After a decade of rapid expansion, artificial intelligence is entering a phase of consolidation. According to Stanford’s AI Index Report 2025, global investment in AI has exceeded $300 billion cumulatively since 2018, but the failure rate of corporate AI projects remains above 45%1. By 2026, researchers at Stanford HAI anticipate a shift: AI will no longer be judged by its spectacular demonstrations, but by its robustness, reproducibility, and measurable value. The central question will no longer be what AI can do, but how, at what cost, and with what real-world effects on organizations and society.
Digital sovereignty as a new strategic front
The concentration of AI capabilities is a major geopolitical issue. By 2025, more than 70% of the computing power dedicated to advanced AI will be controlled by fewer than ten companies, primarily American and Chinese2. According to James Landay, this imbalance is driving many nations to invest in sovereign infrastructure. In Europe, more than €20 billion has been committed between 2024 and 2026 to sovereign cloud and AI projects. This strategy relies as much on the development of national models as on the ability to run and audit foreign models on local infrastructure, in order to regain control over data and algorithmic decisions.
From Solutionism to Measured Utility
According to Erik Brynjolfsson and Angèle Christin, 2026 will mark a period of streamlining in AI adoption. An MIT study shows that only 23% of AI deployments in businesses today generate a clearly measurable return on investment3. In light of this, organizations are developing AI performance dashboards capable of assessing productivity gains per task, sometimes down to the level of a few minutes saved per day per employee. Projects that fail to demonstrate tangible operational value are gradually being shut down, marking the end of opportunistic adoption.
Healthcare as a testing ground for new scientific AI
The healthcare sector appears to be one of the main beneficiaries of this new phase. By 2025, more than 30% of publications on AI applied to healthcare will use self-supervised learning approaches, compared to less than 10% in 20204. Curtis Langlotz anticipates that these biomedical models will reduce the time to diagnosis for certain rare diseases by 20 to 40%. Nigam Shah also notes that several AI systems are beginning to be integrated directly into clinical workflows, with documented gains in diagnostic accuracy of up to 15% in certain complex cases.
Opening the black box: a growing scientific imperative
In research, raw performance is no longer enough. According to Stanford, more than 60% of AI researchers believe that the lack of interpretability is now a major barrier to the scientific and clinical adoption of models5. Russ Altman predicts a widespread adoption of internal network analysis methods, enabling the identification of which representations influence decisions. This requirement for explainability is becoming central, particularly in fields where an algorithmic error can have significant human or medical consequences.
Law and Complex Reasoning Put to the Test by AI
In the legal field, AI is becoming increasingly sophisticated. By 2025, tools capable of simultaneously analyzing dozens of legal documents had achieved an accuracy rate exceeding 85% on factual summarization tasks6. Julian Nyarko points out that this rise in capability imposes new standards, as a simple error or misquote can have major legal consequences. By 2026, the evaluation of these systems will rely as much on their reasoning capabilities as on their traceability and documentary reliability.
Less data, but better selected
The shortage of high-quality data and rising energy costs are profoundly changing development strategies. According to the International Energy Agency, the energy cost of training large models increased by nearly 35% between 2022 and 20257. In response, researchers note that models trained on smaller but better-curated datasets can outperform massive models on specialized tasks. This approach can sometimes reduce energy consumption by 40% without sacrificing performance, marking a turning point toward more efficient AI.
AI with a human-centered focus
For Diyi Yang, one of the major challenges of 2026 will be to reorient AI toward sustainable human goals. Recent studies show that systems designed solely to maximize engagement can amplify polarization and reduce users’ critical thinking skills8. In response, new metrics are emerging, such as long-term well-being, information quality, and diversity of viewpoints. The goal is no longer to maximize attention, but to design AIs that support intellectual autonomy and informed decision-making.
2026: A Year of Maturity Rather Than Upheaval
Stanford experts agree on one key point: 2026 will not be the year of general AI. Fewer than 5% of the researchers surveyed believe that AGI is achievable in the short term9. However, 2026 could become the year when artificial intelligence finally proves its social, economic, and scientific value. A year of maturity, where performance must be accompanied by transparency, demonstrated utility, and accountability—an essential condition for maintaining collective trust.
Learn more
The scenarios outlined by Stanford experts for 2026 cannot be separated from the material and environmental constraints that already weigh on the development of artificial intelligence. To explore this often-overlooked aspect of technological predictions, we invite you to continue reading our analysis of the real ecological impact of AI, focusing on its energy and water requirements and its carbon footprint: Behind AI: Energy, Water, and Carbon—The Environmental Balance Sheet of 2025
References
1. Stanford HAI. (2025). AI Index Report.
https://hai.stanford.edu
2. OECD. (2025). Concentration of AI compute and data.
https://www.oecd.org
3.Brynjolfsson, E. et al. (2024). The productivity paradox of AI. MIT Sloan.
https://sloanreview.mit.edu <
4. Nature Medicine. (2024). Self-supervised learning in healthcare AI.
https://www.nature.com
5. Stanford University. (2025). Survey on Explainability in AI Research.
https://hai.stanford.edu
6. LegalTech Research Group. (2024). AI multi-document reasoning benchmarks.
https://www.legaltechcenter.de//a>
7. International Energy Agency. (2024). Energy costs of AI training.
https://www.iea.org
8. Oxford Internet Institute. (2024). AI engagement and cognitive impact.
https://www.oii.ox.ac.uk
9. Stanford HAI. (2025). Expert survey on AGI timelines.
https://hai.stanford.edu