The hardest problem in AI is no longer the chip but the megawatt. For much of the past three years, the global AI race has focused on semiconductors, with governments competing for advanced chips, technology outfits scrambling to secure GPUs, and investors pouring billions into ever larger datacenters. Yet the binding constraint has shifted from compute to the power required to run it. For anyone trying to energize a new AI cluster today, the bottleneck is rarely silicon; it is grid access, interconnection delays, and aging infrastructure. That was the central message from Envision founder and CEO Lei Zhang at VivaTech in Paris this June, where he argued that AI amounts to an energy revolution as much as a computing one. The steam engine transformed the industrial age by converting coal into motion, and the GPU now transforms the AI age by converting electricity into intelligence. History offers another lesson: James Watt changed industry through the efficient use of energy rather than by producing more steam. AI faces the same problem today, because the binding constraint has shifted from how many chips can be built to how they can be powered. The real risk: AI competing with society for energy The numbers behind the argument are stark. Goldman puts US datacenter power demand at 31 GW in 2025, rising to 66 GW by 2027, while assuming only about 72 percent of scheduled facilities arrive on time because electricity, not construction, is what typically slips. The IEA estimates that datacenters consumed roughly 1.5 percent of world electricity in 2024, a share rising to 3 percent by 2030 as AI-specific demand triples. The structural mismatch sits at the heart of the problem: AI models iterate every six months and chips refresh annually, while power grids have changed little in decades. Rack densities that sat at 5 kW are climbing toward 200 kW, and the IEA notes that AI server power density rose elevenfold between 2020 and 2025, with a further fourfold rise expected by 2027, straining the supply chains for power electronics and transformers that keep a cluster stable. The growing gap raises broader questions about where the energy will come from and who will bear the cost. Around the world, communities are asking whether AI infrastructure should draw on electricity that households, factories, hospitals, and public services also depend upon, with familiar concerns surfacing about consumer bills, manufacturer access to limited grid capacity, and the burden that ever-larger models place on public infrastructure. Those questions have moved beyond the purely technical into the societal, because the future of AI cannot rest on a model in which humanity competes with AI for power. Mission Gobi: Let AI follow energy Envision's answer, Mission Gobi, unveiled at VivaTech, aims to develop 5 GW of green AI computing capacity across deserts and arid regions by 2030. For decades energy followed computing, and Mission Gobi reverses that logic on the premise that in the AI era, computing may need to follow energy. The logic is grounded in geography, because deserts offer some of the world's richest solar and wind resources alongside vast expanses of low-cost land, with the additional advantage of little competing residential or industrial demand. Rather than drawing power from homes, factories, and public services, Mission Gobi seeks to build entirely new renewable energy systems dedicated to AI, expanding the available supply instead of asking society to share a fixed pie. The philosophy reduces to a single idea: compute should chase power, not the other way around. The economics matter because electricity determines whether a facility is viable, with power consistently accounting for the single largest operating cost at a datacenter and some estimates placing it at as much as 60 percent of the operational budget. Building energy-native AI infrastructure Envision splits the system into three layers: an intelligent operating hub, Physical AI powered by its Tianji Weather Foundation Model and Dubhe Energy Foundation Model, and advanced power infrastructure. Together they integrate generation, storage, grid, power electronics, computing, and large-scale AI models into a unified architecture. The challenge lies in coordinating renewable power rather than merely generating it, because AI facilities require stable, high-quality electricity while solar and wind output fluctuate continuously. Envision argues that large-scale predictive models can help balance generation, storage, and demand in real time. The concept has already moved beyond theory. In Chifeng, Inner Mongolia, Envision runs a 2 GW system on 100 percent renewable energy, coordinating wind, solar, storage, hydrogen, and compute in real time, while a gigawatt-scale AI and computing campus in Ulanqab is being developed as a demonstration of what energy-native computing infrastructure could look like. A 5 GW pledge is ambitious, but the underlying read is sound: retrofitting decades-old city grids for gigawatt AI loads is a difficult undertaking, and purpose-built renewable compute, sited where power is cheapest, offers a credible alternative. SpaceX looks up, Mission Gobi looks out Envision is not alone in recognizing energy as AI's defining constraint. Elon Musk's SpaceX has explored concepts for orbital datacenters powered by uninterrupted solar energy in space, and the vision rests on the same recognition: the future bottleneck of AI may lie in energy rather than silicon. Both approaches seek to place computing where energy is most abundant. The two visions diverge in geography, with one reaching upward beyond Earth's atmosphere and the other outward toward deserts and Gobi regions, though both start from the same premise: AI should not compete with humanity for power. A new blueprint for AI infrastructure If the industrial age was built around coal and the electrical age around power grids, the AI age may be built around energy abundance. The success of future AI infrastructure will not be measured by GPU counts and model sizes alone. It will also depend on whether the industry creates new energy supply, eases pressure on communities, and enables technological progress without reducing others' access to power. Whether deserts become the preferred destination for future computing remains to be seen. What is becoming clear is that the next phase of the AI race will be defined not only by who builds the most powerful models, but by who can build the energy systems capable of sustaining them. The path forward runs through creating new energy supply rather than reallocating existing capacity away from households, factories, and public services. Contributed by Envision.
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