Inevitably, the conversation takes a turn: All these waves of AI are having an impact Nowbut if technology improves, what happens next? It’s usually when they look at me, expecting a foreboding of doom or hope.
I’m probably pessimistic, if only because AI predictions are getting harder and harder.
nevertheless, MIT Technology Review I must say, AI has a pretty good track record of getting a sense of where it’s headed. We’ve just published a quick list of predictions for what’s in store for 2026 (where you can read my thoughts on the legal battles surrounding AI), and the predictions on last year’s list all came to fruition. But every holiday season, working out the effects of AI gets harder and harder. This is largely due to three major unanswered questions.
For one, we don’t know if it’s going to get much better with the addition of a larger language model in the near future. Since this particular technology is what underpins almost all the excitement and anxiety in AI right now, with AI powering everything from colleagues to customer service agents, its slowdown would be a big deal. So big a deal, in fact, that we devoted an entire slate of stories back in December to what the new post-AI-hype era looks like.
Number two, AI is wildly unpopular with the general public. Here’s just one example: About a year ago, OpenAI’s Sam Altman stood with President Trump to enthusiastically announce a $500 billion plan to build data centers across the United States to train larger and larger AI models. The pair either didn’t realize or didn’t care that many Americans would vehemently oppose building such data centers in their communities. A year later, Big Tech is taking over from N The battle above Continue to win and build public opinion. Can it win?
The response from lawmakers to all this frustration has been very confusing. Trump has pushed big tech CEOs to make AI regulation a federal rather than a state issue, and tech companies are now hoping to enshrine it in law. But the crowd that wants to protect children from chatbots ranges from progressive lawmakers in California Federal Trade Commissioneach with different goals and perspectives. Will they be able to put aside their differences and hire AI firms?
If sad holiday dinner table conversation gets to this point, someone will say: Hey, isn’t AI being used for reasonably good things? Making people healthier, making groundbreaking scientific discoveries, better understanding climate change?
Well, sort of. Machine learning, an older form of AI, has long been used in all kinds of scientific research. One branch, called deep learning, forms part of Alphafold, a Nobel Prize-winning tool for protein prediction that has transformed biology. Image recognition models are getting better at identifying cancer cells.