The race to secure power for AI data centers has spilled over into some unusual places, including the automotive world. 

Battery recycler Redwood Materials kicked off the trend last year with a new energy storage division and a project that attached old EV packs to a Crusoe data center in Nevada. Then, Ford said it was repurposing some of its battery manufacturing capacity to make grid-scale batteries. And now GM is announcing its own — arguably more ambitious — plans for an energy storage system (ESS). 

GM unveiled on Tuesday two new phases in its attack on the energy storage market. The biggest swing by far is GM’s new partnership with energy storage startup Peak Energy. For that partnership, GM is developing an entirely new sodium-ion battery chemistry tailored for grid-scale deployments.

Outside of China, no automaker has announced plans to build sodium-ion cells. 

“The way we’re getting into the market is the easy way, through ESS,” Kurt Kelty, vice president of battery and sustainability at GM, told TechCrunch. “The performance characteristics are just what is needed in that market.”

GM wouldn’t share with TechCrunch how much money it is investing in this energy storage effort. But we do know the company has committed $900 million to commercialize new battery chemistries, an investment that includes a new battery development center.

Sodium-ion batteries work similarly to lithium-ion, but they swap out key materials to make the cells cheaper, longer lasting, and less prone to overheating. The tradeoff is that sodium-ion batteries need to be larger and heavier to store the same amount of electricity. 

Peak Energy has already been working on energy storage systems that use sodium-ion batteries. Because sodium-ion batteries behave differently from lithium-ion, Peak has developed an energy storage system with that in mind. Its grid-scale batteries don’t have cooling systems or fire suppression systems because there’s less risk of overheating. The setup reduces upfront costs, and it should also eliminate costly maintenance, Paul Menson, director of energy storage commercialization at GM, told TechCrunch. 

“This is the manifestation of the hardest part to engineer is no part at all,” he said. “Eliminate the part, eliminate the problem.”

GM plans to sell sodium-ions cells to the startup, which will then integrate them into its products. But that won’t happen right away.

The first GM cells are expected to enter trial production at the company’s Battery Cell Development Center in 2028. TechCrunch was recently given an exclusive look at the new facility, which GM expects will cut about a year from the commercialization process for sodium-ion batteries, reducing costs in the process. 

GM’s sodium-ion cells are still years away from commercial production, however. In the meantime, the automaker will sell lithium iron phosphate (LFP) cells to LG Energy Solution for use in its energy storage systems. LG Energy Solution already works with GM through its Ultium joint venture, which makes batteries for the automaker’s EVs.

Alongside the partnerships with LG and Peak, GM announced that it was expanding its work with Redwood Materials, the battery recycling and energy storage startup founded by former Tesla executive J.B. Straubel. 

Redwood already buys scrap from GM’s battery factories and used battery packs from its EVs. GM has a pipeline of around 10,000 packs it’s sending to Redwood, and the startup has been operating a 12 megawatt/63-megawatt-hour migrogrid using second-life packs at a Crusoe data center in Sparks, Nevada. GM said it is buying a 7.2 megawatt-hour Redwood system for use at one of its plants in Michigan, which it estimates will save it around $3 million over its lifetime.

The GM installation is “a step one” for Redwood, Cal Lankton, chief commercial officer for Redwood, told TechCrunch.

Data centers, where Redwood already operates, and industrial sites like GM’s are “vastly different things,” he said. Where data centers might use batteries nearly continuously to absorb some of the power fluctuations from GPUs, industrial sites are more likely to use them to shave off peaks in power demand, which can lower monthly power bills, and use them to provide backup power in case of an outage.

“The factory is really excited because now we’ve got a more reliable factory,” Kelty said. “Ultimately, we’ll be having similar installations like this at all of our factories. It just makes good economic sense.”

Sony Electronics is making a massive upgrade to the humble meeting room screen. The company has just unveiled Crystal LED UNIFY, a massive 135-inch all-in-one direct-view LED display designed for boardrooms, meeting rooms, community spaces, and higher education environments.

At a glance, it might look like Sony’s next massive flagship living room TV, but it’s cutting edge display tech arriving to the office space. It is part of Sony’s professional display lineup and sits alongside its existing BRAVIA Professional Displays and Crystal LED portfolio. The model number is ZRL-135SG, and Sony is positioning it as a simpler way for organizations to add a large dvLED display without dealing with the usual complexity of custom LED wall projects.

An easy to setup up giant wall of screen

One of the biggest selling points for the Crystal LED UNIFY is its convenience. It arrives as a complete package with five pre-assembled display units and a control unit. So installation is a relatively straightforward process that can be completed by two people in about an hour. Since direct-view LED installations can get complicated, Sony’s version of the tech isn’t just promising solid visuals. The appeal is the simplified ordering, installation, maintenance, and day-to-day use.

The display units are mounted on wall brackets and connected to the included control unit, while a slide-out, front-serviceable design should make maintenance easier after installation.

Built for big bright rooms

Coming to the fun part, Crystal LED UNIFY uses a 1.5mm pixel pitch, Full HD resolution, and 800 cd/m² brightness. Sony has also added Anti-Reflection Surface Technology, which should help visibility in brightly lit rooms where projectors often struggle. The display also supports 4K input, works with Sony’s Device Management Platform, and offers a familiar interface for organizations already using Pro BRAVIA displays. In other words, it should also slot into conference rooms or multi-display setups with needing an IT team to learn an entirely new ecosystem.

Sony has also put effort in making it look clean on a wall. The Crystal LED UNIFY has ultra-slim bezels, a concealed slide-out control unit, and a depth of under 100mm, or less than four inches, when used with the included wall-mount brackets. So it should fit seamlessly in professional spaces.

The company expects Crystal LED UNIFY to be available in early 2027, with plans for an early showcase at the upcoming InfoComm 2026 event in Las Vegas from June 17 to June 19. Pricing has not been announced yet, but this is clearly aimed at businesses, institutions, and premium professional spaces rather than home theater shoppers with unusually large walls.

On-device AI models have stayed small because the entire weight set has to live in DRAM, capping practical parameter counts well below what server-side deployments use. Enterprise architects evaluating agentic workloads have had to choose between capable cloud-dependent models and limited on-device ones. Apple’s third-generation foundation models, announced at WWDC26, break that constraint by moving the weight set off DRAM entirely.

The AFM 3 family was developed in collaboration with Google and spans five models: two on-device and three server-based, all running within Apple’s Private Cloud Compute boundary. The server-side models, including AFM 3 Cloud Pro for agentic tool use and complex reasoning, run on Nvidia GPUs in Google Cloud. The on-device architecture is Apple’s own. AFM 3 Core Advanced is a 20-billion-parameter model that stores weights in NAND flash rather than DRAM.

“Instead of forcing the entire model into DRAM, the full model is stored in flash memory,” Apple’s research team wrote. “Because NAND-to-DRAM bandwidth is too slow to swap weights token by token, as standard MoE models require, AFM 3 Core Advanced makes routing decisions per prompt.”

How the architecture actually works

The memory wall Apple is working around is one every local AI developer runs into.

“You can’t put 20B parameters in RAM at any reasonable precision,” Awni Hannun, a researcher at Anthropic and former Apple research scientist, posted on X. “To make it work they are using pretty exotic architecture by today’s standards. A small model predicts from the query (or prompt) which experts to load from NAND into RAM.”

That prediction-and-load mechanism has three distinct components, each driven by the hardware constraints of consumer silicon.

The full 20B weight set lives in flash, not DRAM. AFM 3 Core Advanced stores its entire parameter set in NAND flash rather than active memory. Standard on-device deployments require the full model to fit in DRAM, which is what caps their parameter counts. Apple’s approach, which it calls Instruction-Following Pruning (IFP) and developed with its own researchers, treats flash as the model’s permanent home and DRAM as a working buffer for whichever experts a given prompt requires.

Expert routing happens once per prompt, not per token. In a conventional Mixture of Experts model, a router selects different experts for every token generated — which would require continuous weight movement between flash and DRAM at inference speed. NAND-to-DRAM bandwidth cannot support that. AFM 3 Core Advanced routes once at prompt time, selects a fixed expert set, loads it into DRAM alongside always-active shared experts, and generates all tokens from that same configuration.

“The key distinction from a typical MoE is that you do this once per query and then generate all the tokens with the same experts,” Hannun wrote.

Active parameter count scales from 1B to 4B depending on task complexity. Rather than running a fixed model size for every request, AFM 3 Core Advanced adjusts how many parameters it activates based on what the task requires — 1 billion for simpler operations, up to 4 billion for harder ones, all drawn from the 20-billion-parameter pool in flash.

What Apple has and hasn’t disclosed

The architecture paper is detailed on the memory design and sparse activation mechanism. It is less forthcoming on practical deployment constraints.

Apple’s profiling tools expose timing but not the metrics that decide production viability. “Energy, memory bandwidth, thermal? Not in the docs,” Marco Abis, who is building Ziraph, a profiler for local AI on Apple silicon, posted on X. “A notable gap, given those decide most of on-device performance.” 

Abis also did not find a statement in Apple’s documentation — across the Core AI docs, the Foundation Models docs or the Private Cloud Compute security post — of when an on-device request transparently offloads, or whether that routing is visible to the developer or the user. For enterprises that need to document where inference runs, that is a direct compliance problem.

Not all the information is currently available. Apple has indicated a full technical report with benchmarks is coming later this summer.

What this means for enterprise architects

Regulated industries evaluating agentic AI deployments now have a concrete architectural decision to make.

• The DRAM wall for on-device agents just moved. Enterprises evaluating agents that need to run without a cloud round-trip now have a 20-billion-parameter local option to evaluate. The constraint shifts from model capability to device hardware.

• The private/cloud boundary is now an architectural decision, not a default. Simpler requests stay on-device; complex agentic tasks route to AFM 3 Cloud Pro on Private Cloud Compute. Apple has not publicly specified when a request offloads or whether that routing is visible to the developer — a gap that complicates policy decisions for organizations that need to document where inference runs.

• The agentic server tier depends on Google Cloud. AFM 3 Cloud Pro runs on Nvidia GPUs in Google Cloud. The Private Cloud Compute guarantee covers data privacy. It does not eliminate the Google Cloud dependency for server-side inference.

AFM 3 Core Advanced gives enterprises a 20-billion-parameter on-device option that did not exist before WWDC26. Whether it is deployable at scale depends on answers Apple has not yet published. Those details are due in the summer technical report.

It’s also rolling out a vehicle-to-grid firmware update.

OpenAI is preparing a significant overhaul of ChatGPT that shifts the platform’s emphasis away from conversational AI and towards agentic tools and coding capabilities, according to a report from the Financial Times.

The redesign, which the Financial Times reports could arrive within weeks, reflects a growing internal conviction at OpenAI that the chatbot format has run its course as the company’s primary product focus.

That conviction reportedly extends to at least one OpenAI employee describing the situation in stark terms, with the phrase “chat is dead” circulating internally as shorthand for the belief that AI agents represent a more commercially valuable direction than the text-based exchanges ChatGPT built its reputation on.

In a nutshell: Nintendo is bringing back one of its most popular games for a new run on its latest handheld, nearly 30 years after the original. The Legend of Zelda: Ocarina of Time was announced during the Nintendo Direct presentation on June 9, and will be out later this year on the Switch 2.

The minute-and-a-half teaser doesn’t show any actual gameplay, nor does it provide a launch window outside of 2026. We also don’t know how much Nintendo is planning to charge for the game, although it’s probably a safe bet to assume this will be a premium title with a matching price tag. All we really know at this hour is that the game is indeed in the works and is destined for the Switch 2 later this year.

The Legend of Zelda: Ocarina of Time arrived on the Nintendo 64 in late 1998 and was unlike anything seen up to that point. The 3D action adventure game was initially planned for the 64DD, but was eventually moved to cartridge when that peripheral was canceled. Despite having just 32MB of memory to work with, Nintendo put out a masterpiece that pushed the limits of storytelling, puzzle-solving, and combat to new heights.

Nintendo sold roughly 7.6 million copies of Ocarina of Time during its run, good enough for fourth on the list of best-selling N64 games behind GoldenEye 007, Mario Kart 64, and Super Mario 64. In 2022, the game was inducted into the Strong National Museum of Play’s video game hall of fame alongside Dance Dance Revolution, Ms. Pacman, and Sid Meier’s Civilization, cementing its place in history as one of the greatest video games of all time.

The remake is part of Nintendo’s 40th annual franchise celebration, and adds to an already stacked 2026 that includes several high-profile game launches culminating with the release of Grand Theft Auto VI this fall.

For those needing to scratch their Ocarina of Time itch a bit sooner, Nintendo already offers a version of the original game through its Switch Online subscription service.

Killian O’Driscoll, the chief commercial officer at NIBRT, said, ‘The NIBRT team is delighted to be involved in this key WHO initiative to help make the world safer from future emergencies and pandemics.’ 

The World Health Organization (WHO) has selected NIBRT, Ireland’s National Institute for Bioprocessing Research and Training, as its Regional Training Centre for Biomanufacturing for Europe. It will be one of seven such establishments globally forming part of the WHO’s Biomanufacturing Workforce Training Initiative (BWTI).

BWTI was first set up in 2023 as a means of addressing critical skills gaps evident in the biomanufacturing chain, and enabling countries to turn technological innovation into localised and sustainable production – particularly in areas concerning medicines and other healthcare technologies. 

Commenting on the announcement, which was first made at a media briefing in late April, Dr Tedros Adhanom Ghebreyesus, director general of the WHO, said, “Over the past few years, WHO has taken several steps to make the world safer from future emergencies and pandemics. Today, WHO announced that we have designated regional training centres in each of WHO’s six regions, to build the skilled workforce needed to sustain local production of vaccines and biologics. 

“The new training centres are in Brazil, China, Egypt, India, Ireland, Senegal and South Africa. They will operate as part of a coordinated global network, delivering context-specific training aligned with regional priorities, regulatory environments and languages.”

Future responsibilities

As the designated Regional Training Centre for the European region, it will be under NIBRT’s remit to deliver hands-on training aligned with industry needs. The network will also work in close collaboration with the WHO and the Global Training Hub for Biomanufacturing, which is located in South Korea. 

Killian O’Driscoll, the chief commercial officer at NIBRT, said, “The NIBRT team is delighted to be involved in this key WHO initiative to help make the world safer from future emergencies and pandemics. 

“This designation from WHO reflects the quality and reach of the training NIBRT has delivered over many years. As the Regional Training Centre for Europe, we will work with our partners across the region and around the world to help sustain local production of vaccines and biologics for lower and middle income countries.”

The WHO’s additional Regional Training Centres are the Institut Pasteur de Dakar in Senegal; the Council for Scientific and Industrial Research, South Africa; the Oswaldo Cruz Foundation in Brazil; the Translational Health Science and Technology Institute in India; the Center for Continuing Professional Development at the Egyptian Drug Authority in Egypt; and China’s Peking University. 

The life sciences space is booming of late, with many organisations expanding their job and upskilling opportunities. In early June, consultancy Primecore announced plans to create 50 new jobs across their offices in Ireland and the US over the next three years, on top of 100 new roles previously announced. 

In late April, the Advancing Innovation in Manufacturing Centre in Sligo announced an expansion with a new Galway base of operations amid plans to strengthen its links within the medtech and life sciences sectors.

Also in April, OpenAI announced plans to roll out an early version of GPT-Rosalind, an AI reasoning model designed to support research across biology, drug discovery and translational medicine. 

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Valve’s upcoming Steam Machine was recently confirmed to launch this summer, despite uncertainties surrounding its pricing. The console was originally announced back in November 2025, but the ongoing RAM and SSD shortages threw a spanner in its launch timeline.

While the company has not revealed the specific launch date of the console yet, eager fans have been digging through FCC filings and connecting the dots to predict when the console could land. As per their findings, it could happen on or before June 29.

FCC filings may have revealed Valve’s launch window

First spotted by Notebookcheck, Reddit user u/wayTooManyBugs examined the FCC documentation of past Valve hardware and pointed out a pattern in how the Steam Controller’s launch was handled. According to the findings, the Steam Controller was submitted to the FCC on November 24, 2025, but its user manual and product images were kept confidential until May 20, 2026. The interesting part is that these documents only became publicly available after the device had already gone on sale.

The same pattern now appears to apply to the Steam Machine. Valve reportedly submitted the Steam Machine’s FCC documents around the end of 2025, and its user manual and product images are scheduled to become public on June 29, 2026. Going by the Steam Controller timeline, fans believe Valve may launch the Steam Machine before those documents are released publicly.

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In other words, June 29 may not be the exact launch date, but it could act as the latest possible date by which the Steam Machine arrives. All that said, this is just a theory at this point, but since Valve has confirmed that the Steam Machine will debut sometime this summer, it could very well happen around this window.

Pricing could still make or break the Steam Machine

The biggest question is still pricing. Earlier reports have suggested that the Steam Machine could cost upwards of $1,000, which would make it a hard sell given that its GPU performance is already known to trail the standard PS5. Valve could still soften the blow by absorbing some of the hardware cost upfront, especially if the goal is to get more users into SteamOS and the wider Steam ecosystem. For now, though, the price may be the one thing that decides whether the Steam Machine feels exciting or excessive.

One of the most interesting new features is Custom Extensions, which lets you create an extension for Safari in natural language. Lastly, the new version of Safari will work the Passwords app to automatically fix website login passwords that are deemed no longer safe to use.

The other exciting implementation of Apple Intelligence is within the Shortcuts app. The app received some artificial intelligence upgrades last year, but this new update takes things much further. You can now use natural language to design an automated shortcut, no longer requiring the manual work of connecting functions within apps together. Apple’s example was that you could type, “Whenever I’m leaving work, calculate the ETA, and send it to Pedro,” which would use a combination of Maps and Messages.

Other updates include using natural language when creating an event in Calendar, which will then fill in the details with contacts or locations. Image Playground also received a major update, which is now based on Google’s Gemini image-generation tech. It can now cook up photorealistic imagery and looks far less limited in what it can do. Like other image-generation apps, Image Playground will be subject to daily limits, but paid iCloud+ subscriptions can buy you more image generation.

Tweaks to Liquid Glass

While AI is the focus of the update, Golden Gate is also a follow-up to last year’s macOS Tahoe. Liquid Glass represented a major change to the way all the user interface elements of the appear, and in macOS Golden Gate, those UI elements are being refined. Answering complaints about indecipherability and messy menus, macOS now has a refraction effect in its transparency, which more strongly obscures background content and makes the text in the foreground easier to read. This can be customized in System Settings, letting you change the transparency level using a slider.

Apple has also made some smaller refinements to the visual identity of macOS. The Tool bar now has a uniform menu, the sidebars expand to the very edge of the window and the icons within the sidebars have color once again. And perhaps my most requested feature, every window has the same corner radius to the window control buttons.

Beyond the visual elements, Apple says it has made improvements to responsiveness, such as memory usage, CPU usage, display rendering, and app switching. One example on the Mac was that moving between Spaces was more fluid now, as is opening Mission Control.