In a move that casts a shadow across Apple’s upcoming Worldwide Developer’s Conference, OpenAI has announced that it will purchase io, the AI startup founded by acclaimed former Apple designer Sir Jony Ive, who helped create the iMac, iPod, and iPhone.  The deal sees Ive’s hand-picked io team of talented Apple alumni merge with OpenAI. Ive himself stays out the $6.5b deal. He will retain independence at his company LoveFrom but will be taking on “deep design and creative responsibilities across OpenAI and io.” Toward the human interface for AI The intention is to design the user interfaces for AI-enabled machines that will define the future of tech. (I hate to say “I told you so.“) “This is an extraordinary moment,” declares the OpenAI press release announcing the deal. “Computers are now seeing, thinking and understanding. Despite this unprecedented capability, our experience remains shaped by traditional products and interfaces.” While OpenAI doesn’t quite go so far as to say the move means AI is about to enter its iPhone moment, the company quite clearly believes this to be the case. Ive famously left Apple in 2019, working as an advisor for a while until he ceased working for the company completely, just before beginning io. “I have a growing sense that everything I have learned over the last 30 years has led me to this moment,” said Ive. Apple echoes are everywhere For a veteran Apple watcher, there’s a lot of echoes within the announcement. Even the press release has an Apple-like resonance, headed up by a tasteful picture of Ive with OpenAI CEO Sam Altman. Longtime Apple watchers really should not ignore these echoes. Ive and his hand-picked team of historically important former Apple design talent, including Evans Hankey and Tang Tan, will take over design and creative at OpenAI to build AI-enabled devices people can use to make things. If that sounds familiar, think back to Apple founder Steve Jobs and his description of computers as “bicycles for the mind.” That sounds like what OpenAI now intends to make. It isn’t just an intimation of Apple, it’s all about muscling into similar innovation space. “I hope we can bring some of the delight, wonder and creative spirit that I first felt using an Apple Computer 30 years ago,” said Sam Altman. You can watch a short video featuring Altman and Ive discussing their plans here. A change in the balance Of course, Apple has its own relationship with OpenAI, but the appointment of its acclaimed former designer to the company will change the balance of power — particularly as Apple itself is struggling with artificial intelligence. To put the deal into some kind of context, analyst firm Gartner expects worldwide genAI spending to reach a total of $644 billion in 2025, an increase of 76.4% from 2024. This spend includes a huge increase in sales of AI devices, particularly servers and smartphones.  “By 2026, generative design AI will automate 60% of the design effort for new websites and mobile apps,” writes Gartner Market Databook, which anticipates that by 2026, over 100 million humans will “engage robo-colleagues (synthetic virtual colleagues) to contribute to enterprise work.” An analyst perspective So, what does Gartner think the deal means for OpenAI, Apple, and the future of tech?  I spoke with Chirag Dekate, Gartner VP and analyst for quantum technologies, AI infrastructures, and supercomputing. He thinks the arrangement will put OpenAI in competition with all the big hardware players in tech, and, perhaps more importantly, reflects an evolutionary step in AI, one that ends up with far more intelligent devices that feel natural to use. I reproduce his analysis below, as it’s far too wide in scope to paraphrase. What does this deal mean for OpenAI? Dekate: “This marks a next phase of evolution for OpenAI. Market trends as indicated by Google at their I/O event yesterday, Meta, and other innovators, are clear: Leadership in AI is not just about building powerful models anymore, it’s about shaping the entire experience around AI. Bringing Jony Ive on board to design AI-native hardware shows that like Google, Meta, and peers, OpenAI is serious about creating devices where the tech and the design work hand in hand. “Until now, Open AI was reliant on its peers and ecosystems in the cloud to diffuse AI into products and experiences. With this acquisition, Open AI, rather than relying on others to bring its models to life, is stepping into the driver’s seat. OpenAI wants to craft the physical touchpoints of AI themselves, devices that feel intuitive and indispensable in everyday experiences. “This acquisition is also a strategic move. With this kind of vertical integration, OpenAI is positioning itself to go head-to-head with the likes of Google, Meta, and Tesla, not just on software, but on how we experience AI in the real world.” How will this impact Apple and its user base? Dekate: “This is an interesting moment for Apple. With Ive, the company’s longtime design visionary, helping build the next generation of AI devices outside of Apple, it could introduce new ways for people to interact with technology, possibly in ways that challenge Apple’s current product thinking. Today’s iPhone experience — and, more broadly speaking, the Apple experience — leaves a lot to be desired. It is expensive, clunky, and feels dated, especially around AI. “Here the lack of AI nativity within Apple is clear and experienceable for most Apple users. Android experiences from Samsung, and Google Pixel offer more AI native infusion in a way Apple doesn’t. For Apple users, it means more options on the horizon. If OpenAI and Ive succeed, we could see the emergence of new layers of abstraction-designed AI devices that rival Apple’s in terms of experience and aesthetics but are more innovative and ready for AI-native era in a way Apple’s current products aren’t. “That said, Apple isn’t standing still. They’re likely to ramp up their own AI integration, maybe even explore new device categories to stay ahead. It’s not a threat to Apple’s ecosystem yet, but it is a reminder that in an AI-native era, yesterday’s leaders may not be always have an advantage if they do not have AI-native cores.” What’s the bigger picture for the industry? Dekate: “This collaboration is part of a broader shift: AI is moving from digital and into the physical world. We’re seeing it with Google’s robotics and XR efforts, Meta’s smart glasses, Tesla’s Optimus, and Nvidia’s AI platforms. OpenAI’s potential move into devices and physical AI is an accelerant. “The future isn’t just smarter software; it’s intelligent devices that feel natural to use. The industry is heading toward AI-first hardware, designed from the ground up for seamless, human-like interaction. And in that world, design matters more than ever. “As AI becomes part of how we live and work, the companies that can make that experience intuitive, elegant, and even joyful, like Ive has done in his past, will lead the way.” You can follow me on social media! Join me on BlueSky,  LinkedIn, and Mastodon.

An Apple research paper suggests that humanoid robots can be more effectively trained with human instructors as well as robot demonstrators, which is part of a new combined approach the company calls "PH2D."Apple has published a research paper that details a new training method for humanoid robots.On Wednesday, a week after the company revealed its Matrix3D and StreamBridge AI models, Apple published new research on robots and how to train them. The iPhone maker's previous robotics efforts included the creation of a robotic lamp, among other things, but Apple's latest study deals with humanoid robots specifically.The research paper, titled "Humanoid Policy ~ Human Policy," details the inadequacies of traditional robot-training methods and proposes a new solution that's both scalable and cost-effective. Continue Reading on AppleInsider | Discuss on our Forums

Macworld I can’t believe I’m about to state this, but I’m considering switching from iOS to Android. Not right now, but what I once considered an absurd notion is rapidly becoming a realistic possibility. While Apple may have an insurmountable lead in hardware, iPhones and Android phones are no longer on par with each other when it comes to AI and assistants, and the gap is only growing wider. At its annual I/O conference on Tuesday, Google didn’t just preview some niche AI gimmicks that look good in a demo; it initiated a computing revolution that Apple simply won’t be able to replicate anytime soon, if ever. Actions speak louder than words The first thing I noticed during the main I/O keynote was how confident the speakers were. Unlike Apple’s canned Apple Intelligence demo at last year’s WWDC, Google opted for live demos and presentations that only reflect its strong belief that everything just works. Many of the announced features were made available on the same day, while some others will follow as soon as this summer. Google didn’t (primarily, at least) display nonexistent concepts and mockups or pre-record the event. It likely didn’t make promises it can’t keep, either. If you have high AI hopes for WWDC25, I’d like to remind you that the latest rumors suggest Apple will ignore the elephant in the room, possibly focusing on the revolutionary new UI and other non-AI goods instead. I understand Apple’s tough position—given how last year’s AI vision crumbled before its eyes—but I’d like to think a corporation of that size could’ve acquired its way into building a functional product over the past 12 months. For the first time in as long as I can remember, Google is selling confidence and accountability while Apple is hiding behind glitzy smoke and mirrors. Google’s demos at IO showed the true power of AI.Foundry Apple’s tight grip will only suffocate innovation A few months ago, Apple added ChatGPT to Siri’s toolbox, letting users rely on OpenAI’s models for complex queries. While a welcome addition, it’s unintuitive to use. In many cases, you need to explicitly ask Apple’s virtual assistant to use ChatGPT, and any accidental taps on the screen will dismiss the entire conversation. Without ChatGPT, Siri is just a bare-bones voice command receiver that can set timers and, at best, fetch basic information from the web. Conversely, Google has built an in-house AI system that integrates fully into newer versions of Android. Gemini is evolving from a basic chatbot into an integral part of Google’s ecosystem. It can research and generate proper reports, video chat with you, and pull personal information from your Gmail, Drive, and other Google apps. Gemini is already light-years ahead of Siri—and it’s only getting better.Foundry Google also previewed Project Astra, which will let Gemini fully control your Android phone, thanks to its agentic capabilities. It’s similar to the revamped Siri with on-screen context awareness (that Apple is reportedly rebuilding from scratch), but much more powerful. While, yes, it’s still just a prototype, Google has seemingly delivered on last year’s promises. Despite it infamously killing and rebranding projects all the time, I actually believe its AI plans will materialize because it has been constantly shipping finished products to users. Unlike Apple, Google is also bringing some of its AI features to other platforms. For example, the Gemini app for iPhone now supports the live video chat feature for free. There are rumors that Apple will open up some of its on-device AI models to third-party app developers, but those will likely be limited to Writing Tools and Image Playground. So even if Google is willing to develop more advanced functionalities for iOS, Apple’s system restrictions would throttle them. Third-party developers can’t control the OS, so Google will never be able to build the same comprehensive tools for iPhones. Beyond the basics Google’s AI plan doesn’t strictly revolve around its Gemini chatbot delivering information. It’s creating a new computing experience powered by artificial intelligence. Google’s AI is coming to Search and Chrome to assist with web browsing in real time.  For example, Gemini will help users shop for unique products based on their personal preferences and even virtually try clothes on. Similarly, other Google AI tools can code interfaces based on text prompts, generate video clips from scratch, create music, translate live Meet conferences, and so on. Now, I see how dystopian this all can be, but with fair use, it will be an invaluable resource to students and professionals.  Meanwhile, what can Apple Intelligence do? Generate cartoons and proofread articles? While I appreciate Apple’s private, primarily on-device approach, most users care about the results, not the underlying infrastructure. Google’s Try It On mode will use AI to show how something will look before you buy it.Foundry The wrong path During I/O, Google shared its long-term vision for AI, which adds robotics and mixed-reality headsets to the equation. Down the road, the company plans to power machines using the knowledge its AI is gaining each day. It also demoed its upcoming smart glasses, which can mirror Android phone alerts, send texts, translate conversations in real time, scan surrounding objects, and much, much more.  While Apple prioritized the Vision Pro headset no one asked for, Google has been focusing its efforts on creating the sleek, practical device users actually need—a more powerful Ray-Ban Meta rival. Before long, Android users will be rocking stylish eyewear and barely using their smartphones in public. Meanwhile, iPhone users will likely be locked out of this futuristic experience because third-party accessories can’t read iOS notifications and interact with the system in the same way. Apple is running out of time iOS and Android launched as two contrasting platforms. At first, Apple boasted its stability, security, and private approach, while Google’s vision revolved around customization, ease of modding, and openness. Throughout the years, Apple and Google have been learning from each other’s strengths and applying the needed changes to appease their respective user bases.  Apple Intelligence had priomise but Apple has failed to deliver its most ambitious features.Foundry Recently, it seemed like the two operating systems were finally intersecting: iOS had become more personalizable, while Android deployed stricter guardrails and privacy measures. However, the perceived overlap only lasted for a moment—until the AI boom changed everything. The smartphone as we know it today seems to be fading away. AI companies are actively building integrations with other services, and it’s changing how we interact with technology. Mobile apps could become less relevant in the near future, as a universal chatbot would perform the needed tasks based on users’ text and voice prompts.  Google is slowly setting this new standard with Android, and if Apple can’t keep up with the times, the iPhone’s relevancy will face the same fate as so many Nokia and BlackBerry phones. And if Apple doesn’t act fast, Siri will be a distant memory.

During his keynote address at Computex in Taipei today, Foxconn chairman Young Liu warned that generative AI and robotics might soon make low-wage human labor unnecessary on the assembly line. This could have huge implications that go way beyond iPhone manufacturing. A costly shift in the assembly line As reported by The Register, Liu told the audience that after plugging AI tools into Foxconn’s workflows, software now performs roughly 80 percent of the work required to configure equipment for a fresh production run. And it does so faster than humans ever could. He added that while the blend of “bots and brains” cannot replace every human (which they tried), the combo speeds troubleshooting on the factory floor. But that productivity gain might come at a huge cost for what he referred to as “low-GDP” countries: Generative AI and robotics will fill the void. (…) That is the opportunity I see when a country becomes more prosperous. The low-GDP work will be done by GenAI and robotics. (…) I think that is the real challenge for all developed countries. (…) I urge leaders of developed countries to watch this very carefully. Liu also announced that Foxconn has been fine-tuning its own version of Meta’s Llama 3 and 4 models. This manufacturing-centric model, dubbed “FoxBrain,” will autonomously perform specific shop-floor tasks. He said Foxconn intends to open-source the model but didn’t provide a timeline. What this might mean for the iPhone Despite recent events, Foxconn still assembles the vast majority of iPhones in China. While Apple has been expanding production to regions like India, Vietnam, and Brazil, China’s combination of advanced manufacturing expertise and, let’s say… cost-effective labor, remains a significant draw. Integrating AI and robotics into manufacturing processes could potentially reduce the reliance on low-cost labor, making it more feasible for Apple to consider manufacturing in higher-cost regions. That, of course, would require China to just sit and watch it all play out, which would be highly unlikely. But even if that were the case, replicating the efficiency and scale of China’s manufacturing ecosystem elsewhere would remain a complex endeavor, although Foxconn’s automation push could, over time, narrow that gap. Add 9to5Mac to your Google News feed.  FTC: We use income earning auto affiliate links. More.You’re reading 9to5Mac — experts who break news about Apple and its surrounding ecosystem, day after day. Be sure to check out our homepage for all the latest news, and follow 9to5Mac on Twitter, Facebook, and LinkedIn to stay in the loop. Don’t know where to start? Check out our exclusive stories, reviews, how-tos, and subscribe to our YouTube channel

As robots increasingly make their way to the largest enterprises’ manufacturing plants and warehouses, the need for access to critical business and operational data has never been more crucial. At its Sapphire conference, SAP announced it is collaborating with NEURA Robotics and NVIDIA to enable its SAP Joule agents to connect enterprise data and processes with NEURA’s advanced cognitive robots. The integration will enable robots to support tasks including adaptive manufacturing, autonomous replenishment, compliance monitoring and predictive maintenance. Using the Mega NVIDIA Omniverse Blueprint, SAP customers will be able to simulate and validate large robotic fleets in digital twins before deploying them in real-world facilities. Virtual Assistants Become Physical Helpers AI agents are traditionally confined to the digital world, which means they’re unable to take actionable steps for physical tasks and do real-world work in warehouses, factories and other industrial workplaces. SAP’s collaboration with NVIDIA and NEURA Robotics shows how enterprises will be able to use Joule to plan and simulate complex and dynamic scenarios that include physical AI and autonomous humanoid robots to address critical planning, safety and project requirements, streamline operations and embody business intelligence in the physical world. Revolutionizing Supply Chain Management: NVIDIA, SAP Tackle Complex Challenges Today’s supply chains have become more fragile and complex with ever-evolving constraints around consumer, economic, political and environmental dimensions. The partnership between NVIDIA and SAP aims to enhance supply chain planning capabilities by integrating NVIDIA cuOpt technology — for real-time route optimization — with SAP Integrated Business Planning (IBP) to enable customers to plan and simulate the most complex and dynamic scenarios for more rapid and confident decision making. By extending SAP IBP with NVIDIA’s highly scalable, GPU-powered platform, customers can accelerate time-to-value by facilitating the management of larger and more intricate models without sacrificing runtime. This groundbreaking collaboration will empower businesses to address unique planning requirements, streamline operations and drive better business outcomes. Supporting Supply Chains With AI and Humanoid Robots SAP Chief Technology Officer Philipp Herzig offered a preview of the technology integration in a keynote demonstration at SAP Sapphire in Orlando, showing the transformative potential for physical AI in businesses in how Joule — SAP’s generative AI co-pilot — works with data from the real world in combination with humanoid robots. “Robots and autonomous agents are at the heart of the next wave of industrial AI, seamlessly connecting people, data, and processes to unlock new levels of efficiency and innovation,” said Herzig. “SAP, NVIDIA and NEURA Robotics share a vision for uniting AI and robotics to improve safety, efficiency and productivity across industries.” With the integration of the Mega NVIDIA Omniverse Blueprint, enterprises can harness physical AI and digital twins to enable new AI agents that can deliver real-time, contextual insights and automate routine tasks. The collaboration between the three companies allows NEURA robots to see, learn and adapt in real time — whether restocking shelves, inspecting infrastructure or resolving supply chain disruptions. SAP Joule AI Agents are deployed directly onto NEURA’s cognitive robots, enabling real-time decision-making and the execution of physical tasks such as inventory audits or equipment repairs. The robots learn continuously in physically accurate digital twins, powered by NVIDIA Omniverse libraries and technologies,  and using business-critical data from SAP applications, while the Mega NVIDIA Omniverse Blueprint helps evaluate and validate deployment options and large-scale task interactions. Showcasing Joule’s Data-Driven Insights on NEURA Robots The technology demonstration showcases the abilities of NEURA robots to handle tasks guided by Joule’s data-driven insights. Businesses bridging simulation-to-reality deployments can use the integrated technology stack to provide zero-shot navigation and inference to address defect rates and production line inefficiencies without interrupting operations. Herzig showed how Joule can direct a NEURA 4NE1 robot to inspect a machine in the showroom, powered by AI agents, scanning equipment and triggering an SAP Asset Performance management alert before a failure disrupts operations. It’s but a glimpse into the future of what’s possible with AI agents and robotics.

You're probably familiar with the push-to-open mechanism used by fancy cabinets. Pressing the cabinet door pushes an internal latch inward, past a "trigger point" like a cam or notch that had been holding it in place. This then allows a spring behind the latch to release and push outwards, opening the cabinet door. If you need help visualizing it, here are some animated cutaways of different variants:The mechanism's action is somewhat counterintuitive—you push something away from you, to get it to move toward you—but most people grasp it instantly.Now a collaboration at AMOLF and ARCNL, two Dutch research institutes, have developed the opposite mechanism: Something that you pull in one direction, which causes it to contract in the opposite direction. "This surprising behavior," they write, "defies conventional understanding of materials and opens up exciting applications in soft robotics, smart devices, and vibration control systems."Their mechanism consists of three components:Once these components are interconnected, they behave like this:Here's what they do in an opposing horizontal configuration:For applications, the researchers suggest the following:- One-way sliding motion without motors or electronics — potentially useful in soft robots that need to move forward without slipping backward, such as medical robots navigating through the body. - Materials that switch stiffness on demand — ideal for wearable exosuits or prosthetics that need to be flexible during movement but stiffen instantly for support or safety. - Structures that dampen excessive vibration all by themselves — potentially lifesaving in systems like airplanes, wind turbines, or even buildings in earthquake-prone areas. The researchers refer to the phenomenon as "countersnapping." If you're interested in the science behind it, the team lays it all out in a research paper here. There are also some more demonstrations in the video below.

A week ago we saw Tesla’s Optimus robot showing off some nifty dance moves. This week, you can watch it performing a bunch of mundane tasks, though admittedly with a great deal of skill — for a humanoid robot. Instructed via natural language prompts, the so-called “Tesla bot” is shown in a new video dumping trash in a bin, cleaning food off a table with a dustpan and brush, tearing off a sheet of paper towel, stirring a pot of food, and vacuuming the floor, among other tasks.  The performance may not shake the world of humanoid robotics to its core, but it nevertheless shows the kind of steady progress that Tesla engineers are making, with the bot’s actions and movements becoming evermore complex. Recommended Videos Commenting on the latest clip, Optimus team boss Milan Kovac said in a post on X: “One of our goals is to have Optimus learn straight from internet videos of humans doing tasks.” Just to be clear, that doesn’t mean the robot will literally watch videos like a human. Instead, it suggests that the robot will learn from the vast amount of data available in those videos, such as demonstrations of tasks, movements, or behaviors. Kovac said that his team recently had a “significant breakthrough” that means it can now transfer “a big chunk of the learning directly from human videos to the bots (1st- person views for now),” explaining that this allows his team to bootstrap new tasks much more quickly compared to using teleoperated bot data alone. Next, the plan is to make Optimus more reliable by getting it to practice tasks on its own — either in the real world or in simulations — using reinforcement learning, a method that improves actions through trial and error. Tesla boss Elon Musk, who has spoken enthusiastically of Optimus ever since the company first announced it in 2021, has claimed that “thousands” of the robots may one day be deployed alongside human staff at Tesla factories, taking care of “dangerous, repetitive, [and] boring tasks.” The company, better known for making electric cars than humanoid robots, is racing against a growing number of tech firms globally that are intent on commercializing their humanoid robots, whether for the workplace, home, or perhaps some entirely new human-robot ecosystems yet to be imagined.

Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps.  One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem. A Dutch mission to the UK In March, a Dutch delegation landed in the UK to host an “Innovation Mission” with local tech and government representatives. Top Sector ICT, a Dutch government–supported organisation, led the mission, which sought to strengthen and discuss the future of neuromorphic computing in Europe and the Netherlands.  We contacted Top Sector ICT, who connected us with one of their collaborators: Dr Johan H. Mentink, an expert in computational physics at Radboud University in the Netherlands. Dr Mentink spoke about how neuromorphic computing can solve the energy, accuracy, and efficiency challenges of our current computing architectures.  Grab that deal “Current digital computers use power-hungry processes to handle data,” Dr Mentink said.  “The result is that some modern data centres use so much energy that they even need their own power plant.”  Computing today stores data in one place (memory) and processes it in another place (processors). This means that a lot of energy is spent on transporting data, Dr Mentink explained.  In contrast, neuromorphic computing architectures are different at the hardware and software levels. For example, instead of using processors and memories, neuromorphic systems leverage new hardware components such as memristors. These act as both memory and processors.  By processing and saving data on the same hardware component, neuromorphic computing removes the energy-intensive and error-prone task of transporting data. Additionally, because data is stored on these components, it can be processed more immediately, resulting in faster decision-making, reduced hallucinations, improved accuracy, and better performance. This concept is being applied to edge computing, Industrial IoT, and robotics to drive faster real-time decision-making.  “Just like our brains process and store information in the same place, we can make computers that would combine data storage and processing in one place,” Dr Mentink explained.   Early use cases for neuromorphic computing Neuromorphic computing is far from just experimental. A great number of new and established technology companies are heavily invested in developing new hardware, edge devices, software, and neuromorphic computing applications. Big tech brands such as IBM, NVIDIA, and Intel, with its Loihi chips, are all involved in neuromorphic computing, while companies in the Netherlands, aligned with a 2024 national white paper, are taking a leading regional role.  For example, the Dutch company Innatera — a leader in ultra-low power neuromorphic processors — recently secured €15 million in Series-A funding from Invest-NL Deep Tech Fund, the EIC Fund, MIG Capital, Matterwave Ventures, and Delft Enterprises.  Innatera is just the tip of the iceberg, as the Netherlands continues to support the new industry through funds, grants, and other incentives. Immediate use cases for neuromorphic computing include event-based sensing technologies integrated into smart sensors such as cameras or audio. These neuromorphic devices only process change, which can dramatically reduce power and data load, said Sylvester Kaczmarek, the CEO of OrbiSky Systems, a company providing AI integration for space technology.   Neuromorphic hardware and software have the potential to transfer AI running on the edge, especially for low-power devices such as mobile, wearables, or IoT.  Pattern recognition, keyword spotting, and simple diagnostics — such as real-time signal processing of complex sensor data streams for biomedical uses, robotics, or industrial monitoring — are some of the leading use cases, Dr Kaczmarek explained.  When applied to pattern recognition and classification or anomaly detection, neuromorphic computing can make decisions very quickly and efficiently,  Professor Dr Hans Hilgenkamp, Scientific Director of the MESA+ Institute at the University of Twente, agreed that pattern recognition is one of the fields where neuromorphic computing excels.  “One may also think about [for example] failure prediction in industrial or automotive applications,” he said.    The gaps creating neuromorphic opportunities Despite the recent progress, the road to establishing robust neuromorphic computing ecosystems in the Netherlands is challenging. Globalised tech supply chains and the standardisation of new technologies leave little room for hardware-level innovation.  For example, optical networks and optical chips have proven to outperform traditional systems in use today, but the tech has not been deployed globally. Deploying new hardware involves strategic coordination between the public and private sectors. The global rollout of 5G technology provides a good example of the challenges. It required telcos and governments around the world to deploy not only new antennas, but also smartphones, laptops, and a lot of hardware that could support the new standard.  On the software side, meanwhile, 5G systems had a pressing need for global standards to ensure integration, interoperability, and smooth deployment. Additionally, established telcos had to move from pure competition to strategic collaboration— an unfamiliar shift for an industry long built on siloed operations. Neuromorphic computing ecosystems face similar obstacles. The Netherlands recognises that the entire industry’s success depends on innovation in materials, devices, circuit designs, hardware architecture, algorithms, and applications.  These challenges and gaps are driving new opportunities for tech companies, startups, vendors, and partners.  Dr Kaczmarek told us that neuromorphic computing requires full-stack integration. This involves expertise that can connect novel materials and devices through circuit design and architectures to algorithms and applications. “Bringing these layers together is crucial but challenging,” he said.  On the algorithms and software side of things, developing new paradigms of programming, learning rules (beyond standard deep learning backpropagation), and software tools native to neuromorphic hardware are also priorities.  “It is crucial to make the hardware usable and efficient — co-designing hardware and algorithms because they are intimately coupled in neuromorphic systems,” said Dr Kaczmarek.  Other industries which have developed or are considering research on neuromorphic computing include healthcare (brain-computer interfaces and prosthetics), agri-food, and sustainable energy.  Neuromorphic computing modules or components can also be integrated with conventional CMOS, photonics, AI, and even quantum technologies.  Long-term opportunities in the Netherlands We asked Dr Hilgenkamp what expertise or innovations are most needed and offer the greatest opportunities for contribution and growth within this emerging ecosystem. “The long-term developments involve new materials and a lot of research, which is already taking place on an academic level,” Dr Hilgenkamp said.  He added that the idea of “materials that can learn” brings up completely new concepts in materials science that are exciting for researchers.  On the other hand, Dr Mentink pointed to the opportunity to transform our economies, which rely on processing massive amounts of data.  “Even replacing a small part of that with neuromorphic computing will lead to massive energy savings,” he said.  “Moreover, with neuromorphic computing, much more processing can be done close to where the data is produced. This is good news for situations in which data contains privacy-sensitive information.”  Concrete examples, according to Dr Mentink, also include fraud detection for credit card transactions, image analysis by robots and drones, anomaly detection of heartbeats, and processing of telecom data. “The most promising use cases are those involving huge data flows, strong demands for very fast response times, and small energy budgets,” said Dr Mentink.  As the use cases for neuromorphic computing increase, Dr Mentink expects the development of software toolchains that enable quick adoption of new neuromorphic platforms to see growth. This new sector would include services to streamline deployment. “Longer-term sustainable growth requires a concerted interdisciplinary effort across the whole computing stack to enable seamless integration of foundational discoveries to applications in new neuromorphic computing systems,” Dr Mentink said.  The bottom line The potential of neuromorphic computing has translated into billions of dollars in investment in the Netherlands and Europe, as well as in Asia and the rest of the world.  Businesses that can innovate, develop, and integrate hardware and software-level neuromorphic technologies stand to gain the most.   The potential of neuromorphic computing for greater energy efficiency and performance could ripple across industries. Energy, healthcare, robotics, AI, industrial IoT, and quantum tech all stand to benefit if they integrate the technology. And if the Dutch ecosystem takes off, the Netherlands will be in a position to lead the way. Supporting Dutch tech is a key mission of TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets are now on sale — use the code TNWXMEDIA2025 at the checkout to get 30% off. Story by Ray Fernandez Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work ha (show all) Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work has been published in Bloomberg, TechRepublic, The Sunday Mail, eSecurityPlanet, and many others. He is a contributing writer for Espacio Media Incubator, which has reporters across the US, Europe, Asia, and Latin America. Get the TNW newsletter Get the most important tech news in your inbox each week. Also tagged with

Our latest and most advanced technologies — from AI to Industrial IoT, advanced robotics, and self-driving cars — share serious problems: massive energy consumption, limited on-edge capabilities, system hallucinations, and serious accuracy gaps.  One possible solution is emerging in the Netherlands. The country is developing a promising ecosystem for neuromorphic computing, which draws on neuroscience to boost IT efficiencies and performance. Billions of euros are being invested in this new form of computing worldwide. The Netherlands aims to become a leader in the market by bringing together startups, established companies, government organisations, and academics in a neuromorphic computing ecosystem. A Dutch mission to the UK In March, a Dutch delegation landed in the UK to host an “Innovation Mission” with local tech and government representatives. Top Sector ICT, a Dutch government–supported organisation, led the mission, which sought to strengthen and discuss the future of neuromorphic computing in Europe and the Netherlands.  We contacted Top Sector ICT, who connected us with one of their collaborators: Dr Johan H. Mentink, an expert in computational physics at Radboud University in the Netherlands. Dr Mentink spoke about how neuromorphic computing can solve the energy, accuracy, and efficiency challenges of our current computing architectures.  Grab that deal “Current digital computers use power-hungry processes to handle data,” Dr Mentink said.  “The result is that some modern data centres use so much energy that they even need their own power plant.”  Computing today stores data in one place (memory) and processes it in another place (processors). This means that a lot of energy is spent on transporting data, Dr Mentink explained.  In contrast, neuromorphic computing architectures are different at the hardware and software levels. For example, instead of using processors and memories, neuromorphic systems leverage new hardware components such as memristors. These act as both memory and processors.  By processing and saving data on the same hardware component, neuromorphic computing removes the energy-intensive and error-prone task of transporting data. Additionally, because data is stored on these components, it can be processed more immediately, resulting in faster decision-making, reduced hallucinations, improved accuracy, and better performance. This concept is being applied to edge computing, Industrial IoT, and robotics to drive faster real-time decision-making.  “Just like our brains process and store information in the same place, we can make computers that would combine data storage and processing in one place,” Dr Mentink explained.   Early use cases for neuromorphic computing Neuromorphic computing is far from just experimental. A great number of new and established technology companies are heavily invested in developing new hardware, edge devices, software, and neuromorphic computing applications. Big tech brands such as IBM, NVIDIA, and Intel, with its Loihi chips, are all involved in neuromorphic computing, while companies in the Netherlands, aligned with a 2024 national white paper, are taking a leading regional role.  For example, the Dutch company Innatera — a leader in ultra-low power neuromorphic processors — recently secured €15 million in Series-A funding from Invest-NL Deep Tech Fund, the EIC Fund, MIG Capital, Matterwave Ventures, and Delft Enterprises.  Innatera is just the tip of the iceberg, as the Netherlands continues to support the new industry through funds, grants, and other incentives. Immediate use cases for neuromorphic computing include event-based sensing technologies integrated into smart sensors such as cameras or audio. These neuromorphic devices only process change, which can dramatically reduce power and data load, said Sylvester Kaczmarek, the CEO of OrbiSky Systems, a company providing AI integration for space technology.   Neuromorphic hardware and software have the potential to transfer AI running on the edge, especially for low-power devices such as mobile, wearables, or IoT.  Pattern recognition, keyword spotting, and simple diagnostics — such as real-time signal processing of complex sensor data streams for biomedical uses, robotics, or industrial monitoring — are some of the leading use cases, Dr Kaczmarek explained.  When applied to pattern recognition and classification or anomaly detection, neuromorphic computing can make decisions very quickly and efficiently,  Professor Dr Hans Hilgenkamp, Scientific Director of the MESA+ Institute at the University of Twente, agreed that pattern recognition is one of the fields where neuromorphic computing excels.  “One may also think about [for example] failure prediction in industrial or automotive applications,” he said.    The gaps creating neuromorphic opportunities Despite the recent progress, the road to establishing robust neuromorphic computing ecosystems in the Netherlands is challenging. Globalised tech supply chains and the standardisation of new technologies leave little room for hardware-level innovation.  For example, optical networks and optical chips have proven to outperform traditional systems in use today, but the tech has not been deployed globally. Deploying new hardware involves strategic coordination between the public and private sectors. The global rollout of 5G technology provides a good example of the challenges. It required telcos and governments around the world to deploy not only new antennas, but also smartphones, laptops, and a lot of hardware that could support the new standard.  On the software side, meanwhile, 5G systems had a pressing need for global standards to ensure integration, interoperability, and smooth deployment. Additionally, established telcos had to move from pure competition to strategic collaboration— an unfamiliar shift for an industry long built on siloed operations. Neuromorphic computing ecosystems face similar obstacles. The Netherlands recognises that the entire industry’s success depends on innovation in materials, devices, circuit designs, hardware architecture, algorithms, and applications.  These challenges and gaps are driving new opportunities for tech companies, startups, vendors, and partners.  Dr Kaczmarek told us that neuromorphic computing requires full-stack integration. This involves expertise that can connect novel materials and devices through circuit design and architectures to algorithms and applications. “Bringing these layers together is crucial but challenging,” he said.  On the algorithms and software side of things, developing new paradigms of programming, learning rules (beyond standard deep learning backpropagation), and software tools native to neuromorphic hardware are also priorities.  “It is crucial to make the hardware usable and efficient — co-designing hardware and algorithms because they are intimately coupled in neuromorphic systems,” said Dr Kaczmarek.  Other industries which have developed or are considering research on neuromorphic computing include healthcare (brain-computer interfaces and prosthetics), agri-food, and sustainable energy.  Neuromorphic computing modules or components can also be integrated with conventional CMOS, photonics, AI, and even quantum technologies.  Long-term opportunities in the Netherlands We asked Dr Hilgenkamp what expertise or innovations are most needed and offer the greatest opportunities for contribution and growth within this emerging ecosystem. “The long-term developments involve new materials and a lot of research, which is already taking place on an academic level,” Dr Hilgenkamp said.  He added that the idea of “materials that can learn” brings up completely new concepts in materials science that are exciting for researchers.  On the other hand, Dr Mentink pointed to the opportunity to transform our economies, which rely on processing massive amounts of data.  “Even replacing a small part of that with neuromorphic computing will lead to massive energy savings,” he said.  “Moreover, with neuromorphic computing, much more processing can be done close to where the data is produced. This is good news for situations in which data contains privacy-sensitive information.”  Concrete examples, according to Dr Mentink, also include fraud detection for credit card transactions, image analysis by robots and drones, anomaly detection of heartbeats, and processing of telecom data. “The most promising use cases are those involving huge data flows, strong demands for very fast response times, and small energy budgets,” said Dr Mentink.  As the use cases for neuromorphic computing increase, Dr Mentink expects the development of software toolchains that enable quick adoption of new neuromorphic platforms to see growth. This new sector would include services to streamline deployment. “Longer-term sustainable growth requires a concerted interdisciplinary effort across the whole computing stack to enable seamless integration of foundational discoveries to applications in new neuromorphic computing systems,” Dr Mentink said.  The bottom line The potential of neuromorphic computing has translated into billions of dollars in investment in the Netherlands and Europe, as well as in Asia and the rest of the world.  Businesses that can innovate, develop, and integrate hardware and software-level neuromorphic technologies stand to gain the most.   The potential of neuromorphic computing for greater energy efficiency and performance could ripple across industries. Energy, healthcare, robotics, AI, industrial IoT, and quantum tech all stand to benefit if they integrate the technology. And if the Dutch ecosystem takes off, the Netherlands will be in a position to lead the way. Supporting Dutch tech is a key mission of TNW Conference, which takes place on June 19-20 in Amsterdam. Tickets are now on sale — use the code TNWXMEDIA2025 at the checkout to get 30% off. Story by Ray Fernandez Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work ha (show all) Ray Fernandez is a journalist with over a decade of experience reporting on technology, finance, science, and natural resources. His work has been published in Bloomberg, TechRepublic, The Sunday Mail, eSecurityPlanet, and many others. He is a contributing writer for Espacio Media Incubator, which has reporters across the US, Europe, Asia, and Latin America. Get the TNW newsletter Get the most important tech news in your inbox each week. Also tagged with