A hot potato: A proposed Amazon data center in Texas has sparked concerns that its massive on-site power plant could become the single-largest source of pollution in the US, threatening to affect local communities, farmland, and small businesses. The facility is planned for Pecos County as part of Amazon’s growing push into AI and cloud services.

In a statement to The New York Times, an Amazon spokesperson said the data center will be powered entirely by an on-site power plant, ensuring it won’t raise electricity costs for local residents. The plant is expected to run on natural gas, with 35 turbines combining to generate up to 7.65 gigawatts of power.

The facility has regulatory approval to release up to 33 million tons of carbon dioxide per year, which would make it the largest single source of greenhouse gas emissions in the US – more than any other factory or power plant nationwide.

(Not Amazon’s data center) – Satellite imagery shows OpenAI’s Stargate data center transforming the landscape near Abilene, TX. The 1,100-acre campus spans roughly 4 million square feet and is still growing. Credit: Planet Labs PBC

Critics say the permitted emissions level is far too high, noting that the projected output would rival the total emissions of entire countries, including Switzerland, Ireland, and Bulgaria.

Notably, Amazon co-founded The Climate Pledge in 2019, promising to reach net-zero carbon emissions by 2040. Since then, though, its emissions have climbed sharply: the company’s emissions tied to purchased electricity rose 34% in 2025 alone.

Stargate’s gas turbines are permitted to emit over 1.6 million tons of greenhouse gases and 1,000 tons of air pollutants annually, approved without public input. Credit: Evan Simon / Floodlight, via The Texas Tribune

Asked about the pledge, Amazon spokeswoman Margaret Callahan told the Times that “the world looks different now than when we co-founded the climate pledge,” but added that “our commitment [to a carbon-neutral future] hasn’t changed.”

The Pecos County plant falls under the so-called “Ratepayer Protection Pledge,” which requires tech companies to source their own power for new AI data centers rather than drawing from existing grids.

The pledge was signed last March at the insistence of President Trump by several leading American tech companies, including Google, Microsoft, Meta, Amazon, Oracle, OpenAI, and xAI.

US investment in data center infrastructure hit record levels last year, and as major tech companies continue pouring money into AI, demand for electricity is expected to keep climbing sharply in the years ahead.

Across 107 enterprises, agentic orchestration is not a choice of a single platform.

The typical enterprise runs three orchestration platforms at once, and selects them for flexibility across models rather than affinity to any single one. Microsoft leads primary usage while Anthropic leads forward consideration by a wide margin. 

The AI control plane enterprises expect is deliberately hybrid, meaning it includes use of the leading AI providers, but also provider-independent technologies — and the risk they fear most from provider-resident control is not lock-in but the provider’s own security and permissioning limits. 

One in five enterprises still has no real-time way to stop a runaway agent before the bill arrives.

This wave of VentureBeat Pulse Research examines enterprise agent orchestration: which platforms enterprises run on, what drives the choice, what they optimize for, how they expect agent control to be structured, and — most revealingly — how orchestrated their deployed “agents” actually are and how tightly they control the cost of running them.

The central finding is that orchestration has become plural. Eighty-five percent of enterprises run two or more orchestration platforms and 64% run three or more, with a mean of 3.1 platforms per organization. Microsoft AI Foundry / Copilot Studio appears in 70% of stacks and OpenAI’s Agents SDK in 68%, with Anthropic’s Claude Platform in 47%. Asked to name a single primary platform, respondents who gave one unambiguous answer put Microsoft first (41%) and Anthropic second (28%). Nobody in this sample is running one orchestration layer and calling it a strategy.

The selection logic follows from that plurality. Flexibility across models and tools is the leading purchase driver at 29%, nearly three times the share naming model gravity — native alignment with a state-of-the-art base model — at 10%. Enterprises are not choosing the orchestration environment that comes with their favorite model; they are choosing the one that does not commit them to any model. Security and permissions (17%), production reliability (15%), and control over agent execution (15%) fill out a buying logic focused on governance and optionality rather than developer convenience.

A clear majority (53%) expect a hybrid control plane by the end of 2026 — provider-native plus external orchestration — and the risk they most associate with provider-resident control is security and permissioning limitations (37%), ahead of vendor lock-in (23%) and limited visibility (22%). Investment has moved accordingly: agent monitoring and debugging leads the spend at 31%, with security and permissions enforcement at 30%, while workflow tooling draws 19%. Enterprises are spending to see and govern agents, not merely to build them.

Most companies admit that a majority of their “agents” are really just chatbots. A plurality of 47% of respondents say that between 26 and 50% of their agents are genuinely orchestrated, with 37% at a quarter or below and 16% past the halfway mark. 

But fiscal control remains the soft spot: 21% of enterprises track agent spend only through post-hoc logs, with no real-time way to halt a runaway execution loop.

Methodology

VentureBeat fielded this survey as part of its ongoing Pulse Research series, with this instrument focused on enterprise agent orchestration. Responses are filtered to organizations with 100 or more employees (n=107), drawn from a single July 2026 wave; because this is one wave rather than a pooled multi-month sample, the report reads cross-sectionally and does not infer month-over-month trends. All figures in this report come from the July fielding only. Where questions were multiple-select, shares can sum to more than 100%.

This wave draws a notably large-enterprise, technology-heavy sample, and that shapes every finding in it. By organization size, more than half sit at 10,000 employees or above: 50,000+ (26%) and 10,000–49,999 (25%) lead, followed by 2,500–9,999 and 500–2,499 (19% each) and 100–499 (11%). Technology/Software accounts for 53% of respondents, with Government/Public Sector (16%) and Manufacturing/Industrial (10%) next. By role the sample is hands-on and technical: software and ML engineers (22%), product and program managers (21%), directors of data/AI/analytics (17%), and VPs of data/AI/analytics (12%). On purchasing, 90% are recommenders, influencers, or final decision-makers for AI solutions (63% recommender/influencer, 27% final decision-maker).

A note on the primary-platform question. Forty-six of 107 respondents registered more than one selection on a question intended to capture a single primary platform. Because those responses cannot be resolved to one answer, primary-platform shares are reported on the 61 respondents who gave a single unambiguous answer, and are labeled as such wherever they appear. Platform footprint figures — which platforms an enterprise uses at all — use the full n=107 base and are unaffected. The ambiguity is worth noting on its own terms: on a question asking for one platform, more than four in 10 respondents could not or would not narrow to one, which is consistent with the multi-platform pattern documented in Finding 1.

At 107 respondents the sample is robust enough to read directionally with reasonable confidence, though it remains self-selected and is not a probability sample. Because each subgroup here only includes about 50 to 60 respondents, splits between them are less precise than the full-sample findings.

Finding 1: Orchestration is a portfolio, not a platform

The typical enterprise runs three orchestration platforms at once

We asked which agent orchestration platforms enterprises use, and which one they treat as primary. The first answer is that almost nobody has just one.

The defining feature of this layer is plurality. Only 15% of enterprises run fewer than two orchestration platforms; the median organization runs three, and one in six runs five or more. Read that way, the platform “shares” below describe overlapping deployments rather than a divided market — Microsoft and OpenAI each appear in roughly seven of ten stacks precisely because most stacks have room for several.

Asked to name one primary platform, the 61 respondents who gave a single unambiguous answer put Microsoft AI Foundry / Copilot Studio first at 41%, Anthropic’s Claude Platform second at 28%, LangChain / LangGraph at 10%, and OpenAI’s Agents SDK at 7%, with Google, Amazon, Salesforce, and custom in-house builds at 3% each. Microsoft’s lead on primary usage alongside OpenAI’s near-equal footprint on any usage is the signature of an enterprise-weighted sample: the Microsoft platform arrives through an existing enterprise agreement and becomes the default seat of record, while other platforms are added around it for specific work.

A note on reading these shares: As described in the methodology section, the respondents are self-selected, this wave skews heavily toward large technology organizations, and the primary-platform figures rest on a 61-respondent subset. The numbers measure where this cohort has placed its orchestration bets today, within a self-selected audience of AI-active technical practitioners. A sample built this way can diverge substantially from spend-weighted market measures, and each VB Pulse survey draws its own sample with its own company-size and industry mix, so vendor figures should not be compared across our surveys, either.

Respondents rate the platforms they run at 4.17 out of 5 for overall satisfaction, 3.91 for ease of implementation, and 3.63 for value for money — with value for money the weakest of the three by a clear margin. That ordering is itself a finding: enterprises are broadly happy with what these platforms do and distinctly less happy with what they cost, which is the same nerve the fiscal-control finding touches at the end of this report. Satisfaction sits alongside a two-thirds intent to change platforms within the year; this remains a layer enterprises work with rather than settle on.

Finding 2: Flexibility, not model gravity, drives selection

Enterprises buy the orchestration layer that doesn’t commit them

We asked what most influenced the orchestration platform choice, and optionality leads by a distance.

Flexibility across models and tools (29%) is the selection-side explanation for the multi-platform reality in Finding 1: enterprises are choosing orchestration environments on the strength of what they leave open rather than what they lock in. Model gravity — picking the orchestration layer that comes with a preferred frontier model — draws just 10%, less than a third of the flexibility share, which places the pull of any single base model well down the list of what actually decides this purchase.

The next tier reinforces the governance emphasis. Security and permissions (17%), production reliability (15%), and control over agent execution (15%) together account for 47% of responses: nearly half of enterprises pick their orchestration platform on whether they can constrain and depend on what it runs. Ease of development draws 8% and total cost of ownership 4%, an inversion of how these platforms are usually discussed in engineering circles. Performance sits last at 2% — at this stage of adoption the binding constraints are optionality and control, not raw speed.

Finding 3: The job is reliable multi-step execution

Enterprises judge orchestration by whether it completes the work

We asked what enterprises optimize for — their primary success metric for orchestration. Reliability and multi-step workflow management lead, with developer productivity closer behind than in the buying criteria.

Task completion reliability (30%) and multi-step workflow management (27%) together account for 57% of responses: orchestration succeeds, in the enterprise view, when it reliably carries a task through multiple steps to completion. Developer productivity takes a substantial 23% — notably higher than ease of development’s 8% as a purchase driver in Finding 2, which suggests enterprises do not expect to buy developer velocity so much as to earn it once the platform is in place. End-user experience is a minor concern at 7%, consistent with orchestration being an internal execution problem rather than a UX one.

This reliability-first standard is the yardstick against which the portfolio-maturity finding later in this report should be read: enterprises define success as dependable multi-step execution, and a little over a third of them still say a quarter or fewer of their deployed agents do multi-step work at all.

Finding 4: Two-thirds plan to move — and Anthropic leads the consideration set

The installed base and the pipeline point to different vendors

We asked whether enterprises plan to adopt a new, additional, or replacement orchestration platform in the next 12 months, and which platforms they are considering.

Two-thirds of enterprises (67%) intend to adopt a new, additional, or replacement orchestration platform within the year, but the clock runs longer than the intent suggests: the largest cohort sits at 6–12 months (28%) and only 15% expect to move within a quarter. This is deliberate re-platforming on a planning horizon, not urgent churn.

The consideration set is where this finding earns its headline. Among the 72 enterprises in motion, Anthropic leads at 43% — well ahead of Google (31%), custom in-house builds (31%), OpenAI (25%), LangChain / LangGraph (17%), and Microsoft (17%). Set that against Finding 1, where Microsoft leads primary usage and appears in 70% of stacks: the installed base and the forward pipeline point at different vendors. Anthropic draws roughly two and a half times Microsoft’s forward consideration despite trailing it on current primary usage, and custom in-house control planes draw as much interest as any external platform besides Anthropic. A further 18% of movers are evaluating with no shortlist at all.

Read alongside the flexibility-first selection logic in Finding 2, the shape of the next twelve months is legible: enterprises expect to add rather than replace, they are shopping for platforms that preserve model choice, and a substantial minority intend to solve the problem themselves rather than buy it.

Finding 5: Investment flows to watching and governing agents

Monitoring and permissions lead the spend; workflow tooling trails

We asked which orchestration-related investment will grow most next year. Observability and governance take the top two places.

Monitoring and debugging (31%) and security and permissions enforcement (30%) are effectively tied at the top and together account for 61% of planned growth. The money is going to seeing what agents do and constraining what they are allowed to do — the two capabilities that matter once agents are running in production rather than being built toward it. Workflow tooling (19%) and scaling infrastructure (18%) trail, and almost no one is standing still: just 3% report a flat budget.

The emphasis is consistent with the buying logic in Finding 2, where security and permissions was the second-ranked selection factor, and with the control-plane architecture in Finding 6. Enterprises that have decided to run agents across three platforms have a visibility and permissioning problem by construction, and they are funding it directly.

Finding 6: The control plane will be hybrid — and security is why

Enterprises split control, and fear the provider’s permissioning more than lock-in

We asked where enterprises expect the primary control plane for agents to live by the end of 2026, and what worries them most if that control sits inside a model-provider platform.

Hybrid control is the dominant expectation by a wide margin (53%). Taken together, the hybrid, custom in-house, and externally-abstracted options — every architecture that keeps control at least partly outside the provider — sum to 78% of enterprises, against 14% willing to hand control to a provider-managed service outright.

The reason enterprises give is worth separating from the one usually assumed. Security and permissioning limitations lead the risk question at 37%, well ahead of vendor lock-in at 23%, with limited visibility and observability close behind at 22%. Combining the security and visibility answers, 59% of enterprises name a control-and-oversight concern rather than a commercial one. The worry is less that a provider platform will be hard to leave than that it will not let them see or constrain what their agents are doing while they are on it — the same concern funding the monitoring and permissions spend in Finding 5. Only 2% say provider-resident control is not a concern at all.

Finding 7: The chatbot trap is loosening, not broken

“Bridging the gap” is now the modal answer on portfolio maturity

We asked enterprises to assess their portfolios honestly: What share of their deployed “agents” are true multi-step orchestrated workflows versus simple single-prompt chatbot wrappers.

The center of gravity has moved into the middle band. Just under half of enterprises (47%) now put between a quarter and half of their portfolio in genuinely orchestrated, stateful workflows, and 16% are past the halfway mark. The bottom two bands — a quarter or fewer genuinely orchestrated — account for 37%, and outright pure-chatbot portfolios have nearly vanished at 3%. Against the reliability-first success standard in Finding 3, this is a portfolio that has started to do the work the orchestration layer exists for, without most of it being there yet.

Maturity tracks platform count. Enterprises reporting a quarter or less genuine orchestration run 2.8 platforms on average; those in the 26–50% band run 3.5. The organizations furthest into real multi-step work are the ones running the most orchestration platforms at once, which is the practical case for the flexibility-first selection logic in Finding 2 — multi-step portfolios appear to accumulate platforms rather than converge on one.

One split that might be expected does not appear. Organization size makes no difference to portfolio maturity in this wave: 38% of enterprises at 10,000+ employees report a quarter or less genuine orchestration, against 37% of smaller ones, and the shares past the halfway mark are equally close (16% and 15%). Whatever separates the mature portfolios from the immature ones here, it is not headcount.

Finding 8: Fiscal control is still reactive for one in five

A fifth of enterprises learn about a runaway agent from the logs

Finally, we asked how enterprises enforce fiscal control over agent token consumption — the risk that an autonomous loop exhausts a budget before anyone intervenes. The approaches split four ways, fairly evenly.

One in five enterprises (21%) has no real-time, programmatic way to stop an agent before a budget-breaking bill arrives — they learn of it from the logs afterward. Another 30% lean entirely on the native caps and throttles built into their primary platform, a control only as good as the provider’s tooling and one that sits awkwardly beside the hybrid, keep-control-outside posture of Finding 6. Roughly half of enterprises — those building custom gateways (25%) or exploiting cross-model routing to arbitrage cost (24%) — are treating token burn as an engineering problem to be controlled deterministically, and the routing group is doing so in a way that only works because they run several platforms at once.

Unlike previous waves, no size split appears here: 18% of enterprises at 10,000+ employees exercise only reactive control against 23% of smaller ones, a difference well within sample noise. The gap in fiscal control in this wave is not between large and small enterprises but between those that have built a cost-control plane and those still relying on whatever their provider ships. Read against the satisfaction scores in Finding 1 — where value for money was the weakest of three ratings at 3.63 — the picture is of a cohort that is unhappy about what agents cost and, in half of cases, not yet instrumented to do much about it.

The bottom line: Plural by design, governed by intention, metered by hope

Organizations with 100 or more employees describe an orchestration strategy built around optionality rather than commitment. They run three platforms on average, choose them for flexibility across models rather than affinity to any one, and judge them on whether they carry multi-step work reliably to completion. Microsoft anchors the installed base and appears in seven of ten stacks; Anthropic leads forward consideration by a wide margin among the two-thirds planning a change; and a substantial minority intend to build their own control plane rather than buy one. Today’s footprint describes where these enterprises are, and clearly does not describe where they intend to stay.

The governance posture is deliberate and consistent. A hybrid control plane is the majority expectation, 78% intend to keep control at least partly outside the provider, and the reason is not commercial but operational — security and permissioning limits (37%) and limited visibility (22%) outrank vendor lock-in (23%) as the fear attached to provider-resident control. The budget follows the fear: monitoring and debugging and security and permissions enforcement together take 61% of planned investment growth, ahead of the tooling used to build agents in the first place.

Where the strategy thins out is cost. Portfolio maturity has moved into the middle — 47% now report between a quarter and half of their agents genuinely orchestrated, and pure-chatbot portfolios have nearly disappeared — but 21% still cannot stop a runaway agent in real time, another 30% depend on whatever caps their provider ships, and value for money is the lowest-rated attribute of the platforms they run. Enterprises have worked out how they want agents governed well before they have worked out how to meter them.

At 107 respondents in a single July wave, skewed toward large technology organizations, this reads as a clear directional signal rather than a precise measurement. The questions for subsequent waves are whether the middle band of portfolio maturity keeps climbing, whether the forward consideration for Anthropic and for in-house control planes converts into deployment, and whether fiscal control catches up to a cost that enterprises already say they are not getting their money’s worth on.

Based on survey responses from 107 qualified enterprise respondents (100+ employees), drawn from a single July 2026 wave. This is a self-selected sample rather than a probability sample, and figures should be read directionally rather than as precise measurement. Respondents include software/ML engineers, product/program managers, directors and VPs of data/AI/analytics, enterprise architects, and directors of engineering/IT, across technology/software, government/public sector, manufacturing/industrial, and financial services organizations.

French media have asked the country’s competition authority to rein in Google’s use of artificial intelligence, filing a complaint that seeks to put a price on the AI-generated summaries quietly siphoning readers away from the outlets that produced the underlying journalism.

The grievance, lodged by a trade association representing publishers, lands as the fight over AI Overviews and collapsing referral traffic spreads across the industry.

Google’s AI Overviews answer a user’s question directly at the top of the results page, stitched together from articles the search engine has crawled, so readers get the gist without ever clicking through to the publisher, who is left with neither the visit nor a fee for the content that made the answer possible.

The publishers are not asking the watchdog to invent a remedy from scratch. Instead they want the Autorité de la concurrence to mirror a decision it took in July against Meta, which required the company to draw up a payment plan and return to negotiations with traditional media pressing for compensation.

Applied to Google, that would mean forcing the search giant to resume talks it has shown little appetite for and to develop a concrete scheme for paying the newsrooms whose work feeds its models.

Google had no immediate comment on the filing, which is roughly the response the industry has come to expect whenever money and machine-generated summaries are mentioned in the same breath.

France is not a random venue for this fight, and that is rather the point. The country has spent years pursuing Google over “neighbouring rights”, the branch of copyright that governs how news snippets are reused, and it has fined the company before when the two sides failed to agree on terms, which is why its regulators carry more scar tissue and more leverage than most.

That history gives the complaint a sharper edge than a similar move might have elsewhere. French officials have already established, in previous rounds, that Google must negotiate in good faith and pay for the journalism it surfaces.

Thus, the publishers are not arguing an abstract principle so much as asking the watchdog to extend a precedent it has enforced once already.

The timing is no accident either. Publishers across Europe and the United States have reported steep declines in the clicks that once flowed from search, because an AI summary that satisfies the reader on the results page removes any reason to visit the source, and the numbers have grown alarming enough to draw regulators in on both sides of the Atlantic.

Other jurisdictions are already moving. Britain has forced Google to let publishers opt out of AI search results without being punished in the ordinary rankings, an attempt to break the bind in which opting out of the summaries once meant vanishing from search altogether.

The courts are stirring too. A German court has found Google liable for its AI Overviews, a ruling that chips away at the company’s long-held argument that it merely organises information rather than republishing it, and one that European regulators will read closely.

Brussels, for its part, has kept up the pressure through the Digital Markets Act, and Google has already offered concessions on news-search ranking to head off a fresh fine, a sign that even modest regulatory threats can move the needle when the sums involved are large enough.

Whether the French complaint lands is another matter, since the Autorité de la concurrence will take its time, and Google will resist any obligation to pay for answers it insists send traffic the other way.

Yet the venue is well chosen, the precedent is fresh, and Europe’s publishers have decided that the quiet erosion of their audience is worth fighting over before the habit of never clicking through hardens into the way the web simply works.

from the spies-in-the-skies dept

Police departments across the country are lining up to launch drone-as-first-responder (DFR) programs, and hundreds have cleared a necessary hurdle toward making deployment a reality, expanding aerial surveillance and data collection even in areas patrol officers typically can’t reach.

As of February 2026, over 1,000 public safety agencies—including police, fire, and other emergency management agencies—had received Federal Aviation Administration (FAA) waivers needed to automate drone operations and launch a DFR program, according to a recent Freedom of Information Act (FOIA) release listing agencies that have obtained Part 91 waivers since the FAA streamlined and sped up the process in April 2025.

The changes led to a massive increase in the number of waivers issued. Only 976 DFR waivers had been granted since the first DFR program launched in 2018 through April 2025, according to an FAA representative. The agency issued more waivers between April 2025 and February 2026 than it had in the previous seven years combined.

The new FAA process for waivers and the rush of police departments to obtain them signifies a shift in law enforcement’s use of drones: from human-operated aerial surveillance to AI-based autonomous drone use. 

Typically, a drone operator is only permitted to fly in areas that can still be seen by the pilot, and that drone pilot needs to be certified under FAA Part 107. To fly drones “Beyond Visual Line of Sight” (BVLOS) requires additional approval from the FAA, as do flights above 200 feet, due to the risk of colliding with planes and other aircrafts. Without such approval, an officer could not pilot a drone from a desk inside a building and fly it to a call across the city because they could not possibly have line of sight on the drone. 

FAA rules for police drones also required a human operator to manually fly the device to a scene, but DFR technology has become a more common and more automated police technology. DFR programs increasingly rely on artificial intelligence to automate drone flights from launchpads placed around the city, often atop municipal buildings, and make it possible for one drone operator to “fly” multiple devices at once. Though not every police department that has received BVLOS has launched a DFR program yet, by going through this process, every department on this list has signified it has strong enough interest to clear the necessary regulatory hurdles.

Police departments and the companies that sell DFR equipment claim that these drones make it easier for officers to establish “situational awareness” of a scene before they arrive. Early drone adoption centered on similar claims, particularly related to high-risk situations like vehicular accidents or incidents involving an armed suspect. However, these kinds of situations may make up only a small portion of deployments, which often occur in response to low-risk calls for service related to unhoused people, mental health concerns, and loud music, as a Government Technology analysis of the system in Chula Vista, California, found. 

DFR programs have become important sources of revenue for companies like Flock Safety and Axon, the latter of which reported that its DFR platform has become one of the company’s fastest growing sectors. Axon is also known for products like the TASER and the Fusus camera system that lets police integrate viewing of public and private cameras. 

Footage from drone flights is streamed back to a police office, and it can be stored, shared, and analyzed like other video. Turning drone footage into fodder for automated license plate reader (ALPR) networks, for example, requires very little additional software, and Flock Safety was quietly able to turn its drones into “flying ALPRs” last year. 

The normalization of police DFR programs jeopardizes privacy in communities across the country. As flying cameras, drones can capture footage from areas typically inaccessible to a casual patrol officer—backyards, roofs, through windows—at distances that leave subjects of surveillance completely unaware of the spy in the sky. A recent leak of drone footage from the San Francisco Police Department illustrated the ease with which surreptitious drone flights could observe innocent individuals for minutes without them realizing it. EFF’s Atlas of Surveillance contains a list of police departments with drones, including those with DFR programs.

While daytime DFR use grows, police departments are exploring other ways to expand overhead surveillance. In October 2024, the Campbell Police Department in California announced it had received the first FAA approval for BVLOS operations at night, claiming it was the “first to incorporate radar technology with electro-optical sensors to enhance airspace monitoring, enabling a single remote pilot to safely deploy drones both day and night.”  

As communities consider drone use, it’s crucial that they have a say in whether the program is acquired at all, not just how it’s run once purchased. Throughout the process, police should be transparent with the community and comply with local regulations about its adoption.

Many cities provide portals that log the flight paths and reasons for each drone flight, often in real time, an important transparency practice. In California, under AB 481, police departments are required to provide advance notice of intent to acquire drones, establish policies before they’re procured, and provide annual updates on their uses—giving communities and city councils the opportunity, before any contract is signed, to weigh in or object to the acquisition itself. 

For police departments and communities considering drone use, clear policies on appropriate use, transparency around deployment, and regular re-evaluation—including the choice to discontinue a program that isn’t working—are all vital for protecting people’s privacy and security. 

Originally published to the EFF’s Deeplinks blog.

Filed Under: dfr, drone as first responder, drones, faa, police, police drones, surveillance drones

GIGABYTE has launched a new AMD Ryzen 9-powered variant of the AORUS MASTER 16 Gen 2 in India. The latest configuration pairs AMD’s Ryzen 9 processor with an NVIDIA GeForce RTX 5070 Ti Laptop GPU and GIGABYTE’s GiMATE AI companion. The laptop is aimed at gamers, creators, developers, and users working with demanding AI workloads. It also features a 16-inch OLED display and a cooling system designed to maintain performance during intensive workloads.

AORUS MASTER 16 Gen 2 Brings Ryzen 9 and RTX 5070 Ti Performance

The AORUS MASTER 16 Gen 2 is powered by an AMD Ryzen 9 processor and an NVIDIA GeForce RTX 5070 Ti Laptop GPU. The combination is designed to handle demanding workloads, including AAA gaming, AI applications, software development, 3D rendering, and content creation.

For visuals, the laptop features a 16-inch OLED display, which should be particularly useful for gaming and creative workloads where contrast and color reproduction matter. GIGABYTE has also equipped the laptop with an advanced thermal system to help manage heat during sustained workloads.

The laptop includes a range of connectivity options and an upgraded audio system, although GIGABYTE has not detailed the complete port and audio specifications in its announcement.

GiMATE Adds Smart AI Features to the Flagship Laptop

The new AORUS MASTER 16 Gen 2 also comes with GiMATE, GIGABYTE’s AI-powered companion and system control interface. GiMATE allows users to manage various system settings and performance modes from a centralized interface. The software can be used to adjust the laptop based on different scenarios, including gaming, productivity, creative workloads, and other demanding tasks. GIGABYTE says GiMATE is designed to learn user preferences and workflows, bringing more personalized controls to the laptop.

The AMD Ryzen 9-powered AORUS MASTER 16 Gen 2 is launching in India through GIGABYTE’s authorized retail network and leading online platforms. However, GIGABYTE has not yet announced the price of this configuration. The company is expected to share pricing and additional availability details separately.

Sony and Marvel are once again partnering up for a limited-edition PlayStation 5, DualSense Wireless Controller and console covers to build hype for the PS5-exclusive superhero game Wolverine, as the two companies did with the release of Spider-Man 2. This time, however, it’s going to be a bit more expensive to get these items than it was three years ago.

The Limited Edition Marvel’s Wolverine bundle and accessories showed up on the PlayStation Blog on Tuesday and will be available on Sept. 15, the same day as the release of the Wolverine game. The PS5 console, controller and console covers will all cost more than the current elevated price tag for PlayStation 5 hardware and accessories, but they’ll likely still sell out quickly.

The PS5 Digital Edition – Marvel’s Wolverine Battle Yellow Limited Edition Bundle comes in a distinctly Wolverine yellow. On the console itself is the face of the comic book hero and his iconic claw marks. The DualSense controller has the same color scheme and slashes, just inverted.

For those who want just a limited-edition controller, there will be one in battle yellow as well as the adamantium color, which is similar to silver.

PS5 console owners who don’t have the urge, or the funds, to drop almost $700 on a new console can buy the limited-edition console covers. Console covers are easy to install on a PS5 – they snap on and off the system. PS5 Slim owners will be limited to the battle yellow color, while PS5 Pro console owners have their pick between battle yellow and adamantium.

Sony

The prices for the console and accessories are:

• PlayStation 5 Digital Edition – Marvel’s Wolverine Battle Yellow Limited Edition Bundle: $650
• DualSense Wireless Controller – Marvel’s Wolverine Battle Yellow Limited Edition: $85
• DualSense Wireless Controller – Marvel’s Wolverine Adamantium Limited Edition: $85
• PlayStation 5 Console Covers – Battle Yellow Limited Edition: $75
• PlayStation 5 Pro Console Covers – Marvel’s Wolverine Battle Yellow Limited Edition: $75
• PlayStation 5 Pro Console Covers – Marvel’s Wolverine Adamantium Limited Edition: $75

Preorders for the limited-edition PS5, controllers and console covers will start on Aug. 19 at 10 a.m. local time, according to Sony. The limited-edition PS5 and battle yellow DualSense controller will be available at the Sony Direct Store and select retailers, which Sony has not yet detailed. The adamantium DualSense controller and console covers will only be available at the Sony Direct Store. These will likely sell out, and as with the Spider-Man 2 limited-edition items, there will be minimal restocks, if any.

Marvel’s Wolverine, developed by Insomniac Games, is a PS5 exclusive coming on Sept. 15 for $70.

Oscar Gonzalez

Contributing writer

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Oscar Gonzalez is a Texas native who covered video games, conspiracy theories, misinformation and cryptocurrency.

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Sonos chief executive Tom Conrad says model quality will converge and the advantage will sit with hardware that already knows a home. An FCC filing names the first product of that push, the Ace Ultra headphones, expected at a September launch event.

Sonos is betting that the value of AI in the home sits in the hardware rather than the model. “Much of the industry conversation about AI in the home is about who has the best model,” chief executive Tom Conrad told analysts. “We think that’s the wrong question.”

Access to strong models “is going to be everywhere, and the differences between them will narrow,” he said. What lasts instead, in his telling, is “the hardware that can converse with quality across every room, the system that already knows the shape of a home and the way a family lives in it.”

The company has some ground to make that argument from. Sonos counts 53 million connected devices across 17 million homes, and posted third-quarter revenue of $375mn, up 9%. Conrad said it has been “competing with the biggest of big tech for customers in the home for nearly a decade.”

The first hardware of that push arrives next month. A Federal Communications Commission filing published Monday, first reported by Lowpass, names the Sonos Ace Ultra, a sequel to the 2024 headphones, in as many as five colours against the original’s two. Technical details in the filing suggest it supports voice commands, which the first Ace did not.

That original landed badly. It shipped alongside a 2024 app rewrite that broke the platform, cut sales and forced Sonos to pause new hardware releases. The headphones were part of why the app went out early, having been built for the new software and left incompatible with the old.

Reviewers liked the industrial design but found little connection to the rest of the system beyond private TV listening through a soundbar. Buyers who expected to hand music from their speakers to their headphones on the way out did not get it. Conrad has since said shipping without that integration was a mistake.

The do-over falls to a thinner team. Sonos cut senior design and product staff in July, around 3% of the company, in a move Conrad framed as buying speed rather than saving money.

He has also pitched Sonos as a home for third-party AI models, though that has been slow going. Amazon’s Alexa+ is still absent from Sonos hardware nearly a year after the company appeared on a partner slide, while Amazon has moved on to putting Alexa in the search bar.

A marketing campaign under Colleen DeCourcy, who joined as chief marketing officer in January, lands this autumn. Conrad has called it “the clearest expression in a decade of what makes Sonos singular in the world.“

Enterprises running always-on AI agents keep hitting the same tradeoff. Send every task to a frontier model and the bill climbs fast. Build custom routing logic to send easy tasks to cheaper models and that becomes its own engineering project, one that has to be maintained every time a workflow changes.

Nvidia is proposing a fix that touches both ends of that problem at once. The company is out on Tuesday with Nemotron 3.5 Lightning, a 30-billion-parameter open mixture-of-experts model built for high-volume, specialized agent tasks, alongside NeMo Switchyard, an open-source library that routes each step of an agent workflow to whichever model fits it best.

The headline numbers: According to Nvidia, Lightning delivers up to 4x faster output than comparable models in its class, completing agentic tasks roughly 30% faster than Qwen3.6-35B at matching accuracy. Paired through Switchyard, Nvidia says the combination holds frontier-level task completion while cutting benchmark costs to roughly a third of running Opus 4.8 alone.

The timing puts Nvidia in the middle of the busiest open-weight stretch the industry has seen in months. Alibaba, Moonshot, Zhipu and DeepSeek have all shipped competitive open models out of China since the spring, several landing at or near frontier performance while undercutting US labs on size or price. Meta added to that pressure by releasing its own 30-billion-parameter open agentic model, Muse Glimmer. Open weights have gone from a differentiator to table stakes in a matter of months, and Nvidia’s release lands squarely inside that shift rather than ahead of it.

The pairing is the point. A model alone doesn’t solve the cost problem, and a router alone has nothing efficient to route to. Nvidia is betting that open source, applied at both the model layer and the routing layer, is what actually moves the cost needle on agentic AI, not a single cheaper model and not a smarter router bolted onto someone else’s stack.

Switchyard’s real rivals aren’t other open models — they’re Not Diamond, which already powers OpenRouter’s Auto mode, and RouteLLM, the open-source framework from UC Berkeley and LMSYS. Neither ships its own model. Nvidia’s bet is that owning both sides of the decision, under one open license, is what a router-only or model-only competitor can’t match.

“That is the power of a system of models, matching the right model to each step of the workflow,” Kari Briski, vice president of generative AI at Nvidia, said in a briefing.

How the router actually changes the workflow

Model routing isn’t a new category. OpenRouter, LiteLLM and a handful of standalone routing startups already let developers point traffic across multiple providers. Switchyard plugs into several of them rather than replacing them outright.

The core problem Switchyard solves is that the right model changes as an agent moves through a task. An agent’s state shifts as tools return results, errors show up, or a step turns out to be routine rather than complex, and a fixed model choice can’t adapt to any of that.

Briski described routing strategies that respond to that shifting state rather than a static task category.

“It has many types of routing strategies,” Briski said. “You can have a random router, which is not that great, or you can have an agent state route or a classifier route. Depending on your routing strategy, it wants to choose the best model. In some cases you want to go with a model like Lightning for really efficient tasks, and the router will actually choose Lightning if it’s set up in your pool of models.”

Cost enters the routing decision directly, not as an afterthought. In response to a question from VentureBeat, Briski said Switchyard can evaluate model verbosity, meaning how many tokens a given model tends to produce for a task, and use that prediction to steer work toward the cheaper option before the call is made.

The part that keeps this from becoming its own integration project is where Switchyard sits. Nvidia split its partners into two groups: agent frameworks that call Switchyard directly, including Cognition, LangChain and Nous Research, and LLM gateways that have built Switchyard support into their own products, including Kong, LiteLLM and OpenRouter. Kong ships Switchyard natively inside Kong AI Gateway. Briski pointed to that same list of gateway partners when describing how the library fits into the existing routing ecosystem.

“We are an ecosystem lover, and we want to make sure that we are integrated,” Briski said. “We’ve partnered with OpenRouter, LiteLLM and Kong, and they’ve already integrated our routing algorithm, so you can pick it up right where you’re already using the best tools.”

Nvidia shared results from nine companies testing Switchyard, several with specific figures attached. LangChain reported a 74% cost reduction across 145 multi-turn Deep Agents tasks by routing just 7% of calls to a frontier model, at a 6% accuracy tradeoff. Ramp said it matched a frontier model’s performance on Ramp SWE-Bench while cutting costs 58% and runtime 33%. Cognition integrated Switchyard’s staged router into Devin Desktop for internal use and reported near-frontier performance on FrontierCode Main while cutting mean cost 28% relative to routing everything to a single frontier model.

Lightning’s architecture and performance gains

Nemotron 3.5 Lightning is a standalone open model in its own right, built for high-volume, specialized agent tasks rather than general-purpose use.

It extends the hybrid Mamba-Transformer, latent mixture-of-experts architecture Nvidia introduced with the Nemotron 3 family in December 2025, the same line behind Nemotron 3 Super, which Nvidia uses as Lightning’s own baseline in its post-training comparisons. Positioned within a routing setup like Switchyard, it’s built to sit at the fast, cheap end of the decision rather than the frontier end, but it runs and ships independent of any router.

According to the Artificial Analysis Intelligence Index, a general capability benchmark spanning nine evaluations, Lightning scores 24, tied with gpt-oss-120b and behind Nemotron 3 Super, Gemma 4 31B, Claude 4.5 Haiku and Mistral Medium 3.5, all at 30. Lightning isn’t a general-intelligence leader in its size class, and Nvidia isn’t claiming it is.

The actual claim is narrower: according to PinchBench data supplied by Nvidia, Lightning matches Qwen3.6-35B’s accuracy roughly 30% faster and beats Gemma 4 26B’s accuracy at a similar completion time on PinchBench, a real-world agent task benchmark spanning coding, research and file management. That’s a speed-to-accuracy tradeoff, not a capability win.

Post-training is where Nvidia says the bigger gains show up. The company shared before-and-after figures from four early-access partners: CrowdStrike’s malicious-content recall against a Nemotron 3 Super baseline, CodeRabbit’s coding router against a GPT 5.4 Nano baseline, Harvey and Trajectory’s legal task completion against an Opus 4.6 baseline, and Lila Sciences’ energy simulation work against an Opus 4.8 baseline. CodeRabbit’s case is the most specific: Nvidia says the standard NeMo Auto model recipe, trained for one epoch, built into a working router agent for $85 in about two hours.

What this means for enterprises

There is no shortage of competitive offerings in the growing market for open models. The new Nemotron Lightning release will be yet another option for organizations to consider.

On the model side, Lightning’s own benchmark chart picks Qwen3.6-35B as its direct comparison point. Asked by VentureBeat directly how Lightning compares to Chinese models more broadly, Briski didn’t offer a head-to-head benchmark, pointing instead to openness and customizability as the differentiator.

“Our value proposition is not just open and it’s very customizable,” Briski said.

For enterprises building agentic infrastructure, three trends stand out:

The routing decision is becoming dynamic instead of static. Enterprises that built agent pipelines around a single default model are being pushed toward per-step routing based on live signals like agent state and token cost, not a fixed assignment set at design time.

Open source is now a cost lever at two layers, not one. Pairing an open model with an open router a vendor controls end to end is a newer argument than cheaper weights alone, and worth watching for whether other labs follow the same pattern.

The competitive question shifts from best model to best system. As routing libraries mature, the differentiator moves from which model an enterprise defaults to, toward how well its routing layer matches models to tasks in production, a harder thing to benchmark and a harder thing to market.