AI timelines
In plain wordsForecasts of when AI will reach major milestones, such as matching humans at most work. People with short timelines expect this within years; others expect decades, or never.
Forecasts of when AI systems will reach specified capability milestones, most often AGIartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive., transformative AItransformative AI, n.: AI that changes the world as deeply as farming or the Industrial Revolution did. It is defined by its effects on society and the economy, not by how humanlike or clever it is., high-level machine intelligencehigh-level machine intelligence, n.: A yardstick used in surveys of AI researchers: the point at which machines on their own could outdo human workers at every task, in both quality and cost. or the full automation of AI research. Methods include expert surveys, which show wide dispersion and strong sensitivity to question wording; aggregated forecasts on prediction platforms such as Metaculus; extrapolation of trends in training computecompute, n.: The raw computing power used to train and run AI, usually measured by how many specialized chips are used and for how long. With data and algorithms, it is a main driver of AI progress., algorithmic efficiency and benchmark results, notably the length of tasks that AI agents complete autonomously (METR time horizonMETR time horizon, n.: A way of measuring AI progress by asking how long a task — timed by how long it takes a skilled human — an AI agent can complete with even odds of success.); and compute-anchored models such as biological anchorsbiological anchors, n.: A way of forecasting when AI could transform the world, by estimating how much computing it would take to match the human brain and when that much computing would be affordable.. Short timelines usually means AGI expected within about five to ten years. Between 2020 and 2025, median forecasts from most of these sources moved markedly earlier, and several frontier-lab leaders publicly predicted systems matching top human experts by 2026–2030; other researchers expect bottlenecks in data, reliability and learning from real-world experience to push such milestones decades out. Timelines set the urgency of policy but are logically independent of takeofftakeoff, n.: The stretch of time between AI reaching roughly human level and AI becoming vastly superhuman. People argue over whether it would take decades, years, months or just days. speed.
Her timelines got shorter after agents started finishing day-long tasks.
Origin
Predictions of human-level AI are as old as the field, such as Herbert Simon's 1965 forecast that machines would do any human work within twenty years. As a plural noun for AGI forecasts, 'timelines' became standard in effective-altruist and AI-safety circles in the 2010s, through AI Impacts, Open Philanthropy and LessWrong.
Related
- biological anchorsbiological anchors, n.: A way of forecasting when AI could transform the world, by estimating how much computing it would take to match the human brain and when that much computing would be affordable.
- high-level machine intelligencehigh-level machine intelligence, n.: A yardstick used in surveys of AI researchers: the point at which machines on their own could outdo human workers at every task, in both quality and cost.
- METR time horizonMETR time horizon, n.: A way of measuring AI progress by asking how long a task — timed by how long it takes a skilled human — an AI agent can complete with even odds of success.
- takeofftakeoff, n.: The stretch of time between AI reaching roughly human level and AI becoming vastly superhuman. People argue over whether it would take decades, years, months or just days.
- AI 2027AI 2027, prop. n.: A detailed 2025 forecast, written as a month-by-month story, imagining AI becoming superhuman by the end of 2027, with two alternative endings depending on whether humanity slows down.
- scaling lawsscaling laws, n.: Measured patterns showing that an AI model's error shrinks smoothly and predictably as the model is made bigger and given more data and computing power, so labs can forecast a giant model's performance from small, cheap experiments.
- transformative AItransformative AI, n.: AI that changes the world as deeply as farming or the Industrial Revolution did. It is defined by its effects on society and the economy, not by how humanlike or clever it is.
- Epoch AIEpoch AI, prop. n.: A research nonprofit that gathers data on how fast AI is advancing — the computing power, data and money behind it — and publishes forecasts and difficult tests for AI.
Mentioned in
artificial general intelligenceartificial general intelligence, n.: AI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive., METR time horizonMETR time horizon, n.: A way of measuring AI progress by asking how long a task — timed by how long it takes a skilled human — an AI agent can complete with even odds of success., p(doom)p(doom), n.: Shorthand for someone's personal estimate of the chance that AI leads to catastrophe for humanity, such as extinction. People give it as a percentage, from nearly zero to nearly certain., superintelligencesuperintelligence, n.: A mind, usually imagined as an AI, that would leave even the cleverest people far behind in nearly every kind of thinking, not just in one game or skill. None is known to exist., AI 2027AI 2027, prop. n.: A detailed 2025 forecast, written as a month-by-month story, imagining AI becoming superhuman by the end of 2027, with two alternative endings depending on whether humanity slows down., algorithmic progressalgorithmic progress, n.: Improvements in AI methods — not faster chips — that let a model reach the same ability with less computing power. Researchers estimate this has cut the computing needed by half every eight to sixteen months., Epoch AIEpoch AI, prop. n.: A research nonprofit that gathers data on how fast AI is advancing — the computing power, data and money behind it — and publishes forecasts and difficult tests for AI., fast takeofffast takeoff, n.: A scenario in which AI goes from about human level to far beyond it very quickly, in days, weeks or a few months, leaving people little time to react., Ray KurzweilRay Kurzweil, prop. n.: An American inventor of reading machines and speech technology who became the best-known prophet of the 'singularity', predicting human-level AI by 2029., scaling hypothesisscaling hypothesis, n.: The bet that today's AI methods, simply made bigger with more computing power and data, will keep getting smarter — possibly all the way to human-level intelligence and beyond — without needing fundamentally new ideas., Shane LeggShane Legg, prop. n.: A New Zealand-born AI researcher who co-founded DeepMind and helped make 'artificial general intelligence' a mainstream term and goal., situational awarenesssituational awareness, n.: An AI model's grasp of its own position — that it is an AI, how it was trained, whether it is being tested. Also the title of a widely read 2024 essay on the race toward human-level AI., slow takeoffslow takeoff, n.: A scenario in which AI becomes superhuman step by step, over years or decades, as it spreads through the economy, giving society time to spot problems and respond., takeofftakeoff, n.: The stretch of time between AI reaching roughly human level and AI becoming vastly superhuman. People argue over whether it would take decades, years, months or just days., transformative AItransformative AI, n.: AI that changes the world as deeply as farming or the Industrial Revolution did. It is defined by its effects on society and the economy, not by how humanlike or clever it is., Vernor VingeVernor Vinge, prop. n.: An American science-fiction author and mathematics professor who popularized the 'technological singularity': a coming point when smarter-than-human minds change the world so fast that no one can foresee what follows., AGI has been achieved internallyAGI has been achieved internally, phr.: A meme claiming, half-jokingly, that an AI company has secretly built human-level AI and is keeping it hidden; used to tease rumors and hype from AI insiders., AI Futures ProjectAI Futures Project, prop. n.: A small nonprofit research group that writes detailed forecasts and scenarios of how advanced AI could develop, best known for the 2025 scenario AI 2027., AI ImpactsAI Impacts, prop. n.: A small research project that studies how and when human-level artificial intelligence might arrive, best known for large surveys asking AI researchers to predict their field's future., Ajeya CotraAjeya Cotra, prop. n.: An American AI researcher and forecaster whose 'biological anchors' report estimated when transformative AI might arrive, and who argued that standard training could produce deceptive AI., biological anchorsbiological anchors, n.: A way of forecasting when AI could transform the world, by estimating how much computing it would take to match the human brain and when that much computing would be affordable., Daniel KokotajloDaniel Kokotajlo, prop. n.: An American AI forecaster and former OpenAI researcher who led 'AI 2027', a detailed scenario of a race to superintelligence, and campaigns for transparency at AI companies., effective computeeffective compute, n.: A way of measuring AI progress that counts better methods as if they were extra computing power: if a new technique lets a model match one trained with twice the computing, it counts as doubling effective compute., Epoch Capabilities IndexEpoch Capabilities Index, prop. n.: A single number summarizing an AI model's general capability, calculated by the research group Epoch AI by combining its scores on dozens of tests onto one common scale.