Five years from now, AI might be completing software engineering tasks in a month that would take a human the same amount of time. That’s the prediction of a new study that introduces a metric called the 50%-task-completion time horizon—a measure of how long humans typically take to complete tasks that AI models can solve with a 50% success rate. And if current trends hold, AI is on track to automate increasingly complex work, from debugging code to conducting full-scale machine learning research.
AI’s growing time horizon
The study, conducted by the Model Evaluation & Threat Research (METR) group, suggests that AI’s ability to handle long and complex tasks has been doubling every seven months since 2019. Today’s frontier models, like Claude 3.7 Sonnet, already match human performance on 50-minute-long tasks. Extrapolating this growth, AI could reach a one-month time horizon—the ability to autonomously complete tasks that would take a human a month—between 2028 and 2031.
This isn’t just about raw computational power. AI’s improved logical reasoning, tool use, and ability to adapt to mistakes are fueling the trend. Early AI systems would get stuck in loops or abandon problems too soon, but modern models are learning to persist and correct errors—critical traits for automation at scale.
How the study measured AI’s capabilities
The research team designed a benchmark based on 170 tasks across three datasets—HCAST, RE-Bench, and a new suite of shorter software tasks called SWAA. They timed human professionals completing these tasks and compared their performance to AI models spanning from 2019 to 2025. The results showed a clear trajectory:
- In 2019, AI struggled with tasks that took more than a minute.
- By 2023, AI was reliably solving 5–30-minute tasks.
- Today, the best models can handle tasks approaching an hour.
Interestingly, AI’s progress has been remarkably steady, even when tested against new challenges. The study found that the increase in time horizon remains consistent across different types of tasks, meaning AI isn’t just getting better at specific benchmarks—it’s improving across the board.
Are we headed for fully autonomous AI?
While the study confirms rapid AI progress, it also raises concerns. The same ability that allows AI to write complex software could also enable it to perform high-risk activities autonomously. The paper warns that as AI systems become capable of extended autonomous operation, new safety measures will be needed to prevent misuse, such as self-replicating AI or autonomous development of hazardous materials.
Additionally, AI’s performance drops on “messier” real-world tasks—those requiring creativity, strategic thinking, or human collaboration. While AI excels at structured problems with clear objectives, it still struggles in unpredictable environments.
What’s next?
If AI’s progress continues at its current rate, it could reshape industries by automating work traditionally done by skilled professionals. The implications stretch beyond software development—fields like legal research, cybersecurity, and even scientific discovery could see AI playing a much larger role.
But will the trend hold? The study’s authors acknowledge that external factors—such as compute limitations or breakthroughs in AI training—could speed up or slow down progress. One thing is clear: AI isn’t just getting smarter. It’s learning how to work.
Featured image credit: Kerem Gülen/Midjourney
