Six months ago, first lady Melania Trump walked into a White House education summit next to a faceless robot powered by artificial intelligence, which she said could one day give children personalized instruction.
Tech & AI
Trump wants AI in every classroom. Here’s why Republicans are saying no.
“Imagine a humanoid educator named ‘Plato,’” she said. “Access to the classical studies is now instantaneous…humanity’s entire corpus of information is available in the comfort of your home.”
Weeks earlier, her guest at the State of the Union had been a 10-year-old from Alpha School, a fast-growing private school network where children learn reading and math from AI software for two hours a day, and spend the rest of their time on workshops and projects overseen by adults the school calls “guides” rather than teachers.
The Trump administration had chosen AI as the future of American schooling, and wanted everyone to know it. But they picked a bad year to ask Americans to trust the technology with their kids.
Earlier this month, top leaders at Anthropic, OpenAI, and Google DeepMind publicly urged for an AI development slowdown, warning that companies racing to build ever more powerful systems could lose control of them, threatening not only large swaths of infrastructure but even humanity itself. Trump responded by calling the panic a “hoax” and said the US would not “hinder or stifle” the industry, because “whoever wins AI wins.” This “let them cook” mantra has met nearly every AI fear this year, from job loss to data center construction.
- Trump has made AI in classrooms a priority and Alpha Schools his showcase, but Republican-led states like Florida, Iowa, and Utah have passed limits on AI and screens in schools.
- Alpha says its students learn 2.6 times faster with two hours a day of AI instruction. No outside researcher has verified that, though studies may soon launch.
- Republican and Democratic voters want guardrails on classroom tech. Within the GOP fight a fight is brewing over parental choice, innovation, and whether publicly funded schools must prove they work.
Yet the public is not convinced, and parents perhaps least of all. Efforts to bring AI into classrooms have run into organized opposition, and much of it is coming from inside Trump’s own coalition, setting up a fight between the administration’s pro-tech allies and conservatives who think screens have already done enough damage to kids.
In the nation’s two largest school districts, New York City has recently pulled student-facing generative AI from classrooms through eighth grade and Los Angeles blocked it on all student devices. But it’s not just Democratic-run cities that are pushing back. Earlier this year Utah barred students from using generative AI on assignments without a teacher’s say-so, and required schools to notify parents when it’s used in class. Oklahoma followed suit with a law, passed without a single “no” vote, that requires districts to tell parents which AI tools they use and what data they collect, and lets families opt out. Florida went further last week, when its Board of Education adopted rules for parents to opt their children in before they use AI at school, guaranteeing non-AI alternatives, and banning “companion” chatbots outright.
Even at the Heritage Foundation, a conservative think tank with close ties to Trump, leaders are warning about the risks of AI in the classroom. In a March report, Heritage officials cautioned that AI tools built with addictive design features can harm children’s attention, mental health, and learning, and said schools should set limits rather than assume the technology will regulate itself. In July, the Manhattan Institute, another conservative think tank, recommended New York State bar students from using AI until third grade and keep it out of tests at every grade.
All the while, the Trump administration continues to push in the opposite direction, casting the wider AI race as one the US cannot afford to lose to China. Last year, Trump signed an executive order creating a White House task force to bring AI into K-12 classrooms, train teachers to use it, and build an “AI-ready workforce” to keep America as the global leader.
Polling data released in August found voters across the political spectrum concerned about AI in classrooms, with 85 percent worried about students using it to finish assignments without learning anything. Republicans were, if anything, slightly more eager than Democrats for government guardrails, 80 percent to 76 percent. Put differently, Republican voters appear closer to the Heritage Foundation than to the White House, which points to a coming clash inside the GOP over parental rights, innovation, and whether schools that take public money should have to prove their tactics work.
Up to this point much of the AI backlash has focused on regulating the tech in public schools, while some of its most ambitious boosters have concentrated on building private schools and developing new tools. A state can cap screen time in its elementary classrooms and let another family down the road pay thousands of dollars for Alpha, and everyone can call it a win for parent choice.
That peace may start to fray though as more public money comes on the table. Texas recently expanded vouchers to Alpha network schools and the company has also sought to open publicly funded charter schools. Other states are likely to confront these issues as well, meaning the AI education fight on the right may soon be unavoidable — and would land before an electorate whose patience for the tech, in schools and increasingly everywhere else, has been running thin all year.
The Trump administration’s AI bet
The AI-in-schools executive order came just three months into Trump’s second term, and called for AI education to begin as early as kindergarten. Within five months, Education Secretary Linda McMahon was touring Alpha School’s Austin campus, describing it as “the most exciting thing” she’d seen in education in a long time.
Alpha is hardly the first education company to promise a shiny new school model, but it has quickly become an influential player in the growing national debate around AI in schools. Its story so far reflects many of the big questions around AI in the classroom, including how to determine if the tech is helping or hurting kids, who is profiting, and what role governments should play in facilitating its adoption.
Alpha had been a small operation for most of its existence. MacKenzie Price, a Stanford graduate, co-founded it in 2014 after her second-grade daughter complained that school was boring. The tech billionaire Joe Liemandt became Price’s partner and the school’s principal financial backer. As recently as 2024 it served about 300 students across three campuses.
Growth exploded in summer 2025, when Alpha took over campuses from a shuttered Montessori chain and announced plans for 10 new schools. This August it announced plans for 27 more.
Behind the school’s growth is its claim that students enrolled can cover a full day’s worth of academics in just two hours with AI, learning twice as fast as they would at a traditional school. Alpha can “enable the increase in human intelligence,” Price declared on a panel in Washington, DC, earlier this month, because “our academics can be delivered so much more efficiently and effectively.”
Alpha says results from MAP tests, a widely used measure of academic growth, show its students learning 2.6 times faster on average than similar students at other schools. But no outside researcher has audited or independently verified those results.
That may soon change. MIT’s Blueprint Labs, the group behind some of the most rigorous charter school research in the country, told Vox it is designing pilot studies to measure the effects of the school’s programs on achievement and engagement, using experimental designs. Alpha is paying for the research; the lab says its own researchers will set the questions and methods. Harvard’s Human Flourishing Program is separately planning to study students’ life skills and well-being via a longitudinal study during the next academic year, Christina Hinton, who is involved with the research, told me. For now, Alpha’s academic claims remain its own.
Meanwhile, scrutiny of Alpha has grown. A Wired investigation last year described the school’s surveillance system as capable of recording students’ screens, keystrokes, and mouse activity and potentially tracking their eye movements. A 404 Media report published this winter claimed Alpha’s own internal documents said its AI-generated lessons sometimes did “more harm than good.” Alpha has disputed both reports, saying they relied on cherry-picked examples and accounts from fired employees, and provided me with an open letter it says every current family at the Brownsville campus signed, stating that the school Wired described doesn’t resemble theirs.
But education officials are still wary. This month, ProPublica and the Texas Tribune reported that at least nine states, Texas among them, had turned down Alpha’s bids to open publicly funded charter schools, with officials repeatedly pointing out that the model had only been tested on affluent students. (Tuition varies based on location, ranging from $10,000 a year at its Brownsville campus to $75,000 at its most expensive California schools.) Where Alpha had worked with lower-income students, ProPublica and the Texas Tribune found, it either hadn’t released enough data to back its claims of student growth or state test scores fell well short of what its leaders had predicted.
After the Texas State Board of Education rejected Alpha’s charter bid in the summer of 2025, Alpha didn’t reapply. While policy advocacy was “frustratingly slow,” Price wrote, Alpha’s goal was “for students everywhere at every income level to be able to use this model of learning.” They turned instead to Texas’s new voucher program, which lets families spend public money at private schools. Unlike a charter, a private school in the program doesn’t have to give state tests or show anyone how its students perform. The comptroller, which runs the Texas Education Freedom Accounts (TEFA), approved most of Alpha’s Texas schools, which, counting virtual programs and specialty academies, now number more than 30.
None of the concerns that Texas’s State Board of Education had raised about Alpha’s test scores, missing data, or AI applied. “That wasn’t relevant,” Travis Pillow, a spokesperson for the TEFA voucher program, told ProPublica. “Our role is to make sure that a school meets the requirements in the law.”
The five most expensive Alpha schools in Texas opted to leave the voucher program earlier this month, but many Alpha network schools are still participating, including Texas Sports Academy, Montessorium, NextGen Academy, Nova Academy, and Waypoint Academy. TEFA confirmed to me that students are attending Alpha affiliates via vouchers this year.
Anna Davlantes, a spokesperson for Alpha, told me that in lieu of vouchers for their most expensive campuses, her network is offering families financial aid.
Despite Alpha’s enthusiastic embrace in the White House, the introduction of AI in schools is increasingly challenged by forces on the right, from grassroots MAGA parents to social conservatives and technocrats.
The new AI rules in Florida, Oklahoma, and Utah, for example, grew out of a broader conservative push against screens in classrooms that predated the rise of chatbots and was turbocharged by parents’ frustrations with virtual learning during the pandemic.
Earlier this year Iowa capped elementary school screen time at an hour a day in a bill Gov. Kim Reynolds signed with Health and Human Services Secretary Robert F. Kennedy Jr. at her side. Tennessee passed a weakened version of a bill that would have banned devices in elementary schools outright, while similar bills in Missouri and Kansas died after school districts and teachers unions objected.
Uniting these efforts is a growing conviction that the last 15 years of education technology has failed most kids. The sponsor of Kansas’s bill used to sit on his state’s board of education and remembers thinking that ed tech held such promise. “What we found out is that not only is it not the education of the future, but it is destroying the education of the past,” he told Education Week. A growing number of lawmakers and parents have been citing The Digital Delusion, a book self-published in 2025 by neuroscientist Jared Cooney Horvath, that blames the proliferation of screens for declining student achievement. In January Sen. Ted Cruz (R-TX), who is otherwise one of the Senate’s leading opponents of AI regulation, invited Horvath to testify about the harms of AI on student learning and mental health.
The screen skeptics overlap with a more traditionalist wing of the GOP that objects to classroom technology on principle. Missouri’s original bill would have required cursive and kept 70 percent of student work on paper. The classical education movement, which holds up teachers as a moral example, has grown fast on the right. (Of the 895 identified classical schools nationwide, roughly one-third opened or converted to the model between 2020 and 2024, according to a Heritage Foundation tracker.)
The strand of conservatism that cuts closest to Alpha School comes from a third group, the school choice advocates. On paper, Alpha sounds like what the school reformers have wanted for years: It can offer an alternative to underperforming local public schools, its AI programs can theoretically compensate for inconsistent teacher quality, it provides personalized instruction, and it checks mastery on every lesson, so families can easily know whether their child is learning. Those are problems the education reform movement was launched to solve, and Alpha claims to fix them all.
The reformers, though, want proof. Mike Petrilli, who runs the conservative Thomas B. Fordham Institute think tank and has spent decades advocating for charter schools, wrote recently on his Substack that parents who want to pay for Alpha out-of-pocket should be able to, but “that doesn’t mean the rest of us shouldn’t be skeptical, or that we shouldn’t worry about fraud, or that we should allow public funding to flow to these school.”
This split was on display in DC this month at the education conference where Price appeared. Molly Hart, who runs Utah’s public schools and calls herself a school choice supporter, argued that once a school takes taxpayer money, “the game changes,” and parents can’t make a real choice without data about how it performs. Erika Donalds, who chairs the education center at the America First Policy Institute and is one of the Trump administration’s closest allies on schools, said parents, not the government, should decide which results matter. Donalds compared the choice to picking a restaurant on Yelp.
The evidence has started to catch up with parental concern. In a randomized trial involving nearly 1,000 students at a Turkish high school, those given access to a GPT-based math tutor that readily supplied answers scored 17 percent lower on a subsequent exam taken without it. A version designed to withhold answers eliminated that penalty but produced no statistically significant learning gain. A newer working paper tracking nearly 27,000 Chinese students found that adopting AI raised homework scores by 18 percent but lowered monthly exam scores by 20 percent within six months.
Alpha School is working to add more detail to that pile, and other studies will surely follow. It’s at least possible that some aspects of AI in education prove more promising with further study. But public skepticism will remain a major barrier, either way. On an issue where parties are still sorting themselves out, the White House and its allies are swimming against a growing anti-tech tide that extends far beyond just AI or even schools. That’s unlikely to change before 2028, and whoever wants the Republican nomination will have to say which parents they’re listening to.
Tech & AI
Microbes Could Survive On Saturn's Moon Enceladus
New research suggests Enceladus may be an especially promising place to search for extraterrestrial life: microbes similar to those found near Earth’s hydrothermal vents survived in lab conditions designed to mimic the Saturnian moon’s subsurface ocean. A separate study also found that material blasted from Enceladus’ plumes may naturally separate and concentrate salts, organics and potential biosignatures into individual ice grains, potentially making them easier for future spacecraft to detect.
“That is great news in the search for life,” Frank Postberg, lead author of one and co-author of the other of these new studies and professor at Freie Universitat Berlin, said in a statement. “Future spacecraft will have to analyze many individual ice particles in the plume. But if they come across one with microbial material in it, they could identify biosignatures in the particle relatively easy with already available technology.” Space.com reports: Enceladus isn’t the only place in our solar system with water — so, why is it so exciting in the search for life? Well, it has to do with the seafloor of its extensive, liquid ocean. Down deep at the bottom of this body of water, scientists think hydrothermal processes, or movement or reactions with hot water under the surface, are taking place. The plumes shooting upward from the ocean also contain trace amounts of salts and organic compounds. NASA’s Cassini spacecraft found these traces when it flew through the plumes over a decade ago. Between the hydrothermal activity and the organics and minerals in the water, this moon’s ocean has a number of aspects that could be involved in supporting life.
What’s more, using a combination of Cassini data, theoretical models and laboratory experimentation, in Postberg’s new study the team found that the plume’s water droplets blasting out into space at up to 621 miles per hour (1,000 kilometers per hour) don’t freeze as quickly as expected. Before, scientists thought the freeze would happen instantaneously once the droplets reached space, but Postberg and fellow researchers say they found the freezing would actually happen much slower.
They also found that during this freezing process, the salt, organic compounds (and maybe possible signs of life) in the water droplets separate from one another. Not only that, but the team says that as the particles are blasted out into space, they should often collide with the icy cracks of the planet’s surface. This ultimately would leave behind tiny shards of frozen droplets with individually separated out components. Essentially, it’s like the planet has organized its oceanic ingredients into tiny, frozen particle fragments. This work is described in two new studies published in the journal Science Advances here and here.
Read more of this story at Slashdot.
Tech & AI
Nothing’s new Headphone (1) Pro wants to rival Bose and Sony, not just look cool doing it
In context: Android smartphone maker Nothing has launched the Headphone (1) Pro, its new flagship audio product, which it claims combines an elevated listening experience for consumers with studio-grade production and mixing features for professionals. It succeeds last year’s Nothing Headphone (1), which TIME named one of its Best Inventions of 2025.
The biggest change in the new model is a three-driver audio system. It includes a bass dynamic driver designed to add more punch and sensitivity to low frequencies, a precision dynamic driver that adds more texture to vocals and instruments, and a new treble xMEMS driver that Nothing says delivers crisper highs.
Nothing also worked with London’s Metropolis Studios to tune the Headphone (1) Pro, with the collaboration focused on professional audio tools and soundstage characteristics.
The company also highlights an improved adaptive active noise cancellation system that uses a 10-microphone array to more accurately filter ambient noise than the first-gen model. With claimed noise reduction of up to 46 dB, Nothing is positioning the Headphone (1) Pro against premium rivals from Bose and Sony.
Nothing says the improvement in noise reduction should be particularly noticeable in strong winds or noisy traffic. The headphones also feature redesigned ear cushions with an 8mm silicone baffle wall inside, creating a stronger sound barrier and improving the seal around the ears.
The Headphone (1) Pro supports Hi-Res wireless audio and wired playback over USB-C at up to 24-bit/192 kHz. It also offers five modes of Dynamic Spatial Audio with head tracking, along with five new custom EQ profiles. A separate Flat EQ switch is designed to give producers and creators a more neutral sound profile.
The Headphones 1 Pro is built out of aluminum and titanium to provide a durable shell while creating a premium look and feel. It also retains Nothing’s transparent design language, using shatter- and scratch-resistant 9H Panda Glass on both ear cups to reveal parts of the triple-driver architecture.
Despite the increased use of metal, Nothing has reduced the headphones’ weight to 327 grams through a redesigned internal architecture, including replacing steel arms with titanium. Other changes intended to improve comfort over longer listening sessions include a wider headband with thicker padding and softer ear cushions.
The Headphone (1) Pro is positioned to compete with premium models such as the Sony WH-1000XM6, Bose QuietComfort Ultra, and Apple AirPods Max. It is priced at $399 and will be available beginning September 29 through Nothing’s official store at nothing.tech, Amazon, and Best Buy stores across across the US and Canada.
Tech & AI
How brain stimulation could impact how we develop new skills
Ned Jenkinson of the University of Birmingham and Matthew Weightman of the University of Oxford discuss how advancements in brain research might affect how we learn and grow our skillsets.
Whether learning a new piano piece or adapting your tennis serve, acquiring physical skills depends on your brain’s ability to strengthen and refine neural connections. Researchers are exploring whether this process can be accelerated with technology.
Scientists are particularly interested in the potential of non-invasive brain stimulation, a group of techniques that can alter brain activity without surgery.
Some deliver weak electrical currents to the brain through electrodes placed on the scalp. Others use magnetic fields or focused ultrasound waves. Although they work in different ways, they all aim to temporarily change the activity of neural circuits.
If these techniques can successfully enhance neuroplasticity, the brain’s ability to reorganise and form new connections during learning, then they could be of use anywhere where performance depends on learning complex movements, from sport and music to surgery and beyond. Researchers are also seeing if these technologies could help with learning non-physical skills, such as picking up a foreign language.
Elite sport, professional gaming and high-performance workplaces could all become targets for these enhancements if they prove effective. Brain stimulation could also have a big role to play in medicine, helping patients recover physical skills lost through injury or disease, such as stroke.
Studies suggest there’s a lot of potential here. But translating this potential into useful tech that reliably boosts learning physical skills remains a big challenge.
Stimulating findings
Research into enhancing motor learning with electric or magnetic stimulation has been gaining momentum since the turn of the millennium, with early studies garnering considerable excitement.
In a typical experiment, participants might learn a sequence of finger movements similar to practising scales on a piano while receiving stimulation over brain regions involved in movement. Other studies have examined how stimulation could be used for balance training or teaching sports-related skills or surgical techniques.
Some of these experiments produced eye-catching results, finding that participants learned certain movement tasks faster or retained skills for longer if they underwent brain stimulation. But other studies failed to find benefits. And in some cases, researchers struggled to replicate the success of earlier promising experiments when repeating them.
One reason for these mixed findings is that there’s no such thing as a universal ‘learning network’ in our brains. Different skills rely on different combinations of areas near the surface of the brain as well as those deep within it.
Additionally, people can respond very differently to the same stimulation. Factors such as age, anatomy, genetics and even baseline skill level may influence whether stimulation is beneficial. Add to that the infinite number of ways to apply stimulation, the picture becomes murkier.
Despite these challenges, the field continues to evolve in its quest to enhance motor learning. For instance, rather than broadly stimulating the brain, researchers are increasingly targeting specific neural circuits involved in learning.
This is partly thanks to advances in neuroimaging and computational modelling, which has allowed scientists to predict how electrical currents travel through a person’s brain. Newer brain stimulation technologies, such as focused ultrasound, can also now reach deep structures involved in skill acquisition.
The goal is to use these technologies not simply to increase brain activity, but to influence the right neural circuit at the right time during learning. This idea builds on a fundamental principle of neuroscience, often summarised as “neurons that fire together, wire together”. When brain cells are repeatedly activated at the same time, the connections between them become stronger.
By carefully timing stimulation to coincide with the movements made during practice, researchers hope to reinforce the neural pathways involved in learning a new skill. In principle, this could make stimulation more reliable and more effective than current approaches, but researchers are still fine tuning exactly how this would work.
Motoring ahead
Important questions remain. Who would have access? Should stimulation be regulated in competitive environments such as sport? And how much evidence should be required before consumer devices are marketed to healthy users?
These questions are becoming increasingly relevant as brain stimulation moves beyond the laboratory and clinic. A number of at-home devices are now available for people to buy. Some have received regulatory approval, as they’re indicated for treating medical conditions such as depression. But there’s also a growing market for devices for cognitive and performance enhancement. For these uses, no regulatory approval is needed.
The technology is advancing rapidly, but evidence to support it and regulations governing it are still trying to catch up. Proper frameworks for its adoption may simply be bypassed by the ready possibility of ‘DIY’ brain stimulation.
For now, brain stimulation is unlikely to transform anyone into an overnight virtuoso or elite athlete. But as researchers develop increasingly precise ways of targeting the neural circuits that underpin learning, the prospect of enhancing human performance is shifting from science fiction towards scientific possibility.
The challenge today is not simply learning how to influence the brain, but deciding where, when and why we should.
By Ned Jenkinson and Matthew Weightman
Ned Jenkinson is a senior lecturer in human movement sciences at the School of Sport and Exercise Sciences at the University of Birmingham. His research incorporates a range of techniques including non-invasive brain stimulation, electrophysiological recording, eye-tracking, neuroimaging and behavioural techniques. He uses these techniques to investigate how the brain controls movement and how it allows us to learn new motor skills.
Matthew Weightman is a postdoctoral researcher at the Oxford Centre for Integrative Neuroimaging in the Plasticity Group at the University of Oxford, led by Prof Heidi Johansen-Berg. He is broadly interested in the field of sensorimotor neuroscience. His current work focuses on the role of sleep to recovery after stroke. More specifically, he is interested in how we can improve sleep after a stroke, whether improved sleep in stroke patients relates to better functional recovery, and if physiological processes that occur during sleep can be enhanced post-stroke to boost consolidation.
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Tech & AI
Japan’s Keio confirms ransomware attack disrupted business systems
Keio Corporation (Keio), a major private railway operator in Japan, said its network was hit by a ransomware attack over the weekend, disrupting some of its business systems.
Following a system failure in the early hours of Saturday, the company confirmed the attack and shut down its network to prevent additional damage.
The company said it is investigating the extent of the impact and whether the attackers accessed any customer or business partner information.
Keio is a large Japanese railway operator with 85 km of track and 69 stations, as well as a separate hospitality business of 25 hotels. The company has over 2,200 employees and a reported annual revenue of about $2.6 billion.
“In the early hours of September 26, 2026, we confirmed a ransomware attack on our group’s servers. We have reported the incident to the police and are conducting an investigation into the attack’s route and damage with the cooperation of external experts,” Keio says.
The incident appears to have affected only the hospitality side of Keio’s business, not train operations.
A separate announcement published on the company’s Keio Plaza Hotel Tokyo website is warning of possible delays on some customer-facing services.
Local media outlets have reported that the cyberattack disrupted the firm’s payment systems.
At the time of writing, BleepingComputer could not find a ransomware group claiming the attack on Keio.
BleepingComputer has contacted the company to request more information about the incident, and we will update this post with their response once it reaches us.
Tokyo Metro has also disclosed a cyber incident over the weekend in which attackers gained unauthorized access to its systems and accessed 59,000 member email addresses.
Although both Keio and Tokyo Metro are Japanese railway operators, it is unclear if the organizations were targeted in a coordinated campaign by the same threat actor.
Tokyo Metro is a major transit operator that runs nine subway lines covering 195 km and 180 stations, carrying an average of 7 million passengers daily.
The company said the breached systems contained only email addresses and that it has already identified and closed the security weakness the attackers used in this case.
Join Mikko Hyppönen and security leaders from the NFL, CHANEL, and Atlassian for a two-hour digital summit on what AI-speed attacks change, what defenders should stop doing, and how to validate, decide, fix, and re-validate at machine speed.
Tech & AI
Microsoft may have just pushed Copilot into a completely different phase of its AI ambitions
- Home puts chats, delegated work, and Office documents inside one interface
- Code lets non-programmers describe software and have Copilot build it
- Autopilot can continue recurring work without waiting for another instruction
Microsoft has introduced a redesigned version of its Copilot AI platform which claims to combine chat, delegated work, and coding tools into a unified application experience for users.
The company says the update is meant to let individuals and organizations scale artificial intelligence across everyday tasks and long-term projects.
Three new capabilities anchor this release, including Home, Code and Autopilot, each aimed at a different kind of work.
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Home brings Chat and Cowork together
The Home tool brings together two existing modes, Chat for quick questions and Cowork for tasks users delegate entirely, under one shared starting point.
Word, Excel, and PowerPoint now operate inside this same interface, letting users draft documents, budgets, and presentations without switching applications.
In these documents, Copilot is now grounded in Fabric IQ, pulling context from more than 20 million semantic models built in Power BI.
Edits made by colleagues or by the assistant itself appear in real time, so progress stays synchronized across a shared file.
A new plugin registry will let organizations manage Microsoft, partner and custom-built plugins from one central catalog starting this month.
Independent developers and partners can also publish plugins once for use across multiple Copilot surfaces under the new registry arrangement.
The Code tool allows non-programmers to describe an app, tracker or dashboard in plain language and have it built automatically.
This feature runs on the same underlying technology used in GitHub Copilot and can be hosted within a company’s own systems.
Autopilot operates without constant prompting
Autopilot, the third addition, is a persistent agent capable of completing recurring work without needing a new instruction each time.
It can run supplier reviews or similar multi-step processes, build schedules, contact stakeholders, and follow up on outstanding items independently.
Because it operates continuously in the cloud, work can continue late at night or whenever a person’s attention shifts elsewhere.
A related feature called Today, entering private preview in October, will summarize missed messages and pending tasks across mail and chat.
Microsoft is also tying spending controls to these tools through a system it calls FinOps for AI, letting administrators track usage.
Administrators can set spending limits, approve credit requests and restrict which AI models different teams are permitted to use each month.
Everyday tasks like quick answers or first drafts run on a fixed-price subscription, while agentic features use usage-based billing.
Code, Cowork and Autopilot all fall under this usage-based pricing model, alongside frontier models Microsoft refers to as Astra and Fable.
Home and Code are set to roll out through Microsoft’s Frontier program within weeks, and Autopilot enters private preview by month’s end.
Microsoft has not released independent data showing how widely the three features are being adopted, how accurate they are, or how much time they actually save.
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Tech & AI
How To Get Started With Shortcuts On Your MacBook
Automate your Mac with ease using Shortcuts and Apple Intelligence.
Shortcuts has been available on Mac since macOS Monterey, but it’s one of those utilities many Mac users have never explored. At first, it can seem too complicated. You have to know which actions to choose and how to connect them, then hope it all works.
But macOS 27 Golden Gate changes that with Describe a Shortcut, which lets you type exactly what you need and have Shortcuts do the heavy work for you with AI. It doesn’t always work, but it makes the app much easier to use — especially if you’re not an expert.
If you’re not familiar with Shortcuts, it’s an automation tool where you create scripts to handle tasks on your devices. Apple’s own example is a shortcut that texts your spouse with an estimated arrival time based on traffic when you’re leaving work. But you can get much more complex, like a shortcut that checks your calendar and the weather to give you a summary of what to expect today.
There are many possibilities, and now with macOS 27, it’s much easier to master the app.
Creating shortcuts on your Mac is easy
Creating a new shortcut takes a few seconds. Open the Shortcuts app on your Mac and click the Plus button to enter a prompt. The more details you provide, the more likely the app is to get your shortcut right. A command like “Clean up my Downloads” might be too vague for the app to understand what you really want. Instead, try something like “Every Friday, move anything in my Downloads folder older than 30 days into a folder called Archive.” You’re more likely to end up with a working shortcut when you provide clear details.
This is a good example of how Shortcuts are helpful for tasks you often forget to do; no one really cleans out their Downloads folder unless they’re trying to free up space. Plus, you can check the result right away by opening the folder and looking at what moved. If the shortcut moved too much, re-enter the prompt with even more specific details.
A shortcut can also be great for summarizing long text with Apple Intelligence. Try something like “Take the text on my clipboard, summarize it in three sentences and save it to a new note.” Then copy a long article or an email, run the shortcut and you’ll have the short version in Notes.
Make your shortcuts easier to reach
If a shortcut isn’t part of your normal routine, chances are you’ll forget about it after a while. Thankfully, you can assign a keyboard combo to a shortcut, or pin it to the menu bar, so you’ll never forget it.
Choose the shortcut you want to adjust and click Edit. Go to the Shortcut Details menu (the one with the information icon) and select Add Keyboard Shortcut. To add it to the Control Center or menu bar, open Control Center on your Mac (at the top-right) and select Edit Controls. There, all you have to do is add the action from the Shortcuts app, and you’re all set. Exploring the Automation tab is also a good idea for creating a seamless workflow of shortcuts that run on their own when you need them.
Shortcuts isn’t the only element in macOS 27 that acts on your behalf. Visual Intelligence has its own key combo: Shift + Command + Space. After pressing this, select a window on-screen and have Siri answer questions about it or take action, like adding an event to your calendar. Try it on an email with a date buried in it, for example. Siri can also run your shortcuts via voice, speaking of which.
Shortcuts and Siri AI require Apple Intelligence, which means you need a Mac with an M1 chip or later — Intel Mac users are out of luck. Also, some limits may apply when using Apple’s AI models in Shortcuts. More complex prompts could reach a limit faster.
Tech & AI
Peak XV ups Surge seed investment ceiling to $5M, unveils 18-startup cohort
Peak XV Partners, one of the largest venture capital firms investing in markets including India and Southeast Asia with more than $10 billion in assets under management, has increased how much it invests per startup through Surge, its seed-stage investing platform, as it unveils a new cohort of 18 companies.
At least three of the companies in this cohort had already raised outside funding, in some cases from Peak XV itself, before joining Surge.
The new batch, called Surge 12, is the first to operate under Peak XV’s higher investment ceiling of up to $5 million per company, up from $3 million previously. The venture firm invested more than $50 million across the cohort, which has collectively raised over $90 million in seed funding, according to Peak XV. Its median investment per company has also increased, though the firm declined to disclose the figure.
“The bar to raise a Series A has gone up pretty significantly,” Rajan Anandan (pictured above), managing director at Peak XV, said in an interview. He added that the firm is also seeing more capital-intensive companies, particularly in deeptech, that are raising larger rounds at the seed stage.
Surge has become more global with each cohort, Anandan told TechCrunch, with its latest group spanning founders and companies from San Francisco to Sydney. Just five of the 18 startups in Surge 12 are focused on the Indian market, while more than half of the companies are based in India. The remaining 13 target global markets, highlighting the difference between where the companies are built and where they expect to find customers.
Since its launch in 2019, when Peak XV operated as Sequoia Capital India and Southeast Asia, Surge has backed more than 180 startups founded by entrepreneurs representing more than 18 nationalities. Peak XV says the 10 largest companies to emerge from those cohorts now generate more than $1 billion in combined annual revenue.

Anandan described Surge as one way Peak XV invests at the seed stage, alongside its standard seed investing, while the firm still remains an investor as companies progress through later funding rounds. The founders it backs typically include repeat entrepreneurs, experienced operators, and highly specialized technical founders, he said, with about 50% to 60% of a typical cohort made up of people coming from operating roles at established technology companies.
This cohort’s startups span AI, robotics, space, consumer products, healthcare, music, and fintech, ranging from AI safety and personal computing to autonomous robots built for underground pipes and satellites designed to detect radio-frequency signals from orbit.
The Surge 12 cohort
Alma — founded by Nischith Shadagopan M N and Vinod Ganesan — is building a personal computing platform focused on making computer use faster and more affordable. Its founders previously worked at Microsoft Research and were founding engineers at Sarvam AI, a Bengaluru-based startup building AI models for Indian languages.
August AI — founded by Anuruddh Mishra, an IIT-BHU alumnus who started the company in 2022 after a personal medical misdiagnosis — provides a healthcare platform that combines AI with physician-led care, reaching over 9 million users across 160 countries.
Ditto — founded by UC Berkeley dropouts Allen Wang and Eric Liu — works as an AI dating matchmaker inside iMessage, aimed at helping college students turn digital introductions into in-person connections. (TechCrunch wrote more about this one last month.) The company had already raised $9.2 million in a Peak XV-led seed round announced earlier this year.
GameStock — founded by Antoine Mistico, Easton Dana, and Vivek Indlebele Narasimha Prasad — brings competition mechanics to financial markets, turning investing and trading into a more competitive experience. Mistico is a two-time founder and former professional baseball player.
HiLoop — founded by Jad Ghalayini, Karan Brar, and Thomas Boser — helps AI companies adapt general-purpose open-weight models for specific applications using its post-training platform. Its founding team includes former Reducto engineers and a Cambridge computer science PhD who completed his doctorate at 24.
Hoola Health — founded by Deeksha Senguttuva — focuses on care for children and their families, providing consultations, vaccinations, medicines, diagnostics, developmental therapy, and dental services on a single platform. Senguttuvan grew up around healthcare, as her family built and operated a hospital group.
Kello — founded by Mona Gandhi and Subramanya Jingade — is building an AI-powered talent-discovery platform focused on identifying a candidate’s potential and trajectory rather than relying primarily on conventional credentials. Gandhi says she was Airbnb’s first female engineer and she previously founded Upraised, while Jingade previously co-founded AmbitionBox.
Kindling — founded by Adam Miller and Sachin Shah — is building what it calls a “storytelling operating system” for technology startups, using AI to help companies develop and produce their communications and content.
Puralink — founded by Harrison Crowe-Maxwell, Shyeon Delnawaz, and Thien “Long” Tran — is developing autonomous robots that can navigate underground pipe networks. Crowe-Maxwell has been building robots since childhood and turned university research into the patented drive technology behind the startup.
Reinforce Labs — founded by Anish Das Sarma — is developing tools to evaluate, red-team, and remediate enterprise AI systems. Sarma previously founded a company acquired by Airbnb and later served as a director at Google, where he led AI and machine-learning teams.
Riffle — founded by Anurag Choudhary and deo — is building a browser-based platform where musicians can create, collaborate on, and share music, reducing the need to move between separate tools during the creative process.
Rosella — founded by Chris Dwyer and Sean Stuart — is building an AI-native commercial insurance brokerage for U.S. businesses, using AI to automate parts of the traditionally manual process of finding and placing business insurance. Rosella raised a roughly $2.5 million pre-seed round led by Peak XV and Intact Private Capital earlier this year.
Tribe Money — founded by Himanshu Arora and Nikhil Shanker — gives an AI-powered personal finance platform that helps users track their money, research investments and make investing decisions.
ULOOK — founded by Adheesh Boratkar and Siddhesh Ravindra Naik — is building autonomous satellite systems for radio-frequency sensing and spectrum intelligence, targeting customers globally. Its founders have worked on more than 12 satellite missions. The company had already raised roughly $2.3 million in seed funding from growX Ventures and InfoEdge Ventures before joining Surge.
Wingit — founded by Nikunj Kothari and Saksham Khandelwal — is building a beauty platform aimed at India’s growing premium-consumer market. It is focused on how consumers discover and shop for higher-end beauty products.
Three other startups in the cohort have yet to publicly reveal their names or products. Peak XV said they are working in education, applied AI, and medical products.
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Tech & AI
The Metric Is Not The Mission: When The Maps Became The Territory
The Metric Is Not the Mission is a ten-part examination of how Big Tech moved from building and expanding the open internet to increasingly shaping it around its own metrics, incentives and assumptions. Across the series, the argument follows the evolution of the platform economy—from the optimism of the early internet to the growing tensions around power, prediction, geopolitics, accountability and the future of digital life.
The series will be published in two parts each week over five weeks, with each installment building on the one before it. At the end of the series, the complete essay will be brought together in a single PDF edition, providing the full argument in one place.

Part III: When the Maps Became the Territory
In Part II, the story turned on a crucial distinction: measuring behavior is not the same as understanding people. Part III takes that idea further, examining what happens when the platforms’ representations of the world begin to substitute for the world itself.
There is a curious tendency among successful technologies to disappear. Not physically, of course, but cognitively. Once they become sufficiently embedded in everyday life, they cease to be experienced as technologies at all. Electricity is no longer a marvel of engineering but an expectation. We do not admire the plumbing each time we turn on a tap, nor do we reflect on the extraordinary complexity of global logistics every time fresh fruit appears on supermarket shelves in the middle of winter. The greatest infrastructures become invisible because they succeed so completely that we mistake them for part of the natural order.
The internet reached that point sometime during the second decade of the twenty-first century. Yet something else happened along the way that proved far more consequential. As the network itself faded into the background, the platforms through which most people experienced it moved decisively into the foreground. Increasingly, users no longer spoke about “going online.” They spoke about opening an app.
That linguistic shift deserves more attention than it usually receives. Language often reveals structural change before statistics do. To “browse the web” implied movement across an open landscape whose boundaries were undefined. One followed links, discovered obscure websites, stumbled upon ideas that had not been recommended by anyone, and occasionally became gloriously lost. The experience resembled wandering through an unfamiliar city with no particular destination in mind. Serendipity was not a flaw in the architecture; it was one of its defining virtues.
Applications altered that relationship almost without anyone noticing. They replaced geography with destination. Instead of entering a network whose possibilities remained unknown, we entered environments that had already been organized on our behalf. The internet did not disappear, but it became increasingly hidden beneath layers of interface, recommendation and curation. Like passengers traveling through an airport without ever seeing the city beyond the terminal, we continued moving through digital space while encountering only the carefully managed environments that had been prepared for us.
This transformation is often described as an inevitable consequence of convenience. While accurate in its own right, this explanation offers an incomplete narrative. Convenience was certainly the language through which the platforms justified many of their design choices. Friction was treated as the great enemy of the digital age. Every additional click became an obstacle to be eliminated. Every decision that users might otherwise make for themselves could instead be anticipated by software. Recommendation replaced search. Autoplay replaced choice. Infinite scrolling replaced endings. The future, we were told, belonged to experiences so seamless that they would feel almost effortless. And they did.
It is difficult to criticize convenience because convenience is genuinely valuable. Few people wish to return to an internet in which finding information required memorizing obscure web addresses or navigating labyrinthine directories. The platforms did not succeed by forcing people into inferior experiences. They succeeded because, for many years, they built better ones.
Yet convenience has always carried an intellectual cost. Every technology that removes friction also removes moments of deliberation. The elevator spares us the staircase but also the awareness of distance. Satellite navigation ensures that we rarely become lost, while quietly diminishing our ability to construct mental maps of the places through which we travel. Streaming services relieve us of searching for entertainment, but in doing so they also shape the boundaries of what we are likely to discover. Every act of technological simplification transfers a small measure of agency from the individual to the system.
The internet had originally been built on a different assumption. Its underlying protocols did remarkably little. They did not decide which website deserved prominence, which ideas should travel furthest, or which communities ought to flourish. Their genius lay precisely in their restraint. They created conditions under which others could innovate without first requesting permission. The web itself functioned less like a product than like a constitutional order: a simple framework within which extraordinary diversity could emerge.
Platforms gradually adopted the opposite philosophy. They did not merely provide the rules of the game; increasingly, they became active participants in every interaction taking place within it. They selected what deserved attention, inferred what users might prefer before users themselves knew it, prioritized certain relationships over others and determined, through millions of microscopic computational decisions, the contours of everyday experience. The architecture became less constitutional than managerial.
There is an illuminating parallel here with the history of cities. The most enduring cities are rarely the ones that have been planned in every detail. They are those that accumulated layer upon layer of human activity over centuries, adapting continuously to changing needs without ever fully surrendering their unpredictability. One finds unexpected bookshops beside cafés, workshops hidden behind apartment blocks, public squares appropriated for demonstrations one week and festivals the next. Their vitality emerges not from perfect organization but from the freedom they grant people to appropriate space in ways that planners never anticipated.
Shopping malls operate according to an altogether different logic. They are meticulously designed environments in which every entrance, corridor, sightline, and seating area has been carefully considered. Music, lighting, and architecture work together to produce an experience that feels spontaneous while being anything but. There is comfort in their orderliness. They are clean, efficient, and reassuringly predictable. Yet no one mistakes a shopping mall for a city. Its purpose is not to cultivate civic life but to optimize a particular set of behaviors within a privately governed space.
The analogy is imperfect, as all analogies are, but it captures something essential about the transformation of the internet. The early web invited participation because it remained fundamentally unfinished. It assumed that users would contribute to shaping it. Today’s dominant platforms present themselves as complete worlds. Participation still exists, but it takes place within boundaries established elsewhere. Users generate the content while the architecture remains firmly in corporate hands.
Perhaps this is why the language of “community” has begun to feel strangely hollow. Communities, in the classical sense, are rarely designed. They emerge through shared experience, mutual obligation, and a degree of unpredictability that no algorithm can fully reproduce. Platforms, by contrast, increasingly treat community as an engineering problem to be optimized. They recommend friendships, suggest conversations, rank relevance, suppress friction, and amplify interaction according to models whose objectives are necessarily commercial because the organizations that develop them are commercial enterprises.
None of this should be understood as an accusation of bad faith. Many of the engineers responsible for these systems genuinely believed they were improving people’s lives. The difficulty lies elsewhere. Every large institution eventually begins to confuse the optimization of its own internal metrics with the fulfillment of its original purpose. Universities sometimes mistake publication counts for scholarship. Hospitals occasionally confuse efficiency with care. Governments become preoccupied with administrative process rather than public service. Technology companies are no different. The indicators that make sense within an organization slowly become proxies for the world outside it. The metric is not the mission. This is the point at which the maps begin to replace the territory.
The extraordinary quantities of behavioral data collected by digital platforms produce an understandable confidence. When one can observe billions of interactions each day, it becomes tempting to believe that society itself has become legible. Human behavior appears measurable, predictable and, increasingly, governable. The platform begins to resemble reality because so much of reality passes through the platform.
Yet the map is never the territory. It captures what can be measured, not everything that matters. A map records roads but not the reasons people travel. It identifies cities without conveying the lives unfolding within them. Likewise, recommendation systems observe behavior with astonishing precision while remaining largely indifferent to experience itself. They recognize patterns without necessarily understanding meaning.
That distinction mattered little while the platforms continued solving the problems that had made them indispensable. It becomes far more consequential once they begin confronting a world that no longer resembles the one for which they were originally designed. Because societies have changed; politics has changed; and, the internet has changed. The question is whether the companies that grew powerful by interpreting one era have noticed that another has already begun.
Konstantinos Komaitis, PhD, is a veteran of developing and analysing Internet policy to ensure an open and global Internet.
Filed Under: behavior, big tech, metric not mission, open internet, optimization, platforms, understanding
Tech & AI
Boox Announces the Picco, Its Smallest E-Reader Ever (2026)
While smartphones won’t stop getting bigger, e-readers seem to be getting smaller. Boox has been at the forefront with one of the most popular small e-readers, the Boox Palma, and now it is adding an even smaller model.
Boox announced the Picco, with preorders opening today. Its screen is just under 4 inches (3.97 to be exact), making it about the size of a playing card. It’s even smaller than the Xteink X4 Pro I tested earlier this year, which has a 4.3-inch screen (but just slightly larger than the 3.7-inch Xteink X3), and considerably smaller than the upcoming Boox Palma 3’s 6.19-inch screen. I liked the size of the Xteink in my hand, but navigating the interface and getting books were challenging, so I’m excited to see another option in that smaller size from a maker with more accessible ebooks (though still not as convenient as a Kindle or Kobo with their built-in stores).
The Picco will cost $100 and is expected to ship in November. I’ll be testing it soon, but in the meantime, here are the details if you’ve been eyeing a tiny e-reader.
An E-Reader for Productivity
Courtesy of Boox
The Boox Picco has a monochrome screen with a resolution of 235 pixels per inch and an adjustable front light that switches between warm- and cool-toned lighting. The microSD card slot supports up to 2 TB of flash memory storage (a 16 GB card is included). There are both a touchscreen and physical page-turning controls, thanks to the buttons on the side of the device. The case has a magnetic ring so you can attach it to the back of a smartphone, though I’ll have to see how well it fits when I test it, as I had mixed results attaching an Xteink to my phone due to both fit and magnet strength.
Courtesy of Boox
Boox says the Picco will have a streamlined operating system focused on reading and digital utility tools. It’s also the first in what Boox calls its Tiles lineup, which is how you’ll access ebooks on this device. You can also use web and USB-C file transfers (the Picco has Wi-Fi and Bluetooth connectivity) to get ebooks onto the Picco. The Picco also has the Pomodoro, Todo, and Countdown apps, so you can use it as both an e-reader and a productivity gadget—handy, and a bigger motivation to keep it attached to the back of your phone even when you aren’t reading.
I’m intrigued to see it in action. Boox’s most popular e-reader could become the Picco over the Palma 3, but we’ll have to wait for both devices to become available to see which is the better buy. Stay tuned for my reviews of both when they come out.
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Tech & AI
Discord Is Testing A Lightweight Mode To Free Up Resources While Gaming
But a Chromium-based design means it can only be so efficient.
Discord is working on a new mode for its social platform that it says might be less resource-intensive. Screenshots of an option called Game Mode began circulating on social media over the weekend. The description shown for the Game Mode toggle states that it will “Reduce Discord’s CPU and GPU usage while a game is running.” By making the chat platform less resource-intensive, concurrently running software should be able to run more smoothly.
Today, the company confirmed on X that this experimental mode will begin rolling out to its users next week. The brief official announcement about Game Mode added that Discord is “aiming to add more resource-saving features over time.”
Discord is based on the Electron web app framework, which uses Javascript and Chromium for creating software. The open-source Chromium, which is the basis for Google’s Chrome and several other browsers, is not known as the most efficient tool for web development. A feature like Game Mode could offer some performance improvements, especially while also running a beefy AAA game on the same machine, but there may only be so far that Discord will be able to streamline on its current architecture.
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