Lawmakers have failed to extend a surveillance law that allows US intelligence agencies to monitor targets abroad without a warrant.
Congress rejected a vote to extend Section 702 of the Foreign Intelligence Surveillance Act to July 2, which means, for a few days at least, some surveillance will be put on hold, for the first time since the Act was passed in 2008. The next possible chance for a vote will be June 28.
This has significance for CISOs because they need to be aware of how communication between the US and other countries is being monitored. The Act permits US intelligence agencies to collect texts and emails sent to and from foreigners living outside the US without a warrant — and when those communications are to or from an US citizen, it allows them to scoop them up too.
“For too long, the FBI has been able to piggyback on a major national security tool as an unconstitutional backdoor way of reading Americans’ communications,” Electronic Frontier Foundation Senior Policy Analyst Matthew Guariglia wrote in article about the renewal vote this week.
It is uncertain what will happen next. Some commentators expect things will proceed as if the Act had been extended, possibly through an executive order. However, the industry may well revolt against this and we could see some tech providers take legal action.
The freeze affects 41 wind projects in Texas, which produces more wind power than any other state in the U.S., as well as projects in Colorado, Illinois, Iowa, Kansas, New Mexico, Oregon and Washington.
Apple’s executives have been taking questions, hosting seminars, seemingly working around the clock to stress one very important thing: Apple is not using a white label version of Google Gemini to make Siri AI happen. They just pooled resources to get there.
The new Siri AI is faster, more accurate, offers powerful contextual capabilities and shows how Apple has leap-frogged into a good peer position in an AI race critics felt it had already lost. Its market scale — even without the EU — is huge. For most consumers, Apple Intelligence and Siri will continue to be their primary/first engagement with artificial intelligence on a device.
Getting there took a lot of work, and Apple needed Google to get it done. Though there is still some confusion about what that means, Apple’s software chief tried to explain it this week. “We use none of the models that Google deploys to their customers, nor do we use the infrastructure and means by which they employ models to their customers,” Craig Federighi said in a presentation at WWDC.
Apple is not even using Google Search as the foundation of its system, he said. “This is the amount of Google Assistant we use,” Federighi said, pointing at an empty chart. “Nothing.”
Apple not Gemini, Siri AI is not Google’s
What makes this hard to understand is that we all know Apple partnered with Google to build Siri AI; back in January, we were told the next generation of Apple Foundation Models would be based on Gemini models and cloud technology. So, how can we have moved from partnership hero to usage zero?
The answer is, we didn’t. What happened is that Apple built its new Apple Frontier Models (AFMs) (the AI inside Siri AI) by training them using proprietary Apple data and reinforcement learning and then refined those models using “outputs from Google’s Gemini Frontier models.”
In other words, Apple used Google Gemini to help improve its own models, which means the models themselves, the AI in Siri AI, are Apple’s — but they were trained with help from Gemini. They are not white label iterations of those Google models.
Apple also hit a second snag. Its very best model (AFM 3 Cloud Pro) requires more processor power to run than Apple could deliver using its own cloud-hosted Private Cloud Compute servers. Now, we know Apple doesn’t like using other people’s stuff. But it’s a realistic company that understands it sometimes must, and just as it uses AWS to support some of its services, it moved to adopt Google cloud services and Nvidia processors to drive the most demanding requests.
Apple also developed a technological solution that means it can claim the interaction remains just as private as if it were run on your device. Apple has made it possible for independent security experts to confirm this and says it is the only company that can deploy software on those servers, with strong security to ensure your device only interacts with those servers when you want it to. So far, no one has broken this protection.
I came across an interesting report in which Tekonyx Founder and Chief Research Officer Sid Nag explained the significance of Apple finding a way to expand its Private cloud Compute infrastructure beyond its own data centers.
“Apple is effectively arguing that trust in AI systems should come from cryptographic and architectural guarantees rather than trust in the cloud provider itself,” he told Fierce. Where enterprises have faced a choice between access to powerful AI or privacy, Apple has introduced a new solution he called “portable trust.” This could conceivably become a new IaaS offering from the companies involved over time.
Working together for the benefit of all
So, while Apple’s models were built with Google’s help, and while its most advanced models run with support from Google and Nvidia, the models are Apple’s alone.
It’s good for Google, of course. Apple is paying for this use, which helps the search giant claw back some of the value of its massive, muti-billion-dollar AI infrastructure investment. It’s not clear how much Apple is paying; earlier this year, the $1 billion figure was bandied around. But Apple’s decision to tie usage to iCloud subscriptions in some hitherto undisclosed way hints that the deal may also see some token-based usage charges on top of the basic Apple fee. I’ve not come across any details, but that’s what I surmise based on the size of Apple’s ecosystem and the growing realization of how quickly users can consume AI capacity.
What AI models is Apple running?
AFM 3 Cloud Pro is one of five Apple Frontier Models driving Siri AI. Here’s how Apple describes those five models:
On-device models
AFM 3 Core, the next generation of Apple’s 3-billion-parameter dense model. You’ll use this for basic text generation, summaries, conversational replies, all the standard uses. It can also handle indexing, search, App Intents, basic dictation and contextual awareness.
AFM 3 Core Advanced, the most powerful on-device model. This is what makes Siri’s voice match mood or context, does all the high-accuracy dictation, and can process various data to handle tasks across different apps. It’s the AI driving your more involved Siri conversations.
AFM Core Advanced is impressive in its own right, because Apple has managed to cram a 20-billion-parameter model onto a smartphone. It has done this by using a sparse architecture, which means it activates just 1 to 4 billion parameters at a time depending on the request. It is, however, only available to Apple’s most powerful systems — iPhone 17 Pro, iPhone 17 Pro Max, iPhone Air, M4 or later iPad, M3 or later Mac with 12GB+ memory.
Server-based models
AFM 3 Cloud, which Apple calls its server-side workhorse, optimized for speed, efficiency, and performance.
ADM 3 Cloud (Image), for image generation and editing, which unlocks advanced photo-editing tools, the all-new Image Playground, and more.
AFM 3 Cloud Pro, the most capable server-based model, which powers the most demanding use cases, like agentic tool use and complex reasoning.
All five were built using the same common foundation, which was then specialized to reflect the proposed use of that model. It’s interesting to look at the human evaluation tests Apple ran to test how well these models performed; they demonstrate impressive improvement on the company’s original models, effectively justifying the decision to work with Gemini.
A German court has sparked a legal controversy by ruling that Google is responsible for defamatory comments generated by its own AI system. The search giant had argued that it couldn’t be blamed for the false results, but a Munich court has deemed that not to be the case and has ruled in favor of the two unnamed plaintiffs, both publishing companies, who the Google AI Overview inaccurately said engaged in shady business practices.
Google is required to remove the comments and ensure that they are not repeated. The case is certainly going to raise some questions globally. Will this mean that other courts are going to rule against AI vendors?
Bernhard Buchner, a partner at Lausen Rechtsanwälte, the legal firm that acted for the plaintiffs, said, “I believe it shows that online providers such as Google cannot hide behind the fact that a statement was generated by AI, but rather that they can be held liable for its output. It is an important step towards ensuring that providers of AI systems have to take responsibility for their outputs.”
So, does this mean that the decision could be replicated in the US or elsewhere? Alex Shahrestani, managing partner at Austin-based Promise Legal, said, “the short answer is ‘yes’: the Munich ruling travels, because US courts are already making the same move.”
He explained that Section 230 of the US Communications Decency Act, which has been applied to protect online service providers like social media companies from lawsuits based on their decisions to transmit or take down user-generated content, was built for computer bulletin boards, “not for a model that writes its own answers. Once the AI is the author, the company is the publisher.”
This means, he said, “businesses now need named humans at accountability nodes, verification gates before AI output ships, and audit trails that survive discovery, because ‘the model recommended it’ is a legally empty sentence.”
Does the decision mean that other AI providers could find themselves in the same position? Buchner believes it’s possible, although, he said, the situation in this case is unusual; it does not involve a classic chatbot scenario, but one where the AI-generated statements are published as an ‘AI overview’ of a search query.
“Google’s liability here is based not so much on the fact that it operates the underlying AI, but rather on the publication of its output. However, it seems entirely conceivable to me that this could also be applied generally to inaccurate or defamatory AI,” he pointed out.
Nonetheless, said Carolyn Shelby, head of SEO at Yoast, the German ruling should ensure that companies will be more circumspect in how they handle AI in the future, to protect themselves from any legal action. The first thing they should do is to separate low-risk use of AI from major decision-making.
“Using AI to summarize meeting notes, brainstorm campaign ideas, or create a first draft of something is very different from using it to make decisions about customers, employees, finance, compliance, health, legal claims, competitive positioning, or public communications,” she noted.
She pointed out that the effects of AI use could be devastating for companies. “The consequences could include customer complaints, reputational damage, regulatory attention, legal claims, correction costs, loss of trust, and internal disruption,” she said. “Even when a mistake does not become a lawsuit, the operational cost of correcting bad information can be significant.”
However, she noted, things may not change immediately. “Many companies will wait until there is a high-profile court case, regulatory action, or major corporate embarrassment before they take this seriously. That is usually how governance catches up with technology. But the better-run organizations will start treating AI governance as part of normal business risk management now.”
And, said Shahrestani, after the Google decision, everything has changed. It will become more important to ensure that employees remain part of the process.
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