News
- Private drone companies can now request access to US Army testing ranges
- Companies no longer need existing government partnerships before requesting range access
- Five military facilities across America and Morocco are joining the programme
Private drone companies without existing government contracts can now request access to the US Army test ranges nationwide.
The arrangement removes a requirement for companies to have existing government partnerships before requesting access to military ranges.
Army Secretary Dan Driscoll framed the change as an effort to cut through bureaucratic obstacles facing smaller defense contractors.
What companies can access and whereFive ranges now fall under this new access system, spanning four US states and one overseas location.
Dugway Proving Ground in Utah specializes in long-range fires testing for companies developing precision strike capabilities, whilst West Cibola Range in Arizona currently focuses exclusively on drone and counter-drone system evaluations.
Camp Shelby in Mississippi and Camp Grayling in Michigan round out the domestic testing locations available.
The fifth site, the Africa Multidomain Training and Experimentation Center, sits in Morocco.
Beginning in September 2026, Camp Grayling will host a recurring test simulating degraded electromagnetic conditions found in Ukraine.
This quarterly event will let companies trial drone and counter-drone systems against jamming and disrupted signals.
To get access to any of these sites, companies will have to apply through testrange.army.mil, though the Army cannot guarantee a specific site or timeline.
"A company with a good idea shouldn't need a team of lawyers and a program of record just to prove their equipment works," said Dan Driscoll, Army Secretary.
"So we fixed that. One front door — testrange.army.mil — a real person to walk you in, and on the other side, everything the modern battlefield demands: contested airspace, degraded signals and the space to test at real scale."
The pressure driving this shiftThe expanded access reflects urgency inside the Pentagon around accelerating drone and counter-drone development timelines.
The Defense Department recently formed Joint Interagency Task Force 401, a unit built to streamline counter-drone procurement.
That task force has already stood up an online marketplace connecting military buyers directly with technology suppliers.
Behind this push sits a separate concern, with reports suggesting depleted Patriot missile interceptor stockpiles nationwide.
The United States reportedly used roughly two-thirds of its Patriot interceptor inventory during its recent conflict with Iran, as Washington appears to have underestimated how quickly a prolonged confrontation with Iran could consume its most valuable air-defense interceptors.
However, Defense Secretary Pete Hegseth disputed a CNN report claiming military commanders had flagged critically low interceptor stockpiles.
Regardless of that disagreement, the Pentagon continues pressing defense contractors toward faster production of essential weapons systems, and the range initiative sits within a broader goal of hastening development for drones, counter-drone tools, and interceptors.
Leading this effort is the US Army Test and Evaluation Command, working alongside several partner organizations nationwide.
Those partners include the Mississippi and Michigan National Guard units, along with U.S. Africa Command's regional support.
For companies previously locked out by lengthy contracting requirements, this shift represents a meaningfully lower barrier to entry.
Via Defense News
Samsung has had a busy few weeks with the launch of its 2026 foldable and wearable ranges, with pre-orders just wrapping up last week. If you missed out on the pre-order deals or you have your eyes set on something else from the brand, then fret not — Samsung has just unveiled a slew of deals across its product portfolio.
The latest Samsung Secret Sale event features lots of top-rated tech that we’ve reviewed here at TechRadar, with savings of up AU$1,200 or 50% with the code SECRETAUG — but it’s only running for 48 hours, with deals ending at 10am AEST on Wednesday, August 19.
One standout deal is the excellent Samsung Galaxy Watch 8 with a 50% discount, bringing it down to just AU$374.50 for the 40mm LTE model. Given that the new Watch 9 has relatively modest upgrades for AU$699 (with a current deal), this offer is hard to pass up.
Galaxy Watch 8 (40mm, LTE): was $749 now $374.50
Our tester gave the Galaxy Watch 8 a glowing review for its slim design, built-in running coach and advanced health, sleep and AI features. The new Watch 9 has a similar look with relatively modest upgrades (new processor and larger battery), so this deal is a winner if you’re after an LTE-enabled Android smartwatch.View Deal
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Our friends at What Hi-Fi called the Music Studio 7 “a near-perfect product in a perhaps perfect form”, thanks to its Dolby Atmos support, big stereo sound and wealth of streaming options. This deal also makes it especially tempting for those looking to buy two for a stereo setup, which the review recommends for a full music and movie system.View Deal
Galaxy Tab S10 FE (128GB, Wi-Fi): was $1099 now $649
We’re fans of the mid-range FE-series Galaxy Tab slates, with the S10 FE+ getting praise from our reviewer for its excellent display, premium build and IP68 rating — we’ve previously considered it to be one of the best Android tablets. If you’re keen on a smaller size and want 5G connectivity, Samsung has also discounted the S10 FE to AU$749.40 or 40% off.View Deal
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The Samsung Galaxy S26 Ultra was named the best Android phone ever by our reviewer, praising its design, powerful hardware, AI features, excellent cameras and the new Privacy Display. This deal is only available to the online exclusive colourways Silver Shadow and Pink Gold. Also discounted are the 512GB (AU$1,749) and 1TB (AU$2,064) models.View Deal
S95H OLED TV: was $3999 now $2799
Samsung’s flagship OLED TV received a perfect score in our review, with praise for its brightness, colour, gaming features and performance. At this price, it’s still an investment — and note it’s ‘only’ a 55-inch model — but given it’s rare to see savings on Samsung TVs, this is one to consider.View Deal
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Lanterns has finally premiered on HBO Max — and, in a move reminiscent of Invincible's first-ever episode on Prime Video, the new DC Universe (DCU) TV show wastes no time in delivering an almighty shock in its first chapter.
Indeed, the sci-fi show's premiere, titled 'Pilot', ends with a moment that'll completely stun viewers and likely upset DC comic book fanatics.
I'm about to dig into the biggest talking point from Lanterns' debut episode, so consider this your one and only warning: full spoilers immediately follow after the poll below. Turn back now if you haven't seen 'Pilot' yet, or else.
Is Kyle Chandler's Hal Jordan really dead in Lanterns?Green Lantern fans and DC purists are going to have a lot to say about this... (Image credit: John Johnson/HBO Max)It certainly seems that way. Of course, this could be a massive fake out — after all, there are all manner of shapeshifting alien species that can impersonate humans in DC Comics, so it's possible that this is the case here. Nonetheless, I believe that Chandler's Jordan has taken his last breath in the DCU.
Okay, but how did we get here? For starters, this season's premiere wasn't shy about teasing Jordan's demise. Indeed, minutes before his death, which takes place during the show's 2026 storyline, is shown, Jordan, Aaron Pierre's John Stewart, and Kelly Macdonald's Sheriff Kerry Kane survive an attack from a suicidal bomber at Rushville's police station.
This incident, which happens as part of Lanterns' 2016 storyline, is caused by an alien masquerading as a human truck driver called Waylon Sanders. As the unidentified extraterrestrial reveals, their skeleton is retrofitted with a biometric neutron device that they can activate with a single thought.
Hal Jordan interrogates Waylon Sanders, who the former correctly identifies as an alien (Image credit: John Johnson/HBO Max)Prior to detonating the explosive gadget, Sanders goads Jordan over the latter's fearless nature, which Sanders interprets as Jordan wanting someone or something to kill him. In that respect, then, the DCU Chapter One series telegraphs his death before it happens.
Rather than bump off Jordan in 2016, though, Lanterns withholds his passing until a decade later. Indeed, Jordan uses his power ring to form a protective bubble around Sanders just before the assailant detonates his skeletal device, thereby restricting the blast radius and saving the lives of everyone nearby.
But, that's not the end of the story. Five minutes before 'Pilot' ends, we skip ahead to 2026, which reteams us with Stewart as he heads to the same college football field that he and Jordan first met Sheriff Kane, and where their 2016 investigation into the deaths of four football fans began.
There, Stewart reunites with Kane and, after a bit of small talk about Kane's now-teenage son Noah, they head to a section of the bleacher seating where Jordan's snow-covered corpse is eventually revealed to be sitting.
Who killed Hal Jordan in Lanterns episode 1? Assessing the most likely candidatesAnybody else react like this when they saw Hal Jordan's dead body? (Image credit: John Johnson/HBO Max)Alright, so who murdered Earth's first-ever Green Lantern? We don't know, but we can speculate on who pulled the trigger.
The first two — and arguably most likely candidates — are Sheriff Kane and the stadium's groundskeeper. Kane tells Stewart that the latter is the only other individual who currently knows that Jordan is dead. However, it's incredibly unlikely that the groundskeeper will be a prominent character moving forward, so we can rule them out.
We can do likewise with Kane. Sure, she's got a firearm to hand at all times, but I just don't see her wanting to draw heat over Jordan's death, and the potential ire of Stewart, the Green Lantern Corps, and/or the Guardians of the Universe.
Did Jordan's murderer steal his ring, too? (Image credit: HBO Max)So, who else could it be? Of the other characters we've met, I wouldn't be shocked if Garret Dillahunt's Will Macon is behind it, regardless of whether he actually committed the deed or not. Right now, though, he's formed something of an uneasy alliance with Jordan and Stewart, so a massive, relationship-breaking event would need to happen for Macon to be involved in Jordan's demise.
There are bound to be other would-be murderers who could've taken Jordan's life who we've yet to encounter, so we might be adding more names to our shortlist in the weeks ahead.
That said — and hear me out on this before you pass judgement — what if Jordan killed himself? He's a former special forces pilot, so he knows how to handle a gun. For all we know, Waylon was telling the truth about Jordan wanting is life to be over, too — especially if, as an alien, the former has some form of perceptive superpower that allows his species to read someone else.
Who do you think killed Hal Jordan? Let me know in the comment section below. And, for more on the HBO Max show, read my Lanterns review.
Artificial intelligence (AI) is rapidly becoming embedded in the modern workplace, with employees are increasingly turning to AI tools to work more efficiently and boost productivity.
This growing demand for faster, more effective ways of working is driving the rise of shadow AI - the use of AI tools outside approved organizational controls and governance frameworks – which results in organizations quickly losing visibility into data usage and potential risks.
The scale of this challenge is significant. While 90% of executives are confident in their organizations' visibility into AI tools, just 52% of employees admit to using AI tools without approval, often through personal accounts.
As a result, organizations are left grappling with a widening gap between AI adoption and AI governance.
The next frontier of AI riskWhen AI is used without formal oversight, it can bypass governance controls, increasing the risk of errors, regulatory breaches and sensitive data leakages. Organizations are most exposed when AI is already influencing business-critical activities, from customer service and operational decision-making to software development and content creation.
The challenge will intensify as businesses move beyond large language models, which generate information, to large action models and agentic systems that can take action. These systems can diagnose issues, recommend actions and execute workflows with minimum human input, increasing both the speed and scale at which mistakes occur.
A shadow agent operating outside approved governance frameworks could trigger harmful actions before organizations have the visibility and governance capabilities needed to intervene.
There is also a longer-term risk that future AI systems will be trained on synthetic or lower-quality data, weakening performance and decision-making over time. Transparency and traceability will be critical to maintaining accountability, protecting ethical standards and preserving the effectiveness of AI systems as adoption continues to accelerate.
AI governance as an enablerWhat works is AI governance that enables innovation while putting clear guardrails in place that are integrated, transparent, auditable, and aligned with existing risk and compliance frameworks. If AI is to be used safely, firms must be able to successfully identify exactly what went wrong and why when issues arise.
In practice, mature governance starts with an approved AI tool stack that provides safe and trusted options for common use cases. This should be supported by risk-based policies that make clear the data being handled, what can and cannot be shared, which tools are permitted, and where human approval is required.
Low-risk tasks such as drafting or summarizing content should not be governed in the same way as high-risk uses involving customer data, regulated information or business-critical decisions.
Training is equally important. The challenge, beyond only enforcing controls, involves helping employees understand why those controls exist and how to use AI responsibly. As agents increasingly diagnose issues, recommend actions, and execute workflows with minimum input, human oversight and approval processes must scale alongside them.
Interoperability will be critical to making this workable at scale, allowing organizations to operate across jurisdictions and multiple AI models without repeatedly rebuilding governance processes and systems from scratch.
Making responsible adoption the easy choiceFor security and compliance leaders, the goal should be to make responsible AI adoption the path of least resistance. Employees turn to shadow AI when approved tools are unavailable, difficult to access or fail to meet their needs. Companies that focus solely on restricting usage risk driving activity further underground and losing out on the efficiency and innovation gains that AI can deliver.
Organizations that successfully balance AI productivity and control over their systems recognize that shadow AI use is often a symptom of unmet demand. Employees typically turn to unauthorized tools because they are easier to access, faster to use or better suited to the task at hand. Rather than focusing on restrictions alone, leaders should understand where AI is already being used across the business and ensure approved alternatives are available for the most common use cases.
With three-quarters of office professionals saying they would be likely to look for a new job that offered better AI skills development, firms that combine governance with opportunities to build AI skills are likely to see stronger adoption of approved tools and, as a result, less reliance on shadow AI.
Building an AI-enabled culture means giving employees the tools, knowledge and confidence to innovate within clear boundaries. By doing so, shadow AI can be reduced while the speed and agility that workers increasingly expect is maintained.
The organizations best positioned to succeedThe businesses that strike the right balance for AI success will be those that view governance as a foundation for AI adoption and not a barrier to it. By making the secure, approved path the easiest path, shadow AI risk is reduced without sacrificing productivity.
Embedding strong governance, supported by trusted and well-managed data foundations, avoids costly mistakes and allows AI to be deployed and scaled with greater safety and confidence.
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Enterprise AI strategy spent two years chasing a single objective: reach the frontier before competitors do.
The default path was a public cloud account, an API key from OpenAI or Anthropic, and a willingness to absorb cost in exchange for speed.
That reality is now running out of road.
The numbers tell the story. Gartner forecasts worldwide AI spending reaching $2.52 trillion in 2026, up 44% year on year, with $1.37 trillion of that flowing into AI infrastructure alone.
In fact, in mid-2025, they claimed that procurement of AI had entered a “Trough of Disillusionment,” where scaling depends on predictable ROI, rather than visionary pilots.
The pressure has now shifted from how fast enterprises can pilot AI to whether they can sustain, govern, and defend it in production.
The Race to the Front is Over – Now Comes the BillWe are now past AI 1.0, where simple access to cutting-edge AI was the differentiator. Now it’s AI 2.0’s turn, where inference economics, data gravity, latency and control decide the outcomes. Token prices have fallen almost tenfold annually since 2021, but AI spend overall by organizations has increased. That’s because more capable models have enabled greater ambition.
Anthropic, OpenAI, and Mistral are now stratifying offerings between flagship reasoners and lower-cost workhorses precisely because customers refuse to pay flagship prices for every task. McKinsey’s 2025 State of AI survey confirms the pattern - adoption is increasing, but impact at scale remains elusive for most organizations.
Now CIOs have stopped asking which model, but where each workload needs to run and how much it’s going to cost.
Inference Cost InflationBanks delivering the next best action are a good example: the in-app, in-branch, or call-center recommendation served in milliseconds against a customer’s live context. The best banks prove that personalization at this layer can lift revenue by 5-15%. To give a firsthand example, a global bank we work with launched an AI assistant that has already resolved more than 1.5 million customer inquiries in its first year, driving huge efficiencies.
But the inference economics are unforgiving at this scale. A single agentic decision can chain five to twenty model calls, each carrying its own context window. The cost gap between £0.50 and £3 per million input tokens seems trivial in a single-turn demo. Spread across hundreds of millions of customer events, it becomes the difference between a money-making feature and a money-burning one.
This isn’t a hypothetical either. Uber’s 5,000-strong engineering team’s use of Claude Code burned through the company’s entire annual AI budget in the first four months of this year. And AI companies are responding to this market shift. Decagon, after re-architecting onto an open-source multi-model stack on NVIDIA Blackwell, dropped cost per voice query by sixfold. Next best action isn’t a marketing decision anymore; it’s an economic decision.
Organizations making the structural shift now will outcompete those treating model selection as an afterthought.
Complexity Doesn’t Disappear, It Just MovesThe hardest lesson of the past 18 months is that model commoditization does not reduce enterprise complexity but relocates it. Open weights from Mistral or DeepSeek cut experimentation cost, but orchestration, governance, evaluation, and integration burdens move up the stack and sit with the buyer.
Enterprise leaders should be measuring unit economics per useful task, operational burden per deployed agent, and the ratio of inference spent on the governance scaffolding around it. That ratio is typically 1:5 or worse.
A second architectural shift is arriving: sub-quadratic attention.
Approaches from DeepSeek, Google, and Cartesia are collapsing the cost of long-context reasoning by orders of magnitude, with recent benchmarks showing 100x to 300x cost reductions at comparable accuracy.
Large banks will now be able to run whole-portfolio risk modelling, multi-decade fraud detection and cross-jurisdiction Know-Your-Customer (KYC) as single-pass operations - no more chunked retrieval workarounds.
Telcos can make network operations, predictive maintenance and multi-year customer journey reasoning more economically viable at scale. And manufacturers can move full-plant simulation and supply-chain disruption forecasting from periodic batch jobs to continuous reasoning.
The architecture that wins will not be the one with the cheapest token. It will be the one that places compute closest to the data, under the right jurisdiction, with governance that holds.
Sustainable, sovereign, controlled - that’s the new triad. The enterprises that build for it now will define the next decade.
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