AI Agents Podcast The Future of Tech and AI | EP50
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May 27, 2025 David Colwell, an AI leader at Tricentis, shares a wealth of knowledge on the rapidly changing landscape of AI and tech. He discusses the origins and evolution of generative AI, emphasizing both its exciting breakthroughs and inherent challenges, particularly in quality assurance. The conversation delves into automated reasoning models, their implications for the future of work, and the intricate balance between innovation and practical implementation. Colwell highlights the surprising limits of emerging AI tools and the necessity for continuous adaptation in this fast-paced field.
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Early AI Reduced Productivity
- Early generative AI increased work because users spent more time reviewing and correcting AI output.
- The exactness required in QA made early AI productivity negative, prompting a pause in investment.
Power of Reasoning Models
- Reasoning models incorporate self-reflection as a trained component, improving AI's accuracy.
- This advancement enabled building agents that perform human tasks with more precision and productivity.
Emergent Behavior in Big Models
- AI models experienced an unexpected leap in performance at larger sizes, displaying emergent behaviors.
- These leaps challenge human understanding and suggest exponential AI capability growth though with diminishing returns now.