Responsible AI is needed now, and it starts at the chip level

February 16, 2026 by Alain-Serge Porret, Vice President, Integrated & Wireless Systems, CSEM

Collected at: https://www.eeworldonline.com/responsible-ai-is-needed-now-and-it-starts-at-the-chip-level/

As artificial intelligence (AI) becomes even more deeply woven into the fabric of everyday life, conversations continue to evolve around the importance of “responsible AI”. Those discussions have largely focused on the software itself and how it’s managed, including pushes for model transparency, data governance, and bias mitigation. These are necessary, but miss a critical area of responsible AI development.

Rather than solely analyzing the cloud or policy, it is becoming increasingly clear that it is time for a discussion around the hardware itself. That hardware, namely chips, impacts every aspect of how AI behaves and relates to the world, including architecture, infrastructure, energy use, and data flow. With an increase in energy costs and the world grappling with the potential environmental impacts of the data centers underpinning our AI infrastructure, the development of chips at the ground level that can prioritize high-performance with a low-energy output is essential, both for sustainability and affordability.

The rapidly increasing power demands of today’s complex AI models are already beginning to make continued AI growth a potentially damaging prospect to the environment. Data centers and manufacturing require support that is both highly water and energy-intensive, resulting in more emissions and a more negative environmental impact.

These increases are also resulting in rising costs, leading to more potential contributors being priced out of joining the market. Smaller regions, with less natural resources or investment muscle than the global leaders like the U.S. and China, are not able to keep up in the arms race and have found themselves on the outside looking in.

That’s why a different approach, which is more sustainable, high-performance, precise, and low-energy output chip production, is a potential open door, reducing emissions and providing a route for smaller regions around the world to enter the market. Switzerland has spearheaded that model, approaching semiconductor development with these realities in mind.

As global AI deployment accelerates, the Swiss approach, which is grounded in efficient, specialized, and transparent system design, can offer a clear blueprint for how the industry can develop chips and implement AI at scale without relying on overwhelming amounts of limited resources.

Energy efficiency as a strategic imperative

As AI adoption expands globally, energy consumption is becoming one of the defining challenges of the industry. Training and operating large models require vast amounts of electricity, raising concerns not only about cost but also about sustainability and continued access to AI technologies. Recent reports have predicted that in the United States alone, data centers could consume upwards of 68 billion gallons of water a year by 2028, with an estimated three percent of all electricity consumption around the world being tied to AI demands by just 2030.

With skyrocketing costs of energy and water, the model of building increasingly larger and more powerful AI models, with the subsequent data centers to support them, is an unsustainable approach. This is increasingly true for regions around the world without access to capital to match the billions being spent to match the increase.

Additionally, a reduction in energy demands is not only more sustainable but can improve production resilience. By developing more high-performance and low-energy chips, production and models will be less vulnerable to volatile energy prices, infrastructure constraints, and geopolitical disruptions.

As global supply chains become more complex, energy resources become more contested, and digital sovereignty is thrust front and center, efficiency could increasingly become a cornerstone of effective technology research and development.

How precise production can improve sustainability and chip functionality

Energy consumption is not just a byproduct of chip production; it is a constraint that influences deployment feasibility, environmental impact, and system longevity. Producing chips with greater precision, designed to power systems with more specific functions rather than wide-reaching ones, can alleviate these issues.

On the environmental side, having a more precise function can reduce the need for data exchanges and cut down on data center and power demands, lowering costs and the system’s environmental impact.

This low-power, high-performance approach can also improve functionality as well. For instance, systems that consume excessive resources limit where and how they can be deployed and operated. In the case of wearable sensors, the mobile aspect of the devices limits just how much energy any AI system can draw. Focusing instead of refining chips to the point where they are more precise and consume less energy can improve what functions those wearables can offer. Often, low-power AI systems enable broader adoption and longer lifespans, while reducing environmental impact.

Low-power AI architectures designed to process data directly at the sensor level can also dramatically reduce data exposure. When raw data never leaves the device, risks related to data interception, misuse, or regulatory non-compliance are minimized by design — not by policy.

Specialized intelligence can create seats for more at the table

Thankfully, a wider array of developers can leverage this chip development approach than can follow the current market’s trajectory.

Large chip productions have attracted massive investments around the world, as companies and regions try to keep up the pace by supporting larger models and getting ahead of competitors. In fact, according to the Semiconductor Industry Association, over half a trillion dollars (USD) have been committed to be invested in the United States alone in new semiconductor development and production projects.

Naturally, investments of these sizes have left smaller nations around the world priced out of the market. Switzerland, as an example, is in a unique situation that would hamstring more traditional pathways to global chip production relevance, but is still finding a way to carve its own path. For one, it’s a smaller country, leaving it with a lack of natural resources essential to widescale production, limiting its capacity from the outset. On top of that, it’s not a member of the European Union either, and while it has strong trade relationships with Europe, it again limits potential growth.

However, with its approach of boiling chip production down to its finest point and creating low-energy, high-performance outputs, developers in the region are still able to generate at a meaningful level that moves the needle while still operating within their means. These approaches aren’t meant to be a comprehensive solution like some larger models strive to be, but rather are laser-focused on meeting clearly defined criteria at a high level.

This carves out a role for developers who can’t generate chips at that same pace or scale, giving them a valuable secondary approach to focus on.

Manufacturing chips that fit the world we live in

The future of AI cannot be defined or assessed solely by the scale of models or computing power; the world just doesn’t have the energy availability to keep making that happen. Instead, it will be shaped by how well highly efficient and low-power models are able to augment what has been developed already, leading to a more sustainable technological future. Switzerland has provided a potential blueprint for a sustainable future of chip production that simultaneously addresses the need for reducing energy consumption while also providing an approach for smaller regions to support the global innovation landscape.

As the industry moves forward, the challenge will not be to build the largest possible AI systems, but to build the right ones and the right way. What drives those systems are the right chips that are developed and manufactured sustainably and that perform reliably. Switzerland’s experience shows that this path is not only viable but increasingly necessary for the next phase of AI and semiconductor innovation.