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Vertu wants CEOs to run companies from an AI foldable starting at $6,880

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Luxury smartphone brand Vertu on Thursday unveiled a foldable phone powered by an AI agent that connects with enterprise software and coordinates workflows. The company is targeting executives who manage business operations and communications on the move.

Called the Alphafold, the foldable smartphone starts at $6,880 for the calfskin version. Higher-end models feature bespoke finishes including alligator leather, 18K gold, and natural diamond accents, along with customized detailing. This continues Vertu’s long-standing strategy of positioning its phones as luxury status symbols aimed at affluent buyers. The company told TechCrunch that its highest-end standard model is currently priced at $46,800, with further customization options available.

The launch marks Vertu’s latest attempt to reinvent itself for the AI era after struggling to remain relevant in the modern smartphone market. The Hong Kong-headquartered company, once known for luxury handsets and concierge services popular among wealthy buyers before the rise of the iPhone, has changed ownership multiple times over the years as mainstream smartphone makers came to dominate the industry. Nonetheless, Vertu is betting the Alphafold can help reinvent the brand for the AI era by combining luxury hardware with enterprise-focused AI capabilities.

Vertu’s Alphafold comes with Hermes Agent, built on top of the open-source Hermes project by Nous Research. The agent can connect to enterprise systems like ERP and CRM, and coordinate tasks such as approvals, scheduling, sales tracking, travel planning, and operational reporting through natural-language prompts. However, the company said that its Phone-to-ERP and VPS deployments would be customized for each customer depending on their existing enterprise systems, with pricing varying accordingly.

Vertu Alphafold
Image Credits:Vertu

The Alphafold, Vertu said, can route requests across multiple AI models including OpenAI’s GPT, Anthropic’s Claude, Google’s Gemini, and selected open-source models, while also integrating with more than 80 apps and dozens of native phone functions for cross-platform workflows.

Existing AI features on smartphones from major manufacturers remain focused largely on consumer tools such as image editing and voice assistance, Vertu CEO Molly Ma said. This leaves room for more advanced AI-agent workflows tied to enterprise systems. She also pointed to earlier AI-agent smartphone experiments in China that gained popularity before facing challenges over data privacy and cloud-based data collection.

The Alphafold, Ma said, aims to address those concerns through a privacy-focused architecture featuring a proprietary A5 security chip. This silicon is designed to isolate authentication keys, biometric credentials, and sensitive enterprise information from the main operating system, the company said. It added that commercially sensitive data can be processed locally on the device, while prompts sent to external AI models are redacted or tokenized before leaving the phone.

While Vertu has emphasized the device’s privacy and security architecture, including on-device processing and data redaction features, the company said the system has not yet undergone third-party security audits or independent certification. However, Vertu told TechCrunch that independent audits and certification remain on its security roadmap “as an explicit next-stage commitment,” adding that it would “communicate the progress and the results publicly” once the product matures further.

The Alphafold is powered by Qualcomm’s Snapdragon 8 Gen 4 processor and features an 8.05-inch foldable display alongside a 6.53-inch outer screen, a 6,500mAh battery, and satellite communication capabilities. The device also includes a triple rear camera setup with 50-megapixel primary and ultrawide cameras, as well as a 5-megapixel telephoto lens. Vertu said the phone’s hinge uses metal, titanium, and carbon-fiber components and is rated for up to 650,000 folds.

The Alphafold is not Vertu’s first attempt to combine AI with foldable devices. The company last year introduced Agent Q, a clamshell-style foldable smartphone focused on AI-driven automation and productivity features.

However, Ma told TechCrunch that Alphafold represents a significant step forward from Agent Q, arguing that AI-agent technology has matured rapidly over the past year, with improvements in memory, automation and app integration.

Foldable smartphones remain a niche segment globally despite years of investment by major manufacturers including Samsung and Huawei. As many as 20 million foldable smartphones were shipped globally in 2025, accounting for less than 2% of total smartphone shipments, according to IDC data shared with TechCrunch. The research firm said foldables sold at an average price of about $1,300 last year — roughly three times the price of non-foldable smartphones.

Kiranjeet Kaur, associate research director for mobile phones research at IDC, said foldables could eventually benefit from AI-agent workflows because their larger displays are better suited for multitasking and productivity-oriented experiences. She, however, added that enterprise AI adoption on smartphones still lags behind computers, and that most enterprise smartphone decisions continue to be driven by ecosystem integration and device management support rather than AI capabilities.

The first 115-unit batch of Vertu’s Alphafold begins shipping this week across major markets including the U.S.

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Understanding Phase Noise Fundamentals – Wiley Science and Engineering Content Hub

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A stable frequency source is a fundamental requirement in virtually every RF and wireless system, yet all real-world oscillators exhibit some degree of short-term frequency instability known as phase noise. This instability manifests as unwanted sidebands around the carrier in the frequency domain and as timing jitter in the time domain. When phase noise is excessive, it causes spectral regrowth that leaks energy into adjacent channels, reduces receiver sensitivity through reciprocal mixing, and rotates digital modulation constellations to the point where bit errors multiply. Understanding these effects is essential for engineers designing transmitters, receivers, and frequency synthesizers for modern communications standards. This guide walks through the physics of phase noise, its practical consequences for system performance, and the two principal measurement approaches — the traditional spectrum analyzer method and the more sensitive cross-correlation technique used in dedicated phase noise analyzers — giving engineers the knowledge they need to specify, measure, and minimize phase noise in their designs.

 

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Google engineer charged with insider trading after making $1.2M on Polymarket

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The U.S. Justice Department charged Google software engineer Michele Spagnuolo with insider trading, alleging the employee made $1.2 million trading on Polymarket based on confidential business information.

Spagnuolo, who used the name “AlphaRaccoon” on Polymarket, has worked at Google for over 12 years, according to information on LinkedIn.

“As alleged, Spagnuolo violated the duties he owed to his employer and used Google’s confidential business information to make more than $1.2 million in trading profits on Polymarket,” Jay Clayton, the United States Attorney for the Southern District of New York, said in a press release. “Insider trading compromises the integrity of our markets, and the American people want this greed-driven conduct investigated and prosecuted.”

Prediction markets like Polymarket, Kalshi, and others allow users to bet on pretty much anything. Insider trading is not allowed on these platforms because it’s illegal, but some users still commit the offense. The Justice Department recently charged a U.S. Army soldier for allegedly using his insider knowledge of the U.S. military operation to capture Venezuelan president Nicolás Maduro to make $400,000 on Polymarket.

According to the complaint, Spagnuolo risked over $2.7 million on wagers related to Google’s 2025 Year in Search, a marketing campaign in which Google reveals the world’s most popular searches of the year. Spagnuolo allegedly accessed confidential, internal Google Search data about the most-searched celebrities to inform his bets.

“Polymarket worked closely with the U.S. Attorney’s Office for the Southern District of New York and the CFTC, and is the only prediction platform to date whose cooperation has led to insider trading charges in the United States,” a Polymarket spokesperson told TechCrunch. “Blockchain trading is transparent, traceable, and bad actors leave footprints. We are committed to maintaining accurate, fair, and transparent markets as well as enforcing our rules and working with our regulators and law enforcement.”

A Google spokesperson told TechCrunch the company is working with law enforcement on its investigation.

“The employee accessed our marketing material using a tool available to all employees, but using such confidential information to place bets is a serious breach of our policies,” Google said in an emailed statement, “We’ve placed the employee on leave and will take the appropriate action.”

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Why Google’s AI can’t spell Google (or anything else)

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How many Ps are in Google? According to Google, there are two.

There’s also is also “exactly 1 ‘r’ in the word ‘poop’,” Google’s AI Overview says, as well as two ‘d’s in the word journalism, yet spelled it: j-o-u-r-n-a-d-i-s-m. Google did at least identify that there is one P in the last name of the U.S. president, but spelled it as t-r-p-u-m.

You didn’t need to be a prophet to predict that Google’s AI-forward Search overhaul was going to go over poorly. We’ve done this before. The first time Google added AI Overviews to Search, the feature ended up citing satirical posts from The Onion and Reddit, advising people to eat rocks and put glue on their pizza.

This time around, as Google doubles down on its commitment to make generative AI the centerpiece of its 29-year-old flagship product, it’s not surprising to see it stumble.

“Counting within words has been a known challenge for LLMs, and we’re working to fix this particular issue,” Google told TechCrunch in an emailed statement.

These basic spelling errors may seem familiar. LLMs, the kind of artificial intelligence that powers chatbots and other text-generators, are not built to understand spelling. It’s been a running joke for years that whenever a company unveils a new AI model, you should ask it how many ‘r’s are in the word strawberry. These AI models — which can code an app in seconds, or solve problems that have stumped mathematicians for decades — are about as good as a kindergartener at spelling.

Google’s AI overview woes reach beyond silly spelling mistakes though. Google already patched an issue from last week in which searching the word “disregard” would yield what looked like a dictionary definition of the word, only the definition was shown as, “Understood. Let me know whenever you have a new prompt or question!” But these spelling errors have remained amusing because they’re so difficult to quash.

As researchers have previously explained when we’ve asked about these spelling conundrums, AI doesn’t perceive sentences as units of language made up of words and letters. Many LLMs are built on transformers models, which break down text into tokens, which can be full words, syllables, or letters, depending on the model. Instead of “reading” like a human would, the AI converts the text into numerical representations of itself, which are then contextualized to help the AI come up with a logical response.

Image Credits:TechCrunch

“LLMs are based on this transformer architecture, which notably is not actually reading text. What happens when you input a prompt is that it’s translated into an encoding,” Matthew Guzdial, an AI researcher and assistant professor at the University of Alberta, told TechCrunch. “When it sees the word ‘the,’ it has this one encoding of what ‘the’ means, but it does not know about ‘T,’ ‘H,’ ‘E.’”

The token-based architecture that powers LLMs like Google’s AI overview is inherently limiting, and researchers haven’t been optimistic that they can solve the spelling problem.

“It’s kind of hard to get around the question of what exactly a ‘word’ should be for a language model, and even if we got human experts to agree on a perfect token vocabulary, models would probably still find it useful to ‘chunk’ things even further,” Sheridan Feucht, a PhD student studying large language model interpretability at Northeastern University, told TechCrunch. “My guess would be that there’s no such thing as a perfect tokenizer due to this kind of fuzziness.”

This isn’t necessarily an urgent problem on researchers’ minds, since the utility of LLMs doesn’t come in their capacity to spell. But these blatant failures help us remember that AI is not perfect, even if it may sometimes seem like an all-knowing power beyond our comprehension. We cannot blindly trust AI outputs without double-checking their accuracy.

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