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Retired since 2012.
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Something for the digital crate-diggers: The 40 Best Albums From the Last 40 Years That You Probably Didn’t Hear (But Should’ve). I’d only heard of one or two these…
I found this AI Compass quiz genuinely useful for pinpointing how I actually feel about various aspects of AI. At the same time, I don’t think my result (“The Kontextmaschine”) quite fits…
The head of the United Nations has launched an initiative aimed at holding artificial intelligence companies accountable for their exploding environmental impacts, including their carbon emissions, the amount of water and land used for data centres, and the energy they consume.
During a speech at London Climate Action Week on Tuesday, António Guterres noted that AI can accelerate climate solutions, among other key challenges, and said its potential must be harnessed.
“But AI is also hungry for land, water and power,” he emphasised, adding that the data centres needed to run AI models already consume more electricity than most countries.
The UN Secretary-General repeated a call he first made in July 2025 for all big AI companies to commit to power every data centre with renewable energy by 2030.
Some tech firms have announced they are sourcing or building out clean energy to run their hubs, but growing power demand is also contributing to gas-fired generation in the US, according to data from Global Energy Monitor.
The International Energy Agency (IEA) estimates that data centres are set to more than double the emissions from the electricity they use between 2024 and 2030 in a high-growth scenario. But AI’s use could lead to far larger reductions in the energy sector through efficiency gains if adopted widely.
Proposing the new “AI Environmental Transparency Initiative” on Tuesday, Guterres also urged big AI firms companies to measure and publicly disclose the full environmental impact of their systems, including their carbon, water, and land footprints.
“No more hidden costs. No more shifting the burden onto those least able to bear it. It is time to come clean,” he said in a major speech on responding to the world’s twin climate and energy crises. “If AI is to help build a better future, it must be honest about what it costs us now.”
A report issued earlier this month by the UN University Institute for Water, Environment and Health noted that most current assessments of AI’s environmental cost focus on carbon emissions from training models. But, it added, this misses a substantial part of the picture.
Every kilowatt-hour of electricity for AI also carries a water footprint, from cooling and generation, and a land footprint, from infrastructure and supply chains, it said.
Explainer: Will AI data centres make or break the energy transition?
The report estimated that AI data centres globally could consume 945 terawatt-hours of electricity annually by 2030 – more power than all but five countries and roughly twice France’s 2025 consumption.
Offsetting this carbon footprint by 2030 would require growing some 6.7 billion trees over 10 years, it calculated. Producing power for the data centres would consume water equal to the basic needs of 1.3 billion people in sub-Saharan Africa for a year and take up land of more than 14,500 square kilometers, roughly twice the Jakarta metropolitan area.
The European Union said earlier this month it will develop minimum energy-efficiency standards for both new and existing data centres, with a “needs assessment” due by 2027, Reuters reported. It’s also planning a sustainability label for data centres, covering criteria including water use and clean energy supply – but that has been delayed.
Asked after his speech what the response had been, the UN chief said “we’ll see”, without giving more details.
But, he argued that, in his view, the push for transparency “is perfectly reasonable and even positive for the AI industry, because eventually some people will say that they consume much more than they really do”. “I think the truth is essential,” he added.
Concerns about the environmental impacts of AI and the infrastructure needed to run the technology have led to growing opposition in some communities, especially in the US.
This month, Monterey Park in Los Angeles County was the first city in the United States to enact a citywide prohibition on data centres through a voter-approved ballot measure. The developers behind a proposed centre in the area had already pulled the project in April amid an increasingly hostile local environment and regulatory uncertainty.
The vote that stopped a data center: US communities query resource-hungry AI
According to nonprofit Data Center Watch, around $64 billion-worth of data centre projects nationwide were delayed or blocked between May 2024 and March 2025 as communities pushed back against them.
Industry lobby groups argue that data centres can provide economic benefits in their host communities. According to the US-based Data Center Coalition, which represents big operators and developers, data centres generate tax revenue, support construction and technical jobs, and provide infrastructure needed for cloud computing, scientific research and AI development.
The industry has also challenged claims that data centers necessarily raise electricity costs for households.
The UN chief said benefits can be few in the places that are home to the data centre, while “communities are often left in the dark about the environmental impact of the infrastructure rising around them”.
Guterres said companies have an “obligation” to be clear and open about the services they are offering but also the level of resources they require.
“Transparency is essential for the decisions that communities must make – and transparency is essential even for the future of artificial intelligence, and to make sure that artificial intelligence is essentially a force for good,” he told an audience of climate professionals in London
A senior UN official told journalists ahead of Tuesday’s announcement that the AI industry has started to talk about and disclose some of their impacts, but those efforts are not yet comprehensive enough.
The hope is that the new initiative will “encourage the industry to come together and take further action on it”, the official said.
The post UN asks AI companies to reveal full environmental impacts appeared first on Climate Home News.
Scientists have discovered new evidence that the cosmic structures connecting the universe are much larger than previously predicted—persisting over billions of light years—a finding that challenges a core tenet of cosmology and hints at the possibility of new physics, according to a study published on Wednesday in Nature.
The standard model of cosmology, a well-corroborated framework for understanding the universe that is also known as the Lambda cold dark matter (ΛCDM) model, predicts that the large-scale structure of space looks the same in all areas (homogeneity) and in all directions (isotropy). While there is variation in the distribution of matter on small scales, such as thousands or millions of light years, these distinctions should smooth out into a uniform pattern on the scale of the cosmic web, which is a network of large-scale structures made of dark matter, gas, and galaxies that stretches across the universe.
But in recent years, new observational data has started to hint that galaxies cluster in “preferred directions,” forming distinct structures known as “anisotropies” that are not uniform, even across vast distances. Now, a pair of physicists has discovered that these distinct directions and patterns persist even to the scale of a gigaparsec, which is a unit equal to 3.26 billion light years, possibly signalling “the need for a shift in modern cosmology,” according to their new study.
“The structures observed in the real Universe are significantly larger and more persistent than those formed in state-of-the-art simulations based on the standard model of cosmology,” said authors Francesco Sylos Labini of the Enrico Fermi Research Center in Rome, Italy, and Marco Galoppo of the University of Canterbury in Christchurch, New Zealand, in an email exchange with 404 Media.
“The key advance of our analysis is that it allows this difference to be quantified,” they added. “By measuring the spatial extent and coherence of the observed structures and comparing them directly with theoretical predictions, we found that the discrepancy is statistically highly significant. In other words, the largest structures in the real Universe appear to be substantially larger than expected in standard models of galaxy formation.”
According to existing models, the cosmic web emerged from small density fluctuations in the early universe and gradually developed into large-scale filaments and nodes made of dark matter that gravitationally attract gas, galaxies, and other forms of matter.
Last year, the Dark Energy Spectroscopic Instrument (DESI), a major astronomical survey based in Arizona, released the largest high-resolution 3D map of the universe, which has revolutionized cosmology and allowed scientists to test those theories against observational data.
Labini and Galoppo analyzed the DESI release with statistical tools, including the Angular Distribution of Pairwise Distances (ADPD), which is especially effective for detecting and characterizing large-scale anisotropies in DESI’s dataset.
“The idea was to try to really test whether the idea that isotropies reached very large scales is now supported by data,” said Galoppo in a follow-up call. “Even just five or ten years ago, we didn't really have the data to test on gigaparsec scales. But now, we had a chance, so we decided to take it.”
“What we are able to do is to characterize how large are the largest structures inside this sample” of DESI observations, added Labini in the call.
The results revealed that even in DESI’s super-zoomed-out observations, large-scale structures create preferred directions of galaxy distribution, as opposed to an overall isotropic pattern. This contrasts with expectations derived from the cosmic microwave background, the oldest light in the universe, which suggests that directional correlations should fade rapidly at large scales.
“In the standard model, it's not that there aren’t structures,” said Galoppo in the call. “It is just that they are supposed to be smaller and less persistent than what we found. That's the crux of the matter.”
To that end, DESI is expected to release a new batch of observations within a year, and similar datasets will also be forthcoming from Europe’s Euclid space telescope and the Vera C. Rubin Observatory in Chile in the near term. These new and improved views of the universe will help scientists grapple with just how vast these large-scale structures are, and what that means for our understanding of our cosmic surroundings.
“At present, there is no simple or widely accepted modification of the ΛCDM framework that naturally explains structures of this size while remaining consistent with the observed uniformity of the cosmic microwave background,” Labini and Galoppo wrote over email. “That is precisely why these observations are so interesting: they point to a potentially important gap between theory and observation that deserves further investigation.”
“If future surveys continue to find coherent directional structures on even larger scales, the implications for cosmology would be profound,” they concluded.
One aspect of strategy that’s been largely lost in the tech industry in recent years is how to compete against platforms, since the major tech companies have gotten so big that markets are no longer competitive. However, the AI market is still early enough, and users and society are still angry enough, that the Big AI companies can lose.
But for them to lose, everybody else in the ecosystem has to carry out the nearly-lost art of platform strategy. Tech companies (and even open source communities!) used to carry out these tactics in emerging product categories ranging from desktop office suites to operating systems to web browsers, though over the decades, the lesson that big tech learned was, basically, that they should play dirty.
You win platform strategy battles through power and persuasion. We're going to get both.
Historically, we would have relied on regulators or media to help hold bad actors in the tech space accountable, but in the United States, these entities are largely not going to help very much. Some state and local governments may assist, and some independent journalists or smaller media outlets are pushing for accountability, but the most powerful entities are either captured or complicit in many cases, so we don’t have the institutional pushback that had sometimes been present in earlier points of technological change.
The thing that matters right now is that we understand that all of the Big AI companies are extremely vulnerable. The reason they’re making so much noise, and spending so much money, is because they know that they’re vulnerable. Users, and especially users who are developers have an enormous amount of leverage to control where AI goes. And if those communities of users can coordinate, they can put power back into the hands of the people. Today, that means focusing on some technical interventions, along with the cultural and political pushback that’s happening. That’s how we begin to reduce, or even prevent, some of the worst AI harms in the future.
Here are some of the proven tactics that have helped shift the balance of power in prior tech reckonings:
The first and most important technical goal is for everyone to push for all AI usage to be disintermediated — where users access their AI apps or services through open tools or interfaces that aren’t controlled by the Big AI companies. These tools, in the form of “harnesses”, or through text editors or command lines, or just through the familiar chat interfaces that lots of people use, need to move as quickly as possible to being controlled by community-built, open options. The sooner this step happens, the sooner we unlock the ability to shift decision-making power out of the hands of the corporate platforms, and begin to undermine their ability to cement lock-in of users.
Status: Good. There are a number of popular, mature tools in almost every category for users who want to access today’s AI tools through a free, open interface. Most of the work now is to get the word out about these tools, and to continue to polish and improve the user experience so that they offer features and design touches that the commercial tools can’t or won’t.
Another key capability that the open ecosystem must provide is the ability to seamlessly switch between different AI providers on the fly, to reduce costs, to provide better performance, or to get both benefits. In many cases, this will be seamless and automatic, just making the right choice for users so that they get the best option all of the time, but advanced users will want to tweak their settings, like when businesses may want to be very aggressive in minimizing the amount of money that their employees are allowed to spend on AI services.
The important part here is that this forces AI platforms that want to compete to remain compatible with all of their competitors, keeping the market dynamic, and ensuring that all of the big providers are easily replaced with another vendor at any time. Basically, we always have to be able to keep them in their place, and they should know that they could go away at any time. Most companies are aware of these needs, but the more regular consumers are familiar with these kinds of requirements, the more pressure there will be on companies to conform with standards. (This is also what will enable the disintermediation mentioned in point 1.)
Status: Good. This is happening already in business environments, where companies demand this kind of flexibility. Developers have been creating very dynamic systems for switching between AI providers, and the ecosystem encourages this kind of switching by extensively comparing different AI platforms against each other whenever new models are released. The important thing to maintain here is the narrative that none of the individual models matter more than the overall ecosystem — and that even the biggest companies have to conform to the same strict formats and standards as the independent AI systems created by communities around the world.
Another vital concern for shifting power away from the Big AI companies is undermining them economically. Instead of simply following the classic “commoditize the complement” strategy that commercial companies often execute, open source projects created by a community can more straightforwardly pursue a path of enlightened value destruction. Non-commercial LLMs have been roughly keeping pace with the Big AI platforms, following the pattern I described as “frontier minus six”, where free and open models lag about 6 months behind the most cutting-edge AI labs — which means they’re still pretty freaking great for most uses.
In a scenario where there are extremely capable models that cost nothing except for the price of keeping a few servers running, as well as very robust tools that make it effortless to seamlessly switch between models (see point #2!), more and more organizations will shift more and more work away from the Big AI companies, especially as those companies keep raising their prices.
But there’s no reason that these same principles can’t be followed by ordinary consumers as well. Many developers are already using these techniques to switch to free models to save money, and the only barrier to this practice becoming more widespread is that the user experience is still too clunky and technical for most regular people.
Status: Okay. Lots of people are working on this, and in some scenarios, the free AI tools are even pretty great. But for the most part, there are still too many compromises in either the end results or the user experience for this to be a mainstream alternative today. This can change, with the right investments and focus on improving things — and focusing on differentiation in areas where the open community can distinguish itself from all of the Big AI companies.
Pretty much everybody who’s from the 21st century, or anybody who’s a creative person, is pretty furious about AI. Anyone who’s not oblivious to culture is aware of that. Yet all of the Big AI companies keep treating it like some fad that’s going to blow over, or a trend that they can just steamroll with their dollars. This isn’t going to go the way they want.
However, the people who will build the alternatives can actually listen to the values and criticisms of the people who are angry, and make tools that respect and respond to what they’re saying. An Internet of consent is not only possible, it’s all around us, if we choose to respect it. If people hear that they can get some of the conveniences or features that they were previously told were only possible with extractive, exploitative, evil AI tools, but without any of those negatives, they’ll actually be pretty happy to hear it.
Today, usage of AI is high enough that even some of the people who hate AI are using it. Some of this is due to the coercive way that AI is being shoved into everybody’s faces, some of it is due to there being some places that people feel it has utility that they wish they could access without its moral compromises. When people are compelled to use platforms that they object to (as a lot of people feel about using things like social media), the feelings of guilt and resentment that come along with it are deeply toxic.
What we're talking about across these first three points, if taken together, is an entirely new experience for millions of users. And that new set of platforms could respect the consumer backlash against AI and channel it into presenting tools that acknowledge their anger and treat it as legitimate. They might even be tools for fighting back.
Status: This one’s going to be tough. This is the one idea where most people think I’m crazy. People who have a righteous anger about the harms of current Big AI companies say that there couldn’t be any such thing as “good AI”, and I understand their skepticism. People who think AI is an interesting technology but hate the hype (the majority AI view) are usually skeptical that the open community could make offerings that are good enough to compete against the big commercial offerings. And AI enthusiasts are pretty skeptical that AI critics would ever come around to seeing any technology in this category as being acceptable, no matter how thoughtfully it was created or presented.
I think there’s enough anger at the trillionaire predators to go around, though.
It’s been a long time since we succeeded in wresting control of a nascent space away from the tycoons trying to take it over. But it’s pretty clear what the stakes are this time, and it’s also clear that the window for changing the path of the AI world is closing pretty rapidly.
Obviously, this kind of shift won’t be easy, but I think people would be pretty surprised how possible it is. There’s a snowball effect that happens once folks start to understand that there are appealing alternatives to the things that are making them miserable. An entire generation needs to discover that enshittification is not only not inevitable, it is downright preventable, and the power to do so rests in our hands.
If you’re a developer, you have an extra responsibility: are you vetting your work against this list? If nothing else, you need to be doing so just to ensure that you have a chance of having a career over time. But it’s also the right thing to do.
And if you’re not technical in that way, you don’t have to become a developer, but you can familiarize yourself with these concerns broadly — even if you hate AI and never want to touch the stuff! — so that you know what argument to make about how to shift the balance of power.
The most important thing to know is that, as so many people have said, none of this is inevitable. But the way we fight that inevitability is with a more exciting, human, powerful alternative, not merely by repeating what we’re saying no to. We are not simply angrily running away from something, we can all be joyfully running toward something together.
In the early days of tech blogging, one of the biggest reasons that so many people got used to reading Joel Spolsky’s blog was that he’d often write amusing little fables that shared key lessons about product strategy. Strategy Letter V (on commoditizing your complements), or How Microsoft Lost the API War, or Fire and Motion, or… Platforms. If you can squint past the turn-of-the-century mentions of Microsoft Excel, there are lots of interesting lessons there.
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