Behind the DeepSeek Hype, AI is Learning to Reason

When Chinese AI company DeepSeek announced they had built a model
that could compete with OpenAI at a fraction of the cost, it sent
shockwaves through the industry and roiled global markets. But
amid all the noise around DeepSeek, there was a clear signal:
machine reasoning is here and it's transforming AI.


In this episode, Aza sits down with CHT co-founder Randy Fernando
to explore what happens when AI moves beyond pattern matching to
actual reasoning. They unpack how these new models can not only
learn from human knowledge but discover entirely new strategies
we've never seen before – bringing unprecedented problem-solving
potential but also unpredictable risks.


These capabilities are a step toward a critical threshold - when
AI can accelerate its own development. With major labs racing to
build self-improving systems, the crucial question isn't how fast
we can go, but where we're trying to get to. How do we ensure
this transformative technology serves human flourishing rather
than undermining it?


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Clarification: In making the point that reasoning models excel at
tasks for which there is a right or wrong answer, Randy referred
to Chess, Go, and Starcraft as examples of games where a
reasoning model would do well. However, this is only true on the
basis of individual decisions within those games. None of these
games have been “solved” in the the game theory sense.


Correction: Aza mispronounced the name of the Go champion Lee
Sedol, who was bested by Move 37.

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Further reading on Eric Schmidt’s threshold to “pull the plug” on
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 Further reading on Move 37

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