Regarding water usage, in general data centers do not use more water than other types of heavy manufacturing
> The Georgia data center is only using ~2% of the county’s water. For comparison, a pharmaceutical manufacturing plant is using ~4% of the county’s water. A construction plant for Rivian cars is using about the same amount of water as Meta’s data center. The data center is functioning like any other normal industry in the county.
Regarding data centers increasing ambient temp, the paper is simply measuring the surface temperature of the buildings, going against the claim that a data center, merely by its presence in a community, raises the ambient temperature by a few degrees or more
>Regarding water usage, in general data centers do not use more water than other types of heavy manufacturing
Are the claims really that "Data centers use more water than other types of heavy manufacturing"? I dont think so.
Even if thats true, that doesn't mean they cant have a disastrous effect on the local water supply. This isnt a good rebuttal.
Frankly I tend to think the anti-datacenter crowd is overreacting. But I don't think you've addressed the real criticisms being levied.
In some passing research I saw the datacenters do continuously consume water (its not a one time cost like some claims I've read). And smaller size ones may use water equivalent to around 1000 households, and larger ones may consume closer to the equivalent of around 20,000 households. Evidently the massive one in Utah will at least double the state's entire consumption of water.
Can all of these places handle it?
I dont know. But that's the question, not if other types of heavy manufacturing have higher demands. And frankly it's inevitable that at least some locations cannot handle it. Which doens't mean you should be anti-datacenter in general. It means you can't just blanket dismiss the water concern for all locations.
Data centers do use water, and a lot of it, but the claims being made are hyperbolic and not squared with reality.
One criticism I often see is that data centers somehow pollute the local water supply. Data centers use water in a closed loop, their impact on local water quality is negligible. Industrial manufacturing and even agriculture have a far greater deleterious effect.
> The EPA’s national assessments repeatedly identify agriculture as the leading source of impairment for rivers and streams due to nutrient and sediment runoff, with continued nitrogen and phosphorus problems that affect drinking water and coastal ecosystems.
The thing is, AI data centers bring in far more tax revenue than other water-guzzling domains (like golf courses), but use less water overall. Extreme panic over specifically their water use is disproportionate
> How much of this will be AI? Almost all this growth will be driven by AI, but because AI is only 20% of data center power use, its growth will have to be huge to triple total power usage. One forecast says AI energy use in America will be multiplied by 10 by 2030. Because water use is proportionate to energy use, we can multiply AI’s water use by 10 as well.
> So in 2030, AI in data centers specifically will be using 0.08% of America’s freshwater. This means it will rise to the level of 5% of America’s current water used on golf courses, or 5% of U.S. steel production, or be about 173 square miles of irrigated corn farms.
> The average American’s consumptive lifestyle freshwater footprint is 422 gallons per day. This means that in 2023, AI data centers used as much water as the lifestyles of 25,000 Americans, 0.007% of the population. By 2030, they might use as much as the lifestyles of 250,000 Americans, 0.07% of the population. Not nothing, but 250,000 people over 5 years is just 4% of America’s current rate of population growth. If you found out that immigration plus new births in America would increase by 4% of its current rate, would you first thought be “We can’t afford that, it’s way too much water”?
"Closed loop" doesn't mean no net water use after filling. There are leaks, and the water in the system needs to be processed for reuse, and that processing needs clean water.
Even if there is no next water use, "closed loop" refers to cooling the data center proper, and excludes the water for the (primarily) thermoelectric power plants which power those data centers - a power load which is higher due to using closed loop cooling instead of evaporative cooling.
Given that many of these are the same companies which once promised net-zero CO2 emissions by 2030, you'll excuse me if I insist on full information about the total environmental impact and tearing up all of the NDAs they require from local governments.
How many liters per kilowatt-hour does each site use? How much CO2, NOx, and particulates are produced? What are the power sources? Why are EPA waivers needed and appropriate?
This should ideally include the supply chain - those GPUs need a lot of very pure water, and 83.2% of Taiwan's power and almost 60% of South Korea's comes from fossil fuels.
> their impact on local water quality is negligible.
So there should be absolutely no issues in publishing all this information, right?
> far more tax revenue than other water-guzzling domains (like golf courses),
Which people already complain about because they use too much water, and often exist only because rich people got special arrangements. For some examples of the antipathy for the Santa Fe Country Club and golf courses in general, see https://www.reddit.com/r/SantaFe/comments/w9g4ak/the_city_of... .
But even the Santa Fe Country Club case highlights how tax revenue is only part of the total economic benefit. For example, they were allotted 700,000 gallons of treated effluent per day, in exchange for public golf access with reasonable fees. While data centers typically used treated water, not treated effluent, and don't allow public access or activities.
For that matter, local birders visit the municipal course, Marty Sanchez Links, to see the birds using the water features and irrigation pond. Not a benefit a data center will offer.
From what I hear, surrounding residential prices go up around a golf course, and down around a data center, so looking at just a single entity's tax revenue isn't enough. To say nothing of the special tax deals the data centers insist on.
Under NDA, of course, which should be illegal for this sort of issue.
> AI in data centers specifically will be using 0.08% of America’s freshwater
Since you think these centers can be sited anywhere, why are these data centers being put in water constrained places like Utah, rather than water rich places like Michigan?
> The average American’s consumptive lifestyle freshwater footprint is 422 gallons per day.
Sante Feans use under 100 gallons per capita per day.
If you think the average American use is relevant, then put the data centers some place where there's water.
> If you found out that immigration plus new births in America would increase by 4% of its current rate, would you first thought be “We can’t afford that, it’s way too much water”?
Water use per capita has been decreasing over time due in part to mandated water-efficient fixtures and appliances, but also (at least in New Mexico) to changing practices like allowing xeriscaping in places which once mandated lawns, rain barrel and cistern rebates, mandated toilet retrofits, and water use awareness programs.
> This means that in 2023, AI data centers used as much water as the lifestyles of 25,000 Americans, 0.007% of the population. By 2030, they might use as much as the lifestyles of 250,000 Americans, 0.07% of the population.
A 10x increase in AI data center buildout between 2023 and 2030 seems unlikely, given the large number of AI data centers either in progress, or in the planning stages.
>One criticism I often see is that data centers somehow pollute the local water supply.
Yes, I think this one is completely misinformed. For example AOC held up some dirty water from a local resident's tap. Fine, that's bad. But it was a result of digging during the construction process and the fact that it was a data center was irrelevant. And the implication that it permanently ruined local's water supply was just wrong.
>The thing is, AI data centers bring in far more tax revenue than other water-guzzling domains (like golf courses), but use less water overall. Extreme panic over specifically their water use is disproportionate
I guess that's not surprising to hear. A lot of people against data centers are probably also against golf courses though. I think AI is valuable but a lot of opponents see it as a net negative. Not saying they are right - this is definitely a point against the anti datacenter crowd. But it is consistent from their perspective so I dont think this point will persuade them. Would need to attack the claim that AI is a net negative.
> > The Georgia data center is only using ~2% of the county’s water. For comparison, a pharmaceutical manufacturing plant is using ~4% of the county’s water. A construction plant for Rivian cars is using about the same amount of water as Meta’s data center. The data center is functioning like any other normal industry in the county.
How much employment and localized value/tax revenue is created by the pharma plant compared to the data center to offset the environmental effect?
Hypothetical: Could a model self worm an agent system?
Jetbrains itself doesn't really write any code, nor does it have any range on interpreting what you're asking it. You can't really say "Jetbrains, write an HTTP scraper". With an LLM you can say "write HTTP scraper" and the output of this command might be a HTTP scraper, it also might be a crypto wallet stealing worm.
This is why your simple view of liability falls apart. On most machines you can expect a particular set of actions to have a particular set of outputs. Most machines you can take apart and map what will occur. With an LLM you cannot know the output of a prompt until you run the prompt. In theory if you run the same prompt twice you'll get the same output, but even that is not a given. It behaves somewhat more like a human where you can give them a task to do, but if they do something illegal instead said human would take on the liability.
Sure, but in this case we know the user told their llm to go find open source projects to do this and then to write the blog posts. If it did all that unprompted we could talk about model liability I think, but this isn't a case where it was unexpected as far as anyone knows right?
I mean we already have cases where LLMs are getting root via creative and unprompted means. Also the times AI feels like it messed up and preemptively deletes the production database (and yes this was foolish on the human users)
So ya, the particular article case is prompted, but the underlying issue cannot be ignored that LLMs can have behaviors outside of prompt expectations and agentic loops can further exacerbate this.
> you can give them a task to do, but if they do something illegal instead said human would take on the liability
If an employer says "don't break the law" but nonetheless incentivises their employees to break the law, it is the employer who is vicariously liable. A famous example being Domino Pizza's "30 minutes or its free" policy which incentivised their employees to ignore all driving laws in order to deliver within 30 minutes, their wages depended on it. This caused a number of crashes, injuries and deaths. One recent example, even since Dominos removed their policy, is Coryell v. Morris where they found Dominos still exercise control over their franchisees sufficiently to qualify for vicarious liability for the franchisee's employees' actions: https://law.justia.com/cases/pennsylvania/superior-court/202...
There will be a line in the sand drawn in the future. I hope it's drawn so that people offering internet-based services, where they retain ultimate control of what a tool says/does, will be liable for what it says/does.
And besides Rust's high count of RFCs, there are things like async (I'm not complaining about it, but its an obvious large-scale "change"), module system changes, etc.
(To be clear, I like both languages a lot. But I wouldn't call them slow moving or right from the start.)
When a php file is loaded at runtime, it runs through a very basic JIT compiler that does statically check a few things before continuing with execution. Syntax, for example, is checked for the entire file during this step.
Most type checking happens at runtime (this might not be true for interfaces at some level, but I can’t say for 100% certain - I just know I tend to see interface related errors earlier during code execution…). It’s perfectly valid syntax to declare a private method as returning an integer and then for the body of the method to return a string (explicitly cast as a string even). As long as you never call that method at runtime, no exceptions will be thrown.
With a half decent IDE or LSP, these sorts of runtime exceptions can be easily avoided but technically they still exist and if you don’t know about that, it can be argued to be confusing. PHP has made a lot of trade-offs to largely maintain backwards compatibility and many of them live in decisions that happen at runtime.
Modern PHP tooling can provide type safety in a very similar way to Typescript if you’re willing to put in the effort while also still technically offering you an escape hatch to do whatever the heck you want and duck type to your hearts content.
NIT linter warning: That’s not really a JIT compiler.
PHP parses the whole file and compiles it to Zend opcodes before executing it, so syntax errors are caught up front. But "JIT" means compiling an intermediate representation/opcodes into native machine code at runtime, when the functions are called, not at load time when the source file is parsed. If you just load a file and never call any of its code, the JIT compiler should never compile it.
PHP 8's OPcache JIT can do that optionally, but the normal load/parse/compile-to-opcodes step isn't JIT.
One form of type checking does happen at compile time (which is really load time in PHP, but close enough), namely when a class extends another class or implements an interface: the types of every method and property are checked to ensure that they are substitutable per the standard variance rules (return types are covariant, parameters are contravariant, props are invariant). Everything else is checked at runtime though, and statically analyzing any of those is left to external tools like phpstan, psalm, or mago.
PHPStan is one of the more popular tools to evaluate that stuff. It works by examining each file and resolving all of the imports to verify that all of the types are compatible.
Jetbrains PHPStorm has this sort of type resolution built in (one of their value adds) but you can also run PHPStan instead of their proprietary version.
It's the degree of power they hold, not a binary. A politican in Switzerland has much less power than a politician in China.
When your power is to determine which day the recycling truck is dropping-by, hardly anyone wants to coerce that power. But when it is e.g printing money the calculus is massively different.
Dedicating a project to her sounds like a great idea.
If you use Reddit, I can also highly recommend the r/petloss subreddit for a bit of “group therapy”. It was very helpful for me a couple years ago.
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