The Economics of AI Explained | Neil Thompson on AI, Chips, & What’s Next | Techcetra | Salesforce
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Artificial Intelligence is advancing faster than ever—but can this pace continue?
In this episode of Techcetra partnered with @salesforce , Leslie D’Monte speaks with Neil Thompson, Director of the FutureTech Research Project at MIT, to explore the economics driving today’s AI revolution.
While recent breakthroughs have transformed what AI can do, Neil explains that much of this progress has come from scaling compute—using more GPUs, more power and larger investments. But is that model sustainable? What happens when compute demand outpaces supply?
The conversation dives into the economics of AI, chip shortages, NVIDIA’s dominance, large language models, small language models, Agentic AI, AI in scientific research, digital labour, and the future of AI infrastructure. Neil also shares his perspective on India’s AI ambitions, semiconductor manufacturing, local language models and why continuously improving smaller models may be a smarter long-term strategy.
In this episode:
Why we’re living through AI’s “golden age”
The economics behind AI scaling
Why chip shortages could continue for years
Compute vs algorithmic innovation
LLMs vs SLMs
The rise of Agentic AI
AI’s role in scientific research
Can AI compete with human labour?
NVIDIA, GPUs and the future of AI infrastructure
India’s AI roadmap and semiconductor ambitions
Whether you’re an AI enthusiast, business leader, developer, researcher or policymaker, this conversation offers a practical look at the technological and economic forces shaping the future of artificial intelligence.
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