請同意我們的隱私權規範，才能啟用聽新聞的功能。

（中央社記者吳家豪台北27日電）蘋果公司（Apple）今晚在官網發表搭載新一代M6自研晶片的14吋MacBook Pro筆記型電腦及24吋iMac桌上型電腦，採用全新雙16核心神經網路引擎、內建神經網路加速器的繪圖處理器（GPU）架構，鎖定裝置端大型語言模型與代理式AI運算需求。

蘋果公司27日發表14吋MacBook Pro筆記型電腦，由全新M6晶片驅動，提供更強大的人工智慧（AI）運算能力，以及長達24小時的電池續航力。（蘋果提供）中央社記者吳家豪傳真 115年10月27日

市場認為，蘋果將自研晶片世代推升至M6，不僅強化在邊緣運算領域的技術話語權，更有助推動高階個人電腦換機潮。

蘋果指出，M6晶片配備12核心中央處理器（CPU），具有世界上最快的CPU核心：相較於前一代M5，M6內建12核心GPU的圖形處理速度提升30%，無論是3D建模、照片編輯或遊戲都更加流暢。此外，M6在本機端處理如大型語言模型的提示詞時，裝置端人工智慧（AI）工作負載效能比M5提升25%。

新款14吋MacBook Pro搭載Liquid Retina XDR顯示器，提供深邃的黑色與鮮明色彩，電池續航力長達24小時，創下Mac歷來最長紀錄，外接介面提供3組Thunderbolt 4連接埠、HDMI接口及SDXC卡槽，並升級支援Wi-Fi 7與藍牙6等最新高速傳輸規範。

透過 Google News 追蹤中央社

得益於雙16核心神經網路引擎，M6版24吋iMac的裝置端AI效能比M4機型快4倍，讓使用者能在各款App和macOS作業系統上運用個人智慧系統Apple Intelligence的功能、執行本地AI模型，或是透過AI進行「憑感覺寫程式」（vibe coding）。全新iMac機身維持7款多彩外觀設計，搭配升級的1200萬像素視訊鏡頭與音響系統，主打家庭娛樂、教育及中小企業日常生產力場景。

上市規劃方面，配備M6晶片的14吋MacBook Pro售價為新台幣6萬4900元起，24吋iMac售價4萬9900元起，兩款新品將於10月29日凌晨1時10分開放預訂，11月6日發售。（編輯：楊凱翔）1151027

請同意我們的隱私權規範，才能啟用聽新聞的功能。

（中央社記者吳家豪台北27日電）蘋果公司（Apple）今晚在官網發表搭載新一代M6自研晶片的14吋MacBook Pro筆記型電腦及24吋iMac桌上型電腦，採用全新雙16核心神經網路引擎、內建神經網路加速器的繪圖處理器（GPU）架構，鎖定裝置端大型語言模型與代理式AI運算需求。

蘋果公司27日發表14吋MacBook Pro筆記型電腦，由全新M6晶片驅動，提供更強大的人工智慧（AI）運算能力，以及長達24小時的電池續航力。（蘋果提供）中央社記者吳家豪傳真 115年10月27日

市場認為，蘋果將自研晶片世代推升至M6，不僅強化在邊緣運算領域的技術話語權，更有助推動高階個人電腦換機潮。

蘋果指出，M6晶片配備12核心中央處理器（CPU），具有世界上最快的CPU核心：相較於前一代M5，M6內建12核心GPU的圖形處理速度提升30%，無論是3D建模、照片編輯或遊戲都更加流暢。此外，M6在本機端處理如大型語言模型的提示詞時，裝置端人工智慧（AI）工作負載效能比M5提升25%。

新款14吋MacBook Pro搭載Liquid Retina XDR顯示器，提供深邃的黑色與鮮明色彩，電池續航力長達24小時，創下Mac歷來最長紀錄，外接介面提供3組Thunderbolt 4連接埠、HDMI接口及SDXC卡槽，並升級支援Wi-Fi 7與藍牙6等最新高速傳輸規範。

透過 Google News 追蹤中央社

得益於雙16核心神經網路引擎，M6版24吋iMac的裝置端AI效能比M4機型快4倍，讓使用者能在各款App和macOS作業系統上運用個人智慧系統Apple Intelligence的功能、執行本地AI模型，或是透過AI進行「憑感覺寫程式」（vibe coding）。全新iMac機身維持7款多彩外觀設計，搭配升級的1200萬像素視訊鏡頭與音響系統，主打家庭娛樂、教育及中小企業日常生產力場景。

上市規劃方面，配備M6晶片的14吋MacBook Pro售價為新台幣6萬4900元起，24吋iMac售價4萬9900元起，兩款新品將於10月29日凌晨1時10分開放預訂，11月6日發售。（編輯：楊凱翔）1151027

The AI boom has led to a gargantuan surge in energy demand across the country. Critics say this upswell in connections is enacting a stress test on America’s aging grid infrastructure. Worries abound that, in the short term, the energy crunch could lead to surging electrical bills and, in the long term, may eventually lead to outages or other technical problems.

At TechCrunch Disrupt earlier this month, industry insiders tempered much of this criticism with optimism about how the flood of new energy connections could actually be opening the door for grid modernization. Apoorv Bhargava, founder and CEO of WeaveGrid, Tim Heidel, CEO of Veir, and Kristin Eberhard, VP of policy at Rewiring America, said that though the grid faces new challenges, there are also plenty of new opportunities as well.

“It’s a very different electric grid, right?” said Bhargava. “We’re dealing with a ton of new variables, including renewable energy. We’re dealing with a ton of new load in the form of electric vehicles and data centers and heat pumps, and the ability to manage and orchestrate is fundamentally what utilities are now grappling with.”

While all of those new connections are driving an ever larger amount of stress to the grid, Bhargava’s company hopes to provide the kind of load orchestration and management that helps utilities maximize the potential of the current system. WeaveGrid’s software can help organizations efficiently deploy energy while also optimizing connections to reduce utility payers’ bills, the founder said.

Heidel, on the other hand, runs a company that is working to build out and expand the physical infrastructure of the grid, while also making it run much more efficiently. Veir sells superconductive power lines that promise to transfer energy at a much more efficient rate than traditional ones. Veir’s cables can carry power five times farther than traditional cables while also requiring 20 times less space, the company has said. Earlier this month the startup raised $110 million in a Series C funding round.

“The scale of the investment in AI infrastructure is driving a tremendous amount of innovation,” said Heidel. “There’s a ton of new technologies that are coming off the shelves and starting to get to scale that, pulled together, can actually really make sure that data centers are the best community neighbors and citizens they can possibly be.”

He added: “I think that we really need to look at ways that we can really adopt all of that as fast as possible to ease this next stage of growth.”

Eberhard, whose organization advocates for household electrification, also said that while the surge in data center energy demand has spurred a new urgency to the modernization cause, it has also created an environment where industries are ready to try new things.

“I feel like this era of AI and load growth is a very improv era,” Eberhard said. “There’s a lot of yes-and-ing going on. So yes, we need more transmission lines. Yes, we need more centralized power plants, and we also need to make sure that we’re taking advantage of all this other stuff that we had previously been leaving on the table when we didn’t have this urgency of the AI demand.”

Ford announced two new EV chargers today that will allow its forthcoming Fathom pickup truck to serve as a battery capable of delivering power to the home or grid.

The new chargers promise to be about half the cost of Ford’s existing equivalent products, which could make the technology available to more households. Ford said the Fathom itself will start at $28,350.

The Fathom will be capable of delivering “over 10 kW” to a home, Ford said. That would allow the electric vehicle to substitute for a small standby generator. For most U.S. homes, it wouldn’t be enough to power everything, but it could keep a refrigerator and air conditioner operational for about half a day.

Such capability has been touted as an advantage EVs have over fossil fuel vehicles, but not many EVs come properly equipped, and the chargers required to make use of vehicle-to-everything (V2X) services tend to be costly.

Ford’s two devices offer similar but differing capabilities.

The Home Power Station device can help customers shave their utility bill by charging the Fathom when power prices are low and sending electricity back to the home when prices are high. The automaker says it will cost around $650, about 50% less than its first-generation bidirectional charger.

The other option, Home Backup Power, is a package that bundles the Home Power Station with a second device, the Home Backup Panel, to allow the Fathom to also power parts or all of a home when the power goes out. Ford says the package will cost around $2,100, about 60% less than its first-generation product.

None of those prices include installation, which for existing V2X chargers can run up to $10,000, depending on how much electrical work needs to be done to prepare the home. Ford did not announce any partnerships with installers, though a spokesperson told TechCrunch that Ford would release more details at a later date.

Which loads the Fathom can power will vary by household and the specifics of each installation. Things like air conditioners and refrigerators, for example, typically require a large surge of power to start up, after which point their load drops. So-called soft-start devices can help mitigate the surge, though they add cost to the installation.

Both chargers will allow Fathom customers to participate in virtual power plants (VPPs), which use software to control a fleet of distributed energy storage devices, such as EVs.

As strain on the grid grows due to AI data centers, utilities have been exploring whether VPPs can help alleviate congestion at the edges.

Fathom owners who enroll and keep their truck plugged in can sell electricity back to the grid, often at a premium. Ford did not say how much Fathom owners can stand to make, but the payouts can be upwards of $1,000 per year, depending on the grid’s needs and the level of participation.

Automakers, including Ford, have been diving deep into energy storage as demand for such services surges. The Fathom-powered VPP is Ford’s latest foray into the market, but it’s unlikely to be its last.

In Brief

Insurance tech company Guidewire announced Tuesday the acquisition of the agentic underwriting insurance company Hyperexponential for $1.7 billion in cash.

The deal, expected to close sometime next year, is to help bring AI agents to work alongside humans to underwrite, analyze, and price decisions. The deal is worth around $2 billion in total, with $400 million in additional capital reserved if certain revenue targets are hit.

Backed by Andreessen Horowitz and Battery Ventures, Hyperexponential was founded in 2017 and has raised nearly $90 million to date, according to PitchBook. So this marks a significant return for investors. The U.K.-based AI startup says nearly 600 insurers across 40 different countries use its software.

October was a big month for venture funds, per SEC filings. The filings show that established names are currently commanding top dollar from LPs, especially with a specialized focus. Here are some of the most notable ones we found that haven’t been meaningfully announced or have seen important updates.

For one, Trust Ventures filed Monday to raise a $400 million Fund V. The Austin-based firm was founded in 2017 by Salen Churi to back founders building in highly regulated industries. Koch Disruptive Technologies notably backed the firm’s debut fund back in 2018. It previously sought to raise a $225 million Fund IV in 2023, though it’s unclear if or when that fund closed. Deep tech fund Interlagos Capital is also raising a $400 million Fund II.

Elsewhere, Victor Lazarte and Kris Fredrickson, two former Benchmark VCs, have raised $1.252 billion for their debut AI fund called Diffusion ONE. Forbes previously reported back in May that the two had teamed up (after raising smaller funds of their own) to launch this fund with a target of $800 million, meaning it’s far surpassed that original target. The firm has already cut big checks since its debut, including co-leading Harvey’s $550 million Series H with Lightspeed Ventures and taking part in Etched’s $300 million Series C, announced in July.

Speaking of Benchmark, the firm filed what looks like four new partner-only funds, called Benchmark Partners Founders’ Fund T1, Benchmark Partners Founders’ Fund 2, Benchmark Partners Founders’ Fund 2-A, and Benchmark Partners Founders’ Fund T1-A, seeking to raise $220 million, $132 million, $4.5 million, and $7.5 million, respectively. Jack Altman, who recently left his own firm to join Benchmark, is listed in each filing.

Meanwhile, Joe Lonsdale’s 8VC also seems to be seeking to raise new vehicles — including a $400 million fund called 8VC DSS III; a $30.35 million fund called 8VC COSC; a $20.35 million 8VC BRSC fund; and a fund called 8VC AI Fund XI, which has already raised $13.5 million, including from 8VC itself. This follows the July news that 8VC raised $1.5 billion for Fund VII.

Earlier this month, Notable Capital, backers of companies like Anthropic, Airbnb, and Square, filed to raise a fifth fund, while Neil Mehta’s Greenoaks, which co-led Anthropic’s $65 billion Series H, filed to raise a fund called Greenoaks 7. The timing is good as Anthropic just took another major step in its efforts to go public, meaning the firms are already prepared to market themselves based on possible handsome returns. According to PitchBook, Greenoaks has also backed companies like Ramp and Anthropic.

Industry Ventures, meanwhile, has raised at least $1.7 billion for its secondaries fund, which opened in late July. The size of the vehicle shows how important that market has become, as appetite grows to buy into hot exiting private companies. One family office advisor told TechCrunch last month about how popular the secondaries market has become, while Emily Zheng, a senior VC analyst at PitchBook, said that stakes in Anthropic and OpenAI are some of the “most contested real estate in venture.”

General Motors plans to invest in its Lansing Delta Township Assembly plant to handle future production of hybrid-electric vehicles, the latest move by the U.S. automaker to claw back market share by appealing to the shifting tastes of consumers.

The company said on Tuesday that it plans to invest more than $2 billion over the next several years in the broader Lansing region, an amount that includes $1.25 billion to prepare the assembly plant for “future vehicle production.”

The company said it is working with the U.S. Department of Energy to restructure a previously awarded Domestic Manufacturing Conversion Grant and direct those funds to future hybrid production at the Lansing factory. The company was awarded $500 million under the Biden administration to support converting a different factory in Lansing to produce electric vehicles, including hybrid, battery-electric, plug-in electric hybrid, and hydrogen fuel cell electric vehicles. Those funds were never released, according to GM.

Today, GM assembles the gas-powered Buick Enclave, Chevrolet Traverse, and GMC Acadia at the Lansing Delta Township factory, which produced a record 291,000 vehicles in 2025.

GM didn’t provide specifics on when it would begin producing and selling hybrid vehicles. But insiders told TechCrunch to expect hybrids across multiple brands rather than a single vehicle line. A Reuters report, citing anonymous sources, said the company plans to build hybrid versions of the Chevrolet Traverse, Buick Enclave, and GMC Acadia at the factory.

Hybrids have become increasingly popular, thanks in part to rising gas prices. Customers also have more choices than ever before, with most automakers offering a range of hybrid and plug-in hybrid models. Toyota is the U.S. market leader in hybrid sales, followed by Honda. Ford has also gained considerable market share in hybrid sales in the past two years, selling some 228,000 hybrids in 2025.

But GM has been slow to adopt hybrids. Until recently, the company’s only electric-hybrid vehicle was the Chevrolet Corvette E-Ray, which launched as a 2024 model. (GM sold 2,444 of those vehicles last year.) The E-Ray has since been discontinued; GM began deliveries of the hybrid Corvette ZR1X in March and next year will launch a hybrid Corvette Grand Sport X.

GM eschewed hybrids in favor of battery-electric vehicles. The automaker invested billions of dollars to shift its gas-powered portfolio to an all-electric one. That strategy has changed under the Trump administration with the end of the federal EV tax credit and slower-than-expected demand. GM hasn’t abandoned EVs completely, though it has seen EV sales fall year-over-year. The company still sells numerous battery-electric vehicles across its brands, including the Chevrolet Equinox EV, Blazer EV, and Cadillac Escalade IQ.

GM said Tuesday it will share specific details on its hybrid vehicle production plans closer to launch.

Financial giant Bank of America is reminding customers to bank safely by kindly not sharing your banking credentials with your AI agent.

In an email blast to customers titled “Protecting your finances in the Al World,” Bank of America urged customers to never share their username and password with another person or an outside service, “including an Al-enabled tool or agent that offers to act on your behalf.”

You can practically sense the frustration in the person sending the email. Still, customers are increasingly handing over their log-ins as personal AI assistants grow more popular. Sharing your banking username and password with an AI assistant runs the risk that it could do something with your finances without your knowledge, or that your bank thinks someone’s raiding your account and locks you out of it. Worse, a security flaw in the AI assistant could result in it leaking your banking credentials elsewhere, giving financially motivated hackers a way into your bank account.

This latest warning comes as banks are still exploring how AI agents fit into the financial world but are reportedly grappling with unauthorized agents accessing their customers’ money. As we discussed onstage at TechCrunch Disrupt, banks, payment providers, and processors are still trying to figure out how to allow customers access to their bank accounts using AI tools, such as for agentic payments, which allow people to set agents to autonomously buy things at the best price or the right time.

Bank of America did not immediately respond to a request for comment about the email. But heed the bank’s warning, for if things go wrong, it’s on you. Account agreements at most banks hold customers responsible for anyone they give their log-in to. “If you do share your credentials,” its email reads, “keep in mind that you’re responsible for activity resulting from that access.”

(via Bryan Toporek)

Anthropic was founded in 2021 to ensure humanity can control AI and benefit from it in the long-term. As the frontier lab approaches its initial public offering, however, there’s one class of humanity that is being shut out of control: anyone who buys its stock on public markets.

Complex structures that allow founders like Elon Musk or Mark Zuckerberg to control their public companies without owning most of them are nothing new in Silicon Valley. But Anthropic’s obsession with AI safety has resulted in a truly baroque style of corporate governance that will leave common shareholders with little say over most major decisions at the AI company, including who sits on its board.

First, Anthropic is a public benefit company — a for-profit corporation legally allowed to weigh a public mission alongside profit — with the goal of promoting AI safety. That status protects the board’s ability to make decisions about AI safety that aren’t driven entirely by shareholder value without being sued by angry investors.

Right now, the board is chaired by Anthropic president Daniela Amodei and includes CEO Dario Amodei (her brother), Netflix cofounder Reed Hastings, former Microsoft and GM CFO Christopher Liddell, Novartis CEO Vas Narasimhan, and Spark Capital partner Yasmin Razavi. There is one vacancy on the board.

Second, Anthropic has allocated significant control of its board to a Long-Term Benefit Trust (LTBT), an independent panel meant to keep the company focused on mission to benefit humanity. The trust owns a class of stock that gives it the ability to elect and remove a majority of the company’s board, known as Class T directors. The LTBT is chaired by Neil Shah, a global health expert who was managing director at GiveWell, a nonprofit that ranks charities, and includes former Federal Reserve chair Ben Bernanke, national security expert Richard Fontaine, and Harvard physicist Christopher Stubbs. There is one vacancy on the LTBT right now. The existing members will themselves choose who fills it.

Third, Anthropic’s seven founders also jointly own a special class of stock that gives a majority of them 50.1% of shareholder voting power, allowing them to elect three members of the board of directors. If, for whatever reason, there’s no majority among the founders, only then are their shares voted in proportion with holders of common stock.

Finally, the board can put a check on the LTBT. When the LTBT nominates a “Specified” one of its four directors, or removes that director without cause (that is, without alleging misconduct), the board’s nominating committee can decline to approve the move and throw the decision to the company’s employees for a vote.

Here’s what all that looks like:

Image Credits:Tim Fernholz/TechCrunch

The dynamics of such a complex structure are hard to predict. We’ve already seen one notable instance where a similar structure was essentially overthrown: OpenAI’s so-called blip, when OpenAI’s board briefly fired CEO Sam Altman over concerns that he was not being candid with them. Thanks to the loyalty of the company’s employees to Altman, the board backed down, reinstated him, and a new governance structure was put in place. Anthropic’s structure seems to formalize that kind of employee power.

At Anthropic, cracks in the founders’ unified front could lead to common shareholders having more say than expected, while employees can overrule the LTBT on board seats if they back the board over the assembled luminaries.

The founders’ control won’t last forever: Ten years after the IPO, the founders can choose to end their control of the company, or their control will end automatically when fewer than three of them remain. At that point, the LTBT will continue until 85% of common shareholders vote to end it, or it decides to step down with approval from the board.

If and when neither special class of shareholder exists any more — hopefully not because of an AI-driven catastrophe — common shareholders finally get control.

After eight years developing AI for Meta, Rohit Patel is departing the company, TechCrunch has exclusively learned. The former director at Meta Superintelligence Labs has launched Ndoc, a startup he’s pitching as an AI-native replacement for Microsoft Office suite or Google Workspace.

Patel is the latest in a handful of high-profile departures from Meta following the exits of Chief Global Affairs Officer Joel Kaplan, AI researcher Andrew Tulloch, and Head of Product for AI-for-Work Emily Dalton Smith.

Patel designed Ndoc to be more than just another AI tool layered onto an existing workflow. The goal is to replace the fragmented way people work across tools like Excel, Word, PowerPoint, Tableau, Replit, and “ChatGPT copy-paste” with one unified workspace that all colleagues can access, even if they’re not signed up for Ndoc.

Ndoc brings three components together in one layout. The Stage tab is where presentations of key findings live, like slide decks, dashboards, simulations, or mini apps. The Narrative tab features analyses, numbered citations, generated figures, and generally the “why” behind the numbers. The Model tab is where underlying datasets and Python scripts are housed.

The design reflects Patel’s background. During his tenure at Meta, Patel focused on reinforcement learning — a type of machine learning in which AI agents learn to make decisions through trial and error and maximize rewards — and evaluating whether AI output is trustworthy. Patel believes that if AI is going to build the spreadsheets and slide decks that companies use to make decisions, someone should be able to check its work. For instance, users can find the Python scripts used to create the documents.

“Even if you don’t know any Python, you can ask [the AI chat] to explain, ‘Hey, what does this script do? Which line has my formula?’ And you can verify that formula,” Patel said. “There’s nothing hidden behind the curtain here.”

In practice, users can upload a dataset and ask Ndoc to perform an analysis and write a narrative about the findings. They can also add document-specific context, including audience, tone, and memory. “It maintains a memory across your person, that particular document, your team, and your company,” Patel said.

Patel also says Ndoc is built and fine-tuned using a mixture of open source models. Because the platform was developed from scratch, he says it costs considerably less to run than alternatives built on frontier AI models.

“I thought that’s what people would be excited about, but people don’t care about that,” he said, referring to his early trial users. “What people are excited about is that they love seeing all their end-to-end in one place.”

Patel’s larger ambition is to reinvent the file itself. He says documents can be exported as a single “.ndoc.html” file that can be opened in any browser, a format he plans to open-source so other developers can build apps that work with it without being locked into Ndoc the way so many have been locked into Microsoft or Google for years.

His desire to open-source this project was partly why he left Meta and decided to go it alone. At a big company like Meta, getting everyone to agree on an open source format would undoubtedly require the input of “hundreds of people and opinions on how the format should look.”

Patel wanted to move quickly. “Humans can only move at human pace, whereas…I can move at the pace of what AI can do.”

Trying to become the platform where enterprise work happens is a huge bet for a founder with a team of three and no funding, especially when other AI labs are trying to own the same space.

OpenAI, Anthropic, Meta, SpaceXAI, and Google have all come out with enterprise AI tools that allow users to connect to Google Workspace, Microsoft 365, and other workplace apps so their AI models can work directly in those tools. And last month, OpenAI launched Space, a shared workspace inside ChatGPT that’s a more direct competitor to those established tools.

Patel acknowledges that Space could be a threat, but says the labs largely have the wrong idea.

“Companies are basically attaching AI to spreadsheets and it fills your cells for you,” he said. “It just sort of kills my soul. This is like if you just figured out electricity, and then you decided the best use case is to put lights on my bulls that till my soil. No. Get a tractor.”

There’s a reason the AI labs have tried to bring their models to where people are already working, though. It’s hard to change habits and replace entire workflows. Patel says he understands that, which is why he built connectors so customers can import existing data from the tools they already use.

“My bar was that I’m not going to release anything I don’t love using,” he said. “I have very optimized AI workflows for everything I do. If I start using this tool, and I keep going back to my workflows, I haven’t built a good enough tool…I have a graveyard of tools that did not meet that bar.”

Patel has a similar bar for gaining customer traction before he tries to raise funds. “I didn’t want to take money until I had some proof that people love the product,” he said.

As of Tuesday, Ndoc is conducting an enterprise pilot and opening an alpha version to a limited number of customers who join the wait list. It’s free to sign up, but customers will pay for usage and tokens. He hopes to release a beta version in the coming weeks, with a full launch planned for next year.

For Patel, moving as fast as possible is more important than having the perfect product from the get-go.

“Execution is extremely cheap…so go execute on a suboptimal plan and worry about it later,” he said. “By the time you make the best plan, the execution will be done by somebody else.”

Re:Build, the Massachusetts-based manufacturing accelerator that spun out EV startup Slate Auto in 2023, has raised $2.1 billion

The company said Tuesday that it will use the funding to “expand Re:Build’s capacity to manufacture hard tech products and launch new industrial startups,” which will “ensure the most innovative products can be made in America for generations to come.” The investment was led by General Catalyst and JPMorganChase’s Strategic Investment Group. Other investors weren’t disclosed.

The funding round comes as increasing attention and capital is flowing to the idea of building up manufacturing and supply chains in the United States, especially as a hedge away from China.

“We started Re:Build because the deindustrialization of America is an economic failure and national security risk,” Re:Build co-founder Jeff Wilke said in a statement. “This capital allows Re:Build to go faster and further than would otherwise be possible, and build a great manufacturing business and a platform for American industrial leadership. The products and companies emerging from our ecosystem prove we can help restore America’s industrial might.”

General Catalyst’s CEO Hemant Taneja echoed those sentiments in a statement, saying, “General Catalyst has long believed that rebuilding American industrial capacity is one of the most important economic and national security imperatives of our time.”

Taneja added: “To compete with China, re-industrialize the U.S., and revitalize the towns and jobs that have been left behind, we need platform companies that can operate at genuine scale — and we need our innovation ecosystem to learn how to build hardware again.”

Founded in 2020, Re:Build bills itself as “America’s answer to Shenzhen,” and it has numerous ties to Amazon. It was co-founded by Wilke, who was Amazon’s former commercial CEO. Wei Gao, a longtime Amazon VP and technical adviser to Bezos, is Re:Build’s chief digital and product officer. Multiple other Re:Build executives also worked for the e-commerce giant.

Re:Build claims to have “launched hundreds of products and production systems” over its six-year existence. The company tends to operate two ways: It enters partnerships with industrial companies to “design, engineer, and manufacture products at scale across verticals including aerospace and defense,” and it incubates startups in “critical, underserved markets.”

Re:Build says its network of companies employs more than 2,100 people in the U.S. and houses more than 2.4 million square feet of manufacturing capacity.

Wilke and his co-founder Miles Arnone came up with the idea of Slate Auto in 2022 and decided to spin it out in order to help it attract more capital. The company has since gone on to raise at least $1.4 billion, including funding from Bezos’ family office, in an attempt to build an ultra-low-cost, bare-bones pickup that can be customized to a customer’s liking.

In Brief

The Dallas-based investment firm Disruptive, whose past bets include Palantir, Airbnb, and Spotify, announced a $10 billion fund to back at least 10 late-stage companies, with checks of up to $1 billion, it said Tuesday.

It’s one of the biggest fundraises of the year so far, aiming to back (what else?!) the capital-intensive fields of AI and defense. The Wall Street Journal first reported earlier this month that the firm was seeking to raise the fund and by then had already secured at least $7.5 billion in capital commitments.

The firm was founded in 2012 by Alex Davis, grandson of billionaire oil magnate Marvin Davis.

This is technically the firm’s first blind-pool fund, meaning investors commit money without knowing which companies it will back. Until now, the firm raised money through deal-by-deal special purpose vehicles. Disruptive has nonetheless made headlines in recent years for pouring money into companies like chip startup Groq and the AI voice-generation startup ElevenLabs. Bloomberg reported that the firm told LPs it has returned more than $6 billion on the roughly $1.9 billion it has put into startups.

According to the Dallas Business Journal, the new vehicle is the largest venture fund raised in Texas history.

OpenAI released solutions to three blockbuster math problems that have long vexed researchers. One was a surprise rejection of the Exponential Time Hypothesis (ETH), a durable assumption in computer science that plays a large role in how we understand what is difficult to compute.

As with previous proof-drops from the frontier lab, scientific communities responded with a mixture of thrill and dismay. Anthropic mathematician Levent Alpöge suggested that OpenAI was basing its solutions on pre-existing work, and hours later, Anthropic subsequently released its own refutation of ETH.

However, OpenAI’s process is evolving. This time, the lab structured its releases differently for each of the three disciplines involved: Mathematicians got the work dropped on GitHub; the computer scientists formed a working group, asked for the proofs to be shared publicly and OpenAI to fund a conference to discuss them; the physicists formed a working group that will determine how their result will be released.

MIT professor Ryan Williams is the computer scientist who is organizing his field’s response to the new finding. Williams, whose work focuses on these questions of computational complexity, has criticized OpenAI’s approach in the past, calling its proof dump earlier this October a “DDOS attack on mathematicians.”

However, he agreed to collaborate with the frontier labs in order to help shepherd ideas that are vital to the field, he told TechCrunch. The lab asked him to help interpret the solution, along with UC San Diego computer scientists Russell Impagliazzo and Ramamohan Paturi, who first published the ETH in 1999.

Their first step was to release an open letter calling for feedback from scientists interested in these problems and a discussion of the norms around AI contributions to their work.

“Some parts [of OpenAI’s proof] are, maybe you could say routine, but then yes, there are two or three alien-looking parts, and it’s exactly these parts that I saw that made me think, wow, this is something that the whole community needs to work on together,” Williams told TechCrunch.

Are you down with P versus NP?

The ETH is part of a broader class of problems known as P versus NP, which has animated computer science and many other fields for decades. Reductively, researchers are trying to understand if any problem whose solution can be easily verified (NP) can also be easily solved (P).

A simple example is the game Sodoku: Once a solution to a puzzle is in place, it can be quickly verified, but finding the solution is time-consuming. That dynamic is true across a range of important problems, and the insight underpins cryptographic systems: When you have the key to an encoded message, you can reveal it, but actually cracking an effective code takes too long to be useful.

However, scientists have yet to prove whether that is always true. “P = NP is saying that, not only can you verify Sudoku solutions when they exist, you can just generate them,” Williams said. “P = NP is asserting that this is true for every single problem you can verify quickly — if you can verify it quickly, you can solve it quickly. So this just seems too good to be true.”

That’s why most scientists assume that P ≠ NP. ETH provided evidence for that. It basically says that even the fastest computer algorithm needs exponentially more time to find a solution as the number of variables grow in a specific class of tricky logic puzzle.

The claims from OpenAI and Anthropic is that their models have come up with algorithms that solve those puzzles in “sub-exponential” time, meaning faster than the speeds previously thought possible. If true, they may have proven the EFT to be false — refuted it, in scientific terms — and maybe opened up a path for algorithms that generally perform P = NP.

“Every computer scientist should probably put a little more weight on the plausibility of P = NP, but one’s weight may vary,” Williams said. “I do think that it kind of calls into question a lot of the intuitions that we thought we had about … how tractable [hard NP problems] are. I also think that this is probably not the final answer either. I don’t think that OpenAI’s algorithm could possibly be the best algorithm you can get.”

The work ahead for computer scientists

The durability of ETH has lead to it being included in many scientific papers over the past decade that will need to be revisited if the AI labs claims that they’ve proven it wrong holds up, Williams said.

“This is partly why I was asking or pushing OpenAI to support a conference on this — it’s going to affect many researchers’ works, and we need to figure out, okay, what happens next besides understanding the algorithms. We need to understand, how should the foundations change compared to what we had before?” he said.



Before scientific concerns, there are cybersecurity questions. Williams said that an improved algorithm could imply that cryptographic systems relying on certain key sizes could be considered broken. So one of the first questions he asked OpenAI science lead Dan Roberts “was if they had checked that this affects any crypto systems … is anything insecure now? And apparently, they checked, and they were reasonably confident that this didn’t … imply any breaks.”

This new AI paradigm of scientific discovery is still a work in progress, but Williams says he is optimistic despite real concerns, particularly about training the next generation of scientists. Being a computer scientist who does high level math has perhaps made the adjustment easier for him than for pure mathematicians.

Still, he says AI is “forcing people to be a little more ambitious about the kind of work that they do, and to think more deeply about, okay, if all the things I used to work on are now low-hanging fruit for AI, how can I raise the bar for the kind of scientific questions I want to ask?”

Google DeepMind released its newest world model, Genie 4, in research preview on Tuesday. The latest model generates the most photorealistic and interactive worlds yet, allowing users to type prompts mid-simulation to change the scene. The model also generates real-time audio that matches the simulated world, including music, ambient noises, and interactive dialogue.

The release of Genie 4 comes a couple of months after AI video startup Runway released its second world model, GWM Worlds 2, a playable model that responds dynamically to user inputs and includes audio generation. At the same time, the world model industry is beginning to consolidate. Fei-Fei Li’s World Labs is being acquired by AMD, Amazon is in talks to buy Decart, and Rivian just acquired Third Dimension to bring simulation capabilities in-house for self-driving testing.

Genie 4 is Google’s latest move to stay ahead in the crowded field.

Google released its predecessor, Genie 3, as a research preview a little over a year ago. Aside from being far more photorealistic and featuring audio, Genie 4 allows for free-form edits, like changing the weather or camera angle in real time or adding new people or objects. It can maintain a consistent scene for up to two minutes, double its predecessor’s limit, and offers better physics and more humanlike characters.

Gemini, Google’s AI model, is also more closely integrated with Genie 4, acting as the director that plans steps and checks frames while Genie creates the visuals. In a research experiment tied to Gemini Robotics, a Gemini 4 Argon model broke down tasks into steps that Genie 4 simulated in real time. The experiment shows a possible way to test robot scenarios in simulation before they happen in the real world.

“What we’ll increasingly see in the future are these hybrid world models, where you have a combination of Gemini writing code and text, and Genie ingesting that in real time and simulating reality at different levels of abstraction,” Tejas Kulkarni, research director at Google, said during a briefing.

As with other world models, the pitched commercial use cases include gaming, interactive learning, and robotics. One speculative example of interactive learning would be entering a painting of a historical scene and chatting to the painting’s subjects about the event portrayed.

But “trusted testers” — the people who get first access to Google’s cutting-edge products — are pointing to a possible new use: prototyping films.

“The real time aspect becomes most valuable in the areas where keeping a state of flow in the creativity space becomes incredibly useful,” Diego Rivas, a Google DeepMind engineer, said during a press briefing. “For example, in the ideation phase, in the storyboarding phase — anytime you’re kind of in need of rapid ideation.”

Google also offers Veo, its AI video-generation model that makes high-definition clips with built-in synchronized audio from text, image, or video prompts — a tool that has been growing in popularity among filmmakers.

Rivas added that users could, eventually, rely on Gemini Omni, a multimodal large language model that can generate video from images, audio, and text, to turn those prototypes into creative assets. Though those capabilities, he says, are still a ways off.

Throughout the briefing, Rivas stressed that it’s still early days for Genie 4 and that it’s not yet clear what the end-to-end product experience will be. The team also acknowledged that Genie 4 still has gaps: Details and objects can drift the longer a simulation runs, and they still haven’t quite figured out how to ensure the audio will be accurate and any in-world text will be legible.

Genie 4 is available now as a research preview for trusted testers. In the coming weeks, the model will roll out to Google AI Ultra subscribers in Project Genie, Google’s AI tool for generating interactive worlds from text or image prompts, and early access partners of the Gemini API and Google AI Studio.

U.S. cybersecurity agency CISA has confirmed that hackers are now actively exploiting a pair of critical security vulnerabilities in the widely used PHP web software and has ordered civilian federal agencies within the U.S. government to fix their systems within the next three days.

The urgent new warning to patch comes less than a week after the makers of the decades-old PHP web programming language rolled out patches for the two bugs, which allow hackers to remotely break into a vulnerable system over the internet without the need for passwords. The PHP software is reportedly used by some 70% of web-facing servers, putting major websites and applications at risk. The severity of the bugs prompted some governments and big companies to take their sites offline to fix them.

While the bugs are rated critical, meaning they can be highly damaging if exploited, security researchers said that developing a working exploit to hack into vulnerable systems was initially challenging — but not impossible. Confirmed in-the-wild exploitation now puts extra pressure on organizations to patch their systems to prevent malicious compromises, disruption, and data breaches.

CISA did not provide details about who was exploiting the bugs in its Monday statement, but its statement comes soon after security researchers said they found evidence that the PHP bugs were being exploited.

The bugs, known as PHP2Shell, exploit vulnerabilities in the software that went undiscovered for years. The bugs were initially uncovered two years ago but the nonprofit foundation that maintains the software said there was no practical way for hackers to exploit them, leaving the bugs unpatched. That was until earlier this year, when researchers at security research firm Calif uncovered a way to exploit the two dormant vulnerabilities with the assistance of an AI model.

The two bugs in PHP are among countless vulnerabilities that have not yet been discovered or have been deprioritized for years but now pose a risk due to advances in AI models’ ability to find and exploit vulnerabilities, including by chaining multiple flaws together.

PHP is one of many aging web technologies that help keep the internet and modern technologies running, but that have been under-resourced and under-funded for years, making it difficult for open source projects like PHP to pay for security reviews that might have uncovered flaws sooner.

Apple on Tuesday unveiled its new 14-inch MacBook Pro, powered by its latest M6 chip. The new MacBook Pro has 24 hours of battery life and now supports Wi-Fi 7 and Bluetooth 6 connectivity. The laptop starts at $1,999 and is available in space black and silver.

Apple also announced its latest iMac, which is also powered by the M6 chip. The tech giant revealed the new devices just a day before its “Pro and Tell” event scheduled for Wednesday. On Monday, Apple announced its new iPad mini.

The tech giant says the M6 chip features a 12-core CPU with the world’s fastest CPU core and a 12-core GPU that delivers more responsive performance, up to 30% faster graphics, and up to 25% faster AI workloads compared with the M5 chip.

The M6 delivers up to 13 times the LLM prompt-processing speed of the M1 MacBook Pro and up to 1.3 times faster performance than the M5 model, Apple says.

Image Credits:Apple

The M6 MacBook Pro starts with 16GB of unified memory, configurable up to 32GB, and has memory bandwidth of up to 170GB/s, up from the 153GB/s on the M5 chip.

It includes HDMI, MagSafe 3, three Thunderbolt 4 ports, an SDXC card slot, and a headphone jack. Like previous models, the laptop features a 12-megapixel camera.

The M6 MacBook Pro supports fast charging, reaching up to 50% charge in 30 minutes with a compatible high-wattage USB-C power adapter.

As for the new M6-powered iMac, it features a faster CPU and GPU, with Neural Accelerators built into every core. Apple says the M6 delivers smooth multitasking and powerful performance for everyday apps, photo editing, and video production.

Image Credits:Apple

The new iMac starts at $1,499 and comes in blue, green, pink, yellow, purple, orange, and silver.

The company also notes that the new iMac delivers up to five times faster on-device AI performance than the M4 model. It features a 24-inch 4.5K Retina display, an advanced camera and audio system, up to four Thunderbolt 4 ports, color-matched accessories, and macOS 27.

Both the new MacBook Pro and iMac can be preordered starting October 28 and will be available starting November 6.

Discord announced on Tuesday it will make some conversations in its public servers searchable and easier to find on the internet. The new update targets conversations in “Discoverable Servers,” public Discord servers that administrators have made eligible to appear in the platform’s Server Discovery feature.

The aim is to help people find useful information that might otherwise remain buried in Discord conversations to a wider audience.

According to Discord, more than 63,000 Discoverable Servers host discussions on everything from gaming strategies and fandoms to language learning and niche interests. By making some of these conversations searchable, Discord hopes to extend the reach of its communities beyond the platform itself.

The move also taps into the appeal of traditional internet forums, where users can find answers by searching through publicly accessible discussions. Gaming communities could draw attention to walkthroughs, developers could reach more people with technical advice, and content creators could attract new audiences without relying solely on Discord’s internal discovery tools.

Image Credits:Discord

Starting November 16, every Discoverable Server will have its own web page, which may feature automated summaries of discussions in active public channels. Beginning in mid-December, selected messages from public channels may also appear in search engine results.

Server administrators will be able to choose which channels are included, while individual members can opt out of having their messages made publicly searchable. Published messages will not display usernames or avatars. Private servers, direct messages, and group direct messages are excluded.

The announcement has sparked backlash over privacy. Some users are concerned about exposing conversations to audiences far beyond their original context, arguing that posting in a public Discord server does not necessarily mean users want their messages to be discoverable across the wider internet.

Others have raised concerns about whether these conversations could be used to train AI models. Similar arguments emerged during controversies surrounding Reddit’s data-sharing practices and AI training, when users questioned whether their posts and comments should be used to train AI without their explicit consent or compensation.

Discord says users can manage their preferences in settings, while server administrators retain control over which channels participate. Notably, the option to make messages discoverable is turned off by default in the EU, U.K., Canada, Brazil, Australia, and South Korea because of legal restrictions. Teen users are also excluded from the feature.