Notes on ‘Tech’s Midlife Crisis’ with Slate’s What Next: TBD
Tech companies are struggling with middle age. Instead of being edgy and fun, people now think of them as being like any other “evil” big business.
Tech companies are struggling with middle age. Instead of being edgy and fun, people now think of them as being like any other “evil” big business.
“When you start getting labeled big, that means people are worried about you or fear you. They don’t love you anymore,” said Will Oremus, technology news analysis writer for The Washington Post, on Wednesday at the Crosscut Ideas Festival.
It’s difficult to be young and fun when facing new federal regulations, disdain from the general public and stagnant business growth. Oremus shared his perspectives with host Lizzy O’Leary on a live recording of Slate’s What Next: TBD podcast.
Things have changed in recent years as tech companies have failed to follow through on their idealism.
“I think we’re finding out ‘Big Tech’ can’t have it all,” Oremus said. “If they want to make a crap-ton of money – and we’re clearly seen that’s their number-one imperative – they’re going to make choices all the time where they’re not a force for good.”
Oremus said he expects a messy process to reign in Big Tech, similar to the midlife experience of other industries – think pharmaceuticals, tobacco and automobiles.
“In every case, it took a long time, it took a change in attitudes, it took regulation, it never changes quickly,” he said.
Oremus and O’Leary also discussed AI, or artificial intelligence: training machines to mimic the functions of the human brain. The face of AI has been ChatGPT, a chatbot that can do everything from write essays to generate computer code – while generating controversy and amusement at the same time.
Even though big tech companies may be stagnating and not innovating as much, that doesn’t mean they can’t get their fingers into innovation. Microsoft, for example, is investing in OpenAI, the developers of ChatGPT.
The ones who will benefit initially from AI’s success are the companies “who have giant clouds of computing power that the models have to be trained on and run on,” Oremus said.