FORBES
Below are 10 predictions about what will unfold in the world of artificial intelligence in 2025, from technology to business to policy and beyond.
1. Meta will begin charging for use of its Llama models.
Meta is the world’s standard bearer for open-weight AI. In a fascinating case study in corporate strategy, while rivals like OpenAI and Google have kept their frontier models closed source and charged for their use, Meta has chosen to give its state-of-the-art Llama models away for free.
So it will come as a surprise to many next year when Meta begins charging companies to use Llama.
To be clear: this is not to say that Meta will make Llama entirely closed source, nor that anyone who uses the Llama models will have to pay for them.
Instead, expect to see Meta make the terms of Llama’s open-source license more restrictive, such that companies who use Llama in commercial settings above a certain scale will need to start paying to access the models.
Technically, Meta already does a limited version of this today. The company does not allow the very largest companies—the cloud hyperscalers and other companies with more than 700 million monthly active users—to freely use its Llama models.
Back in 2023, Meta CEO Mark Zuckerberg said: “If you’re someone like Microsoft, Amazon or Google, and you’re going to basically be reselling [Llama], that’s something that we think we should get some portion of the revenue for. I don’t think that that’s going to be a large amount of revenue in the near-term, but over the long term, hopefully that can be something.”
Next year, Meta will substantially expand the set of organizations that must pay to use Llama to include many more large and mid-sized enterprises.
Why would Meta make this strategic pivot?
Keeping up with the LLM frontier is incredibly expensive. Meta will need to invest many billions of dollars every year if it wants Llama to stay at or near parity with the latest frontier models from OpenAI, Anthropic and others.
Meta is one of the world’s largest and most deep-pocketed companies. But it is also a publicly traded company that is ultimately answerable to its shareholders. As the cost of building frontier models skyrockets, it is increasingly untenable for Meta to devote such vast sums to train next-generation Llama models with zero expectation of revenue.
Hobbyists, academics, individual developers and startups will continue to be able to use the Llama models free of charge next year. But 2025 will be the year that Meta gets serious about monetizing Llama.
2. Scaling laws will be discovered and exploited in areas beyond text—in particular, in robotics and biology.
No topic in AI has generated more discussion in recent weeks than scaling laws—and the question of whether they are coming to an end.
First introduced in a 2020 OpenAI paper, the basic concept behind scaling laws is straightforward: as the number of model parameters, the amount of training data, and the amount of compute increase when training an AI model, the model’s performance improves (technically, its test loss decreases) in a reliable and predictable way. Scaling laws are responsible for the breathtaking performance improvements from GPT-2 to GPT-3 to GPT-4.
Much like Moore’s Law, scaling laws are not in fact laws but rather simply empirical observations. Over the past month, a series of reports have suggested that the major AI labs are seeing diminishing returns to continued scaling of large language models. This helps explain, for instance, why OpenAI’s GPT-5 release keeps getting delayed.
The most common rebuttal to plateauing scaling laws is that the emergence of test-time compute opens up an entirely new dimension on which to pursue scaling. That is, rather than massively scaling compute during training, new reasoning models like OpenAI’s o3 make it possible to massively scale compute during inference, unlocking new AI capabilities by enabling models to “think for longer.”
This is an important point. Test-time compute does indeed represent an exciting new avenue for scaling and for AI performance improvement.
But another point about scaling laws is even more important and too little appreciated in today’s discourse. Nearly all discussions about scaling laws—starting with the original 2020 paper and extending all the way through to today’s focus on test-time compute—center on language. But language is not the only data modality that matters.
Think of robotics, or biology, or world models, or web agents. For these data modalities, scaling laws have not been saturated; on the contrary, they are just getting started. Indeed, rigorous evidence of the existence of scaling laws in these areas has not even been published to date.
Startups building foundation models for these newer data modalities—for instance, EvolutionaryScale in biology, Physical Intelligence in robotics, World Labs in world models—are seeking to identify and ride scaling laws in these fields the way that OpenAI so successfully rode LLM scaling laws in the first half of the 2020s. Next year, expect to see tremendous advances here.
Don’t believe the chatter. Scaling laws are not going away. They will be as important as ever in 2025. But the center of activity for scaling laws will shift from LLM pretraining to other modalities.
3. Donald Trump and Elon Musk will have a messy falling-out. This will have meaningful consequences for the world of AI.
A new administration in the U.S. will bring with it a number of policy and strategy shifts on AI. In order to predict where the AI winds will blow under President Trump, it might be tempting to focus on the president-elect’s close relationship with Elon Musk, given Musk’s central role in the AI world today.
One can imagine a number of different ways in which Musk might influence AI-related developments in a Trump administration. Given Musk’s deeply hostile relationship with OpenAI, the new administration might take a less friendly stance toward OpenAI when engaging with industry, crafting AI regulation, awarding government contracts, and so forth. (This is a real risk that OpenAI is worried about today.) On the flip side, the Trump administration might preferentially favor Musk’s own companies: for instance, slashing red tape to enable xAI to build data centers and get a leg up in the frontier model race; granting rapid regulatory approval for Tesla to deploy robotaxi fleets; and so forth.
More fundamentally, Elon Musk—unlike many other technology leaders who have Trump’s ear—takes existential AI safety risks very seriously and is therefore an advocate for significant AI regulation. He supported California’s controversial SB 1047 bill, which sought to impose meaningful restrictions on AI developers. Musk’s influence could thus lead to a more heavy-handed regulatory environment for AI in the U.S.
There is one problem with all these speculations, though. Donald Trump and Elon Musk’s cozy relationship will inevitably fall apart.
As we saw time and time again during the first Trump administration, the median tenure of a Trump ally, even the seemingly staunchest, is remarkably short—from Jeff Sessions to Rex Tillerson to James Mattis to John Bolton to Steve Bannon. (And, of course, who can forget Anthony Scaramucci’s 10-day stint in the White House?) Very few of Trump’s deputies from his first administration remain loyal to him today.
Both Donald Trump and Elon Musk are complex, volatile, unpredictable personalities. They are not easy to work with. They burn people out. Their newfound friendship has proven mutually beneficial to this point, but it is still in its honeymoon phase. I predict that, before 2025 has come to an end, the relationship will have soured.
What will this mean for the world of AI?
It will be welcome news for OpenAI. It will be unfortunate news for Tesla shareholders. And it will be a disappointment for those concerned with AI safety, as it will all but ensure that the U.S. government will take a hands-off approach to AI regulation under Trump.
4. Web agents will go mainstream, becoming the next major killer application in consumer AI.
Imagine a world in which you never have to directly interact with the web. Whenever you need to manage a subscription, pay a bill, schedule a doctor’s appointment, order something on Amazon, make a restaurant reservation, or complete any other tedious online task, you can simply instruct an AI assistant to do so on your behalf.
This concept of a “web agent” has been around for years. If something like this existed and worked, there is little doubt that it would be a wildly successful product. Yet no functioning general-purpose web agent is available on the market today.
Startups like Adept—which raised hundreds of millions of dollars with a highly pedigreed founding team but failed to deliver on its vision—have become cautionary tales in this category.
Next year will be the year that web agents finally start working well enough to go mainstream. Continued advances in language and vision foundation models, paired with recent breakthroughs on “System 2 thinking” capabilities as a result of new reasoning models and inference-time compute, will mean that web agents will be ready for primetime.
(In other words, Adept had the right idea; it was just too early. In startups, as in much in life, timing is everything.)
Web agents will find all sorts of valuable enterprise use cases, but the biggest near-term market opportunity for web agents will be with consumers. Despite all the recent AI fervor, relatively few AI-native applications beyond ChatGPT have yet broken through to become mainstream consumer successes. Web agents will change that, becoming the next true “killer app” in consumer AI.
5. Multiple serious efforts to put AI data centers in space will take shape.
In 2023, the critical physical resource that bottlenecked AI growth was GPU chips. In 2024, it has become power and data centers.
Few storylines have gotten more play in 2024 than AI’s enormous and fast-growing energy needs amid the rush to build more AI data centers. After remaining flat for decades, global power demand from data centers is projected to double between 2023 and 2026 thanks to the AI boom. In the U.S., data centers are projected to consume close to 10% of all power by 2030, up from just 3% in 2022.
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