Predicting the Unpredictable: The AI Outlook

The history of technological advance is characterized by disruption, yet we rarely witness a technology emerge with propensity to shock like artificial intelligence (AI).

This year started with a bang when Chinese startup DeepSeek launched R1, an open-source, low-cost large language model (LLM) that blindsided global tech, knocked nearly $600 billion off U.S. chipmaker Nvidia’s market value, and provoked a serious spat with OpenAI over proprietary technology. No one (or few, at least) saw that coming.

Future shockwaves look inevitable. Yet, despite its unpredictability, AI is ripe for speculation: What’s next for generative artificial intelligence (GenAI) and intelligent agents? How will lawsuits and regulation play out? Will solutions emerge for AI’s sticky sustainability problem?

As the DeepSeek dust settles, AI experts from different fields were asked what breakthroughs and challenges they expect to see in the rest of 2025.

Tracking the technological trends

LLMs continue to be at the front of researchers’ minds, but there are many other advances to watch.

Sonja Schmer-Galunder, Glenn and Deborah Renwick Leadership Professor in AI and Ethics at the University of Florida, predicted an increase in the “democratization” of AI development. “We will see more open source and proliferation of competitive models, especially now, after DeepSeek. This could help balance the current concentration of AI power,” she said.

According to Schmer-Galunder, there is also “lots of talk” about AI agents, despite the lack of established ethical guidelines and standards for their safe use. “Agents will likely mature further and become more task-specific. The problem is often that real-world implementations show limitations or unintended uses,” she said.

Isabelle Augenstein, an expert in natural language processing (NLP) at Denmark’s University of Copenhagen and a co-lead of Denmark’s Pioneer Centre for Artificial Intelligence, expects to see new LLMs focused on non-English languages. “Moreover, I hope to see increased multi-modal capabilities of LLMs, an area where they are still lacking,” she said.

Augenstein also flagged breakthroughs in the development of small, high-performance models that are less compute- and cost-hungry. “This would be important both from an environmental perspective and for adoption in more scenarios,” she said.

Keiland Cooper, a cognitive scientist and neuroscientist at the University of California, Irvine, and president of non-profit research organization ContinualAI, anticipates advances in AI for scientific research. In the drug development space, Cooper highlighted a recent analysis by researchers at the Boston Consulting Group of the performance of AI-discovered drugs in clinical trials. The researchers found that “AI-discovered molecules have an 80-90% success rate, substantially higher than historic industry averages.”

Advances in robotics should be expected too, said Cooper. “The barriers of mapping the physical to the digital space—while not completely overcome— have been aided by clever methods learned from training LLMs, and applying and mixing them with new data types to the robotics space.”

Juan David Gutiérrez is an associate professor at the Universidad del Rosario in Bogotá, Colombia. Like Augenstein, Gutiérrez expects to see growth in non-English-language LLMs and pointed to the development of Latam-GPT, a 50B-parameter LLM based on a Spanish-Latin American corpus.

Across Latin America, predicted Gutiérrez, governments will continue to expand their use of AI. His current research has identified over 500 AI systems already used by governments in the region. “The expansion is not just quantitative, but also in terms of the diversity of sectors of government where AI is deployed,” he said.

However, Gutiérrez anticipates AI advances could be impacted by U.S. restrictions on exporting hardware and wider data access. “Difficulties in accessing data of the required quality and volume will be a significant barrier for companies that are trying to develop new models that are customized for the needs of the region.”

AI challenges are becoming ever more complex and global

The unfolding story of AI is more complicated than the sum of its technological breakthroughs. Who gets to build AI, how fast it is rolled out, and how it is legislated (or not) are critical topics up for debate in 2025.

Said Schmer-Galunder, “Big tech companies seem to be completely engulfed in an accelerating race towards AGI (artificial general intelligence), under the assumption that whoever gets there first, wins. This has global geopolitical ramifications affecting national security and economic power.” She pointed to international cooperation and the adoption of frameworks for developing safe AI as potential solutions.

Augenstein identified a growing “disconnect” between academia and industry in the research space, as well as between big tech and other stakeholders. “This is both due to the closed-source nature of many high-performance language models, as well as the computational costs of training and experimenting with LLMs on an academic compute budget,” she said.

Gutiérrez expects to see AI regulation adopted more widely across Latin American in 2025, at both the national and subnational levels. “In countries such as Chile, Brazil, and Colombia, the bills have advanced and may be approved by the end of the year,” he said. However, there are concerns: while issuing legislation may be reasonably straightforward, “Building implementation capacity is difficult for Latin American countries, given the financial constraints that most states face,” he said.

The ongoing debate over societal impact and sustainability

Responsible AI that is sustainable and built for the public good is an often-cited aspiration in academia and industry, yet consensus on responsible solutions remains elusive.

Said Cooper, “While sustainability has been in the back of many researchers’ minds for some time, further techniques to drive down the cost and energy use of training models is a growing concern as models continue to grow larger.”

GenAI’s popularity is exacerbating the sustainability issue, said Schmer-Galunder. “We are building giant datacenters that use lots of unnecessary energy for silly prompt requests, sometimes just for our entertainment.”

At a societal level, Schmer-Galunder raised concerns about the absence of collective decision-making as AI continues to fundamentally reshape our lives. “This change is happening even though we lack a coherent vision for the type of society we want, or want to create, for generations to come,” she said.

Schmer-Galunder suggested that while technology can boost efficiency, we may not want it to penetrate every aspect of our lives. “We need to really deeply think about that and, collectively, decide where we don’t want AI—either because we want humans to stay in control, or because it doesn’t lead to improvements in well-being.”

The thrilling potential of technological breakthroughs mixed with challenges around competition, legislation, geopolitics, and sustainability loom large in the AI outlook. The likelihood of shockwaves also seems high. In Cooper’s words, “As always, I’m most excited about the advances that we didn’t see coming.”

That AI shocks will happen in 2025 is perhaps predictable now, but what those shocks look like and what their consequences will be are the big unknowns.

Karen Emslie is a location-independent freelance journalist and essayist.