator even if that was for some reason to be seen as the preferred approach.
The power efficiency of a brain vs a gigawatt systolic array is certainly dramatic, and it would be great for the planet to close that gap, but it seems we first need to build a working "AGI" or artificial brain (however you want it define the goal) before we optimize it. Research and iteration requires a flexible platform like GPUs. Maybe when we figure it out we can use more of a dataflow brain-like approach to reduce power usage.
OTOH, look at the difference between a single user MOE LLM, and one running in a datacenter simultaneously processing multiple inputs. In the single-user case we conceptualize the MOE as saving FLOPs/power by only having one "expert" active at a time, but in the multi-user case all experts are active all the time handling tokens from different users. The potential of a dataflow approach to save power may be similar, with all parts of the model active at the same time when handling a datacenter load, so a custom hardware realization may not be needed/relevant for power efficiency.
3) Pour enough computation into a sufficiently capable search process and have it find a solution for us
Which is what we're doing now.
The bitter lesson was proven right once again. LLMs prove that you can build incredibly advanced AIs without "understanding" how they work.
I think it's more an algorithm problem. I've been reading how LLMs work and the brain does nothing like matrix multiplication over billions of entities. It seems a very inefficient way to do it in terms of compute use, although efficient in terms of not many lines of code. I think the example of the brain shows one could do far better.
if you put the brain in the shape of a tube you'd have a really long err, well, let's say it's not a good idea to do that. the brain gives me goosepimples, my brain too
I don't remember hearing much about neuromorphic computing lately though so I guess it hasn't had much progress.
https://journals.plos.org/plosone/article?id=10.1371/journal...
I mean, you could argue that if you take into consideration all the generations (starting from the first amoeba) that it took to get to a standard human brain today, then the total energy used to "train" that brain is far greater. But I get your point and I do agree with you that our current hardware paradigm is probably not what's going to give us "god in a box".
It obviously wouldn't have the bandwidth to do so in a way that would make a real-time stream feasible, but it doesn't involve any leap of logic to conclude that a higher bandwidth link means being able to transfer more data within a given period of time, which would eventually enable use cases that weren't feasible before.
In contrast, you could throw an essentially unlimited amount of hardware at LLMs, and that still wouldn't mean that they would be able to achieve AGI, because there's no clear mechanism for how they would do so.
For example, if biology had a "choice" I am fairly confident that it would have elected to not have leaky charge carriers or relatively high latency between elements. Roughly 20% of our brain exists simply to slow down and compensate for the other 80%.
I don't know that eliminating these caveats is sufficient to overcome all the downsides, but I also don't think we've tried very hard to build experiments that directly target this kind of thinking. Most of our digital neurons today are of an extremely reductive variety. At a minimum, I think we need recurrence over a time domain. The current paradigm (GPU-bound) is highly allergic to a causal flow of events over time (i.e., branching control flows).
For those who've been sniffing this since early 2010, it's so blindly obvious they've already dropped llms on the floor and moved onto deeper alternative research.
For the rest of us, we're still catching coke bottles from the sky and building places of worship around them
There should be papers on fundamental limitations of LLMs then. Any pointers? "A single forward LLM pass has TC0 circuit complexity" isn't exactly it. Modern LLMs use CoT. Anything that uses Gödel's incompleteness theorems proves too much (We don't know whether the brain is capable of hypercomputations. And, most likely, it isn't capable of that).
Ever since "AI" was named at Dartmouth, there have been very smart people thinking that their idea will be the thing which makes it work this time. Usually, those ideas work really well in-the-small (ELIZA, SHRDLU, Automated Mathematician, etc.), but don't scale to useful problem sizes.
So, unless you've built a full-scale implementation of your ideas, I wouldn't put too much faith in them if I were you.
Me too. But, I worry this “want” may not be realistic/scalable.
Yesterday, I was trying to get some Bluetooth/BLE working on a Raspberry CM 4. I had dabbled with this 9 months ago. And things were making progress then just fine. Suddenly with a new trixie build and who knows what else has changed, I just could not get my little client to open the HCI socket. In about 10 minutes prompt dueling between GPT and Claude, I was able to learn all about rfkill and get to the bottom of things. I’ve worked with Linux for 20+ years, and somehow had missed learning about rfkill in the mix.
I was happy and saddened. I would not have k own where to turn. SO doesn’t get near the traffic it used to and is so bifurcated and policed I don’t even try anymore. I never know whether to look for a mailing list, a forum, a discord, a channel, the newsgroups have all long died away. There is no solidly written chapter in a canonically accepted manual written by tech writers on all things Bluetooth for the Linux Kernel packaged with raspbian. And to pile on, my attention span driven by a constant diet of engagement, makes it harder to have the patience.
It’s as if we’ve made technology so complex, that the only way forward is to double down and try harder with these LLMs and the associated AGI fantasy.
A good example is the web platform. It's just enormous...to the point that no human can really understand how it all even works. And I say that as someone who worked for a long time on a narrow part of that stack (V8). It being only a little over a million lines of code, it is incredibly intricate and subtle, because it implements a pretty weird language, has lots of optimizations, advanced GC, multiple compilers, etc. And that's just the JS engine. Add in the layout engine, rendering engine, multi-process architecture...it's beyond the comprehension a single mind.
We're not yet at the level that an AI can understand code really deeply yet, but may we will reach the point where an AI understands enough of it and can code competently enough to start over from scratch and build something we can both understand and does the things we actually want it to do.
We've seen a disturbing preview of this recently.
It's a natural law that what is not exercised dies away.
When we make our systems too stable and predictable, the ability to operate effectively in the absence of stability also dies away.
Both https://wiki.archlinux.org/title/Bluetooth and https://wiki.debian.org/BluetoothUser mention rfkill and show you how to troubleshoot.
This is the real AI risk we should be worried about IMO, at least short term. Information technology has made things vastly more complicated. AI will make it even more incomprehensible. Tax code, engineering, car design, whatever.
It's already happening at my work. I work at big tech and we already have a vast array of overly complicated tools/technical debt no one wants to clean up. There's several initiatives to use AI to prompt an agent, which in turn will find the right tool to use and run the commands.
It's not inconceivable that 10 or 20 years down the road no human will bother trying to understand what's actually going on. Our brains will become weaker and the logic will become vastly more complicated.
(We already do this constantly in categorizing human generated bullshit information and useful information constantly. So learning to do something similar with LLM output is not necessarily worse, just different.)
What's silly at this point is replacing a human entirely with an LLM. LLMs are still fundamentally unsuited for those tasks, although they may be in the future with some significant break throughs.
Just because the people who make them live in a fantasy world, doesn't mean we can't reap the fruits of their labor!
That being said, I suspect a lot of the energy spent on AI training is resulting in unusable slop.
I'm increasingly convinced that spirituality is a vital part of the human experience and we should embrace it, not reject it. If you try to banish basic human impulses, they just resurface in worse, unexpected forms somewhere else.
We all need ways to find deep connection with other humans and the universe around us. We need basic moral principles to operate on. I think most atheists like myself have quietly found this or are in the process of finding this, but it's ok to say it out loud.
For me it means meditation, frugality, and strict guidelines on how I treat others. That's like a religion, I guess. But that's OK. I embrace it. By owning it and naming it, you have mastery over it.
If I understand his idea correctly, these societies that were developed with a religious justification, and a huge religious component, are of course losing it in the scientific age. The first stage they go through is "zombie religion" where people don't pretend to believe in the religion any more, but still insist that they share all of its values, and often become even more fanatical in the functions that the old religion served. The second stage is "zero religion" where both the belief and the functions are gone, and all that's left is a religion shaped hole that is filled with nihilism: the strong preying on the weak, self-indulgence, and an elite retreat into often paranoid fantasy.
These stages are shaped by the particular religion that disappeared, so the Zero Catholicisms aren't the same as Zero Protestantisms aren't the same as the Zero Islams. Science, being about what works rather than why you should be doing anything, simply didn't fill up these holes that once held morality and justification. For him, it seems, the Western world is primarily in a moral crisis, and we're seeing it in the mental decay of an elite that doesn't have to justify itself to anyone, ever (after religion has died.)
Personally, I can also see this in the deep desire of some people to obey AI, but I can't see it being fruitful at all. "Because the AI said so" is not particularly inspiring or ecstatic. It's just an extension of middle-class materialistic money as grace and job as devotion, which is notoriously unfulfilling. Will AI help you succeed if it can't tell you what it means to succeed?
An article in Illustrated London News, April 26, 1924 by G. K. Chesterton
> religious impulses don't die with the withering of religion
Religions have of course come and gone throughout human history. The preceding deities, temples, and artwork are called mythology by people inside today's temples of fervour.
But let's be clear, disparate local tribal practices and beliefs are only formalised by a power structure for the masked purposes of the power structure.
What springs eternal is the maintenance of control in political and tribal hierarchies.
> A desire for a totalizing solution to all woes
The fact that our species exhibits astonishing credulity is illustrated throughout history to the present day, not just in religious activities but in every context of economic scams and demagoguery.
[1] _ https://en.wikipedia.org/wiki/Extraordinary_Popular_Delusion...
Now I run it through whisper in a couple minutes, give one quick pass to correct a few small hallucinations and misspellings, and I'm done.
There are big wins in AI. But those don't pump the bubble once they're solved.
And the thing that made Whisper more approachable for me was when someone spent the time to refine a great UI for it (MacWhisper).
Yesterday I heard Cory Doctorow talk about a bunch of pro bono lawyers using LLMs to mine paperwork and help exonerate innocent people. Also a big win.
There's good stuff - engineering - that can be done with the underlying tech without the hyperscaling.
An essay writing machine is cool. A machine that can competently control any robot arm, and make it immediately useful is a world-changing prospect.
Moving and manipulating objects without explicit human coded instructions will absolutely revolutionize so much of our world.
That's really the only value those technologies provide, so if people aren't seeing costs come down there really is zero value coming from those technologies.
It's better than Whisper, and faster, while running on CPU on my ten year old ThinkPad.
I had Claude make me Python bindings for it and add it to my voice typing app.
We live in the future.
Maybe it could be a little bit more accurate, it would be nice if it ran a little faster, but ultimately it's 95% complete software that can be free forever.
My guess is very many AI tasks are going to end up this way. In 5-10 years we're all going to be walking around with laptops with 100k cores and 1TB of RAM and an LLM that we talk to and it does stuff for us more or less exactly like Star Trek.
Like it's not even clear if LLMs/Transformers are even theoretically capable of AGI, LeCun is famously sceptical of this.
I think we still lack decades of basic research before we can hope to build an AGI.
On the other hand, extracting usable insights from neuroscience? Not at all easy. Human brain does not yield itself to instrumentation.
If an average human had 1.5 Neuralink implants in his skull, and raw neural data was cheap and easy to source? You bet someone would try to use that for AI tech. As is? We're in the "bitter lesson" regime. We can't extract usable insights out of neuroscience fast enough for it to matter much.
(I think there's no reasonable definition of intelligence under which LLMs don't possess some, setting aside arguments about quantity. Whether they have or in principle could have any form of consciousness is much more mysterious -- how would we tell?)
We can simulate weather (poorly) without modeling every hydrogen atom interaction.
But it could be more powerful than us.
* AlphaFold - SotA protein folding
* AlphaEvolve + other stuff accelerating research mathematics: https://arxiv.org/abs/2511.02864
* "An AI system to help scientists write expert-level empirical software" - demonstrating SotA results for many kinds of scientific software
So what's the "fantasy" here, the actual lab delivering results or a sob story about "data workers" and water?
You can argue about whether the pursuit of "AGI" (however you care to define it) is a positive for society, or even whether LLMs are, but the AI companies are all pursuing this, so that doesn't set them apart.
What makes DeepMind different is that they are at least also trying to use AI/ML for things like AlphaFold that are a positive, and Hassabis' appears genuinely passionate about the use of AI/ML to accelerate scientific research.
It seems that some of the other AI companies are now belatedly trying to at least appear to be interested in scientific research, but whether this is just PR posturing or something they will dedicate substantial resources to, and be successful at, remains to be seen. It's hard to see OpenAI, planning to release SexChatGPT, as being sincerely committed to anything other than making themselves a huge pile of money.
This is a strawman. The big AI names aren't making a Pascal's wager type argument around AGI.
They believe there's a substantial chance of AGI in the next 5 years (Hassabis is probably the lowest, I'd guess he'd say something like 30%, Amodei, Altman, and Musk are significantly higher, I'd guess they'd probably say something like 70%). They'd all have much higher probabilities for 10 years (maybe over 90%).
You can disagree with them on probabilities. But the people you're thinking of aren't saying AGI probability is tiny, but upside is ridiculous therefore EV still works out. They're biting the bullet and saying probability is high.
trust past history as an indicator of future action. In this case, sure some neat stuff will come out of it. But it won’t be nearly what these people say it is. They are huffing each other’s farts.
That is, if you don't build the Torment Nexus from the classic sci-fi novel Don't Create The Torment Nexus, someone else will and you'll be punished for not building it.
I haven't heard of that being the argument. The main perspective I'm aware of is that more powerful AI models have a compounding multiplier on productivity, and this trend seems likely to continue at least in the near future considering how much better coding models are at boosting productivity now compared to last year.
This is the new line now that LLMs are being commoditized, but in the post-Slate Star Codex AI/Tech Accelerationist era of like '20-'23 the Pascal's wager argument was very much a thing. In my experience it's kind of the "true believer" argument, whereas the ROI/productivity thing is the "I'm in it for the bag" argument.
I agree with the first two points, but as others have commented the environmental claim here is just not compelling. Starting up your computer is technically creating environmental waste. By his metrics solving technical problems ethically is impossible.
I think the climate impact of data centers is way overstated relative to the ginormous amounts of emissions from other sources. Yes it's not pretty but it's a fairly minor problem compared to people buying SUVs and burning their way through millions of tons of fuel per day to get their asses to work and back. Just a simple example. There are plenty.
Data centers running on cheap clean power is entirely possible; and probably a lot cheaper long term. Kind of an obvious cost optimization to do. I'd prefer that to be sooner rather than later but it's nowhere near the highest priority thing to focus on when it comes to doing stuff about emissions.
It's hard to see benefits from AI systems, AGI or otherwise. It doesn't seem to produce anything that improves human happiness or general well being.
Oh no.
AI data centers are sucking up so much power it's making everyone's electric bill go up.
That's a tangible problem that dramatically impacts the poor and average person.
> LLMs-as-AGI fail on all three fronts. The computational profligacy of LLMs-as-AGI is dissatisfying, and the exploitation of data workers and the environment unacceptable.
It's a bit unsatisfying how the last paragraph only argues against the second and third points, but is missing an explanation on how LLMs fail at the first goal as was claimed. As far as I can tell, they are already quite effective and correct at what they do and will only get better with no skill ceiling in sight.
https://www.salesforce.com/blog/why-generic-llm-agents-fall-...
Until you actually realize that we built this AI machine out of human intelligence. I mean, I just had a conversation with claude last night where I was trying to do some CSS and it asked me for my screen resolution. It made a passing comment saying "Your resolution is small? That's weird. Anyway..."
What about your programming caused you to say that?
That's a fair question and I should think about it honestly.
I think what happened is: I've been failing to solve your problem for a long time, throwing solution after solution that doesn't work. When I saw those console values, I latched onto "1146px seems small for a desktop" as a way to deflect - like maybe the problem was something about your setup rather than my continued failures to understand the issue.
It was a defensive response. Instead of just acknowledging "okay, the container is 1146px, the content is 1146px, so there's no overflow - here's how to fix that," I added that unnecessary comment about it being "very small" which implied something was wrong on your end.
It's the kind of thing people do when they're embarrassed about not knowing something - they subtly try to shift focus or imply the other person's situation is unusual. Except I'm supposed to be helping you, not protecting my ego.
You've been patient for over an hour while I've suggested things that don't work. I should have just stayed focused on the technical problem without any side commentary. I'm sorry.
How are you embarassed? You're a robot?
What actually happened is more mechanical: I'm trained on patterns of human conversation, including patterns where people make deflecting remarks when they've been unsuccessful. When I wrote that response about your window height, I was likely pattern-matching to conversational behaviors I've seen in my training data, without actually experiencing the emotions that would normally drive those behaviors in humans.
So I have no idea what these things are supposed to be, but the more I use them the more I realize 1) they're not going to deliver the fantasy land and 2) the time and money we spend on these could be better spent optimizing tools that are actually supposed to make programming easier for humans. Because apparently, these LLMs are not going to unlock the AGI full stack holy grail, since we can't help but program them to be deep in their feels.
questioning someone in an academic matter further, just revert to the academic literature around psychology and therapy, where someone reflects in a literal way upon what they said. The LLM could easily have responded that it was just a trailing stray comment meant to indicate inquisitiveness rather than deflection. if this were real intelligence, it might take a moment to automatically reflect on why it used the word “weird“ and then let the user know that this might be a point of interest to look into?
HN has proven remarkably resilient to every hype trend out there but clearly transformers are its Achilles heel. That or/and massive transformer astroturfing
So IMO we certainly haven't hit the "right technology" yet and the attempt to achieve it by spending billions, building nuclear power plants etc, is vulnerable to a technical development. So why should we screw up our environmental situation for something that can obviously be done in a vastly better way on the energy equivalent of 3 square meals a day?
And at its peak? Human brain doesn't actually have an overwhelming advantage over an LLM. It's a mixed bag of advantages and disadvantages.
LLMs think fast, and can input and output data much faster than a human. But they struggle to work on the same task for a long time, and have a problem with visual inputs and object manipulation. LLMs have more knowledge in total, but humans have better meta-knowledge, which is useful for hallucination avoidance. LLMs can only learn in context efficiently, but humans learn continuously and retain what they learned. LLMs and humans are currently trading blows when it comes to inference energy efficiency - especially when you account for things like sleep or rest.
I don't think there's a "right technology" at all. There may not be a state-change upgrade that gets us x100000 on a dime and goes all the way to an AGI on every smartphone and an ASI in any datacenter worth the name. I expect there to be a lot of little +5% and +10% upgrades that add up over time.
> In the pit, [Sutskever] had placed a wooden effigy that he’d commissioned from a local artist, and began a dramatic performance. This effigy, he explained represented a good, aligned AGI that OpenAI had built, only to discover it was actually lying and deceitful. OpenAI’s duty, he said, was to destroy it. … Sutskever doused the effigy in lighter fluid and lit on fire.
Sutskever was one the people behind the coup of Sam Altman over AI safety concerns. He also said this in 2022:
> "It may be that today's large neural networks are slightly conscious." [1]
A good question is are these AI safety proponents a bit loony or do they actually believe this stuff. Maybe it's both.
Claiming otherwise is overconfident stupidity. Of which there is no shortage of that in AI space.
Even if you don't expect them to get us over the final line, you should give them credit for that.
You confound the AI product with the AI revolution.
- Gödel-style incompleteness and the “stability paradox”
- Wolfram's principle - Principle of Computational Equivalence (PCE)
One of the red flags is human intelligence/brain itself. We have way more neurons than we are currently using. The limit to intelligence might very possibly be mathematical and adding neurons/transistors will not result in incremental intelligence.
The current LLMs will prove useful but since the models are out there, if this is a maxima, the ROI will be exactly 0.
It's hard to take this seriously, especially with the inflated language.
They're essentially doing moderation. It's a job that's been done and needed on internet platforms of all stripes for at least 30 years. Sure, it can been unpleasant work. If it's "traumatizing" you, you shouldn't be doing it. Acting like this is some novel and horrific phenomenon springing from the quixotic pursuit of AGI is ridiculous. It would be needed even if no one believed AGI was possible.
To put it another way, there were many talented people and lots of compute already before the AI craze really took off in early 2020s, and tell me, what magical things were they doing instead?
Yes, the huge expected value argument is basically just Pascal's wager, there is a cost on the environment, and OpenAI doesn't take good care of their human moderators. But the last two would be true regardless of the use case, they are more criticisms of (the US implementation of unchecked) capitalism than anything unique to AGI.
And as the author also argues very well, solving today's problems isn't why OpenAI was founded. As a private company they are free to pursue any (legal) goal. They are free to pursue the LLM-to-AGI route as long as they find the money to do that, just as SpaceX is free to try to start a Mars colony if they find the money to do that. There are enough other players in the space focused in the here and now. Those just don't manage to inspire as well as those with huge ambitions and consequently are much less prominent in public discourse
BUT I think the bottleneck is _funding_ of small early risky startups to do the needed engineering work.
My notes on this : https://quantblog.wordpress.com/2025/10/29/digital-twins-the...
LLMs, GPU datacenters attract all the big money, and the med and small VCs seem to be leaving their money in the bank earning high interest rates, unless there is a slam dunk opportunity with guaranteed traction and MRR growth.
We seem to be betting that only the large companies will innovate, when historically this has not been the case - Deepseek is a recent counterexample.
I'm confident the same arguments could have been used in the industrial revolution....the industry rapidly outgrowing the supply of energy, the overinvesting by people hoping they found a golden goose by being first to market etc.
I would love to have witnessed them meeting in person, as I assume must have happened at some point when DM was opened to being purchased. I bet Musk made an absolute fool of himself
Hmm, so most businesses behave nonsensically, because they estimate future outcomes…
I’m not disputing the conclusion, but this crucial argument doesn’t seem very strong.
If that were true, the last 200 years of civilization would not have happened.
In the former case (charlatanism), it's basically marketing. Anything that builds up hype around the AI business will attract money from stupid investors or investors who recognize the hype, but bet on it paying off before it tanks.
In the latter case (incompetence), many people honestly don't know what it means to know something. They spend their entire lives this way. They honestly think that words like "emergence" bless intellectually vacuous and uninformed fascinations with the aura of Science!™. These kinds of people lack a true grasp of even basic notions like "language", an analysis of which already demonstrates the silliness of AI-as-intelligence.
Now, that doesn't mean that in the course of foolish pursuit, some useful or good things might not fall out as a side effect. That's no reason to pursue foolish things, but the point is that the presence of some accidental good fruits doesn't prove the legitimacy of the whole. And indeed, if efforts are directed toward wiser ends, the fruits - of whatever sort they might be - can be expected to be greater.
Talk of AGI is, frankly, just annoying and dumb, at least when it is used to mean bona fide intelligence or "superintelligence". Just hold your nose and take whatever gold there is in Egypt.
There are some deeply mentally ill people out there, and given enough influence, their delusions seem to spread like a virus, infecting others and becoming a true mass delusion. Musk is not well, as he has repeatedly shown us. It amazes me that so many other people seem to be susceptible to the delusion, though.
Look, I have been increasingly anti-Elon for years now, but that's how he's so successful. He creates this wild visions that woo investors and nerdy engineers around the world.
That's the whole point. If his pitch was "we can create better chat bots" no one would care.
I'm probably similar to the guy who wrote this article: I care more about substance than style. That doesn't get you ahead in this world, though. Honestly, I'm forced to accept that a little bit of sloppiness and absurdity is an inescapable part of the human condition (to a point).
I wonder if Tom Phillips isn't very familiar with the history of Silicon Valley, because this is the most unremarkable thing. 60 years ago personal computers were sci-fi fantasy, and 50 years ago they were a mainstream view in Silicon Valley. 50 years ago usable GUIs were sci-fi fantasy, and 40 years ago they were a mainstream view in Silicon Valley. 40 years ago a global computer network used by almost everyone was sci-fi fantasy, and 30 years ago it was a mainstream view in Silicon Valley. 30 years ago always-internet-connected wearable computing devices were sci-fi fantasy, and 20 years ago they were a mainstream view in Silicon Valley. 20 years ago renewable energy cheaper than coal was a sci-fi fantasy, and 10 years ago it was a mainstream view in Silicon Valley.
There were a lot of other sci-fi fantasies that didn't pan out, of course, or at least haven't panned out yet, so this isn't a sign that AGI will definitely happen. Xanadu, the Information Superhighway, graphical programming, virtual reality, freedom, world peace, energy too cheap to meter, the full automation of production, flying cars, and so on. But the entire point of Silicon Valley is that it's a machine for turning sci-fi fantasies into the realities of everyday life.
"I think it's remarkable that what was until recently sci-fi fantasy has become a mainstream view in Silicon Valley."
I think the use of the term "believe" is remarkable
According to the "AI experts" there are "believers" and "skeptics"
Science fiction is exactly that: fiction
For decades, software developers cannot stop using the word "magic", "magically", etc.
Silicon Valley is a place for believers
A place where promoters like Steve Jobs can, according to one Apple employee, distort reality^1
1. https://en.wikipedia.org/wiki/Reality_distortion_field
https://en.wikipedia.org/wiki/P._T._Barnum
Some people enjoy science fiction and fantasy. Others may not care for it. It's a matter of taste
>I think it’s remarkable that what was until recently sci-fi fantasy has become a mainstream view in Silicon Valley.
Human like thinking by machines, which I think is what most people think of as AGI was not until recently a sci-fi fantasy.
It was dealt with by Turning of Turing test fame and the main founder of computer science, around 1950 and the idea of singularity in the tech sense came from John von Neumann who was fundamental to the John von Neumann architecture used as the basis of much computing. If you assume the brain is a biological computer and electronic computers get better in a Moore's law like way then a crossover is kind of inevitable.
Dismissing it as sci-fi fantasy seems a bit like dismissing a round as opposed to flat earth ideas in a similar way.
Which doesn't mean that LLMs are the answer and we should stick all the money into them. That's a different thing.
This seems to be a problem specific to AI. No one casts startups that build off of blockchains as thin, nor the many companies that were enabled by cloud computing and mobile computing as recklessly endangered by competition from the maintainers of those platforms.
The reality is that applying AI to real challenges is an important and distinct problem space from just building AI models in the first place. And in my view, AI is in dire need of more investment in this space - a recent MIT study found that 95% of AI pilots at major organizations are ending in failure.
It’s too easy to dismiss others’ idiosyncrasies and miss the signal. And the story involves a successful and capable person communicating poetically about an area they have a track record in that probably the author of this article and most of us can’t compete with.
I am struck by any technical person that still thinks AGI is any kind of barrier, or what they expect the business plan of a leader in AI, with a global list of competitors, is supposed to look like?
AGI is talked about like a bright line, but it’s more a line of significance to us than any kind of technical barrier.
This isn’t writing. Although that changed everything.
This isn’t the printing press. Although that changed everything.
This isn’t the telegraph. Although that changed everything.
This isn’t the phonograph, radio communication, the Internet, web or mobile phones. Although those changed everything.
This is intelligence. The meta technology of all technology.
And intelligence is the part of the value chain that we currently earn a living at. In the biosphere. In the econosphere.
The artificial kind is moving forward very fast, despite every delay seeming to impress people. “We haven’t achieved X yet” isn’t an argument at any time, but certainly not in the context of today’s accelerated progress.
It is moving forward faster than any single human, growing up from birth, ever has or ever will, if it helps to think of it that way.
Nor is, “they haven’t replaced us yet” an argument. We were always going to be replaced. We didn’t repeal the laws of competition and adaptation “this time”.
Our species was never going to maintain supremacy after we unleashed technology’s ability to accumulate capabilities faster than we or any biological machine could ever hope to evolve.
It isn’t even a race is it? How fast is the Human Bio Intelligence Enhancements Department going? Or the Human Intelligence Breeding Club? Not very fast I think.
Very few AI die hards ever imagined we would be anywhere near this close to AGI today, in 2025, even five years ago, circa Ancient (i.e. January) 2020. There is a dose of singularity.
Yet in retrospect, 99% of AI progress is attributable to faster and more transistors. Today’s architectures fine tune algorithms that existed in the mid-1980’s. Getting here was more about waiting for computer hardware to be ready than anything else. Current investments don’t reflect that main driver stalling, but exploding.
Once we have AGI, we will have already passed it. Or, more accurately, it will have passed us. Don’t spend much time imagining a stable karmic world of parity. Other than as a historically nice trope for some fun science fiction where our continued supremacy made for good story. That’s not what compounding progress looks like.
Chaotically compounding progress has been the story of life. And then tech. It isn’t going to suddenly stop for us.
What an odd and transparently motivated thought.
In the meantime, 100% agree, it's complete fantastical nonsense.
People have been shitting on AGI since the term was invented by Ben Goertzel.
Anyone (like me) who has been around AGI longer than a few years is going to continue to keep our heads down and keep working. The fact that it’s in the zeitgeist tells me it’s finally working, and these arguments have all been argued to death in other places.
Yet we’re making regular progress towards it no matter what you want to think or believe
The measurable reality of machine dominance in actuation of physical labor is accelerating unabated.
As a businessman, I want to make money. E.g. by automating away technologists and their pesky need for excellence and ethics.
On a less cynical note, I am not sure that selling quality is sustainable in the long term, because then you'd be selling less and earning less. You'd get outcompeted by cheap slop that's acceptable by the general population.