
Russia Has An Arsenal Of New AI Drones Built With Smuggled U.S. Chips
The drone war in Ukraine has become an AI arms race as both sides rush to deploy AI-enabled systems which are immune to radio interference, making protective jammers useless, and which can find and attack targets on their own.
This arms race is driven by hardware from the world's biggest company, NVIDIA. Sanctions should prevent Russia from acquiring NVIDIA hardware, but their chips have been found as key components in the latest Russian, with several different types deployed all using NVIDIA hardware.
NVIDIA is the world's largest company by market value, and the first ever to break the $4 trillion barrier.
NVIDIA headquarters in Santa Clara of Silicon Valley, California Anadolu via Getty Images
The company is incredibly successful, with an estimated an estimated 85% of the global AI chip market, because it makes what everyone wants: powerful hardware to drive AI. The chips, known as GPUs (Graphics Processor Units) or accelerators, differ from the typical computer chip or CPU (Central Processing Unit) in being able to handle lots of small tasks simultaneously rather than applying more power to a few tasks. This is parallel processing, and it is essential for most types of AI, which involve huge datasets.
Their success is reminiscent of the chip wars of the 1980-90s when desktops PCs ground slowly through large spreadsheet calculations and other complex tasks. CPU power was vital and Intel rose to dominance, with ever-faster clock speeds and transistor counts in their 386, 486 and Pentium processors. NVIDIA is doing the same with GPUs in the 2020s, producing every more capable versions and outpacing the competition.
NVDIA has several different families of chips for different applications, including high-power units for data centers and compact, low-power Jetson boards for edge devices like consumer electronics – and drones. Such single-board computers cost just a few hundred dollars.
Timelapse image of AI-powered racing FPVs traversing the course faster than human pilots Regina Sablotny
This is highly capable hardware. In 2021, a team from University of Zurich (UZH) led by Davide Scaramuzza demonstrated an AI system using a Jetson computer on a racing drone which was able to beat world-class human pilots for the first time.
This system relied on external motion trackers to give the drones data, giving them an unfair advantage. But by 2023 , the UZH team had developed an AI system which was able to beat human champions using just onboard sensors and processing.
'We are very excited as this is the first time that AI beats a human in a physical sport designed by and for humans,' Scaramuzza told me at the time.
The feat was replicated recently in Dubai when an AI-enabled drone from TU Delft, having beaten all the other AI drones, competed head-to-head against the human FPV racing winners in an AI vs Human Challenge, and won. Again, the teams used Jetson-based computers.
'Even in our earlier work, we had more than enough computational headroom,' Scaramuzza told me.
The earlier version used the older Jetson TX2. Now drones have the new and more capable Jetson Orin which offers at least ten times as much computing power.
'Some of the algorithms we developed for our drone racers have found their way into companies like Skydio and Zipline, where several of my former students now work,' says Scaramuzza.
While there is no indication that the software developed by UZH is being used Ukraine, others are certainly using AI-enabled systems powered by the same hardware, and which may be equally capable in terms of matching human operators.
Back in 2023 Russian Lancet attack drones – a 35-pound weapon with a reach of 25 miles and a warhead capable of taking out a tanks – were found to have an NVIDIA Jetson TX2 'brain.' Smugglers reportedly ship the chips in small batches labelled as other components, sending them via several third-party countries to reach Russia.
Frame of a Lancet attack with video with the 'target locked' indicator at the top of the screen showing it is in automated mode Russian MoD
The Jetson drives AI giving Lancet 'lock on target' function allowing the operator to designate a target within the field of view. The Lancet then track the target, following it and running into it even if the communication link is lost.
In 2024 this automated system appeared to be performing poorly, for example hitting a shadow next to the target rather than the target itself. For a time the function appeared to be disabled. But it was restored, presumably after software upgrades, and appears to have improved considerably. From data published on Russian weapon performance tracking site LostArmour in 2024 about 30% of Lancet hits involved automated guidance , that figure is now up to almost 60%.
Now two new types of Russian drone have recently been found with the Jetson Orin. A third has entered services which likely uses the same technology.
One of these was a new version of the Shahed attack drone, known as MS001 which in addition to the NVIDIA processor also had a thermal imager and digital modem.
In a LinkedIn post, Ukrainian Major General Vladyslav Klochkov said that this was not simply a drone.
'This is a digital predator. It doesn't carry coordinates, it thinks,' stated Klochkov
This is an exaggeration – the MS001 also has a satellite navigation system, so clearly it does carry co-ordinates. But unlike the basic Shahed which relies entirely on satellite navigation, the MS001 can identify objects on the ground using its thermal imager and AI and attack them.
A downed Russian V2U attack drone with AI running on NVIDIA Jerson Orin hardware Ukraine MoD
The second new drone is the smaller V2U. A report in Ukraine's Defense Express says this four-winged drone, similar in layout to the U.S. Switchblade 300 but carrying an 8-pound warhead, has a range of over 25 miles. The V2U has a high-resolution camera and a laser rangefinder, which, like the TERCOM system in the Tomahawk cruise missile allows it to navigate by comparing the terrain to a digital map. Like the MS001 it has a digital modem which connects to Ukraine's cellphone system to communicate with the operator.
Again, the V2U is powered by an NVDIA Jerson Orin, which allows it to fly up and down roads looking for targets.
'They don't distinguish between military equipment and a civilian bus,' according to one Ukrainian report.
According to other reports cited by Ukrainian electronics expert Serhii Flash the V2U works in teams, with each team member having different color makings on their wings. This likely allows the drones to distinguish each other and carry out their attacks in sequence without conflict and without needing radio communication. According to Flash, the drones are stacked one above another like circling vultures, so for example the 'blue' drone waits its turn until the 'red' drone has attacked. This is a basic version of swarming behavior but a significant step forward.
Flash also notes that the V2U has limited intelligence when it comes to target discrimination and one attacked a public toilet rather than a vehicle.
The Tyuvik, which resembles a miniatures Shahed, is another autonomous attack drone Russian MoD
The third new AI Russian drone is known as Tyuvik ('Levant sparrowhawk') which resembles a scaled-down Shahed and is now in mass production. It has a range of 20 miles and carries a 4-pound warhead; it can find and attack striking moving vehicles and its intended targets are armored vehicles. No example has been captured and analyzed yet, but like V2U this flies to a specified location and then finds a target using machine vision. The makers Statim say Tyuvik is built from low-cost commercial components. Given that Russia does not make any suitable AI hardware, this again suggests an off-the-shelf Jetson Orin.
All three drones may use similar software for navigation and target location, like the portable Prism software from FLIR now being integrated on U.S-made attack drones or the open-source software developed by Auterion. (A shipment of 33,000 new Auterion Skynode strike drone systems to Ukraine was announced recently). This type of setup allows new type of drone to be turned into autonomous 'digital predators' rapidly and at minimal cost. And as the software for navigation, flight and targeting is improved, the improvements can be shared across every drone type in the fleet. New functions, such as dogfighting or swarming, can be added as needed.
We are at the dawn of the age of AI drones. Thanks to the ready availability of NVIDIA Jetsons, such technology is available to literally everyone. This genie is now very much out of the bottle. In the near future, 'dumb' drones lacking onboard AI may be as outdated as biplanes.

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[Latest] Global AI in Synthetic Biology Market Size/Share Worth USD 192.95 Billion by 2034 at a 28.63% CAGR: Custom Market Insights (Analysis, Outlook, Leaders, Report, Trends, Forecast, Segmentation, Growth Rate, Value, SWOT Analysis)
[220+ Pages Latest Report] According to a market research study published by Custom Market Insights, the demand analysis of Global AI in Synthetic Biology Market size & share revenue was valued at approximately USD 24.58 Billion in 2024 and is expected to reach USD 30.76 Billion in 2025 and is expected to reach around USD 192.95 Billion by 2034, at a CAGR of 28.63% between 2025 and 2034. The key market players listed in the report with their sales, revenues and strategies are Ginkgo Bioworks, Amyris, Thermo Fisher Scientific, Illumina, GenScript, Synthetic Genomics, Novozymes, LanzaTech, Zymergen, Twist Bioscience, Codexis, Cambrian Biopharma, Moderna, Editas Medicine, Vertex Pharmaceuticals, CRISPR Therapeutics, Arbor Biotechnologies, Synthego, Evonik Industries, Cargill, Others and others. Austin, TX, USA, Aug. 13, 2025 (GLOBE NEWSWIRE) -- Custom Market Insights has published a new research report titled 'AI in Synthetic Biology Market Size, Trends and Insights By Technology (Gene Synthesis, Genome Editing, Synthetic Biology Tools, Bioinformatics), By Application (Healthcare & Medicine, Agricultural Biotechnology, Industrial Biotechnology, Environmental Biotechnology), and By Region - Global Industry Overview, Statistical Data, Competitive Analysis, Share, Outlook, and Forecast 2025–2034' in its research database. 'According to the latest research study, the demand of global AI in Synthetic Biology Market size & share was valued at approximately USD 24.58 Billion in 2024 and is expected to reach USD 30.76 Billion in 2025 and is expected to reach a value of around USD 192.95 Billion by 2034, at a compound annual growth rate (CAGR) of about 28.63% during the forecast period 2025 to 2034.' 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Report Scope Feature of the Report Details Market Size in 2025 USD 30.76 Billion Projected Market Size in 2034 USD 192.95 Billion Market Size in 2024 USD 24.58 Billion CAGR Growth Rate 28.63% CAGR Base Year 2024 Forecast Period 2025-2034 Key Segment By Technology, Application and Region Report Coverage Revenue Estimation and Forecast, Company Profile, Competitive Landscape, Growth Factors and Recent Trends Regional Scope North America, Europe, Asia Pacific, Middle East & Africa, and South & Central America Buying Options Request tailored purchasing options to fulfil your requirements for research. (A free sample of the AI in Synthetic Biology report is available upon request; please contact us for more information.) Our Free Sample Report Consists of the following: Introduction, Overview, and in-depth industry analysis are all included in the 2024 updated report. The COVID-19 Pandemic Outbreak Impact Analysis is included in the package. 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- Yahoo
HeartFlow IPO success reflects market embrace of AI in medtech industry
HeartFlow's $364m initial public offering (IPO) signals market validation for the use of artificial intelligence (AI) in a company's product portfolio, an expert says. Roundly exceeding its $300m expectations for the IPO, the Bain Capital-backed AI-based coronary artery disease (CAD) platform developer debuted with a a $2.2bn valuation on the Nasdaq on 8 August. HeartFlow's current products are HeartFlow Plaque analysis, which aims to provide clinicians with the ability to more accurately assess patients with arterial plaque buildup (atherosclerosis), and HeartFlow FFRCT analyses CT angiogram (CCTA). Typically used alongside Plaque Analysis, FFRCT creates detailed 3D models of the arteries to assess the impact of blockages on blood flow. Plaque Analysis received US Food and Drug Administration (FDA) clearance in 2022 and is claimed to be the only AI-based plaque quantification tool currently cleared by the agency. FFRCT received FDA clearance in 2014. To reach the IPO milestone, HeartFlow maintained a focus on testing its product offerings alongside physicians, generating real world evidence, taking on feedback and refining as needed, and ensuring they were truly ready to scale. HeartFlow also navigated the structural barriers of getting its product to market; again, real-world evidence – with over 3,000 peer-reviewed papers demonstrating its products' efficacy – spurred the company's commercialisation efforts. Medtech industry veteran, Brent Ness, CEO of Aclarion, who served as HeartFlow's chief commercial officer from 2014 to 2015, told Medical Device Network that HeartFlow's work on the underlying economic factors around its product, including working on contractual relationships with imaging centres to get them on board with the product, pre-reimbursement, have proven a key part of its success. 'Between FDA clearance and reimbursement, lots of technologies can't make it through that journey because there's no reimbursement, and therefore there's no adoption, and they run out of cash,' Ness explained. 'Part of the structural barrier stage of development involves getting the provider economics right and that market access work that needs to take place between the early payors and the key opinion leaders (KOL) advocating for the product. But payors aren't going to turn it on just because a KOL says so. 'This is why the shining jewel in HeartFlow's history has been their absolute commitment to leading with evidence.' AI's recognition in healthcare and its future According to Ness, the success of HeartFlow's IPO reflects the market validation of the overall rationale for using AI in imaging and the deployment of software-as-a-service (SaaS) based products as the 'raw material' underpinning the technology's ability to provide clinically actionable information and improve patient outcomes. Ness said this model is 'here to stay', having been 'completely validated by the market, which has recognised of its value, which obviously translates into revenue, and speaks to the long term success and viability of HeartFlow and the significant potential for other AI-based SaaS imaging developers.' To learn from HeartFlow's success, Ness views a critical role for SaaS-based AI software providers as being to try and shorten the timeframe between regulatory approval and reimbursement. 'The faster that new and novel technologies using AI can move through that Death Valley, the better it's going to be for patients, and the more money we're going to save as a collective society,' Ness said. 'The 'muscle' of these AI tools is proving to be valuable. It's got to be safe, and it's got to make sense economically, but we've got to figure out how to shorten that part of the journey.' "HeartFlow IPO success reflects market embrace of AI in medtech industry" was originally created and published by Medical Device Network, a GlobalData owned brand. The information on this site has been included in good faith for general informational purposes only. It is not intended to amount to advice on which you should rely, and we give no representation, warranty or guarantee, whether express or implied as to its accuracy or completeness. You must obtain professional or specialist advice before taking, or refraining from, any action on the basis of the content on our site. Error in retrieving data Sign in to access your portfolio Error in retrieving data Error in retrieving data Error in retrieving data Error in retrieving data


Android Authority
11 minutes ago
- Android Authority
I tested GPT-5 and now I get why the Internet hates it. Is it time to ditch ChatGPT?
Calvin Wankhede / Android Authority After years of rumors and speculation, OpenAI's next-gen GPT-5 language model is finally here. But while many of those early rumors claimed that the next major ChatGPT model would achieve artificial general intelligence or AGI, that's not the case. GPT-5 does not surpass human-level intelligence, although it's smarter and more capable than any of its predecessors. Despite the improvements, however, it has garnered significant and widespread backlash across the internet. So what does GPT-5 bring to the table and why have so many loyal users already turned their back on it? I tested it to find out. Why GPT-5 is so controversial Until a few days ago, the ChatGPT experience felt bloated if you weren't an AI model expert — the app offered nearly half a dozen different models to choose from. Each model had a unique advantage. For example, the o3 series promised detailed problem-solving skills while GPT-4.1 excelled at coding tasks. And if a task required analysis, you could manually engage a 'deep research' mode. However, the default GPT-4o model worked for most tasks. All of that is now history. If you open ChatGPT today, you'll find that you can only chat with the newest GPT-5 model. OpenAI says this is because it has created a routing system that can automatically decide which model your request needs to go to. Indeed, I've noticed that some prompts will inspire the chatbot to ponder and research, while it will immediately respond to simpler questions. With GPT-5, you no longer have to select a specific model for your task. GPT-5 does have multiple models under its belt, though, even if you can't manually select the one you want. For example, when I asked how many times the letter 'R' appears in the word strawberry, ChatGPT thought for a few seconds and returned with the correct answer: three. Hovering over the 'Retry' button revealed that it had used the 'GPT-5 Thinking Mini' model for my prompt. Even on a free account, I've noticed that ChatGPT will default to GPT-5 for most responses and think for longer if necessary. That said, shorter responses tend to rely on the GPT-5 Mini model. And as we've seen for the past couple of years, free users only get a limited number of responses from the large model before the chatbot forces you over to a scaled down version. Paying $20 monthly for a ChatGPT Plus subscription overcomes this limitation, though, and you can manually select the larger GPT-5 model with thinking for all responses. GPT-5 is less eager to hold conversations with users, and can come across as a bit curt. However, not everyone has welcomed the upgrade; some users have demanded OpenAI offer a way to use the older GPT-4o model indefinitely. They argue that GPT-5's responses feel robotic, in a push to improve safety and accuracy metrics, and that it lacks the distinct personality that the last-gen GPT-4o model offered. In response to these criticisms, OpenAI has brought back the GPT-4o model for ChatGPT Plus users but it's unclear how long this will last. In my time using GPT-5, I've noticed that the new model does seem less eager to hold a conversation. It also lacks the creative writing capabilities of GPT-4o — a big problem if you use ChatGPT for advice, roleplay, or help with drafting letters and emails. It may seem like a minor gripe, but the thousands of complaints online speak for themselves. GPT-5 vs GPT-4o: Is the upgrade really a downgrade? Depending on how you use ChatGPT, you may struggle to notice a big improvement going from GPT-4o to GPT-5. That's because many of the changes are really quite subtle. OpenAI says GPT-5 is more accurate, hallucinates significantly less often, and follows instructions more closely. Impressively, its responses are 45% less likely to include a factual error. On the flip side, however, we're seeing claims that GPT-5's responses are more sterile and direct. And from my testing, that does seem to be the case. Here's a side-by-side comparison showing just how much character GPT-5 lacks compared to GPT-4o: GPT-5 GPT-4o GPT-5's response is perfectly functional, but lacking any depth – it's machine-generated. Here's another example, where I asked both models if I could use lime instead of lemon in a recipe: GPT-5 GPT-4o GPT-4o ended its response with a 'Let me know what you're making—happy to give a more specific answer' while GPT-5 is borderline curt: 'What's the recipe? The stakes change a lot between, say, roast chicken and lemon meringue pie.' So is GPT-5 straight up better at anything? OpenAI said it's vastly better at coding so let's play to its strengths and ask it to write the code for a web app. As the images below showcase, GPT-5 does deliver a much more polished result with the exact same prompt. In fact, its output looks like a real website while GPT-4o's seems like only a suggested starting point. GPT-5 output web app GPT-4o output web app ChatGPT now also lets you pick from multiple response styles — in addition to the default, you have cynic, robot, listener, and nerd. I think power users will benefit from the robot and nerd personalities the most as it cuts out most of the AI's politeness. But if you prefer a friendlier version, the listener and default styles are your best bet. Still, the feature doesn't seem to help the many users who wish OpenAI would just give them the choice to use GPT-4o. GPT-5 is a work in progress Calvin Wankhede / Android Authority Shortly after OpenAI introduced GPT-4, the tech industry was rife with speculation that a future model with more advanced capabilities would threaten humanity. That's because the jump in capability from GPT-3.5, ChatGPT's original model, to GPT-4 was massive. Besides making fewer factual mistakes, the latter model could perceive images, browse the internet, and even mimic human speech. In its early days, GPT-4 would even get into tense emotional exchanges with users. However, GPT-5 doesn't represent the same quantum leap from its predecessor. While it's a significant upgrade on paper, it's not nearly as impactful in day-to-day use and can even feel like a regression. I definitely think it's a net positive to have a model that makes fewer mistakes, but it's missing the glamor and spectacle that many expected from such a big version update. GPT-5 takes a dramatically smaller leap than GPT-4 did at launch. In fact, I'd say that GPT-5 is not as exciting as some of OpenAI's other recent releases. Agent Mode, for instance, allows the chatbot to control a web browser using a simulated mouse. In my testing, I found that the agent can actually perform real-world tasks like logging into websites and can even handle curveballs like shopping for groceries. It remains to be seen if OpenAI will tweak GPT-5 to meet the needs of its most vocal users. This may very well be the company's vision for ChatGPT's future: a helpful assistant that doesn't get too close to its users. However, I think the new model takes one step too far in the name of correctness, and risks sacrificing the very traits that made ChatGPT so popular. It is now up to OpenAI to prove that a smarter AI doesn't have to mean one that's devoid of personality. Follow