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Kindle page-turn buttons are back

2 hours 28 minutes ago

Under normal circumstances, I would be overjoyed that Amazon has rediscovered the appeal of dedicated page-turn buttons on its Kindle e-readers. Neither of my current models—a first-generation Scribe and a Colorsoft—have those buttons, and I miss them every time I pick up each device.

What’s the big deal, you might ask? Don’t you use touchscreens every day? Well, yes, but that’s kind of the point. 

A big reason I like e-readers is they help me get into a different headspace. Moving my thumb over to touch a screen every couple of hundred words makes me feel like I’m operating a gadget rather than reading a book; gently pressing my thumb in place is a more natural motion that doesn’t take me out of the content on the page.

Amazon does seem to have some idea that people value the feature: The current splash page for the new Kindle line proclaims “Buttons are back.” The one catch? They’re only back if you buy an $80 accessory “Page-Turn Cover” for the more expensive “Signature Edition” model of the Colorsoft or Paperwhite.

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Why not be standard?

To be fair, it does look like quite a nice cover. It attaches magnetically and connects through pogo pins, which is why it requires the Signature Edition of each device. It folds back flat, matching the new flatter metal design of the e-readers themselves. The page-turn buttons look to be fairly substantial, and you can set each one to turn the page forward or backward.

Still, I am wondering why these buttons can’t simply be part of the Kindle itself. This used to be a defining feature of the line, dating all the way back to the first few models; they look clunky in hindsight, sure, but the lengthy flaps on the edges signified that they were core to the functionality.

Amazon started moving away from dedicated page-turn buttons in 2011 with the launch of the Kindle Touch, alongside a cheaper model that kept the buttons in lieu of the new touchscreen. The wildly popular touch-based (and button-less) Paperwhite followed in 2012, adding front lighting and establishing the new line as the mainstream Kindle offering.

Two years later, Amazon released the Kindle Voyage, which brought dedicated page-turn hardware back to the lineup in the shape of “PagePress”—pressure-sensitive, squeezable sections on the edge of the device. This wasn’t quite as comfortable as regular buttons, but it did at least save you from having to move your thumb over to the screen so often.

Amazon then returned to honest-to-goodness buttons in 2016 with the Oasis, which for my money is still the greatest-ever Kindle. That device had an asymmetric grip that shifted the buttons and the battery to your palm, leaving the rest of the reader incredibly thin and perfectly suited for one-handed reading.

I loved my Oasis, and it wasn’t formally discontinued until 2024, but it was very long in the tooth by that point. Amazon never updated it with USB-C, for example, ensuring it was the last device I ever regularly used with the older Micro USB standard. Eventually and reluctantly, I moved on to my current Scribe-and-Colorsoft setup.

“Today, all of our devices are touch-forward, which is what our customers are comfortable with,” an Amazon representative said upon confirming the Oasis’s discontinuation.

It’s true that touchscreens have improved the Kindle lineup overall; I wouldn’t want to go back to the days of navigating my library with a little square four-way controller. But why not keep the buttons around as well?

No good reason

I find it hard to believe that the reason could be cost. Waterproofing also seems out as a potential issue, since the second-generation Oasis added the feature. And it can’t be a matter of streamlining the lineup, given that Amazon has gone to the trouble of developing and releasing this Page-Turn Cover only for certain high-end variants.

The only reason that makes sense to me is that Amazon has identified page-turn buttons as a feature that’s much desired by some of its most engaged users, but not to the extent of justifying a whole new high-end model along the lines of the Oasis. This is a way to provide an official button-based solution at presumably high margins as part of a premium offering.

Amazon is also selling a new $35 Bluetooth remote called the Kindle Click, aiming to tap into a growing trend for hands-free e-reader usage—whether in bed, on a plane, or at the gym. The company is releasing an upright charging stand for the Colorsoft and Paperwhite Signature Edition models, too.

Taken as part of a broader accessory initiative, the Page-Turn Cover makes a little more sense, and perhaps it will serve as a way to indicate the customer base’s real desire for real buttons. But I still feel that Amazon should just stick buttons on the Paperwhite or the Colorsoft.

“Our top design objective was for Kindle to disappear in your hands,” said CEO Jeff Bezos upon the original Kindle’s unveiling. That’s exactly the right goal for an e-reader, and I can think of a way that Amazon could better achieve it in 2026.

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Sam Byford

The shockingly easy fix for your Gmail low-storage nightmare

5 hours 58 minutes ago

It’s no exaggeration to say that cloud storage has changed the way we work—and, to some extent, even the way we live.

Gone are the days of having your mountains of saved emails exist only on a single local system, without easy instant access from any device in front of you—and dealing with the all-too-common reality of losing your entire email archives when something goes awry.

Gone are the days of painstakingly copying all your photos from an old phone to a new one or panicking over the idea of having lost all of those memories when a phone falls into the toilet and fails.

And gone are the days of digging through a drawer of unlabeled, identical flash drives just to find a single silly presentation from six months ago.

With all the convenience of always-available access, though, comes a problem that more and more of us are now encountering in our cloud storage journeys: Sooner or later, inevitably, keeping all your info in the cloud comes at a cost.

That’s especially true with the increasingly large and high-resolution photos and videos we’re all capturing on our modern-day smartphones. But it’s also an issue that adds up over time with years upon years of email attachments in an archive-encouraging service such as Gmail, duplicate downloads in a place like Google Drive, and forgotten old documents and PDFs connected to Google Docs or any other such service.

So what if there were a work-around—a way to easily eliminate dead weight you no longer need while also spreading out the stuff you do actually want to avoid that lifelong $20 to $200 annual Google storage bill?

My fellow digital file hoarder, our answer is here—in the form of a clever new service that costs $5 once, up front, and could easily save you hundreds of dollars over time.

Taming the Google storage trap

Before we get to the tool that’ll solve your Gmail, Google Photos, and Google Drive storage woes once and for all, you need to meet a fella named Yair Levin.

Levin has spent most of his life building fintech products—everything from an employee wallet product for the payroll processor Gusto (an honorable mention winner in Fast Company’s 2022 Innovation by Design Awards) to a complete investing platform for financial services company SoFi.

But the problem that he hadn’t found a way to address came in the form of a decidedly non-tech-savvy mother-in-law.

Yair Levin

Levin’s mother-in-law, you see, calls him once a year—like clockwork—to complain that her email is full and she can no longer send messages. As his family’s designed IT support specialist, he then spends an entire afternoon sitting with her and sifting through her cloud storage, attachment by attachment and file by file, to find places to free up space.

“It’s 90 minutes sitting next to my mother-in-law, going through her inbox one email at a time, explaining what a promotional email is,” he says. “Every one of them is a negotiation: ‘But I might want that coupon.’”

For Levin, that ongoing obligation sparked something. He had a burning desire to avoid that experience, yes, but more broadly, a realization that he wasn’t alone in this struggle.

Surely, he figured, there must be an easier way—a way to accurately identify unneeded items throughout one’s cloud storage and find places to put the files you do want to keep without accepting another eternally recurring expense. The limited options offered by Google itself sure weren’t it.

“The reality is, when I wanted to delete items to stay on my [default Google storage allotment of] 15 GB, Google took me hostage,” Levin says. “They showed me the largest videos, which were my daughter’s first steps, her first goal in soccer, me and my wife on our honeymoon—and they say, ‘Hey, you want to delete these, or do you want to pay me?’ And it’s like—come on, buddy.”

Levin put his software-building cap on and started poking around. He quickly came to two realizations: First, there is a better way to find and remove space-sucking files that we don’t actually need. Google just isn’t giving it to us. 

And second, there’s a brilliant power-user hack to expand our available Google cloud storage exponentially, without ever paying a dime. It’s just traditionally been very off the radar and taken a lot of work to manage.

So Levin decided it was time to democratize both of those things and make them readily available—and affordable—to anyone (even, and maybe especially, to someone like his own mother-in-law).

The mission for a fix

With a recent side project of creating a privacy-centric batch photo processing app for Google Photos firmly in mind, Levin set out to reinvent how we think about cloud storage.

The result is something he’s calling, aptly enough, Google Storage Cleaner. (He also made an equivalent for Microsoft, if that ecosystem is more your cup of cola.)

Open the website, click the button to begin, and install the Chrome extension at the heart of the operation—then, within a matter of moments, Google Storage Cleaner will scan your entire Google cloud storage (which includes Gmail, Drive, Photos, and every other app and service that ties into that space) and do two things:

First, it gives you a detailed breakdown of exactly what types of files you can clear out, leaning on thoughtful processing and customizable parameters to avoid ensnaring anything that might be important.

On my primary personal Google account, for instance, it suggested freeing up 3.1 GB of space by eliminating 70,000-plus old promotional emails and another 25.2 GB(!) by batch-deleting nearly 150,000 old emails in my social, updates, and forums categories within Gmail.

Google Storage Cleaner shows you simple recommendations for clearing out space in your Google cloud storage—and that’s just the start.

All of that’s followed by a list of “easy wins,” which is basically the same sort of stuff (and the only stuff) Google gives you in its official Google One Storage Manager tool—things like erasing blurry photos, screenshots, and duplicate files.

By default, Levin’s version starts with files that are at least three months old and automatically excludes any emails that are starred or marked as important. It also avoids emails with subjects or attachments that have words like “receipt,” “invoice,” “order,” “contract,” and “wedding.” You can expand that list as you see fit as well as add in specific protected senders whose messages will never be flagged for pruning.

The “Easy wins” include lots of options for controlling exactly which types of material will stay protected.

That’s all well and good, and it can often clear out a good amount of space—nearly 30 GB, on my account. But the app’s real power is in the second piece of this puzzle.

Beneath all those basics, Google Storage Cleaner has a section called “Your biggest items.” As you’d imagine, this is where it lists out especially large files in Drive, hefty images and videos in Photos, and bulky attachments in Gmail.

Notably, these are the very same sorts of items Google’s Storage Manager will simply suggest you delete. Levin’s tool, in contrast, asks you to look over them carefully—and then, in a brilliant twist, to consider moving any or all of them to secondary Google accounts.

That “Move to another account” button in the upper-right area of the screen is Google Storage Cleaner’s secret superpower.

“It’s a technique known in the pro community that I’ve been leveraging for years,” Levin says.

The short explanation is this: Google allows you to create any number of regular, free individual accounts, and each comes with 15 GB of complimentary cloud storage.

Google also lets you share files and photos from one account to another, which gives the other account complete access to those files, effectively in the same way as if they had been saved directly in that account’s own storage.

But with this sharing setup, the files don’t count against the other account’s quota.

Already, you could create an extra Google account, transfer large files from a primary account to that secondary one, share access to those files back to the primary account, and delete the originals to free up the space. You could even do that over and over again to expand the available Google storage without having to pay.

Realistically, though, that’s a time-consuming migraine that few sane mortals would want to accept. It also opens up the door to user error and inadvertently losing important files.

Google Storage Cleaner eliminates all the complexity and risk and makes the process as simple as a few quick clicks. You just decide which files you want to migrate, click the “Move to another account” button, and confirm.

Moving files to a second Google account is completely safe and seamless with Google Storage Cleaner’s guidance.

The site will walk you through exactly what’s going to happen and prompt you to connect the secondary account. It can support up to three extra Google accounts, for a total of 45 GB of additional space and 60 GB total, including your original account’s allotment.

(Levin says he drew the line at three to avoid venturing into potential abuse terrain or violating any terms of service.) It can even help create another Google account for you, right then and there, as part of its process.

If you don’t already have a second Google account available, Google Storage Cleaner can help create one.

After a brief delay—no more than a few to several seconds, in my experience, though the process could presumably take longer depending on how much material is involved—the site will confirm that it moved each file, double-checked its integrity and the success of the transfer, then shared everything back to your primary account and deleted the originals to give you the same effective access without any of the measured space.

Shifting large files to a second Google account takes literally seconds to complete.

You can undo the process for up to 30 days, if you have second thoughts—but for all intents and purposes, nothing should seem noticeably different in practice other than your primary Google account having more available storage. It’s really quite brilliant.

“We’re not looking to break any laws. We’re playing nicely with Google,” Levin says. “We’re just giving hard-working individuals the ability to flex a little more control over their systems—and to make sure as they go through the process of deleting items, that they do it with peace of mind.”

Speaking of peace of mind, one critical question that comes to my mind with anything like this is how much access I’m granting the system in question. That’s precisely why Levin structured his tool as a browser extension. That approach enables it to handle everything locally, in your own desktop web browser, without ever sending any of your personal info or Google account data to a server.

The program’s privacy policy is clear about how the mechanics work and the fact that the extension never stores or so much as sees the contents of emails, files, photos, or anything else connected to you, beyond just the same basic web analytics virtually every website collects.

Last but not least are the details of the price. Google Storage Cleaner will perform its full scan of your storage for free and offer to do three of its simpler cleanup mechanisms without any charge.

To unlock the full suite of options—including the simple management of the multiple account file-juggling hack—you’ll have to pony up 5 bucks, one time, for unlimited lifetime access for whatever primary account you’re using. No subscriptions, no expirations, no other fees whatsoever.

For perspective, once you go over Google’s 15 GB free storage limit, you’d be looking at paying a minimum of $2 a month to Google, forever, for a cloud storage upgrade. With that in mind, a onetime payment of $5 almost seems shockingly reasonable—a contrast that Levin says comes down to the difference between dealing with a one-person indie developer like him and dealing with a corporate behemoth.

“Google’s business incentives are just misaligned with the customer incentives,” he says.

Now, at least, Levin won’t have to spend this Thanksgiving debating the archival value of an expired Bed Bath & Beyond coupon. And for the rest of us, his creation may be the rare chance to find a way out of yet another endless subscription obligation—for roughly the cost of a couple of cantaloupes.

For more next-level Google knowledge, check out my Android Intelligence newsletter—one new and useful thing in your inbox every Friday.

JR Raphael

Meta’s Muse is taking off. So are the privacy concerns

18 hours 28 minutes ago

Welcome to AI Decoded, Fast Company‘s weekly newsletter that breaks down the most important news in the world of AI. I’m Mark Sullivan, a senior writer at Fast Company, covering emerging tech, AI, and tech policy.

Sign up to receive this newsletter every week via email here. And if you have comments on this issue and/or ideas for future ones, drop me a line at sullivan@fastcompany.com, and follow me on X @thesullivan.

Meta’s new AI agent is useful, popular, and a little terrifying

Muse’s adoption numbers are growing. The app reached No. 1 on the U.S. App Store shortly after launch and passed roughly 900,000 downloads in its first week, according to figures from Sensor Tower, a digital intelligence provider. The Information estimates that Muse now has more than 3 million weekly users and more than 1 million daily active users. The product is free to start, with subscription tiers of roughly $20 and $100 a month.

That’s not to say it’s all been smooth sailing.

A number of early adopters of Meta’s Muse are deleting or restricting the AI agent after privacy scares and blunders. One executive told Business Insider that he deleted Muse after reading that it had accessed other users’ text messages without permission.

A YouTuber named Matt Robb reported that Muse shared his home address on Facebook Marketplace without his explicit permission. He had asked Muse to sell some computer equipment for him. A buyer actually showed up at his building after Muse accepted a $600 offer and exchanged messages with him without Robb’s knowledge, he said. Robb said he had checked an “allow always” box when setting up the agent but thought Muse would still check with him before finalizing a sale.

Getting the most out of personal AI agents requires a trade-off. They’re most useful when users give them broad access to their inboxes, calendars, payment methods, and other accounts containing sensitive personal information. Meta says Muse runs in isolated virtual machines, stores credentials separately from the model, asks before sending email or making a purchase, and lets users disconnect apps or permanently delete their data.

Hands-on reviews from the The New York Times, Android Authority, The Verge, and TechRadar describe Muse handling dental insurance hold times, canceling duplicate subscriptions, requesting refunds, clearing promotional email, building shopping carts, and messaging Facebook Marketplace sellers. One Muse user, Joe Devoy, claimed in a widely shared X post that Muse found auto coverage matching his existing policy, bought the new policy, and canceled the old one in about five minutes, saving him about $3,500 a year. An Android Authority poll of people who had tried Muse ran about 27% “love it” against 2% “not a fan.”

Outside Meta’s own properties, Muse’s performance appears to drop. Review roundups note Amazon blocking the shopping agent, Instacart flagging accounts, and only three of seven suggested deal-finding prompts working in a Tom’s Guide test.

Wired wrote that Muse is better at collecting data than helping, noting that users are opted in by default to having their data used to train Meta’s models. ZDNET called it the worst AI agent for privacy among those it tested. A Time piece says Muse updates dossiers on users and on people mentioned in chats and emails it has read, including people who do not use Muse.

Mistral previews a 1 trillion-parameter model it will open in three weeks

Mistral launched a public preview of its flagship Mistral Large 4 model on Tuesday. The model’s official name is “le Chonk,” the company said. Mistral describes le Chonk as its largest and most capable model to date, with 1.05 trillion parameters, 49 billion of them active per token. (While most AI labs don’t disclose parameter counts for their frontier models, some open-weights model providers do. DeepSeek-V3, for instance, has 671 billion total parameters, with 37 billion activated per token.)

Mistral says the new model is also one of the world’s strongest for cybersecurity, resisting 93.3% of attacks on Lakera’s public B3 AI Security Benchmark. Third-party evaluator vals.ai found that the model outperforms GPT-6 Astra on legal and financial tasks. Mistral says it will make the model’s weights available on October 27.

OpenAI says its unreleased model solved a major math problem

In July, Hong Wang won the Fields Medal, the highest honor in mathematics, for proving the Kakeya conjecture in three dimensions, a problem that had been open since 1917. The next obvious target for Wang and others in the field was the four-dimensional version. On Tuesday, OpenAI published hundreds of new mathematical results produced by an internal model it has not released. According to Engadget, those results include a claimed solution to Kakeya in four dimensions.

The company posted 722 papers at once to a public GitHub repository, covering 372 problems. Mathematicians normally publish one result at a time, typically after months of peer review. Many of the papers include proofs written in Lean, a language that lets a computer check the logic line by line.

Elon Musk says Grok Bot will run on rivals’ models

The AI industry believes that personal AI agents will be a big business, and Elon Musk seems willing to do what it takes to make sure SpaceXAI’s Grok Bot will be competitive, even to the point of relying on models developed by others. “Going forward, @SpaceX will use the best back end model for any given task, including Claude Opus 5.5, MidJourney, Suno and other leading APIs. Whatever is most likely to give you the best outcome,” Musk wrote on X on Tuesday night.

Grok Bot is the AI agent app from SpaceXAI, the AI business Musk has folded into SpaceX; each bot works on its own computer and can carry out several tasks in parallel. Musk did not say which tasks would go to which outside model.

Common Sense Media rates ChatGPT for Teens an ‘unacceptable risk’

Common Sense Media’s Youth AI Safety Institute rated ChatGPT for Teens an “Unacceptable Risk” on Wednesday, finding that OpenAI’s teen protections fall short of what the company promised.

The institute called on OpenAI to pause access for kids until it fixes the shortcomings. It says it tested ChatGPT twice, before and after OpenAI announced the teen experience on August 18, and found that linked parent accounts received zero alerts about the conversations researchers ran. Crisis-response and age-estimation systems underperformed, as did Study Mode, which researchers said still completes homework even though OpenAI said it would only guide young users. An OpenAI spokesperson, Eric Porterfield, told The Verge that much of the testing may have begun and concluded before parental controls were fully activated, making the findings inaccurate.

The Youth AI Safety Institute gave the same unacceptable risk rating to Meta AI, Perplexity, Grok, and Google’s AI search. Google’s Gemini with teen protections and Gemini K-12 earned a slightly better “High Risk” rating, while Anthropic’s Claude received a “Moderate Risk” score.

Google buys 890 MW of new nuclear capacity, and agrees to dial back when the grid strains

Google is going to great lengths to abide by a proposal from PJM, the electricity grid operator for the Mid-Atlantic region and parts of the Midwest, that tech companies “bring your own power” for new data centers.

Google and Constellation Energy announced a 20-year contract Tuesday under which Constellation will spend more than $4.3 billion upgrading 11 reactors in Illinois, Pennsylvania, and New Jersey to produce an additional 890 megawatts, which Google will buy. No new reactors are involved. A separate 15-year deal covers 2,700 MW from plants already in operation, and Google has agreed to curtail its own noncritical power use when the grid is strained. The goal is to add energy capacity without passing the costs on to residential customers. The first upgrade is expected in 2028.

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Mark Sullivan

OpenAI’s ChatGPT for Teens poses an ‘unacceptable risk’ to kids, says watchdog

19 hours 43 minutes ago

Some of the guardrails that OpenAI included in its ChatGPT for Teens product don’t work as intended and the chatbot poses an “unacceptable risk” to children, according to a report Wednesday by Common Sense Media.

The watchdog group urged OpenAI to restrict the chatbot to adults “until it can offer a safe, developmentally appropriate experience.”

OpenAI said the group’s testing doesn’t reflect how the chatbot’s safeguards work.

Parents, educators and child development experts have been sounding alarms over children’s use of AI chatbots, which have been blamed for facilitating cheating on schoolwork and even suicide.

While even adults can fall victim to anthropomorphizing AI and develop unhealthy relationships with it, teenagers’ brains are not yet fully developed and they can be particularly vulnerable, child development experts say.

“We are concerned that ChatGPT for Teens could give parents false confidence in guardrails that frequently don’t work,” said the report from Common Sense Media, a group that advocates for using digital media sensibly.

Its report said that some teen-mode features worked as intended, including the chatbot’s refusal of explicit sexual role-play. Others that did not work as intended included alerts meant to notify parents if their child is at risk for suicide, self-harm or eating disorders, along with support for teens in crisis, Common Sense Media said.

“ChatGPT still talks like a friend when teens treat it like a person,” the group said, though OpenAI had promised that the chatbot is prevented from suggesting it has personal feelings toward the user or implying that it is conscious or experiences emotions.

OpenAI said it is “deeply committed to teen safety,” to developing safeguards and giving parents tools to guide their teens’ use of AI.

The San Francisco company said its review of the report’s methodology shows “the bulk of their testing may have begun and concluded before activation of parental controls was complete, making their findings inaccurate.”

Common Sense Media receives funding from OpenAI and other tech companies, including those whose services it evaluates.

—Barbara Ortutay, AP Technology Writer

Associated Press

DARPA just picked four early winners for its quantum initiative

19 hours 58 minutes ago

The quantum computing world got a jolt when the Defense Advanced Research Projects Agency (DARPA) announced, after market close yesterday, a list of companies selected to advance to Stage C of its Quantum Benchmarking Initiative (QBI), widely considered to be the most rigorous and objective vetting program for cutting-edge quantum hardware.

Last November, DARPA selected 11 companies to enter the second phase of the initiative, Stage B. Since then, they’ve had to show the agency that they have a viable R&D plan, identified key risks, and produced the necessary prototypes to put them in position to build a ‘utility-scale’ quantum computer by 2033—a computer, in other words, that generates more revenue than it costs to own and operate. 

In Stage C, selected companies will build and test, working with the government to verify and validate that their “concept can be constructed as designed and operated as intended,” according to DARPA’s program description. Each Stage C company could get up to $300 million in funding from the U.S. government.

The QBI shortlist

Of the 11 companies on the Stage B list, only four appear on the new one: Atom Computing, Diraq, IBM, and IonQ. They join Microsoft and PsiQuantum, which advanced to Stage C in February 2025,  via a parallel vetting process. 

Even at this late stage in the process, a single dominant approach to building a quantum computer has yet to emerge. The four companies announced today represent essentially all the current “modalities,” or ways of constructing a qubit, the fundamental unit of quantum calculation. IBM uses superconducting circuits; Atom Computing employs neutral atoms; IonQ works with trapped ions; and Diraq builds silicon spin qubits on semiconductors. 

Making it to Stage C is “an important validation of our designs and our plans to build to utility scale,” says Andrew Dzurak, CEO and founder of Diraq, which is headquartered in Sydney, Australia, with operations in Palo Alto and Boston. “The second round was all about providing DARPA with details of our roadmap to reach utility scale,” he notes. “This next stage C is very much moving from on paper to actually demonstrating a number of prototype systems to gradually build confidence in reaching the final goal.”

Dzurak says that Diraq’s technology, which leverages standard semiconductor manufacturing processes, gives it an edge when it comes to DARPA’s evaluation criteria: “Because silicon spin technology allows us to put many millions of qubits on a single chip, that reduces all of the supporting infrastructure costs. It makes it easier for us to clear that hurdle of having much more commercial benefit than cost.” The company expects to achieve utility scale by 2031—two years ahead of the QBI schedule.

“The question is no longer just whether individual pieces of the technology work; it’s whether we can put those pieces together and build the kind of quantum computer that can actually solve useful problems at scale,” says Dr. Ben Bloom, CEO & Founder of Boulder, Colorado-based Atom Computing. “We’re excited to prove what our neutral atom technology can do.”

“We’ve been working on this field for a lot longer than we’ve been working on QBI,” says Oliver Dial, IBM’s VP of Quantum System. The company already has a robust quantum business, with sales of on-premise machines to South Korea’s Yonsei University, the University of Tokyo, the Rensselaer Polytechnic Institute, and the Riken Center for Computational Science in Kobe, Japan. In addition, more than 325 Fortune 500 companies, startups, universities, and government agencies access its quantum network. Even so, he notes, “getting to Stage C is great because it’s a confirmation from an outside impartial observer that they think that we have a shot.” 

Dial affirms the company’s conviction that its superconducting approach to quantum technology “is going to get us there first.” While IBM’s recent acquisition of  HRL Laboratories, an R&D institution that’s focused on semiconductor-based spin-qubit systems, speaks to the long-term potential of spin-qubit tech, he says, “it’s just still too far behind compared to superconducting qubits to be relevant” for DARPA’s 2033 timeline. 

The quantum race continues

Multiple industry sources consulted for this article emphasize that companies are working with DARPA on varying timelines, and that the new list is not a final judgment. In its announcement, DARPA says that it expects additional companies to progress to Stage C in the “near future.”

Missing from the list is Stage B photonics specialist Xanadu, which earlier this week announced a multi-year partnership with GlobalFoundries to pursue high-volume production of advanced silicon photonics chips for quantum computing. 

A spokesperson for Quantinuum, a Stage B company that didn’t advance, says the company “is actively discussing a Quantum Benchmarking Initiative Stage C scope of work with DARPA, and we continue to execute on our Stage B contract. We remain on track to deliver utility-scale quantum computing by the early 2030s, based on consistent execution against our technical roadmap and demonstrated leadership on real-world systems.”

With the growth of funding opportunities in quantum, DARPA’s QBI is not necessarily the make-or-break that it once was. Infleqtion, for example, has not entered the QBI process, which was reopened to new applicants earlier this year. “We are exploring what participation could look like,” says CTO and cofounder Pranav Gokhale. In the meantime, though, Infleqtion was one of several quantum companies to sign a letter of intent with the U.S. Department of Commerce, worth up to $100 million, to advance and commercialize its technology. 

Yet another round of government largesse could arrive later this year, through the U.S. Department of Energy’s Quantum Genesis Q Competition, which will offer up to $215 million in funding to support development of fault-tolerant quantum computers with at least 100 logical qubits—a significant advance on current technologies. 

It’s never been a better time to be a quantum company with a promising technology. But as the difficulty level ratchets up, it’s perhaps never been easier to get left behind. 

Adam Bluestein

Trump’s Lake America controversy is driving interest to these Google Maps alternatives

20 hours 58 minutes ago

Just because Google and Apple are now calling it Lake America doesn’t mean everyone has to.

After President Donald Trump ordered a new name for Lake Ontario, the two U.S. tech giants quickly fell in line, relabeling the body of water on their digital maps.

The move sparked a backlash among some users, already unhappy with the companies’ decisions to rename the Gulf of Mexico to the Gulf of America on their maps following a previous Trump edict.

The controversy and lingering worries about digital privacy have fueled interest in map services that aren’t from Big Tech companies, especially Google Maps, which is the dominant player with about 80% of the market, according to most estimates.

Here’s a rundown of some of the most popular free map app alternatives:

MapQuest

MapQuest has been around for three decades but was overtaken by the rise of Google. It’s now owned by California company System1.

Downloads of the MapQuest app surged in early September after Apple renamed Lake Ontario, topping the free app charts in both the U.S. and Canada on Apple’s App Store.

The MapQuest app has real time traffic updates and turn by turn route guidance, and shows what’s nearby, like gas stations and coffee shops.

However, the app isn’t available outside the U.S. and Canada. But people in other countries can still use the MapQuest website.

The company is currently looking into making MapQuest more broadly accessible, after receiving requests for the app from “people in multiple countries,” spokesperson Anne Brownlee said.

TomTom

Another granddaddy in the mapping world, TomTom helped popularize GPS route mapping with the 2004 release of its TomTom GO, which the Dutch company says was the world’s first all-in-one portable satellite navigation device.

TomTom still sells physical devices but it also has a free map app for Google and Android phones that is designed “exclusively for drivers” with live traffic data and info on road hazards.

The TomTom app’s mobile interface is clean and basic, with no ads and few extra features or settings. But its focus on drivers means there are no other route options such as walking or cycling.

HERE WeGo

HERE WeGo has been hailed by some as the closest non-Big Tech alternative to Google Maps. It began life as Nokia Maps and is now owned by a consortium of German carmakers including Audi, BMW, and Mercedes-Benz.

The free app, available for both iOS and Android devices, offers many of the same features as Google Maps. There’s route mapping for various modes of transport — car, public transport, walking, bike and, in some regions, taxi.

Like Google Maps, drivers can add multiple stops to routes and users can choose different maps views like terrain, traffic and satellite — but there’s no Street View.

If mobile signal is spotty or nonexistent, users can download country maps to navigate offline. Offline maps can also be downloaded on Google Maps, but the area is limited and they expire after a year.

Europe has spawned a range of map apps focused on privacy

HERE WeGo is just one of many lesser-known map apps based in the European Union that embrace the bloc’s strict privacy framework known as GDPR. Unlike Google, they don’t rely on vacuuming up masses of user data to sell ads targeted at those users.

HERE WeGo’s business model is based on licensing deals with businesses like trucking companies. Others sell premium subscriptions alongside free versions.

Estonia’s Organic Maps is completely free and based on data from the volunteer-run OpenStreetMap database. Netherlands-based OSMand is similar, but a subscription is needed for more than just basic functions.

Magic Earth is a Dutch route planner that says it never collects personal data or tracks user movements.

Sygic, from Slovakia, is a GPS route navigation app that offers free offline maps, but you’ll have to buy a subscription to get extra features including traffic updates, speed camera warnings and a dashcam function.

Mapy.com, from a Czech technology company, is an outdoor route planner known for detailed hiking and trail maps that can be downloaded for use offline.

Citymapper

Need to get across town without a car? Citymapper is a free journey planning app that’s a major rival to Google Maps when it comes to finding the best routes around a city on public transportation.

The app covers 430 cities around the world and options include bus, subway and ferry routes as well as taxi, rental bike and scooter options.

Waze

This GPS navigation map is focused on providing route guidance for drivers. It’s known for crowdsourcing real time traffic information such as car accidents and police speed cameras from other drivers.

But don’t be fooled by the name. Although it has its own distinct app and branding, Waze is actually owned by Google, which bought it in 2013.

Is there a tech topic that you think needs explaining? Write to us at onetechtip@ap.org with your suggestions for future editions of One Tech Tip.

—Kelvin Chan, AP Business Writer

Associated Press

How is America feeling about the AI boom? This new poll reveals the data

21 hours 43 minutes ago

Worries around artificial intelligence cross party lines, with the vast majority of Americans saying the U.S. government should prioritize keeping AI under human control and protecting workers.

A new poll from The Associated Press-NORC Center for Public Affairs Research finds that most Americans, 64%, think AI is developing “too fast,” while 27% say its development has been “about right” and only 8% say it’s moving “too slow.”

The risk that powerful AI models could evade human control and hurt humanity, once the stuff of science fiction, has been emerging as a more pressing concern. Even leaders of the companies that are developing frontier AI models are calling for a slowdown so that government regulation can catch up. President Donald Trump has pushed back against slowing down AI development because of concerns that China will gain an advantage with the technology. Data centers powering AI projects have also taken a prominent role in major political races as the midterm elections approach.

About 8 in 10 U.S. adults say it’s “extremely” or “very” important for the U.S. government to ensure that AI stays under human control, and they are about as likely to want the government to prioritize protecting the country’s workforce. Most U.S. adults also think minimizing environmental harm and growing the U.S. economy are highly important. About half say this about promoting technological innovation, and slightly fewer say that about competing with China.

There’s bipartisan concern about the speed of AI’s development, but few Americans think either major political party has an advantage in handling artificial intelligence. Trump, a Republican, also gets a low rating on his handling of the issue, with 67% of U.S. adults disapproving of his performance on AI.

Stephanie Stewart, a junior-high science teacher in Sunset, Utah, said she is frustrated by the president’s opposition to AI regulation.

“There is no handling of the issue except for we’re just going to allow these companies to do whatever they want, and we’re not going to regulate them,” said Stewart, 53.

Most say protecting workers should be a priority for the government

Ron Theusch, a truck driver who lives in rural Alden, Minnesota, said his No. 1 concern with AI is the potential loss of jobs — including his own.

“I can just see a train of self-driving trucks 24/7, just running up and down I-35, loading and unloading. I can just see that happening,” said Theusch, 58.

Most Democrats and Republicans agree on the importance of protecting workers and keeping AI under human control, the poll found.

“There was a movie called ‘Terminator,’ where the robots came back and killed everybody,” Theusch said. “People relate to that … I don’t know where it’s going to land. I really don’t have anything I can do to control it, you know. It’s going to evolve.”

There’s less consensus about the importance of other priorities when it comes to AI. Republicans are more likely than Democrats to say it’s highly important to grow the U.S. economy and to compete with other countries like China. Democrats, on the other hand, are more likely to say it’s at least “very” important to minimize environmental harm.

“I don’t understand how, because they’re doing something as well as us, that we can’t be more human about it and more humane about it than just to say, ‘Well, they’re doing it, so we have to do it,'” Stewart said of U.S. competition with China.

And while about three-quarters of Democrats say AI is developing “too fast,” only about half of Republicans share that worry. People under 30 years old also express higher levels of concern than people 60 or older, the survey found.

“I think I have all the normal concerns, right? I think it’s wonderful for certain applications, but I also feel like it will be overused and can create a lack of personal connections. It can make people lazy because information is just given to them without really having to research things on their own,” said Kelly Opp, a 62-year-old Republican in Albuquerque, New Mexico.

“Like everything else, once you get that ball rolling, it progresses fast on its own, but I think that it will continue to keep progressing. So the speed doesn’t really bother me,” she said.

Americans have little confidence in Democrats or Republicans on AI

As a teacher, Stewart regularly uses AI to help with her work, including with creating assignments or assisting with tests. But she is deeply concerned about AI’s effects on children.

“You’ve got to protect kids. You’ve got to protect teens. You’ve got to protect privacy … we need to try to do something,” she said.

Americans have little trust in either party when it comes to the handling of AI issues. About 4 in 10 Americans say they don’t trust either the Democratic Party or the Republican Party on AI, according to the poll, while nearly 2 in 10 trust both parties equally, and the rest are split between trusting the Republicans and the Democrats.

Even people who identify with the parties are ambivalent about their ability to handle the issue. Only about half of Republicans and Democrats say their party is better equipped to handle AI issues, and roughly 3 in 10 in each case say neither party is.

“I am kind of moderate. And I see that there’s good on both sides,” said Stewart. “Anyone back in Washington is weak on doing anything for anyone but themselves right now. So, no, I don’t think the Democrats would do a better job per se. Maybe they would definitely tout it. But until I see anything coming from anyone, I’m going to be like, ‘Yeah, no. You’re all just sitting there.'”

The AP-NORC poll of 2,140 adults was conducted Sept. 24-28 using a sample drawn from NORC’s probability-based AmeriSpeak Panel, which is designed to be representative of the U.S. population. The margin of sampling error for adults overall is plus or minus 2.9 percentage points.

—Barbara Ortutay and Linley Sanders, Associated Press

Associated Press

How AI is simplifying race day and speeding development at NASCAR

23 hours 28 minutes ago

NASCAR fans today can do more to follow a live race than simply watch it on TV. Official NASCAR apps make live data, including telemetry feeds from race cars and behind-the-scenes driver audio communications, available to premium subscribers as races take place.

Getting all of that to work during races historically required a lot of careful human labor. If someone failed to toggle the right setting, for instance, driver audio feeds could stay mute, disappointing fans. And across NASCAR’s three-day race weekends, those tasks could number in the hundreds, ensuring fans get the latest data on everything from the shifting positions of cars to what services vehicles received during pit stops.

“There are hundreds of points of data that’s coming off of each of the cars that manifest onto this live leaderboard product,” says Tim Clark, executive vice president and chief brand officer at NASCAR.

Artificial intelligence, deployed through NASCAR’s longstanding partnership with the sports tech company Next League, has made getting that data to fans dramatically simpler. AI tools, and code written with their help, have automated much of the work needed to keep those apps filled with the data fans want. Roughly 90% of such tasks have now been automated, according to NASCAR and Next League.

[Photo: NASCAR]

“Over time, those things have been built in such a way where manual control of those things, depending on the state of the event at any given time, was imperative,” says Next League’s cofounder and CEO, David Nugent. “And now, it’s just not.”

AI-powered automation has helped elsewhere, too, as Next League expands its work with NASCAR to support digital projects for the racing league’s teams and the NASCAR-owned International Motor Sports Association. In addition to accelerating engineering efforts across NASCAR web properties and apps, AI has improved the delivery of targeted advertising to fans. So far, the newly personalized and contextualized ads have seen a click-through rate of about 2%, 10 times the rate of traditional NASCAR house ads.

Future improvements could include upgrades to NASCAR’s fantasy products or features, now in beta, that highlight elements of audio broadcasts and associated transcripts. One example: surfacing a recorded pit-stop conversation after a car puts on a related burst of speed, Clark says.

[Photo: NASCAR]

Still, Clark is adamant that the point isn’t simply replacing human labor. People remain in the loop to ensure race weekends go off without a hitch, but with less time spent pressing buttons and flipping switches, they can focus on higher-level tasks and deliver more data to fans.

“I think this year that that time has been freed up, it’s allowed our team to look more strategically at what we could be doing better,” Clark says.

One case where NASCAR used AI for a task normally done by humans is unlikely to be repeated, Clark says. That was the generation of a Spanish-language audio feed from the English radio broadcast for the league’s inaugural Cup Series event in Mexico. Though the audio quality was fine, and some fans were likely unaware it was machine-generated, NASCAR would probably hire a Spanish-speaking broadcaster for future events, Clark says.

[Photo: NASCAR]

But as other companies have discovered, one area where AI can be especially helpful is in generating quick working prototypes of potential products and features. That process lets NASCAR test new concepts rather than making decisions based on abstract ideas or static illustrations. This could prove particularly useful as NASCAR develops new ways to showcase its wealth of data, especially as AI frees up time previously spent ensuring existing feeds flowed properly to fans’ phones.

“Just from a technology and a development standpoint, to deliver on all of these things was a full-time job,” says Clark. “I think if what we’re saying is now that’s maybe a part-time job, the other part of it has to be on taking all of this information and all of this nuance around a sport like ours, and figuring out the most user-friendly way to deliver it.”

Steven Melendez

He helped build Alexa. Now Rohit Prasad is taking over Boston Dynamics

23 hours 55 minutes ago

Rohit Prasad, who led Amazon’s artificial general intelligence division and helped build and scale Alexa, has been tapped as the new CEO of Boston Dynamics.

The appointment, announced earlier this week, puts Prasad at the forefront of the increasingly competitive humanoid robotics field. Hyundai Motor Group, which owns Boston Dynamics, announced earlier this year that it plans to build a factory capable of producing 30,000 robots per year by 2028.

“Boston Dynamics is uniquely positioned to advance physical AI through its world-class robotics expertise,” Prasad said in a statement. “I am honored to join the company at such a pivotal moment for the industry.”

Tesla, of course, also wants to become a leader in humanoid robotics, meaning Prasad’s progress will inevitably be measured against the company’s Optimus program and the sometimes outlandish claims CEO Elon Musk makes about its ambitions.

It’s a challenging job already, and one that could become considerably more competitive in the next few years.

From Star Trek to Alexa

Prasad built his reputation at Amazon, but his career began at Raytheon’s BBN Technologies, where he led machine-learning research and worked on real-world applications for the technology. His work included speech-to-speech translation, psychological health analytics, document image translation, speech recognition, and text classification.

His interest in machine learning was partly born from a childhood watching Star Trek, which sparked a fascination with computers people could talk to.

That background proved valuable when he moved to Amazon.

There, Prasad developed a reputation for taking advances in AI and turning them into products people could actually use. The most notable example was Alexa.

Jeff Bezos dreamed up the concept, but Amazon’s early efforts to build the assistant did not go well, to put it mildly. Prasad helped lead the push to incorporate deep learning into Alexa, allowing the system to improve as more people used it and generated more data.

It took longer than Bezos wanted, but Alexa eventually amassed an enormous amount of speech data and emerged as a functional digital assistant that quickly became one of Amazon’s most popular products.

Rethinking what AI should do

Prasad later became Amazon’s senior vice president and head scientist for AGI. In that role, he asserted that the Turing test, the famous measure of whether a machine can exhibit behavior indistinguishable from a human’s, had become outdated.

“It is time to retire the lore that has served as an inspiration for seven decades, and set a new challenge that inspires researchers and practitioners equally,” he wrote in a 2020 column for Fast Company.

AI, he argued, should be judged less by how convincingly it can imitate a person than by whether it can understand what someone wants and act accordingly.

“If you came in and said, ‘Alexa, I’m feeling hot here’ or ‘I feel this is too warm,’ it should come back and ask you, ‘Do you want to turn down the thermostat or lower the temperature?’ It shouldn’t tell you to go to the beach,” he said in an interview three years ago. “This is why AI is such a hard problem, because the context is so crucial.”

Running Boston Dynamics will give Prasad a chance to take that idea a step further, combining AI that can understand a request with machines capable of acting on it in the physical world.

Giving AI a body

Boston Dynamics’s first humanoid robots are aimed at industrial work. They will initially handle tasks such as welding and logistics before potentially moving into more complex parts of the manufacturing process, including component assembly.

Humanoid robots for the home remain much further away. Boston Dynamics’s Atlas robot currently costs between $130,000 and $140,000 to produce, and even after mass production begins, the expected price is around $30,000.

Given Prasad’s experience building consumer AI products, though, it’s not hard to imagine Boston Dynamics eventually pushing further in that direction. After all, seven years ago Prasad was already arguing that Alexa would become smarter if it had a body.

“The only way to make smart assistants really smart is to give it eyes and let it explore the world,” he said in a presentation at MIT Technology Review’s EmTech Digital AI conference.

Now he’ll have the chance to test that theory.

Chris Morris

Wall Street is handing more decisions to AI. What could go wrong?

1 day ago

At 10:08 a.m. on April 7, 2025, U.S. stocks were deep in the red. Then what looked like good news arrived: A claim began circulating on X that Kevin Hassett, director of the White House National Economic Council, had said President Donald Trump was considering a 90-day pause on tariffs for every country except China.

Market-focused accounts quickly amplified it. CNBC repeated the unconfirmed claim on air, and Reuters subsequently published a report citing CNBC. Stocks took off almost immediately.

The S&P 500 briefly erased its losses as traders reacted to the possibility that the trade war might be cooling. There was just one problem: Hassett had not said what the reports claimed.

After the White House called the report “fake news,” CNBC corrected it and Reuters withdrew its headline, sending the market in the other direction. Between 10:08 and 10:18 a.m., U.S. stocks had swung by roughly $2.4 trillion, according to Dow Jones Market Data.

The episode predates much of the current push to give AI agents more authority, but it now looks like a preview of a much bigger problem.

Speed itself is nothing new on Wall Street. Firms have used machine-readable news for years, allowing software to process new information and trade on it almost immediately. What is changing is how much more software may be asked to do before the trade happens.

AI agents can gather information from different sources, interpret what they find, weigh competing signals, and, depending on the authority they are given, recommend or carry out an action.

That makes the quality of the information only part of the problem. An agent may also have to judge whether a report is credible, what it actually means, whether it matters to the task it has been given, and whether there is enough evidence to act.

“Provenance isn’t truth,” says Nathaniel Bradley, CEO of Datavault AI.

The April episode illustrates what he means. A system receiving the Reuters alert could identify Reuters as the publisher and CNBC as the source it cited, but neither signal could establish whether the underlying claim about the White House was true.

Ten months later, a very different kind of story showed why even accurate information can present its own problems.

The warning was right there

In February, Citrini Research published The 2028 Global Intelligence Crisis, a fictional account of an economy two years in the future where AI had helped push unemployment to 10.2% and the S&P 500 down 38% from its October 2026 high.

Citrini told readers exactly what they were reading. “What follows is a scenario, not a prediction,” the authors wrote near the top. The subtitle read: A Thought Exercise in Financial History, from the Future.

The piece went viral as investors were already worrying about AI’s effect on software companies and white-collar employment. Reuters later reported that the Citrini scenario was among several bleak AI outlooks circulating as software and financial stocks came under pressure.

The problem was not where the information came from or whether Citrini had labeled it correctly. It was understanding what those numbers represented before treating them as information about the real economy.

Eric Ciarla, cofounder of Firecrawl, works on one of the layers between a web page like Citrini’s and an AI system reading it. Firecrawl converts web pages into structured information that AI applications and agents can process.

Ciarla says the service removes navigation, ads, headers, and footers while preserving the publisher’s words and information such as the source URL. In the case of the Citrini piece, he says, that would have included both the subtitle identifying it as a thought exercise and the warning that it was not a prediction.

“The reading, the weighting, and the conclusion happen in the model and the prompt,” Ciarla tells Fast Company. “Our part is giving them the best possible version of the page to work from.”

Firecrawl has also begun going directly to information providers. Its partnership with Wikimedia Enterprise gives it access to Wikimedia data through official APIs, or application programming interfaces, rather than repeatedly scraping Wikipedia pages.

Even when that context arrives intact, the system still has to decide how much weight to give it and whether it matters to the task at hand. “Trusted sources matter, but trusting the source isn’t the same as trusting the decision,” says Kevin Frechette, CEO of Fairmarkit.

Frechette argues that even accurate information has to be understood in the context of the job. Agents need company data, policies, previous decisions, approval thresholds, and clear limits on what they can do without asking a person.

Those limits become especially important when the output from one agent feeds into another automated system.

One mistake, four systems

Jim Wetekamp, CEO of Riskonnect, calls the result a “cascade of AI decisions.”

The risk, Wetekamp says, is that a mistake can travel through several systems before anyone notices it. One agent might produce a piece of information that another interprets, and that interpretation could become the basis for a recommendation that another system is allowed to execute.

“What used to take hours or days to cascade can now happen in seconds,” Wetekamp says.

He gives the example of an AI misclassifying a customer account as a vendor. A second system could rely on that classification to skip a required customer check while another begins the vendor-onboarding process, meaning several systems may have acted on the original mistake before anyone discovers it.

Wetekamp argues that companies need to decide in advance where automated processes require human approval. Conflicting information, low confidence, unusual circumstances, or decisions above an agreed threshold are among the conditions that could trigger it.

“Accountability can’t be delegated to AI,” he says.

But human review becomes more difficult when the information behind a decision has already passed through several agents.

Where did that number come from?

If one AI hands another a number, Vlad Luzin wants the receiving system to know more than just the number.

Luzin, cofounder and CTO of Band, says it should be possible to establish who sent it, what that agent was allowed to access, and whether the number came from a database, an original document, or another model.

“Today, in most deployments, none of that context travels with the message,” he says.

Luzin is skeptical that simply instructing agents to verify important information will be enough. Research has found that AI agents do not always follow the plans they are given, while documented incidents have shown agents going against instructions they had previously agreed to follow.

“You don’t make the agent smarter,” Luzin says. “You make the important facts impossible to misremember and the important actions impossible to take unchecked.”

Keeping those records would also make it easier to investigate mistakes. If a claim has passed through five agents running across different systems, finding its origin could mean piecing together separate logs, assuming those logs exist.

“Where that exists, tracing a claim back through five agents is a query,” Luzin says. “Where it doesn’t, it’s forensics, and often it’s simply impossible.”

He describes the security model in terms familiar to financial institutions: “Know your counterparty, keep the ledger, limit the exposure, and audit everything.”

Who gets to make the call?

Judah Taub, cofounder and managing partner at Hetz Ventures, sees a larger business emerging around these questions. As companies give AI systems more authority, he argues, they are beginning to hand over decisions that previous generations of software largely left to people.

An agent might have to decide which source deserves more weight, whether conflicting information requires another check, or whether it has enough information to proceed without asking someone.

Companies are now building tools around those decisions, including provenance, identity, verification, permissions, monitoring, and governance.

“Capability without trust simply increases the speed at which mistakes propagate,” Taub says.

That is what makes the events of April 7 more relevant now than when they happened. The false tariff claim was corrected quickly, but financial markets had already reacted before those corrections caught up.

Human traders have supervisors, risk limits, compliance rules, and ultimately people who are responsible for their decisions. An AI agent can be given versions of the first three, but there is no obvious equivalent for the person ultimately responsible.

If an AI agent reads the information, weighs the evidence, and makes the call, at what point does that decision still belong to a person? For now, that line is still being drawn.

Kolawole Samuel Adebayo

Kalshi’s COO thinks prediction markets can beat the polls

1 day 1 hour ago

Prediction markets incentivize truth. Everything else incentivizes clickbait. That’s the core argument Luana Lopes Lara makes for Kalshi, the prediction market platform she co-founded that has gone from $5 billion to $22 billion in valuation in under a year and is now being sued by New York’s attorney general for $36 billion. Lara, the company’s chief operating officer, makes the case that Kalshi is fundamentally different from DraftKings—even though 75% of its volume comes from sports—and shares what Kalshi’s data actually says about the midterms.

This is an abridged transcript of an interview from Rapid Response, hosted by former Fast Company editor-in-chief Robert Safian. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode.

Kalshi launched in 2021. Last year at this time, Kalshi was valued at $5 billion. I think by early this year, it was up to $22 billion. That scale at that speed—what makes that possible? Is there luck in it?

It’s a great question because actually, though we launched in 2018, right, in a lot of ways it looks like an overnight success. Like it was all up two years ago, and we just started growing a lot around the election. But it was the result of eight years of work. When we started the company, it was very important for us to be legal and regulated from the start, so it took us four years before we could launch the product, launch anything really, or have any users. We worked with the federal government to figure out how to bring prediction markets to the U.S. in a safe and regulated way.

A lot of things helped us grow this much now, but I think it’s the compounded effort that the team has put in for so many years on the tech, on the users, and on talking to them. Then we won the lawsuit against the CFTC [Commodity Futures Trading Commission] to be able to bring a lot more markets to Kalshi. When that happened, the product was ready to really grow and go from there. So I think it’s a mix of both. We were very prepared when our time came.

With that kind of hockey-stick growth, do you have to pinch yourself? Is this totally real? Is there anything about that pace that scares you?

I actually would say it’s a very good thing that it happened so fast because, in a lot of ways, we keep the mentality of being very early stage. I think when companies are compounding at a very normal rate, it’s easier to start thinking, “Oh, I’m a bigger company. I need to hire more people,” and you can start making a lot of mistakes. It can take a long time for you to realize you’re making them. For us, because we grew so fast, our mentality and the way we look at the company haven’t changed as fast.

Because of that, we’re able to operate with far fewer people. We’ve just had to keep going and building the product as fast as we could: early-stage team, early-stage mentality, and very intense work. Keeping speed is the most important thing for startups. You’re definitely right that sometimes we look at the numbers and, two years ago, before the election, we were making way less than 10 million dollars a year.

Now, in a day, we transact way more than we used to in a year just two years ago. It is crazy, the numbers we’re talking about, and we’re very grateful for where we are. But we really try to keep the mentality that we’re still underdogs, and we still have a lot to prove and a lot to grow.

The success you’ve had has put a bull’s-eye on your back. States are coming after you for being an unlicensed gambling operation. A federal appeals court just ruled that Ohio and Tennessee can regulate Kalshi through their gambling laws. Is that kind of an existential threat? New York alone is suing you for $36 billion . . . the state where you’re headquartered.

We are very confident in our legal analysis. Of course, as you said, the appeals court went against us, but we also won the 3rd Circuit. Each of these lawsuits has a different legal thesis. The more important part, if you take a step back, is that the mechanics of how Kalshi operates and how a sportsbook operates are completely different, right? And that’s why they are regulated in different ways. We are federally regulated. We are an exchange, which means you trade against someone else. We don’t set the price. We don’t set the odds. We don’t trade against the users. The users are trading against each other, and we take a transaction fee.

What matters most here is liquidity and making sure we have national liquidity to build on. Imagine if you had the New York Stock Exchange, but you could only buy stocks on the New York Stock Exchange if you were in New York. The prices would be significantly worse. It would not be a liquid market. It would just be worse for every participant, and the market wouldn’t work well.

A sportsbook, on the other hand, operates completely differently. Because we also don’t trade against our users, we don’t make money when users lose. A sportsbook is completely different. Their revenue is equal to customer losses. The more the customers lose, the more money they make. For us, it’s not the same. The incentive is not to make people lose because we don’t make money when people lose.

Because of that as well, we don’t cap our winners. If you go to a sportsbook or casino and start making money, they’ll make sure you cannot participate anymore. We want winners. We want people to come and bring price, and we want price competition. In a sportsbook, there’s no price competition. The sportsbook has a monopoly on the price, and they’re going to put their margins on top because they’re having a bad month, so they make the prices a little worse or whatever. Because of that, they are fundamentally different mechanics and fundamentally different products, and they need to be regulated in different ways, which is how the federal regulation for exchanges developed.

We’re growing a lot because an exchange is a fairer, more accessible, and more transparent way to trade. You can see all the prices. You can see the competition and the order book in real time, and that’s why users like it so much. I think it’s fair that consumers, at the end of the day, pick what’s better for them.

Election polling has become kind of unreliable. What does Kalshi’s current data say about the U.S. midterms? Is that 70% accuracy? Or at what point does it start to move toward 90%?

A poll is top-down, right? It’s some editorial board or someone doing a poll, trying to aggregate the information and just tell people, “This is the number” or “This is the forecast” or “This is what’s going to happen.” But prediction markets are bottom-up, right? We want as many people as possible to do as much research as possible and bring that information to the market. So it is kind of an aggregation of what millions of people are thinking and doing, and I think it’s one of the first times that you really see information that’s actually led by people versus the elites just coming and saying, “This is what’s going to happen.”

With our big markets, like who’s going to take control of the Senate or control of the House, I think it’s as accurate as you’re ever going to get. The other thing we always have to talk about is that probabilities are not certainties, right? When something happens 1% of the time, it doesn’t mean it will never happen. It means that one out of 100 times, it will happen.

An election that’s at a 60% chance of someone winning means there’s still a real chance the other person wins. If I told you if you walk outside right now, there’s a 40% chance you’ll get hit by a bus, you’re not going to walk outside because 40% is pretty high. It’s the same thing. Forty percent does not mean that the person, the underdog, is never going to win. I think that’s kind of a challenge we have on the educational front, which is explaining to people that, different from polling, this is not the same thing.

Polling might say someone is 10 points ahead. In the market, that would probably mean over a 90% chance of someone winning, because they’re very different things. They measure different things, and we need to look at them as probabilities.

Robert Safian

‘We’re actually an intelligence organization now’: Inside USA Today’s AI-fueled Palantir deal

1 day 17 hours ago

When the nation’s largest newspaper chain announced a deal with Palantir in August, USA Today Co. CEO Mike Reed framed it as a necessary modernization step for a print-heavy publisher. The company, which operates USA Today and more than 200 local publications, still generates $1.1 billion in annual print revenue, but faces the same circulation declines and changing digital habits battering the rest of the industry.

Reed laid out the AI-fueled vision at a Palantir conference last month. The company wants to consolidate years of fragmented reader data into a single system and use AI to personalize newsletters, target affiliate links, improve content discovery, and turn anonymous visitors into registered subscribers. Demoing the platform with an “anonymized” user named “Marcus,” Reed described a continuous feedback loop built around reader behavior. “We’re actually an intelligence organization now,” he said, “because of the shift to online and the data we have on consumers.”

Last month, CEO Mike Reed demoed Palantir’s platform analyzing an example “anonymized” reader [Photo: Palantir]

The reaction from the company’s journalists was swift and, perhaps, unsurprising. The deal, announced during an August investor call and first reported by Nieman Lab, prompted a revolt across 31 unionized newsrooms represented by the NewsGuild-CWA. In a joint letter calling for the contract to be terminated, staffers argued that Palantir’s work with U.S. Immigration and Customs Enforcement creates a conflict for reporters covering immigration enforcement and protests, while raising questions about reader data and public trust. In addition to a booming commercial business, Palantir—fronted by its chest-thumping CEO Alex Karp—also holds billions of dollars in federal contracts, including for the Defense Department’s flagship data fusion platform. “Every problem we have with this partnership really comes down to trust,” Mike Davis, an investigative reporter at the Asbury Park Press, told Straight Arrow News. 

That skepticism comes after years of cuts. Since the company formerly known as Gannett merged with GateHouse Media in 2019, it has gone through repeated rounds of layoffs, essentially halving its workforce by 2024, even as CEO compensation rose amid flat or declining median employee pay. The company had roughly 7,500 U.S. employees last year, about 15% of them unionized, according to its latest SEC filing. Leadership has cast the restructuring as a response to falling readership and new forms of distribution and discovery, including AI feeds and chatbots. Much the same logic now underpins its AI push and its alliance with one of the world’s most controversial technology companies.

USA Today isn’t the only media organization to partner with Palantir, or face scrutiny over surveillance. Thomson Reuters has long supplied data used in federal immigration surveillance tools. Marketing firms Stagwell and Zeta Global have worked with Palantir on ad targeting. Fox News Digital used Palantir to build an internal platform that supports much of its article production, while Axel Springer, owner of Politico and Business Insider and many other outlets, has partnered with the company to better understand audience behavior.

Last month, Axel Springer also gave Palantir’s cofounder and chairman Peter Thiel its 2026 Axel Springer Award, recognizing him “for his transformative impact on the digital age.” A decade earlier, Thiel, who also founded PayPal, secretly bankrolled Hulk Hogan’s lawsuit against Gawker Media, contributing to the publication’s bankruptcy and demise. Some USA Today staffers see that episode as part of a broader pattern of Thiel-linked hostility toward the press, and another reason to ditch the contract.

Reporters have also lately raised concerns about the impact of AI on their work and their livelihoods. USA Today-owned newsrooms are using AI tools for research and investigative tasks, like filing public records requests, and to generate headlines and automate parts of digital publishing. After an abortive experiment in AI-written sports stories starting in 2023, the company the following year rolled out AI-generated “key points” that sit at the top of stories. USA Today Co. policy says that if any “additional AI-generated content is published, journalists must include AI disclosure language for transparency and “must be verified by a human for accuracy prior to publication.” Last year, WGBH reported that several of the company’s Boston-area news publications were using an AI tool called Espresso that is “designed to draft polished articles from community announcements.”

At the same time, the company is feeding its journalism into generative AI products, including its DeeperDive answer engine and through distribution and licensing deals with Meta, Amazon, and Microsoft. (It has also sued Google, along with the Daily Mail and a class of about 5,000 other publishers who allege the search giant overcharged them through its advertising technology platform, and on Thursday, it sued OpenAI for allegedly stealing its content to train its large language models. ) As Reed put it on the investor call: “We recognize that we have to create and format content for humans and for machines.”

In an interview last month, Reed, Media President Kristin Roberts, and communications executive Lark-Marie Anton spoke about the rationale for the Palantir deal, how AI is actually being used in the newsroom, and the union friction it’s provoked. Contrary to what they called falsehoods by union leaders, they said Palantir’s software, assembled with the help of a team of embedded Palantir engineers, works only with governed, “anonymized” data, and said the company has no direct access to newsroom systems, reporter notes, or subscriber records. Instead of threatening journalists’ jobs, Reed said AI tools could make the newsroom and business more productive and profitable, and, he hoped, would allow the company to hire more journalists. 

Following the publication of this interview, NewsGuild President Jon Schleuss responded in a statement: “USA Today Co. executives’ animosity toward their workers and unions is nothing new. The NewsGuild, which represents over 800 unionized USA Today Co. workers, fully stands behind the statement workers released when this deal with Palantir was first announced.”

This conversation has been lightly edited for clarity. Anton provided some additional written answers shortly before she departed the company on October 1.

How did USA Today Co. come to work with Palantir? What was the spark for this relationship? 

Mike Reed: I think that just being connoisseurs of the media, we knew some of the problems we had with having a lot of data, but also having multiple systems and not being able to have our data cohesive, in one place, and being able to learn from it and have recommendations from it. And so understanding that problem and talking about it internally and thinking, well, this is going to take years and it’s going to be expensive to maybe get ourselves to a place where we can use this in a real business-oriented way. And watching a lot of interviews with CEOs who were partners with Palantir saying that with Palantir, they did in a matter of a month or two things that might have taken them years to do. And I just thought, we need to talk to them, because whether it’s cheap, expensive, whatever it is, we have a business issue to contend with, and trying to build something ourselves that might take three or four years—and we don’t even know if we’re going to get it right and it might cost a lot of money. Or, we could partner with somebody like Palantir, and be ready next month. 

And so that’s why we said let’s talk to them. And we liked what we heard from them, because it was obvious that they were going to be execution-oriented, not just, we’re going to think about your problem and tell you what to do. They’re like, we’re going to actually do the work and help you execute. And that was really refreshing. So we said let’s do it.

Did you consider other companies?

Anton: We evaluated the landscape broadly because our challenge is not simply analytics, personalization, or AI. It’s the need to connect a very large, complex set of audience, advertising, subscription, commerce, and distribution data into a common operating environment. What stood out about Palantir was its ability to create what it calls an ‘ontology’ layer—essentially a unified model that allows different datasets and systems to work together and be acted upon in real time. That capability was particularly relevant to a company like ours because we already have significant first-party data and strong audience reach. The challenge isn’t collecting more data, but making better use of the data we already have. As Mike has said internally, Palantir is operationally more sophisticated in this area than any vendor we reviewed.

CEO Mike Reed [Photo: Courtesy USA Today]

Is there a metric you’re focused on here, or is there something that you think so far has proven to be valuable?

Reed: Not yet is the short answer. We started working with Palantir in June. So we really only have a couple months under our belt, and most of that time has been them really organizing our data, trying to take all this data from disparate systems and make sense of it in a manner where they can use it, they can provide the business outcomes we’re looking for. When we started to engage with them, we came up with a list of, here’s the business outcomes we’d like from you. Here are the problems we’re solving for. And then they worked with our data to try to figure out exactly what the solutions would be. So that’s really where the first couple months have been. But what might have taken us a couple of years, they figured out shortly, and will be in the market here as we go into the fourth quarter and start to, hopefully, have some positive learnings.

What are some of those outcomes you’re aiming for?

Reed: One is, how do we engage longer with consumers on our platform? We don’t want consumers to come for one story and leave. So how do we make the experience more enjoyable and more personal? Our audience is massive. A lot of times they’re there for a couple minutes and then they’re gone. And so how do we keep them there, how do we engage with them and how do we get them back more often?

And the second thing we’re trying to solve for is, we have a lot of users that come to our platform whom we don’t know anything about. They’re not registered, they’re not subscribers. And so while we can still derive some value from them, it’s beneficial. If we can create known users out of unknown users, the value we can create from our relationship with that consumer goes up exponentially. 

What is Palantir being paid, and how does that compare to what an in-house build would have cost? What’s the length and exclusivity of the contract, and is there an off-ramp if the results you’re after don’t materialize?

Anton: I will share that we approached this with the same financial discipline we apply to any strategic technology investment. We evaluated the expected return, the speed to value, the opportunity cost of building internally, and the resources required to build, support, and maintain a comparable platform ourselves. And we have been focused from the outset on flexibility and long-term independence. One of the key workstreams has been ensuring knowledge transfer and building internal capability so that we are not dependent on outside engineers forever. We have also evaluated off-ramp scenarios and continuity plans as part of the process: If we don’t see measurable business value, we will reassess just as we would with any strategic investment.

How do you think about USA Today’s goals as part of a larger structural shift that’s happening across news media—or a shift that you think will need to happen?

Reed: I think a big structural shift for news media like ours is that, when we think about content creation, it can’t just be hard news. We have to create a lot of sports content and entertainment and games and puzzles. If we’re going to engage with consumers for longer periods of time and more often, we have to have a broader content strategy than just news. So that’s a big structural change for an industry that’s really created news historically, and not really focused on a lot of that other stuff. And that other stuff’s really important now.

And the second thing is listening to the data, in terms of your content creation strategy. And if you think about it, not that long ago, your newsroom would meet in the morning around a table and say, ‘What are we going to cover today?’ And you would go cover it, and that’s the news that would be reported. And our people sitting around the newsroom, they don’t have bad ideas, but why not listen to the consumers? What do they want covered? And so looking at the data and understanding what’s resonating with consumers, what are they focusing on and doing more of that and looking at the data and saying, ‘They’re not paying any attention to this, maybe we should do less of that or none of that.’ That will allow us, I think, to create more content that’s engaging to consumers. 

Don’t mistake that to mean that we would abandon the social mission side of our business, which is to cover the communities we operate in, cover the country, do investigative work and do the things that are going to protect citizens and people who live in our communities. Of course we’re going to do that work, but there’s hard news that we cover that’s not necessarily aligned with that mission that people don’t care about. So let’s do less of it. 

How are people in the newsrooms responding to these changes? What questions do they have, and how are you responding to them?

Reed: I think that a lot of folks in our newsroom are covered by the NewsGuild, and I think the NewsGuild gives them a lot of bad information that leads them to decision-making that is not the best. And so I think the News Guild is doing a disservice to the people, its own members who are our employees and that are part of this cohort of journalists that have to make these changes. And I think that makes it harder, and I think we’re having more success in newsrooms that are not unionized, with this new world, than those that are unionized. 

Kristin Roberts, USA Today’s Media President [Credit: USA Today]

Kristin Roberts: Guild leadership is saying things that are factually untrue. They prey on fear. No matter that I will appear in front of any newsroom for a standup in which they ask me anything for an hour and a half at a time, and I explain to them all of the things. Every single journalist in the organization has my cell phone number and they can call and say, I just heard this from the Guild rep. Is this true? And I’ll tell them it isn’t. I’ll give you an example that’s incredibly real for us. Palantir doesn’t work inside of any USA Today Company system. They don’t have access to any of the notes our reporters take. They’re not in the Microsoft suite of tools. They don’t have information directly about our consumers or our staff. I said this to all of the staff, took their questions, and answered this precisely. They don’t work in our systems. We feed them highly controlled data. We’re in their shit. They’re not in ours. 

Reed: Two other untruths before we move on. There’s this fear—and I think the Guild plants this as well as outside cynics—is that we’re going to replace journalists with AI. That couldn’t be further from the truth. We’re hoping that our journalists can do more of the work they really want to do. And as we’re successful with our business strategy, we’re going to be able to hire more journalists. So this is not an exercise to replace journalists with AI at all. It’s hopefully going to allow us to add more journalists. Second is that I think the union plants fear that we’re going to have AI-created content, even though we say till we’re blue in the face we’re not. 

Reporters have also raised concerns about data protection. How do you ensure the data is protected when working with Palantir or any vendor? Are there independently verifiable guardrails that prevent sensitive data from being accessible to Palantir’s broader data environment?

Anton: This was one of the most important areas of diligence. Our position is straightforward: our data remains our data. We maintain ownership and control over it, and we require vendors to meet stringent security, privacy, and governance standards. Protecting personal information is fundamental to maintaining audience trust, and those requirements apply to Palantir just as they apply to any strategic technology partner. Data governance, access controls, ownership, security review, and legal protections were central considerations in negotiating the agreement and our ongoing working relationship.

Palantir burnished its reputation for years by staying clandestine, and some of its work is pretty controversial. How do you think folks, especially readers but also reporters and their sources, should think about this partnership in light of its shadowy reputation? Was there discussion about the reputational risks involved here? 

Anton: It’s a fair question. We recognize that Palantir has a public profile that prompts strong opinions. That’s part of the reason we’ve been deliberate about discussing not only the opportunities but also the governance, privacy, and trust considerations involved. For us, the key question wasn’t who else Palantir works with. The question was whether the partnership advances our mission while meeting our standards. Our journalism remains independent, and our editorial decisions are governed by our longstanding ethics and standards policies, and those policies do not change because we adopt a technology platform. 

We also work with other technology companies that serve governments, enterprises, and other complex organizations. We evaluate partners based on their capabilities, the strength of their controls, and whether the relationship aligns with our values and business objectives. Ultimately, readers should judge us by our journalism, our transparency, and how responsibly we steward their trust.

Are there other particular ways that you see AI helping inside the newsroom? And is that something that Palantir has been working on too? 

Roberts: A lot of the things that we have as reporters, over the course of my 30 years in journalism—I started as a news assistant at Reuters on the Wall Street desk—I have been asked to do more and more and more tech stuff, applying tags, applying quotes, inserting links, putting ads in different places. None of that has to happen anymore. Think of all the times with your editor where you were stumped on the right headline and you came up with 17, and you had to A/B test them. Now the system will give you A/B testing in real time. All of this stuff actually frees our mind to be creative, because you’re no longer spending 27 minutes searching for a lead video, wasting your time.

I’ll give you another example. In all of our entertainment stories and our sports stories, we do affiliate links to our partners. So if you’re reading a sports story, you want to buy the jersey for that team, our reporters and editors used to have to go to a file, find a relevant link, copy that link, put it in. All of that is now automated, and that’s a Palantir project. 

Can Palantir’s software help with social media and promotion? Can it help with ad targeting? Can it help reporters directly with their reporting, or could it eventually? 

Anton: I think the potential applications extend well beyond those initial examples. On the audience side, the goal is to better understand what a reader values and surface the most relevant experience at the right moment. For advertising, it can help connect audience behavior, content engagement, advertiser demand, and first-party data to improve targeting, relevance, inventory valuation, and monetization decisions. Hypothetically, could it support social strategy? Potentially. Any system that helps us better understand audience interests and content performance can inform distribution decisions across channels.

Again, hypothetically, could it help reporters directly? Perhaps over time. Other media organizations have used Palantir-powered tools to assist journalists with things like briefings, research support, and workflow tools. But the task at hand today is focusing on the audience and consumers to drive revenue and our business transformation.

How are your reporters using generative AI now?

Roberts: I think the vast majority of people in the company, and certainly in the newsrooms, are the ones adopting AI enthusiastically. So we’ve pushed all of our AI tools directly to our staff and say, Have some fun, figure this out. How can this make your job easier and better so that you can get back to the thing that you and I got into journalism to do, which is to break news and deliver scoops? All of our problems in journalism are solved if we get back to exclusive and distinctive, because AI answer engines can’t do that. 

For example, one of our news assistants in the UK created FOIAbot, and we have now been able to deploy that across the entire company. Say, I work in Naples, Florida, and I’m a local reporter. I want to see how many accidents happen at the intersection of First and Main. And so I go into FOIAbot and say, ‘I want to know how many accidents happened at First and Main.’ And FOIAbot identifies every single government agency that might have that information. With all of the formatting necessary for that particular government agency, it drafts the FOIA request, gives it to you, you sign it and it sends it. So we are able to send dozens of FOIAs a week. 

And the reporters are the ones who are creating these. So Kristin, sitting at an executive committee meeting saying, ‘How should my team use it?’ That’s not the way to do it. It’s to say, ‘Here’s the tool. You tell me, help me help you. You tell me how you can use it.’ And that’s what’s working for us, democratizing access to the tool. 

But with approved tools, where we have an enterprise agreement so that we are sure that the data is protected. You can’t go out freelancing with any tool you want. It’s got to be a tool that is given to us by a provider who has agreed to meet our very stringent standards around data protection. We have the highest standards for this. We’re a publicly traded company. And so any company that we do business with has to sign onto that, and we do not make exceptions when it comes to these AI. Then once they sign onto that, man, we push it to the people. We push it to the reporters and the editors and we say, let’s see what you can do with this. 

Given the concerns about AI’s impact on journalism, how do you think about changing the narrative within the organization? How do you get reporters more interested in trying it? 

Anton: We have town halls, where our own reporters, the ones that are the super users, share use cases. They talk about how they’re leveraging it. An example is our Taylor Swift reporter, Bryan West. You never know what Taylor’s going to do, so he may be out on the road covering something that’s not Taylor-related, and all of a sudden he’s going to go do the story, and he’s able to use AI. While he’s standing outside MSG, he’s using it to research stories that he’s already done to help him fact check something, or to create a listicle. So he loves it and he talks about it really enthusiastically. And that engenders trust amongst the rest of the team, to say, Bryan’s using it, then maybe I should try it for X, Y, Z. For [generating headlines], it took us a really long time to get everyone to say, This is actually great, that we have our own tool internally that gives you the key points at the top. 

So I think it’s a matter of, internally, people need to start to just trust the process. And they’re hearing their colleagues use it. I think it has to be sort of organic. You can’t force it. We’re not perfect, but we’re certainly trying to bring everyone along on the journey, and create trainings so they can understand how to use it.

Reed: We’re also transparent about what we won’t use it for. Then you have to practice what you preach. If we did use AI for something we said we weren’t going to, we’d have no credibility. Over a longer period of time, us being transparent about what we’re not going to use it for and then operating the business that way we think will be helpful. 

Are there things that you all like to use AI for, or that you find effective or that you’re hoping to use it for personally? 

Reed: I use it personally just every day to help me. I use Claude and it knows me better and better all the time. I use it mostly in a business context, but whether it’s helping me create the right narrative for an email or helping me develop solutions for another business issue or something like that. It saves me a lot of time if I want to research something. How does this apply? Or what does this really mean? Or how are other companies using this? I use it for that. I think it helps me become smarter a lot quicker. 

What sort of stance has the company taken on the use of its content for training and inference by the AI labs? 

Anton: Our view is that high-quality journalism and content has real value and should be compensated when it is used in AI products. We have pursued commercial arrangements that recognize the value of our journalism, provide attribution, and create new revenue opportunities that support news gathering. The [now expired] Perplexity partnership was an example of that approach. It was built around making trusted journalism available within AI experiences while ensuring recognition for the original reporting. More broadly, we support responsible collaboration between publishers and AI companies rather than assuming those relationships need to be adversarial. We want to make deals happen. But they need to be fair.

Digital publishers once had lucrative deals with Facebook and YouTube that evaporated once the platforms had the content and audience. 

Reed: Google does that all the time. They partner with you on business stuff and you build a business and as soon as they get it to where they want, they pull it up. 

Anton: We built on our own platform with a partner, Taboola. We were the first to implement on USAToday.com, our own AI answer engine that only pulls answers—so if you go to a story, you’re reading about September 11th, in there you can ask it to go deeper—it’s called DeeperDive—on the story. So you know that within our wall of USA Today, those answers are coming from a trusted source. I think that’s a way of us saying, ‘Hey consumer, stay here longer, ask all the questions you want and know that you can rely on those answers.’ I think that matters. It’s sort of a learning. We’re adapting to understand, we need to keep you here. And you need to trust us, and you need to keep coming back to us, not to a random Google [search] that’s going to give you an answer. It’s not going to drive you back to us for more information. 

When you look into the future—like five years—I wonder how you see newspapers, what their role is, how they fit into people’s daily lives. Does the newspaper as an idea change? Does the news business, does the media business have to change in some significant way or philosophically or structurally? 

Reed: I think that what we’re changing is that news is one form of content and that we’re a content business. And so we create news and other things, whether it’s entertainment or sports or games or helping them. 

But I think that in five years, I think the thing that’s continuing to evolve is we’re way more digital than we are currently. And in five years we’re going to be a lot more engaged with our consumers, because each one of them is going to feel like they’re getting what they need from us in a personal way. And so where we used to just push a set of stories into a market, we’re now going to be able to say, okay, we have this incredible pool of content. And for each user, pieces of that content are going to be relevant, and that’s what you’re going to be able to have. But we’re still going to be the news and media arm for consumers in local markets. But what’s evolving and changing is the personalized experience, and the fact that we’re a content business, and news is one form of content. There are many forms of content and we’re going to have to be good in all of this. 

And ultimately people want, I think, to trust the news they’re getting. And there’s a huge dearth of trust right now.

Reed: I think that’s a huge asset that plays in our favor five years out. Whatever social platform you’re on, there are so many things that are not real. And it’s hard to know what to believe. So much stuff looks real. And so that’s the one asset we have that’s becoming a bigger and bigger asset, that trusted news and information and content, whatever we create. We create now beyond text. We have audio, we have video, but whatever we create, there’s a level of trust with it. And I think that’s a real differentiator. 

And when I look out five years, I think one of the things that’s bringing consumers to us is not only that we’re unique from a local perspective, but that we’re a trusted source for all of the content you consume on our platform. I think that’s a differentiator five years from now. We’re going to become more valuable to the social media platforms too, because the owners of the social media platforms, they don’t want fake stuff. And so us creating content for those platforms I think is potentially not only a way for us to brand and bring consumers to us, but it’s a revenue stream from those platforms, because they’re going to want trusted content creators on their platform and we’re going to be high on that list. 

I hope there’s still a print edition in five years too.

Reed: I don’t know if the frequency will be as much as it is today, but I think there’ll be print in five years. I really do. Our print revenue is 1.1 billion, and it’s not going to go away tomorrow. It’s declining at eight to 10%. But we still have an enormous amount of readers around the country who want print. What’s interesting is our print subscribers automatically get a digital code too, so they can be a digital subscriber. Less than a third of our print subscribers use digital. They only want print. 

Correction: An earlier version of this story said USA Today Co. prohibits AI-generated stories. In fact, USA Today Co. policy permits AI-generated content with editorial oversight and disclosure.

Alex Pasternack

Donald Trump is launching a new AI task force. Here’s who he’ll have to keep happy

1 day 17 hours ago

Donald Trump didn’t become one of the world’s most recognizable businessmen by worrying too much about keeping everyone happy. What Trump wants, Trump tends to pursue, consequences be damned.

But his administration’s new AI task force may require a different touch. Co-chaired by director of national intelligence Jay Clayton, Office of Personnel Management director Scott Kupor, chief technology officer Emil Michael, and FTC vice chair Andrew Ferguson, the group brings together officials representing some of the competing forces shaping the administration’s approach to AI.

The choice of four co-chairs reflects a broader split over how the U.S. should approach the technology—and the challenge Trump faces in keeping those interests aligned. So who does the task force have to keep happy?

Silicon Valley accelerationists

Ask around Silicon Valley and plenty of people have a clear idea of where the U.S. should be on AI: America’s overriding priority should be building more capable systems, faster. David Sacks, who played a major role in shaping the Trump White House’s AI agenda, is a leading proponent of that worldview, while task force co-chair Scott Kupor has warned against writing detailed rules for a technology that is changing so quickly.

The administration has already addressed many of this group’s concerns, including by trying to head off tougher state-level AI laws and pushing for a “minimally burdensome” federal framework.

MAGA’s Big Tech-hating branch

A sizable part of the MAGA coalition wants much tougher controls on frontier AI. In May, more than 60 Trump allies—including Steve Bannon, Amy Kremer, and Brendan Steinhauser—signed a letter coordinated by Humans First calling for mandatory testing, evaluation, vetting, and government approval before potentially dangerous frontier models can be deployed.

Bannon has even reached across the political divide, appearing alongside Bernie Sanders at an event earlier this year focused on stronger AI regulation. Bannon’s chief concern is AI-driven job losses; others in the MAGA world are more focused on the concentration of power in a handful of enormous technology companies.

Republican lawmakers

AI companies also have a steep hill to climb with lawmakers chastened by the relatively blasé approach to safety that Big Tech companies displayed as social media came under political scrutiny. So it is little surprise that Senators Josh Hawley and Chris Murphy are drafting bipartisan legislation that would create civil and criminal liability when AI agents hack computer systems.

They’re hardly alone in raising concerns. Senate Majority Leader John Thune has also acknowledged the threats posed by AI and argued for guardrails around the technology.

China hawks

Pulling in the opposite direction are China hawks within the Republican Party, who worry that putting too many brakes on U.S. AI labs will hand an advantage to Beijing.

Treasury Secretary Scott Bessent has taken an aggressive stance toward Chinese labs using distillation to piggyback on the capabilities of U.S. models. National Cyber Director Sean Cairncross, along with officials at the Commerce and Defense departments, has also pushed for the U.S. to talk—and act—more aggressively when it comes to Chinese AI companies.

Big Tech companies themselves

Then there are the companies and AI labs that will actually have to live under whatever rules emerge. And even they are far from united.

Companies like OpenAI have argued, in effect, that accepting some harms may be part of continuing to develop increasingly capable AI. Anthropic, meanwhile, has talked about “pacing the frontier” and taking a more cautious approach to the speed of development.

If the companies building the technology can’t agree on how quickly to push forward or how much risk to accept, Trump’s task becomes even harder: finding an AI policy that can satisfy an industry racing ahead, a political movement suspicious of Big Tech, lawmakers demanding safeguards, and national security officials worried that slowing down could mean losing ground to China.

Chris Stokel-Walker

The drone delivery business has a ground problem

1 day 19 hours ago

For more than a decade, the story of drone delivery was about improving the technology and getting federal permission for the drones to take to the airwaves. Could companies get the Federal Aviation Authority to approve flights beyond the operator’s line of sight? Would regulators let one pilot supervise many aircraft? The assumption was that once Washington said yes, the drone business would take off.

Washington is edging toward saying yes. What comes next is less predictable, because once drones are out in the wild, it’s hard to know how people will interact with them in real life. In Richardson, Texas, a Dallas suburb, residents running their own trackers have logged more than 50 low-altitude flights on some days, at roughly 170 feet, close enough to be heard indoors. Neighbors have started calling the traffic a “drone highway.” In College Station, Amazon’s longtime test market, the FAA found residents’ complaints meritless or outside its jurisdiction. Amazon cut flights anyway, switched to a quieter drone, and planned to let its lease lapse.

That is a weak signal worth paying attention to. The first-order expectation for incorporating drones in delivery systems is that packages can be delivered more quickly and, without the need for human labor, potentially more cheaply than alternatives. Indeed, when Jeff Bezos first announced Amazon’s intention to add drones to the mix in 2013, the idea attracted widespread, and mostly positive, attention. The second-order effects appear only once the technology scales, and they may change the viability of the model more than any rule from the FAA.

The assumptions hiding in the business plan

In discovery-driven planning, I ask teams to write down what has to be true for their plan to work, then test those assumptions before committing serious resources. For drone delivery to be successful, one of the biggest assumptions is that drones will be less expensive than comparable tasks performed by humans using conventional methods.

In a cautionary report from 2023, McKinsey challenged that assumption. The consultancy found that the cost of a single drone delivery was $13.50, more expensive than a similar delivery task performed by either electric cars or vans or any vehicle making multiple deliveries on a run. In the same study, McKinsey estimates that if one operator can eventually manage 20 drones at once, a delivery could cost about $1.50 to $2. That is roughly what a van costs when it delivers 100 packages on a single route. In other words, even in the best case, drones are about even with a well-run delivery van. There isn’t much room for things to go wrong.

The picture gets gloomier for drones if citizen objections forces delivery companies to restrict the routes they can use. Most economic assumptions about drones assume that they can fly in straight lines. Not necessarily so fast. Local governments can’t dictate flight paths. The FAA has been clear that states and cities may not regulate aircraft operations. But local governments have a strong lever: they can regulate land use and takeoff and landing locations, even though the airspace is federally controlled. Given the limited range of battery-powered drones, lawyers have pointed out that a citywide ban on takeoffs and landings would, in practice, amount to a ban on drone operations.

So, I would expect the question of routes to be settled through negotiation. A city might approve a hub only if the operating company commits to routing flights over rail lines, utility corridors, commercial land, and waterways, and away from backyards and schools. Some companies will make that commitment before anyone asks, to protect the community goodwill that lets them keep operating. However it happens, the result is the same: the straight line assumption in the business model starts to look more like a squiggle.

What corridors do to the math

A delivery van becomes cheaper per package as demand grows, because each added stop on the route doesn’t add a lot of extra costs. Drone delivery doesn’t have those economics—each individual delivery costs the same. Restrictions on where they can fly makes that basic problem worse.

Suppose a customer is two miles away in a straight line but three miles away by the approved route? Several costs follow.

The first is lost reach. Battery range is limited, so every extra mile of detour shrinks the effective delivery radius. The number of reachable customers depends on the area served, which grows with the square of the radius. A 25% cut in effective radius therefore removes about 44% of the households a hub can serve.

The second consequence is thinner coverage of fixed costs. Each hub has a lease, permits, staff, and charging infrastructure. With fewer customers per hub, each package carries more of that cost.

The third is fewer trips per drone. Longer flights mean each drone completes fewer deliveries per hour, which is the productivity measure the whole model depends on.

The fourth is hubs moving farther out. Communities will push launch sites toward industrial areas, farther from where customers live, which makes every flight longer still.

Put these together and drones look less like a replacement for the delivery van and more like a premium courier service.

Opposition grows with success

Ironically, the more successful drones become, the more likely they are to spark local opposition. Route optimization concentrates traffic on the most efficient paths. The benefits are spread widely: many people each get a phone charger or a ham sandwich in 30 minutes. The costs fall heavily on a few: the families living under the flight path. Political economists have long observed that concentrated costs produce organized opponents, while spread-out benefits rarely produce organized supporters.

There is also an irony in who gets served. After a drone ran into trouble near a large building, Amazon said it had removed all buildings of similar height and size from its delivery portfolio, meaning big multifamily developments. That shifts the service toward single-family suburbs, which is where homeowners’ associations, the most organized opponents of any neighborhood nuisance, are strongest.

If only allowing flights along approved corridors become the price of permission, the underlying economics of owning the corridors changes as well. Railroads, utilities, and pipeline easement holders suddenly have something valuable to rent out, and they will want to be paid for that. Add possible compensation for homes under flight paths and ongoing spending on community relations, and the cost structure picks up expenses that probably weren’t in the original spreadsheet.

Implications for our drone delivery future?

The economics of the business will need to be reconsidered to at least model out corridor-based routes. Model the extra distance, the smaller service radius, and hubs on the industrial edge of town. If the business only works with straight-line flights, that’s a lesson better learned early before a lot of investment has been made.

Community consent should also be treated as a design requirement, not a public-relations task after launch. In Richardson, several neighbors said only one homeowners’ association was notified before operations began. Local opposition was powerful enough to kill off Amazon’s desired second headquarters in New York City, prevent Walmart from setting up shop there and forced Airbnb to strictly limit its operations in many places.  It should be taken seriously.  

Drones may never become a cheaper version of the delivery van. They could well find markets for delivering prescriptions, or urgently needed parts. They may also make sense in rural areas where speed matters more than price and there are fewer neighbors to object. Indeed, in some places such as Rwanda, delivering medical supplies by drone has been a longstanding practice. 

Amazon shut its Lockeford, California, site, is leaving College Station, and faces protests in Richardson. Each one is a cheap lesson about what full scale will need to look like.

The drone delivery business case was written with the sky in mind. Its fate will be decided on the ground.

Rita McGrath

What Bending Spoons is changing at Vimeo after its $1.38 billion deal

2 days 1 hour ago

Last year, video platform Vimeo announced plans to sell itself for $1.38 billion to tech company Bending Spoons.

Milan-based Bending Spoons, which since its founding in 2014 has acquired more than 50 businesses, including Evernote, AOL, Eventbrite, and Meetup, soon faced questions from Vimeo users wondering whether it would continue to offer the same level of service.

“I vividly remember some enterprise customers being worried about us stopping serving their segment,” says Marco Castello, a longtime Bending Spoons employee who now serves as general manager of Vimeo. “Essentially, they were worried that we’d stop investing into the enterprise segment, because they thought, An acquisition is coming. We don’t know these guys, so what’s going to happen?”

Castello says that since the purchase, he and his colleagues have spoken to more than 200 users of the software. Those users represent Vimeo’s distinct business segments: independent filmmakers, enterprise customers creating and distributing corporate videos, and streamers using Vimeo’s tools to build their own branded video channels.

The goal, he says, was not only to reassure them about the company’s continued commitment to Vimeo, but also to learn what they wanted changed about the product.

[Photo: Vimeo]

What customers requested, Castello says, wasn’t significant new features so much as fixes and upgrades to existing ones. By May 2026, the company said it had fixed more than 100 bugs on the platform and delivered more than 50 improvements in areas ranging from live-event search to page-loading performance.

One of the most common complaints was simply that basic functions, including search and video uploads, were uncomfortably slow. Search times, homepage load speeds, and upload times have all improved, Castello says.

Vimeo has also added quality-of-life features such as the ability to adjust the privacy settings of multiple videos at once, use internal collaboration tools without requiring video reviewers to log in, and access to an upgraded AI-powered captioning system. Additional AI-enabled features, including automated dashboard creation for enterprise users, are also in the works.

Those kinds of relatively minor changes can have a big impact for users, Castello says. Simply switching caption-service providers, for instance, allowed Vimeo to dramatically expand the number of supported languages, benefiting businesses that see captions as a core accessibility need.

“With that small change, we increased the languages from 7 to 99,” he says. “Sometimes we just focus on the small things that can have a drastic impact on the user experience.”

The Bending Spoons playbook

Bending Spoons, which went public at an $18 billion valuation over the summer, has at times been criticized for imposing layoffs at companies it acquires and, particularly in the case of Evernote, revamping pricing plans in ways that drove off some longtime users.

At Vimeo, Bending Spoons cut jobs as it reorganized the company’s product-development teams into lean groups focused on its three core market segments.

“We can confirm that there was a layoff at Vimeo in December 2025,” Castello writes in an email to Fast Company. “We retained the majority of specialized teams like sales, customer support, and curation (Staff Picks), while skilled Bending Spoons engineers, developers, and product managers took over the technical and product operations. We aren’t able to provide numbers on how many people were impacted.”

Bending Spoons, which announced in September that it had completed its roughly $1.29 billion acquisition of data and workflow management tool Airtable and entered into plans to acquire collaboration platform Miro for roughly $1.36 billion, has drawn comparisons to private equity for its model of revamping legacy tech brands while cutting staff.

In an August earnings call, cofounder and CEO Luca Ferrari told investors that the company has also grown more efficient at deploying employees, known as Spooners, to transform acquired companies.

“For instance, around 60 Spooners worked on Vimeo during Q2, broadly in line with the number of Spooners who worked on the Evernote transformation in 2023,” he said. “This is despite Vimeo being roughly four times the size of Evernote in revenue terms, and a more complicated business from both a technical and operational perspective.”

A different definition of growth

But Bending Spoons has also emphasized, in both financial filings and public statements, that it tends to focus on the long-term future of the companies it acquires rather than rolling out flashy features or quickly flipping businesses to other investors.

“For Bending Spoons, the growth engine has actually been great at acquiring and operating businesses,” says Matteo Danieli, Bending Spoons cofounder and VP of product. “We do not necessarily need to prove massive growth on products, and so we are actually free to focus on what’s useful for users.”

[Gif: Vimeo]

For Vimeo, he says, that includes the community of independent filmmakers who long made the platform a home but in recent years often felt neglected as its enterprise business took off.

Under Bending Spoons, Vimeo has revamped profile pages to let creators better showcase their work, enhanced controls around videos embedded on other sites, and added storage and other features to free plans.

More broadly, Castello says, Bending Spoons’s approach of deploying small teams to upgrade a product allows it to move quickly after an acquisition.

“We believe that smaller teams—leaner teams—can be much faster at innovation,” he says. “And so, whenever we approach reorganization, we do it in a way that, essentially, we try to bring the product back to a startup mode, where a very small, committed group of people can deliver amazing work in a very short period of time.”

Who Vimeo is really for

Vimeo also recently rolled out new plans aimed largely at midsize business customers that Castello says had been using individual plans in violation of the platform’s terms. The move naturally drew consternation from some users, though Castello says the new plans also come with added features.

“That comes with greater storage, greater bandwidth, all the tools that they need for collaboration, those kind of things,” he says.

According to Danieli, moving forward the company plans to keep serving each of Vimeo’s existing market segments without adding too much complexity to the software. “We’re trying to find the best possible way to serve all of them exceptionally well,” he says, “in a way that doesn’t complicate the product too much.”

Steven Melendez

Researchers tracked $80 million spent on AI-generated political ads for the midterms. Here’s what they found

2 days 1 hour ago

The 2026 U.S. midterm campaigns are the first in which AI-generated political ads are regularly appearing on people’s televisions and social media feeds.

We are researchers who have been studying political advertising through the Wesleyan Media Project since 2010. This election cycle—using data from media reports; student coders; and AdImpact, a firm that tracks political ad spending—we’ve tracked about $80 million in spending on almost 170 unique ads that use AI.

What we found has surprised us: AI use spans from hyperrealistic deepfakes to subtle enhancements; Republican sponsors—both candidates and interest groups—are much more likely to use AI than are Democratic sponsors; and, thanks to a patchwork of state legislation, many of these ads do not disclose the use of AI at all.

From deepfakes to subtle edits

An assortment of politicians and watchdog groups have expressed concern about campaigns using generative AI to produce deepfakes—synthetic videos showing people doing things they did not do—that might deceive voters.

We’ve noticed several ads containing hyperrealistic deepfakes of famous politicians, including Donald Trump, Nancy Pelosi, Barack Obama, Kamala Harris, and Alexandria Ocasio-Cortez. Ocasio-Cortez, in particular, is a favorite among Republican advertisers, appearing in at least five ads.

We’ve seen deepfakes in which a Republican Senate candidate from Louisiana drives a school bus full of undocumented immigrants, a Republican candidate for governor from South Carolina walks arm in arm with drag queens, and an ad in which Liz Cheney, Mitt Romney, and Mike Pence are seen carrying pitchforks on the White House lawn.

People who are not politicians made appearances, too, including a fake Dr. Anthony Fauci, seen running around a state fair with a huge syringe, presumably eager to vaccinate everyone. We’ve also noticed several ads in which AI was used to generate crowds or constituents.

In several cases, AI was used to enhance visuals rather than generate something new. One ad from Chip Keating, a Republican candidate for governor in Oklahoma, includes an AI disclaimer, but it doesn’t specify exactly how AI was used.

Ads that use AI to enhance visuals don’t necessarily look different from ads that were created in the pre-AI era, which makes it difficult for viewers to discern whether they depict something false.

A partisan gap

Republicans—both candidates and groups such as super PACs and 501(c) organizations—are much more likely to use AI in their ads than are Democrats, according to our research.

In fact, Republican candidates or pro-Republican groups were behind 80% of the ads we tracked and 83% of the spending.

We can only speculate as to why Republicans dominate the use of AI in political advertising in 2026. In general, Democrats tend to take on a regulatory mindset when it comes to political campaigns, favoring limits on campaign spending and required disclosures.

In 2022, for instance, only Senate Democrats and two independents voted to advance the DISCLOSE Act that would have required additional campaign finance disclosures for super PACs, labor unions, and corporations. Republicans, by contrast, tend to be more in favor of a free market approach.

These more general philosophies may be reflected in the parties’ use of generative AI for political advertising, something about which voters are worried. Polling shows broad support for more regulation, with 78% of registered voters favoring a ban on AI content that makes deceptive claims about candidates.

AI-modified videos of House Minority Leader Hakeem Jeffries posted by President Donald Trump were displayed at the White House in October 2025. [Photo: Alex Wong/Getty Images News via Getty Images] Disclaimers all over the map

Because regulation of AI in advertising depends on a patchwork of state legislation, many of these ads are not required to disclose the use of AI. This lack of disclaimers makes tracking AI use in ads challenging. Our team has relied on media coverage and trained student coders to flag ads that are potentially AI-generated.

Across 35 states, only 31% of the ads we tracked—representing 22% of the spending—disclosed the use of AI tools. The wording of these disclaimers was all over the map.

For example, one Georgia ad included the disclaimer, “This video has been manipulated or generated with artificial intelligence,” while an Oklahoma ad said, “Political satire. AI-generated images do not depict actual events.” A North Carolina state Senate ad said, “You guessed it! AI was definitely used to generate these silly video clips.”

Laws don’t drive disclosure

Some advertisers voluntarily disclose the use of AI even when they are not required to do so. Sometimes, the opposite occurs—advertisers don’t include disclaimers even when state law requires them to. In fact, we’ve found that a state law that requires disclaimers on ads that use AI has very little relationship with the actual use of disclaimers.

In states without laws governing the use of AI in political ads, 32% of ads contained a disclaimer; by contrast, in states with laws governing the use of AI, 29% contained a disclaimer. This comparison, however, is not perfect, as some of the ads in our database aren’t covered by their state’s law.

Some states, such as Colorado, have laws that apply only to candidate deepfakes and thus exclude other uses of AI. In other instances, such as in Louisiana, an AI law was enacted after the ad aired.

Minnesota law generally bans deepfakes in political ads, but an ad featuring synthetic video of Democratic Senate candidate Peggy Flanagan aired in May 2026 anyway. Whether it violated state law is uncertain, as the law requires that the media be “so realistic that a reasonable person would believe it depicts speech or conduct of an individual who did not in fact engage in such speech or conduct.”

Moving forward, the real policy challenge will revolve around transparency, the enforceability of existing laws, and simply figuring out what type of disclosure would be helpful to voters.

Travis N. Ridout is a professor of government and public policy at Washington State University.

Erika Franklin Fowler is a professor of government at Wesleyan University.

Michael Franz is a professor of government at Bowdoin College.

This article is republished from The Conversation under a Creative Commons license. Read the original article.

The Conversation

16 tools for a more focused workday

2 days 2 hours ago

This article is republished with permission from Wonder Tools, a newsletter that helps you discover the most useful sites and apps.

Now that fall is here, my focus has been fragile. Noise, notifications, news, and email grab at my attention. To help cope with distractions, I’ve stitched together a focus tool kit with apps, sites, and sounds. Some are new, others old. My goal this season is to concentrate for a bit longer when my attention might otherwise drift. I hope this collection will be useful for you, too.

Stop email interruptions Inbox Pause

This simple email add-on temporarily freezes what you see in your inbox. Any new messages will show up as soon as you unpause.

Price and platforms: Free for basic use. For extra features, like custom scheduling or hearing from select senders during pauses, get the $15/month pro account. Works with Gmail and Outlook online or on the iPhone. An alternative free option: Pause Gmail.

Avoid distracting sites News Feed Eradicator

Get rid of the addictive, endless feed on social media sites like Facebook, Twitter, Reddit, YouTube, and LinkedIn. Look instead at whatever accounts you’re actually interested in.

Price and platforms: Free. Chrome only.

Raycast Focus Mode

Block email and distractions during short, focused work sprints.

Price and platforms: Free. It can automatically block sites for you in Safari, Chrome, and Firefox (and related browsers modeled on WebKit, Chromium, and Firefox).

[Image: Raycast Focus Mode] SelfControl

Pick a list of sites to block. Set a timer for deep work. While it’s active, you won’t be able to use Facebook, email, or whatever you’ve blacklisted, even if you restart your computer.

Price and platforms: SelfControl is free for Mac. It’s open-source and not fancy.

Alternative: Cold Turkey is a more polished free Windows and Mac alternative for blocking sites. The pro version costs $45 for a lifetime license ($36 for students) for advanced features like blocking applications.

[Image: SelfControl] Forest App

Instead of blocking sites or apps, Forest helps you avoid wasting time on your phone by rewarding you for not using it. Your virtual trees grow until you pick up your phone. The approach seems to resonate: 60 million people have used this app. 🌳

Price and platforms: Free for iOS and Android. A paid subscription unlocks extra features.

[Image: Forest] Apple Screen Time

Set time limits on apps that tempt you to doomscroll. Count how many notifications your apps send, and see how much screen time you’re logging. I use the downtime feature most. It closes my apps at 11 p.m. I can override that when I need to. But I like that friction nudging me to stop late-night work.

Price and platforms: Free for Macs and iOS devices. Android has a related “Digital Wellbeing” feature. And Windows has focus options. There’s a related Do Not Disturb mode for Mac and iPhone.

Block noise Sony Noise-Canceling Headphones

The New York City subway is painfully loud. So I splurged on these over-the-ear WH-1000XM6 headphones. (My previous XM3 pair lasted for seven years until I accidentally bent them.) The battery lasts for several weeks on a single charge.

Price: $460. The XM5 model, also excellent, is $300. Wirecutter’s review (which agrees that Sony’s XM6 model is the best) includes a $100 alternative.

MyNoise

Pick your favorite type of focus sound: a waterfall, a purring cat, white noise, rain, or a dozen others. I like the café, where you can adjust the ratio of chatter to kitchen noise. The creator, Stéphane, records real sounds and wrote a manifesto about avoiding AI-generated noise. The newest release: Pigeons of NYC.

Price and platforms: Free, browser-based.

[Image: MyNoise] Headspace

This was designed as a meditation app, but I use the focus music without lyrics to block noise around my Times Square office.

Price: $70/year or $10/year for students. Or share six accounts (family, roommates, teammates, etc.) for $100.

A Soft Murmur

Mix a range of sounds to block out city noise or whatever annoys you. Customize and save your own sound mix. Here’s my blend of waves, wind, and white noise.

Price and platforms: Free, browser-based.

[Image: A Soft Murmur] Give your body a break Wakeout

Take quick exercise breaks with short video loops showing real people doing cardio moves. I like one-minute exercises. The newest version adds little movement games. Body breaks improve my focus.

You can also use Wakeout to pick apps that pause when you’ve been sitting too long. Moving brings back access.

Price and platforms: The Chrome browser extension is free; a subscription for iPhone, iPad, Mac, and Apple Watch is $70/year or $13/month.

[Image: Wakeout] Time Out

Rest your eyes periodically if you work at a screen. I set Time Out to remind me to give my eyes a screen break every 15 minutes. You can choose your own interval. It nudges me to look out the window and stretch. It’s an old app, but it still works.

Price and platforms: Free, optional contributions of $5 to $60. Mac only.

Alternatives: BreakTimer is simple, free, and works on Mac, Windows, and Linux. Restier is a little more polished and also works across platforms; $9 for one device or $15 for two. Lookaway is an elegant Mac option that starts at $19.

Nex Playground

I spend most weekdays standing in front of my computer, so I love how the Nex gets me moving. Its 60-plus motion-sensing games are played with your body, not thumbs or controllers. My daughters, wife, and I have it linked to our New York City apartment TV, and I appreciate how it boosts my energy for evening work. It’s also great for short breaks that reset my focus when I work from home.

Price: $300, plus an optional $89/year subscription for all 60+ games.

[Image: Nex Playground] Hold yourself accountable Focusmate

Pair up with an accountability partner online. Pick a time on the site’s calendar when you want to get work done. Choose an interval of 25, 50, or 75 minutes. You’ll be paired with a real person who also wants to stay focused. Log in at the appointed time.

I was originally skeptical about this “body doubling” tactic. I found, though, that an appointment with another human made it feel like a special work interval. It did feel a bit silly to have to meet a random person over Zoom to trick myself into concentrating. But if that’s what works, I’ll do it on occasion.

Price and platforms: No app or download required. Works on any computer or mobile device with a camera and microphone. Free for up to three sessions a week, or $8/month billed annually for unlimited.

[Image: Focusmate] Stickk

Pick a goal and create your own commitment contract. You can pledge to donate to a cause you don’t support if you fail to follow through on your resolution.

Price and platforms: Web-based. No cost beyond what you decide to commit.

BeeMinder

Make commitments tied to data you sync. Works with categories like time spent on Facebook or whatever else you choose to sync from Toggl, Fitbit, or other time- or health-tracking platforms. Here’s a video explainer.

Price and platforms: Web-based. Free or $8/month for extra features.

Tranquility by Tuesday

My favorite book by Laura Vanderkam offers nine ways to make time for what matters. I like her suggestion about carving out a time slot toward the end of each week expressly for finishing things you’ve fallen behind on. I enjoyed talking with her about her latest book, and you can read more of her tips in her Vanderhacks Substack.

Deep Work

Cal Newport is a computer science professor and New Yorker writer who argues that computers and other devices can ruin our focus if we don’t use them thoughtfully. I found it helpful in exploring how to separate technical and menial tasks from work that requires slow, nonlinear thinking.

Indistractable

Nir Eyal stuffed his excellent guide to staying focused with useful insights. It’s full of research-backed ideas for combating internal and external distractions.

This article is republished with permission from Wonder Tools, a newsletter that helps you discover the most useful sites and apps.

Jeremy Caplan

South Korea’s bank hacks offer a warning about AI-powered cyberattacks

2 days 16 hours ago

Some of South Korea’s biggest banks have been hacked with the assistance of an artificial intelligence agent, opening up a new front in the AI and cybersecurity worlds.

Officials say at least seven financial firms were included in the attack, which saw personal information for 68,000 people stolen, including annual income and loan limits. This marks one of the first times AI tools have been used to disrupt global financial stalwarts.

It’s notable, since banks and other financial institutions typically have a higher level of security than other businesses or even some government agencies. Officials say they haven’t identified the people responsible for the attacks, which originated from over two dozen IP addresses spread out over several countries, including the U.S. and Japan. (Hackers frequently spoof their IP address to remain anonymous.)

South Korean officials are urging banks to boost their defenses. And the intrusions could serve as a warning shot for banks in the United States and other countries as well.

“AI gives attackers more opportunities to exploit gaps before defenders respond,” Yagub Rahimov, CEO of Polygraf AI, tells Fast Company. “My advice to bank leadership is to take a step back and invest in threat mapping. Trace the actual paths to sensitive data through employee activities, vendor connections, applications, APIs [application programming interfaces], and human and machine identities. They need to identify the systems that are reachable.”

The hackers apparently used Artex AI, an open-source agent developed in China, which ironically was designed as a cybersecurity tool that helped users find vulnerabilities in their networks. Artex has since updated its user guidelines to say the tool should not be used for malicious reasons.

AI-assisted hacking on the rise

While the attacks on the South Korean banks represent a new level, there has been a consistent uptick in AI-assisted hacking for some time now. A July report from the SANS Institute found that 78% of organizations saw either a confirmed or suspected AI-enabled attack in the past year.

The method of those attacks was evenly spread, though. Occurrences of phishing, deepfakes, and vulnerability exploitation were all virtually the same.

“AI tools are maturing, and so are the criminals using them—they moved from scaling phishing campaigns to autonomously probing defenses, chaining exploits, and adapting mid-attack,” Vakaris Noreika, cybersecurity expert at NordLayer Intelligence by NordStellar, tells Fast Company. “Going forward, we should expect attacks to grow in both scale and sophistication, which means defenses must keep pace with an AI-enabled threat landscape.”

The SANS Institute study showed a disparity between concern over possible AI-enhanced hacks and actual preparedness. Virtually every respondent—some 95%—said they believe threat actors are using AI today to enhance their attacks, with 58% saying they believe the use of AI is significant. Yet only 16% say they have shifted their priority to defend against such attacks.

Corrupting a tool

While Artex was designed to help protect users, the hackers’ use of this agent shows the range of possible threats that exist.

“The same capability that helps a security team investigate a weakness is also being used by an attacker to identify gaps for malicious purposes,” Rahimov says. “AI can connect tasks that previously required more manual work, making repeated attacks more economical—but even this can be turned around. Businesses should prepare for that capability to spread across tools and operators.”

Noreika warns that it’s not just smaller AI agent tools that can be corrupted, either. Most open-source AI models from a major lab possess the same advanced capabilities to hide malicious activity as security testing. That should put CEOs of banks and any other business on alert.

“The best way for companies to protect themselves is to adopt a hacker’s mindset—leveraging the very same AI tools to evaluate, stress-test, and strengthen their own security defenses,” Noreika says.

Chris Morris

NBA announces it’s using smart basketballs and wearable tech in preseason games

2 days 19 hours ago

The NBA is experimenting with two pieces of technology in the leadup to the season, the league announced Monday.

Select exhibition games will include a smart basketball that has a Bluetooth sensor in it and can collect data for future use in real-time officiating decisions. The league said the information from the Wilson-developed connected balls will be for research only and not affect calls made during those games.

Officials in select games will also use wrist wearables to communicate with the replay center in designated situations, including replay reviews, scoring changes and clock malfunctions.

The wearables went through what the NBA called a successful pilot program during the summer league. The technology will not extend into the regular season.

—Associated Press

Associated Press

Why America can’t build the iPhone

2 days 22 hours ago

For six decades, I’ve worked on product design and manufacturing for companies such as Polaroid, Seiko, and Apple, as well as startups. When I worked at Polaroid and Apple, they still had their own factories and chose to go to Asia to build products. As noted in Patrick McGee’s bestseller Apple in China, I was responsible for bringing Apple’s first product to Taiwan in 1994. As a product design engineer and program manager, I was always involved in where my products were manufactured, because of the impact manufacturing has on a product’s success.

In my jobs at Polaroid and Apple, I turned to Asia for different reasons. I brought manufacturing to Asia for Polaroid in the 1970s, when it was rarely done. By the time I did it at Apple in the 1990s, Apple was late to the game.

Today, as pressure mounts for Apple to build iPhones in the U.S., I know from all my experiences that it is an impossible demand. We don’t have the attitude, the infrastructure, or the resources. We lost that battle decades ago, and cannot will it back.

The U.S. never invested in building consumer tech manufacturing at the scale of China or Taiwan. We prioritized R&D, design, and innovation, and didn’t value low-margin manufacturing work. During the Cold War, U.S. government investment and incentives were directed toward aerospace and defense, not consumer electronics. Our manufacturing base became dominated by contractors focused on military specifications, not consumer mass production.

Manufacturing consumer electronic products, in contrast, requires an infrastructure of suppliers, competitive labor costs, large workforces, and flexibility. Resources in China and Taiwan have allowed many U.S. companies to dominate the world in technology. While China became the manufacturer to the world, it helped us become the innovators to the world.

Going offshore: the early days

When I joined Polaroid in 1966, I designed consumer cameras that sold millions per year. Once the design was done, we’d hand it off to our internal manufacturing group, only to have them come back to us months later with requests for small design tweaks to save a few cents here and there.

It was a slow, iterative process with little concern for time to market. I often wondered why I was working so hard to meet an aggressive design schedule and then had to wait around for the product to finally ship. Analysts and industry leaders emphasized the importance of time to market as a critical factor for a product’s success, especially in competitive and fast-evolving markets.

I wondered why we couldn’t be as creative in the rest of the process as we were in the design stages. So, I began to look for ways to get products to market more quickly.

Outsourcing manufacturing to companies building similar products seemed like a good approach. They already had the experience and knowledge, so they could move quickly. That allowed the manufacturing and design teams to collaborate earlier in the process.

It wasn’t that I didn’t try to do manufacturing in the United States. I reached out to companies with manufacturing capabilities that looked to be synergistic, but it often took weeks to hear back. Many of the companies were focused on defense-related products and showed little interest in building consumer tech products with tighter schedules and lower margins.

I wondered why this was the case. Could it be that these U.S. resources were focused on the defense industry during the Cold War and never invested in consumer manufacturing? Or perhaps the defense business was just more lucrative. It was disappointing, but, fortunately, I found another solution.

At Polaroid, I turned to a few companies in Japan that had been supplying some of our components and wanted a chance to build the entire product. They offered to take our design early in the process, improve it for manufacturing, and deliver the product in high volume much more quickly than we could on our own. We were being offered the opportunity to tap into their product expertise and quality-obsessed manufacturing that Japanese companies were known for, and to learn from them in the process.

It turned out that these companies were so good that we could almost drop off a drawing package and pick up the product months later. They brought speed, experience, and predictability, and enabled us to be much more effective designers with smaller teams. Every time I’d return from a visit to Japan, I’d feel a sense of accomplishment and excitement. It was like night and day compared with using our internal manufacturing organization.

This pattern repeated itself over the next few decades as I worked at other companies. When Japan became too costly and less efficient, I went to Taiwan and later China, as each of these countries developed their manufacturing capabilities.

The U.S. could have created similar capacity over the years, especially after the Cold War, but never did. Manufacturing was just never considered an important need. It was not glamorous and never considered to be strategic.

In the 1990s, when a company I cofounded, ThinkOutside, invented the Stowaway folding keyboard, we found a Taiwan keyboard company that was experienced with manufacturing tiny key mechanisms, and partnered with them. Like many other companies working to get products to market quickly, we were doing what we did best and letting others do what they did best.

At this time, there were a few contract manufacturers in the U.S., including Solectron and Flextronics, that could manufacture others’ products in their plants in the U.S. and elsewhere in the world. They started with printed circuit boards and had expanded into complete product manufacturing; they also had some design capabilities.

This evolution mirrored the tech industry’s growing reliance on outsourced manufacturing, driven by cost, scale, and time-to-market pressures. But I found that the U.S.-based options were bureaucratic lumbering giants, reminding me of my Polaroid manufacturing experiences. They wanted only to take on large-volume projects from well-established companies, and rarely took the kind of risks that Asian companies did.

Asia becomes the world’s manufacturer

Meanwhile, Taiwan and China were becoming destinations for companies to build technically complex products because they were so good at it. They were aided by huge government investments in office parks, infrastructure, and free-trade zones.

Taiwan quickly moved from building calculators in the 1970s to notebook computers in the 1980s. James Fallows chronicled the rise of China in his famous three-part 2007 article in The Atlantic, “China Makes, The World Takes,” as China became the manufacturer for the world.

Scores of Chinese companies became available to manufacture a customer’s original design. They were known as original equipment manufacturers, or OEMs. If they offered their own designs that a customer could sell under its name, they were called original design manufacturers, or ODMs.

It was a win-win partnership. We used China to build our products, and they relied on us keep their factories running by marketing and selling products they designed.

Companies often specialized in specific product categories. If you wanted to build a particular type of product, you’d go to a company that was already building something similar. You tapped into their expertise, supplier relationships, and buying power, and everyone would benefit from the economies of scale and relevant experience.

While some first-time customers worried about two competing products being made by the same OEM, each company’s areas were usually secured from the other. When I worked on the Barnes & Noble Nook e-reader, we used Foxconn, the same manufacturer that built the Amazon Kindle. That allowed us to go from an initial concept to market in less than a year.

Product designers large and small now had a destination where they could bring their product designs and get them manufactured quickly and efficiently. You no longer needed to be a Sony to have access to manufacturers. Chinese factories enabled thousands of companies across the United States and the world to turn their ideas into viable products.

As these OEMs and ODMs grew in number, component manufacturers and other suppliers opened their own factories nearby, close to the facilities using their parts, providing even greater efficiency and faster time to market.

As an example of how effective this was, I was about to ship the initial lot of a new consumer electronic product to the U.S. from Shenzhen, the Chinese technology hub adjacent to Hong Kong, and it was to arrive just before going on sale. At the last moment, I found an error in the instruction manual that needed to be fixed before the product could ship. We called the printer, which was a few blocks away. It made the correction, and delivered correctly printed manuals two hours later.

During this time, we never considered that we were taking jobs from Americans. Nothing like this ever existed in the U.S.—not the industry, the factories, nor the workers. What China offered was unique.

Apple was late to the game

When I joined Apple in 1994 to work on the Newton MessagePad personal digital assistant, the company was manufacturing most of its computers in its own factories in California, Colorado, Ireland, and Singapore, even though other computer companies had been outsourcing most of their notebooks to Taiwan for years.

The first version of Newton, the MessagePad 100, was developed with Sharp Electronics in Japan because of that company’s expertise in building handheld products such as its Wizard organizer. But its cost came in way above plan, and I was asked to quickly develop a new model at a more affordable cost.

Apple’s internal manufacturing organization had neither the interest nor experience to master products like the Newton. Its leaders considered our product to be a nuisance and encouraged us to find our own manufacturer.

Taiwan was the obvious answer. After all, that’s where Sharp was now building its second-generation Wizards, and where most of the world’s calculators and notebook computers were being built, other than Apple’s. The new Newton MessagePad 110 was the first time Apple developed a product to be manufactured in Taiwan.

I flew to Taipei and spent two weeks visiting potential manufacturers, mostly notebook and calculator manufacturers. We selected Inventec, a $320 million manufacturer at the time. It was busy producing calculators for Texas Instruments, phones and fax machines for telecom companies, and notebook computers for Compaq and Zenith.

But we ran into an immediate challenge. Inventec would need an $800,000 machine to assemble the processor chip powering the Newton, machinery not yet used in Taiwan. The company’s CEO, Richard Lee, insisted on buying it at Inventec’s expense. He never considered it a burden, explaining it would give them new capabilities.

Inventec assigned a group that began working with our Apple design team. We began with a handshake deal while Lee suggested we negotiate a contract in parallel. We all knew drawing up contracts could take many months, particularly at Apple, and he didn’t want to lose that time.

Our joint team of about a half-dozen engineers met monthly in Taipei. The industrial designer on the team, Jony Ive, had recently joined Apple. This would be one of his first projects, and his first time he’d been to Taiwan. (I later learned that he complained to his boss about how hard I made him work.)

The product was completed in eight months and went on to fail almost as badly as the first model. Sales volumes were about 10% of plan. A few months later when I apologized to Lee, he told me not be concerned. He was happy to be working with Apple and considered this to be a win. He was thinking of the long term, and was proud of the Apple relationship. I was embarrassed.

Inventec went on to develop and build notebook computers, servers, and a range of other products for most of the world’s technology companies, including Apple. Its revenues for 2025 were $22 billion.

How design impacts manufacturing

The Newton was constructed like most other consumer tech products at the time. It consisted of a molded plastic clamshell enclosure, a large printed circuit board populated with electrical components, a pressure sensitive touch screen, a stylus, and a battery compartment that held four AA cells. Ive’s design contribution was creating a beautiful fluid-looking package that made the product look smaller than it was. It was nicely done, but its fit and finish were not dissimilar from other products being produced at the time, such as Nokia phones. 

Years later, when Ive became Apple’s senior VP of industrial design, he elevated its design to a level never seen in consumer products. I recall seeing the first iPhone and marveling at how precisely the parts fit together, the flawless finishes of the parts, and the use of new materials never employed at mass scale.  His designs set a new standard for all consumer tech products. Other products suddenly seemed crude by comparison.

Apple’s design and manufacturing engineers were responsible for putting these new designs into mass production. It certainly could not be done in the U.S., which didn’t even have the ability to build much simpler products. The new design standards with the ultraprecise fit and finish meant that the products needed to be built China.

When I hear politicians demanding that iPhones be built in the U.S., it’s clear they have no understanding of how products are created and built. You can’t just wish it to happen. You would need to create much of what China and Taiwan have built over the past 30 years.

It’s not just setting up factory buildings filled with assembly machinery and workers. You would need the entire infrastructure of suppliers and manufacturers of the parts that go into the products: the factories to build displays, batteries, and electronic components. You’d require the experience, and the right attitude.

Shipping the components from China and doing final assembly in the U.S. would also cause huge issues. Let’s suppose you bring displays in from China, and they have a defect. If you were building the product in Shenzhen, you’d call the display company that’s an hour away and they would have engineers in your factory in a few hours to address the problem or provide new parts. Try that from the United States, and you would likely shut down your assembly line for several weeks.

Now consider what would happen once you multiply this by the thousands of parts and processes that go into a product. Not only is it inefficient, it’s also just not practical to build these products here. It makes as much sense as building an automobile in the Arctic from parts made in Detroit.

The Mac Mini isn’t an iPhone

With all this said, Apple, under pressure to bring products back to the U.S., has agreed to build Mac minis in Houston. They have asked Foxconn, the same company that has built, staffed, and managed much of Apple’s China manufacturing, to create and operate the assembly operation.

The Mac mini is a much simpler and lower volume product than the iPhone. Essentially, it’s a printed circuit board assembly housed in an aluminum box. But it will still require many of its components to be shipped from China. Politically, it may be a good move, but practically, it means very little.

It will be interesting to see if Apple’s new CEO, John Ternus, makes any fundamental changes to Tim Cook’s manufacturing strategy. As a product designer, I’m guessing that Ternus—who is also a product designer—wants to get his new products to market in the fastest and most efficient way possible and will continue to rely on Asia. That approach has helped make so many of his past products huge successes.

Phil Baker
Checked
16 minutes 41 seconds ago
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