The person who connected Jeff Bezos to one of the most ambitious electric vehicle startups in America has left its board. Melinda Lewison, who manages the Bezos family office and was listed as a director on Slate Auto’s corporate filings, has departed the company’s board months before its first truck is scheduled to roll off the production line in Warsaw, Indiana.

The departure follows a pattern of leadership changes at the startup that has raised 1.4 billion dollars on the strength of an idea, a factory, and a name. That name, more than any specification sheet or reservation count, has been the organising principle of Slate Auto’s public identity since TechCrunch revealed Bezos’s involvement in April 2025.

The backing

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Slate Auto was incubated inside Re:Build Manufacturing, the industrial conglomerate founded by Jeff Wilke, who served as chief executive of Amazon’s worldwide consumer division before retiring in 2021. Wilke and Miles Arnone, Re:Build’s chief executive, created the company under the working name Re:Car before spinning it out as an independent entity in 2023.

Bezos’s connection to Slate was indirect but unmistakable. Lewison, the head of his family office, appeared on corporate filings as a director. The arrangement gave Slate the most valuable asset a pre-revenue startup can possess: the implicit endorsement of the world’s second-richest person, without requiring Bezos himself to make public statements, attend events, or stake his reputation on production timelines.

Bezos has separately committed to a 10 billion dollar physical AI laboratory called Project Prometheus, and his family office has backed ventures across space, media, agriculture, and nuclear energy. The pattern is consistent: large bets on capital-intensive physical infrastructure, managed at arm’s length through intermediaries. Lewison’s board seat was the mechanism through which that pattern extended to Slate. Her departure removes it.

The changes

The board departure is the second significant leadership change at Slate in three months. In March, the company replaced chief executive Chris Barman with Peter Faricy, a former Amazon Marketplace vice president who had been advising Slate alongside work with McKinsey and Bessemer Venture Partners. Barman moved to the role of president of vehicles.

The timing of both changes is notable. Slate opened preorders in June 2025 and crossed 100,000 refundable reservations within two weeks. The reservation count has since grown to more than 160,000. The company closed a 650 million dollar Series C in April 2026, led by TWG Global, the investment firm run by Los Angeles Dodgers owner Mark Walter and Thomas Tull. Total funding reached 1.4 billion dollars.

A startup that changes its chief executive and loses a high-profile board member in the months before first production is not necessarily in trouble. Leadership transitions at this stage can reflect the shift from fundraising mode to operational execution, and Faricy’s Amazon logistics background is arguably better suited to manufacturing scale-up than Barman’s earlier role. But the optics matter for a company whose brand has been built on the Bezos connection.

Amazon-backed ventures have reached significant milestones recently, including nuclear startup X-Energy’s 1.02 billion dollar IPO in April. But X-Energy is a company where the Amazon relationship deepened over time, culminating in a 500 million dollar investment and a five-gigawatt power purchase commitment. At Slate, the Bezos connection appears to be narrowing rather than expanding.

The truck

The vehicle at the centre of this is deliberately unglamorous. Slate’s electric truck is priced in the mid-20,000 dollar range before federal incentives, which could push the effective cost below 20,000 dollars. It offers a 52.7 kilowatt-hour battery with 150 miles of range in the standard configuration, or an 84.3 kilowatt-hour battery with 240 miles in the extended version. Payload capacity is 1,400 pounds. The design is boxy, utilitarian, and deliberately analog, with physical controls and minimal software.

The positioning is anti-Tesla in every dimension. Where Tesla’s Cybertruck is a 80,000 dollar stainless steel statement piece, Slate is pitching a work truck for tradespeople, small business owners, and first-time EV buyers who want something that functions like the cheap trucks Detroit stopped making a decade ago. The company offers more than 100 accessories and a do-it-yourself SUV conversion kit.

The Warsaw, Indiana factory, a former R.R. Donnelley printing facility, has received approximately 400 million dollars in investment and is projected to create more than 2,000 jobs in Kosciusko County. Production is scheduled to begin late 2026, with preorders opening in June alongside official pricing.

The market

A dozen electric vehicle models have been discontinued in the United States as tariffs, tax credit changes, and import costs reshape the market. The result is a landscape that structurally favours domestically manufactured vehicles, particularly those priced below the 55,000 dollar threshold for the federal EV tax credit. Slate’s price point and Indiana factory position it squarely within these incentive boundaries.

The affordable EV truck segment is no longer uncontested. Kia has confirmed an electric pickup and plans to deploy Atlas robots in its Georgia factories, targeting the same domestic-manufacturing advantage that Slate is pursuing. Hyundai, Scout Motors, and several Chinese manufacturers exploring US assembly are all eyeing the segment below 40,000 dollars.

Volkswagen has overtaken Amazon as Rivian’s largest shareholder after a one billion dollar software milestone payment, illustrating how quickly investor relationships shift in the EV startup landscape. Rivian, which went public at a 153 billion dollar valuation in 2021 and saw its market capitalisation collapse by more than 90 per cent, remains the most prominent cautionary tale for EV startups that raise billions before achieving sustainable production economics.

Slate’s 160,000 reservations, collected at 50 dollars each on a fully refundable basis, represent intent rather than commitment. The conversion rate from reservation to binding order will determine whether the Warsaw factory’s capacity is a strength or an albatross.

The question

Every electric vehicle startup that has reached the production stage has experienced some version of the transition Slate is undergoing. The founders who attract early capital and generate excitement are not always the operators who can run a factory, manage a supply chain, and deliver vehicles on time. Faricy’s appointment suggests Slate’s investors understand this. Lewison’s departure suggests the Bezos orbit has decided its involvement has reached a natural conclusion, or that the risk profile of a pre-production automaker no longer fits the family office’s portfolio strategy.

What Slate has that most failed EV startups did not is a realistic product for a market that exists. The truck is not a hypercar, a flying taxi, or a autonomous robotaxi. It is a cheap, simple vehicle for people who need to move things, built in a state that wants the jobs, priced for a tax credit that currently exists, and manufactured domestically in a trade environment that punishes imports.

The question is whether the company can execute without the halo. Bezos’s name opened doors, attracted co-investors, and generated media coverage that a startup building affordable trucks in Indiana would not otherwise have received. The 1.4 billion dollars is in the bank. The factory is under construction. The reservations are on the books. And the person who represented the most famous investor in the building has walked out the door, six months before the first truck is supposed to drive through it.

from the security-theater dept

Utah has a long track record of short-sighted internet policymaking, but the latest example really does take things to a new level of stupid. As of yesterday, Utah’s “Online Age Verification Amendments” bill, Senate Bill 73, has taken effect. It is a piece of legislation that effectively tries to ban VPNs as a desperate attempt to stop people from bypassing the state’s already problematic (and likely unconstitutional) age verification requirements.

Signed by Governor Spencer Cox on March 19, the controversial law establishes that a user is considered to be accessing a website from Utah if they are physically located there, regardless of whether they use a VPN or proxy to mask their IP address. It also prohibits covered websites from sharing instructions on how to use a VPN to bypass age checks.

We’ve been highlighting the various attempts to ban VPNs as short-sighted legislators fail to grasp how necessary they are for basic security. But, now, Utah has touched the stove and is going to find out what it feels like.

While an earlier version of the law would have simply held a provider liable for not doing age verification, the amended version says service providers have to determine whether the person is physically located in Utah — even if they’re using a VPN to appear to be from somewhere else:

An individual is considered to be accessing the website from this state if the individual is actually located in the state, regardless of whether the individual is using a virtual private network, proxy server, or other means to disguise or misrepresent the individual’s geographic location to make it appear that the individual is accessing a website from a location outside this state.

In short, the genius legislators in Utah have decided that websites should do the impossible: either block all access from VPNs or somehow magically “know” that users whose digital footprints suggest they’re connecting from outside Utah are actually lying about their location. That is, in any understanding of the law, an effective ban on VPNs, because the only way to deal with that would be to block off huge segments of IP addresses associated with known VPN servers.

Even worse, the law says it’s a violation to tell people how to protect themselves with a VPN, which seems like a First Amendment violation on its own (you can’t ban a service from telling users how to use another service):

A commercial entity that operates a website that contains a substantial portion of material harmful to minors may not facilitate or encourage the use of a virtual private network, proxy server, or other means to circumvent age verification requirements, including by providing:

(a)instructions on how to use a virtual private network or proxy server to access the website; or

(b)means for individuals in this state to circumvent geofencing or blocking.

Lia Holland at Fight for the Future pointed out the absurdity of this in a statement, noting that the logic of the bill doesn’t even survive a basic reality check:

This is the sort of slop that if you asked the chatbot whether or not its previous statement was accurate, it would apologize profusely. Why? Because you cannot require a website doing age verification to determine where someone using a reputable VPN is browsing from—this feat is literally impossible by design for even the best hacker.

Such language and lack of logic begs the question—do Utah lawmakers actually understand what a VPN is? Let’s set the record straight: VPNs are an essential tool for online privacy, security, and liberty that everyone from abuse survivors to small businesses use to keep themselves safe. VPNs do this by totally hiding where a person is browsing the Internet from. Thus, when a person is using a VPN, the website they are browsing definitionally can’t tell whether or not they are in Utah.

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It’s fairly astounding the level of technological ignorance legislators will openly admit in their efforts to demand technology do the impossible. Insisting that VPNs need to be banned should be a disqualifier from holding public office.

EFF’s Rindala Alajaji notes that what Utah is demanding here is technologically incomprehensible:

Blocking all known VPN and proxy IP addresses is a technical whack-a-mole that likely no company can win. Providers add new IP addresses constantly, and no comprehensive blocklist exists. Complying with Utah’s requirements would require impossible technical feats.

The internet is built to, and will always, route around censorship. If Utah successfully hampers commercial VPN providers, motivated users will transition to non-commercial proxies, private tunnels through cloud services like AWS, or residential proxies that are virtually indistinguishable from standard home traffic. These workarounds will emerge within hours of the law taking effect. Meanwhile, the collateral damage will fall on businesses, journalists, and survivors of abuse who rely on commercial VPNs for essential data security.

Again, Fight for the Future explains the real impact of such a law:

Websites are left with three choices: either try to block everyone around the globe who’s using a VPN (which they can’t actually do), or require age verification for everybody in the world no matter if they’re in Utah, or censor all content that meets Utah’s nebulous “harmful to minors” standard for age verification.

Oh wait, there’s a fourth option: sue Utah.

Ignoring the law or suing the state appear to be the only rational responses.

Age verification already has a long list of well-known problems, many of which put users at risk. An effective ban on VPNs just makes it that much more dangerous for anyone in that state to use the internet. The fact that they’re doing all of this under the pretense of “protecting” children, when the actual impact will put everyone at greater risk, is just the icing on the cake — performative headline-chasing dressed up as policy.

Filed Under: age verification, location, sb 73, security, utah, vpns

Every LangChain pipeline your team hardcodes starts breaking the moment the query distribution shifts — and it always shifts. That bottleneck is what Sakana AI set out to eliminate.

Researchers at Sakana AI have introduced the “RL Conductor,” a small language model trained via reinforcement learning to automatically orchestrate a diverse pool of worker LLMs. Conductor dynamically analyzes inputs, distributes labor among workers, and coordinates among agents.

This automated coordination achieves state-of-the-art results on difficult reasoning and coding benchmarks, outperforming individual frontier models like GPT-5 and Claude Sonnet 4 as well as expensive human-designed multi-agent pipelines. It achieves this performance at a fraction of the cost and with fewer API calls than competitors. RL Conductor is the backbone of Fugu, Sakana AI’s commercial multi-agent orchestration service.

The limitations of manual agentic frameworks

Large language models have strong latent capabilities. But tapping these capabilities to their fullest is a great challenge. Extracting this level of performance relies heavily on manually designed agentic workflows, which serve as critical components in commercial AI products. 

However, these frameworks fall short because they are inherently rigid and constrained. In comments to VentureBeat, Yujin Tang, co-author of the paper, explained the exact breaking point of current systems: “While using frameworks with hard-coded pipelines like LangChain and Mixture-of-Agents can work well for specific use cases … In production, an inherent bottleneck arises when targeting domains with large user bases with very heterogeneous demands.” 

Tang noted that achieving “real-world generalization in such heterogeneous applications inherently necessitates going beyond human-hardcoded designs.”

Another bottleneck for building robust agentic systems is that no single model is optimal for all tasks. Different models are fine-tuned to specialize in distinct domains. One model might excel at scientific reasoning, while another is superior at code generation, mathematical logic, or high-level planning. 

Because models have these varying characteristics and complementary skills, manually predicting and hard-coding the ideal combination of models for every query is practically impossible. An optimal agentic framework should be able to analyze a problem and delegate subtasks to the most suitable expert in the pool.

Conducting an orchestra of agents

The RL Conductor is designed to overcome the limitations of rigid, human-designed frameworks. As the name implies, it conducts an orchestra of agents by dividing challenging problems, delegating targeted subtasks, and designing communication topologies for a set of worker LLMs. 

Instead of relying on fixed code or static routing, the Conductor orchestrates these models by generating a customized workflow. For each step in the workflow, the model generates a natural language instruction for a specific aspect of the task, assigns an agent to carry it out, and defines an “access list” that dictates which past subtasks and responses from other agents are included in that agent’s context.

By defining everything in natural language, the Conductor builds flexible workflows tailored to each input. It can construct simple sequential chains, parallel tree structures, or even recursive loops depending on the problem’s demands. 

Importantly, the model learns these strategies not by human design but through reinforcement learning (RL) and reward maximization. During training, the model is given a task, a pool of workers, and a reward signal based on whether its answer and output format are correct.

Through a simple trial-and-error RL algorithm, the model organically discovers which combinations of instructions and communication structures yield the highest reward. As a result, it automatically adopts advanced orchestration strategies such as targeted prompt engineering, iterative refinement, and meta-prompt optimization. 

The model learns to dynamically adjust its strategies and leverage the distinct strengths of its worker agents without any human developer having to hard-code the process.

Conductor in action

To test RL Conductor in action, the researchers fine-tuned the 7-billion parameter Qwen2.5-7B using the framework. During training, the Conductor was tasked with designing agentic workflows of up to five steps. It was given access to a worker pool containing seven different models: three closed-source giants (Gemini 2.5 Pro, Claude-Sonnet-4, and GPT-5) and four open-source models (including DeepSeek-R1-Distill-Qwen-32B, Gemma3-27B, and Qwen3-32B).

The team evaluated the Conductor across a variety of highly challenging benchmarks, comparing it against individual frontier models acting alone, self-reflection agents prompted iteratively to improve their own answers, and state-of-the-art multi-agent routing frameworks like MASRouter, Mixture-of-Agents (MoA), RouterDC, and Smoothie. The small 7B Conductor set new benchmarks across the board. It achieved an average score of 77.27% across all tasks, hitting 93.3% on the AIME25 math benchmark, 87.5% on GPQA-Diamond, and 83.93% on LiveCodeBench, according to the researchers.

Remarkably, it achieved these marks while remaining highly efficient. While baseline models like MoA burned through 11,203 tokens per question, the Conductor used an average of just 1,820 tokens, taking an average of only three steps per workflow.

A closer look at the experimental details shows exactly why the framework is so effective. The Conductor automatically learned to measure task difficulty. For simple factual recall questions, it often solved the problem in a single step or used a basic two-agent setup. However, for complex coding problems, it built extensive workflows involving up to four agents with dedicated planning, implementation, and verification phases.

The Conductor also learned that frontier models have different strengths. To achieve record scores on coding benchmarks, the Conductor frequently assigned Gemini 2.5 Pro and Claude Sonnet 4 to act as high-level planners, and only brought in GPT-5 at the very end to write the final optimized code. In a particularly clever display of adaptability, the Conductor would sometimes completely abdicate its own role, handing the entire planning process over to Gemini 2.5 Pro and allowing it to dictate the subtasks for the rest of the pool.

Beyond math and coding benchmarks, Sakana AI is already putting the underlying architecture to work in front-office utility. “We have been using our Fugu models based on the Conductor technology internally for various practical enterprise applications: software development, deep research, strategy development, and even visual tasks like slide generation,” Tang said.

Bringing orchestration to the enterprise: Sakana Fugu

While the 7B model described in the research paper was an exploratory blueprint and is not publicly available, Sakana AI has productized the Conductor framework into its flagship commercial AI product, Sakana Fugu. Now in its beta phase, Fugu serves as a multi-agent orchestration system accessible through a standard OpenAI-compatible API.

Tang noted Fugu targets “the large market of industries where AI adoption has yet to bring large productivity gains due to the generalization limitations of current hard-coded pipelines, such as finance and defense.”

For enterprise developers, this allows seamless integration into existing applications without the headache of managing multiple API keys or manually routing tasks across different vendors. Behind the API interface, Fugu automates complex collaboration topologies and role assignments across a pool of models. To support varying business needs, Sakana released two variants: Fugu Mini, built for low-latency operations, and Fugu Ultra, designed for maximum performance on demanding workloads.

Addressing governance concerns around autonomous agents spinning up invisible workflows, Tang pointed out that the interpretability risks are functionally similar to the hidden reasoning traces of current top-tier closed APIs, and the system is managed with established guardrails to minimize hallucinations. 

For enterprise architects weighing when to deploy RL-orchestration versus traditional routing, the decision often comes down to engineering resources. “We believe the absolute sweet spot comes whenever users and their teams feel they are spending a disproportionate amount of time guiding their underlying agents,” Tang said. However, he cautioned that the framework isn’t necessary for everything, noting that “it’s hard to beat the economic proposition of a local model running directly on the user’s machine for simple queries.”

As the diversity of specialized open- and closed-source AI models continues to grow, static hardcoded pipelines will inevitably become obsolete. Looking ahead, this dynamic orchestration will likely extend beyond text and code environments. “There is indeed a large potential to fill this gap with cross-modal Conductor frameworks becoming the foundation for more autonomous, self-coordinating physical AI systems,” Tang said.

• Panthalassa’s valuation now sits near $1 billion after fresh funding
• Peter Thiel led a $140 million investment round into the ocean tech company
• Investors see ocean energy as a vast, untapped computing resource

A US-based ocean technology company, Panthalassa, is advancing its plan to relocate data processing into open waters, backed by fresh funding that places its valuation near $1 billion.

The start-up has spent ten years developing wave energy technology and is now backed by PayPal co-founder and early Facebook investor Peter Thiel, who led a $140 million investment round into the company.

Kodiak AI’s stock tumbled 37% in after-hours trading Thursday after the self-driving truck startup disclosed it had raised $100 million by selling shares at a steep discount — a sign that investors were willing to back the company but not at its current market price.

The company sold shares at $6.50 each, well below its closing price of $9.10, according to a filing with the Securities and Exchange Commission (SEC). The raise also included warrants — instruments that give investors the right to buy additional shares later at a set price, in this case as low as $6.

The financing came from existing backer Ares Management and several unnamed institutional investors.

The influx of capital comes Kodiak pushes forward on the expensive task of scaling its self-driving trucks business, which covers off-road industrial settings and public highways, with the ultimate goal of eventually spending less than it earns. Kodiak reported revenue of $1.8 million in the first quarter, up from the $1.4 million it logged in same period a year prior. The company’s loss from operations was $37.8 million, twice what it reported in the same period last year.

Those numbers help explain why the discount terms rattled investors. The company is burning cash fast, and the raise — while sizable — does little to change that math in the near term.

Kodiak has made some recent progress on the business front, including a new commercial contract with Roehl Transport, a pilot program to test Kodiak-equipped autonomous trucks at West Fraser Timber Co.’s log-hauling operations in Alberta, Canada, and a collaboration with the military vehicle maker General Dynamics Land Systems to create autonomous ground vehicles for defense applications.

Under the deal with Roehl, which was also announced Thursday, Kodiak-equipped trucks will autonomously haul freight between Dallas and Houston on four round trips per week. The trucks operate autonomously on the entirety of the trip, but Kodiak keeps a human safety operator behind the wheel as a precaution.

Kodiak founder and CEO Don Burnette said the company is on track to move to driverless trucking on public highways later this year as it ramps up operations.

“We have tons of over-the-road long haul initiatives, and bringing on new partners continues to show momentum,” he said in an interview. “We’re excited about the progress that we’re making as we march toward our driverless launch later this year.”

For now, Kodiak owns the trucks, provides the safety driver, and carries the freight for Roehl along with its other existing on-highway customers, which include Werner, J.B. Hunt, Bridgestone, Martin Brower, and C.R. England. But that arrangement will change once it goes to driverless trucking operations.

“Our intention is to not own the trucks at that point [but to] operate our driver-as-a-service model, where [customers] own and operate the trucks,” Burnette said. He added that this is the system it uses with its off-highway customer Atlas for its driverless deployment in the Permian Basin of Texas.

While Kodiak plans to pull the safety driver by the end of 2026, Burnette said it won’t start driverless operations on public highways until it has finished validating the technology.

“It’s already operating under all of the conditions that we expect to launch driverless, but there’s a lot of validation work that we need to do, and that’s where we bring in our autonomy readiness measure,” Burnette said, describing the initiative — released Thursday — as a zero-to-100 score tracking how much of Kodiak’s internal safety validation is complete. As of April, Kodiak was at 86%, Burnette said.

The company, which was previously called Kodiak Robotics, went public in September via a merger with special-purpose acquisition company Ares Acquisition Corporation II, an affiliate of Ares Management. The deal valued the startup at about $2.5 billion. 

At the time, Kodiak raised $275 million in financing. More than $212.5 million came from certain institutional investors, including $145 million in PIPE funding (Private Investment in Public Equity, a method by which investors purchase shares directly from a public company) and about $62.9 million in trust cash from Ares. That trust cash shrank from its initial $562 million as some SPAC investors redeemed their shares — a standard provision that lets SPAC investors recover their money before a merger closes.

Earlier this year, Toyota revealed its first three-row electric SUV in the Highlander EV. Now, it’s Lexus’ turn to put its spin on this segment with the upcoming TZ, which boasts a more luxurious design, seating for up to six and a top range of around 300 miles.

Like its cousins the Highlander EV and Subaru Getaway, the TZ is based on Toyota’s e-TNGA platform and will be available with two battery sizes (76.9kWh or 95.8kWh) and an upgraded Direct4 AWD system. While Lexus has yet to provide specific info about power, based on the output available from other models sharing this platform, we’re expecting around 400 horsepower (or more) depending on the exact configuration. It’s a similar situation when it comes to range, because while we’re still waiting on an official figure from the EPA, Lexus estimates a TZ with the larger 95kWh power pack will go for around 300 miles between charges. 

Meanwhile, at 200.8 inches, the TZ is actually slightly longer than the Highlander EV, while sporting a similarly brawny exterior with lots of hard lines along and Lexus’ signature spindle-shaped grille. Other features include Dynamic Rear Steering (up to four degrees) that should provide better maneuverability at low speeds and increased stability at high speeds. Unfortunately, the TZ’s 400-volt architecture doesn’t look very impressive, with charging speeds that top out at just 150kW that should deliver 10 to 80 percent charging times of around 35 minutes. Thankfully, the car does come with a native NACS port and, for times when you need to charge your other gadgets, Lexus is making a dedicated accessory adapter that plugs into an AC inlet in the cargo area.

On the inside, the TZ’s infotainment is centered around a 14-inch main display with a secondary 12.3-inch digital instrument cluster for the driver. Lexus says the TZ will also support a Smart Digital Key+ that allows you to unlock the car with your phone or smartwatch, and will continue to work even if the gadget runs out of battery. Also, aside from the base infotainment system, the TZ supports both Android Auto and Apple CarPlay.

Lexus

The TZ’s platform and exterior are quite similar to the Highlander EV and Subaru Getaway, so Lexus seems to have really leaned into the EV’s interior as a way to distinguish itself from its rivals. The company claims the TZ has the quietest cabin of any of its SUVs (both EV and ICE) and that quest for muted peace and relaxation seems to have been a core design goal for the vehicle, as Lexus uses the word quiet eight separate times in its official press release. The TZ also features a number of sustainable materials scattered throughout the car including forged bamboo panels, a plant-based UltraSuede and recycled aluminum for components like its roof rails and tonneau cover frame.

Unfortunately, we’re still waiting for official info regarding the TZ’s pricing and availability, configurations and trim levels, which Lexus plans to release closer to the EV’s on sale date sometime later this year.

Samsung has started rolling out One UI 8.5, and it’s a pretty big one.

The update is landing on more than 40 Galaxy smartphones and tablets. It brings a refreshed interface and a new wave of AI features to devices that, in some cases, are years old.

The rollout began in Korea today, with other regions expected to follow from mid-May. As usual with Samsung updates, it won’t arrive everywhere at once. Even though the list of supported devices is already extensive, some users will still have to wait.

At the front of the queue are Samsung’s newest flagships, including the Galaxy S25, S25+, and S25 Ultra, along with the Galaxy S25 FE and S25 Edge. But the update doesn’t stop there; Samsung is also pushing One UI 8.5 to older generations like the Galaxy S24 and S23 series. This includes their Ultra and FE models.

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Foldables are included too, with support for the Galaxy Z Fold 5 through Fold 7 and Galaxy Z Flip 5 through Flip 7. Additionally, a wide range of tablets including the Galaxy Tab S9, S10, and S11 series will be supported. Even mid-range and entry-level devices like the Galaxy A05 are on the list.

That breadth is the main story here. Rather than limiting new software to premium devices, Samsung is once again pushing its latest One UI version across almost its entire ecosystem.

So what’s actually new? One UI 8.5 brings a visual refresh in parts of the interface, including updated menus and navigation elements. But the bigger focus is AI. Samsung is expanding tools like Photo Assist, which helps refine and adjust AI-generated images, alongside improvements to its Bixby assistant, which is becoming more context-aware and responsive.

It’s not a radical redesign, but it does continue Samsung’s steady shift toward AI-driven features across both hardware tiers and older devices.

As with most major Android updates, availability will vary depending on region and model. It may take months before every eligible Galaxy device receives it. Still, for a rollout that includes everything from flagship phones to budget A-series models, this is one of Samsung’s broader software updates in recent memory. In effect, it is a free upgrade sitting in the settings menu for millions of users.