Generative artificial intelligence (GenAI) has experienced intensive development this past year, solidifying AI’s influence across nearly every facet of modern life. The field of artificial intelligence intersects with an ever-increasing variety of business processes and operations in healthcare, retail, the financial sector, as well as the manufacturing industry. With AI and GenAI systems occupying a growing part of our lives, it is more important than ever to keep up-to-date with current trends in AI. As we look toward 2025, it is worth exploring the latest generative AI trends so that we can see what lies beyond the horizon.
1. Agentic AI
AI agents have recently become a hot topic among AI enthusiasts, with major figures like NVIDIA’s CEO Jensen Huang among their prominent advocates. These are advanced AI systems that combine the natural language processing capabilities of large language models (LLMs) with a wide array of tools to operate independently. Unlike traditional LLMs, which primarily respond to prompts based on their training data, LLM agents are designed to take action, make decisions, and interact with their environment in more sophisticated ways.
These agents can understand context, maintain both short-term and long-term memory of interactions, and utilize various external tools and APIs to accomplish complex tasks. By leveraging advances in AI systems, machine learning, and AI algorithms, these multimodal AI agents are redefining the boundaries of what AI-driven systems can achieve in practical applications.
That being said, it is vital to mention that AI agents, as great as they are, may not be a viable option for every application. With greater autonomy comes a higher risk of errors, which could prove difficult to recognize and rectify. While AI agents represent a significant advancement in AI technology, bridging the gap between passive language models and active, goal-oriented AI systems, their complexity requires careful consideration.
For simpler tasks, traditional LLM-powered AI tools using strategies like RAG, fine-tuning, or basic function calling might be more appropriate and manageable. The key is to understand that while LLM agents show tremendous promise in shaping the future of artificial intelligence, they should be deployed thoughtfully, with their enhanced capabilities balanced against the potential risks and complexity they introduce.
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2. Generative AI capabilities on mobile devices
The integration of AI capabilities into mobile devices represents another trend, with both Android phones and iPhones now coming equipped with their own AI systems—Gemini and Apple Intelligence, respectively. And yet, however promising this perspective might be, it is not all plain sailing. One of the main issues is the gigantic amount of computational power required by generative AI. Not only is this a huge headache for tech companies, forcing them to drop sustainability targets and consider powering new data centers with nuclear power, it is also way too much for home devices to provide. That means the computations have to be handled by cloud servers. This presents a number of issues, such as challenges with data privacy and the potential exposure of trade secrets or sensitive information, as the manufacturing industry and other sectors explore new AI applications.
One solution that will likely see more use in 2025 is the use of small language models (SLMs) in mobile devices. While less powerful than large language models, these smaller AI models require less computational power, which eliminates the need for cloud processing, allowing the artificial intelligence to run locally. Aside from solving privacy concerns, this approach reduces latency and lowers operational costs.
Regardless of all the issues with AI-powered mobile devices, it is all but inevitable that AI trends for 2025 will include new applications of generative AI tools to be used on mobile devices.
3. AI trends—edge computing
Another area where small language models might find use is in edge computing environments where computational resources are limited. Edge devices are increasingly being equipped with specialized, lightweight machine learning models and AI tools that can perform data analysis and inference at the network’s periphery. This evolution is part of broader trends in AI that emphasize decentralization, efficiency, and real-time processing.
This approach allows for real-time processing and decision-making without the need to transmit sensitive information to cloud servers. By leveraging smaller, more efficient language models, manufacturers can integrate AI capabilities into IoT devices and industrial equipment while maintaining computational efficiency and data security.
These compact models are particularly valuable in scenarios requiring immediate, context-aware responses, such as on-device translation, personalized virtual assistants, and predictive maintenance in industrial settings. Small language models represent a strategic approach to ensuring AI access across technological ecosystems, ranging from budget to flagship systems.
4. Generative AI in search engines
Another key trend in AI is using artificial intelligence in search engines to enhance user experience. They combine machine learning and LLMs’ understanding of natural input with the power of search engines to produce appropriate responses to prompts. Among the most popular are:
Microsoft Copilot
Thanks to Microsoft’s early cooperation with OpenAI, Microsoft Copilot has been a trailblazer in AI-powered productivity, being the first to offer this type of search solution. Integrating Bing’s search capabilities with OpenAI’s GPT-4, the platform exemplifies the AI capabilities of large language models to deliver highly relevant, context-aware results.
A standout feature is its source verification where each search result comes with direct citations, ensuring transparency and credibility, while generative AI is meant to enable the system to understand and interpret complex queries with nuance to provide intelligent, contextually rich information that feels more like a conversation with a knowledgeable assistant.
Moreover, Copilot is integrated into other MS apps, allowing users to easily loop the chatbot into text conversations on Skype or Teams. One of the most useful features of Copilot is its integration with the Edge web browser, allowing it to directly respond to queries about the contents of the website currently open in browser.
It has to be noted, however, that these standout AI features still often feel like a work in progress. The cited sources are often superficial, secondary, or only tangentially related to the AI-generated answer and all seem to come from the first page of Bing’s search results. Copilot also often has trouble following a conversation, either ignoring previous exchanges or, conversely, repeating a previously generated answer when it should have already dropped it. Even its ability to read the content of the user’s browser does not always work. While Copilot is a multimodal AI, capable of both image generation and analysis, these features are limited for free accounts and often result in error messages. Nevertheless, with its head start over other search engines in terms of artificial intelligence, it is definitely a strong contender to watch in 2025.
Google AI Overviews
Powered by Alphabet’s Gemini models, Google AI Overviews is supposed to elevate search experiences through precise and intelligent information synthesis. Its main advantage is contextual understanding, aiming to deliver relevant results even for complex or ambiguous queries. The enhanced summarization feature should provide users with quick, detailed overviews of intricate subjects, making information consumption more efficient.
What sets Google’s approach apart is its sophisticated personalization, tailoring search results based on individual user preferences and historical search behavior. This is meant to create a more intuitive and adaptive search experience that feels increasingly tailored to each user’s unique information needs and interests.
While Google’s adventure with AI has so far been marked by a series of spectacular (if not always all that serious) PR disasters, we should not rush to dismiss the search giant’s potential in the realm of artificial intelligence. Google is still the most-used search engine on the Web and has access to downright incomprehensible amounts of historical data—from search histories, through advertising and consumer behavior data, to users’ personal information and histories. Generative AI thrives on data, and Google is in a position to leverage its vast stores of information to catapult its search engine AI models to the top.
Perplexity
Perplexity is one of the first generative AI tools to focus on search. It distinguishes itself through a commitment to transparency and user clarity. Every statement generated by the search engine is backed by footnotes, providing direct links to original sources and empowering users to verify information independently. The platform’s interface prioritizes simplicity and direct communication, offering clear and concise answers. The emphasis on credibility and ease of understanding addresses key user concerns about content generated by artificial intelligence, building trust through a rigorous approach to information validation and presentation.
However, my personal experience with Perplexity has been, although not negative, underwhelming. While it does seem to provide better answers than Copilot, the difference is not enough to outweigh access to the latter’s other functionalities.
Comparing these AI search engines reveals a landscape of innovation driven by distinct strategic approaches. So, will one of these AI tools dominate the search landscape or will various tools be used for different purposes in a complex search ecosystem? As we look at AI trends for 2025, this is certainly one question to keep an eye on. As flawed as these search engines are at the moment, we can expect that through 2025, they will continue to be developed further. With AI tools, search will fundamentally change from how it has been until now.
5. The flood of AI-generated content
Anyone with access to the Internet can use generative AI, be that through ChatGPT, Claude, or other AI models, to write articles, blog posts, and other pieces of writing previously only penned by actual people. The issue here is that there are currently no infallible ways of proving whether text was generated by AI or not—detection software’s capabilities end at giving a percentage score of how probable it is that artificial intelligence was involved.
Far from condemning the practice, platforms such as LinkedIn, Facebook, and X encourage their users to use generative AI tools to enhance their presence—so-called AI “slop” can be found in social media posts on all platforms. This growing prevalence of content generated by artificial intelligence raises questions about data quality, originality, and potential AI bias, all of which are emerging as major concerns for content creators and consumers alike.
This situation also bears an unnerving similarity to the so-called “dead Internet theory.” As we enter 2025, this fringe conspiracy theory has evolved into a compelling thought experiment about artificial content’s dominance online. While the original theory claimed that most Internet activity was already being generated by AI bots and algorithms rather than humans, today’s reality is more nuanced but perhaps more unsettling.
With generative AI now producing an ever-expanding amount of social media content, product reviews, and blog posts, and even helping craft personal messages, the line between authentic human interaction and AI-generated engagement is becoming increasingly blurred. Things look even more bleak when SEO practices are added to the mix—one reason why search seems to be getting worse is that the Internet is increasingly flooded by AI-generated content only meant to be read by Google’s search algorithm.
Yet unlike the theory’s paranoid origins, this shift hasn’t happened through some shadow AI conspiracy—instead, we’ve walked into it willingly, choosing generative AI-curated experiences over messy human ones in our quest for convenience and optimization. The question facing us in 2025 is not whether the Internet is “dead,” but whether we can preserve meaningful human connection in an increasingly synthetic online world.
The controversial practice of allowing content generated by artificial intelligence on social media platforms also raises important questions about plagiarism, human input, and its role in the originality of text. Was a given text obtained through generative AI plagiarism? If yes, does human intervention make it original? The answers to these questions will be a vital point to be addressed in 2025 by philosophers, lawmakers, and generative AI users alike.
6. Privacy concerns and AI technologies
Issues of intellectual property and originality are mirrored by concerns around generative AI and data privacy. Training data is crucial for the development of AI models. The more real-world data a machine learning algorithm trains on, the better its responses. Synthetic data, though useful in some circumstances, can quickly corrupt the AI models that use it.
One approach is to form partnerships with brands to use their content, such as the one between OpenAI and Condé Nast, which allows ChatGPT and SearchGPT to use content from the publisher’s websites such as those of Vogue, The New Yorker, WIRED, and more. However, sometimes AI companies resort instead to questionable methods to gather training data. This not only jeopardizes data privacy but also poses risks to corporate trade secrets, fueling debates about how to regulate AI responsibly.
With AI companies scraping every data point they can, it is entirely within the realm of possibility that some sensitive data may become part of a publicly accessible LLM. In such a situation, it is certain that litigation over data breaches would follow. Such an event would be a pivotal moment for AI regulation. With such an acute data shortage, it is likely that such an issue will emerge in the following year.
7. AI regulation
All these concerns around the intersection of artificial intelligence, intellectual property, and data security warrant an expectation that AI regulation will become a major concern and one of the major trends in AI in 2025. The AI regulatory landscape is characterized by a delicate balance between innovation and control, as governments worldwide grapple with the rapid advance of artificial intelligence. Efforts like the AI Act from the European Union highlight the need to establish frameworks for regulating AI while addressing challenges such as AI bias, data security, data breaches, and the ethical use of emerging technologies.
The European Union AI Act has set a global benchmark, creating ripple effects far beyond European borders as companies scramble to align their AI development with these stringent standards. Meanwhile, the United States has moved beyond its initial executive orders toward more comprehensive federal legislation, though a fragmented state-by-state approach to AI regulation continues to create compliance challenges for tech companies. China’s dual-track system of encouraging development of AI systems while maintaining strict control over their applications has emerged as an influential alternative model, particularly among developing nations seeking to harness AI’s economic benefits while preserving control.
The focus of AI regulation has shifted from broad frameworks to specific use cases, with particular emphasis on foundation model oversight, synthetic media authentication, and AI-powered surveillance systems. Regulatory bodies are now requiring mandatory safety evaluations and algorithmic audits for high-risk AI applications, while implementing more stringent transparency requirements for content generated by artificial intelligence.
8. LLMs as a universal user interface
The trend of LLMs as a universal user interface may take more than a year to develop, and it would be an exaggeration to claim it will definitely occur in 2025. However, out of all the latest AI trends, it stands out as a fascinating possibility for an implementation of AI technology that could fundamentally reshape how we interact with tech.
The large language models can act as the user interface, while the data used is extracted from other systems—databases, CRM systems, ERP systems, industrial IoT systems, etc. These implementations have already proven effective at simplifying complex data interactions across various platforms.
What may happen is that this trend will be developed into more universal user interfaces that will carry out tasks more complicated than currently thought possible. Imagine LLMs serving as a unified gateway to all your digital tools, seamlessly translating natural language requests into precise actions across multiple platforms. This evolution could eliminate the need for traditional interfaces in many cases, leveraging AI to make complex systems accessible to users regardless of their technical expertise.
AI trends to look out for in 2025—conclusion
In 2025, generative AI will continue driving innovation while raising crucial questions about ethics, privacy, and AI regulations. From the promise of agentic AI and the practicality of small language models to the ever-expanding presence of AI in mobile devices and search engines, and as AI-generated content transforms the digital realm and sparks debates about authenticity and intellectual property, the need for robust AI regulations and thoughtful deployment will become paramount.
Looking ahead to 2025, the most impactful AI trends may not only redefine how we interact with technology but also challenge our assumptions about human connection, creativity, and the Internet’s role in society. As we navigate this rapidly evolving field, striking a balance between innovation and responsibility will be essential in order to harness AI’s potential for the benefit of all.
