Keynote-Lars Reger
19 Feb 2026 16:45h - 17:00h
Keynote-Lars Reger
Summary
The session opened with Speaker 1 thanking the ministerial panel and introducing Lars Reger, CTO of NXP Semiconductors, as the keynote on AI-enabled semiconductor technology [1-4]. Reger began by questioning the current focus on powering large data-centers, emphasizing instead the need to define the purpose of AI and its real-world applications [9-12]. He described a world that “anticipates and automates,” driven by megatrends such as demographic shifts, infrastructure upgrades, supply-chain pressures and energy constraints [18-24]. In that vision, homes become barrier-free shelters that monitor health, wealth and security without user touch, while manufacturing eliminates most manual tasks and pilots operate intelligent robots rather than aircraft [26-41]. Cars are portrayed as “rolling cocoons” or living rooms that can serve as mobile offices, a trend already visible during the COVID-19 pandemic in China [43-48]. Reger identified four universal ingredients for any of the projected 50 billion smart robots: sensing, thinking, connecting and acting, and argued that trust-implemented through functional safety and robust security-is the prerequisite for their adoption [54-65]. He suggested that semiconductor designers should copy biological architectures, citing the human spine and cerebellum as models for real-time, safety-critical control systems [74-82]. Citing insects, he noted that most AI tasks can be handled by tiny, efficient, tailor-made models at the edge, and explained that NXP is building modular “Lego-block” chips to support small to large devices [90-95]. As an illustration, he presented an India-made AI accelerator from Kinara that runs a 10-billion-parameter language model at only 7 watts, enabling edge applications such as smart fridges and medical imaging [100-104]. Reger warned that without ultra-low-power, secure architectures, the projected 50 billion connected devices would demand three times the planet’s available energy, making scalability impossible [141-144]. NXP’s strategy therefore focuses on scaling silicon “Lego bricks” that combine low-power AI accelerators with secure, functional-safety cores, exemplified by plug-in drone control units and building-automation modules [149-154]. He also highlighted the emergence of common communication standards (Meta standards) that allow devices such as home gateways, blinds and solar managers to interoperate, further enabling the edge-centric AI ecosystem [124-128]. He concluded that the democratization of AI-bringing appropriate AI capability to every device in Togo, India, Germany and beyond-must happen at the edge rather than in centralized data centres [155-158].
Keypoints
Major discussion points
– A future “anticipate-and-automate” world – Lars paints a picture of barrier-free homes, autonomous manufacturing, and “rolling cocoons” that act as living rooms, all driven by billions of smart, connected robots. He links this vision to megatrends such as demographics, infrastructure upgrades, supply-chain and energy constraints. [18-33][44-49]
– Trust, safety and security as the foundation of AI – He stresses that without functional safety (e.g., fail-safe braking) and robust cybersecurity, users will revert to manual control. Trust is framed as the essential layer that enables low-power, battery-run devices to be adopted at scale. [65-67][66]
– Edge AI over massive data-centers – The speaker argues that 80 % of AI tasks will run on tiny, efficient models at the edge, not in large cloud farms. He showcases NXP’s 7-watt AI accelerator that runs a 10-billion-parameter language model, illustrating how edge compute can deliver sophisticated services locally. [93-95][101-104][155-157]
– NXP’s modular semiconductor strategy – NXP is building “Lego-brick” silicon blocks (AI accelerators, ultra-wideband, low-power sensors) that can be mixed-and-matched for drones, cars, homes, and industrial equipment. The approach aims to scale from tiny to large form factors while keeping power consumption ultra-low. [95-100][118-124][141-149]
– Energy and scalability constraints – To realise 50 billion connected devices, power efficiency is critical; the planet cannot supply the energy required for always-on AI. Thus, ultra-low-power designs and physics-level innovations are presented as non-negotiable. [22-24][142-144]
Overall purpose / goal
The discussion serves to persuade policymakers, industry leaders, and the broader audience that the next wave of AI must be democratized through edge-centric, secure, and ultra-efficient semiconductor solutions. By outlining a compelling future vision and demonstrating NXP’s concrete hardware roadmap, the speaker aims to align governmental AI ambitions (e.g., “AI for everyone”) with practical, scalable technology pathways.
Overall tone
The tone begins formally and celebratory, shifts into an enthusiastic, visionary narrative, then moves to a more technical and persuasive style when describing trust, safety, and hardware specifics. Throughout, it remains optimistic, using rhetorical questions and pop-culture analogies (“Dumbledore”, “Yoda”, “Superman”) to keep the audience engaged, and concludes on a hopeful note that the envisioned edge AI ecosystem is already within reach. No major tonal downturns are observed; the progression is from broad vision to concrete technical confidence.
Speakers
– Lars Reger
Role/Title: Executive Vice President and Chief Technology Officer, NXP Semiconductors
Area of Expertise: Semiconductor design, edge AI hardware, functional safety, secure and efficient AI systems[S1]
– Speaker 1
Role/Title: Event host / moderator of the ministerial conversation[S2][S4]
Area of Expertise:
Additional speakers:
– (none)
The session opened with Speaker 1 thanking the ministerial panel and introducing the keynote speaker, Lars Reger, Executive Vice-President and Chief Technology Officer of NXP Semiconductors, emphasizing the premise that “artificial intelligence runs on chips.” [1-4]
Reger began by questioning the prevailing data-centre-centric approach to AI, asking “What is this AI for?” and urging a shift from merely scaling compute power to defining a concrete purpose for AI. [9-12]
He then outlined the megatrends-demographic shifts, infrastructure upgrades, supply-chain pressures and tightening energy constraints-that drive an “anticipate-and-automate” future in which homes, factories and transport become self-monitoring and self-optimising. [18-24][26-33]
In that future, barrier-free homes continuously monitor health and wealth, factories run autonomously, and pilots-many now in their 70s-operate intelligent robots rather than aircraft. [30-33] Cars evolve into “rolling cocoons”, mobile living rooms that extend office space, a trend already observed in China during the COVID-19 pandemic. [43-49][44-48]
Reger identified four universal functional blocks that every intelligent system must possess-sense, think, connect and act-and argued that without “trust”, i.e. a combination of functional safety and robust cybersecurity, users will revert to manual control. The functional-safety principle is illustrated by the automotive requirement that a braking system never fails. [54-58][66-68]
To guarantee safety and real-time responsiveness he advocated a biomimetic architecture. He used a concrete “90-kg bag of water with a couple of bones” as a biological-robot analogy, mapping the spine to a deterministic, low-latency reflex loop, the cerebellum to a safe functional-control layer, and a higher-level AI layer to higher cognition. [74-82][78-82] He also employed colourful super-power analogies such as telepathy and X-ray vision to highlight the desirability of richer sensor capabilities. [130-135]
Reger highlighted the scale of AI workloads: insects such as ants, with only about 100 k neurons, can perform complex navigation, showing that most edge tasks require only tiny, highly efficient models. Industry data suggest that roughly 80 % of AI tasks will run on edge-optimised models. [90-95][S41][S38]
NXP’s response is a modular “Lego-brick” semiconductor strategy that offers interchangeable AI accelerator blocks, ultra-wideband radios and low-power sensors that can be combined for drones, cars, medical devices and building-automation systems. Concrete examples include a drone-control unit and the India-made Kinara AI accelerator, which can run a 10-billion-parameter language model at just 7 W, demonstrating that sophisticated inference is feasible on battery-powered edge devices. [95-100][149-154][101-104][118-124]
Ultra-wideband (UWB) technology underpins the interoperability vision. It enables use-cases such as gate opening and car-key functions from a wristwatch, and sub-millisecond, mile-range car-to-car communication that can give priority to ambulances at intersections. Emerging “meta-standards” provide a common language that lets home gateways coordinate blinds, solar managers and other appliances. [118-124][125-128]
Reger warned that deploying 50 billion connected devices would require roughly three times the planet’s available energy unless ultra-low-power designs are adopted, making energy efficiency a non-negotiable prerequisite for scalability. [141-144][22-24][66][S13][S45]
Recent autonomous-vehicle fatalities were traced to a “bug in the brain structure” of the autonomous system rather than a mechanical failure, underscoring the need for deterministic safety layers. [84-86]
In closing, Reger linked the technical roadmap to global policy ambitions, citing Prime Minister Modi’s call for AI for everyone and asserting that the democratisation of AI-for citizens in Togo, India, Germany and beyond-must happen at the edge, not solely in centralized data centres. [155-158][4]
Ladies and gentlemen, I thank our elite panelists who were a part of this ministerial conversation. Her Excellency, Ms. Togo, His Excellency, Nizar Patria, His Excellency, Rafat Hindi, Honorable Ministers from Togo, from Indonesia, and from Egypt, and I thank Ms. Debjani Kosh for moderating this ministerial conversation. And now I would like to invite Mr. Lars Recher, Executive Vice President and Chief Technology Officer, NXP Semiconductors. As we all know, artificial intelligence runs on chips, and Lars Recher is at the frontier of designing the semiconductors that will power the next generation of edge AI. In cars, in medical devices, in industrial systems. NXP’s work on secure, efficient, real -world AI hardware is essential to everything on the stage.
Ladies and gentlemen, please welcome the Chief Technology Officer of NXP Semiconductors, Mr. Lars Reger.
Namaste. Hello everyone and thanks for having me here. When we are talking about AI, at the moment there is a lot of talk about how do we pump AI in big data centers, how are we energizing these big data centers, but very honestly, there is a lot of questions. What is this AI for? What is this AI at all doing? And if I’m looking at my own lifespan, I’m coming from an analog world, was born in the 1970s. Then there was some heavy digitization in there over the last 20 years, when someone stuffed a laptop into… into a mobile phone and they called it smartphone. So we had a data display device. We could run topics that were on demand.
So on demand, I need a pizza, I need an Uber, I need to switch on the climate control in my house. And now my Marcom people would say, Lars, we are entering a phase of the world that anticipates and automates. And this little world that anticipates and automates is driving megatrends around us. And these megatrends are unchanged over the last 15 years. We have demographics changes. We have infrastructure upgrades. We have supply chain constraints. We have renewable and we have energy constraints. So out of all of these drivers, what is this modern world that anticipates and automates able to do for us? Well, jumping forward maybe 20 years, how is the cocoon that I’m living in going to look like?
I will have a shelter. I will have my house, and that house is totally barrier -free. That house will check about my health, my wealth, will protect me. I can enter and I can live. I can live. without touching anything. No one else can do the same and my property is protected very seamlessly. No barriers for me, but maximum safety and security. How will be my manufacturing landscape look like? Well, most of the manual tasks are gone. I need better education and I may be the most advanced equipment operator in the world. Look at airplane pilots 70 years ago. They were guys my size. These type of muscles and arms were flying in thunderstorms, real heroes, mechanical pilots.
Today we have more pilots, but they are all genders, shapes and sizes because they are operating flying intelligent robots. So when I come from Germany here to India, a pilot, mechanically, I’m not a pilot. has to work for 30 seconds at the end of the runway, pull up the plane, and all the rest is happening already today autonomously. And that’s going to get better in the industrial world. And of course, also in the transportation world. How are cars going to look like in 20 years? Well, they are rolling cocoons, rolling robots, and these cars are rolling living rooms. You have seen this during the COVID pandemics in China, for example. A lot of people use their cars as their office extensions.
Too many people in the house at home, the kids were too noisy. You go to these type of places, so you have a rolling cocoon again that is anticipating and automating what you want to do, what you want to achieve. And what does this all have in common? I mean, most of the people are asking me now, okay, Lars, nice. You are predicting that there is 50 billion of these smart connected robots out there in 10 years from now. But they have so different form factors. What does that mean? Well, simple answer. They have all the same ingredients. Each of these little robots has to sense its environment. So what’s happening around me? Has to connect to the cloud to get the data.
Last ones to drive from here to Mumbai, how is the traffic situation? Getting the information from the web. And then you start thinking of a smart advice. This is where AI comes into the play. At that moment, you have to think of what is my best advice to the arms and legs to my robot. And whether these arms and legs are an automotive powertrain and a steering wheel, is a manufacturing arm, or is the wireless connection to my climate control from my smart thermostat. I don’t care. Sense, think, connect, act are the ingredients for every of these 50 billion robots. Now, the only thing is, that all is nothing if you cannot trust. Because if your fridge starts ordering 500 liters of milk for the next weekend, you go shopping alone if your car does erratic driving you start driving manually again and if your thermostat sets your house on 50 degrees centigrade and your flowers are dried out and your cat is dead you go organizing it all manually again so trust is the essence and how does a nerd like me define trust that’s very simple this is functional safety like in automotive make sure that your braking system never ever fails and make sure that your connected device your car or whatsoever is never ever being hacked and then you can trust your device you can be sure that it doesn’t turn against you so these underlying levels make sure that you are energy efficient because otherwise you cannot be battery powered make sure that you are trustworthy so safe and secure you and then make sure that you can sense, think, connect, and act.
And you can build every robot in the world. And that is, of course, interesting for a semiconductor maker because for us, volume matters in these semiconductor chips. Now, you will ask me, but Lars, we have so long already these discussions on autonomous vehicles. In 2018, the entire press community thought, in 2020, my kids are going to the kindergarten without a steering wheel and without me, autonomously. That did not happen. Why? Because we have designed these robots wrong. And how do you design the robots right? Well, try to copy from nature. That normally works. And here on stage is a 90 -kilo bag of water with a couple of bones, or in other words, a biological robot. And that robot has a certain architecture.
That robot has different layers. That robot has a real -time system, highly functional safety, and that is my spine. And that is my spine. and if I stumble, the reflexes in my spine tell me already straighten your leg. In real time, highly undisturbed, very, very fast. No AI, not big AI, very deterministic system. Then I have in green my cerebellum that is working also in a highly functional, safe environment for heartbeat, stomach control, stability control. I can stand here and stand in a stable way because only the blue part is trying to find out what is the next sentence that I’m firing towards you. And green and orange are working to manage the infrastructure in a functional, safe way that is standing here in front of you.
So why don’t we copy these approaches into vehicles, into cars, into houses, into planes again? Well, there are simple architectural constraints and building mechanisms. There are building blocks that we need and we need to scale. But how big does the AI really have to be? So that AI in these systems. can be comparatively tiny. If you’re talking about transportation robots and how these transportation robots should look like, well, look at intelligent transportation robots, insects, for example. These insects have 100 ,000 neurons and an ant is already a very, very nice, very sexy transportation device. It’s not as intelligent as a human being with 90 billion neurons, but for most of the tasks, it is also not needed in this way.
And Ashwini Vishnath said it very nicely in Davos. 80 % of the AI tasks around us will be on very tiny, efficient, and very, very tailor -made models at the famous edge, so in the end devices. And this is what we are designing for. So in other words, NXP is trying to build all these Lego blocks where you can start scaling small, medium, and large devices. You have these devices here. This is, for example, sorry, very small devices. This is a complete drone control unit. And this is a complete drone control unit. that also flies with AI, artificial intelligence, and reaches targets, not only remote control, but is operating the entire drone and is finding via the camera its way.
What I have here is an India -made AI accelerator from Kinara and Hyderabad that NXP has acquired. This is carrying 10 billion parameters in a large language model. So it is not as big as JetGPT. But the combination of those two systems carries a large language model and operates an intelligent system at the edge for a power consumption of 7 watts. So in other words, you can build these type of plug -in combinations and you have a system, for example, at a computer tomograph that is taking my entire X -ray pictures and is writing the doctoral report that is operating at my fridge and tells me how many bottles of milk are missing. you do not have to have it always on and always operational so these seven watts are only consumed the moment where the fridge tries to find out what is missing and then you can go to sleep again that is the answer for this global quest of how many nuclear power plants do we need when we send one question to chat gpt and that is what the edge is going to solve for all of us but beyond all of that we are always talking about ai and the brain structure of these robots most of the cars that have created fatalities in the last 10 years these autonomous vehicles didn’t create these fatalities on the roads because they had a bug in the brain structure they created these issues because they were more short -sighted than i so wouldn’t it be great to have these robots with superhero senses wouldn’t it be great if these robots out there would have telepathic capabilities?
I do not need to touch anything, but the stuff around me is arranging, like Dumbledore. One move of the magic wand, everything is arranged. Wouldn’t it be great to know what is ahead of your line of sight, like Yoda, telepathy? Wouldn’t it be great to have X -ray vision like Superman? You look in rain, in snow, and in fog what is around you. Wouldn’t it be great for the very old ones amongst us to be like in Hitchhiker’s Guide through Galaxy? You have one bubble fish that you plug into your ear and you understand the entire universe, every language that is spoken. A German can understand Hindi without a big barrier in between. And wouldn’t it be great if our robots would have better ears than Daredevil or an owl in real life and would be able to hear what is being spoken out there in the outer ranks?
If we would have that. then the driving robot that replaces Lars is way better than Lars the driver himself. But I am the entry ticket for driving 250 kilometers an hour on the left lane of a German highway with my car. Now, you think we cannot have that for our robots next to this little bit of AI that we need? Well, let me tell you, we have it already. We have ultra wideband technology that is opening gates and car keys from my watch to everything around me. I have car to car communication over more than one mile of distance in three milliseconds. I can immediately tell the device there is an ambulance rushing into the crossroads, switch the traffic lights to green for that ambulance and to red for me.
Telepathy. We have radar systems over 300 meters that see two persons sitting like you next to each other. And we can detect. I’m in rain and snow and in focus. We have meta standards. So the English for smart connected devices. There is a common language in place and all devices are talking to each other. The home gateway is talking to the window blinders, is talking to the solar cell management. This is the entry tickets for this democratization of AI functionality and for the entrance of these tiny devices here with a little bit of AI, a lot of functional, safe and secure architectures to building the right devices. And what we have done with a little bit of AI and a couple of microphones in cars, we can take the in -car microphones, the sound in a way that we hear a bicycle bell behind the cars.
And we can easily detect whether there is vulnerable road users, for example, behind the cars. We can do this in any other settings as well. But automotive is there a very nice one. So in other words, where are we at? At the moment, when I’m talking to my fellow nerds and the and the semiconductor researchers. it is not about ai alone it is how you can build systems that you absolutely can trust how can you go low power and then the key question how big does the brain has to be and the answer is somewhere between a hundred thousand and a hundred billion neurons beyond all of that there is very very interesting questions that we have to solve and where india is deeply with the europeans in research and in the activities how do we make the wiring harnesses how do we battery operate all of that how we are sensing in the right way how do we think so all of these separate topics and to not make it too nerdy and too complex all of these silicons here are driving then these form factors a lot of people are only talking about humanoids and sorry to say humanoids are the tiny fraction of robots because why should a robot look like a human being I mean, that only makes sense in a very human environment, climbing stairs or whatsoever.
Otherwise, you have robots that are looking like ultrasonic devices, that are looking like infant monitor devices on neonatology stations in hospitals. There is no need to look like me. But all of this, we are equipping already with silicon in the hundred thousands today, and the ingredients are always the same. And just to get this pitch here down on the runway, to say it in the drone language, what do you need to do? What do we need to work on? What does the industry do at the moment? Well, in a very simple way, we are working on safe and secure architectures that are ultra low power, ultra energy efficient. Again, otherwise, this dream of 50 billion smartphones.
Connected devices will not work. because these 50 billion smart connected devices need three times the energy that Mother Earth can provide. So that is the absolute must for these markets to come into play. Then what we need to work on is we have to push the boundaries, the envelope of physics, and we are doing. We are sensing better than human beings in the meantime. And then what we need to do is just a simple game that semiconductors have done since 50 years now. We need to scale in the right way. So we need to build these little Lego bricks and say, okay, here is a complete drone control unit that you can fly autonomously. You want to fly with large language models and very, very smart AI slalom between the trees.
Plug this little dongle in, and you have everything on board that you can do. And the same you can do for building control systems with manuals. You can do this for any form factor that you like. And that is what we are doing at the moment. while the AI models are getting much, much more efficient, smaller, and we carry them here. So my pitch is, when PM Modi says he wants to bring AI to everyone, this is the answer. The answer is not data centers. They will exist. But the democratization of AI and equipping everyone in Togo, as we heard earlier, or in India, or in Germany, with the right levels of AI that create the world that anticipate and automates, the answer lies at the famous edge in the end device.
Thank you.
“Pilots—many now in their 70s—operate intelligent robots rather than aircraft.”
The knowledge base notes that today there are more pilots of various ages operating flying intelligent robots, confirming the shift from traditional aircraft to robotic systems [S8].
“Robots are already being used in production facilities and private households, enabling barrier‑free homes and autonomous factories.”
Source S14 explicitly states that robots are in use in production facilities and private households, supporting the claim about autonomous factories and smart homes [S14].
“Megatrends such as demographic shifts, infrastructure upgrades, supply‑chain pressures and tightening energy constraints drive an “anticipate‑and‑automate” future.”
S50 discusses how future work and societal change are driven by technological change, demographic shifts and other megatrends, providing broader context for the claim [S50].
“Trust, defined as a combination of functional safety and robust cybersecurity, is essential; the automotive braking system is used as an illustration of a safety‑critical requirement that must never fail.”
S61 emphasizes that trust (including safety and security) is a prerequisite for technology adoption, and S62 describes functional-safety requirements for robotic systems, adding nuance to the safety-critical example [S61] and [S62].
The discussion shows a clear consensus that future AI deployment hinges on semiconductor innovation, edge‑focused low‑power designs, and robust safety/security mechanisms.
High agreement on the technical foundations (chips, energy, trust) but limited overlap on broader social or policy dimensions, indicating a focused but narrow consensus.
The transcript contains an introductory segment by Speaker 1 that thanks the panel and introduces Mr Lars Reger, followed by a single, uninterrupted presentation by Lars Reger. No other speaker offers a contrasting viewpoint, and therefore there are no observable points of contention, either direct or indirect, between participants.
Very low – the discussion is essentially a monologue after the opening remarks, so no disagreement emerges. This implies that the session was designed more as an informational showcase of NXP’s AI‑edge strategy rather than a debate on policy or technical directions.
Lars Reger’s remarks repeatedly redirected the conversation from hype‑driven, data‑center centric AI narratives toward a pragmatic, human‑focused vision anchored in trust, safety, and ultra‑low‑power edge computing. Each pivotal comment introduced a new dimension—purpose, biological design analogies, edge hardware feasibility, communication latency, form‑factor relevance, and energy sustainability—that collectively reshaped the discussion’s trajectory. By interweaving vivid future scenarios with concrete technical examples, he deepened the analysis, challenged prevailing assumptions, and set a clear agenda for how AI should be democratized through scalable, trustworthy, and energy‑efficient semiconductor solutions.
Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.
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