Predicting Weather Patterns More Accurately One of the most significant contributions of AI is in predicting weather patterns with enhanced accuracy. Traditional models often struggle to predict sudden weather changes,

But while AI has led to significant advancements, like tracking greenhouse gas emissions and sand dredging, its rise comes with an environmental cost.

While many businesses still have reservations about using AI in the supply chain, it can prove crucial in reducing operational expenses and improving efficiency.

Only a couple of months into 2025 and the global AI race shows no signs of slowing down – in the past few weeks alone the artificial intelligence landscape has been further revolutionised by groundbreaking advancements and unprecedented investments.

This is an independently submitted op-ed for our Quill section. Views and statements made in this article do not necessarily reflect the opinions of The Tower. Image Courtesy of BWCFLA Do you remember middle school science class?

We are also seeing a growing intersection of AI and blockchain in sustainability. By integrating AI-powered measurement and monitoring with immutable records on blockchain, businesses can create auditable, tamper-proof sustainability claims – critical for regulatory compliance and investor confidence.

The environmental cost of AI has been well documented, with the development and deployment of large-scale AI models consuming massive computational power. Training advanced models requires significant energy,

Artificial Intelligence Forecasting System is “first operational open AI model” for weather forecasting, according to European Centre for Medium-Range Weather Forecasts.

Video posted on the president’s Truth Social platform included clip of Trump and Netanyahu sipping cocktails and lounging topless beside a pool

On the energy and climate side, it's understandable that many would assume that this will mean we'll use less energy. But this is a mistake. Cheaper, more efficient, more accessible AI will exponentially increase the use cases for AI, thereby also increasing demand for its inputs.

But while AI has led to significant advancements, like tracking greenhouse gas emissions and sand dredging, its rise comes with an environmental cost.