Prof. Victor Menaldo in The Los Angeles Times writes an opinion piece about what the DeepSeek AI news is all really all about,
DeepSeek’s creators claim to have found a better way to train their AI by using special parts, improving how the AI learns rules and deploying a strategy to keep the AI running smoothly without wasting resources. According to the company’s report, these innovations drastically reduced the computing power needed to develop and run the model and therefore the cost associated with chips and servers...
At first glance, reducing model-training expenses in this way might seem to undermine the trillion-dollar “AI arms race” involving data centers, semiconductors and cloud infrastructure. But as history shows, cheaper technology often fuels greater usage. Rather than dampen capital expenditures, breakthroughs that make AI more accessible can unleash a wave of new adopters, including not only tech startups but also traditional manufacturing firms and service providers such as hospitals and retail.
Microsoft Chief Executive Satya Nadella called this phenomenon a “Jevons paradox” for AI. Attributed to the 19th century English economist William Stanley Jevons, the concept describes how making a technology more efficient can raise rather than lessen consumption. Steam and electrical power followed this pattern: Once they became more efficient and affordable, they spread to more factories, offices and homes, ultimately increasing use.
Please link here for the full article.