What’s next for AI in 2025

For the last couple of years MIT team have had a go at predicting what’s coming next in AI. A fool’s game given how fast this industry moves. But we’re on a roll, and the team is doing it again.  

Generative virtual playgrounds .  If you guessed generative virtual worlds (a.k.a. video games), high fives all round.  We got a tiny glimpse of this technology in February, when Google DeepMind revealed a generative model called Genie that could take a still image and turn it into a side-scrolling 2D platform game that players could interact with. In December, the firm revealed Genie 2, a model that can spin a starter image into an entire virtual world.  Other companies are building similar tech.   That could also be used to train robots. World Labs wants to develop so-called spatial intelligence—the ability for machines to interpret and interact with the everyday world.

Large language models that “reason”.  Most models, including OpenAI’s flagship GPT-4, spit out the first response they come up with. Sometimes it’s correct; sometimes it’s not. But the firm’s new models are trained to work through their answers step by step, breaking down tricky problems into a series of simpler ones. When one approach isn’t working, they try another. This technique, known as “reasoning” (yes—we know exactly how loaded that term is), can make this technology more accurate, especially for math, physics, and logic problems.  It’s also crucial for agents.

It’s boom time for AI in science.  One of the most exciting uses for AI is speeding up discovery in the natural sciences. Perhaps the greatest vindication of AI’s potential on this front came last October, when the Royal Swedish Academy of Sciences awarded the Nobel Prize for chemistry to Demis Hassabis and John M. Jumper from Google DeepMind for building the AlphaFold tool, which can solve protein folding, and to David Baker for building tools to help design new proteins.  Expect this trend to continue next year, and to see more data sets and models that are aimed specifically at scientific discovery. 

AI companies get cozier with national security.  There is a lot of money to be made by AI companies willing to lend their tools to border surveillance, intelligence gathering, and other national security tasks.   The US military has launched a number of initiatives that show it’s eager to adopt AI, from the Replicator program—which, inspired by the war in Ukraine, promises to spend $1 billion on small drones—to the Artificial Intelligence Rapid Capabilities Cell, a unit bringing AI into everything from battlefield decision-making to logistics. European militaries are under pressure to up their tech investment, triggered by concerns that Donald Trump’s administration will cut spending to Ukraine. Rising tensions between Taiwan and China weigh heavily on the minds of military planners, too. 

Nvidia sees legitimate competition.  Nvidia emerged as undisputed leader of chips used both to train AI models and to ping a model when anyone uses it, called “inferencing.”  A number of forces could change that in 2025. For one, behemoth competitors like Amazon, Broadcom, AMD, and others have been investing heavily in new chips, and there are early indications that these could compete closely with Nvidia’s—particularly for inference, where Nvidia’s lead is less solid.   A growing number of startups are also attacking Nvidia from a different angle. Rather than trying to marginally improve on Nvidia’s designs, startups like Groq are making riskier bets on entirely new chip architectures that, with enough time, promise to provide more efficient or effective training. In 2025 these experiments will still be in their early stages, but it’s possible that a standout competitor will change the assumption that top AI models rely exclusively on Nvidia chips.

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