Podbit · Modern Wisdom
In 1983, Geoffrey Hinton was an obscure neural network enthusiast telling a young journalist that cheap microprocessors and massive parallelism would change everything. He was completely right — and then, by his own account, found the result scarier than he ever expected.
Where this was said
At 4:18 · chapter starts 3:15
Wright stakes out his core position plainly: AI could bring wonders, and it could go terribly wrong — and the answer to whether it will end well depends entirely on whether humanity approaches it wisely. Chris adds texture by referencing what he believes is an FT-produced chart identifying three possible AI futures: total catastrophe, exponential growth unlike anything humanity has seen, or a modest 0.2% annual GDP increase. The spread itself — from near-irrelevance to species-defining upheaval — captures why the debate is so charged. Wright then reaches back to 1983, when he interviewed a young, obscure Geoffrey Hinton who was advocating a maverick approach to neural networks with total enthusiasm and zero concern. Hinton's prediction — that cheap microprocessors and massive parallelism would change everything — proved exactly right. What he did not predict was that he would eventually find the result scarier than he expected. Wright also reveals he had Eliezer Yudkowsky on his podcast roughly 15 years ago, when Yudkowsky was mid-transition from singularity optimist to doomer; Wright wasn't persuaded then, but grants that his respect for the sci-fi doom arguments has since grown substantially.
Robert Wright interviewed Geoffrey Hinton in 1983 when Hinton was an obscure neural network advocate with no doom concerns — who later became the most prominent AI safety alarm-raiser.
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