"Frontier AI" is a term that will only get more common in the years to come, but what it describes is constantly changing. That is because "frontier" does not describe an actual property of an AI but rather how it compares to other existing AI. The definition is relative and therefore changes every time someone releases another powerful model.
That said, frontier AI models do typically share common characteristics. First, they have extremely large computational budgets, being built using server farms running for months at a cost of hundreds of millions to billions of dollars. Second, they have generality, performing at the highest quality across a wide range of tasks. Last, they top the charts in the areas of reasoning, coding, math, and language.
Who Builds Frontier Models
As of mid-2026, a small group of labs produce what the industry calls frontier models. In the United States, the four labs are OpenAI (GPT-5.5), Anthropic (Claude Opus 4.8), Google DeepMind (Gemini 3.1 Pro), and xAI (Grok 4.3). Each lab has a different set of values it tends to represent, and they create models that lead to different benchmarks each release. The result of this concentration of power and fierce race to market expansion has been constant personnel trading between the labs and companies taking financial risks to gain users despite already having large losses.
What Frontier Models Can Actually Do
The capabilities of frontier AI are massive as well as wide reaching. PhD experts were outperformed by frontier AI in biology and chemistry by up to 60%, and Gemini earned a gold medal at the International Mathematical Olympiad. Furthermore, when taking "Humanity's Last Exam", an academic AI benchmark featuring 2,500 expert-created questions across dozens of specialized fields, frontier AI went from 8.8% to 50%+ correct in one year.
Beyond those benchmarks, there are plenty of anecdotal stories. For example, an Australian man, Paul Conyngham, used ChatGPT, Gemini, and Grok to isolate cancer mutations in his dog and develop a specialized treatment.
The Regulation Problem
One of the most consequential things about frontier models is their increasing rate of release. Flagship models are now released every few months, and AI researchers warn of a time when frontier models can improve on themselves to constantly push the boundaries further. On the other hand, the EU AI Act took three years to pass and the US federal government is still working on passing an in-depth regulatory framework for AI. By the time any regulatory framework is passed, it is already describing the previous generation of AI. This makes it especially important that AI regulation is built to last. It is a structural problem that lawmakers across the world must struggle with.
Frontier AI is and will continue to be one of the hottest topics in this decade and the next. While it is easy to focus on the harm it may cause, it is worth thinking equally about the good it may do. What Paul Conyngham accomplished is just a glimpse into a future where frontier AI is used to treat diseases, push the boundaries of research, and help solve problems that have faced humanity for tens, hundreds, even thousands of years.