5 A.I. Trends to Watch in 2025: Agents, 3D A.I., Synthetic Data & More

In the first eight months of 2024, Microsoft (MSFT), Meta (META), Google (GOOGL) and Amazon (AMZN) collectively recorded a staggering $125 billion in A.I.-related capital expenditures (CapEx) and operating costs, according to a September JPMorgan report. The cumulative CapEx of these four tech giants alone is expected to soar past the $200 billion mark for the entirety of 2024.

A.I. startups, meanwhile, received unprecedented amounts of funding from investors eager to cash in on the technology’s lucrative potential. OpenAI is set to finish out 2024 as the most well-funded A.I. company, most recently valued at $157 billion. Its rival Anthropic is gearing up for new fundraising that cold value it at $40 billion.

Flush with cash, leading A.I. companies are now tasked with the challenge of proving to investors—and the public—that their pricy bets on the new technology will pay off. From an ongoing pivot into “agentic A.I.” to emerging new scaling laws and wide-ranging explorations of A.I.’s myriad capabilities, here’s a look at what 2025 will bring to the world of A.I.:

Agentic A.I. will be “the next giant breakthrough”

The buzzword refers to autonomous A.I. assistants able to complete tasks without human oversight. The potential of A.I. agents to enhance workplaces and everyday life quickly caught notice in Silicon Valley, with companies like Salesforce embracing agents as their next major product.

Microsoft, too, has rolled out a slew of A.I. agents in recent months. In November, it unveiled several A.I. assistants customized for its Microsoft 365 suite, including an agent able to provide translation in nine different languages.

OpenAI is also on the “agentic A.I.” train, with an upcoming model expected to be able to perform tasks like booking travel and writing code. A.I. agents are “the thing that will feel like the next giant breakthrough,” said Sam Altman, OpenAI’s CEO, during a recent AMA on Reddit.

The global market for A.I. agents is currently valued at more than $5 billion, according to the research firm MarketsandMarkets. By the end of the decade, this figure is expected to soar to $47 billion, driven in part by demand for agents amongst enterprise clients.

Test-time compute could be a solution to A.I.’s training data crisis

One of the key components of A.I.’s success in recent years has been the mass amounts of data fed into A.I. models. But there’s only a finite amount of text, images and videos on the internet. To avoid a plateau in the technology’s development, A.I. companies are turning to alternative ways to train their models. One of the most promising solutions is test-time compute, where A.I. models improve by reasoning and taking longer to think about potential answers before responding—a theory most recently demonstrated by OpenAI’s o1 model.

On an earnings call in November, Nvidia (NVDA) CEO Jensen Huang described OpenAI’s new model as “one of the most exciting developments” in scaling and noted that “the longer it thinks, the better and higher-quality answer it produces.”

Huang isn’t alone in his optimism. Microsoft CEO Satya Nadella also pointed towards test-time compute as a new scaling law in November, while OpenAI co-founder Ilya Sutskever earlier this month highlighted it as a progression of A.I.’s pre-training era.

Synthetic data is another promising solution

Another solution to A.I.’s data crisis is replacing traditional data with information generated by the technology itself. The market for synthetic data is expected to skyrocket to $2.1 billion by 2028, representing an over 450 percent hike from 2022, according to BCC Research.

Altman hinted at the potential of synthetic data a year ago when discussing A.I.’s dwindling data supply, remarking in an interview that “as long as you can get over the synthetic data event horizon, where the model is smart enough to make good synthetic data, I think it should be all right.” OpenAI, alongside competitors like Anthropic, Meta, Microsoft and Google, have all reportedly begun using synthetic data in some way to train and fine-tune models.

In October, the A.I. startup Writer unveiled a new A.I. model trained entirely by A.I.-generated data. The approach allowed the company to cut significant costs in developing the model, which totaled at a mere $700,000 in comparison to the millions doled out by other companies. OpenAI’s GPT-4 model, for example, cost more than $100 million to train.

“Large world models” will create 3D A.I. worlds

Until now, much of A.I.’s visual outputs have remained two-dimensional, something tech pioneers are looking to shift in the coming years. “Large world models” are an emerging form of A.I. that aims to build interactive three-dimensional scenes advancing the worlds of movies, games and simulators.

One of the largest players in this space is World Labs, a new startup established by Stanford A.I. pioneer Fei-Fei Li that raised $230 million earlier this year. The venture is looking to build large world models with “spatial intelligence,” a form of intelligence that understands and interacts with the real world. To demonstrate this concept, Li has previously used the example of a cat reaching out to topple over a glass of milk and humans’ ability to predict the consequence of this event and therefore take action to prevent the glass from falling.

At the beginning of December, Google DeepMind launched its own large world model in the form of Genie 2, which simulates virtual environments that will be used to train and evaluate A.I. agents. The area will likely be a key focus for the lab going forward, as evidenced by its recent hiring of Tim Brooks, a former OpenAI researcher overseeing its video generator Sora. In an X post welcoming Brooks to his team, Google DeepMind CEO Demis Hassabis noted his excitement at “working together to make the long-standing dream of a world simulator a reality.”

A.I. search engines will reshape online search

Google has long had a seemingly untouchable dominance on the search market. As artificial intelligence continues to advance, a wave of AI-powered search engines is poised to challenge Google’s dominance in the tech industry.

Google itself has not shied away from embracing AI technology. In 2024, the company introduced AI Overviews, a feature that offers users AI-generated summaries instead of traditional search links. CEO Sundar Pichai is optimistic about the feature’s potential, predicting it will attract over 1 billion monthly users and enhance overall search usage and user satisfaction.

However, Google faces stiff competition from other players in the search engine market, such as OpenAI and Microsoft, who are leveraging AI to enhance their offerings. Meta is also rumored to be developing its own AI-powered search engine, while the startup Perplexity AI has emerged as a key player in the industry. With a valuation of $9 billion, Perplexity AI’s AI search tools now handle around 20 million queries daily, a significant increase from the beginning of 2024.

This influx of AI-powered search engines signals a shift in the tech landscape, with companies vying for market share by harnessing the power of artificial intelligence to revolutionize the way we search for information online.