In 2026, Stanford AI experts predict a pivotal shift from AI hype to real-world evaluation, as the field moves beyond promises of transformation to focus on measurable impact, ethical use, and practical limitations. Across disciplines, faculty from Stanford HAI and related departments highlight a growing demand for rigor, transparency, and accountability in AI development and deployment. James Landay, Co-Director of Stanford HAI, anticipates no breakthrough in artificial general intelligence (AGI) this year, but a surge in global efforts toward AI sovereignty. Countries are increasingly investing in their own AI infrastructure—building local large language models or running foreign models on domestic hardware to protect data and political independence. This trend is driven by major data center expansions in regions like the UAE and South Korea, and by global tech firms like Nvidia and OpenAI expanding their international presence. Landay warns of a potential AI investment bubble, noting that the current focus on massive compute and data may be reaching its limits, especially as data quality declines and new models show that smaller, better-curated datasets can outperform larger, noisier ones. He also expects a rise in custom AI user interfaces beyond simple chatbots, as well as real-world applications in AI-generated video, which are finally maturing enough to be used in production. However, this progress will likely intensify copyright and intellectual property debates. Russ Altman, a senior fellow at HAI, emphasizes the need to “open the black box” in scientific and medical AI. He predicts increased focus on understanding how high-performing models make decisions—using tools like sparse autoencoders and attention mapping to uncover the internal logic of neural networks. In medicine, this is essential: researchers and clinicians need not just accurate predictions, but insight into how those predictions are made. Altman also foresees the development of comprehensive evaluation frameworks for medical AI, addressing not just technical performance but also impact on hospital workflows, staff efficiency, patient outcomes, and return on investment. In the legal field, Julian Nyarko predicts a move from asking whether AI can write to asking how well, at what risk, and for what outcome. Firms and courts will demand domain-specific benchmarks tied to real legal results—accuracy, citation integrity, and time savings. AI will also take on more complex tasks, such as multi-document reasoning, requiring new evaluation methods like LLM-as-judge and preference ranking to assess performance on intricate legal work. Angèle Christin, a communication professor, sees a growing realism about AI’s actual capabilities. She expects a slowdown in the frenzy of investment and deployment, as evidence mounts that AI can mislead, displace workers, and create new forms of labor. The focus will shift to fine-grained, empirical studies of where AI truly adds value and where it causes harm. Curtis Langlotz foresees a “ChatGPT moment” in medicine, driven by self-supervised learning that reduces the need for expensive, expert-labeled data. This will enable the creation of powerful biomedical foundation models capable of diagnosing rare diseases and improving care across specialties, even with limited training data. Erik Brynjolfsson predicts the rise of real-time AI economic dashboards that track productivity gains, job displacement, and new role creation at the task and occupation level. These tools, using payroll and platform data, will allow executives and policymakers to monitor AI’s impact in near real time, moving beyond abstract debates to actionable insights. Nigam Shah warns of a growing trend where AI tools bypass traditional health system approval by going directly to end users through free, app-based services. This shift increases the need for transparency and patient understanding of how AI decisions are made. Finally, Diyi Yang calls for a deeper focus on long-term human-AI interaction. He stresses the importance of designing systems that support human development, critical thinking, and well-being—not just short-term engagement. The goal should be AI that augments people meaningfully, not replaces or manipulates them. The year 2026, the experts agree, is not about what AI can do, but how it should be used to serve people and society.