BrainBox AI shapes the global dialogue at the AI for Developing Countries Forum, United Nations Geneva Summit
Artificial intelligence (AI) is rapidly reshaping economies, industries and societies. But as AI adoption accelerates, an equally important question is coming into focus: how do we ensure that the next era of AI delivers progress without creating unsustainable demands on energy systems, infrastructure, and the communities they are intended to serve?
That question was at the heart of the AI for Developing Countries Forum (AIFOD) United Nations Geneva Summit 2026, where Jean-Simon Venne, President, Founder, & Chief Technology Officer of BrainBox AI, joined global leaders, researchers, and experts to help advance the dialogue around responsible, accessible, and energy-conscious AI.
Throughout the Summit, Jean-Simon brought a perspective shaped by years of applying artificial intelligence to one of the world’s most energy-intensive sectors: buildings. His participation included chairing the AIFOD Industry Review Board on Energy and leading a General Assembly discussion examining whether the continued pursuit of ever-larger AI models is necessarily the right measure of technological progress.
Together, these conversations pointed toward a broader principle: the future of AI should not be defined simply by how much computing power we can deploy, but by how intelligently and efficiently we use it.
Pictured 1: Jean-Simon (center left) chairing the Industry Review Board on Energy
Rethinking the relationship between AI and energy
Chairing the AIFOD Industry Review Board on Energy, “AI and Climate Change: The Kind of AI that Adds Load and the Kind that Removes It,” Jean-Simon set the stage for one of the defining tensions of the AI era: where AI adds pressure to the grid, and where it can ease it."
AI has extraordinary potential to optimize physical systems, eliminate inefficiencies, and accelerate decarbonization. At the same time, the computing infrastructure required to train and operate increasingly powerful models is creating significant new demand for electricity.
The challenge is not whether the world should deploy more AI, it is what kind, where, and whether the value justifies the resources that AI consumes.
Under Jean-Simon’s leadership, the Industry Review Board on Energy focused on turning that challenge into an actionable framework. The members were tasked with identifying a central finding, evaluating viable alternatives and developing a recommendation that could be carried forward to the Summit’s General Assembly.
Pictured 2: Photo from AIFOD (af.net)
The Board identified a fundamental imbalance: AI-related energy consumption is rising rapidly while many of the systems consuming and distributing that energy remain insufficiently optimized.
That gap represents both a challenge and an enormous opportunity.
The discussion highlighted three pathways toward a more efficient AI ecosystem:
The Board also examined how economic policy could accelerate adoption of these approaches. Among the approaches discussed by the Board were energy pricing structures that could create stronger economic incentives for the adoption of energy-efficient AI technologies.
The underlying idea is powerful and, surpisingly, straightforward: energy efficiency should not simply be a technical aspiration. It can become an economic advantage.
The Board’s conclusions were adopted and are expected to contribute to AIFOD’s forthcoming final report.
Jean-Simon also delivered a speech and chaired a General Assembly discussion around another increasingly important question: “Is Bigger Always Better in AI?”
For much of AI’s recent evolution, progress has often been associated with scale: more parameters, larger datasets, greater computing capacity and increasingly powerful foundation models.
But scale alone does not determine value.
The discussion challenged participants to consider a broader definition of AI performance, one that includes not only raw capability, but also cost, accessibility, specialization, environmental impact and relevance to local needs.
For developing economies in particular, this distinction is critical.
The most valuable AI solution may not be the largest or most computationally intensive model available. It may instead be a highly specialized system designed to solve a specific problem efficiently, economically, and at scale.
A central conclusion emerged from the discussion:
The best AI model is not necessarily the biggest or the smallest. It is the one that delivers the required outcome with the right balance of performance, efficiency, specialization, accessibility and impact.
That principle has implications far beyond model architecture. It challenges the technology industry to think differently about what innovation should ultimately optimize for.
The conversations in Geneva also underscored a wider shift taking place across the global economy. AI is moving beyond digital applications and increasingly becoming part of the infrastructure that powers cities, buildings, energy systems, agriculture, healthcare and education.
That transition creates an opportunity to rethink the relationship between technological growth and resource consumption.
Buildings offer a compelling example.
They represent one of the world’s largest sources of energy demand, yet many continue to operate using static controls and legacy systems. Applying AI to continuously optimize those environments demonstrates how intelligence can be used not simply to consume computing resources, but to generate measurable efficiency in the physical systems.
It is a model for a broader future in which AI becomes part of the solution to the very energy challenge its growth is helping to create.
Pictured 3: AIFOD Industry Review Board on Energy
Beyond the formal sessions, the Summit provided a window into the breadth of AI innovation taking place around the world.
Participants shared applications spanning energy, agriculture, healthcare and education, demonstrating how countries and organizations are adapting AI to their own economic, social and infrastructure realities.
One of Jean-Simon’s strongest reflections from the Summit was the pace of innovation underway across developing countries. As AI models become more accessible, the ability to innovate is becoming increasingly distributed. Breakthrough applications are no longer limited to a small number of technology hubs or organizations with access to enormous computing resources.
Instead, countries and communities are finding new ways to apply AI to highly specific local challenges, often with ingenuity, speed and an acute understanding of the outcomes that matter most.
That shift may ultimately become one of the most consequential developments of the AI era.
The global AI conversation is entering a new phase.
The question is no longer simply what AI can do. Increasingly, leaders must ask what AI should do, how efficiently it can do it and who benefits from the value it creates.
The discussions at the AIFOD United Nations Geneva Summit reflected the growing importance of those questions.
For BrainBox AI, they also reinforce a conviction that has shaped the company from the beginning: intelligence should translate into meaningful real-world impact.
As AI becomes increasingly embedded in the systems that power the global economy, the organizations building and deploying it have an opportunity and a responsibility to ensure that greater intelligence leads to greater efficiency.
The next frontier of AI will not be defined by scale alone.
It will be defined by our ability to apply intelligence where it matters most.