Antarctica is a global climate technology company that enables enterprises to reduce both the environmental impact and operating costs of their IT systems. Established in 2018, the company delivers an AI-driven platform that provides real-time measurement, deep observability, and intelligent automation across cloud environments, AI workloads, and core IT infrastructure.
Moreover, the platform unifies cost, energy, and carbon data into a single operational layer. It generates actionable FinOps and GreenOps insights, strengthens sustainability reporting, and accelerates enterprise decarbonization strategies. As a result, organizations optimize financial performance while advancing measurable climate outcomes. In doing so, Antarctica integrates climate accountability directly into technology governance and digital operations.
In an exclusive conversation with The Interview World at the India AI Impact Expo 2026, Mathieu Francois, Chairman & CEO, Antarctica explains how the platform tracks the carbon and energy footprint of AI systems in real time. He addresses the systemic environmental risks driven by escalating AI-related energy demand. He also articulates the strategic urgency of achieving Net Zero and outlines how Antarctica’s platform supports that transition. Finally, he shares his vision for the next wave of innovation the company plans to introduce over the coming five years.
The following are the principal insights from that discussion.
Q: How does your platform monitor the carbon and energy footprint of AI systems in real time, and what capabilities does it provide to reduce environmental impact without compromising performance?
A: We assess the environmental impact of AI with precision and discipline. To do so, we measure the true footprint of every AI interaction, whether it is a token, a prompt, a session, or an API call. In other words, we quantify the impact of every computational event tied to AI.
Specifically, we track three critical variables: cost, performance, and energy consumption. In parallel, we calculate the associated carbon emissions generated by each token processed. While most organizations know the market price of a token, they rarely understand its underlying operational burden. They do not see what it truly consumes in compute resources, energy intensity, or carbon output.
Therefore, we focus on real-time measurement at the most granular level. By exposing the full economic and environmental cost of AI workloads, we enable enterprises to optimize their systems with clarity and control. As a result, organizations can make informed decisions that improve performance while reducing both financial and environmental waste.
Q: How does your organization address the systemic environmental challenges posed by AI-driven energy demand growth?
A: This challenge is both urgent and systemic, particularly in a country like India, where nearly 70 percent of electricity generation depends on coal. Consequently, we have built the most power-intensive digital infrastructure in human history. Yet despite its scale, we lack real-time instrumentation to measure its true resource consumption.
As a result, when users interact with systems such as ChatGPT, Claude, or Gemini, they remain unaware of the full environmental cost of each prompt and response. They do not see the underlying compute load, the energy consumed, the water withdrawn for cooling, or the carbon emissions generated. These externalities remain invisible. Likewise, developers integrating AI APIs into applications rarely quantify the operational and environmental burden embedded in each call.
However, for enterprises, this opacity translates into tangible risk. It creates financial exposure and operational vulnerability. For example, rising cloud expenditures, whether on AWS or other hyperscale platforms, often reflect upstream increases in energy prices and grid intensity. Without granular measurement, organizations cannot identify or mitigate these drivers.
Therefore, we introduce real-time visibility across energy, water, and carbon metrics. By measuring and reducing resource intensity at the workload level, enterprises can optimize both infrastructure and spending. In doing so, they strengthen financial resilience while advancing environmental responsibility.
That integration of economic discipline and climate accountability defines the core purpose of Antarctica.
Q: Net Zero has become a defining leadership imperative globally, particularly for developing economies. How does your solution materially accelerate progress toward Net Zero targets, and what quantifiable impact does your solution demonstrate?
A: The Net Zero imperative now shapes policy, capital allocation, and corporate governance worldwide. In Europe, decarbonization frameworks are advancing rapidly. Meanwhile, India is accelerating regulatory enforcement. The Securities and Exchange Board of India (SEBI), through its Business Responsibility and Sustainability Reporting (BRSR) framework, now mandates disclosure of Scope 1, Scope 2, and Scope 3 emissions, that is, direct emissions, purchased energy emissions, and value-chain emissions. Companies must quantify and report these exposures with rigor and transparency.
Accuracy is not optional. Regulators demand it. Moreover, capital markets increasingly reward it. Investors evaluate ESG performance before allocating funds, and sustainability disclosures directly influence ratings, valuations, and access to capital. In this context, credible emissions accounting becomes both a compliance requirement and a strategic advantage.
We address the Net Zero challenge through a closed-loop model. First, we establish a precise emissions baseline by measuring energy consumption and carbon output at the operational level. Next, we deploy targeted optimization strategies to reduce that footprint in measurable increments. Finally, for unavoidable residual emissions, we enable the generation and monetization of carbon credits. Organizations can trade these credits in regulated or voluntary markets, thereby converting sustainability gains into financial instruments.
In effect, we transform decarbonization from a compliance burden into a value-creating mechanism. We help enterprises reduce emissions systematically, and then capitalize on the progress they achieve.
Q: How do you envision the evolution of your platform over the next five years, and what new technological innovations are you prioritizing?
A: One of the most consequential shifts we are witnessing is the rise of agentic AI. Unlike episodic AI workloads, autonomous agents operate continuously. They run 24/7. As a result, AI no longer generates intermittent demand; it creates persistent base load.
Consequently, energy consumption rises structurally rather than incrementally. This trajectory will accelerate. Organizations will deploy more agents, expand their autonomy, and integrate them deeper into core operations. Therefore, the aggregate compute intensity, and the associated energy demand, will increase materially over the coming years.
In this environment, measurement becomes indispensable. We must quantify the impact of deploying each agent. We must attribute cost, performance, and energy consumption to specific agents, users, workloads, and geographies. Without this level of granularity, organizations cannot manage scale responsibly.
Ultimately, measurement is not a reporting exercise. It is the control mechanism. Only through precise attribution and real-time visibility can enterprises govern agentic AI sustainably and economically.
