Think many would disagree with you Oliver…
While Artificial General Intelligence (AGI)—AI systems with human-level capability across all domains—is generally considered theoretically possible, consensus on its timeline is split between a few years to several decades.
In a nutshell
- Four key factors are driving AI progress: larger base models, teaching models to reason, increasing models’ thinking time, and building agent scaffolding for multi-step tasks. These are underpinned by increasing computational power to run and train AI systems, as well as increasing human capital going into algorithmic research.
- All of these drivers are set to continue until 2028 and perhaps until 2032.
- This means we should expect major further gains in AI performance. We don’t know how large they’ll be, but extrapolating recent trends on benchmarks suggests we’ll reach systems with beyond-human performance in coding and scientific reasoning, and that can autonomously complete multi-week projects.
- Whether we call these systems ‘AGI’ or not, they could be sufficient to enable AI research itself, robotics, the technology industry, and scientific research to accelerate, leading to transformative impacts.
- Alternatively, AI might fail to overcome issues with ill-defined, high-context work over long time horizons and remain a tool (even if much improved compared to today).
- Increasing AI performance requires exponential growth in investment and the research workforce. At current rates, we will likely start to reach bottlenecks around 2030. Simplifying a bit, that means we’ll likely either reach AGI by around 2030 or see progress slow significantly. Hybrid scenarios are also possible, but the next five years seem especially crucial.
The godfather of AI himself:
