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Great Expectations

Dear Google, don’t pull an Apple and advertise features you can’t deliver

Google’s developer conference this year made a lot similar promises to Apple’s last year — ones that failed to materialize.

Rani Molla

At Google’s developer conference this week, AI was everywhere and being shoved into everything. Generally analysts were happy with the company’s performance and promises, but what’s ultimately more important is whether Google will actually be able to execute all that AI supposedly “coming soon.”

For a cautionary tale, look no further than Apple, which at its developer conference last year made many similar promises. Then, like now, AI was going to be integrated across apps and its smart assistant Siri was going to become truly smart. The iPhone maker promised the holy grail of UI: having your device correctly anticipate and act on your needs and wants.

The goal was to “make Siri more natural, more contextually relevant, and, of course, personal to you,” Apple AI and machine learning project manager Kelsey Peterson said at the time. The assistant, recreated from the ground up with AI, would be able to pull from your texts and emails and cross-reference real-time flight tracking, for example, to tell her when to pick up her mom from the airport.

The promises went on:

Thanks to Apple Intelligence, [Siri] has awareness of your personal context. With its semantic index of things like photos, calendar events and files, plus information thats stashed in passing messages and emails like hotel bookings, PDFs of concert tickets and links that your friends have shared, Siri will find and understand things it never could before. And with the powerful privacy protections of Apple Intelligence, Siri will use this information to help you get things done without compromising your privacy. Youll be able to ask Siri to find something when you cant remember if it was in an email, a text, or a shared note, like some book recommendations that a friend sent you a while back or for times when youre filling out a form and need to input your drivers license, Siri will be able to find a photo of your license, extract your ID number and type it into the form for you.

One year later, ahead of this year’s Apple developer conference, much of what was advertised hasn’t panned out. Many of the promised features aren’t available and those that are don’t work as promised. Perhaps as a result, Apple Intelligence failed to drive a meaningful iPhone upgrade cycle, Apple is behind its peers in the AI space, and Apple’s head of services, Eddie Cue, has warned, “You may not need an iPhone 10 years from now.”

When the company’s software chief, Craig Federighi, tested the upgraded Siri weeks before its planned release in April, “he was shocked to find that many of the features Apple had been touting — including pulling up a driver’s license number with a voice search — didn’t actually work,” Bloomberg reported recently. The promised Siri upgrades are still “months away from shipping,” and Apple doesn’t plan on discussing them much at this year’s WWDC. Indeed, the company will stop announcing new features more than a few months before their launch.

Let’s look now at Google’s recent promises. Here’s Josh Woodward, VP of Google Labs and Gemini, at the developer conference Tuesday:

Our goal is to make Gemini the most personal, proactive, and powerful AI assistant, and it starts with being personal. What if your AI assistant was truly yours... an assistant that learns you, your preferences, your projects, your world, and you were always in the drivers seat?

He went on, demonstrating a number of features that would certainly be nice to have, but felt a bit like a bridge for sale.

See today, most AI is reactive: you ask, it answers. But what if it could see whats coming and help you prepare even before you ask? Imagine youre a student youve got a big physics exam looming and instead of scrambling, Gemini sees it on your calendar a week out, but it doesnt just remind you. It comes with personalized quizzes crafted from your materials, notes from your professor, even photos, handwritten notes. Thats not just helpful. Its gonna feel like magic.

Yes, of course — if it works. As we learned from Apple, getting this stuff to actually work is a far cry from conjecturing in a demo. Truly useful AI, it turns out, is hard. Apple’s Genmoji, where you use real language to make custom AI emoji, take a long time to generate, usually don’t look that great, and can even overheat your phone. Apple’s text summaries of news were shut down after not just being comical but flat-out wrong. Siri’s upgrades haven’t materialized and the assistant can seem worse than ever.

We’ll check back in this summer when “personal context” is slated to roll out across Google products to see if it’s all it was talked up to be.

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$26B

Nvidia is planning on spending $26 billion to train its own AI open-weight models, according to a 2025 financial filing. Wired was first to report the information. Nvidia has released several of its own AI models, including the Nemotron reasoning model, as well as specialized ones for specific tasks.

Nvidia making its own large frontier models could allow the company to go head-to-head against some of its biggest AI customers.

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Musk blurs the boundaries of his companies even more with joint xAI-Tesla AI agent project

Tesla and SpaceX CEO Elon Musk said Wednesday that Tesla and xAI, which is part of SpaceX, would work on a joint AI agent project called “Macrohard,” also referred to as “Digital Optimus,” as part of Tesla’s $2 billion investment in xAI. The collaboration would pair Grok with what Musk described as a real-time computer-controlling AI agent running on Tesla hardware.

In his post, Musk said Grok would serve as the higher-level “System 2” reasoning layer directing “Digital Optimus,” a faster “System 1” layer that processes the last five seconds of screen video and keyboard/mouse inputs to take action. He said the system would run inexpensively on Tesla’s low-cost AI4 chip alongside more expensive Nvidia chips at xAI, and suggested it could, “in principle,” emulate the function of entire companies. “No other company can yet do this,” he said.

Business Insider reported earlier Wednesday that Tesla was taking up the AI agent mantle as xAI’s similar project stalled, but Musk’s post suggests the initiatives are more intertwined than previously understood.

The collaboration marks the latest example of Musk’s companies working closely together, further blurring the lines between Tesla and the recently merged SpaceX-xAI entity.

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Meta doubles down on custom inference chips after reportedly scrapping training chip

Meta said today that it’s expanding its custom silicon development to include four new generations of Meta Training and Inference Accelerator (MTIA) chips. The announcement comes just weeks after The Information reported that the social media company had scrapped its most advanced AI training chip, dubbed Olympus, after facing design challenges. In the meantime, it signed outside chip deals with Nvidiaand Advanced Micro Devices.

Early in its recent conference call, Broadcom CEO Hock Tan sought to reassure investors that the custom chip specialist’s relationship with the social media giant was only getting stronger.

“Now contrary to recent analyst reports, Meta’s custom accelerator MTIA road map is alive and well,” he said. “We’re shipping now.”

The new road map suggests Meta’s in-house chips will focus more on inference, which has more predictable workloads, over training — a technically more demanding area dominated by Nvidia:

“MTIA 300 will be used for ranking and recommendations training, and is already in production. MTIA 400, 450 and 500 will be capable of handling all workloads, but we will primarily use these chips to support GenAI inference production in the near future and into 2027.”

Meta CFO Susan Li told attendees at Morgan Stanley’s tech conference earlier this month that the company “eventually” plans to expand its custom chip design to include training models.

Early in its recent conference call, Broadcom CEO Hock Tan sought to reassure investors that the custom chip specialist’s relationship with the social media giant was only getting stronger.

“Now contrary to recent analyst reports, Meta’s custom accelerator MTIA road map is alive and well,” he said. “We’re shipping now.”

The new road map suggests Meta’s in-house chips will focus more on inference, which has more predictable workloads, over training — a technically more demanding area dominated by Nvidia:

“MTIA 300 will be used for ranking and recommendations training, and is already in production. MTIA 400, 450 and 500 will be capable of handling all workloads, but we will primarily use these chips to support GenAI inference production in the near future and into 2027.”

Meta CFO Susan Li told attendees at Morgan Stanley’s tech conference earlier this month that the company “eventually” plans to expand its custom chip design to include training models.

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Google completes acquisition of Wiz — its biggest ever

Today Google said it has completed its $32 billion acquisition of cybersecurity startup Wiz, the largest deal in the company’s history.

“This acquisition is an investment by Google Cloud to improve cloud security and enable organizations to build fast and securely across any cloud or AI platform,” the company wrote in the press release.

The companies agreed to the all-cash purchase last year, after quite a bit of back-and-forth.

Alphabet updated acquisitions chart
Sherwood News
Alphabet updated acquisitions chart
Sherwood News

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