EV maker Rivian is forming partnerships right and left—including two new initiatives announced Thursday at Tech Brew’s Power Shift event in New York City.

Wassym Bensaid, Rivian’s chief software officer, announced two separate efforts aimed at making EV charging easier for Rivian drivers: Rivian is joining the automaker-backed EV charging network IONNA, and the EV maker will roll out a smart charging program later this summer.

IONNA—in which BMW, General Motors, Honda, Hyundai, Kia, Mercedes-Benz, Stellantis, and Toyota are all involved—is in the process of establishing a fast-charging network across the US.

“We continue to invest in the Rivian charging network and provide that seamless experience to our customers,” Bensaid said. “But then in parallel…we are big believers in a seamless, open ecosystem. And we want to provide choice and options for our customers.”

He noted, for example, Rivian drivers’ ability to use the Tesla Supercharger network and access features like “plug and charge” capabilities.

“We continue to do that with other players in the ecosystem,” Bensaid said.

The IONNA partnership, he said, “will follow a similar approach to what we did with the Tesla team, which is offer that seamless digital experience from the vehicle, so that you have access to live data, you have access to plug and charge…A great charging experience should be that customers don’t need to think about it—it just works.”

Keep reading here.—JG

Amid growing discussion around how AI might be taught to “think” in novel ways, Google claims its new coding agent can apply creative problem-solving to tasks like chip design and data center improvement.

Earlier this month, Google DeepMind announced a system called AlphaEvolve that uses Gemini to create algorithms, then iteratively improves them with an automated evaluation system. The company said the agent has already improved data center efficiency, chip design, and even the training of its own AI models.

The announcement came ahead of a flurry of other news at its I/O developer conference, including advances in video, image, and coding models; a new AI Mode for search; and the replacement of Google Assistant with the more advanced Gemini.

But AlphaEvolve is more focused on back-end processes than consumer uses. The system “propose[s] computer programs [through LLMs] that implement algorithmic solutions” to given tasks. The automated evaluators then test the answers the program produces, and an evolutionary framework then makes improvements to optimize for a given outcome.

“This makes AlphaEvolve particularly helpful in a broad range of domains where progress can be clearly and systematically measured, like in math and computer science,” the research team wrote in the announcement.

Keep reading here.—PK

AI tools produce substantial carbon emissions, which is one of the reasons why DeepSeek’s purportedly more-efficient-than-others AI model made such a splash in the industry. And in a recent study, French sustainability software company Greenly put DeepSeek’s claims to the test.

According to Greenly, training DeepSeek models takes less time and uses fewer Nvidia chips. When training DeepSeek’s V3 model and Meta’s Llama 3.1 on the same scenario, DeepSeek used 2.78 million graphics processing unit hours and Meta’s model used 30.8 million. As training is usually the most carbon-intensive step in operating an AI model, DeepSeek’s faster training time ups its efficiency. Additionally, DeepSeek uses 2,000 Nvidia chips, while Meta’s model uses over 16,000 and ChatGPT uses more than 25,000—and DeepSeek’s chips are less “energy intensive” than those used by ChatGPT.

“The company had to develop these innovations out of necessity—due to US sanctions restricting access to Nvidia’s most advanced AI chips,” Greenly’s study said. “This limitation forced DeepSeek to design models that maximize efficiency rather than relying on large-scale computing power.”

Keep reading here.—TC

Usually, we write about the business of tech. Here, we highlight the *tech* of tech.

Maybe don’t try it on: The Atlantic tested out Google’s new AI shopping tool, Try It On, which lets online shoppers get a sense of what they’d look like in a given article of clothing. Atlantic staffers Lila Shroff and Matteo Wong used photos of themselves and famous individuals to test it out, and they were able to coax some alarming visuals out of the tool. Perhaps, for now, the best way to see if a garment fits is still the old-fashioned way: standing in a fitting room with your best friend, sweaty and agitated, complaining that the umpteenth pair of pants still just isn’t quite right.

Awash in slop: Any time you’re staring at a screen, you run the risk of encountering AI slop. But The New York Times proposes that the slop sensibility has leaked out of our screens and pervaded our physical existence in the form of Shein hauls and fast-casual lunches. The whole read sort of makes you feel like abandoning the internet and going full Walden.