Stanford University's Institute for Human-Centered Artificial Intelligence (HAI) has published the ninth edition of its independent AI Index report. The 425-page report lays out, with independent data, the global trajectory of artificial intelligence—from research and development to technical performance, from the economy to medicine, and from education to public policy. This year's core finding is striking: as AI's capabilities accelerate, the systems built around the technology—governance frameworks, evaluation methods, education, and data infrastructure—are failing to keep pace. As Mirleon AI, a domestic startup that develops AI-based decision support systems, we wanted to assess this report in terms of what it tells us about both the ecosystem and companies like ours.
Capabilities Aren't Plateauing, They're Accelerating
The report's clearest message is that progress in AI capabilities is not slowing down. In 2025, more than 90 percent of notable frontier models were produced by industry, and these models met or surpassed human baseline levels on PhD-level science questions, multimodal reasoning, and olympiad mathematics. On SWE-bench Verified, a benchmark widely used in software engineering, performance rose in a single year from 60 percent to nearly 100 percent of the human baseline. Organizational adoption reached 88 percent, and four out of five university students now use generative AI. This picture shows that the baseline is rising rapidly for every startup developing decision support solutions, and that keeping products in step with this momentum has become imperative.
The Jagged Frontier, Powerful but Uneven
The report draws attention to a phenomenon researchers call the "jagged frontier": AI can be both extraordinary and surprisingly weak at the same time. A model can win a gold medal at the International Mathematical Olympiad, yet read an analog clock correctly only 50.1 percent of the time. On OSWorld, which tests computer tasks, AI agents' success leapt from 12 percent to roughly 66 percent in a single year—yet they still fail on one out of every three attempts on structured benchmarks. While robots achieve up to 89 percent success in controlled laboratory environments, they can complete only 12 percent of household tasks. For us, this unevenness confirms an important design principle: especially in high-sensitivity fields such as healthcare, AI should be positioned not as a replacement for humans, but as a tool that strengthens human judgment and keeps a human in the loop.
Responsible AI Is Falling Behind
This may be the most critical heading for startups. The report shows that responsible AI is unable to keep pace with the rise in capability: while nearly all frontier model developers report results on capability benchmarks, reporting on responsible AI benchmarks remains spotty. The number of documented AI incidents rose from 233 in 2024 to 362. Moreover, recent research reveals that improving one dimension, such as safety, can degrade another, such as accuracy. For startups developing KVKK-compliant, transparent, and human-centered solutions, this gap is not merely a responsibility but also an opportunity for differentiation. In an environment where trustworthiness has not yet become the standard, building trustworthiness into the design from the outset turns into a competitive advantage.
Sovereignty, Open Source, and Domestic Production
The report treats "AI sovereignty" as a defining element of national policy. National AI strategies are expanding rapidly, particularly among developing economies, and state-backed investments in supercomputing are rising. Although model production remains largely concentrated in the U.S. and China, open-source development is beginning to redistribute participation: on GitHub, contributions from the rest of the world have surpassed Europe and are approaching the United States, paving the way for more linguistically diverse models and benchmarks. In an environment where the model performance gap between the U.S. and China has effectively closed, and where outh Korea leads the world in AI patents per capita, this picture offers a meaningful window for countries like Türkiye and for domestic startups: with the advantage of open source and local data, standing on the producing side is no longer reserved for large players alone.
The Economy: Productivity Is Rising, but It's Uneven
The report shows that the U.S. leads private AI investment by a wide margin at USD 285.9 billion, and ranks first in entrepreneurship as well, with 1,953 newly funded AI companies in 2025. Generative AI reached 53 percent population adoption within three years, spreading faster than the personal computer or the internet. Productivity gains, on the other hand, are uneven: while gains of 14 to 26 percent are seen in customer support and software development, the effect is weak or even negative on tasks requiring more judgment. A notable finding is that entry-level employment is beginning to decline in the very fields where productivity gains are clearest. This once again reminds us of the importance of designing AI not as something that automates the entire job, but as a lever that complements human judgment.
The Systems Must Catch Up to the Technology
The 2026 AI Index does not point in a single direction; it depicts a field that is scaling faster than the systems around it can adapt. As capabilities rise rapidly, trustworthiness, evidence, education, and governance lag behind. As Mirleon AI, we believe this very gap is a meaningful field of opportunity for startups developing responsible, domestic, and human-centered solutions. With our understanding of AI as a tool that protects human dignity, strengthens decisions, and creates value, we will continue to stand on the side of building not the technology itself, but the trust and value around it.