In 2024, artificial intelligence was all about putting AI tools to work

Just over two years after the debut of ChatGPT, the world is getting a little savvier about AI.

If 2023 was a year of wonder about artificial intelligence, 2024 was the year to try to get that wonder to do something useful without breaking the bank.

There was a “shift from putting out models to actually building products,” said Arvind Narayanan, a Princeton University computer science professor and co-author of the new book “AI Snake Oil: What Artificial Intelligence Can Do, What It Can’t, and How to Tell The Difference.”

The first 100 million or so people who experimented with ChatGPT upon its release two years ago actively sought out the chatbot, finding it amazingly helpful at some tasks or laughably mediocre at others.

Now such generative AI technology is baked into an increasing number of technology services whether we're looking for it or not — for instance, through the AI-generated answers in Google search results or new AI techniques in photo editing tools.

“The main thing that was wrong with generative AI last year is that companies were releasing these really powerful models without a concrete way for people to make use of them,” said Narayanan. “What we’re seeing this year is gradually building out these products that can take advantage of those capabilities and do useful things for people."

At the same time, since OpenAI released GPT-4 in March 2023 and competitors introduced similarly performing AI large language models, these models have stopped getting significantly “bigger and qualitatively better," resetting overblown expectations that AI was racing every few months to some kind of better-than-human intelligence, Narayanan said. That's also meant that the public discourse has shifted from “is AI going to kill us?” to treating it like a normal technology, he said.

On quarterly earnings calls this year, tech executives often heard questions from Wall Street analysts looking for assurances of future payoffs from huge spending on AI research and development. Building AI systems behind generative AI tools like OpenAI's ChatGPT or Google's Gemini requires investing in energy-hungry computing systems running on powerful and expensive AI chips. They require so much electricity that tech giants announced deals this year to tap into nuclear power to help run them.