𝐁𝐞𝐲𝐨𝐧𝐝 𝐭𝐡𝐞 𝐇𝐲𝐩𝐞: 𝐓𝐡𝐞 𝐈𝐧𝐯𝐢𝐬𝐢𝐛𝐥𝐞 𝐂𝐨𝐬𝐭 𝐨𝐟 𝐀𝐈 𝐂𝐮𝐫𝐢𝐨𝐬𝐢𝐭𝐲

In this episode, we explore the measurable expenses of pushing AI boundaries, from CO2 emissions to workforce stress, drawing on recent research.

Key Learnings:

• The energy cost of LLMs, with training and usage emitting thousands of tons of CO2, comparable to powering entire countries by 2026.
• Career impacts, including stress and adaptation demands on professionals navigating automation and new roles.
• Operational challenges, such as mitigating hallucination and bias, requiring resource-intensive techniques like retrieval methods and dataset curation.
• The financial scale of AI investment, with $1 trillion at risk, contrasted by potential environmental gains if experimentation focuses on efficiency.
• The importance of evaluating these costs independently to understand their implications for AI/ML, data management, and career trajectories.

Speaker - ⁠Priti⁠— Founder, Human in Loop Podcasts

Check the references below. Dig deeper if you’d like! We all see things our own way, so feel free to explore.

References:

• LLM Reliability (Rush Shahani).
• Carbon emissions of the ChatGPT usage: environmental impacts of the ChatGPT in different regions (Scientific Reports, 2024).
• Explained: Generative AI’s environmental impact (MIT News, January 2025).
• 9 top AI and machine learning trends to watch in 2025 (TechTarget, January 2025).
• Energy and Policy Considerations for Deep Learning in NLP (University of Massachusetts, 2019) — Relevant for historical energy context.
• ChatGPT says our GPUs are melting as it puts limit on image generation requests (The Verge, March 2025) — Relevant for GPU strain from trends.