In recent years, the rise of foundation models like ChatGPT, Bard, Claude, and Gemini has revolutionized how we interact with artificial intelligence (AI). These large language models (LLMs), trained on vast datasets, can perform a wide range of tasks—from generating code to simulating human conversation (Bommasani et al., 2021). As we move beyond the capabilities of ChatGPT, next-generation foundation models are poised to reshape society in profound ways.
A defining feature of the next generation is multimodality. While current LLMs focus primarily on text, emerging models can process and generate across multiple data types—text, images, video, and audio—creating a more holistic understanding of the world (OpenAI, 2024). For instance, OpenAI’s “Sora” introduces video generation from text prompts, while Google’s Gemini 2 integrates visual reasoning and programming logic into a single platform (Google DeepMind, 2024). These advances point toward a future where AI systems are capable of context-aware perception and decision-making across real-world tasks.
Another key trend is personalization. Future models will adapt to individual users’ needs, behavior patterns, and even emotional states. This has implications for fields like education and mental healthcare, where AI tutors or therapists could offer culturally and emotionally intelligent support (Marcus & Davis, 2023). However, such personalization raises concerns about surveillance, consent, and psychological influence.
The societal impact of these models is not limited to utility—it includes ethical risks. As foundation models generate increasingly convincing content, issues of misinformation, deepfakes, and political manipulation become more pronounced (Floridi, 2023). The European Union has already taken steps toward regulating AI use through the AI Act, aiming to ensure transparency, data privacy, and accountability (European Commission, 2024). However, global consensus on governance remains fragmented.
Economically, the transformation is twofold: displacement and augmentation. Jobs involving repetitive cognitive tasks are being automated, but new roles in AI safety, alignment, and prompt engineering are emerging (Brynjolfsson & McAfee, 2023). Without proactive reskilling policies, there is a risk of exacerbating socioeconomic inequalities, especially in developing countries with low digital literacy.
Transparency and explainability are becoming central demands. Many current models are criticized as “black boxes” with limited insight into how decisions are made (Raji et al., 2020). Calls for responsible AI and explainable AI (XAI) are growing, with emphasis on open-source alternatives, independent audits, and interdisciplinary oversight.
In conclusion, the next generation of foundation models brings both promise and peril. Their integration into daily life will be transformative—but whether they empower or control society depends on the decisions made today. Ethical development, inclusive access, and robust governance are essential to ensure that these powerful tools serve the public good.
References
- Bommasani, R., Hudson, D. A., Adeli, E., et al. (2021). On the opportunities and risks of foundation models. Stanford Center for Research on Foundation Models. https://crfm.stanford.edu/report.html
- OpenAI. (2024). Introducing Sora. https://openai.com/sora
- Google DeepMind. (2024). Gemini 2: A new frontier for AI. https://deepmind.google/technologies/gemini
- Marcus, G., & Davis, E. (2023). Rebooting AI: Building artificial intelligence we can trust. Pantheon.
- Floridi, L. (2023). The Ethics of Artificial Intelligence. Oxford University Press.
- European Commission. (2024). Artificial Intelligence Act: Proposal for a Regulation. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
- Brynjolfsson, E., & McAfee, A. (2023). The Second Machine Age. W. W. Norton & Company.
- Raji, I. D., Smart, A., White, R. N., et al. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 33–44.
