In this episode, Scott Hanselman and Mark Russinovich dive deep into the promises and pitfalls of AI-assisted coding. They debate whether large language models can truly handle complex software projects, discuss the limitations of current AI systems in areas like synchronization, and explore the difference between human learning and machine pattern-matching. Along the way, they touch on the dangers of over-anthropomorphizing AI, the rise of “thinking tokens” in new models, and the impact these tools may have on junior developers learning the craft.

Takeaways:

• The ongoing debate: can AI scale into true general intelligence or not?
• The risks of relying too heavily on AI when you don’t understand your own code
• What junior developers may lose and gain in a world of AI-assisted programming

Who are they?

View Scott Hanselman on LinkedIn

View Mark Russinovich on LinkedIn

Watch Scott and Mark Learn on YouTube

Listen to other episodes at scottandmarklearn.to

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