🚀 Exploring AI and Energy at Quantum Capital Group! 🚀
In our recent Quantum Capital Group AI Working Group session, Tuhin Srivastava, CEO of Baseten, and Brian O'Shea provided valuable insights into the evolving AI infrastructure landscape, focusing on the differences between foundational model training and inference. 🌐💡
Key Takeaways:
🌍 Open Source Momentum: The rise of open models like Llama and Deepseek is reshaping the AI landscape, addressing cost, privacy, and compliance concerns—especially for regulated industries.
💰 Infrastructure Shifts: Access to high-quality models is changing capital expenditure strategies, increasing demand for GPUs and distributed data centers.
⚡ Energy Impact: As inference costs decrease, AI usage increases, leading to higher power demand and data center capacity needs, reflecting the Jevons paradox in modern tech.
🔧 Training vs. Inference: Training foundational models requires significant compute resources and capital investment, traditionally dominated by tech giants. In contrast, inference—using these trained models for tasks—demands scalable, efficient infrastructure to handle real-time applications and high-volume usage.
📈 Practical Applications: From domain-specific chatbots to real-time analytics, modern models enable smooth transitions and minimal reengineering, enhancing efficiency.
🤖 Future Outlook: Agentic AI applications are on the horizon, set to become integral to business operations within the next decade, improving speed and focus.
These discussions at Quantum Capital Group AI Working Group sessions help our portfolio become smarter investors and more savvy in digital and AI.
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