HARDWARE

Apple's Mac Studio pushes frontier of AI hardware

Apple continues to push the cutting edge in one part of the AI industry that could emerge as the next frontier of the ecosystem over the next 6-12 months. 

I've been testing the M5 Ultra Mac Studio and the M6 Mac mini and both devices have extended Apple's lead at the frontier of AI hardware for individual consumers and professionals. The M6 Mac mini is better than ever for running an always-on agent like Perplexity Computer, Hermes, or a variant of OpenClaw. The M5 Ultra Mac Studio is a workhorse that runs at the speed of a racehorse for the most demanding AI builders and small teams. 

Both machines can save you a ton of money on AI tokens by running the latest open models locally, such as the ones from Google Gemma, Nvidia Nemotron, DeepSeek, Alibaba's Qwen, Kimi, GLM, and others. But beyond the cost savings, the hardware can often run AI jobs a lot faster. And of course, since none of the data leaves your machine it’s a lot more private and secure, which is critical for working with PII and sensitive data. 

Apple's last-generation Mac mini and Mac Studio boxes were already terrific for AI and have faced long wait times for backorders since early 2026. Apple didn't have to make a new generation of hardware. It could have just increased production of its last-generation products and it would have likely sold every device that it could make. 

But I'm glad that Apple didn't rest on its laurels and chose to push the envelope instead. I haven't started fully benchmarking the machines yet, but I have no doubt that when I do, the numbers are going to be eye-popping. The TLDR is that you can be confident that if you buy one of these machines, they are going to be future-proofed for the next 2-3 years. 

I'm also currently testing the Nvidia DGX Spark and the AMD Ryzen Halo. Both are excellent little AI boxes that sit somewhere in between the Mac mini and the Max Studio and have many of the same benefits. These machines are based on the same GPU hardware that runs much of the world's most popular AI chatbots and agents in data centers. And what's wild is that the highest-end Mac Studio has 5x the memory bandwidth of the Nvidia and AMD boxes to deliver bleeding edge performance. That speaks to Apple's lead in chip design and vertical integration in consumer AI devices. 

Still the AMD Ryzen Halo is great if you want a machine running Windows and the Nvidia DGX Spark is perfect if you want a headless desktop AI appliance running Linux. And both Nvidia and AMD are working with hardware vendors to build their own AI computers to compete with Apple in the years ahead. But, make no mistake, they are still largely playing catch-up.

On-device AI is expected to be one of the most important AI trends of the next year, for all three of the reasons I mentioned above: cost, performance, and privacy. While the past year has been about agents and AI getting a lot more useful, the result has been an explosion of token use and skyrocketing costs. One CTO I spoke with recently said that for each engineer her company is spending 1.5x their total compensation in token costs. That could quickly net out to $15,000 to $20,000 per month. A maxed out Mac Studio costs $18,299, so it's easy to see where it could pay for itself pretty quickly. But beyond those extreme use cases for AI builders, on-device AI has excellent potential to become a much bigger part of the future beyond just saving money. As the AI models and harnesses get smarter and more capable, there are more things they could do to be useful every day. For example, I'd love to use them to run a workflow every 10 minutes scanning specific sites and dropping updates into a Slack channel, but that job burns through too many tokens using today's cloud-based AI. I'd also love to use AI to scan my home security cameras and my health data and ping me when there are important updates or anomalies, but that's highly sensitive data that I wouldn't trust to send to any of today's leading AI providers. On-device AI can solve those problems and a lot more like them, and Apple's new desktop Macs remain the friendliest and the most powerful ways to take advantage of it.

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POLICY

The US-China AI race is more tangled than it looks

Despite the ongoing narrative that the US and China are in a heated race against one another for increasingly-capable AI, the reality of the relationship is far more complicated. 

This week, the Trump Administration is scheduled to host Chinese President Xi Jinping at the White House. Ahead of the meeting, Treasury Secretary Scott Bessent said this weekend that the US has proposed a "notification mechanism" to sound the alarm on AI incidents that could impact national security. 

Bessent told the press on Sunday that the US wants a "shared vision of common goals and common threats" related to AI. "We think that just like any cross-border activity, that moving from opaque to more transparency between the No. 1 and the No. 2 AI powers in the world is very important."

A notification system would not be the only example of how intertwined the US and China are when it comes to AI. However, those connecting threads aren't always above board: 

• Chinese AI labs, such as DeepSeek, Moonshot and MiniMax, have been accused both by major AI labs and US government agencies of "industrial-scale distillation campaigns" involving US-made frontier AI. 

• Meanwhile, US companies are increasingly relying on Chinese open source AI models as a cheaper alternative to the pricy APIs from frontier labs. July data from OpenRouter found that the share of companies using Chinese models on its platform sits anywhere from 30% each week to up to 46%.

• The US and China are also interwoven on the hardware side: While chips have long been a point of contention, Chinese components like transformers, batteries, and switchgears are a building block of US data centers.

Still, despite the tangled nature of the AI relationship between the two superpowers, frontier labs and US government officials have invoked the narrative that the nation must win the heated AI race. 

For instance, the Trump Administration's AI Action Plan from last July explicitly says that the US must achieve "global dominance" in AI. Anthropic, meanwhile, wrote in a May paper entitled "2028: Two scenarios for global AI leadership" that AI supremacy is essential to "stay ahead of authoritarian governments like the Chinese Communist Party, or CCP," and OpenAI has used Chinese competition to justify its massive infrastructure buildout. 

However, this dichotomy isn't necessarily a race to build out two separate, warring ecosystems, Thomas Randall, a research director at Info-Tech Research Group, told The Deep View. Rather, because the ecosystems are so intertwined, "It is a contest for control within a single, shared system." However, neither can sustain dominance on their own, he said, as both rely on a network of international suppliers, research, talent and more. That reliance is not "symmetric or stable," and is constantly shifting. 

"The rivalry is better understood as a state of shifting exposure, in which the US and China each work to weaponize whatever asymmetric position they hold within the interdependent system while simultaneously trying to correct, unilaterally, for the exposure the other has already gained," said Randall.

US government officials and Silicon Valley alike have long used the competition with China as a means to justify the ruthless forward push to build bigger and better AI. However, as discussions of a slowdown and fear over the security risks of AI start to reach a fever pitch, the US government's alert system proposal may be an acknowledgement that this argument has its limitations. The proposal may simply be a diplomatic way to address the AI risk that these powerful systems present, without actually saying the quiet part out loud: That the US and China's AI ecosystems are inextricable from one another. 

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PRODUCTS

Gemini-first laptops are Google's new AI device play

Apple rode the AI boom, with Macs winning early access to cutting-edge technology and capturing AI users. Now Google is rebranding Chromebooks to compete.

On Monday, Google launched Googlebooks, a new category of laptops that are built on Android while maintaining ChromeOS. Google is making two major selling points: 

1. Android phone users get a much more seamless experience working together with these laptops

2. Gemini Intelligence is integrated into the core experience

Google backs up the claim that Googlebooks were designed for Gemini Intelligence with new features that make getting Gemini assistance more seamless. For instance, with the Magic Pointer feature, users can wiggle their cursors to give Gemini access to whatever is on the screen. Rambler, the Google voice-to-text feature that functions similarly to Wispr Flow, is now located next to the Quick Insert key. This is meant to help users quickly access the feature and get accurate transcriptions wherever they compose text. 

It is also integrating AI to help people build. The Create My Widget feature allows users to customize the desktop by using natural language to create widgets. For people interested in taking it a step further, Google Antigravity, Google's development platform for AI, comes with every Googlebook. Developers also have access to a full Linux terminal environment, where they can use tools like Claude Code or Antigravity CLI, according to Google. 

To flex the AI arm even further, every Chromebook purchase includes 12 months of Google AI Pro, with 5TB of cloud storage and Gemini Advanced tools. Other freebies include 3 months of YouTube Premium, Adobe Photoshop, and up to $300+ in apps. If it feels like AI is the central focus, that's what some of the earliest user critiques of Googlebooks have been focused on, though initial reactions from the media have trended more positive.

The launch partners include HP and Dell, whose Googlebooks are powered by the Qualcomm Snapdragon X Elite chip. Lenovo and Acer, whose laptops use the Intel Core Ultra 5 Series 3 (Panther Lake). And there's also ASUS, with the Googlebook 14 powered by the Intel Core Ultra 5/7 (Panther Lake). While these chipsets are powerful and can definitely handle AI workloads, this is probably the hardest area for Google to compete in, as Apple Silicon remains more advanced. The laptops are available for pre-order today and launch on October 4 in the US, and start at $899.

Apple has been winning big in the on-device AI race delivering the chips and devices needed to fuel it, and unsurprisingly, more manufacturers want a slice of that pie. Google's Chromebook rebrand as "Googlebook" is its attempt at doing just that. The biggest draw, I think, will be for Android users who can now seamlessly hand off between their phones and laptops, a feature Apple users have enjoyed for years between iPhones and Macs. The Gemini pull will be a tougher sell, though, since Mac users can already access some of the best AI features available in Google's suite of tools. Competing will be especially difficult as Nvidia's RTX Spark chipsets power a new generation of laptops from many of the same manufacturers, including Acer, Asus, Dell, and Lenovo. Another critical factor is that this marks Google's first real entry into Apple's turf of premium devices, whereas Apple has already found early momentum entering Google's turf with the budget-friendly MacBook Neo that targets the space formerly owned by Chromebooks and budget-friendly Windows machines.

LINKS

• OpenAI to launch advisory group for mathematicians on math-related AI

• Calif. Gov. Gavin Newsom to sign package of bills aimed at data centers

• OpenAI, Experian partner to let users ask about credit score in ChatGPT

• China's Z.ai disables AI coding assistant features after security issues 

• Treasury Secretary says OpenAI is responsible for Hugging Face incident

• UN says that the world "cannot afford a race to the bottom" on AI safety

• Kimi Code Desktop: A redesigned interface making Kimi Code available on desktop

• Motion: Introduced Muse but for motion design

• Qwen-Image-2.1: most balanced and cost-effective image generation model in the Qwen-Image series now has open weights 

• Muse AI: The Muse AI agent is now open to third-party agent developers

• Grok 4.7: SpaceXAI's most powerful model yet for coding and knowledge work

• Hill's Pet Nutrition: AI Creative Technologist

• Niural AI: AI Product Operator

• Unlearn.AI: AI Engineer

• Booz Allen Hamilton: Agentic AI and Data Engineer

GAMES

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