AI’s Economic Benefits Surpass Emissions Concerns According to IMF

by News Editor

April 27, 2025

The International Monetary Fund (IMF) has recently highlighted the potential economic benefits of artificial intelligence (AI), projecting a global output boost of approximately 0.5% per year from 2025 to 2030. This growth is expected to surpass the environmental costs associated with higher carbon emissions from AI-driven data centers.

The report, showcased at the IMF’s spring meeting, emphasizes the need for equitable distribution of these economic gains while managing the adverse effects on our climate. The forecast indicates that AI’s contribution to GDP growth will outweigh the financial impacts of emissions, though it points out the necessity for policymakers and businesses to mitigate societal costs.

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Energy Demands and Environmental Footprint

AI is set to escalate global electricity demand, potentially reaching 1,500 terawatt-hours (TWh) by 2030, mirroring the energy consumption of countries like India today.

The increasing demand for data processing capacity could result in higher greenhouse gas emissions, but the AI industry aims to offset these with advancements in renewable energy technologies.

AI: A Driver for Energy Efficiency?

Analysts suggest that AI could potentially reduce carbon emissions through improved energy efficiency, fostering advancements in low-carbon technologies across sectors such as power, food, and transport. Grantham Research Institute stresses the significance of strategic action from governments and industries to facilitate this transition.

The role of AI in the global economy continues to evolve, stirring debates not only about its economic potential but also its environmental impact.

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Mirendil Signs $100 Million Google Cloud Deal To Advance Self-Improving AI

by News Editor

August 6, 2026

AI startup Mirendil has signed a multi-year agreement worth more than $100 million with Google Cloud to secure computing infrastructure for its self-improving AI research.

The partnership reflects growing competition among AI companies to lock in access to high-performance computing, while cloud providers race to attract promising startups developing next-generation AI models.

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Backing The Next Stage Of AI Research

Mirendil plans to use Google’s Tensor Processing Units (TPUs), Nvidia GPUs and managed training infrastructure to develop AI systems capable of improving their own performance over time.

Known as recursive self-improvement, the concept focuses on building AI that can refine its knowledge and capabilities with minimal human intervention. The technology is attracting growing interest across the industry, with several startups and leading AI labs exploring similar approaches.

According to co-founder and Chief Executive Behnam Neyshabur, the long-term goal is to develop AI that can automate scientific research and accelerate discoveries in fields such as medicine, biology and materials science.

Compute Capacity Becomes A Strategic Asset

Training increasingly advanced AI models requires enormous computing resources, making long-term infrastructure agreements a critical competitive advantage.

Mirendil said Google’s combination of TPUs and GPUs allows workloads to be matched with the most suitable hardware, improving efficiency while reducing costs for customers.

For Google Cloud, the agreement strengthens its position in the race to provide infrastructure for frontier AI developers, while giving the company exposure to one of the industry’s emerging approaches to next-generation artificial intelligence.

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