With the rapid evolution of technology, experts and companies are making predictions about the technological trends we can expect to see by 2025. Particularly notable are the areas of cybersecurity, artificial intelligence (AI), chip design, and the integrating of renewable energy with AI technologies.
According to Kaspersky, the year 2024 saw increasing interest in malware for stealing cryptocurrency assets, with this trend set to continue through 2025. During this time, other threats such as data breaches are also expected to rise significantly. The company has observed up to 40% more advertisements for data selling on shadow forums compared to the previous year.
“Not all data leak ads on the dark web stem from real-world incidents; some are simply public information or previously leaked data,” explained Alexander Zabrovsky, a digital footprint security expert at Kaspersky. This underlines the changing dynamics of cybersecurity threats, with attackers likely to steer away from platforms like Telegram toward more obscure dark web forums.
Research indicates a rise of sophisticated law enforcement actions targeting cybercriminal groups by 2025, as well as the fragmentation of ransomware groups which could complicate tracking efforts. Threat models are shifting, especially within the Middle East, where geopolitical tensions have sparked increased hacker activism.
Meanwhile, Arm, known for its pivotal role in semiconductor design, has laid out extensive predictions for upcoming technological trends. The company discusses the need to rethink chip design as traditional manufacturing processes become increasingly complex. Chiplets are expected to become significant components as the industry explores new methods to break through traditional barriers such as Moore’s Law.
Arm emphasizes the growing importance of standardization platforms which will enable different components to work seamlessly together, enhancing interoperability. Collaborations with over 50 technology partners are being ramped up to push the standardization of chiplet markets. Notably, this will benefit the automotive industry, where flexible and interoperable computing solutions can significantly reduce development timelines.
AI is foreseen to become more deeply integrated within chip design processes, allowing for optimized layouts and power distribution. Arm predicts increasing use of AI-driven chip design tools which could democratize access for smaller firms by accelerating development cycles.
“AI will not replace human engineers but will become a significant tool to effectively tackle the complex nature of modern chip design, especially within high-efficiency AI accelerators,” stated the company’s insights.
Looking toward the future, AI-enhanced hardware designs are anticipated to dominate discussions. The rise of edge computing will facilitate AI workloads running locally rather than relying on large data centers. This shift not only saves power and costs for businesses but also improves user privacy and security.
Smaller, more efficient AI models, such as small language models (SLMs), are forecasted to gain traction, with these new technologies allowing devices with limited resources to perform sophisticated tasks. By 2025, such models are expected to proliferate, enhancing user interactions through more localized AI systems.
Arm also sees the emergence of multimodal AI models capable of processing various types of data. “Current AI models primarily rely on textual training data, but the integration of audio and visual inputs will allow models to perceive and understand environments more like humans do,” the company explained.
On the application front, AI is predicted to drive hyper-personalization, with devices becoming more intuitive and responsive to user needs. Healthcare will increasingly adopt AI technologies, from predictive analytics to the development of new therapies. For example, AI's role in predictively analyzing complex protein structures has already demonstrated its potential to drastically shorten drug development timelines.
Sustainable practices are also becoming intertwined with AI advancements. Companies are exploring how AI can be applied to optimize energy consumption and reduce carbon footprints during model training. This involves choosing low-carbon locations and times for training operations, possibly leading to new industry standards.
The interconnection of AI and renewable energy solutions is set to reshape the energy sector. AI is forecasted to refine demand predictions and improve the efficiency of energy consumption, providing precision management of power grids. Such predictive analyses will facilitate advancements not only in performance but also reliability among renewable energy frameworks.
With microchips becoming increasingly specialized, industries are working to perfect various computing engines to meet diverse AI requirements, ensuring efficient deployment across devices. This trend emphasizes the necessity for companies to leverage flexible computational platforms to maximize the efficacy of AI applications.
Finally, as virtual prototypes become more prevalent, they are set to revolutionize chip and software development processes particularly within the automotive sphere. By allowing companies to test software even before physical chips are ready, development cycles can be significantly shortened.
All these trends reflect the remarkable intersection of multiple technological domains, where collaborations across industries are likely to redefine both capabilities and user experiences as we look toward 2025 and beyond.