OnePlus has introduced the N6 in India, a new budget-tier handset with a huge 8000mAh battery.

That battery rating puts the N6 in a narrow group of cheap Android phones where endurance rather than processing power is the key selling point. The cell is also double that of the iPhone 17, which sits at 3692mAh.

Charging support runs to 45W via SuperVOOC wired, and the N6 also supports 5W reverse wired charging, a feature that lets the handset function as a power bank for other devices. This remains uncommon across budget Android hardware at this tier.

Beyond standard charging, the N6 incorporates bypass charging, a feature that routes incoming power directly to the processor and display during intensive tasks rather than cycling it through the battery. This reduces heat accumulation during extended gaming sessions and helps preserve long-term cell health.

Power comes from the MediaTek Dimensity 6360 Max, a chipset paired with either 4GB or 6GB of RAM plus 128GB of internal storage and a microSD card slot.

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The display stretches to 6.8 inches with an IPS LCD panel running at 120Hz, an HD+ resolution, and a peak local brightness of 1200 nits.

Camera hardware centres on a 50MP rear sensor with an f/1.8 aperture alongside an 8MP front-facing camera, and the N6 ships with OxygenOS 16.0 based on Android 16, backed by a commitment to two major OS updates and three years of security patches.

The N6 launches in India on 4 July through Amazon India and OnePlus India in Fresh Mint and Midnight Green, with the 4GB/128GB configuration priced at INR 22,999 (approximately £215) and the 6GB/128GB variant at INR 24,999 (around £234), while pricing and availability for the UK and other international markets have not been confirmed at this stage.

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Oracle is burning hundreds of billions to finance AI datacenters for the likes of OpenAI. Now, the company is admitting they may not pay off.

Amid the usual boilerplate, Big Red cited numerous risk factors related to its AI infrastructure investments in a regulatory filing published late last month.

“To grow our OCI business, which requires increased computing capacity, we must incur significant capital and operating expenditures to increase our existing data center capacity and to establish data centers in new geographic locations,” the filing reads, using the TLA for “Oracle Cloud Infrastructure.”

These investments, the company notes, are tied to long-term commitments for infrastructure and datacenter capacity. Unlike the big three cloud providers, Oracle prefers to lease datacenter capacity from partners like Crusoe, rather than build them itself.

While the filing doesn’t mention OpenAI explicitly, Oracle’s success as an AI infrastructure provider is inextricably tied to the model dev and its cult-of-personality leader, Sam Altman.

In early 2025, Oracle joined OpenAI, SoftBank, and MGX to put its name on the so-called Stargate initiative, an ambitious project to pave the planet with half a trillion dollars worth of bit barns.

As we later learned, Oracle had signed up to provide $300 billion of capacity over five years as part of a long term agreement with OpenAI, which would also see the database provider manage the model dev’s flagship facility in Abilene, Texas. In addition to the OpenAI deal, Oracle claims to still have about $155 billion in remaining performance obligations from other customers.

This puts Oracle in a tough spot. If it underestimates demand, it could lose customers to competing infrastructure providers. On the flip side, Oracle says if it overestimates demand, or any of its key customers can’t make rent, it could end up footing the bill for the datacenter capacity it leased on their behalf.

Oracle’s OpenAI deal will reportedly contribute up to $30 billion in revenues annually, with revenues expected as early as next year. But OpenAI still hasn’t managed to turn a profit, which means its ability to pay its bills depends entirely on its ability to continue raising capital.

“Our business is, and may continue to be, exposed to risks of customer non-payment and non-performance,” the company wrote. Well, yes.

And even if they pay up, there’s no guarantee its customers will renew their leases. “If customers do not renew their contracts, we may be unable to re-lease, repurpose or assign such capacity on acceptable terms, if at all,” the filing reads. 

Customers’ ability to pay their bills may not be the only risk factor facing Oracle’s AI gamble. As the company notes, it is already having trouble securing enough power at fair prices to fuel its datacenter buildout.

“We have faced, and may continue to face, challenges with securing reliable and cost-effective power sources for our data center energy demands, which are constrained globally due to the significant increase in demand for and limited availability of energy to power AI compute,” the company wrote. “In addition, power prices can be volatile, including due to extreme weather events and market structure in certain regions, and increases in energy costs can adversely affect our margins, particularly where customer pricing is fixed or committed.”

Oh, and then there’s the fact that building datacenters is not for the faint of heart in the first place. Anything that could go wrong … could go wrong. Let’s go to the tape:

“Our data center expansion depends on access to suitable, permitted build sites; reliable and predictable power sources; networking hardware; and server availability, including graphics processing units, memory devices and other critical components. Data centers in geographies that we rely on may be unavailable on commercially reasonable terms or at all. Government-imposed limits or moratoria on data center construction in a given market could hinder our ability to execute our expansion plans or prevent us from completing planned data center projects. Even where suitable sites and capacity are available, our data center expansion plans are complex and subject to execution risks, including, among others, delays or cost increases related to design, engineering, permitting, construction, utility interconnection, equipment delivery and contractor performance. Our ability to build and operate data centers also may be affected by existing and evolving laws, regulations and policies relating to land use and zoning, environmental permitting, energy usage, grid reliability, greenhouse gas emissions, water usage, building codes, health and safety, tax incentives and data localization.”

Whew.

But Oracle is in too deep to call it quits.

“We have made significant investments in AI initiatives, including investments in infrastructure and headcount, and we expect to continue to invest significant resources to build and support our AI products in support of our growth strategy,” the company warned investors. “If we do not continue to invest significant resources to develop and support our AI products, we may fall behind technological developments and evolving industry standards, which would likewise harm our ability to compete.”

In other words, damned if they do and damned if they don’t, so what’s left to do other than burn, baby, burn?

And that’s exactly what Ellison and crew plan to do. During its Q4 earnings call last month, the company said it planned to spend $70 billion on capital expenditures during the 2027 fiscal year, up from around $55 billion spent during its 2026 fiscal year.

To support this spending spree, Oracle will have to take on additional debt. In 2027, the company hopes to raise around $40 billion in debt and equity. That’s on top of the $18 billion in debt it raised back in September.

Stock market bettors aren’t sure they like these odds. The company’s stock is down more than 40 percent in the last month.®

This week, two teachers take a hard look at what happens when you hand a problem to a tool and trust it to solve that problem. David Webb, a school teacher based in Jakarta, India, spent a year vibe coding an AI-powered library app called LibraryAid and discovered exactly where the algorithm ends and the educator begins. Then, California high school teacher Gabe Nitro makes a counterintuitive argument: the phone pouches sweeping his district may be swallowing the very instructional time they were designed to protect.

WHAT YOU’LL LEARN

• David Webb built LibraryAid, a personalized book recommendation app, using vibe coding techniques with no prior computer science background, and the tool now tracks approximately 30 factors, including student interests, reading history, and classroom topics, to generate personalized reading recommendations.

• One student reading two grade levels below placement made three times the average reading progress after the app matched him to a book series he loved, demonstrating both the power and the limits of algorithmic recommendation.

• A study from the National Bureau of Economic Research found that Yondr pouches had no statistically significant impact on standardized test scores for high schoolers in English, a finding that surprised even teachers who had adopted the pouches.

• Gabe Nitro argues that phone pouches consume up to 49 minutes of instructional time per school day in enforcement alone, and that the real distraction problem simply shifts to Chromebooks once phones are sealed away.

Listen to the episode:

This Week with EdSurge is produced by the EdSurge newsroom. Subscribe to the EdSurge newsletter for the latest in education news delivered straight to your inbox.

The IPO has valued Bending Spoons at roughly $18.4bn.

Bending Spoons, the Italian company that owns Vimeo and Eventbrite, is set to debut on the US Nasdaq market today (1 July), after raising $1.68bn in its public listing.

The company sold around 58m shares at $29, above its targeted range of between $26 and $28 per share.

The initial public offering (IPO) valued Bending Spoons at roughly $18.4bn – above the initial expectation of around $17.8bn.

Bending Spoons’ IPO comes at a time when investor attention is largely turned to AI, after SpaceX raised a record $75bn and Anthropic announced its intentions to go public.

OpenAI, which also intended to go public, is now reportedly mulling over delaying its plans, after SpaceX shares dropped sharply following its listing.

SpaceX shares, which were priced at nearly $202 apiece at their peak on 16 June had slumped to around $153 by 26 June. They have since risen to around $171 a share today (1 July).

Founded in 2013, Bending Spoons, led by Matteo Danieli, Luca Ferrari, Francesco Patarnello and Luca Querella, acquires and revamps existing digital businesses.

It’s 50-plus portfolio includes Evernote, WeTransfer, Eventbrite and AOL.

In 2024, the company acquired five businesses for a total of $876m. In 2025, that rose to about $1.9bn for six businesses, while it spent $2bn for just two businesses so far this year.

Bending Spoons said it has around 500m monthly active users across its portfolio of digital products, including more than 9m monthly paying customers.

It made a compound annual growth rate of 84pc between 2023 and 2025, while making more than $600m in the first quarter of 2026.

The Italian company has identified more than 1,000 businesses as potential acquisition targets for the future, it said in a government filing.

Don’t miss out on the knowledge you need to succeed. Sign up for the Daily Brief, Silicon Republic’s digest of need-to-know sci-tech news.

from the you-said-it dept

Legal systems have always struggled to keep up with rapid technological change, and things are no different in the world of generative AI. There are still relatively few rulings on the new issues that the roll-out of AI-based services is raising. That makes a ground-breaking judgment from a court in Germany particularly important. It concerns the AI Overview that sits at the top of the Google’s search results. The Decoder summarizes the court’s ruling:

The Regional Court of Munich hit Google with a temporary injunction barring the company from spreading false claims about two Munich-based publishers through its AI-generated search overviews (case no. 26 O 869/26). The court classified Google as a direct infringer because the “AI overview” is its own content, not just a list of search results.

Google’s AI overviews had falsely tied two publishing companies to scams, subscription traps, and shady business practices for certain search queries. According to the court, the AI mixed up information about other, genuinely sketchy companies with the plaintiffs and drew connections that didn’t appear in any of the linked sources. The publishers sent Google a cease-and-desist letter, but Google didn’t respond appropriately.

The legal innovation here is that the local German court held Google liable for the content of its AI Overview. Unlike traditional search results, which simply point to external sources of information, Google’s AI Overview made statements that were original, the court said:

Google’s AI overviews work nothing like traditional search results, the court argues. The AI rewrites and judges results “in its own words and according to its own structure,” the ruling says. In the case at hand, for example, it opened with confident claims like “Yes, [company] is known for dubious business practices,” then built its own structure with a summary, red flags for the alleged scam, and tips for users.

The court also found that the AI overview made claims “that are not even made in the search results.” None of the linked sources drew any connection between the plaintiffs and the shady companies the AI mentioned. The court called these “the defendant’s own statements.”

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Google argued that people using its search engine could check the results, but the court dismissed the idea that this was the responsibility of the users. Leaving aside the fact that research from the Pew Research Center last year found that “Google users are less likely to click on links when an AI summary appears in the results,” there is also the difficulty of checking statements that have been made up (as in this case), which therefore come with no reference links. The court also dealt with the issue of free speech protection for AI-generated content:

An AI’s opinion is “not the expression of an acquired conviction of the persons expressing it, but the result of an algorithm,” the court wrote.

Offering AI-powered research is “above all an expression of Google’s business activities” and “at most a secondary expression of an interest in being able to freely express one’s opinion and beliefs.”

In a statement given to The Decoder, Google said “We invest deeply in the quality of AI Overviews to ensure that the overwhelming majority of responses provide accurate information, and they are designed to reflect the information that exists on the web.”

Since there is no way to ensure that AI responses are 100% correct, this judgment is a big problem for Google, not least because the company plans to place AI Overviews at the heart of its new AI-saturated search engine, as Techdirt reported recently. Not surprisingly, Google has announced that it will appeal against the ruling, which comes from a local German court. If a higher court upholds the judgment, one solution would be for Google to remove AI Overviews in Germany. That would be messy, but doable. But it’s not clear how other AI companies such as chatbots could do the same, since the AI-generated response generally forms the basis of the whole service. Some might choose to discontinue their operations completely in any jurisdiction that adopts a similar position to the Munich court. That would make the roll-out of international services more difficult.

In a post on his blog, the security guru Bruce Schneier points out that if the ruling stands and is adopted elsewhere, it could have important implications not just for things like Google’s AI Overviews and chatbots, but also for the increasingly popular AI agents:

More generally, liability concerns could mean that many current use cases for agents won’t be commercially viable. Companies may not be able to profitably operate AI lawyers, doctors and media influencers if they are held responsible for what they say and do.

Schneier says that he is “OK with this outcome”:

There’s nothing in the law that requires us to accommodate AI systems if they are fundamentally untrustworthy, just as we don’t need to accommodate untrustworthy human systems. Any company that won’t stand by the statements its agents make—whether human or AI—doesn’t deserve users’ time or money.

Clearly this question of AI and agentic liability requires urgent legal clarification. The German decision should at least help to concentrate people’s minds on the topic.

Follow me @glynmoody on Mastodon and on Bluesky.

Filed Under: ai agents, ai overview, algorithm, doctors, free speech, generative ai, germany, influencers, lawyers, liability, links, scams, search, trust

Companies: google, pew research center, the decoder

At a conference last year, Ford CEO Jim Farley said that artificial intelligence is “going to replace literally half of all white-collar workers in the US.” Just last week, Ford executives said that the automaker had quietly rehired more than 350 of what it internally calls “gray beard” engineers over the past three years to help fix the AI quality-control systems that weren’t getting the job done.

Over the last decade, US automakers have cut more than 20,000 jobs, nearly a 20% reduction in workforce between Ford, General Motors and Stellantis combined. While Ford hasn’t said for sure how many of these gray beard rehires were originally fired to make way for AI and how many are simply returning retirees, Farley’s recent statements on automation-fueled worker replacement certainly paint an awkward picture.

Representatives for Ford and the United Auto Workers union did not immediately respond to requests for comment.

Not getting the desired results

“Artificial intelligence is a fantastic tool, but it’s only as good as the information you use to train it,” Charles Poon, Ford’s vice-president of vehicle hardware engineering, told reporters last week. “Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high-quality product.” 

Kumar Galhotra, Ford chief operating officer, was even more blunt about the realities of AI in manufacturing, saying that Ford had been “relying more and more on automated quality systems and not getting the desired results.”

More than a simple oopsie, automation issues have been costing Ford billions in warranty costs and recalls. A study from iSeeCars, an automotive marketplace and research company, ranked recent Ford models among the most recalled vehicles in the industry. Ford’s statements and the rehiring of experienced workers are essentially an admission that moving too quickly into AI was a big mistake.

Many major corporations in almost every aspect of tech and manufacturing have been naming artificial intelligence as an excuse for large workforce reductions, often without fully accounting for what gets lost when that human factor walks out the door. Entire industries have been crunching the uncomfortable numbers of replacing human judgment with automated systems, with some even backtracking on their decisions when the true cost of AI proves too high. 

What happens now?

Last week, Ford announced that, for the first time in 16 years, it had captured the number one spot among mainstream brands in JD Power’s 2026 Initial Quality Survey, up from tenth last year. The automaker credits the rise, in part, to the contributions of the rehired gray beards. But before you get too excited about the triumph of these modern-day John Henrys over the machines set out to replace them, don’t forget what ultimately happened to that folklore hero: He was still replaced by the steam engine.

Galhotra said the rehired specialists — some former Ford employees, others drawn from industry suppliers — were brought back specifically to “hunt for failure points before a part ever reaches the plant floor.”

Ford isn’t abandoning AI. Instead, the returning gray beards are doing two things: training younger staff who never worked alongside those veterans and helping to rebuild the data pipelines that the AI tools run on. 

Essentially, they’ve been brought back to fix and train the automated software systems that replaced them. Ford also said it has built a dedicated 40-person software quality assurance team and added more than 100,000 AI-powered automated tests to catch edge cases late in development.

Technology marches on.

Ford just happened to learn the lesson loudly enough to become a case study, but I don’t think it will be the last. There may not always be gray beards to call on to save the day.

Wenting Zhang looked at the M5PaperS3 and decided its e-ink screen could do more than display static pages. The compact development board from M5Stack carries an ESP32-S3 processor, a 4.7-inch 960-by-540 e-ink touchscreen, a simple buzzer, and a microSD slot. He turned the whole package into a handheld that runs original Game Boy software at a steady 60 frames per second.

Most e-ink displays are noted for being a little slow. It has a lot to do with how charged particles in the panel take their time drifting into place, thus a full refresh can take hundreds of milliseconds and frequently produces a faint afterimage. Zhang stomped on those physical constraints. He did not simply accept the status quo and dismiss those restrictions as the maximum. Instead, he created a bespoke driver that only works with the display’s low-level parallel interface. This driver holds a super-compact state record for each pixel, with only four bits per dot.

That state buffer receives a new set of image data every sixtieth of a second, while the driver nudges each pixel into place using a series of voltage changes. It’s a clever approach that renders the standard global lock, which requires the entire panel to complete one full cycle before receiving fresh commands, completely obsolete. The Game Boy panels are small (160 by 144 pixels), therefore a crisp threefold upscale with slight dithering is well within the processor’s capabilities. Fortunately, all of the active buffers fit inside the ESP32-S3’s extremely fast internal SRAM.

The two cores on the board function together, with one running the emulator itself. The second core is responsible for processing display updates and sending new data via DMA transfers timed to the vertical sync signal. This preserves the 60 Hz rhythm perfectly on beat even when the emulation workload changes. Zhang tried several Game Boy emulator cores before settling on CrankBoy, an optimized fork of Peanut GB. It provided the best trade-off between speed and compatibility on this hardware. Most games run close to full speed. The system will skip the odd frame to keep the timing just perfect for sound and input in the event of a demanding situation. Unfortunately, Game Boy Color titles remain out of reach for the time being due to their roughly doubled processing demand.

The sound emanates from a single buzzer on the board. Zhang created a rapid-switching scheme that cycles between crude approximations of the four original Game Boy audio channels. The end output is recognized chiptune music rather than exact waveform playback, but it preserves the essence of the games without the need for additional gear. The touchscreen includes all of the controls. The panel’s lower portion displays a Game Boy-style directional pad and various action buttons. Any taps register right away. There is experimental Bluetooth LE gamepad support, although it only works with a limited number of controllers right now.

Saved data is stored on the microSD card, and the hardware power button only cuts power, with no shutdown process (Zhang added a manual save button to the screen). Quick save states add an extra degree of security during lengthy gaming sessions. The entire project’s source code (known as Paper Boy S3 in some places) is now available on GitLab. There’s also a separate proof-of-concept JIT recompiler that another developer worked on, demonstrating one approach to go even faster if someone wants to take it a step further.
[Source]