Artificial intelligence (AI) is constantly reshaping everything we do. Across industries, it is changing the way we do work, but that rapid expansion can’t continue without bumping up against real tangible limitations.
Most notably, planning for hyper scaled data centers across the world and increasingly complex cloud computing infrastructures and AI systems are leading to difficult conversations around energy pricing, generation and availability.
Around the world, electricity consumption is increasing at some of the fastest rates seen in decades, and there are no signs of it slowing down. The International Energy Agency (IEA) projects global electricity demand growth of 3.3% in 2025 and 3.7% in 2026, driven heavily by those same data centers, AI deployment, and other advanced industrial expansion.
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The IEA has also warned that electricity demand from data centers is expected to double by 2030, with AI-focused facilities alone projected to triple their power use over the same period.
Vice President, Integrated & Wireless Systems at CSEM.
The financial implications and policy blowbacks are already starting to be felt. With limited expansions of electrical grids, more consumers are fighting for less resources, causing prices to only go up. In fact, according to S&P Global, some regions with AI data centers have seen wholesale electricity prices surge by more than 250% in the past five years.
This growing tension between AI advancement and energy availability is beginning to reshape how the technology sector thinks about the future of innovation. For years, the dominant assumption was that progress in AI would mainly come from scaling model size and centralized compute infrastructure.
But the next wave of value creation will also come from AI embedded in the physical world: machines, devices, buildings, industrial assets, medical wearables, and infrastructure that continuously sense, act, and adapt. In that context, the question is not only how to train larger models, but how to process massive streams of real-world data with minimal latency and minimal energy.
That is why alternative architectures, including low-power and decentralized AI, are becoming strategically important.
What low-power AI systems are
Low-power AI are systems specifically designed to minimize the resources required for inference and online learning, particularly energy consumption, while still delivering on high-performance expectation. Rather than relying entirely on massive cloud-based infrastructure and centralized data centers that guzzle down energy, these systems prioritize resource-efficiency at every level of the technology stack, from semiconductor architecture to data processing and deployment.
Low-power AI is not a single breakthrough at model level. It is a system-design discipline that spans sensing, signal conditioning, embedded processing, semiconductor architecture, algorithm optimization, and deployment. The real challenge is to co-design hardware and software for a specific use case so that intelligence is delivered where it matters, with the lowest possible energy budget.
This is precisely where research-transfer institutions such as CSEM can contribute: by combining expertise in sensors, edge computing, ultra-efficient IC design, and application-driven system integration to translate AI into robust real-world solutions rather than generic demonstrations.
Most of the focus in AI development has been in creating systems that are trained and operated on generalized architecture, handling a wide array of tasks simultaneously. These systems are immensely powerful but rely on the same models that require copious amounts of energy to keep them functioning.
On the contrast, low-power AI systems focus on more highly specialized systems, limited in scope and capabilities to a well-defined set of tasks that allow them to be less reliant on vast infrastructure and energy resources to function.
This includes edge AI, where data is processed directly within devices and systems rather than being sent continuously to remote cloud infrastructure. That shift matters even more in the era of physical AI. When intelligence is embedded into the real world, the volume of potentially relevant data generated by sensors, machines, vehicles, buildings, and other assets becomes enormous.
Sending everything to the cloud is not only inefficient, but often too slow and too costly. Many decisions must be taken locally, in real time, with strong constraints on power, bandwidth, privacy, and reliability.
Low-power AI therefore becomes essential not just to reduce energy use, but to preprocess data close to where it is generated, extract the small fraction of information that is meaningful, and enable the broader system to be monitored and optimized for performance, resources, and health.
Perhaps most importantly, low-power systems expand where AI tools can realistically operate. Wearable medical devices, industrial sensors, remote monitoring systems, transportation infrastructure, and smart manufacturing environments all require AI systems capable of functioning within strict energy constraints.
These contexts show places where sustainability is not only a cost-effective measure, but a functional requirement. At the sub-milliwatt level, some systems can even move beyond battery dependence and become energy-autonomous, harvesting ambient energy from light, heat, or vibration to enable a true fit-and-forget lifecycle.
Why efficiency is becoming an imperative
Power generation capacity, transmission infrastructure, cooling resources, and semiconductor supply chains are all facing mounting, simultaneous pressure. The assumption that future competitiveness depends solely on building larger and more power-intensive systems may no longer hold true, with further expansion likely bringing with it exponentially higher costs.
Organizations capable of delivering efficient, highly targeted distributed AI systems could gain major strategic advantages and offers a pathway toward greater technological resilience, as their design natively makes them more resistant to fluctuations in electricity pricing, supply disruptions and geopolitical instability.
Additionally, a more sustainable option can bring value by reducing environmental impact, while still not sacrificing function. The conversation around responsible AI therefore cannot remain focused solely on software governance and ethical frameworks but needs to be talking about how systems are powered, and how and where they process information.
A strategic opportunity for smaller nations
The rise of low-power AI also bears the opportunity to redefine who can meaningfully participate in the global AI race.
The United States and China have been postured as global tentpoles when it comes to the development of AI, and subsequently massive AI investments that have followed suit.
Both are examples of large nations that have the resources to invest billions into data centers, chip production and other infrastructure. On first glance, this paradigm forces many smaller nations to miss the financial and innovation benefits of the AI movement.
But with low-power and distributed AI systems, smaller countries do not need to compete on scale alone. They can compete through specialization, precision engineering, and the ability to translate research into deployable systems for demanding applications.
My home nation of Switzerland provides a useful framework for what this looks like in practice. Similar to most nations across the world, we cannot outspend the largest economies, but we do possess strong capabilities in microelectronics, embedded intelligence, sensing technologies, and high-value industrial and medical applications.
By recognizing these strong foundations, technology transfer organizations like ours can then play an important role in bridging these unique national strengths with industrial deployment, helping transform AI from a cloud-centric paradigm into efficient intelligence embedded in the physical world.
Even for relatively small nations with limited natural resources, there is an opportunity to be a leader in AI development by embracing low-energy system design. Chip producers with less resources will have to increasingly focus on creating specialized, energy-efficient technologies optimized for targeted applications to let them compete on the global stage.
As energy constraints become more severe, demand will likely grow for AI systems capable of operating efficiently in real-world conditions rather than exclusively within massive, centralized infrastructure environments.
In many ways, low-power AI could democratize portions of the AI boom by rewarding efficiency, precision, and specialization as opposed to simply providing opportunities for regions that can match scale. It can be said that virtually every country on earth has some level of specialized technical expertise that can be bridged to an AI use case.
The next generation of AI will see a shift from chat interfaces and cloud platforms to physical systems that shape daily life and industrial productivity. In that setting, efficient local intelligence is not a secondary optimization; it is a core architectural requirement.
Physical AI will depend on the ability to sense the world continuously, interpret it selectively, and act on relevant information without moving every raw data stream through centralized infrastructure.
The democratization of AI brings with it the need for more democratized solutions and opportunities for all to participate.
Looking ahead
While the early years of the AI boom have been defined by large, multi-purpose models and increasing scale, the next chapter will likely be written by developers and ecosystems that are able to utilize precision engineering to focus on low-power distributed systems that are by nature more resilient and sustainable.
Energy availability is no longer a secondary consideration in AI development and will increasingly be one of the defining variables shaping the future of the industry, and subsequently, how the global economy is built. That reality is making low-power AI an emerging necessity.
The next generation of AI systems must be designed with these restrictions in mind, requiring advances in semiconductor design, edge computing, specialized architectures, and intelligent energy management.
Countries and companies that embrace these more efficient and targeted systems may ultimately be better positioned for long-term competitiveness than those that rely instead on growing as large as possible as quickly as possible.
The European Union’s Joint Chips Undertaking is already bringing together its member nations, as well as some outside partners like Switzerland, to develop pathways to technologies like low-power chips. With that in mind, the future of AI may not belong solely to the biggest players, but to the smartest and most efficient ones.
For the global economy, that may prove to be one of the most important transitions of the AI era which only started to come to prominence recently with the growing controversies regarding hyperscale data centers.
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