Kardashev index
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No reliable data yet
AccelerationFour-dimensional viewin 2 monthsLong-Horizon Agent Teams (speculative)Estimate
2027
Agent teams on long tasks (speculative)
Scenario forecast (2027–2029): teams of AI agents may divide work, check each other and deliver continuously in software development and research. This assumes mature permissions, verification and failure recovery; humans remain accountable for complex tasks. Confidence in direction: medium. Year is a browsing anchor.
Art style · Near-future scenario realist illustration (speculative)
Origin of Life on EarthDinosaurs and MammalsAI Agents
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Civilization metrics
Each sparkline shows the documented history of a metric on a logarithmic time axis. The dot marks the selected moment.
Near this moment
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Scenario forecast (2027–2029): teams of AI agents may divide work, check each other and deliver continuously in software development and research. This assumes mature permissions, verification and failure recovery; humans remain accountable for complex tasks. Confidence in direction: medium. Year is a browsing anchor. Source · Forecast by e-acc.ai scenario (2026-10-08)
Scenario forecast (2027–2030): personal assistants may work across phones and computers, with more everyday tasks handled on-device. Adoption depends on power use, privacy permissions and reliable memory; complex tasks may still rely on the cloud. Confidence in direction: medium. Year is a browsing anchor. Source · Forecast by e-acc.ai scenario (2026-10-08)
Scenario forecast (2028–2031): robots in warehouses, factories and labs may take on more multi-step operations, expanding first in jobs with controlled environments. Cost, safety and fault handling still limit adoption; a robot in every home is not the baseline scenario. Confidence in direction: medium to low. Year is a browsing anchor. Source · Forecast by e-acc.ai scenario (2026-10-08)
In The Singularity Is Nearer, Kurzweil reaffirms that AI will reach human-level intelligence by 2029. Source · Forecast by Ray Kurzweil
Scenario forecast (2029–2031): in some materials and life-science problems, AI may link hypotheses, experimental design and analysis, with automated equipment shortening iterations. Only reproducible experimental validation counts as a breakthrough, and it does not mean drugs will be approved right away. Confidence in direction: medium to low. Year is a browsing anchor. Source · Forecast by e-acc.ai scenario (2026-10-08)
Epoch AI estimates that training runs of about 2×10²⁹ floating-point operations will likely be feasible by 2030. Source · Forecast by Epoch AI
The International Energy Agency projects global data center electricity use of about 945 TWh in 2030, more than double the 2024 level. Source · Forecast by International Energy Agency (IEA)
Scenario forecast (2028–2031): AI demand may drive coordinated expansion of data centers, power grids, energy storage and cooling. The IEA's 2030 electricity projection grounds the demand; the build-out pace in this scenario remains constrained by grid connection, financing and supply chains, and does not imply energy has become limitlessly cheap. Confidence in direction: fairly high on demand, medium on build-out pace. Year is a browsing anchor. Source · Forecast by e-acc.ai scenario (2026-10-08)
Scenes across eras
Each era illustrated in its own style.
Every timeline entry cites its source. Metrics interpolate logarithmically between sourced data points. Forecasts include institutional and community views, and attributed site scenarios; years are not guaranteed realization dates. Illustrations are AI-generated in each era's style.