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 Meta today launched a new ?Meta? AI app for the Mac, which is available in a beta capacity.
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The new Google Pixel 11 Pro XL doesn't dramatically reinvent the company's flagship smartphone formula. From a hardware perspective, this year's model represents an incremental update. But after spending time with Google's latest full-featured Pixel, I've found that some of its most meaningful improvements for business users aren't necessarily the things that stand out on a spec sheet or benchmark chart.
Starting at $1,299 with 256GB of storage, the Pixel 11 Pro XL features a 6.8-in. Super Actua LTPO OLED display, Google's new Tensor G6 processor, a 5115mAh battery and an updated triple-camera system. Google also pledges seven years of operating system, Android security and Pixel Drop updates.
The hardware is very good, but Google's deeper integration of Gemini with Android and other thoughtful software features are what make this device more interesting.
Tensor G6 performance: The numbers don't tell the whole story
Google's Tensor processors have never been benchmark chart-toppers, and the Tensor G6 doesn't change that. In Geekbench 6, the Pixel 11 Pro XL scored 2,736 single-core and 7,399 multi-core numbers. That's a healthy improvement over the Pixel 10 Pro XL's 2,326 and 6,413, respectively, but well behind devices like Samsung's Galaxy S26 Ultra (at 3,683 and 11,243) powered by Qualcomm's latest SoCs.
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The ChatGPT maker is also beefing up its security systems.
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Meta rolled out a new 30-billion-parameter AI model optimized to run on a PC or Mac with a single GPU on Monday, offering a way to run always-on agentic workflows locally rather than relying on the cloud.
The company has dubbed it Muse Glimmer. However, its hardware demands, including a GPU with a minimum of 24GB of VRAM, could make it difficult to justify for deployment at scale.
Although analysts and consultants agree that there is a tremendous enterprise appetite for running models locally, determining whether switching more systems from cloud to local makes fiscal sense is much more complex.
The hardware costs are tricky to calculate even today, with the VRAM needed depending on the particular applications to be run. But the far bigger consideration is that there is no way to determine what RAM costs will look like over the next 12-18 months, and there is an identical lack of visibility into how cloud prices might increase during the same timeframe.
That makes determining the better financial choice impossible.
Agents become capex, not opex
Noah Kenney, principal consultant at Digital 520, noted that since RAM costs have increased "exponentially" over the last 12 months, cloud AI providers are also going to have to increase their prices. Beyond that, IT needs to anticipate logistical is
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New details of how ChatGPT maker OpenAI failed to notice that its models had launched a hacking spree raise questions about the industry's approach to safety.
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