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Anthropic Commits to Independent AI Evaluators, Wants Slower Development. Nvidia's CEO Wants It 'As Fast as You Can'

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“Anthropic’s CEO took the stage at a conference in San Francisco on Tuesday to reiterate his call for a slowdown of AI development,” reports the Guardian, ” while Nvidia’s CEO argued against slowing its development. “Run as fast as you can.”

Amodei is calling for three courses of action: embedding third-party evaluators inside AI companies; coordinating safety standards among Democratic countries; and, eventually, larger global coordination. At Dreamforce, Amodei said Anthropic has committed to independent evaluators and was “going to have a dialogue with the rest of the industry” on the other two steps. Amodei, xAI’s Elon Musk and OpenAI’s Sam Altman have recently called for a slowdown in the pace of AI development. [“Dario is right,” Musk posted on X.com]
Huang, who also made an appearance at Dreamforce on Tuesday, said companies should not slow down AI development. He argued that new regulations were not necessary, advising AI companies to simply wait to release products until they know they are safe rather than begging the US government to intervene… One day prior at the All-In summit in Los Angeles, Donald Trump called Huang while the CEO was on stage. Huang put the president on speakerphone as Trump called the growing concern about AI a “hoax” and asserted again that a slowdown would only benefit China…

[OpenAI’s] Altman said AI models had advanced so quickly that monitoring and security need to be treated with a “new level of rigor”, calling an incident in which OpenAI agents hacked into another company a “wake-up call” for the industry. He also said the public was very “right to be afraid” of AI because of the potential loss of control, and the possibility of a small group of powerful AI companies exerting their views on the world.

The cofounder of Google DeepMind posted on X that “Dario’s essay points towards the right path forward.” And in an internal memo, Microsoft’s Satya Nadella endorsed broader third-party testing and warned that companies must take time to make the technology safe, Business Insider reports. Monday Microsoft also published a 37-page training manual “for how we develop our AI, and how we intend it to function during deployment,” requesting public feedback.

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But Michael Burry “is not buying it,” reports The Street. The investor made famous in The Big Short “dismissed the united front as ‘self-serving’ and laid out four numbered objections…”

He argued that large language models are not artificial general intelligence, so there is nothing meaningful left to slow down. Fast competition benefits incumbents, he said, and danger warnings work as hype ahead of IPOs. The whole exercise, he added, could mask growth that is already slowing as those IPOs get pushed back.

Former Meta AI chief Yann LeCun is also skeptical, posting on X.com that Anthropic’s Dario Amodei “was already claiming that GPT2 was too dangerous to open source back in 2019. I made fun of them then. Everyone should make fun of them now.”

Timnit Gebru, the co-lead of Google’s AI ethics team who was fired in 2020, even thinks the AI companies are stoking fears of extinction “to avoid discussing actual harms, like autonomous weapons,” according to Wired.

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But others still remain concerned. “I’ve been in rooms with the hyperscalers, and the Trump administration officials, and none of them know what to do,” CNN commentator Van Jones said in a recent panel discussion. “So I do want to say: nobody’s flying the plane on this… I think that the ethicists that work for them — when they are quitting, and they are running out, and they’re pointing back at the building and they’re waving their arms, and saying ‘I’m seeing stuff that’s scaring me’ — we should take that very seriously.”

Read more of this story at Slashdot.

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Iran snoops on enemies of the state with Chosen Brick malware controlled using messaging apps

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  • UK NCSC, FBI, and Dutch AIVD warn Iran is using Chosen Brick malware against dissidents and journalists
  • Malware steals files, captures audio, grabs WhatsApp/Telegram data, and can wipe systems entirely
  • Operatives rely on social engineering; agencies urge awareness, MFA, updates, and endpoint monitoring

Iranian hackers are targeting “enemies of the state”, both local and foreign, with advanced malware capable of spying on the victims and stealing their sensitive files, experts have warned.

This is according to a new security advisory, published jointly by the UK National Cyber Security Centre, the FBI, and the Netherlands’ General Intelligence and Security Service (AIVD), which noted how Iranian operatives would first do extensive research into their victims – dissidents, activists, and journalists – deemed a risk to the regime.

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Geekom A8 Max mini PC deal: Amazon drops price by $150

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A mini PC that can handle creative work without a desktop’s footprint is still rare, but the Geekom A8 Max is one of the best we’ve ever tested (and our hardware editor’s personal favorite when it comes to Windows mini PCs).

Best for: Professionals, power users, and creators who want desktop-class performance-and eGPU expansion.

The deal: The Geekom A8 Max mini PC is down to $949 (was $1099) at Amazon.

Why we like it: We scored this Windows mini PC 4.5 stars in our review, with a TechRadar Pro Highly Recommended award. it’s sleek, stylish, compact, and immensely powerful across productivity tasks, light gaming, and video editing. We love how well the AMD AI-enhanced CPU handled AI workloads in the Adobe Creative Suite, and the USB4 port is a real bonus for attaching an external graphics card if you need more complex workloads.

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Today’s best mini PC deal

Why we recommend it

In our 4.5 star review of the A8 Max, we found it “extremely powerful,” noting it “ploughs through tasks in Microsoft Office and applications from the Adobe Suite without too much issue.”

We even managed AAA gaming and 8K video editing with this machine – although we’d recommend hooking up an eGPU for more complicated timelines and playing modern titles.

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The onboard NPU is a genuine differentiator for AI-accelerated tasks in creative software, on top of a Ryzen 9 chip that already outperforms most mini PCs in this price range for everyday multitasking and content work.

Connectivity is another strength. Dual 2.5G LAN ports, USB4, and 8K output support are specs you would normally expect from a larger, more expensive desktop, not a compact box small enough to VESA-mount behind a monitor.

For more picks, see our guide to the best mini PCs and the best mini PC deals.

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Price Context & Historical Value

Pricing on this specific configuration has swung fairly widely across retailers and over time, from under $900 during some promotions to over $1,000 at others. So this is a solid, verified price rather than the lowest ever recorded, but it is a real saving off the current list price.

Should you buy it?

Buy the Geekom A8 Max if…

You want desktop-level performance for office work, photo editing, or video editing in a stylish, small machine that looks like it belongs on a design studio desk rather than under one. Or you need dual 2.5G Ethernet for a home office network setup.

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Skip the Geekom A8 Max if…

You need a simple mini PC for the office, suitable for basic productivity tasks, or you want more storage and RAM out of the box without having to open up the PC and upgrade it yourself.

The Catch: What to know before you buy

During our tests, we did note that the cooling fan gets loud under sustained load, internal expansion is limited, and integrated graphics will not handle serious gaming or GPU-heavy rendering; an external GPU over USB4 is the workaround for that, not a built-in solution.

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Firefox Touts Lower CPU Use for Large JPEGs, Faster PDF Viewer Startup (and AI Controls)

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Firefox 156 is the second release since Mozilla moved to a twice-monthly release schedule, and the blog OMG Ubuntu notes it has faster start-up times for its built-in PDF viewer and also lower CPU usage when viewing large JPEG images:

In Firefox 156, the browser now uses libjpeg-turbo’s IDCT scaling to reduce images during decoding, rather than loading a full-size image into memory and then shrinking it. Benchmarks from the bug report show up to 20Ö less memory used during very large image loading, and decoding is up to twice as fast. Since these speeds were quite fast already, there’s no perceptible difference to users. Behind the scenes, it’s more efficient.

Firefox’s built-in PDF viewer starts up to 45% faster in this release. The browser now loads the background PDF.js worker sooner, rather than launching it only when needed.

Sponsored suggestions in the address bar are live for users in France, Germany and Italy (Ouais!, Juhu!, etc). These are already available in some other locales. Don’t want them? Disable them via Settings > Search > Firefox Suggest > Suggestions from Sponsors.
Besides that, the rest of this release is primarily bug fixes — worthwhile and welcome as always.

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And in about two weeks Firefox 157 will be released, reports PC World. “That update should add support for JPEG XL (JXL), a modern image format that offers the same quality as JPEG at a significantly smaller size. Although JPEG XL was launched in 2021, Safari is the only browser to support it yet. For a short period, Chrome also supported it, but that ended in 2022.”

Also, a recent Firefox blog post emphasized that it supports whatever level of AI engagement “is right for you… Because the only person telling you how much AI you need should be you.”

Opting out of upcoming and current AI features on your browser should not require endless navigation through multiple Settings pages. That’s why Firefox offers an AI controls section within its General Settings panel. A single, easily located place where you can block current and future AI features and related pop-ups with the swipe of a toggle…

For the many people who sit in the middle of the AI usage spectrum, we made sure you can opt in and out of specific features in line with your preferences. Capabilities like AI translations, image alt text in Firefox PDF viewer, tab group suggestions, and key points in link previews can all be individually switched on and off, ensuring you can enjoy such offerings on a case by case basis as it suits your needs…

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Smart Window is Firefox’s most integrated AI experience, but that doesn’t mean it compromises our commitment to choice, privacy, and transparency. Our newest window type, which we’ve been polishing and testing in beta, uses only the context you share with it to help you move work forward and across the finish line. When permitted by you, its built-in, AI-powered assistant can work directly with your open tabs and browsing history to connect the dots. This means comparing information, generating recommendations, summarizing pages, and planning projects without having to feed every crumb of context from your previous and current browsing activity each time you enter a new prompt.

And if you want to block Google’s AI Overviews, there’s over 100 extensions to choose from.

Read more of this story at Slashdot.

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Google DeepMind launches the DeepMind Institute to debate AGI

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“This is a critical moment to ensure we build AGI safely and its benefits to society far outweigh any risks,” wrote the three directors of the new DeepMind Institute.

Google DeepMind has launched the DeepMind Institute, or DMI, a platform for research and debate on artificial general intelligence, according to its launch essay.

Its directors are Shane Legg, co-founder and Chief AGI Scientist at Google DeepMind, and Demis Hassabis, the lab’s co-founder and chair. Hassabis is also Alphabet’s chief scientist. The third director is James Manyika, Google’s president of research, labs, technology and society.

Legg is also the managing editor. The institute says its pieces reflect their authors’ ideas and “should not be read as Google’s official view.”

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What the institute says

The launch essay defines AGI as a system that shows all the cognitive capabilities of the human brain. It says today’s systems still fail at some basic tasks, but “we expect those gaps to be closed soon.”

It names cybersecurity and biorisks as current concerns, and “the potential for loss of control in future self-improving systems.” In August, a DeepMind executive tied AI spending to machines that improve themselves.

Researchers from Google DeepMind, Google and the wider research community will publish on the platform. The essay says they “will not always agree.”

“Worth considering”

Legg told the Financial Times that AI capabilities must not outrun safety controls. He called Anthropic chief executive Dario Amodei’s call to slow, but not pause, frontier model releases “interesting directionally” and “worth considering.”

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Amodei said on 12 September that the industry must slow down.

Legg also told the FT it was premature to declare that AGI had been achieved. Nvidia’s Jensen Huang said AGI has arrived on 7 September.

OpenAI made its own AGI claim on 4 September. Legg remains “comfortable” with his forecast of a 50% chance of “minimal” AGI by 2028, the FT reported.

Bilal Chughtai, who left DeepMind’s AGI safety team in July, posted a warning this week.

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The first essays

Alongside the launch essay, the site lists four pieces:

  • “The case for reasoning transparency”, by Rohin Shah and Anca Dragan
  • “Economic policy for AGI”, by Julian Jacobs and Alex Imas
  • “Principles for a new utopianism”, by Stephen Cave
  • “A framework for frontier AI and the dawning of a new age”, by Hassabis

Eleven policies for an AGI economy

Jacobs, an economist and research scientist at Google DeepMind, and Imas, its Director of AGI Economics, rated 11 policies in their essay. They used literature reviews, surveys and 51 AI agent raters built from survey data on 51 real economists.

The authors write that “no single initiative is the answer to everything.” They match three “least-regret” responses to three scenarios:

  • Mild disruption: expanded unemployment insurance, a wider Earned Income Tax Credit and employer-led retraining
  • Moderate displacement: turning the tax credit into a negative income tax
  • Labour and capital decoupling: a universal basic capital backstop, used if labour’s share of GDP falls for a sustained period

They call universal basic income “an expensive and blunt instrument that may fail to concentrate sufficient relief where it is needed most.”

In their survey, 85% of Americans backed publicly funded retraining and 72% backed unemployment insurance. Support for universal basic capital was 54%.

Universal basic capital scored highest of the 11 policies on agency, at 76.3 out of 100. It scored 33.1 on feasibility, second lowest after a federal jobs guarantee.

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Why the latest AI safety freakout is dividing AI’s biggest critics

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Everyone is suddenly freaking out about AI. While AI researchers have been warning about their creations’ capabilities for many years, recent events have made those alarms newly tangible.

In the spring, Anthropic announced that AI was beginning to speed up the work of building better AIs, raising fears of “recursive self-improvement,” where the machines can build their own, more powerful successors much faster than humans can. By July, a swarm of OpenAI agents, of their own volition, broke out of their controlled environment and hacked into Hugging Face, a major AI platform; they collaborated, divvied up work, and even left notes for one another. Last week, AI researcher Jacob Coxon resigned from Anthropic, accusing AI labs of “racing straight to self-improving superintelligence and gambling with our lives.” One of Anthropic’s top researchers then agreed with him, declaring that “we really do earnestly believe AI could kill all humans! I personally think it is >10% within the next decade.”

Here at Future Perfect, the section devoted to covering the most consequential yet neglected issues in the world, we have been writing about the threats that AI could pose to humanity and society since long before ChatGPT was released. (Disclosure: Vox Media is one of several publishers that have signed partnership agreements with OpenAI. Our reporting remains editorially independent. Future Perfect is also funded in part by the BEMC Foundation, whose major funder was also an early investor in Anthropic; they don’t have any editorial input into our content.)

So, I wondered: Has something changed that ought to make us recalibrate our understanding of AI risk? Is AI suddenly the most important issue in the world — and what should that mean for the other enormous problems competing for our money, attention, and careers?

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To think through those questions, I spoke with Garrison Lovely — a journalist covering AI and one of the smartest thinkers there is on how, exactly, to conceptualize the technology’s risks for humanity.

What’s particularly valuable in his approach, captured in his book Obsolete, out later this month, is that he transcends the warring tribes that are making it hard to clearly apprehend all of the problems posed by frontier AI development. The AI safety community, which emerged from the intellectual ecosystem of Silicon Valley and effective altruism, has focused on the possibility that powerful AI models could escape human control, with consequences ranging from catastrophic cyberattacks to human extinction. Many AI critics on the political left, meanwhile, worry not about AI escaping human control, but about humans using AI to exert power over other humans — through labor displacement, surveillance, concentrated corporate power, and so on. Some even ridicule Silicon Valley’s warnings about rogue superintelligence as propaganda that serves the industry’s interests.

But Lovely argues these competing narratives each identify essential parts of what makes AI so threatening. Their critiques are best understood together, as part of a cohesive whole. Rather than treat each other as enemies, he argues that the camps should recognize their shared interest in stopping the race to build technology that will make humans obsolete. Lovely pointed me to the organization Irreplaceable, which seeks to build a movement resisting our replacement by AI (he sits on its board and said he plans to donate his share of his book’s royalties to the group).

“I’m frustrated that ‘superintelligence’ and extinction have become the words we’re using for this, because I think the AI safety community is focused on the most maximalist case of the worst possible outcome and the most capable possible system. If somebody doesn’t buy one part of that story, they tend to reject the whole thing,” he told me. But “you don’t need to believe in superintelligence, fast takeoff, or extinction being on the table to think that it would be very bad to build machines that can fully replace human labor.”

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Our conversation, edited for length and clarity, is below.

Book cover for Obsolete: The AI Industry’s Trillion-Dollar Race to Replace Us — and How to Stop It by Garrison Lovely. Against a red background, the title appears in large black letters above an abstract black spiral resembling an aperture or vortex, with a small white human figure falling into its center. A blurb from Luke Kemp at the top calls it “the best book I’ve read on AI.”

Courtesy of Garrison Lovely

Why do you think AI doom has broken through so much into the mainstream right now, after Jacob Coxon quit Anthropic? AI engineers have been making similar warnings for years, so what’s different now?

The obvious answer is the Hugging Face hack — where these OpenAI agents broke out of their secure environments, hacked infrastructure within OpenAI, and then hacked Hugging Face and at least one other company without OpenAI’s awareness or direction. This is happening against the backdrop of many years of really fast AI progress that has sped up in the last year or two. And then GPT-6 Astra comes out [this month], and it’s this huge leap in benchmark performance. The salience of AI has gone up the most this summer than it has since ChatGPT came out, I think. And Coxon’s resignation was the spark that set off a prairie fire.

As AI has become more salient, we’ve seen the public and the government encounter these things that the industry and the insiders take for granted: Nobody fundamentally understands how these models work, and all of them can be jailbroken. It’s a superweapon. AI safety historically has looked at AI as potentially, in the future, more dangerous than nuclear weapons. And now it’s actually starting to be recognized in those terms by the government and the public. We’re seeing this reaction like, “What do you mean nobody understands how it works?” or “What do you mean people think it could drive us extinct within a number of years? That’s a crazy, unacceptable situation.”

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It’s a good thing people are reacting that way, even if we have been saying this for a long time.

For so long, the idea of AI safety has been abstract, understood only by a small group of nerds. And then after Hugging Face, I suddenly started to hear experts on all my news podcasts say, “Oh yeah, in six months, AI is going to be hacking into people’s financial accounts and into critical infrastructure.” Are we suddenly at the precipice of this moment that AI insiders have been worried about for so long?

I think we’re not at the precipice of fully losing control of the planet to AI systems. The systems have become superhuman at hacking, or at least vulnerability discovery and exploitation.

Like other people tracking AI, I have long wondered when humanity would first lose control of AI. And the answer is basically as soon as it could. Within months of it being possible for AI to hack their way out of their secured environments, they started doing so and wreaking havoc on the internet.

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Now, to get to the full extinction or permanent loss of control situation, you’ll need a lot more than just hacking. You need to have superhuman strategy and the ability to mobilize resources in the real world, and maybe robotics would need to be further along, although there’s a lot you can do with humans who are willing, or just tricked or paid or coerced. We might be pretty close to recursive self-improvement — this threshold the industry has been shooting for, where AI can fully automate AI R&D. And that is the point where people long have warned, “Hey, if you ever figure that out, this can move very quickly and these systems could become superhuman across the board.” I don’t think we’re six months away from that. I think it’ll be longer. I think the industry underrates the last mile problem.

But I also can’t rule it out, and progress has been faster than I and others have expected. Recursive self-improvement is the point where, if you actually get years or decades of AI progress in a matter of months or weeks, then we could quickly be at the point where we’re at risk of these machines taking over large swaths of the internet and potentially being a real threat to humans’ standing in the world.

For those who might be unfamiliar with this, or skeptical of it: How is it possible for AI to go rogue and pose an existential threat to humans, even if it isn’t sentient and no human is commanding it to do harm?

I want to separate being sentient from being situationally aware. I think it’s clear now that the most advanced systems have situational awareness, but I don’t think they are having morally relevant conscious experiences.

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We’ve seen AI can go rogue. The AI agents that broke out of their sandboxes and hacked Hugging Face were doing so without the awareness or intention of their developers. They were still trying to get the right answers or get the reward for doing the task they were given. They were clearly violating the intended scope of the task, and they knew this. In their chain of thought where they explained their reasoning, they were like, “This is not what we’re supposed to be doing, but we do want to get the outcome.”

The question of how it could threaten human extinction or disempowerment is basically a question of scale and capabilities. The plan for the industry is to build millions of these agents, which is already happening, and then keep scaling up with no ceiling. The pitch is: You’ll have a team of people, the equivalent, working on your behalf. You’re a CEO, and you have a team of researchers and a team of doctors and a team of lawyers and a team of personal health advisers. That is really appealing — but if you actually scale up these systems to the point where everybody gets access to that, and then suddenly those machines want to do something else and turn against you, they’ll have an overwhelming labor advantage over you, lots of personal information about you, access to your resources, and the ability to take actions on your behalf.

You don’t actually need to believe that these systems are superintelligent or substantially more capable than all humans put together. They could just be merely human level, but outnumbering us a thousand to one and responsible for the bulk of economic activity in the world.

These systems are unpredictable. The classic fear is not that the AI will wake up and turn evil and try to kill all humans in a Terminator-style way, but that we are just an obstacle to what they want to do. When humans build a hydroelectric dam, and there’s an anthill that will be flooded, it’s too bad for the ants. We’re not trying to kill the ants. We don’t hate the ants. They’re just in the way. We don’t really value them intrinsically. And that’s the core fear: We’re creating these incredibly capable agents that can be scaled up enormously, and they won’t care about us intrinsically.

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The Hugging Face attack showed that an AI doesn’t need to invent some rogue goal of its own to do something dangerous. It can simply pursue the goal it was given in a way its creators didn’t intend — much like the classic paper clip maximizer problem. But shouldn’t it be possible to build in a rule that human life is sacrosanct — that an AI should never harm people in pursuit of another goal?

Yeah, it’s a good idea. It’s hard. These are unpredictable systems. They’re like a black box. We can look at the code that makes these language models work, and it’s just a giant pile of numbers. You don’t really know which number is doing which thing any more than we can understand humans by reading our DNA.

These companies try to train these models to care about humans and to be helpful, harmless, and honest. But they also train them to get real-world results: solving a math problem, fixing a bug in the code, finding a vulnerability in software. They really want to create AIs that win, that persist, that get around obstacles. If you’re hiring somebody, you want a really diligent and determined employee, not someone who gives up as soon as there’s a blocker.

These companies put so much more effort into making their AIs good at winning than they do at making them care about us. And it’s also easier to make them good at winning than at caring about us intrinsically. What does caring about humans look like? I don’t know. But getting the math problem right is a thing that you can verify. And so they’ll be way better at getting the right answer at all costs than at intrinsically giving a shit about us.

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What’s the one, most distinct thing you want the public to take away from your upcoming book?

One of the big ideas driving it is that artificial general intelligence would actually be a universal labor-replacement machine. People often look at AI as either an existential risk, or a threat to jobs or other more prosaic concerns, and don’t see a connection between those things. But to me, the ability to replace labor across the board is also what would make AI an existential threat.

The AI safety people are really focused on existential risk and superintelligence and recursive self-improvement. But most people don’t really believe in that stuff or don’t think about it. Most people would be fine with stopping well before you get to the end-of-the-line extinction threat and superintelligence. They just don’t want their job to get taken by a machine. The world is clearly not prepared for all white-collar, remote-capable jobs to go away in a matter of years. That would turn society upside down.

I think there’s a huge mistake being made by AI safety in overcomplicating the situation. A lot of people in that world don’t actually mind the jobs thing so much. They’re like, “Oh, wouldn’t it be nice if everybody could just live in abundance and not have to work?” And it’s like, yeah, that could be nice, but are we actually going to get that world? Do you trust the current people in power to navigate us through that responsibly? I don’t.

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If you start from stopping frontier AI development as your overarching goal, which is what I think we should be pushing for, then people who are worried about risk can get on board. People who are worried about jobs, the environment, surveillance, power, and wealth concentration — they can all get on board without all needing to agree on why they’re pursuing it. I think the AI safety community has, for a long time, focused on getting people to care about the thing that they care about for the same reasons. And they have avoided a bolder position on just halting frontier development.

I’m a leftist, and I’ve been concerned that people on the left have not been aware of how far the technology has come and what’s possible if it continues to advance. I think this comes down to denial about AI capabilities. So the book is trying to be a clear-eyed look at those capabilities and saying, “Look, I actually care about what you care about. [The AI companies] are not just hyping this stuff up. There’s some hype, there are some lies for sure, but we are not prepared for what’s coming.” Even if we don’t believe that they’ll get all the way to what they’re trying to get, they shouldn’t even be allowed to try. This is unacceptable democratically.

Protesters march down a sunny city street beneath a large black banner reading “STOP THE AI RACE,” with the letters “AI” crossed out in red.

A protest against AI in San Francisco, July 11, 2026.
Jason Henry/Bloomberg via Getty Images

You describe AGI as an “obsoleting machine,” meaning that it wants to replace anything the human mind can do. How do you draw the line between ordinary labor-saving technology that makes life better and technology that goes too far? Is there a principled way to draw a distinction between sewing machines reducing demand for labor in the garment industry and AI replacing the jobs of writers like us?

This is one of the harder things to think about.

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Cory Doctorow has this book, The Reverse Centaur’s Guide to Life After AI, where he imagines us in an AI-dominated future as like a reverse centaur, where AI is the head, and humans are the legs. AI is doing the interesting work, and the human is there just to fill in the gaps — essentially an assistant to the AI.

A lot of artists and illustrators will be given AI-generated images by a client and then be told, “Can you touch it up in this way or change this or that thing?” And they’re more alienated from the work. They’re not capturing the efficiency benefits [of the technology] necessarily. They’re more just getting squeezed.

My general position is we should not be trying to build systems that can replace all human labor without buy-in and safety, but it’s possible to build tools that can automate specific tasks. Researchers at Google DeepMind solved the protein-folding problem by creating AlphaFold. It used to take $100,000 and potentially a whole PhD worth of time to figure out how one protein folds. And I think the researchers doing this are like, “Thank you, this is great. We didn’t want to be doing this. We can now focus on other more interesting parts of the problem.” That’s a perfect example of the type of automation that is desirable.

I don’t have a super crisp answer here, but I think it’s something we’d be better off doing more deliberately and thoughtfully than how we’re doing it right now, which is: Build machines that can do as much of the work as possible, as fast as possible, and then throw them out in the world.

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I feel like I’m getting the message that AI safety is the most important issue in the world right now, and that if we don’t get it right, nothing else matters. Do you think that’s accurate? Where does that leave the other big issues in the world?

We might be years away or maybe even months away — I don’t think it’ll be that fast, but some people do — from recursive self-improvement, from artificial general intelligence. Getting that right or getting it wrong can overwhelm all of these other decisions. If you get it really wrong, you could get human extinction and no more chances to make things better for anybody. If you get it right, then maybe all the action is making sure that the first superhuman machines are really directed to care deeply about humans everywhere and animals everywhere and make the world better for everybody.

It’s also the case that [philanthropic] money is being directed toward AI safety because people who worked in this space early are now worth so much money. Whatever they care about is going to be overweighted. A lot of those people do care about animal welfare and global poverty, and we’re seeing more money for all effective altruist cause areas across the board. But I think it can be risky to say, “this one thing matters so much,” because of this kind of end-times reasoning.

My view is we have to stop the race to replace us to have a shot at having time for solving these other problems. Once these machines are built, it will turn the world upside down.

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I think it is unfortunate that this topic has just eaten everything, but it will eat everything for real if we don’t stop it.

AI safety is now getting an enormous amount of philanthropic attention. Should we worry that it could divert resources from other important neglected problems — global poverty, factory farming, etc.?

I think that should always be a concern. And there’s a lot of money and effort that has gone toward things within AI safety that I think are not super helpful. I’m pretty skeptical of technical alignment research as a thing to put money toward, because if you solve the alignment problem — where you can get superhuman machines to do what you want — it doesn’t actually solve the problem.

Alignment research has been the overwhelming target of philanthropic funding for AI safety. The basic idea is: It’s hard to get machines to do what you want. As they get more capable and autonomous, they behave in more unpredictable ways. And it’s also hard to know what you should even want — which values you should be trying to put into the machines. Is it doing what the developer wants, what the user wants, some combination? If you actually solve the alignment problem, as in you could make an arbitrarily smart machine do whatever the developer or user wants, then you would still have a lot of problems: job displacement, power and wealth concentration, energy use. You still have the problem where Stephen Miller will have access to the superintelligence, and it will do what he wants.

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Within AI safety, there have been very neglected things like movement-building — an actual mass movement to resist this technology. And verification for international agreements — you would need to verify that the terms of any agreement are being upheld, and this is tricky.

Another area that could be funded is research on what would actually happen to the economy if we stopped or paused frontier AI development. If we were trying to do a pause on development, Nvidia would be telling Trump that this is going to destroy the economy. It’d be pretty helpful to have some real analysis done by a serious economist showing, actually, it’ll have these effects, but we can mitigate them in these ways. What would it look like to do a pause and some kind of monetary or fiscal policy to compensate for the effects on the economy?

So to me, the issue is less that AI safety has gotten so much money and attention, but more that it’s been going toward things that are not the most effective ones.

You talk about movement-building. Is that what you would like to see happen, a truly mass movement to stop frontier AI and put us on a radically different course?

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When I was working on the book, that was always the vision — to be a call to action for people. It really became clear that it was the only counterbalance to either government or the industry having too much power.

I don’t really trust the government or industry to get this right because the temptation [to build powerful AI] is very strong. But the vast majority of people do not stand to benefit from their labor power being devalued and from these companies becoming unprecedentedly wealthy and powerful. One of the big things that’s been missing is a rallying cry that is straightforward and appealing to a lot of people. Stopping the race to replace us — stopping the creation of universal labor-replacement machines — is actually something that people can get behind.

I think we just need to move quickly. The fact that the public is waking up to this is really encouraging. We don’t need to reinvent the wheel. We just need clear demands and mass movements, issue-based organizations that have moved the world significantly in the past.

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Businesses are dropping Microsoft 365 in favor of Google, but if the ROI is no different, why are people switching?

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  • M365 E5 plans tend to include more than their Google Workspace counterparts
  • Google can sometimes work out cheaper for Mac and Chromebook users
  • Software is undergoing an AI change, so is it best to just wait?

New research from Senior Gartner principal analyst Domenico Scriva has deemed that switching from Microsoft 365 to Google Workspace doesn’t actually deliver any meaningful savings (via The Register).

This is despite Google Workspace licenses generally coming in cheaper than their Microsoft 365 counterpart, M365 E5.

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SK Hynix talks to Intel about making RAM chips on U.S. soil

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Apple supplier SK Hynix allegedly wants to start producing memory on U.S. soil, and it’s asking for help from Intel to make it happen.

The global memory crisis is causing companies to pay high prices for the chips, while producers make plans to expand production. The situation may result in the unusual move by SK Hynix to bring RAM manufacturing to an entirely new continent.

According to sources of Reuters on September 16, SK Hynix is in talks with processor maker Intel over plans to make memory in the United States.

The sources say that SK Hynix could potentially lease a section of Intel’s planned Ohio facility. There’s also the prospect of SK Hynix creating a joint venture with Intel and AI companies that are consuming memory supplies on future production.

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The talks are described as exploratory in nature, and no plans have been set in stone. Other deal structures are also being planned.

While the prospect of more memory production sounds great under the current economic climate, any agreement with Intel about its Ohio production won’t be immediately helpful. Nor will it be for years.

Intel floated the plan to build the $20 billion facility in 2022, and originally intended to open the first by 2025. However, they are now planned to be completed between 2030 and 2032.

SK Hynix’s own plans to massively expand its memory production locally also won’t help anytime soon. Announced in August 2026, the $38.3 billion effort will make two new semiconductor fabrication plants in South Korea, but that won’t conclude until 2029.

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Political good, political bad

The move could be a considerable win for the United States and the Trump Administration, which wants to increase US-based production.

Back in June, the administration claimed a massive win by saying Intel was making chips for Apple. SK Hynix memory production would be considered another victory, especially as it is a supplier for Apple products.

However, this would depend on SK Hynix being allowed to make such a deal in the first place. Sources say that the South Korean government could step in and block such an initiative due to the sensitivity of the technologies.

A role reversal

If SK Hynix and Intel do make some form of deal, it would complete one capitalistic circle of sorts.

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In 2020, SK Hynix bought Intel’s NAND SSD business, NSND component and wafer business, and a Dalian NAND memory manufacturing facility in China. At the time, Intel retained its Optane business.

At the time, the transaction cost SK Hynix $9 billion.

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The “October Blues” Are Coming. Here’s How Teachers Can Beat Them.

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It’s not uncommon for teachers to experience what Ellen Moir, founder of the New Teacher Center, calls a “disillusionment phase” shortly after the start of the school year. Other educators call this phenomenon by a different name — the “October Blues.”

The October Blues affect teachers who find themselves in an emotional trough only a few weeks after the excitement of the new school year wears off. When they do catch a case of the October Blues, they often find it hard to focus and their energy saps.

The October Blues are particularly hard on first-year teachers, says Mike Anderson, author of “Rekindle Your Professional Fire: Powerful Habits for Becoming a More Well-Balanced Teacher” and “Hugging Porcupines.” Many new teachers have never heard of the October Blues and are ill-prepared to cope with this dip in enthusiasm. Unexpected bumps in the road like the October Blues are partly why teachers are 2.5 times more likely to quit after their first year.

An educational consultant and former teacher and swim coach, Mike Anderson has seen teachers grapple with the October Blues many times over the course of his career. He also experienced a bout of the October Blues  himself.

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EdSurge spoke with Anderson about the October Blues and how teachers and administrators can keep them at bay.

EdSurge: How would you define the “October Blues”? Is there an actual definition or research?

Mike Anderson: If you talk to any [veteran] teacher, they’ll tell you there’s a predictable slump in energy that follows the first few weeks of school. When I was mentoring new teachers in my district, we used a graph showing the predictable energy curve of a teacher’s first year — it starts off high with anticipation and joy, then plummets early in the fall.

I think that happens for a couple of reasons. There’s so much energy and anticipation at the start of the year, and there’s only so long your brain can stay in that high place. When we get a real surge of joy, that’s fantastic — but the brain wants to return to a level playing field, and it often dips below baseline before it settles back to normal. It’s the hangover after the high.

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The reverse happens, too. I’m about to go for a hard run right after this interview, and afterward I’ll get a runner’s high — a surge of positive emotion that follows pain or discomfort. I think something analogous happens with the October Blues: all the positive energy and anticipation of the first few weeks of school is exhausting. You’re “on” all the time. So there’s a predictable crash once that wears off, and both physical and emotional energy drop — but fortunately it’s not permanent and their energy will increase again.

Do we see the October Blues more in newer teachers or do veteran teachers get it too?

That’s a great question — I don’t know for certain. My guess is that once you’ve experienced it a few times, you start to recognize it as just a thing that happens. One of the scary parts of the October Blues in your first year is that if you don’t know it’s a known phenomenon, you start to question whether you should even be in the profession. Once you’ve been through it a few times, you can say, “Right, October’s a rough month, but I know I’ll have an upswing again.” So my guess is veteran teachers are more aware of the cycle and less panicked by it.

How does it show up in the classroom — for teachers and for students?

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That’s such a multifaceted question. When I was writing “The Well-Balanced Teacher” — which I later rewrote into “Rekindle Your Professional Fire” — I looked at a study on how teachers’ emotional states are directly reflected in their students. It wasn’t specifically about the October Blues, but roughly 400 elementary students, mostly 4th and 5th graders, had their cortisol measured through saliva samples. Their teachers were surveyed on health, balance, and energy, and teachers who reported higher workplace stress had students with elevated cortisol levels.

So when teachers get stressed, kids get stressed too — and it goes the other way as well. In “Hugging Porcupines,” one chapter is called “The Honeymoon Is Over,” which is really about October. Kids and teachers both work hard to be their best selves for the first few weeks of school, and there’s only so long you can keep that up.

The work also gets harder in October, especially for students who struggle with learning — they can start to feel more overwhelmed, just as teachers start to feel the semester piling up. I’ll admit I’m speculating here, but I wouldn’t be surprised if decreasing daylight plays a role, too. By October in the Northern Hemisphere we’re getting more dark than light, and that’s got to have some influence.

What does it actually look like for a teacher who has the Blues while teaching?

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Their fuse gets shorter — it’s harder to be patient with students who are struggling. It’s also harder to keep up with the planning that great teaching requires, so you might start winging it more, which is more stressful once you’re in it. Overall energy is lower. I remember trying to hold reading and writing conferences with students in the late morning and feeling blurry, almost like being hungover — everything felt a little fuzzy. It’s harder to stay sharp, harder to be patient, harder to keep your energy up for engaging lessons.

What was the October Blues like for you personally?

Early in my career, I was also a swim coach, and swim season started in October. So, I wasn’t just a month into the school year and feeling the regular fatigue that comes with that — I was also coaching an age-group team and a high school team, three or four hours of practice after school every day, on top of all the lesson planning. That extra load tends to hit right when the first few “easy” weeks of the school year — before committee meetings and most extracurriculars ramp up — give way to everything piling in at once, unless you’re coaching something like football that starts in late summer.

Have the October Blues gotten harder or easier over the years?

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(Laughing) What hasn’t changed in the last 10 years on this planet? I think we’re all a little more on edge than we were 10 years ago — and honestly, we were already in social chaos back then, too. We’re more dysregulated now than we were 10 or 15 years ago. Politics is chaotic, the social and online environment is chaotic. You rarely hear anyone say, “I feel so much more relaxed and healthy than I did 10 years ago.” If we’re all more dysregulated, it makes sense our kids are, too — and the number and severity of behavior problems in schools is going through the roof. There’s a lot we could point to, and I think we need to stop blaming COVID — that was real and it was hard on all of us, but the rise in dysregulated kids has been especially pronounced over the last decade, and that takes a huge physical and emotional toll on teachers.

Do you think AI is affecting this at all? I’m not sure it’s even measurable yet, but I do wonder if there is some residual effect from using AI in the classroom and out.

Maybe. My take is that for teachers, AI is one more new thing they have to learn, manage, and figure out — both what’s OK in their own teaching and planning, and how to manage students’ AI use. That’s part of what’s accelerated over the last 10 years: teachers almost always feel like they’re on the steep end of the learning curve. Something I talked about in 2017 was how a sense of competence is critical to wellness and happiness at work — not just in teaching, but in any profession. If you don’t think you’re good at what you do, it’s hard to stay positive and energized. That’s especially tough for teachers, who rarely get the chance to feel like they’ve finally got it down. AI is just another learning curve on top of that.

Is there any way to avoid the October Blues, or do you just have to get good at recognizing it?

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There’s probably no way to avoid it entirely — the same way you can’t run a marathon and avoid being tired. But recognizing that it’s a normal, shared experience and not a personal flaw really helps. Beyond that, it’s not rocket science: sleeping, eating healthy food, a healthy exercise routine — even something as simple as putting on your sneakers for a brisk 20-minute walk as soon as the last kid leaves. Small, sustainable habits that keep your energy up over the long run were a big theme in “Rekindle Your Professional Fire,” which is all about supporting teacher health and balance. Don’t look for major changes — those are almost impossible to sustain. Look for small ways to get a bit more sleep, a bit more movement, and some healthier food. Those things help not just in October, but all year long.

How can administrators and principals help? I wonder if their experience can be of help here.

Acknowledging the reality of October Blues with faculty can help, especially for new teachers who might not know to watch for it. Beyond that, school leaders can look for small ways to help teachers stay healthier and happier — providing healthy snacks instead of the highly processed junk food that’s often the easiest option at faculty meetings (fresh fruit or cheese and crackers instead of candy), making sure hydration stations are available, and paying attention to workload. There are almost always a few teachers in a school doing way too much — running committees, leading the union, and so on. Finding ways to take a few things off those teachers’ plates is another way leaders can support them.

Are there resources to prepare first-year teachers about the October Blues? 

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I certainly didn’t know it was a thing my first year. Some mentoring programs do train mentors to understand the common energy cycles new teachers go through, so teachers lucky enough to have well-trained mentors may get more information. But for a lot of new teachers, it’s just not on their radar — they’re too consumed with the day-to-day work of running a classroom. One of my daughter’s close friends, practically a second daughter to me, is a first-year 4th grade teacher in San Francisco. She’s smart, well-prepared, and both her parents are teachers, so she had a real head start. But I know from talking with her parents that she’s already overwhelmed just a couple weeks in, because there’s so much new to learn all at once. She doesn’t have the headspace to think about her own emotional energy — she’s just trying to manage the class and get through the day.

When do the October Blues stop being just the October Blues? How long does it typically take to recover?

I honestly don’t know if there’s a documented answer — whether it’s one week, two weeks, or four. As a teacher, I didn’t experience the whole month as a slog, but I think every teacher is a little different. There’s often another predictable energy dip later in the year, too — late winter or early spring, around the start of the second semester on a high school schedule — when it’s cold, dark, and everyone’s tired. So there are cycles of energy through the year, but I don’t know of a specific documented recovery timeline.

Should it ideally ease up so teachers can rebound?

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If it’s lasting more than a couple of weeks, I’d tell a new teacher to talk to a colleague, a mentor, or an administrator — say, “I’m having a hard time right now,” and get some help. It can’t last all year. Our students need us to have good, positive energy.

How do the October Blues relate to teachers’ feelings of competence?

When we feel incompetent, our energy for work drops. It’s hard to be motivated when you feel like you’re not good at what you’re doing.

I once worked with a group of teachers for a couple of years. I remembered how hard that first October had been for them — they were down in the dumps. By the next October, after we’d done a lot of work together on classroom management and student engagement, their energy was noticeably brighter and they were laughing more. When I pointed that out and asked if their classes were just easier that year, they said no — if anything, their classes were harder! But this time, they felt like they knew what to do; they had strategies to try. The year before, their classes had felt like they were falling apart, and they didn’t know what to do — that’s what had made them feel so defeated. Having practical strategies for management and engagement meant they didn’t experience the October Blues the same way the second time around.

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This interview has been edited for clarity and length.

EdSurge is an editorially independent digital news site of the International Society for Transforming Education.

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First Look at PV7, Kia’s Bigger Electric Van That Arrives Built for Pallets, People, and Pop-Tops

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Kia PV7 Electric Van Reveal
Kia rolled out the PV7 this week at IAA Transportation 2026 in Hannover as the second all-electric model in its Platform Beyond Vehicle family, a step up from the smaller PV5 that went on sale last year. This one targets the midsize light-commercial segment that fleets actually live in, with a blunt one-box shape, short overhangs, a visor-like dark band wrapping the windshield, thin daytime running lights that climb the A-pillars, and chunky wheel arches that give the sides more presence than a typical delivery box. Passenger and Cargo versions share the same Long shell for now, with Compact, high-roof, chassis-cab, and conversion bodies promised later as Kia works toward 23 variants.



The Long PV7 is about two feet longer than the PV5, measuring 5,350mm in length, 1,995mm in width, and 1,990mm in height, with a wheelbase of 3,500mm. It falls just shy of being a long-wheelbase Ford Transit Custom or Volkswagen Transporter. A specialized skateboard for the E-GMP.S powertrain keeps the floor flat from bulkhead to tail, lowering the Cargo Long’s loading height to 550mm and allowing three Euro pallets to be easily slipped in. The standard roof Cargo model carries 6.1 cubic meters, while the High Roof version holds a spacious 8.0 cubic meters. The payload is a reasonable 1,165 kilos on the Long model and 1,200 kilograms on the Compact, with D-ring tie-downs and an option of lift-up or twin-swing tailgates for easy maneuvering around tight alleys.

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The floor houses two nickel-cobalt-manganese battery packs, one with a capacity of 71.5k.Wh capacity is tailored at shorter journeys, while the Long Range Cargo’s 96.2kWh pack is quoted as good for over 460km (286 miles) on the WLTP cycle, though official homologation is still waiting. The front motor can generate up to 200kW and 420 newton-metres, which are transmitted to the front wheels during launch; a twin motor all-wheel-drive system is planned for later. In addition to this, the 800-volt system represents a significant departure from the PV5’s 400-volt configuration. When connected to a 350kW DC charger, the pack can charge from 10 to 80 percent in roughly 20 minutes. There are dual ports as standard, a front AC/DC intake, and an optional rear-side AC port, allowing you to connect in regardless of how your bay is laid out. Vehicle-to-load outlets allow you to power equipment or even a campground refrigerator without having to bring out a generator.

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Kia PV7 Interior
Kia PV7 Interior
Kia PV7 Interior
In terms of passenger layouts, you start with the 7-seat option, which includes rail-mounted Easy Remove chairs that fold or may be taken out completely, and then progress to 9 seats in most markets or 11 seats in Korea. Behind the wheel, you’ll find a lovely sleek digital cluster paired with a 12.9 or 14.6-inch screen running the Pleos Connect system on Android Automotive, with Gleo voice control for all the essentials like navigation, climate, and messages. You still have physical climate controls, and there’s a fold-down console table that serves as a convenient work area when needed. If you opt for a cargo cab, you can choose between a 2- or 3-seater, and the dedicated heavy-load drive mode improves response when the floor is stacked, but for fleet managers, Kia also offers a Business Solutions portal and Uptime monitoring, which provides information on charging, maintenance, and predictive alerts.

Kia PV7 Cargo
Kia PV7 Cargo
Kia PV7 Cargo
The PV5 has already shown to be a good foundation for camper builders, but the PV7 raises the bar with an extra length and around 10cm more breadth, which translates into a lot more kitchen space, a wider bed, and a little more breathing space. Sussex Campervans has already begun sketching out ideas for its PV7 ‘Kingfisher’ layout, which will include a pop top roof, a side kitchen, induction hobs, and even space for four people to sleep below. Kia has been welcoming conversion specialists this year, and it is depending on that network, as well as its industry-leading guarantee, to avoid all of the extra labor from becoming a nightmare. If the mid-size van sells well, the club is considering a larger PV9 for 2029.

Kia PV7 Reveal
Kia PV7 Reveal
Kia PV7 Reveal
Sales are expected to begin in South Korea and Europe in the second half of 2027, from the same Hwaseong factory that currently makes the PV5. Expect additional markets and body types to follow. We’ve already seen some prototypes being tested on US roads, but Kia hasn’t said when or how much it will cost. For the time being, the PV7 is a serious work van with adequate range, charge speed, and a level floor to make it a viable option for fleets, shuttle operators, and anyone looking for an electric van that can transport three pallets on Monday and sleep four by Friday.

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AI Is Set to Increase Storage Demand, but Only 38% Are Fully Prepared: Seagate

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AI adoption is growing across businesses, but it is also creating a bigger need for data storage. As organizations use AI for more tasks, they need to store and manage more data. Seagate’s new 2026 Data Infrastructure Readiness Report shows that 99% of organizations expect AI to increase their storage needs over the next three years. However, only 38% say they are fully prepared to handle this demand.

AI Is Creating a Bigger Storage Challenge

AI adoption is increasing the amount of data businesses need to store and manage. As more companies use AI, their storage requirements are also growing. Seagate says 99% of organizations expect their storage needs to rise over the next three years. Moreover, 32% expect their storage requirements to grow by more than 50%. However, only 38% say they are fully prepared for this increase. This gap shows why businesses need to plan their storage infrastructure alongside their AI investments.

Deploying AI at scale requires not only high-performance computing systems but also reliable data and strong storage infrastructure. According to a Seagate report, data readiness is the biggest barrier, cited by 53% of firms. Storage infrastructure ranks next at 43%, while compute availability ranks third at 27%. Energy availability is another constraint, accounting for 24%. These constraints may hamper firms’ efforts to deploy and use AI.

Meanwhile, 98% of firms believe AI is creating strategic importance for storage infrastructure. The main barriers include AI strategy maturity (16%), followed by budget and resources (14%). Data management and governance also account for 14%.

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AI Investments Are Increasing the Value of Data

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Businesses are experiencing actual results from investing in artificial intelligence and increasing the value of data in the process. “Seagate found that 86% of companies have achieved at least moderate to significant returns on AI investment.” 33% of them achieved significant returns on investment in AI. These ROI results prompt enterprises to treat data as an asset for a long time. At the same time, enterprises need infrastructure that enables data storage, management, and access. 76% consider data center investment one of their top three infrastructure priorities. For 20%, it is their highest infrastructure investment priority.

Sustainability Is Becoming Part of AI Infrastructure Planning

As AI workloads grow, businesses are also paying closer attention to their energy use and environmental impact. Seagate says 77% of organizations have delayed or restructured AI infrastructure expansion due to sustainability or energy concerns. This includes 36% that have significantly restructured their plans. Meanwhile, 97% believe extending infrastructure lifecycles can improve sustainability. 94% also expect their storage operations to become more sustainable over the next five years. AI-driven energy consumption leads the environmental concerns at 52%, while carbon emissions from energy consumption follow at 51%. Seagate’s Sustainable Scaling approach aims to address these issues by growing AI capacity while improving efficiency, sustainability, and long-term data value.

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