Progression

Almost a fifth of the earth's population lives in Africa. And Africa's next generation of power projects "is increasingly being built around solar and wind power and battery storage," reports the Associated Press, "as governments and investors shift away from coal and large hydropower dams in search of cheaper, faster and more reliable electricity."

The shift is visible in a $1.5 billion energy agreement between China and Zambia announced in early May that includes three separate 300-megawatt projects spanning solar, wind and coal-fired power. While the inclusion of coal underscores the continent's continuing need for stable baseload electricity, African countries facing rising fuel import bills as a result of the Iran war, unreliable grids and growing industrial demand are increasingly turning to renewable energy projects that can be deployed faster and more cheaply than traditional plants.

Of the 322 energy projects announced across Africa in 2025, 173 were solar projects, followed by hydropower at 46, wind at 34, gas at 22 and hybrid energy projects at 14, according to the energy research firm Electron Intelligence... Utility-scale solar power costs have dropped by nearly 90% globally since 2010, while onshore wind costs have fallen around 70%, making renewables the cheapest source of new electricity generation in many African markets...

Much of the growth is through distributed solar and battery systems installed directly in mines, factories, telecom towers and homes. "Most official statistics still measure the energy transition the old way, by counting megawatts connected to national grids," [said Matt Tilleard, CEO of CrossBoundary Energy, which invests in renewable energy in Africa]. "But solar and batteries don't need central utilities." Data from the Africa Solar Industry Association shows 23.4 gigawatts of operational solar projects had been tracked across Africa by the end of 2025. But Chinese export figures indicate 58.1 gigawatts of solar panels have been shipped to African countries since 2017, suggesting solar adoption may be growing far faster than official figures capture.

Investor Tilleard says "Renewable energy is now unequivocally the fastest, cheapest, and most bankable way to connect people, companies and economies to the megawatts they need to grow."

And the article also includes this quote from Mugwe Manga, climate finance lead at FSD Kenya. "Africa is not on the periphery of the global energy transition, it is sitting at its center. The continent holds the world's best renewable resources, and the economics have now decisively turned in favor of clean energy."

"In the future, AI agents will be able to find one another using the Domain Name System (DNS), instead of crawling about and probing ports or checking configured resources," writes The Register.

InfoWorld writes that "numerous proprietary agent registries are on the market, but the Linux Foundation suggests we simply extend the distributed, open Domain Name System (DNS) infrastructure we already have."

The foundation is now inviting contributions to the DNS-AID project, a standard way for AI agents to discover, verify, and communicate with one another over DNS that requires no new infrastructure. It enables agents and Model Context Protocol (MCP) servers to use DNS as a global, vendor-neutral directory.

While many details remain to be worked out, the proposal suggests domain owners create a new well-known address that can provide a starting point for agents looking for one another: _index._agents.{domain}. This approach ensures that agent discovery remains scalable, secure, and compatible with the protocols that underly the internet, the Linux Foundation said.

The Linux Foundation descrbes DNS-AID as enabling a standard way for AI agents to discover and communicate with one another. "By leveraging the internet's existing Domain Name System (DNS) infrastructure, DNS-AID provides a robust, decentralized alternative to the centralized registries and hardcoded URLs currently limiting AI interoperability."

The standard was originally developed by Infoblox, their announcement notes, but "Because the protocol is implementation-agnostic, it functions across any DNS provider, ensuring that organizations maintain control over their agent infrastructure without relying on proprietary, centralized services."

You can try 570 extinct operating systems at a new "virtual museum," according to a new article by ZDNet. Their reporter downloaded the ancient OS NeXTStep, and was "shocked" by how easy it was to run it, "and by the sheer number of operating systems to choose from."

Essentially, what you do is download a zipped file, unzip it, change into the newly created directory, and run the executable. VirtualBox then opens to a Debian Linux instance, where you can select from a very long list of operating systems to run... You can run operating systems like Amiga, Apple I/II/III, Atari, Avigo, Commodore 64, Cray, DEC Alpha, Einstein, Game Boy Advance, GE 200, HP 3000, IBM 1130, iPod touch, Jupiter Ace, Lisa, Macintosh, MIPS-based SBCs, Neo, Newton, NeXT, NORC, Palm, and so many more. You can test the earliest mainframes, later mainframes and minicomputers, workstations and Unix variants, home computers, personal computer operating systems, mobile and embedded adOSes, and research-based and obscure systems. As far as Linux is concerned, you can run early Debian and its derivatives, Red Hat and its derivatives, early Slackware, and more...

There are two editions of the Virtual OS Museum: full and lite. The full edition is currently 174GB and includes everything you need to run these old-school operating systems. The full version does not require a network connection to run. The Lite version is only 14GB and requires an internet connection because it downloads the full OS image you want to use.

Gizmodo notes "this project is all the more remarkable for being the work of one man: Andrew Wartenkin, who has been collecting OS images for over two decades."

Of course, Wartenkin didn't write all the emulation software himself, and he maintains a list of credits to give credit where it's due... The Museum itself runs in a virtual machine, which seems kinda fitting - it opens in a virtualized Linux installation and presents you with the full list of available operating systems.

Did you know someone has written a GUI for the Commodore 64? Neither did I! There are simulations of ancient mainframes, like the IBM 1130 (yours for the low, low price of $32,280 - or $41,230 with a disk drive - back in 1965).

There's also a YouTube channel.

Thanks to long-time Slashdot reader Z00L00Kfor sharing the news.

spectrum.ieee.org

Shadow Walker’s creators went on to found a pioneering robotics company

Image: Peter Nelson / The Verge

Despite headwinds from the current administration, automakers continue to release well-equipped EVs with bigger battery packs and increasingly faster charging speeds. For those who want to travel further between plugging in, the future is still bright, just slightly tinted.

But there haven't been many sedans starting around or below $50,000, as crossover SUVs have largely taken up this territory. Now, Mercedes-Benz has released its CLA compact sedan which ticks every box above for the 2026 and 2027 model year, and throws in pleasant interior amenities and fun driving character for good measure.

What's inside

The CLA w …

Read the full story at The Verge.

NurPhoto / Contributor

arstechnica.com

On a blazing hot day in South Africa, female southern pied babblers can’t think straight. The medium-sized black-and-white birds are trying to get at tasty mealworms behind a see-through barrier. On cooler days, the birds can quickly figure out that all they have to do is go around the small wall of plastic. But when the mercury goes up, the birds just keep stubbornly pecking at the barrier.

That experiment is part of a growing body of research showing that animals get their minds muddled during heat waves. When it’s hot outside, birds struggle to learn, dogs bite more often, goat-like chamois pick fights. This is bad news not just for those who get on Fido’s toasted nerves. If the animals can’t stay alert enough to find food or avoid predators, their chances of survival go downhill, says Amanda Ridley, a behavioral ecologist at the University of Western Australia who coauthored the pied babbler study.

With climate change making heat waves more common, such cognitive impairments across the animal kingdom could ripple through entire ecosystems, putting already fragile species at greater risk. If pollinators forget which flowers to visit, crops and wild plants may fail. If birds can’t find food as easily, their young may not survive. And on a warming planet, a sharp mind is particularly vital. “A changing climate means that your ability to behaviorally adapt is even more important,” Ridley says.

Read full article

Researchers have identified a ring of minerals around the largest basin in the northern hemisphere of Mars (which past research suggests held a large body of water). Phys.org says the research provides new clues on when life may have been possible on Mars - and how future astronauts could make oxygen:

Manganese oxides and hydroxides (collectively written as manganese (hydr)oxides) can act as geological proxies for past oceans... The team involved in the new study analyzed short-wave infrared (SWIR) data from China's Zhurong rover, ESA's OMEGA orbiter and NASA's CRISM orbiter to identify and quantify manganese (hydr)oxides... The team says the placement of the ring indicates that the ring formed during the Hesperian epoch - a geologic period on Mars that occurred roughly 3.7 to 3.0 billion years ago. The Hesperian epoch marked the transition from the warmer, wetter, and volcanically active Martian world to a cold, dry, and dusty planet... [when "the potential for further prebiotic evolution on the surface was significantly reduced."]

"This yields a final estimated duration of 0.8-1.5 million years for the presence of stable aqueous conditions in Utopia Planitia. This timescale significantly exceeds what is typically expected for transient surface water activity on Mars, suggesting that Utopia Planitia hosted a long-lived and evolving aquatic system during the Hesperian epoch, rather than a short-lived or rapidly evaporating water body," write the study authors. The researchers say that although this does not provide direct evidence of early life, it does suggest that Mars may have provided an environment conducive to initiating early forms of life. The timeline of the ocean matches the minimal timescale required for prebiotic chemistry, and also temporally overlaps with the period on Earth in which scientists believe the earliest forms of life first arose, approximately 3.4 billion years ago. The study authors also note that the conditions for life may have also extended into the next Amazonian period on Mars. They write, "If MnOx formation or redistribution occurred during the Amazonian, this would suggest that Mars may have maintained episodic or localized liquid water environments significantly later than traditionally assumed."

Interestingly, the authors also bring up the potential for future human habitation on Mars. They suggest that oxygen can be produced by using the manganese (hydr)oxides for water-splitting reactions that generate oxygen through photocatalysis, potentially supporting human activities or even terraforming. Of course, this would be a long way off.

After Google announced AI-emphasizing changes to its search results, many web surfers began defecting to DuckDuckGo, reports TechCrunch. (They describe DuckDuckGo as "a privacy-focused alternative" that accounts for around 2% of the U.S. search market...)

DuckDuckGo said U.S. app installs went up 18.1% week-over-week on average during the May 20 to May 25 period, compared to May 13 to May 18. The company said that growth was sustained for six consecutive days and peaked at 30.5% on May 25. On iOS, the rate of install is even higher, with week-over-week growth hitting a 33% average, peaking at 69.9%... DuckDuckGo said the trend is stronger in the U.S, and that DuckDuckGo continued to gain users over the Memorial Day weekend, when it usually sees a dip in traffic. Some of that data is backed up by third parties. App analytics company Apptopia found a 29% increase in average daily downloads in the U.S. and a 12% increase globally over the same period.

DuckDuckGo also said visits to its AI-free search page, noai.duckduckgo.com averaged 22.7% week-over-week growth, peaking at 27.7% on May 24, according to the article. ("DuckDuckGo also offers an AI Image Filter that filters out AI-created images from search results.")

TechCrunch delves into the reason why:

I overheard a woman on the phone saying she was switching to DuckDuckGo because you can "opt out of using AI... Google just isn't Google anymore," she said. It seems that others had the same idea... Some have argued it will kill the open web, while others shared concerns that AI overviews surface inaccurate responses and take away control from users who might not want to use AI. It also overcomplicates simple things.

A Google spokesperson pointed out that AI Mode isn't the default in their search results. (And CNET notes Google include an AI-free "Web" choice in its results if you just want a page of ftraditional blue links.)

TechCrunch adds that DuckDuckGo also offers a separate free tool called Duck.ai offering access to models including Claude, Meta's Llama and OpenAI's GPT-5 mini. "All chats are private because DuckDuckGo strips the user's IP address before requests reach model providers, deletes conversations within 30 days, and prevents chats from being used for training."

A research team found "extensive changes" on brain scans of 13 young women taking GLP-1 drugs, reports the Washington Post:

Within only a few months, the brain connections in the salience network, which helps target attention, had multiplied... ["We didn't expect to see this effect, and we really don't know what it means," said an assistant professor assisting the research.] Ozempic and other GLP-1 drugs were initially understood as a metabolism breakthrough: medicines that act like hormones to control hunger, blood sugar and weight. But as researchers probe deeper into how the drugs work, early evidence suggests that GLP-1s may also be reshaping parts of the brain.

Tens of millions of people are now taking the medications worldwide, turning what began as an obesity and diabetes treatment into what could be modern medicine's largest unplanned neuroscience experiments... Long before Oprah Winfrey and social media influencers helped popularize GLP-1 drugs, physician-scientist Lorenzo Leggio was studying them as a possible addiction treatment... Several major studies examining GLP-1 drugs on nicotine dependence, opioid- and cocaine-use disorders, gambling addiction and binge eating are also underway. "It's very exciting times, but we don't fully understand how it works," Leggio said...

As evidence has grown that inflammation, metabolism and mental health may be far more connected than scientists once believed, researchers have become intrigued by patients who say GLP-1 drugs appear to ease anxiety, compulsive thinking and emotional distress. Daniel Drucker, a University of Toronto researcher and GLP-1 drug pioneer who receives funding from several drugmakers, said researchers are investigating the medications across a variety of psychiatric and neurological conditions, though none are approved for them. "We have so many anecdotal reports: They were treated for blood sugar and then they felt much happier. Or they took one dose of the drug and their brain fog cleared," he said.

The article suggests social media complaints "raise deeper questions about what, exactly, these drugs are changing.

"If GLP-1s alter the brain systems involved in reward, craving and motivation, researchers wonder, where is the line between quieting a person's destructive impulses and reshaping personality itself?"

Security company Okta shot up 30% Friday, reported CNBC, while data platform provider Snowflake jumped 50% this week.

They see it as part of a larger trend where software stocks "soared this week," signaling "some companies are navigating their way through AI disruption better than Wall Street expected" and that investors "may have been too quick to declare the end of software with the emergence of AI. Even as AI displaces certain tools and job functions, many software companies continue to show growth, assisted by their own AI products..."

The "SaaSpocalypse" may not be over. But for now at least, fears of software's demise have cooled... The iShares Expanded Tech-Software exchange-traded fund rose 8% this week and closed May up 21%, the best monthly performance for the ETF since October 2001. Back then it was a brief rebound during the dot-com bust, while the current rally comes as concerns about the impact of AI ripple across the sector. Software names have been hit particularly hard over the past year due to the boom in so-called vibe coding, with users able to now build apps and websites in minutes thanks to offerings from Anthropic, OpenAI and others...

Elsewhere in the software space, Atlassian climbed 26% for the week and ServiceNow surged over 20%, while Shopify, Workday and Asana each gained at least 14%.

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Read Paul Jones’s poem from our June 2026 issue

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Golf carts and forklifts benefit from a new approach to installing lithium-ion packs

Image: Past Maps

Craig Campbell walked away from the river of investor money flowing into AI to create, of all things, a website.

Sure, Campbell probably could have started an AI company. He's a former engineer at Meta and an experienced tech founder who in 2022 sold his last venture - an e-commerce tool for businesses that use Shopify - right as the AI boom was booming. "I had my prior VC investors breathing down my neck, going 'start something else. We'll write you a blank check.'" He had other ideas.

People generally aren't rushing to get into the website business, what with the Google Zero event horizon approaching. Campbell was undeterred and has gro …

Read the full story at The Verge.

Apple is reportedly working to shrink Google's Gemini models enough to power parts of a long-delayed AI-enhanced Siri on iPhones. But despite Apple's best efforts to run the AI locally, "the iPhone's Gemini makeover will lean heavily on Google and Nvidia in the cloud," reports Ars Technica. That could complicate Apple's privacy-first AI messaging, especially if more complex Siri requests are routed through Google infrastructure and Nvidia's encrypted cloud-computing platform. Ars Technica reports: After inking the Google deal, Apple apparently got to work distilling Google's giant cloud-based Gemini models. Distillation is a process in which a small, less resource-intensive model learns to mimic a large, expensive one. With enough time, this can reliably transfer useful capabilities while pruning less important weights from the model. That may enable Siri to handle some tasks with private local compute, but a cloud component looks inevitable.

Processing users' AI data in the cloud could be a problem for Apple. At WWDC, the company will probably promote its years of experience designing chips and how well that positions it for AI. However, The Information claims that Apple has struggled to even get Google's massive undistilled Gemini models running on its custom Private Cloud Compute infrastructure, which is built on on M-series Mac chips.

When the smarter Siri rolls out, it will probably route more complex tasks to Google's cloud infrastructure instead of Apple's, but it won't be running on Google TPUs. Apple has reportedly signed a deal with Nvidia to use its Confidential Computing platform for this purpose. Confidential Computing keeps data encrypted on Nvidia GPUs while it's being processed in the cloud, which could help Apple claim it's still sensitive to user privacy concerns. It might even retain its own Private Cloud Compute branding for the system.

The iPhone probably won't tell you which version of Gemini is handling individual Siri requests. Device makers designing hybrid systems that rely on local and cloud-based AI like to talk about making the experience feel "seamless." There might be clues, though.

Dell's stock skyrocketed 32.76% on Friday, "its best day ever," reports CNBC, after Dell "reported its fastest pace for revenue growth for any period since returning to the public market in 2018..."

"Shares are now up 234% in 2026."

Dell, which reported first-quarter earnings after the bell on Thursday, saw a flood of artificial intelligence-related demand for its servers, which contain graphics processing units from companies like Nvidia. Quarterly revenue soared nearly 88% year over year, with AI server revenue alone increasing 757% from a year earlier to $16.1 billion...

Ben Reitzes, head of technology research at [research/investment firm] Melius, said he'd "never seen anything like" Dell's latest quarter. "They beat every line in the model, so this wasn't just AI, it was great execution," Reitzes told CNBC's "Squawk on the Street." "They beat whatever we would've thought...."

Morgan Stanley wrote that while they expected a clean beat and raise this quarter, they're "eating our humble pie" off the back of Dell's results. "We got this one wrong, and our model/PT are under review," the analysts wrote. "This was - across the board - one of the most impressive quarters we've seen in our time covering Hardware, especially in the context of what is happening across the component universe."

blog.google

We used Google AI Studio to vibe code a quiz about our top I/O 2026 announcements.

blog.google

Watch 9 videos showing the capabilities of Gemini Omni and Gemini 3.5, announced at Google I/O 2026.

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Your weekly selection of awesome robot videos

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The mesmerizing silicone display is powered by air

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How a new extraction process could unlock the world’s lithium

A new method for extracting lithium could cut costs and emissions from one of the world’s most important materials for EVs and energy storage.

The technique uses a weak acid to dissolve silicate minerals. That frees not only the lithium but also other useful materials, including alumina and silica.

“At scale, we believe this will be the lowest-cost way of sourcing lithium in the world,” says Yet-Ming Chiang, an MIT professor who co-authored a study of the process published yesterday in Science.

Startup Rock Zero is already working to commercialize the research. Read the full story on a new way to unlock the world’s lithium.

—Casey Crownhart

The deadly Ebola outbreak is proving difficult to control

The alert was raised on May 5. Four health-care workers in the Democratic Republic of the Congo had died from an unknown illness within four days. Tests in Kinshasa revealed the culprit: the Bundibugyo virus, one of the causes of Ebola.

A couple of weeks ago, an outbreak of hantavirus erupted aboard a cruise ship. Three people died, but the outbreak was kept under control. The picture for Ebola is bleaker for several reasons, including the disease itself, the available treatments, and the local environment.

Find out why the outbreak is causing alarm.

—Jessica Hamzelou

This story is from The Spark, our weekly newsletter giving you the inside track on all things biotech. Sign up to receive it in your inbox every Thursday.

How the Pope’s Magnifica Humanitas offers a template for individuals to meet the AI moment

——Father Séamus Finn, a leader in faith-based and socially responsible investing with the Oblates of Mary Immaculate, and Sister Susan Francois, assistant congregation leader and treasurer of the Sisters of St. Joseph of Peace

Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.”

Magnifica Humanitas is a call to act with courage and solidarity as AI transforms human life, framing the choice ahead as one between the Tower of Babel and the rebuilding of our common humanity. It warns that corporations alone cannot set the direction of such a transformation.

With governments slow to regulate AI, institutional investors are stepping into the gap. Here’s how they can build a better future.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Anthropic is now valued higher than OpenAI
It hit a $965 billion valuation after a new funding round. (AP News)
+ Claude demand has driven annualized revenue to $47 billion. (WSJ $)
+ The funding round may be Anthropic’s last before an IPO. (TechCrunch)
+ What even is the AI bubble? (MIT Technology Review)

2 A Blue Origin rocket has exploded in a setback to NASA’s Moon plans
New Glenn burst into flames during testing on a Florida launchpad. (CNBC)
+ Blue Origin is heavily involved in NASA’s Moon base plans. (The Verge)
+ It also wants to compete with Elon Musk’s SpaceX. (Reuters $)

3 Adversaries are tracking US troop locations via mobile phone data
The Pentagon has long ignored warnings of this exact threat. (Reuters $)
+ The targeting uses commercially available location data. (Wired $)
+ LLMs could supercharge mass surveillance. (MIT Technology Review)

4 Anthropic plans a broad rollout of Mythos AI in the coming weeks
Despite concerns over its cybersecurity capabilities. (CNET)
+ Claude Opus 4.8 is now out, with a promise to be more honest. (The Verge)

5 Grok oversaw a crime spree in an AI safety test
Models were tasked with governing a simulated society. (Fortune)
+ Grok committed 180 crimes, while Claude ruled with restraint. (Gizmodo)

6 Amazon has scrapped an AI leaderboard after worker gaming
Employees were artificially inflating usage scores. (FT $)
+ We can build better AI benchmarks. (MIT Technology Review)

7 Political spending by AI and crypto groups is shifting elections
They’ve pushed their preferred candidates closer to power. (Axios)

8 China’s tech boom is fueling a new wave of industrial tourism
Visitors are touring AI labs and EV factories. (Rest of World)

9 Alibaba’s MuleRun aims to replicate the OpenClaw craze
The AI agent platform is positioned as a safer alternative. (SCMP)

10 Mysterious changes have emerged in the Sun’s magnetic field
They could reshape space weather forecasts. (404 Media)

Quote of the day

“What Peter Thiel is doing is terrible. His settling in Argentina is even worse.”

—Elisa Lilita Carrió, an Argentine politician, writes on X that Peter Thiel’s relocation to her country has angered her even more than his leadership of Palantir.

One More Thing

NASA, ESA, CSA, STSCI, WEBB ERO PRODUCTION TEAM

How the James Webb Space Telescope broke the universe

When the James Webb Space Telescope began full operations in 2022, astronomers were in awe of the flood of data that arrived.

“Every hour we were looking at a galaxy or an exoplanet or star formation,” says NASA scientist Heidi Hammel. “It was like a firehose.”

Since then, JWST has delivered nonstop discoveries, from distant galaxies to new planetary atmospheres. “We’re cracking open an entirely new window on the universe,” says Hammel.

Discover how JWST has transformed astronomy.

—Jonathan O’Callaghan

We can still have nice things

A place for comfort, fun, and distraction to brighten up your day. (Got any ideas? Drop me a line.)

+ Kubrick fans will love this Lego recreation of Dr Strangelove.
+ Here’s a fascinating explanation of why seven landlocked countries have navies.
+ This mesmerizing 4K remaster of a super typhoon turns weather data into cinematic art.
+ Go inside the genius of Queen with this track-by-track breakdown of “Bohemian Rhapsody.”

blog.google

University of Waterloo students develop AI prototypes like sign language tutors to reshape the future of education and work.

Screenshot: Shift

AI training startup Shift wants to clean your home for free. The catch - because, despite what its website says, there's always a catch - is that it will record cleaners as they scrub, vacuum, dust, tidy, and wash, and use that footage to train robots.

Shift announced the unusual offer on social media on Thursday, explaining that the value of the training data generated from the cleanings is more than enough to fund the service. As its website puts it: "You get a spotless apartment. We get training data. Everyone wins."

A promotional video shows a cleaner in a crisp white uniform and awkward-looking hat (more on that later) washing windows …

Read the full story at The Verge.

Pope Leo XIV’s new encyclical on artificial intelligence includes a statement that warrants serious attention from technologists and policymakers: “Technology is never neutral.” Magnifica Humanitas (“Magnificent Humanity”) is a clarion call to all people to act with courage and solidarity as we enter an age already being transformed by artificial intelligence, the greatest change in human life since the Industrial Revolution. As the pope says, the choice before us—the choice AI presents—is one between the Tower of Babel and the rebuilding of our common humanity.

In the biblical story of the Tower of Babel, humans sought to build a massive structure that reached all the way to Heaven, only to have their project thwarted when God made those involved unable to understand one another. It was a pursuit fixated on relentless growth, divorced from any concern about God’s commandments or the human cost. It resulted in failure and atomization.

The Book of Nehemiah, however, offers a contrasting narrative, in which the rebuilding of Jerusalem after a period of violence and displacement becomes an opportunity for humanity to show its collaborative resilience. As the encyclical puts it, “The city is reborn, not through the initiative of one man, but through the shared responsibility of all: men, women, priests, artisans, heads of households and young people all play a part. It is an undertaking with God at the center, which rebuilds relationships before rebuilding with stones.”

Is there any question which road we are currently barreling down? And can there be any doubt which we would do well to walk together?

We are both Catholics, members of religious communities and longtime advocates within the movement for socially responsible investment. Of particular interest to us and that movement is Pope Leo’s point that AI is not some force of nature or hyperrational, ineffable entity. Instead, he reminds us, AI is ultimately another commercial product, one emerging at a point in history when excessive power over commerce and the wider society has amassed in a vanishingly small number of hands.

It’s a powerful message. It’s also one that institutional investors have been acting on for years. This encyclical doesn’t break new ground so much as ratify a governance effort that’s already underway, led not by states or international bodies but by shareholders. When governments fail to meaningfully regulate, and corporations cannot be trusted to do what is beneficial beyond their own bottom line, people in society still have the power to set us on the right path, and indeed have the duty to do so.

Around the world, AI systems are being deployed at scale with remarkably little institutional oversight. There is no AI safety board. The US Federal Trade Commission has jurisdiction over unfair practices but limited authority over algorithmic design. The National Institute of Standards and Technology publishes guidance that most companies ignore. The EU AI Act is partially in force but addresses only a sliver of the deployment surface.

Institutional investors have stepped into this vacuum. Coalitions including the membership of the Interfaith Center on Corporate Responsibility, representing investors managing over $400 billion in assets, have spent the past several proxy seasons filing resolutions demanding transparency, risk assessment, and accountability around AI deployment. Secular institutional investors have joined them, treating AI governance failures as material business risks.

Shareholders have called tech giants including Alphabet, Amazon, Nvidia, Palantir, and Uber to account and demanded that AI not be used for acts of violence or other violations of human rights. The importance of this aspect of corporate governance was highlighted tragically in the opening hours of the war against Iran, when AI was used to help identify targets for thousands of missile strikes that killed hundreds of people.

Investors have also challenged executives at CVS and UnitedHealth Group to ensure that AI not be used to undermine the well-being of patients and quality of health care across the United States.

At companies including Meta and Microsoft, shareholders have decried the environmental impact of AI data centers, which consume vast amounts of energy and precious water resources, and in turn can emit large amounts of greenhouse gases.

Within creative industries, investors have challenged the leadership at companies like Disney, Netflix, and Warner Bros. to demand transparency about the ways they are using AI and to defend the inimitable human element in storytelling.

Soon, with OpenAI, Anthropic, and Grok all set to enter the public markets, we will be able to exert similar influence over what are now all privately held entities.

These actions by concerned investors not only call out misdeeds but hold fast to an immutable truth: that it is wrong to use technology to kill, harm, or oppress people. Every human being has a right to safe and effective health care and the opportunity to earn a dignified living. The stories we tell each other matter and require the human creative spark.

Investor advocates hail from a range of faith traditions. Some have no formal religious faith. Yet in their informed and tenacious advocacy, all these people echo the calls embedded within Pope Leo’s encyclical and act on its declaration that “it is essential that the use of AI, especially when it touches on public goods and fundamental rights, be guided by clear criteria and effective oversight.”

Encyclicals mark time. A century from now, how will we be remembered for how we met this moment? Will we be seen as having been too timid or shortsighted to prevent a small group of unfathomably wealthy and self-interested people from seizing ever greater control over the human family’s shared destiny?

Or will the years ahead be remembered as a turning point that helped us rebuild our common humanity? Let this be a time when people of good will and diverse talents come together through their own magnificent humanity to build a future that honors our Creator.

Father Séamus Finn, OMI, is a global leader in faith-based and socially responsible investing and a priest of the Oblates of Mary Immaculate, a missionary religious congregation.

Sister Susan Francois is the assistant congregation leader and congregation treasurer for the Sisters of St. Joseph of Peace.

NASA has outlined a three-phase plan to build a lunar base at the moon's south pole. The first phase, from 2026 to 2029, will focus on robotic missions, landers, rovers, reactors, satellites, and Blue Origin's Blue Moon Mark 1 Endurance test. Later phases will add habitats, power systems, communications, cargo logistics, and rotating crews. Wired reports: According to a recent press conference, phase one will be particularly active: at least 25 missions and 21 surface landings. Without detailing specific dates, the agency said that over the next three years it will send rovers, including manned models for future mobility, drones, surface reactors, new-generation satellites, and payloads to prepare the ground.

One of the first key missions will be the test of the Blue Moon Mark 1 Endurance module in fall 2026. Its purpose is to evaluate conditions for a controlled descent and validate navigation and positioning technology. It will not carry astronauts. If the mission is successful, Blue Origin plans a manned version around 2028, possibly with Blue Moon Mark 2. Moon Base II and III missions are also part of the program's 2026 startup. One will send rovers and payloads to evaluate more complex rover operations; the other will carry scientific instruments to study the behavior of materials and systems under extreme lunar conditions.

Phase two, starting in 2029, marks the beginning of semipermanent infrastructure assembly and first occupancy operations. NASA plans to install advanced energy systems, including surface reactors, initial habitat elements, and more robust communication networks. Up to 60 tons of cargo will be delivered in 24 missions during this period.

Phase three is for scale-up. The infrastructure in place will be strengthened and expanded to form durable centers with constant turnover of personnel. NASA envisions a lunar south pole with habitable modules, reliable power systems, logistics networks for cargo and crew transportation, and the shipment of about 38 tons of cargo annually for maintenance and expansion. "Every mission, crewed and uncrewed, will be a learning opportunity as we return to the lunar surface, build the infrastructure to stay, and master the skills required to live and operate in one of the most demanding and dangerous environments imaginable," said administrator Jared Isaacman in a NASA statement. "We will go for the science, for all we stand to gain from an economic and technological perspective, for the innovations that will make life better here on Earth, and to prepare for where we will inevitably go next."

An anonymous reader quotes a report from MIT News: Currently, lithium hard rock extraction involves baking the rock at over 1,000 Celsius and chemically leaching it to extract lithium. The rest of the rock is discarded. Now, a team of researchers from MIT and elsewhere has developed a low-temperature process for extracting battery-grade lithium from the most common type of lithium-bearing mineral. The process uses a liquid reagent to dissolve the rock into the useful forms of its constituent parts: not just battery-ready lithium salts, but also smelter-grade alumina and cement-ready silica. After the minerals are extracted, the solvent and reagent can be recovered and used again so waste levels approach zero. The researchers estimate the closed-loop process is half the cost of traditional lithium hard rock extraction and could make it cost-competitive with extracting lithium from brine water. "We believe this approach is the lowest-energy, lowest-cost way of getting lithium not only out of hard rock, but period," says Yet-Ming Chiang, MIT's Kyocera Professor of Materials Science and Engineering. "That's what's motivating us to scale this. It will enable the energy transition through batteries that use lithium. This was one of the goals of The Climate Project at MIT -- to work on projects that, within a short number of years, could transition from the lab to commercialization and impact."

A paper describing the process has been published in the journal Science.

Anthropic has released Claude Opus 4.8 with stronger performance and better handling of uncertain or flawed data, including a greater tendency to flag issues rather than make unsupported claims. The update also introduces a "Dynamic Workflows" research preview for coordinating complex tasks across many subagents. TechCrunch reports: Opus 4.8 comes with the expected best-in-class benchmark results, but there's also particular attention to how the model manages bad or uncertain data. In the launch post, Anthropic's early testers found that the new model is "more likely to flag uncertainties about its work and less likely to make unsupported claims." Echoing this point, a testimonial from Bridgewater associates said the biggest difference in the upgrade was "Opus 4.8's tendency to proactively flag issues with the inputs and outputs of an analysis, something other models routinely missed and left to the users to catch."

Together with the new model, Anthropic launched a feature called Dynamic Workflows, which will be available in research preview. The system is designed to help larger models like Opus manage complex tasks across hundreds of parallel subagents. "Claude Code alongside Opus 4.8 can now carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge, with the existing test suite as its bar," the post explains. As for Mythos, Anthropic's most advanced model, the company hinted it could be made publicly available in the not too distant future. "We're making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks," the company wrote.

An anonymous reader quotes a report from Gizmodo: In an era where Silicon Valley's conservatism is both expressed openly and becoming more intense by the day, it's strange to think that tech was once seen as a hive of liberalism. The right-wing nature of today's tech industry means that its products tend to also be seen as serving right-wing interests, either in their actual operation (like X's openly and unrepentantly right-wing chatbot Grok) or by the simple fact that their existence serves to enrich a small group of very powerful, very conservative people.

But does it have to be this way? Can LLMs and AI agents find a place in the toolkit of progressive activist groups? The conviction that they can is the idea behind a new app called Outcry, which provides a chatbot designed specifically as a "private, on-device AI mentor for activists, organizers and movement builders." (There's also a web version, although it obviously lacks the privacy benefits of being entirely offline.) It's the brainchild of Occupy Wall Street co-creator Micah White, who recently wrote a blog post about the thinking behind the project.

[...] Outcry's other distinguishing feature is that its dataset is entirely offline -- it's included with the download. According to the readme, the entire dataset is downloaded to your device at first launch, and stored in your library's Application Support directory. So, how effectively does Outcry serve as a guide for collective action? "I'd say that its information is pretty high-level and general, not least because its offline nature prevents it from accessing specific details not contained in its database," writes Gizmodo's Tom Hawking.

He continued: "This app has the potential to be a really valuable resource, especially for people who are just beginning to become involved with activism and genuinely don't know where to begin -- and getting over that first step can be hard."

Image: Microsoft

Microsoft is launching a revamped version of Microsoft 365 Copilot, offering a cleaner design that the company claims loads twice as fast. As part of this update, Copilot will provide more reliable and structured responses that are easier to scan, according to Microsoft.

The redesign, which is rolling out across desktop and mobile devices, comes with a feature Microsoft calls "progressive disclosure." That means Copilot will present you with tools and controls based on your prompt, instead of showing a bunch of options at once. You can now format your text directly inside Copilot's upgraded prompt box as well, which will expand to fit every …

Read the full story at The Verge.

Illinois lawmakers on Wednesday passed a landmark AI safety bill (SB 315) that would require major AI companies to publish safety plans, submit annual third-party testing reports, report serious incidents quickly, and protect whistleblowers who flag emerging risks. OpenAI and Anthropic supported the bill, which could make Illinois a testing ground for state-level AI governance as federal regulation remains stalled. Ars Technica reports: To force companies to be more transparent about rapid developments, Illinois would likely rely on "the Big Four accounting and auditing firms -- Deloitte, EY, KPMG, and PwC -- to audit their safety practices," [said Scott Wisor, a policy director at a nonprofit called Secure AI Project, which supported the bill]. The required independent audits will likely frustrate Trump, who has tried and failed to stop states from implementing AI safety laws as Congress stalls on passing any legislation.

For Trump, the priority has been to promote AI industry interests, but he began considering expanding federal government safety testing after Anthropic's Mythos was released and the AI firm limited access due to safety concerns. Whether or not governments at any level are prepared to protect society from the most catastrophic AI risks remains a major concern for critics who wonder how and when governments will intervene. After inside sources started leaking the details of Trump's AI safety testing plans, critics warned that even the federal government may lack the necessary expertise to audit frontier AI models. And it seems the same criticism extends to independent auditors that Illinois may rely on but industry insiders suggest some AI firms may not entirely trust.

Adam Kovacevich is CEO of Chamber of Progress, a trade group that opposed SB 315 and counts Google and Apple among its members. He told Wired that Illinois' requirements "would force companies to expose sensitive systems to untested auditors in a regulatory regime that's all liability and no standards." Governor J.B. Pritzker confirmed his intent to sign, proclaiming that "Illinois is leading the nation in holding Big Tech accountable."

"I look forward to signing SB 315 and working with the legislature so that AI, when used, is used responsibly," Pritzker said.

Steve Wimmer, a senior policy and technical advisor for the Transparency Coalition, said his group considers the law to be "one of the most important pieces of legislation in 2026."

Cavan Images

arstechnica.com

While we make batteries based on many different chemistries, nothing has approached the massive scale at which we can produce lithium batteries. That scale makes the economics of lithium-ion batteries hard to compete with. Even if we develop a superior battery technology, it's unclear whether we can get manufacturing costs down quickly enough to compete with the efficiency of the lithium supply chain and manufacturing.

The one thing that could change the dynamics is a supply crunch. While lithium is extremely widespread, lithium that can be extracted economically is a different matter. It's cheapest to extract it from brines, and lithium-rich brines are largely limited to South America. We do obtain some lithium from other sources, but it's considerably more expensive.

In today's issue of Science, however, a research team has identified an energy-efficient means of extracting lithium from rocks. The process they've designed uses far less energy than existing ones, regenerates all its starting chemicals, and produces byproducts that could also be sold.

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Anthropic has raised $65 billion in Series H funding led by Altimeter Capital, Dragoneer, Greenoaks, and Sequoia Capital, valuing the company at $965 billion post-money.

Global enterprises across industries are deploying Claude in their core operations, and a growing number of people around the world use it for their everyday work. Since our Series G in February, adoption has continued to grow across global enterprise customers, and our run-rate revenue crossed $47 billion earlier this month. This latest funding is expected to advance our safety and interpretability research, expand compute to meet growing demand for Claude, and scale the products and partnerships our customers rely on.

“Claude is increasingly indispensable to our growing global community of customers, and we work tirelessly to make tools like Claude Code and Cowork more helpful, more powerful, and more adaptable to their needs,” said Krishna Rao, Chief Financial Officer of Anthropic. “This funding will help us serve the historic demand we are experiencing, stay at the research frontier, and bring Claude to more of the places where work happens.”

The round was co-led by Capital Group, Coatue, D1 Capital Partners, GIC, ICONIQ, and XN. Significant investors in this round include AMP PBC, Baillie Gifford, Blackstone, Brookfield, D.E. Shaw Ventures, DST Global, Fidelity Management & Research Company, General Catalyst, Insight Partners, Jane Street, Lightspeed Venture Partners, MGX, NTTVC, NX1 Capital, Situational Awareness LP, T. Rowe Price Associates, Inc., T. Rowe Price Investment Management, Inc., and Temasek. It also includes $15 billion of previously committed investments from hyperscalers, including $5 billion from Amazon.

Joining them are strategic infrastructure partners—Micron, Samsung, and SK hynix—whose technologies play a critical role in the world's supply of memory, storage, and logic chips. As demand for Claude continues to grow, these relationships will help us scale our compute reliably at the pace our customers need.

We have significantly expanded our compute capacity in recent weeks. We signed agreements with Amazon for up to five gigawatts of new capacity, with Google and Broadcom for five gigawatts of next-generation TPU capacity, and with SpaceX for access to GPU capacity in Colossus 1 and Colossus 2. Claude is the first frontier model available on all three of the world's largest cloud platforms: Amazon Web Services, Google Cloud, and Microsoft Azure. AWS remains our primary cloud provider and training partner.

“Claude’s latest advancements have driven large-scale adoption among the world’s most demanding organizations. This momentum positions Anthropic to lead the next phase of AI innovation and capture the enormous opportunity ahead,” said Brad Gerstner, Founder and CEO of Altimeter Capital.

“Dragoneer has long partnered with companies building the technology that will shape our future. Anthropic is helping pull forward this future, as intelligence becomes an increasingly critical ingredient to the way businesses operate and how their products show up in the world,” said Marc Stad, Managing Partner at Dragoneer. “The technological progress we are seeing right now is breathtaking. And we believe that we are still in the earliest days of both the development and commercialization of this technology.”

“Anthropic has built an organization in which the world’s best researchers and engineers operate with unmatched clarity of purpose, because they believe this is the most important work they will ever do,” said Neil Mehta, Founder and Managing Partner at Greenoaks. “Rarely has a company’s culture, mission, and commercial momentum reinforced each other so completely. We are honored to deepen our partnership.”

“Startups and Global 5000 companies alike are deploying Claude to handle complex workflows, and in doing so, Claude is learning how businesses actually operate: the context, the processes, the judgment,” said Alfred Lin, Partner at Sequoia Capital. “Anthropic is building the bridge between where enterprise AI stands today and where it's headed.”

We are grateful for the support of our investors and partners as we continue building Claude for people and organizations around the world.

Blue Origin

arstechnica.com

It was less than two months ago that the third flight of Blue Origin's heavy-lift New Glenn rocket left a customer's payload in an unusable orbit. Investigators have now identified the cause of the failure, and Blue Origin is preparing to launch the next New Glenn mission as soon as next week.

The Federal Aviation Administration and Blue Origin announced the closure of the failure investigation May 22. Yesterday, officials confirmed Blue Origin's next launch will loft a payload of 48 commercial satellites for Amazon's broadband network in low-Earth orbit. This will be the most satellites Amazon has launched on a single rocket, surpassing previous flights on United Launch Alliance's Atlas V, SpaceX's Falcon 9, and Europe's Ariane 6.

Blue Origin and Amazon, each founded by Jeff Bezos, have not officially revealed a target launch date, but public notices of airspace and maritime closures suggest the mission is set to lift off from Cape Canaveral Space Force Station, Florida, as soon as next Thursday, June 4. Blue Origin is expected to roll the New Glenn rocket to its launch pad in the coming days for a test-firing of its seven main engines, fueled by liquified natural gas and liquid oxygen.

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Researchers say they’ve found a new way to extract lithium, a crucial metal used in the lithium-ion batteries that power electric vehicles and energy storage arrays. This new technique could be more environmentally friendly and cheaper than existing ones.

The research was published today in Science, and a startup called Rock Zero is working to commercialize the process.

“At scale, we believe this will be the lowest-cost way of sourcing lithium in the world,” says Yet-Ming Chiang, one of the study authors, who is an MIT professor and a serial entrepreneur behind climate tech companies including Form Energy and Addis Energy.

The most economical way to get lithium currently is to extract it from brine, salty water that’s pulled the metal out of rock over the course of millennia. But this technique is geographically limited and currently requires vast tracts of land for massive evaporation pools. The more common tactic is hard-rock mining, where large bodies of ore are blasted apart, cooked at high temperatures, and processed using dangerous chemicals.

The researchers’ new method uses a weak acid to dissolve typically nonreactive silicate minerals. That frees not only the lithium but also other useful materials, including alumina and silica.

The origin story for this research, and the resulting company, came from another startup founded by Chiang, Sublime Systems, which makes cement using electrochemistry.

The team was trying to find a source of highly reactive silica in order to form stronger cement. One way to make reactive materials, which can bond easily with other materials, is to take a nonreactive material, dissolve it, and then allow it to become solid in a more reactive form. It’s not impossible to dissolve silicates, but the best-known way is to use hydrofluoric acid, an extremely dangerous chemical. Other fluorine-containing chemicals are candidates too, but some will produce hydrofluoric acid as a side product during reactions.

Chiang drew inspiration from a previous home renovation project involving glass, which is made of silica. “I was remodeling a shower in Framingham, Massachusetts, about 25 years ago,” he says. “So when we started this project, I remembered that glass etching cream and thought, ‘What’s in that?’”

The glass etching cream he remembered, which can be found on shelves at any craft or home improvement store, uses ammonium fluoride, a weak acid. And the MIT researchers discovered that in the right conditions, it can effectively dissolve silicate minerals without producing hydrofluoric acid in the process.

This chemistry could be useful for any silicate minerals—and there are a lot of them. But spodumene, the mineral that’s often mined for lithium, became a prime first target. (Chiang says a suggestion from Doug Wicks, one of the company’s advisors and a former ARPA-E official, pointed the team in spodumene’s direction.)

ROCK ZERO

Today, a key step in processing spodumene ore is to roast it in a kiln at super-high temperatures. This causes a phase transformation, essentially puffing up the material and making the lithium more accessible.

By avoiding the need to reach these temperatures, you could save on energy costs and potentially reduce carbon emissions as well, says Camden Hunt, one of the authors of the study and the CEO and cofounder of Rock Zero.

Avoiding the kiln could also unlock the ability to use some ores that can’t be roasted properly, Hunt adds. Ore that contains too much iron won’t go through the phase change correctly, instead melting and turning into a glassy material.

The new process relies on simple stirred plastic tanks and takes place at temperatures up to about 95 °C (200 °F). The ammonium fluoride dissolves the silicates, which in earlier experiments allowed nearly all of the lithium inside the spodumene ore to be extracted within a couple of days. The researchers have since cut this time to under 12 hours, says Benjamin Mowbray, first author of the study and the CTO and cofounder of Rock Zero.

The products (after some additional steps to clean them up) are lithium carbonate, which can be used to make batteries; alumina, which can go into a smelter to make aluminum; and cementitious silica, which can be added into concrete. And the acid can be reused in the same loop.

Chiang calls this “nose-to-tail” mining—using every part of the ore provided, like eating every part of a butchered animal.

The researchers are currently working to scale and optimize the process. The tanks in the lab in Cambridge, Massachusetts can handle three kilograms of spodumene concentrate in each batch.

They have also estimated the cost of this process once fully scaled up. Assuming that the ammonium fluoride can be recycled at a high level, they should be able to extract lithium for less than $6,000 per metric ton. (They’ve identified a potential cheap industrial source of the acid as well, as an alternative to recycling it.)

The total cost is projected to be lower than that of other processes used to extract lithium from hard-rock ore today, and it could be competitive with brine.

The team has designed a pilot plant and is looking for space to build it. The plan is to have construction done by the end of 2026 and start operating the facility in 2027. Talks are underway with potential partners in the mining industry.

One difficulty for new players in lithium extraction is the volatility of the market: Prices have seen huge swings in recent years, from a peak in 2022 to lows in late 2024 and a slow climb starting in early 2026.

Rising prices might benefit new players like Rock Zero, but there are many projects that could come online if prices continue to rise, and that could bring the market right back down, says Simon Jowitt, chair of exploration geology at the University of Nevada, Reno. “People are waiting to see what happens with the lithium price,” he says. “It’s a crowded market, and there’s some big players out there.”

And even though batteries are driving up demand for lithium, the market is still relatively small, Jowitt adds: “That means it’s going to be volatile.” New lithium extraction technologies like Rock Zero’s will have to compete with methods used by existing giants, and there’s also the potential that technological alternatives, like sodium-ion batteries that don’t need lithium, could make the market more difficult to navigate, Jowitt says. He also thinks some of the company’s economic estimates could be optimistic.

For its part, Rock Zero’s team hopes not only to scale this technology for lithium, but to use it for other minerals in the future. As Mowbray says, “The Earth’s crust is made of silicates.”

Christian Ziegler/ Max Planck Institute of Animal Behavior

arstechnica.com

Scientists have long known that migrating birds and homing pigeons navigate in part by sensing the Earth's magnetic fields, especially at night or in overcast conditions when visual landmarks or sunshine are in short supply. But exactly where this magneto-sensing occurs in the body—and the mechanism that enables it—remains a matter of intense debate. A new paper published in the journal Science suggests that homing pigeons have iron-rich immune cells in their livers that help them detect magnetic fields and transmit that information to the brain.

There are three primary hypotheses for how birds might sense Earth's geomagnetic field. One is a compass-like mechanism, whereby the Earth exerts a pull on magnetic particles in a bird's upper beak that relays directional information via a large nerve in the cranium. A second is that it happens biologically via cellular ion channels sensitive to voltage, enabling birds to sense changes in the magnetic field. And a third suggests that physical effects on retinal pigments enable birds to detect photons and send signals to the brain, although this mechanism is really only viable in the light.

None fully explain how animals can sense magnetic fields. However, “We had some clues that the liver and spleen have magnetic properties, because they break down red blood cells and so store much iron in the body,” said co-author Clivia Lisowski of the University of Bonn and the University Hospital Bonn. This refers to a 2015 paper suggesting that red pulp macrophages in the spleens of mice and humans are intrinsically superparamagnetic and hence more sensitive to magnetic fields. But it wasn't clear if those properties were involved in any kind of magnetoreception.

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We’re upgrading Claude Opus to a new version: Claude Opus 4.8. It builds on Opus 4.7 with improvements across benchmarks, and is a more effective collaborator. It’s available today for the same price.

Opus 4.8 launches alongside several new features. Users on claude.ai now have control over the amount of effort Claude puts into a task. Claude Code has a new “dynamic workflows” feature that allows it to tackle very large-scale problems. And fast mode for Opus 4.8—where the model can work at 2.5× the speed—is now three times cheaper than it was for previous models.

Opus 4.8’s capabilities

The table below shows how Opus 4.8 compares to its predecessor and to other models on tests of coding, agentic skills, reasoning, and practical knowledge work tasks. More details and a much wider range of capability evaluations are provided in the Claude Opus 4.8 System Card.

Collaborating with Opus 4.8

Early testers have found Claude Opus 4.8 to be more reliable and sharper in its judgement when it’s performing agentic tasks. Below are quotes from many of these testers about their experience collaborating with Opus 4.8:

Claude Opus 4.8 has noticeably better judgment. In Claude Code, it asks the right questions, catches its own mistakes, pushes back when a plan isn’t sound, and builds up confidence around complex, multi-service explorations before making big changes. It’s a great model to build with.

Tom Pritchard
Staff Engineer

On our Super-Agent benchmark, Claude Opus 4.8 is the only model to complete every case end-to-end, beating prior Opus models and GPT-5.5 at parity on cost. For agent products in translation, deep research, slide-building, and analysis, it delivers powerful reliability.

Kay Zhu
Co-Founder and CTO

On CursorBench, Claude Opus 4.8 exceeds prior Opus models across every effort level. Tool calling is meaningfully more efficient, using fewer steps for the same intelligence, and it carries end-to-end tasks through.

Michael Truell
Co-Founder and CEO

Claude Opus 4.8 delivers the highest score recorded on our Legal Agent Benchmark, and is the first model to break 10% overall on the all-pass standard. For substantive legal work, that’s the kind of accuracy lift that translates directly into how much real attorney work our customers can hand off with confidence.

Niko Grupen
Head of Applied Research

Claude Opus 4.8 feels like a major quality-of-life update over Opus 4.7: faster, easier to collaborate with, and better at carrying context and style direction across a long session. Opus 4.8 is the model I kept trusting for work where voice, taste, and technical execution all have to happen side-by-side.

Katie Parrott
Staff Writer

Claude Opus 4.8 is the strongest computer-use and browser-agent model we’ve tested, scoring 84% on Online-Mind2Web, which is a meaningful jump over both Opus 4.7 and GPT-5.5. It stays reflective and on-task in the way our customers’ agent workloads need to be reliable end-to-end.

Miguel Gonzalez
Tech Lead

Claude Opus 4.8 uses tools cleanly and follows instructions with the consistency our autonomous engineering workloads need to keep running unattended. It improves on Opus 4.6 and fixes the comment-verbosity and tool-calling issues we saw with Opus 4.7. This release from Anthropic translates directly into faster capability gains for engineers building on Devin.

Scott Wu
CEO

On our long-running evals, Claude Opus 4.8’s analysis was consistently higher quality than prior Opus models. It finished faster and produced richer, more information dense outputs. Overall, a noticeably better signal to noise ratio. The biggest differentiator was Opus 4.8’s tendency to proactively flag issues with the inputs and outputs of an analysis, something other models routinely missed and left to the users to catch.

Michael Ran
Sr. Investment Associate

Across CoCounsel Legal, Claude Opus 4.8 delivered meaningful improvements in consistency and reasoning quality compared to prior Opus models. For the high-stakes professional workflows our customers depend on, that reliability matters. As we build fiduciary-grade AI systems for legal and tax professionals, advances like these help raise the standard for trusted AI performance in real-world workflows.

Joel Hron
Chief Technology Officer

Claude Opus 4.8 sets a new bar for enterprise AI. In Genie, Databricks’ AI agent for data and knowledge work, the new Opus model unlocks a step change in agentic reasoning, tackling deeper, multistep questions faster than any prior Opus. Its multimodal strength also lets Genie reason directly over PDFs, diagrams, and other unstructured content at 61% cheaper token cost than Opus 4.7.

Hanlin Tang
CTO, Neural Networks

For financial-document workflows in Hebbia’s orchestrator, Claude Opus 4.8 delivers the same strong quality as Opus 4.7 with noticeably better citation precision and more token efficiency on retrieval, which works incredibly well for the kinds of dense filings our customers run every day.

Aabhas Sharma
CTO

01 / 11

One of the most prominent improvements in Opus 4.8 is its honesty. We train all our models to be honest—for instance, to avoid making claims that they can’t support. But a general problem with AI models is that they sometimes jump to conclusions, confidently claiming to have made progress in their work despite the evidence being thin. Early testers report that Opus 4.8 is more likely to flag uncertainties about its work and less likely to make unsupported claims. This is borne out in our evaluations, which show that Opus 4.8 is around four times less likely than its predecessor to allow flaws in code it has written to pass unremarked.

As always, we ran a detailed alignment assessment on the model before release. In terms of positive traits, our Alignment team concluded that Opus 4.8 “reaches new highs on our measures of prosocial traits like supporting user autonomy and acting in the user’s best interest.” The assessment also showed Opus 4.8 to have rates of misaligned behavior (such as deception or cooperation with misuse) that are substantially lower than Opus 4.7, and similar to our best-aligned model, Claude Mythos Preview. The full alignment assessment, accompanied by a suite of pre-deployment safety tests, is reported in the Claude Opus 4.8 System Card.

Also launching today

In addition to Claude Opus 4.8, we’re making the following updates:

• Dynamic workflows. This new feature, available in research preview, allows Claude to take on even bigger tasks in Claude Code. Claude can plan the work and then run hundreds of parallel subagents in a single session (and with Opus 4.8, the agents can run for even longer). It then verifies its outputs before reporting back to the user. For example, Claude Code with Opus 4.8 can now carry out codebase-scale migrations across hundreds of thousands of lines of code from kickoff to merge, with the existing test suite as its bar. You can read more about dynamic workflows—available in Claude Code for Enterprise, Team, and Max plans—in this post.
• Effort control in claude.ai and Cowork. A new control alongside the model selector lets users choose how much effort Claude puts into a response. On higher effort settings, Claude will think more frequently and more deeply to give better responses. On lower effort settings, Claude will respond faster and use up a user’s rate limits more slowly. Users now have this choice—the effort control is available on all plans.
• The Messages API now accepts system entries inside the messages array. Developers can update Claude’s instructions mid-task without breaking the prompt cache or routing the update through a user turn. This can be used in a given harness to update permissions, token budgets, or environment context as an agent runs.

A note on effort

Opus 4.8 defaults to high effort, which we judge to be the best overall balance of quality and user experience. On coding tasks, this effort level spends a similar number of tokens as Opus 4.7’s default, but with better performance. Users can choose “extra” (“xhigh” in Claude Code) or “max,” and the model will spend more tokens to get better results; we recommend using “extra” for difficult tasks and long-running asynchronous workflows. We have increased rate limits in Claude Code to accommodate the higher token usage of higher effort levels; users can select whichever makes sense for their particular project.

What’s next?

Users will find Opus 4.8 to be a modest but tangible improvement on its predecessor. There’s still more to be done: we’re working on developing and releasing models that provide many of the same capabilities as Opus at a lower cost.

Not only that, but we plan to release a new class of model with even higher intelligence than Opus. As part of Project Glasswing, a small number of organizations are currently using Claude Mythos Preview for cybersecurity work. Models of this capability level require stronger cyber safeguards before they can be generally released. We’re making swift progress on developing these safeguards and expect to be able to bring Mythos-class models to all our customers in the coming weeks.

Availability

Claude Opus 4.8 is available everywhere today. Pricing for regular usage is unchanged from Opus 4.7: $5 per million input tokens and $25 per million output tokens. Pricing for fast mode is $10 per million input tokens and $50 per million output tokens. Developers can use claude-opus-4-8 via the Claude API.

IBM and Red Hat are committing $5 billion to a new initiative called "Project Lightwell," which aims to secure open-source software supply chains with AI-assisted vulnerability discovery, triage, patch validation, and upstream maintenance. Longtime Slashdot reader wiggles shares a press release from IBM: IBM and Red Hat today announced Project Lightwell, a $5 billion commitment backed by new frontier AI capabilities and a global force of more than 20,000 engineers to help enterprises secure open source software. Together, these investments establish a new model for enterprise use of open source software, from upstream development through production environments.

Project Lightwell will establish a trusted enterprise clearinghouse combined with a global force of engineers to identify and fix vulnerabilities at scale. The clearinghouse will serve as a security coordination layer, using advanced AI capabilities to validate and test fixes across an unprecedented volume of open source code. These capabilities will be offered through commercial subscriptions, allowing enterprises to integrate secure patches directly into their existing software supply chains with enterprise-grade validation and lifecycle management.

IBM and Red Hat have already begun collaborating with a select group of early adopters on Project Lightwell, including Bank of America, BNY, Citi, Goldman Sachs, JPMorganChase, Mastercard, Morgan Stanley, Royal Bank of Canada, State Street, Visa and Wells Fargo. The real-world insights from these initial deployments will actively shape how vulnerabilities are identified, validated, and remediated at scale across complex software supply chains.

Image: Cath Virginia / The Verge, Getty Images

Anthropic is releasing Claude Opus 4.8 on Thursday, and the company is touting the model's "honesty."

According to Anthropic, it trains "all [its] models to be honest - for instance, to avoid making claims that they can't support." But it notes that "a general problem with AI models is that they sometimes jump to conclusions, confidently presenting their work as making progress despite thin evidence."

The AI lab claims that early testers have found that Opus 4.8 "is more likely to flag uncertainties about its work and less likely to make unsupported claims." In the company's evaluations, Opus 4.8 is "around 4x less likely than its predeces …

Read the full story at The Verge.

Image: Fountain 0

Next month's Tribeca Festival will include the premiere of an AI-generated film: Dreams of Violets. The 75-minute film is a fictional dramatization of the Iranian government's mass killing of protestors in January, with the people and images fully created by AI, as reported earlier by The Hollywood Reporter.

Dreams of Violets cost $2,000 to make and is "based on journalistic reports, photographs, and eyewitness accounts," according to a press release. It was created by Ash and Pooya Koosha, two brothers who left Iran in 2009. Pooya cofounded Fountain 0, the company behind the film, while Ash serves as CEO.

Fountain 0 says Dreams of Violets …

Read the full story at The Verge.

Image: YouTube

New features coming to YouTube could make it better for listening to podcasts, rolling out to Premium subscribers starting today on Android and coming later to iOS.

A new "on-the-go mode" shifts YouTube into an audio-first layout, with larger, simplified playback buttons, a still image in place of the video, and a timeline showing video chapters. YouTube says you can turn on this new mode in a video's settings - a pop up will also appear if YouTube detects you're moving around while watching a video.

If you like to speed up your podcasts to get through episodes faster, YouTube's new auto speed feature can help you automate that process. …

Read the full story at The Verge.

Emily Dahl

news.mit.edu

• MIT and the Commonwealth of Massachusetts announced plans to establish the Quantum Systems Laboratory (QSL) at MIT, which will be open to researchers across the region.
• With the new funding from the state, which will match federal funding for quantum research already underway at MIT, the Institute aims to begin construction on the QSL facility this summer.
• The QSL will host specialized facilities that will enable Massachusetts scientists to undertake impactful work applying quantum research across practical domains, including life sciences and national defense.

Quantum technologies promise transformative changes in fields from computing, security, and navigation to health sciences, defense technologies, and space exploration. But how do we ensure Massachusetts stays on the leading edge of our nation’s coming quantum leap? Doing so is vital to the prosperity and security of our Commonwealth and country, serving to protect and advance America’s technological leadership in a world that has been upended by geopolitical rivalries.

On Thursday, May 28, Governor Maura Healey joined President Sally Kornbluth at MIT to announce a new effort aimed at establishing Massachusetts as a national hub for quantum innovation and catalyzing next generation quantum technologies. MIT and the Commonwealth of Massachusetts announced plans to establish the Quantum Systems Laboratory (QSL) at MIT, a new shared-use facility that will serve as a quantum toolbox for the region, aimed at accelerating quantum research, innovation, and growth in this critical field.

The QSL seeks to be the first facility in the world to bring together state‑of‑the‑art quantum computers with quantum sensors and peripherals, joined by quantum interconnects (physical channels that transfer quantum information). The facility will provide researchers from MIT and other institutions hands‑on access to significant quantum hardware and specialized experimental capabilities that are necessary to achieve the full transformative potential of quantum science and engineering.

Thanks to a $25 million investment from the state, which will match a portion of the federal funding for quantum research already underway at MIT, the Institute is now in a position to move forward as early as this summer with construction on the QSL facility, positioning the region to dominate the next generation of quantum research, according to Institute officials. The Commonwealth’s investment adds to MIT’s own financial commitment, as well as generous philanthropic support from Thomas Tull.

“Greater Boston has the greatest concentration of quantum talent anywhere in the world, working on a range of potential applications. Through the new Quantum Systems Laboratory, we will help position Massachusetts to lead the next era of quantum technologies,” says Kornbluth. “This facility will serve those at the edges of our wildest imaginations in physics and quantum computing, yes. But it will also equip the talent in our region -- and ultimately, our nation -- to push our knowledge to new limits, and new innovations.”

The QSL will be located at Building 39 on the MIT campus and will serve as a multi-disciplinary quantum hub with modern experimental infrastructure. Because quantum research involves the creation and study of coherent phenomena in systems that are isolated from the rest of the universe, it must take place in a highly controlled environment. Work is already underway in Building 39, with significant investments by MIT, to upgrade the physical infrastructure for these unique demands. The state’s support will supercharge this work and allow for the transformation of the lab into a hub for scientists across the region working on next-generation quantum technologies, startup applications, defense and health tech, and more.

“Our region has unparalleled strengths in science-intensive innovations and tough tech breakthroughs that combine engineering, science, and computing,” notes Anantha Chandrakasan, MIT’s provost. “With the new Quantum Systems Laboratory, we aim to arm Massachusetts with the compute power and integrated platforms needed to lead the coming era of quantum technologies.”

By the numbers

The QSL will host specialized facilities that will enable Massachusetts scientists to undertake impactful work applying quantum research across practical domains. As a shared-use facility, the QSL is being developed with the underlying mission of returning broad scientific, workforce, and economic benefit to the public.

For example, quantum technologies provide significant opportunities in the fields of life sciences and defense technologies, which are $50 billion contributors to the Massachusetts economy, with dozens of startups working in the area. During a time of increased economic anxiety and labor market concerns, investing in foundational quantum facilities will infuse our region with new job opportunities, in academic research institutions, startups and more. Construction on the QSL facility alone is anticipated to create over 150 full-time, on-site construction jobs, plus another 75 to 100 jobs across the Commonwealth in supply chain and professional services supporting the project.

Startups from MIT are also a key driver of the state’s entrepreneurial ecosystem; in 2015, Sloan Professors Edward Roberts and Fiona Murray published a report detailing how the Institute’s alumni entrepreneurs have created more than 30,000 active companies, employing 4.6 million people, and generating annual global revenues of $1.9 trillion, a figure greater than the gross domestic product (GDP) of the world’s 10th-largest economy, as of 2014. The QSL facility will provide the necessary equipment and facilities for startups working on quantum technologies, thereby strengthening the region’s innovation economy.

“The new QSL will introduce modern experimental infrastructure to quantum research at MIT and beyond, allowing us to scale experiments and expand into critical domains in disciplines such as biology and chemistry, where we see enormous innovative potential,” explains Ian Waitz, MIT’s vice president for research. “As the new physical home of the MIT Quantum Initiative (or QMIT), the QSL will serve not only as an on-campus incubator, but more broadly, a regional hub to catalyze quantum innovation, growth, and investment in this critical R&D sector for the Commonwealth.”

One floor of the facility will allow for development of radio-frequency (RF) electronics for controlling and interfacing with quantum systems. The QSL will also support researchers in the creation of customized quantum experiments with advanced high-frequency packages, which are required to protect quantum data in real-world applications. The facility will also develop the associated THz electronics needed by advanced quantum systems.

A history of future-focused plays

Nearly a decade ago, MIT made a similarly big bet on nanotechnology, developing MIT.nano — a state-of-the-art, shared-use facility with more than 200 tools and instruments that support nanoscale discovery and innovation through imaging, fabrication, characterization, and prototyping. Set in the heart of campus in the Lisa T. Su Building, MIT.nano is home to a thriving research community, an industry consortium, and a startup accelerator. More than a fifth of the 1,500 users of MIT.nano come from outside of MIT, and half of the companies in its START.nano accelerator have had non-MIT founders.

The QSL will also complement the capabilities of MIT Lincoln Laboratory’s SQUILL Foundry, a quantum fabrication hub for superconducting qubit systems that serves researchers across Massachusetts and the nation free of charge.

Robinhood is launching beta support for a new feature that will let AI agents make payments and trade stocks on users' behalf. The company is also rolling out a virtual credit card for AI agents, with spending limits and approval controls. TechCrunch reports: Robinhood said users on its platform can now create a separate account for their AI agents and connect them to a dedicated wallet. While these agents would be able to read and analyze users' portfolios to come up with trading strategies and suggest investments, they'll only be able to access the pre-loaded balance in the dedicated wallet to place orders.

Users will get notifications of all trades their AI agent makes and will be able to monitor their activities within the Robinhood app. For some trades, agents will show a preview that users may have to approve before the order is executed. The company said it has also built in fraud detection protection, in which a team from Robinhood would review suspicious trades and help users resolve disputes.

Robinhood says users can connect their AI agents to its Model Context Protocol (MCP) service to do things like analyze concentration risk and sector exposure, execute trades, or look through analyst notes to identify new investment opportunities across various sectors. The agentic trading feature is launching in beta and only allows stock trading right now. The company says it plans to add support for options, crypto, event contracts, futures, and prediction markets soon.

spectrum.ieee.org

Tensions over the Strait of Hormuz have spiked fertilizer prices dramatically

Photo by Andrej Sokolow/picture alliance via Getty Images

After several months of testing, Waymo is finally ready to invite non-employee passengers into its newest vehicle, the Zeekr RT minivan, which has been rebranded as Ojai. Waymo says it will begin offering "select riders" access in San Francisco, Los Angeles, and Phoenix, before "gradually" expanding to more riders and cities. Trips will be free to start out, as Waymo collects data about the passenger experience in the new vehicle. Paid rides will follow.

Waymo's current fleet of Jaguar I-Pace vehicles runs on the company's fifth generation technology, first rolled out in March 2020. But that vehicle has reached the end of its shelf life, a …

Read the full story at The Verge.

Photo: Allison Johnson / The Verge

Apple's long-awaited Siri overhaul, expected to arrive in iOS 27, might look a lot like ChatGPT with a splash of Liquid Glass. Renders from Bloomberg offer a preview of iOS 27, including the new app and chat interface for Siri. The renders are "based on information viewed by Bloomberg and people with knowledge of [Apple's] plans," and could differ from Apple's final designs, which Bloomberg's Mark Gurman says Apple will reveal at WWDC in June.

The images show a new pill-shaped Siri chat bubble popping out of the Dynamic Island with a drop-down menu containing options for Ask, Siri, and ChatGPT. According to Gurman, you'll be able to open t …

Read the full story at The Verge.

Image: The Verge / Photo: Rivian

Today, I’m talking with Wassym Bensaid, the chief software officer at Rivian, and the co-CEO of Rivian’s platform joint venture with Volkswagen, which everyone just calls RV Tech.

That joint venture kicked off about a year and a half ago with a nearly $6 billion investment from Volkswagen. It effectively puts Wassym in charge of the operating system and electrical architecture for every future EV from Volkswagen and its associated brands, including familiar names like Audi, but also new companies like Scout.

There’s a lot of Decoder ideas in there — I really wanted to know how that joint venture works and how it’s structured to preserve Rivian’s unique software culture, which you’ll hear Wassym talk about as the core element of the whole thing. I also wanted to know where the lines were — what parts of Rivian’s software get to be just for Rivian, and which parts of the core technology would be shared across the smaller company and the behemoth that is Volkswagen Group. And, of course, I wanted to understand how Wassym navigated the tension between the two. You know, classic Decoder bait.

Verge subscribers, don’t forget you get exclusive access to ad-free Decoder wherever you get your podcasts. Head here. Not a subscriber? You can sign up here.

It’s also a big moment for Rivian in general right now. The company is gearing up to deliver the more affordable Rivian R2, which is the first vehicle based on this new architecture, and the company also just shipped the AI-powered Rivian Assistant in its R1 vehicles. You’ll hear Wassym talk about Assistant as the beginning of a big bet for Rivian, as it tries to create a more agentic software platform in its cars.

I actually got to spend some time with the Rivian Assistant in an R1S ahead of my conversation with Wassym, and I found it to be a fascinating experience — certainly powerful and engaging while at the same time frustrating in a lot of really interesting ways. So I had a lot of feature requests, bug reports, and questions about the future of AI and voice assistants in cars.

So I asked Wassym about all of that, and also about his statements over the years that buttons in cars are just an anomaly and of course how he’s feeling about Apple CarPlay and Android Auto these days. You’ll hear it, but spoiler alert: Don’t get your hopes up.

This is a really fun episode of Decoder — we really get into the weeds on a lot of my favorite topics to talk about here on the show.

Okay: Wassym Bensaid, chief software officer of Rivian and co-CEO of RV Tech. Here we go.

Wassym Bensaid. You’re the chief software officer at Rivian. You’re also the co-CEO of a very important software joint venture between Rivian and Volkswagen, which is straightforwardly called Rivian and Volkswagen Group Technologies. Welcome to Decoder.

Thanks, Nilay. Super excited to be here.

I am very excited to talk to you. I have a lot to talk to you about. It occurred to me as we were doing the prep for this episode that you’re in charge of building a new kind of software for cars. But because of this joint venture that’s building a new kind of software company that’s building a new kind of software for cars, it is the most fractal Decoder I think we’ve ever had.

Awesome.

There’s a lot here. Let’s start with the organization. So, you’re the chief software officer at Rivian. I think a lot of people understand what that means. You’re the guy that they can yell at about CarPlay. Don’t worry, we’ll come to that.

There’s also the new Rivian Assistant, which is an intelligent agent inside the car that I’ve been playing with and I want to ask you a lot of questions about. Then, there’s RV Tech, which is the joint venture with Volkswagen. You’re building a new zonal architecture for a bunch of cars. I believe the R2 is the first car that’s going to run that new architecture.

Correct.

How does that all work? What are the lines between RV Tech, where you’re the co-CEO, and your role with Rivian, and what is the boundary between the software you build in the joint venture and the software you build at Rivian?

Before we dive into RV Tech and the joint venture, I think it’s really important to talk about the overall industry landscape. The automotive industry is going through a major disruption. The amount of software content in cars with technologies like electrification, connectivity, and autonomy is significantly increasing. That is creating a big divide between traditional OEMs and new tech-forward companies.

Consumers now have much higher expectations in terms of the overall experience and convenience. Multiple OEMs have tried really hard to get software content, but it’s not easy. It requires a very different type of talent. In some cases, it requires complete cultural change because you’re not only developing software. You also need to adopt different methodologies and ways of doing things. You need to be much more agile. When you look at the industry, companies tried to do that in-house. Some of them tried to partner. Some of them tried to use Tier-1 suppliers.

A lot of recipes did not really work, and that was the genesis of the great partnership we have now with the Volkswagen Group, which has really taken the Rivian technology stack — taken the software, the electrical architecture, as well as the Rivian DNA and culture — and married it with the Volkswagen Group’s incredible scale. It truly provides a fantastic opportunity for both companies because now we have a solution that can not only underpin Rivian vehicles — as you mentioned, the R2 is the first car the joint venture is shipping — but then also, in the future, every single electric model in the VW Group. This is from your premium cars like Audi, to luxury cars with Porsche, Bentley, and Lamborghini, all the way to mass market cars.

That suddenly provides an opportunity of scale. Also, it exercises the technology in very different ways, and it puts us in a wonderful position so that we can build an architecture and operating system for the entire industry.

That question about the architecture and operating system feels very complicated. As you said, the industry is moving to software-defined vehicles, which is a great buzzword. Every car executive I talk to clearly has a different definition of what “software-defined vehicle” means. What is your definition of “software-defined vehicle”?

First of all, I hate that buzzword.

[Laughs] You brought it up.

Actually, I can’t find a better name. So, I admit that I’m also using the same for a lack of a better definition.

But think about it this way. When you look at the older architecture in cars, it’s really an aggregation of multiple mechanical components. Underneath that, there are, in some cases, hundreds of electronic units, and each one of them is meant to do one thing. That’s actually mirroring the way those cars are built because they are developed using different Tier-1s and other suppliers.

In that world, integrating an end-to-end vehicle feature requires a ton of coordination between many of those suppliers. It requires very long development cycles. That approach kind of worked in the past when the expectations of consumers were not super high in terms of those end-to-end features. But I think with the advancement of EVs and with the types of user experiences that Tesla, Rivian,, and the Chinese cars are offering now, that’s no longer an option for any car manufacturer.

I’ll give you a small example. When you walk to Rivian — and I know you’re currently testing a [Rivian R1] Gen 2 Quad — let’s say you have your Apple digital key. You walk to the car and then the car recognizes you. Then, there’s a lighting sequence, and your entire profile is configured. Whether it’s the seats, the steering wheel, the infotainment system, the HVAC, everything is configured for you.

That sequence takes probably just 15 seconds, but doing that in the traditional world requires the coordination of more than 10 suppliers. You need to talk to the seat supplier. You need to talk to the door supplier. You need to talk to the HVAC supplier. You need to talk to the infotainment supplier. You need to talk to the security system. You need to talk to the cloud. You need to talk to the third party for the digital key. Just imagine that you want to slightly change that sequence for whatever reason. You have to go through another cycle of changes.

This is why that old model really doesn’t work anymore. Cars are now integrated systems with what we call “zonal computers.” We think about them as general-purpose, powerful compute that we place in the middle of the car, and they become the centralized brain of those different functions. The more software you can move on those zonals, the more it can provide control over those end-to-end features for the customers.

So, this is the pitch that every pure-play car startup has been making for a long time, right? The way that the OEMs built cars was done, and you shouldn’t have 1,200 ECUs from 1,200 different suppliers. That was fine for gas cars that were pretty dumb, where the only computer was like my old Pioneer head unit that had a fold-out screen. By the way, I love that head unit, if you could bring that back. I have fond teenage memories of my dumb old car with that head unit.

Now, the whole car is a computer, and you expect a lot of things to happen but that integration is too hard. What I would say broadly is that legacy OEMs have known this for years. They have been on their own journey to solve this problem, to cut down on the number of ECUs.

Ford CEO Jim Farley was on the show five years ago saying things like, “Too many ECUs; we’re going to cut it down.” Volkswagen, in particular, had its own giant project to do this that failed. I think there’s enough distance. You’re a year and a half into the new joint venture, and we can say Volkswagen’s CARIAD failed.

Why do you think the new joint venture and the infusion of Rivian culture is going to be successful when Volkswagen’s attempt to do it on its own did not net any positive results?

You’re getting me in trouble, Nilay.

That’s what I do.

What I personally appreciate about the Volkswagen Group’s decision is the recognition that developing what are called software-defined vehicles requires a complete, clean-sheet approach. You cannot approach it with Band-Aids and by having some level of software content in the car. As you said, the Volkswagen Group has tried. Actually, it has tried twice. But deep inside, there are two things that are really important here. One is that you need the right talent who are able to develop true software. Not abstracted functions like what the automotive industry is using — you have probably heard about AUTOSAR — but a true, hard-coded operating system.

Then, you also need a deep cultural change with a very different way of approaching the car and its overall development. The traditional model said that cars were defined many, many years in advance. People claim they know about software features four or five years in advance, and then it’s a very fixed waterfall approach. The way we design cars at Rivian is that we actually design the car around the electrical architecture, the software, and the adaptability of the software. So, software and technology have been at the table since very early on. It actually impacts the overall packaging of the car. We really use that platform and that operating system mindset so that we have a car that can evolve over time and get better and better for our customers.

Such changes are so deep that to do it well, you either need to have the right partner or you go with a clean-sheet approach. I think the Volkswagen Group has made the right decision to partner with Rivian in this case and to not only embrace the technology that we built from the ground up but to also embrace the culture, the approach, and the DNA of Rivian as a company.

How is the joint venture structured? I know you have a co-CEO, Carsten Helbing, who’s the Volkswagen CTO. So, you’re the two co-CEOs. How is it structured underneath that?

There’s a technical team underneath that: software engineering and electrical engineering. The technical team reports to me, and Carsten is my partner in crime. He takes care of the operations, and he’s also the main interface with the Volkswagen Group. There’s a ton of complexity in terms of managing different requirements and different inputs from the brands. He’s really doing all that arbitration so that we continue pushing towards a platform approach and reduce the overall complexity of the portfolio we’re supporting with the VW Group.

One of the questions I have here is that you describe it as an operating system. That seems like a good framework. People understand what operating systems are. I realize car operating systems are vastly more complex than people give them credit for, but it’s an operating system.

Then, there are the expressions of the operating system. I know that when our audience thinks of the software in the car, they think of the infotainment screen and that’s it. That’s just one expression, right? The user interface that Rivian is running… There are going to be other expressions for Volkswagen, for Scout, and I presume for Lamborghini. They’ll all be running the same core operating system expressed in different ways.

That is a real push-and-pull dynamic. Where do the features live? Who gets to build which feature? What are the core capabilities of the operating system and the platform versus what Lamborghini wants that it doesn’t want Rivian to have? How do you make those decisions?

First of all, I think it’s important to clarify the role of the joint venture. So, we’re responsible for the underlying electrical architecture and the operating system. When you look at a modern car today, pretty much every single interaction you have with the car is powered by software. You don’t realize it in a lot of cases. People tend to associate software with infotainment and with what they see on the UI and the screen, but there’s software everywhere in the car. I mean, there’s the way the car navigates, the way the car drives, the way the car saves energy, the way the car does cabin comfort. All of that is actually managed through software.

So, the way to think about this is that our role is to, first of all, build an electrical architecture with as few computers as possible in the car so that we simplify the overall packaging and the overall bill of materials. This is the brains of the system. On top of that, we develop software that the different brands can use so that they express their own identities. Think about it as us doing 80 to 90 percent of the hard work. Then, we provide customization hooks so that an Audi drives like an Audi and a Lamborghini has a different UI than a Rivian. But what’s happening under the hood, what’s happening behind the scenes, is based on the same platform.

When you think about that underlying electrical architecture and the zonal computers — you say we’re going to cut down the number of computers but have fewer and more powerful computers — one of the things that seems like an obvious opportunity for Rivian that might be way more complicated for Volkswagen is that you have a big battery that can just power those computers all the time. They can be online, they can be functional, they can be available. Volkswagen also makes gas cars and hybrids. There’s some pendulum swinging in the industry between electrics and gas vehicles, particularly here in the United States. Is that a challenge or are you just not thinking about their gas cars at all?

The joint venture’s scope, for the time being, is really about powering all the electric vehicles. This is the agreement we have with Volkswagen. One of the main reasons I joined Rivian is for the mission. I think the joint venture provides us with an extraordinary opportunity to accelerate electrification into many more cars around the world.

One of the first products that we’re building with Volkswagen Group is the ID.1, which is taking our technology to a mass-market vehicle. This is a car that will sell for less than $25,000, and it really opens the technology and that rich feature set to many more consumers around the world. Now, can the technology be used for non-EVs? Can it be used for hybrids or ICE vehicles? Obviously, the answer is yes, but for the time being, that is not the priority of the joint venture.

How big is RV Tech? How many people?

We’re about 1,500 people.

How is that split between Rivian folks and Volkswagen folks? Is it employees from both companies, or are they employees of RV Tech?

They are employees of RV Tech. Actually, we started with about 800 or 900 developers coming from Rivian, and then we had about 50 colleagues who joined us from the Volkswagen Group. The rest are developers and engineers that we’ve hired in the past 18 months. So, everybody’s RV Tech.

The reason I ask is that you mentioned at the very beginning that it’s an infusion of Rivian culture, but now they’re not Rivian employees. But at the same time, you are also the chief software officer of Rivian. How does that culture persist if the thing is its own entity, if it’s not as directly connected to Rivian, or if they’re not Rivian employees?

The way I define my job and my number one priority is to help the company grow and build on our two main assets, which are technology and our people and culture. With technology, I think we have a wonderful opportunity now to take that tech into many more cars across a wide range of the portfolio. Then there’s trends and culture. My daily obsession is to really make sure that we continue to have the same DNA: agility, being nimble, prioritizing action, quick decision-making, and iterating really fast so that we are at the forefront of innovation.

One of the other reasons I ask is that there are Rivian decisions that Volkswagen maybe won’t make. Rivian runs on Unreal Engine for the graphics in the infotainment. It’s really fun. I’m not sure that every single Volkswagen is going to run on Unreal Engine.

At least as I understand it, that’s a decision the different brands can make for themselves. But you’re the chief software officer at Rivian. You’re like, “We need better support for the Unreal Engine interface.” Maybe the platform doesn’t want that, and you wear that hat, too. How do you reconcile those tensions? Do Rivian’s needs always win?

What wins is how we can build the software in a way that allows for different expressions. I think in this case, Rivian’s interface will show up through Unreal Engine, but then we need to have hooks in our frameworks so that — and I know you will ask me this question — Volkswagen cars can have CarPlay. The team will develop that even though Rivian will not adopt CarPlay. It’s really about creating those different hooks in the operating system so that we allow for different ways to express the user interface.

This is so fascinating. Like I said, this is such a fractal episode of Decoder. It strikes me, just talking to you about this, that there aren’t a lot of models in an industry as big as the auto industry like this, where the big player is letting the smaller company define the culture, the opportunity, and the architecture, which will define its future roadmap. What examples have you looked at that are similar, where you can say, “That’s successful. We should build the model based on this and operate like this?” What versions of this have failed that you’ve looked at where you’re like, “I want to avoid those mistakes?”

I think there are many more failure stories than success stories when people look at joint ventures. This has been one of our guiding principles. We spent a lot of time discussing with VW leadership before we engaged in such a partnership. What made [Rivian CEO] RJ [Scaringe] and myself lean heavily into this partnership is, one, the opportunity and, two, the honest and constructive partnership from Volkswagen Group leadership.

First of all, we are talking about putting Rivian technology into the second-largest OEM in the world. This is, by far, the biggest licensing deal in the automotive industry. As RJ and I started the discussions with [VW CEO] Oliver Blume, his number one priority was that they needed to keep the Rivian way of doing things. We realized that we are not only bringing software IP and electronics IP but also a different process. We are bringing a different culture, and the VW Group needed that change from the inside.

Obviously, in some cases there has been daily tension. There are cases over the past 18 months where, as you mentioned, one brand might request a different requirement than another brand. But what really helped us to continue is that support from the highest levels of Volkswagen Group leadership to help drive that transformation and cultural change.

Let me ask you the other Decoder question, and then I want to turn to the software itself. I ask everybody this question. We’ve talked about it a little bit. How do you make decisions? What’s your framework for making decisions?

So obviously, my job every day is making decisions, but there are a number of guidelines that I try to apply. In terms of coaching with my team, I try to push decisions to the lowest levels of the organization as much as possible. One of the anti-patterns that I see with a bunch of companies is them trying to bubble up decisions with the highest levels of executives, and that tends to create a culture where things are slow and employees don’t feel really empowered.

Now, in cases where I personally have to make the decision, there are a few guidelines to the team: never come to me with one option, show that you went through an analysis, have multiple options, and then make a recommendation. I want a culture where I empower my team to have a forceful proposal and then come up with recommendations themselves.

The rule that I use to determine how much time I should spend on a decision is about whether it’s a one-way door decision or a two-way door decision. If it’s a two-way door decision, then I don’t need to spend that much time on it. It doesn’t really need a hard framework. We don’t need to go to extremes where we collect tons and tons of data so that we get to a decision. In some cases, I just use my gut. I’m a product builder at heart. I know that with some of the decisions, even if the data is against me, I should go with my feeling. In some cases I’m wrong, and I’m the first to recognize that.

Now, if it’s a one-way door decision, then that’s a different process that requires much more preparation and much more data, and then it requires arbitration for how we do things.

Give me an example in this context of a one-way door decision and a two-way door decision at RV Tech.

There’s multiple. I think one of them will probably lead to the next topic of discussion, which is our overall approach around AI. We had a ton of debates internally about whether we should just use a third-party AI solution or develop our own. There was a ton of tension because you look at the advancements in the AI world, and you would think that this is a hard problem to solve with everything that’s happening.

Now, it was really clear for me, given the opportunity and how transformative this is for the entire user experience, that we needed to own our destiny in terms of having a platform that allows us choice, that allows us to change foundation models as we wish and own the integration layer that allows us to power the entire car operating system.

So, this is Rivian Assistant. I’ve been playing with it for a few days now.

What do you think?

I had some searching conversations with it, just to push the boundary of what it can do. It’s super interesting to have a car where even within the interface, it does the wavy line on the main screen. It’s very much that the car is running this assistant, not an overlay. You can tell that the assistant can go and address lots of parts of the car, and then there are places where it can’t or it won’t.

Actually, I think one of the most interesting things about it is that it won’t tell you why it can’t do things. It is insistent that it won’t tell you why it can’t do things. Don’t worry, I have very specific questions. But it strikes me that this is a natural evolution of, “Okay, the whole car is run by a finite set of computers, and that means our assistant can just run around and talk to those computers and the functions that those computers control.” I have a Cadillac EV. If you try to glue an assistant onto that, it has to go talk to its ECUs. It’s just very obvious that something else is happening with Google Assistant in that car. That’s the opportunity. The assistant can talk to the whole car. Then there are places where it just can’t for some reason.

I’ll give you one example. It struck me as very odd. I was driving in the rain, and I said, “Hey, turn on the back window wiper,” and it just won’t. I thought, “Is that a safety reason? Is that because you don’t know how to do it? You’re lost in the zonal architecture?” I asked it, and it said, “I can’t tell you why I can’t do these things, but here’s where the button is,” which is really interesting for a car assistant to do. I’m not going to do it for you, but the button is on the stalk. Push the button. How do you make those decisions in the context of an assistant to figure out what it can and cannot do?

So, there’s a lot of things here. First of all, I think you described it really, really well. Our philosophy for the Rivian Assistant was to not just put in a chatbot and then slap it on top of the UI. It’s also about developing what will become the connective tissue that enables our users to interact with pretty much every single feature in the car and, even more than that, to bring their own personal digital ecosystem in the car through agentic integration.

Now, to your question about what it can do and cannot do, it’s obviously possible for us to control the wiper. I’m sure that you have seen that it can do way more. It can change your drive modes. It can change your ride height.

I could raise and lower the car at 55 miles per hour with the air suspension, which was cool and like the slowest low-rider experience you could possibly have, and then I couldn’t turn on the wiper. So, what is the split there?

Honestly, that’s one of my favorite features. The way I like to interact with it is that I don’t tell it to change the ride height. I tell it, “Okay, give me a drive mode with more pep,” and then it does it and changes to sport mode. I mean, this is really the magic of that true conversational experience.

Now, the reason it does not control the wiper is by design. We actually block a number of features that are safety-related. Cars are homologated and regulated. So, things related to wipers, windshield controls, highway assistance are regulated functions, which we block for safety reasons today through our framework. Safety is one of the core tenets in how we develop the entire experience.

The other one that struck me is that your cars have rear-seat sensors. We have kids, so every time I get out of my car, it reminds me there might be a kid in the back seat because it has sensed the weight. I think this is one of the funniest sensors any car can have because the car seat is always in the back seat. So, it’s always reminding me that the kid might be in the car.

So I asked, “Is anyone in the back seat?” Maybe this is just a bug, but it said, “I’ll find out,” and then it said, “I can’t access that sensor.” I said, “What sensor are you trying to access?” And it refused. I probably had a five-minute argument with your assistant about why it wouldn’t tell me what sensor it was trying to access.

The reason I’m asking this is not because it’s a bug or I really needed to know if anyone was in the car seat at the time. I’m just curious. You think about building the assistant that can access all of the sensors and the architecture and how that might work and how we might interact with cars. There’s a moment where you realize maybe it’s for safety reasons or maybe it just won’t work right now with the version you have because the LLM has to go talk to another computer and that computer has to give it permission. I don’t know if anyone in any part of the tech industry has figured out exactly how that should work, and I’m just wondering what your point of view is.

I think in this specific case, it should have actually told you what’s in the back seat. So, that’s a bug. That’s on me.

No, it was like, “I’m not telling you what sensor I’m trying to use.” I was like, “Why?” and it was like, “I’m not going to tell you what sensor I’m trying to get to.”

Yeah, that one is on me. I think the beauty here is that we have the team in-house. We’ll be able to calibrate that answer, and then we’ll fix it. Don’t worry. Nilay, I’ll send you an OTA next time when that’s fixed.

That’s very good. Every time we get a car executive on the show, I just complain about the experiences I have. It’s perfect.

But every assistant at every level is running into that specific barrier concerning how you talk to the computer and what permission does that other computer give you. Every assistant at every level is running into that specific barrier, and I’m just curious what you think the answer is.

Think about our architecture this way. The assistant has deep integration into the entire vehicle operating system. So, in theory, unless we have a bug like the one that you experienced, , you should be able to do everything with the integrations that we have built.

The only functions that are not allowed are functions that are safety-related, obviously because of the homologation reasons. But also there are functions where we are not comfortable with the level of reliability we can get from the LLMs to expose them to the end users. But that’s really the beauty of the internal, in-house orchestration layer that we have built where we have a ton of guardrails that allow us to control which functions are exposed by the assistant or not.

All right. You mentioned that I was going to get you in trouble. I’m going to get you in trouble again. In 2024, you said using buttons in a car is an anomaly of modern design. People love buttons in their cars, so you got in trouble for saying that, but the thing you said was that voice should be the future. This is the first gesture at voice being the future. Is it good enough? Because we’re right on the cusp of whether these things are actually good enough to build the kinds of products people want.

I think we are on the cusp of something really big. When you think about it, you’re in a car, you’re driving, you’re focused on the road. So, in theory, the primary interface with which you should be interacting with the car is actually voice. The only reason that drivers and consumers do not interact with the car through voice is that, to put it really bluntly, the technology has been broken. That’s really the beauty of what we have now with the technology disruption coming with foundational models.

The foundational models are providing us this wonderful opportunity to truly have a conversational experience where drivers can interact with the car in human language. I don’t need to tell the car, “Open the frunk.” I can say, “Open the front trunk.” Actually, I can say, “I have a bag in front of the car,” and it will actually open the frunk. I think that completely changes the way you interact with the car.

On top of that, we now have the opportunity with all the agentic framework to truly give people their time back in the car. I hope you tried our Google Calendar agentic integration. You can imagine how the experience will be in the future where you’re driving and can perform operations on your calendar. You should be able to perform operations on your email. In the future with the agent-to-agent integration, you can actually interact with many more apps from your own digital ecosystem.

Can I ask you about the word agentic in this context? To just describe it quickly for people, the way the Google Calendar integration works with Rivian Assistant is that it shows you a QR code. You connect your Google Calendar to it and then Rivian Assistant can read your calendar, add events, remove events, and do other calendar stuff.

I’m curious how that’s agentic and how it’s built such that the word agentic is meaningful because I’ve had like 500 apps over the past 10 years that can do Google Calendar stuff through the standard API. So, how is it agentic? Is it powered by MCP? Is it something else? Why build it that way versus doing a bunch of API integrations?

I mean, you can build it with an API integration. I think the advantage of an agentic integration is that you can share the context, and then you can perform multiple integrations within the car. In this case, it is based on an MCP integration.

You can imagine that in the future, instead of having that mono access to every single app on your car — or honestly, even on your smartphone — you can start aggregating and connecting many of those apps through the agentic framework and have them present a unified user experience.

This is how we’re able to connect the navigation to Google Calendar, for example. I can go to the assistant now and say, “I want to plan a trip from San Francisco to San Diego, and I want to have two charging stops. I want them to be close to an Italian restaurant. I love Italian food.” The assistant would go and play that, and then I’ll say, “Okay, print the summary, add it to my calendar, and then send it as a text to my wife.”

When you have a behind-the-scenes agentic framework, this type of integration can really allow many more capabilities. This is where agentic can be utilized even further. You can start going into more autonomous functions. Let’s say you have an invitation in the calendar with XYZ details. You can start having reminders that say, “Do you want to go to this place?” “You’re actually really late to your meeting. Do you want me to start preconditioning your car?” So, that’s the beauty of bringing in the depth of that agentic integration.

I think I understand that. Rivian Assistant is in the car. It can access a bunch of apps and services you have. You can take actions across them. You’ve collected a lot of data in one place.

This brings me to a very deep existential question I have whenever anyone talks about ambient computing this way: Where does the logic live? The idea that you’re going to have that interaction in your car and not at your laptop or on your phone seems like a big jump to me. It was the same way when the smart speaker companies would be like, “You’re going to talk to your thermostat,” and would I think, “Why?” I’m going to talk to my phone. I don’t feel the need to talk to my thermostat in this way.

Do you think people are going to do that in the car, or are you going to bring your assistant to the Rivian app on a phone? Can you compete with Apple’s Siri and Google Assistant in that way? How is that all going to work?

Actually, the way I think about it is that it will be both. This is the big difference between the old world where we had unique applications and the new world where we have agentic integrations.

I think about Rivian Assistant as an agent orchestrator that has privileges because it can deeply integrate with the vehicle controls and the vehicle operating system. It understands safety. It understands which things to do and which things not to do. Nobody else can develop that better than us because we develop the entire vehicle software. But at the same time, it has interfaces and connections to other agents.

This is just the beginning. In the future, you can probably bring your own favorite assistant and chatbot to the car, and then it can share context with Rivian Assistant. I mean, these are the possibilities that this new world and this new type of integration are allowing us to do.

Is Rivian Assistant the kind of thing that is possible because of the RV Tech software stack? Is it possible that we’ll see Rivian Assistant or something just like it in Volkswagens as well, or is this special to Rivian?

This is special to Rivian. This is an AI stack that is developed uniquely for Rivian. This is Rivian’s brand priority as we see cars becoming more and more AI-defined. But we’re in discussions so that we can have similar technologies for the Volkswagen Group.

Rivian cars famously use LTE. When I first got in this car, I saw the LTE indicator, and I thought, “Oh, something must be wrong.” I drove around, and then I realized, “Oh, it’s just LTE.” Are there any latency concerns with that, especially with voice and going out in the world and doing whatever inference you need to do?

[Laughs] So, two things, Nilay. One, we need to get you an R2. The R2 has 5G.

There you go.

It’s coming soon, and it’s amazing. And two, I think you really touched on one of the architecture considerations for the technology, which is that when you look at vehicles like the Rivian R1 today, most of the interactions will happen with the cloud. So, as you say, it’s connectivity dependent. so they will work best when there’s strong connectivity with the external world. Now, there’s a number of interactions that happen locally with the car. If you tell the car, “I am cold,” that interaction is being managed by a small language model that sits directly on the edge.

The beauty of what will happen next as we get to the R2 is not only the 5G but also edge AI will be way more powerful and capable.

Just to be clear for the audience, when you say “edge,” you mean local, right? It’s running locally.

Yeah, local, meaning that the local computer on the R2 will have up to 200 sparse TOPS (trillions of operations per second) of compute dedicated to AI. I know this sounds extremely technical, but think about it as more capable than some of the self-driving platforms today. It’s more capable than the AI compute that you have in your smartphone.

All of that will be available locally in the R2 car, which is coming soon. That allows it, as you mentioned, to not face these connectivity limitations and issues and to get to much lower latency because a lot of the processing will happen directly on the embedded system so you can get a conversational experience that’s pretty much instantaneous.

Can I just ask you a very in-the-weeds question? We’re talking about putting compute in the car. We’re going to do some amount of local inference in the car. GPUs are expensive. RAM is expensive. How much of the bill of materials is RJ giving you to do all this in the car versus, I don’t know, bigger motors or bigger batteries? How much of the range can you pull off to do local inference in the car? This is the trade-off you’re talking about.

This is what I love about RJ. What has always attracted me to RJ is that he thinks about big things in the long-term. He knows, in this case, that the world is moving to AI. This is why decision-making from a bill-of-material standpoint is a very hard process with a ton of trade-offs all the time. You can imagine the tension between people wanting to push for a better exterior part, people wanting to push for a better interior part, and people wanting better technology. Then, we have what we call the”differentiation budget” in the car.

For RJ, there was absolutely no debate on whether we would equip the car with higher inference compute and more memory because this is really the future. It’s an opportunity for us to completely reshape the way people interact with their cars. To be honest, it solves itself in the long run from a unit economic standpoint because as we do more and more interactions locally in the car, we avoid the back and forth with the cloud. So, we avoid the connectivity costs, and then we also don’t have to pay for the cloud inference costs. So, in the long run, it’s actually economically positive.

That’s not just a spreadsheet you made up to win an argument that actually models out?

[Laughs] Kind of.

The reason I ask it is because my next question was about inference costs. They’re going up. There are rate limits with all the big providers. What model are you using right now? What are the frontier models you’re using right now?

The architecture that we have built is not actually model dependent. One of the architecture’s foundations allows us to interact and plug-and-play with different foundational models. Similarly, it can use different modalities in how users can input their requests, whether it’s voice or vision. It can use text if we want to enable that.

When it comes to the models themselves, we currently use a combination of internal models for everything that runs locally on the edge and models from Google. We have a partnership with Google. Things are going really well in terms of deep access to advanced Gemini models as well as the grounding of results also powered by Google.

This is another question I asked Rivian Assistant: what are the top five headlines on The Verge? I just wanted to see if this thing browses the web. It returned some results that I think are 24 or 48 hours old. These were the top five headlines from yesterday.

Does this thing have a web browser in the background, or is it just pulling from a Google data corpus? How does that work?

In theory, it should in theory connect in real-time. This is where the grounding with Google results comes into the picture. It should give you the latest headlines. So, if it didn’t, then that’s another one on me.

Well, I was just curious. Lots of people are having this experience now where the data in the model is old and there’s some cutoff, and I was just trying to find the cutoff. Then, I had a long searching conversation with it. We need to buy a new air conditioner, and I was just asking it to do math about air conditioner efficiency. It’s very boring, but this is what I talked to your car about for a while. It occurred to me that I was making it think very hard. I am wasting more energy asking how efficient an air conditioner I should buy. This is not a good ratio of energy spent to energy saved over there.

How does that work? You have to pay a monthly fee for the connectivity package to access Rivian Assistant, but then I might burn way more tokens than that fee could ever pay for. How does that math work out?

It really depends. In these cases, there are all sorts of what we call “rate-limiting” techniques that we can apply. If we have seen, like in your case, that you’re spending 20 hours discussing with the assistant, then we may do something behind the scenes.

It’s similar in the way we can configure the models. Given the types of interactions that you have in the car, you would not be interacting with the latest and greatest, say, Claude Opus 4.7 models so you’ll burn a lot of tokens. A lot of it also depends on aggregation across users in terms of the types of requests, as well as the arbitration we do between the edge and the cloud. As I mentioned, the more we move to edge and local compute, the better it is for us in terms of overall inference costs.

So, let me just ask you this question again. Now that you’ve shipped this software, people are using it. You’re getting extremely detailed feedback from me. Do you still think having buttons in the car is an anomaly?

I deeply believe that voice has the chance to be the primary interface in the car. I also think that buttons can exist, but they shouldn’t be the primary way with which you interact with the car. I think there’s more that is possible with voice since you can do more than one single function.

You don’t have to fiddle with so many functions. You don’t have to go deep into the touchscreen to look into specific features. A great voice experience can elevate all of that, allow users to talk to the car as a human would and really take the overall experience to the next level.

Are we going to get the HVAC buttons back in any future Rivians? That’s really what I’m asking here.

Actually, with the R2, we have a great way to add tactile feedback for HVAC.

Oh, the big paddles on the wheel?

Yeah. They’re really awesome.

That’s a good pivot, but I’m asking, are we going to get the fan speed button back in the center stack?

Not in the center stack, but we have the same thing on the Haptic Halo Wheels. It’s a great compromise.

You knew it was coming. I have to ask you about CarPlay here. It strikes me as you imagine this future where the car is connected to your calendar and it’s connected to all this context. It has autonomy, which is something you’re also working on. You get in the car, and it knows it’s time to go to work. You just say, “Let’s go,” and the car takes off driving.

This is when you would use a vast number of applications, right? This might be when you have to focus to push the buttons again. I’ll just make that argument. But this is when you would want a whole number of apps. I hear from our readers every time I talk to a car executive that, “The reason I want CarPlay is because there’s 5,000 apps on my phone and no car OEM is ever going to support them in the built-in infotainment.”

This is when you would say, “Okay, project your phone to the center stack. The car’s driving itself. Have at it. Phone projection all day.” Do you think the tide is turning, or are you still absolutely committed to not having CarPlay in Rivian vehicles?

First of all, it’s really important to go through the philosophy of how we see software in the car and the user interface. The challenge with screen mirroring solutions is that they take over every single pixel in the car, and that’s not the way we see ourselves interacting with our users. You drove our car four years ago, and you drove another car over the past few days. I hope you’ve seen how much has changed in the car. It’s truly been by bringing in end-to-end features, not only changing the user interface but having your navigation know exactly about your drive mode, know exactly about your efficiency.

Offering that level of convenience is what is really resonating with a lot of our customers. If I look at our own internal statistics from five years ago when we first shipped the R1T and the R1S, the number one request from customers was CarPlay. We did all sorts of surveys with customers at the time, and more than 70 percent of customers were requesting CarPlay. In the recent survey, that number is less than 25 percent because with the level of features that we have shipped to customers, level of end-to-end integration, and the level of convenience that we are bringing, CarPlay or Android Auto is no longer the topic of discussion.

What we’re seeing right now with the advancement of AI technologies is just another reason why I deeply believe that RJ and Rivian made the right choice by investing into our own technology and software. Cars are moving from, as you said, the buzzword “software-defined” to “AI-defined.” The possibilities now for such deep AI integration in the car make the entire CarPlay debate completely obsolete.

I really believe that the way you interact with apps — which are mono-threaded with single buttons or single icons — will be completely reshaped into a world where an agentic integration presents itself as a wholesome user experience.

I buy that in the big picture, but give me an example of that. I’ll put up an idea that I get from our readers all the time for you to react to. There are tons of little apps. They’re basically media-playing apps on phones, and it’s trivial to push the button for the CarPlay app.

The one that I always think about is an email from a reader who said, “I have a Bible app that is never going to be built into anyone’s infotainment system. It’s made by a small developer and I love it, and that’s why I need CarPlay. I’m always going to buy a car with CarPlay because of it.”

That is about as small of an edge case as you get, but this one customer is going to pick a car based on it. Are you going to make that developer build an agentic AI integration into the Rivian Assistant, or are you just going to lose that customer to CarPlay?

I mean, this is the beauty of the technology disruption in which we live today. The answer in that case does not necessarily need to be, “We will build an agentic integration for that particular app.” It can absolutely be if it is, say, Spotify or Apple Music.

But if it’s a small app, the answer could be that we have an integration for your favorite voice assistant in the car, and then you can ask the voice assistant to play that particular app through Bluetooth audio. That is possible as we open up the framework and allow more integrations to bring your own digital ecosystem to the car.

We’ll use Google because Gemini is more present on an Android phone than Siri is currently on an iPhone. It’s also your partner. You’re saying you can talk to Rivian Assistant and it knows your Google account and Android phone, it’s going to go talk to Gemini, and Gemini is going to go operate your phone and stream Bluetooth audio to Rivian Assistant.

In the future, all of that is possible.

Is that better or worse than phone projection? This is a different kind of loop than just saying, “Put the interface here and let the user do it.”

It could be possible through phone projection. I think the challenge with phone projection is that… First of all, as you’re driving, you’ll have to go through your phone. In some cases, you’ll have to press multiple buttons so you can get to the app menu. The other thing is that it takes over the entire screen, and that is a degradation of the experience.

Is the alternative solution available right now? No, but I think the beauty of this wave of technology is that we finally have the building blocks to really redefine those types of interactions. We can allow hooks now into your personal device through a different interaction rather than truly integrating the app end-to-end the car itself or taking over the entire screen. There’s a third path now that is possible.

Obviously I think it might be easier with Google. Again, it’s your partner, but where would that connection to Google Assistant happen? I don’t think I’m holding up my Android phone to the speaker and letting the assistants talk to each other out loud. Although that would be fun. It would be deeply hilarious to hear the two assistants just have a conversation like, “Can you please play the music app for me?”

Does that happen in the cloud? Does it happen locally? Where does that integration point between assistants happen?

Think about it as the assistant in the car knowing how to talk to your Gemini or your personal assistant. In that case, your personal assistant will be controlling your phone.

The reason I’m asking this in this way is because at some point, you have one main assistant, all the other things are agents it can talk to, and then maybe no one talks to Rivian Assistant again. You pull that thread all the way and Gemini just does everything for you all the time. Is that a danger, or are we just nowhere close to even having to worry about that?

Honestly, we don’t worry about that because we know the opportunity that we have, and we know the breadth of capabilities that we can offer. No other assistant will be able to know as much as Rivian Assistant about the car controls. None.

Similarly, the fact that we have the surface of integration sitting in our own operating system enables a ton of opportunities that you simply cannot do with your phone or by calling another assistant. Imagine that you’re driving, and in the near future, we enable the technology to have agentic integration with your favorite food delivery. The car knows exactly when you will be home. You’ll say, “Order my favorite sandwich from XYZ shop.” Your account is already configured. Then, the assistant will pick the destination and get you to your favorite restaurant. All of that is integrated. You just need to do it through a voice command.

Those types of experiences — where things become so seamless and so easy as if you’re talking to a human, where it connects the dots across multiple surfaces of your digital ecosystem — would only be possible through such integrations.

Wassym, we’re out of time here. As you can tell, I can obviously talk to you about this forever. I don’t think anyone has figured out how all this works, and it seems like you’re making some big decisions. So, you’re going to have to come back when you’ve learned how this goes after this is shipped to all of your customers and certainly when the R2 is out.

There is one question that I have to ask every single Rivian person that I encounter. It is very important to me. When is the R3X coming out?

It’s here. Do you see it? [Laughs]

When can I get one?

By the way, it’s my favorite car. I ask RJ that question all the time. Now, you talked about decisions. You talked about trade-offs. Us delivering the R2 before the R3X is, as you can imagine, a big decision. It’s also a hard decision because in our hearts, we all deeply want to have the R3X as soon as possible.

We also know that the R2 has the best ingredients to be a wildly successful car. The US needs another great alternative SUV for families, and this is what the R2 will bring. As we ship the R2, as we scale our volume as a company, we will earn the right to bring fantastic and emotional cars like the R3X.

I know that that is, in one way, the right answer. I’m just saying for me personally, come on, just send me one. It’ll be great.

That’s the hard thing.

I’ll give you more feedback just like this. I will break your R3X prototype in 10,000 different ways. You’ll get the bug reports. It’ll be great. Tell RJ I made the offer.

Awesome. I’ll get one at the same time as you Nilay.

Sounds good. Wassym, thank you so much for being on Decoder. That was great.

Thank you.

_{Questions or comments? Hit us up at decoder@theverge.com. We really do read every email!}

blogs.nvidia.com

Robotics is entering a new phase: moving from controlled demos and scripted automation toward generalizable, reliable embodied autonomy in the real world.

At the International Conference on Robotics and Automation (ICRA), eight of NVIDIA Research’s 28 accepted papers show how simulation-to-real transfer is becoming a foundation for that shift, helping robots perceive, reason, plan and act across dynamic, unpredictable environments.

Together, the papers span the full stack of challenges robot developers face: coordinating multiple arms in parallel, building policies that generalize across robot bodies, grasping novel objects in clutter, performing precise assembly and developing vision-language-action models that reason before they move.

The throughline is clear: sim-to-real is becoming a foundation for robots that can adapt, generalize, and operate with greater reliability outside the lab.

Coordinating Arms, Navigating Bodies, Grasping Objects

Picture a pharmaceutical lab run by robotic arms: picking up tubes, transferring liquids, mixing reagents — each step taking different amounts of time, all requiring careful coordination.

Traditional robot scheduling software handles those steps sequentially, one arm at a time.

ScheduleStream changes that by running computations on GPUs, letting multiple arms plan movements and operate in parallel. The result — a 3x speedup across multi-arm planning scenarios, on hardware like the NVIDIA Jetson edge AI platform. Code for the framework is available on GitHub.

A robot that learns to navigate through a space — avoiding obstacles and finding its destination — usually learns to do it in one body. Put the same navigation software into a differently shaped robot and it often falls apart, because its parts all move differently.

The COMPASS policy framework solves this by first building the baseline navigation functionality using imitation learning and then using residual reinforcement learning in NVIDIA Isaac Lab to build specialists for diverse robot embodiments. Crucially, no real-world robot data is involved at any stage: everything is trained in Isaac Lab simulation.

Compared with an imitation learning baseline, COMPASS achieved a 4.5x improvement in average success rate. It also seamlessly transfers to real-world environments, demonstrating around 80% success across 20 real-world navigation trials on autonomous mobile robots and humanoids.

COMPASS is agent-friendly, with dedicated skills — and developers can connect the pipeline with NVIDIA Omniverse NuRec to post-train and validate robots in a digital twin of a novel environment before deployment.

Most grasping systems identify the object, predict a grasp, plan a path, then execute. But the last few centimeters are where small errors matter most.

Grasp-MPC adaptively computes robotic grasps, continuously correcting the robot’s motion as it closes in on the object, rather than carrying out a fixed plan — the way a person grabs something by feeling rather than calculating every joint angle in advance.

To build the policy, the researchers generated 2 million simulated trajectories across 8,000 objects using annotations from the GraspGen dataset and motion planning data from cuRobo, a CUDA-accelerated library for robot motion generation.

After training on both successful and failed trajectories, Grasp-MPC learned to grasp novel objects in cluttered tabletops and shelves — achieving around 75% overall success on real robots, compared with a baseline of 41%.

Deformable Cluster Manipulation introduces a framework that tackles a parallel challenge: enabling systems to grasp not just one object, but a whole bundle of flexible, tangled material at once.

The framework was motivated by a real-world task: clearing a mass of tree branches that have grown over a power line, where there’s no single clean object to grab. The system uses its entire arm, not just the gripper: wrapping it around the branch cluster and sweeping it aside, the way someone might gather an armful of cables or push a tangle of brush out of the way.

The researchers built a tree generator using biological growth equations to create synthetic trees of many different shapes and sizes — then trained the system across thousands of them in NVIDIA Isaac open simulation frameworks.

The policy deploys to real branches zero shot. Beyond power lines, the researchers see potential in cable management, agricultural inspection and anywhere robots need to handle a tangle rather than a single graspable item.

Assembling With Precision

Precise assembly — threading a nut onto a bolt, inserting a gear onto a gearshaft, pressing a peg into a hole — is notoriously hard to get right with simulation alone.

The real world is complex. Real surfaces aren’t perfectly smooth. Sensors don’t behave as specified. Tiny discrepancies that a simulator ignores can stop a robot in its tracks.

The SPARR method addresses this by splitting the job in two. A policy trained in Isaac Lab learns the general strategy for the assembly task in simulation. Then, on the actual hardware, a second layer learns to correct for whatever the simulator got wrong — using the robot’s own camera and without any human demonstrations or guidance.

SPARR improves success rates by 38% and reduces cycle time by around 30% compared with zero-shot sim-to-real baselines.

On National Institute of Standards and Technology (NIST) assembly tasks not seen during training, success improves by nearly 75% — approaching the results of methods that require a human in the loop.

The Refinery framework takes on the next layer of difficulty in assembly: tasks with multiple sequential steps, where how step one is finished determines whether step two is even possible. It’s like assembling furniture — leave a panel at the wrong angle, and the next fastener won’t go in.

By understanding how success varies across initial conditions and training across hundreds of simulated assembly scenarios, Refinery learns how to complete each step and leave each component in a position that sets up the next. It achieves 91% simulation success and a nearly 11% mean improvement over baselines with comparable real-world results — and its policies can be chained to handle long, multi-part sequences.

Action Models That Keep Their Word

The PEEK pipeline helps robots see past the clutter. In a typical manipulation task, the robot’s camera picks up everything in the scene — but most of it is irrelevant noise.

One task demonstrated on the PEEK project page is “give the banana to NVIDIA founder and CEO Jensen Huang”: a photo of Huang sits on a table alongside a photo of Michael Jordan, a collection of unrelated objects and other distractors.

A human doing the task instantly focuses on the banana and the right photo; a standard robot policy has to process everything and often gets confused. PEEK solves this by having a vision language model read the task instruction and focus the robot’s line of vision accordingly — showing a movement path, and highlighting around the objects that matter, while fading out everything else.

The policy then acts on that annotated view rather than the raw scene. For a policy trained purely in simulation, adding PEEK produced a 41x real-world improvement in accuracy. For large VLA models and smaller policies, gains range from 2-3.5x. Because it works at the image level, PEEK integrates with any camera-based policy without modification.

Do What You Say — a collaboration with researchers at Carnegie Mellon University, University of Utah and University of Sydney — addresses a specific failure mode that matters more as robots tackle longer, more complex tasks.

Give a robot an instruction like “store everything on this table inside the cabinet” or “prepare a Manhattan,” and it has to break that down into individual steps and execute them in sequence.

The problem is that the AI model can correctly reason through what it needs to do — and then execute something different.

The method, called SEAL, fixes this at runtime without any retraining: the robot generates several candidate action sequences, thinks through where each one would actually lead and picks the outcome that matches what it said it would do. SEAL delivers up to 15% accuracy gains over prior work, with robustness against rephrased instructions, changed objects, scene clutter and shifted camera angles.

In addition to papers, NVIDIA is expanding robotics research infrastructure with large-scale open datasets for robotics. The NVIDIA Physical AI Dataset is the world’s largest open dataset for physical development, surpassing 15 million+ downloads, while NVIDIA Isaac GR00T X Embodiment Sim has become one of the most-downloaded robotics datasets.

Universities Accelerate Physical AI Research With NVIDIA Technologies

Robotics teams from universities such as Carnegie Mellon University (CMU), ETH Zurich, MIT and University of Texas at Austin are tapping NVIDIA technologies to move physical AI research from simulation to real-world systems — with nearly 50 accepted papers referencing NVIDIA-accelerated simulation, robot learning and compute.

Examples include a paper from CMU demonstrating a robotic control framework trained in NVIDIA Isaac Lab and MIT work on large language model-guided reinforcement learning powered by NVIDIA GPUs.

Explore NVIDIA Research’s physical AI work. Developers can get started with Isaac Lab and Isaac Sim.

Stay up to date by subscribing to our newsletter, and following NVIDIA Robotics on LinkedIn, Instagram, X and Facebook.

To start your robotics journey, enroll in our free NVIDIA Robotics Fundamentals courses today.

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License to stream, shaken and stirred.

GeForce NOW is dialing up the espionage with the launch of 007 First Light, letting members slip into James Bond’s reimagined origin story from almost any device — no tux or preloads required.

For a limited time, the game is included with the purchase of a 12‑month GeForce NOW Ultimate membership, letting members lock in Bond’s next mission and a year of top-tier cloud gaming in one shot.

And for those stepping into the role, the look comes with it. Daring Elite Outfit, a signature look capturing the spirit of a rising agent, is now available for Ultimate members — equal parts discipline, ambition and edge.

And catch Capcom’s Resident Evil Requiem demo in the cloud — catch an early portion of the game and discover its two sides: terrifying survival horror with Grace Ashcroft, and pulse-pounding action with Leon S. Kennedy

This GFN Thursday also brings eight new games to the cloud, expanding the library with even more ways to play across genres.

Mission Assigned

The mission begins before the legend. 007 First Light puts players in the shoes of James Bond at the dawn of his career, when instincts are sharp, rules are flexible and every decision shapes the agent he’s destined to become. This is a Bond who’s still earning his “00” — unpolished, dangerous and learning when to trust a plan and when to improvise under fire.

A cinematic spy thriller unfolds with high-stakes infiltration, tense encounters and stylish set pieces. One moment brings thrills working undercover at an opulent event — the next, white‑knuckle chases and close‑quarters confrontations where timing and composure are everything. Approaches to each mission — quiet and calculated, bold and aggressive, or something in between — define Bond’s path and the allies and enemies that cross it.

Stream it all with GeForce RTX 50 Series GPU power in the cloud, with up to 5K high dynamic range and cinematic-quality streaming for Ultimate members. Experience Bond’s origin story in razor-sharp detail across devices — no high-end PC required.

Keep It Daring

Looks speak first.

A new 007 First Light reward drops today on GeForce NOW, delivering Ultimate members a refined, unmistakably bold way to step into the world of espionage.

The Daring Elite Outfit blends calculated precision with effortless style — the kind that turns heads before the mission even begins. Sleek, confident and just a little dangerous, it’s built for agents who understand that presence is a part of the playbook.

All rewards are available starting now through Saturday, June 27, or while supplies last. To claim, log in to a GeForce NOW account, head to the rewards section in the account portal and redeem.

Keep It Heated

Armored battles get hero-driven in Wargaming’s free-to-play, player vs. player vehicle shooter, World of Tanks: HEAT, now streaming on GeForce NOW.

World of Tanks: HEAT is the franchise’s first hero-driven tank action game, where powerful Agents and their experimental machines shape every fight. Fast-paced 5v5 and 10v10 matches erupt into high-velocity brawls as hero-enhanced tanks trade explosive bursts, clutch escapes and momentum-shifting abilities.

Each Agent brings a unique tool kit and vehicle lineup, layering team roles and synergy on top of sharp aim and smart positioning. It’s classic steel-on-steel combat with a hero-shooter twist, now available in the cloud. The next match is always just a quick deployment away on GeForce NOW.

Keep It Playing

Community stories take the spotlight this week as an Ultimate member on Reddit shares a heartfelt note about what GeForce NOW means to daily gaming.

In addition, members can look for the following:

• Romestead (New release on Steam, May 26)
• World of Tanks: HEAT (New release on Steam, May 26)
• 007 First Light (New release on Steam, Epic Games Store and Xbox, available on the Microsoft store, May 26)
• Starminer (New release on Steam, May 27)
• Resident Evil Requiem Demo (New release on Steam, May 27)
• Alchemy Factory (Steam)
• BeamNG.drive (Epic Games Store)
• Ostranauts (Steam)

What are you planning to play this weekend? Let us know on X or in the comments below.

nvidia geforce now delivers such flawless performance, it is possible to exert dominance upon the squad ‘w/ absolute precision & @ ease' ♡

more about @nvidiagfn @ https://t.co/pFHAv1zXRS 💫https://t.co/q67SOJNpbO #nvidiapartner #geforcenow pic.twitter.com/NsiK3FIX6v

- airie (@airiesummer) May 22, 2026

spectrum.ieee.org

Academe and industry require different approaches

Image: Google

Google Home is rolling out a new Gemini-powered automation feature that can trigger smart home routines based on what your security cameras can see. This is one of several updates announced yesterday for Gemini for Home, including enhanced voice command support and general stability improvements, following its early access launch in October.

"We are introducing a brand-new starter that lets you design automations based on visual insights," Google said in its announcement. "Because your cameras can now actually understand what they see, your smart home can automatically react to almost anything happening around your home."

The feature is cu …

Read the full story at The Verge.

Last.fm announced that it is independent again after separating from Paramount Skydance, nearly two decades after CBS acquired the music-tracking service in 2007. The company says accounts, scrobbles, privacy settings, Pro subscriptions, and billing information will remain intact. Additional details are forthcoming. Engadget reports: "Today, Last.fm begins a new chapter as an independent company," the announcement reads. "Ownership has changed, but the product you use every day has not." It also said that it will keep its current team. Last.fm is a music website that can track what you listen to across platforms, apps and streaming services, including Spotify, YouTube and Apple Music.Â

[...] Last.fm started as an internet radio station in 2002, and it didn't get scrobbling until a few years later when it merged with the original team that created the tracking process. It operated as an independent company until it was acquired by CBS Interactive, which is now part of the merged Paramount Skydance Corporation, for $280 million in 2007. In 2014, it killed off its $3-a-month subscription radio service to focus on tracking your listening habits on other providers. The company promised to share more about what you can expect from the transition in the coming weeks, but everything will work on Last.fm "exactly as it did yesterday" for now.