If we assume the doubling time for time horizon is maintained at 122 days (the faster pace post-2024) this implies 50% time horizon on SWE and ML tasks greater than 40 hours occurs 12/27/26, and the 80% time horizon on SWE tasks greater than 8 hours occurs 1/09/27 (using public launch of GPT-5 as start date).
If the slower doubling time of 213 days is used this pushes back to 1/09/28 and 2/2/28 respectively.
It seems reasonable (as a non-AI researcher) that the doubling time will continue to accelerate as labs increasingly leverage AI to speed up research, so the timeline is likely earlier than Dec '26.
The accuracy of 50% doesn't seem that good (as an industry outsider) - is there a mechanism to use AI to validate the solutions and toss wrong ones such that 50% accuracy is workable at-scale, or is the right way to think about disruption to labor as it needs to be 95%+ accuracy so the timeline for real disruption is further out.
Regardless, incredible piece to read and thank you and OpenAI for sharing.
If we assume the doubling time for time horizon is maintained at 122 days (the faster pace post-2024) this implies 50% time horizon on SWE and ML tasks greater than 40 hours occurs 12/27/26, and the 80% time horizon on SWE tasks greater than 8 hours occurs 1/09/27 (using public launch of GPT-5 as start date).
If the slower doubling time of 213 days is used this pushes back to 1/09/28 and 2/2/28 respectively.
It seems reasonable (as a non-AI researcher) that the doubling time will continue to accelerate as labs increasingly leverage AI to speed up research, so the timeline is likely earlier than Dec '26.
The accuracy of 50% doesn't seem that good (as an industry outsider) - is there a mechanism to use AI to validate the solutions and toss wrong ones such that 50% accuracy is workable at-scale, or is the right way to think about disruption to labor as it needs to be 95%+ accuracy so the timeline for real disruption is further out.
Regardless, incredible piece to read and thank you and OpenAI for sharing.