Summary

This report explores pathways through which AI could develop transformative capabilities within the next ten years (i.e. by 2035), referred to here as ‘short timelines’ to transformative AI (TAI). It focuses on two primary mechanisms for rapid AI capabilities progress – compute scaling and recursive improvement – which play central roles in the most influential stories of short TAI timelines. After examining how these mechanisms might or might not enable rapid capabilities progress over the next ten years, the key argumentative threads of the report are captured under a set of seven distinct scenarios in which TAI is developed by 2035. The analysis of the report culminates in an argument that short timelines are plausible.

Compute scaling

The history of AI development indicates that AI capabilities can be improved by increasing (effective) compute – and we’ve observed fast growth in this input, fuelled by increases in microchip density, hardware efficiency, algorithmic progress, and investment. Some experts believe these trends will persist over the next decade. If so, they could result in TAI before 2035.

Sceptics argue that this pathway will soon face challenging bottlenecks (concerning e.g. data, investment, power, and limitations of traditional LLMs) that would slow progress. However, even if compute scaling becomes seriously bottlenecked on something before TAI arrives, other mechanisms – such as recursive improvement – could still achieve enough traction to produce TAI within the next ten years.

Recursive improvement

If AI systems are deployed to automate AI R&D, they could initiate powerful feedback loops in the AI field. Some argue that this would not only break bottlenecks to compute scaling, but drive exponential or even super-exponential growth in AI capabilities, resulting in the arrival of TAI (and perhaps even more advanced systems) before 2035.

Sceptics argue that the effects of these feedback loops would be counteracted by increasing bottlenecks and diminishing returns on effort as low-hanging fruit in capabilities improvements is exhausted. They also highlight constraints which would limit the size or speed of each capabilities improvement ‘step’. However, even if recursive improvement cannot drive exponential growth in AI capabilities, it could still enable fast enough progress to achieve TAI by 2035.

Short timeline scenarios

On examination, it seems that there are many different routes through which TAI could arrive by 2035. To illustrate this, the author generates and describes seven plausible scenarios with short TAI timelines. In five of these, progress is based on compute scaling and/or recursive improvement; the other two highlight pathways to short TAI timelines which don’t significantly rely on these mechanisms. The existence of a plurality of plausible short timeline scenarios strengthens the evidence base for short timelines.