The article examines how expectations around artificial general intelligence, defined as an Artificial Intelligence system that matches or exceeds human cognitive abilities across a wide range of tasks, have shifted toward earlier timelines. Drawing on 15 surveys and prediction markets that include responses from 8,590 Artificial Intelligence researchers, scientists, entrepreneurs, and community forecasters, the analysis finds that many now expect a 50% probability of AGI around 2040, compared with earlier estimates around 2060. Entrepreneurial and community forecasts are more aggressive, often clustering between ~2030 and the mid-2030s, while academic surveys tend to favor broader ranges for when machines might surpass humans in most economically relevant tasks.
Survey data since 2009 shows a consistent pattern of experts assigning a 50% probability of AGI arrival between 2040 and 2061, with several studies also projecting that superintelligence could follow within a few decades. One 2012/2013 survey of 550 Artificial Intelligence researchers concludes that AGI will probably (over 50% chance) emerge between 2040 and 2050 and is highly likely (90% chance) to appear by 2075, and that super-intelligence could then arrive within a window ranging from as little as 2 years (10% probability) to about 30 years (75% probability). More recent work, such as a 2023 expert survey on progress in Artificial Intelligence, estimates high-level machine intelligence by 2040, while earlier surveys of NIPS and ICML authors suggested a 50% chance around 2059 or 2060. Other studies on labor displacement project that Artificial Intelligence systems could perform 99% of current paid tasks before 2068 and within 100 years for 75% of respondents.
Prediction markets and entrepreneurs skew toward shorter timelines, often citing rapid advances in large language models, scaling trends, and growing compute. Metaculus forecasts based on more than 3,290 participants expect the first weakly general Artificial Intelligence system by 31 Oct 2027, a long, informed, adversarial Turing test to be passed by 2029, the first general Artificial Intelligence system publicly announced by 2030, and top forecasters expecting AGI by 2035. Individual leaders are even more bullish: Dario Amodei argues that AGI will likely occur within a few years (2027), Demis Hassabis assigns roughly a 50% chance by 2030, Eric Schmidt places arrival within 3-5 years as of April 2025, Elon Musk predicts an artificial intelligence smarter than the smartest human by 2026, and Masayoshi Son forecasts it in 2-3 years (i.e., 2027 or 2028). Others, including Jensen Huang, Louis Rosenberg, Ray Kurzweil, Geoffrey Hinton, Sam Altman, Ajeya Cotra, Patrick Winston, and Jürgen Schmidhuber, offer dates ranging from 2029 to 2050, reflecting both optimism and caution.
The article stresses that present systems remain narrow despite impressive breakthroughs. OpenAI’s GPT-5, announced on August 7, 2025, is described as a strong step forward in narrow Artificial Intelligence that is “PhD-level” in reasoning, coding, and writing, but critics such as Carissa Véliz and Gaia Marcus argue that it still only mimics human reasoning and that regulation lags behind capability. DeepMind’s Gemini, in its Deep Think mode, achieved gold-medal performance at the 2025 International Mathematical Olympiad by solving five out of six problems within the 4.5-hour contest window using natural language proofs. At the same time, analyses of training compute suggest that resources for models like GPT-4 and Gemini Ultra have grown at about 4-5x per year, with language models reaching up to 9x/year growth until mid-2020 before slowing, and one 2024 report argues it is feasible to train models requiring up to 2e29 FLOPs by 2030.
Supporters of short AGI timelines point to exponential trends in compute, model scale, and task duration. One study finds that the longest tasks frontier models can complete with 50% reliability have doubled roughly every seven months, progressing from seconds for GPT-2 to nearly an hour for Claude 3.7 Sonnet and o1, and projecting that future systems could handle tasks taking humans days or weeks. Another analysis of 4-5× annual growth in training compute underpins arguments that AGI may be achievable within one or two decades if current power-law performance scaling persists. Advocates of the scaling hypothesis argue that simply increasing parameters, data, and compute for transformer-based architectures could be sufficient, especially as compute costs are driven down by engineering innovations and, potentially, by future quantum computing that could train neural networks more efficiently once Moore’s law slows or ends.
Critics of near-term AGI contend that intelligence is multi-dimensional, that economic value depends on more than cognition, and that modeling the human brain may require breakthroughs beyond mere scale. Yann LeCun argues for retiring the term AGI in favor of “advanced machine intelligence” and emphasizes that human minds are themselves specialized collections of skills, while historical over-optimism, such as Geoff Hinton’s 2016 prediction that radiologists would be obsolete by 2021 or 2026, is cited as a cautionary tale. Others note that IQ above ~$40k of income is not strongly correlated with net worth and that investors seek “unfair advantages” like intellectual property or exclusive access, which cannot be replicated by intelligence alone. Theoretical arguments invoke the Church-Turing hypothesis to claim that brain-level computation is, in principle, simulatable, but also acknowledge that there is no proof that this can be done without prohibitive time or memory.
The article also highlights measurement and safety issues that complicate AGI forecasting. Traditional benchmarks such as the Turing test are considered obsolete, while emerging tests like ARC-AGI aim to measure abstraction and generalization yet may still be vulnerable to data contamination and overfitting. Recent research reports very low scaling exponents (~0.1) for large language models, implying that massive increases in data or compute yield only small accuracy gains, and warns of a potential Degenerative AI regime in which models trained on synthetic or repetitive data accumulate errors faster than they are corrected. Benchmarking approaches like LiveBench and AIMultiple’s own AGI benchmark and ARC-AGI variants use frequently updated questions and holdout sets to reduce data leakage. Beyond benchmarks, some observers propose macro indicators: Satya Nadella suggests that 10% growth in the developed world would indicate AGI, and the article’s authors expect AGI to reduce white-collar employment to 10% of its global peak while GDP growth continues.
Labor statistics from the U.S. Bureau of Labor Statistics show that the ratio of white collar workers to total employment has fluctuated between 45% and 48% from 2019 to 2024, which the authors interpret as short-term stability rather than a long-term trend and expect more dramatic changes as automation and Artificial Intelligence adoption accelerate. At the conceptual level, voices like David Silver, Ilya Sutskever, Ray Kurzweil, and Yann LeCun offer different visions: Silver describes AGI as versatile learning across tasks that will require multiple breakthroughs and gradual progress, Sutskever predicts AGI within 5 to 10 years while emphasizing alignment and safety, Kurzweil forecasts AGI by 2029 and a technological singularity by 2045 with advances such as AI-generated cures, digital clinical trials, and longevity escape velocity, and LeCun proposes alternative architectures to reach human-level intelligence. The article concludes that predictions for AGI have shifted toward 2026-2035, driven by large language models and compute growth, but that deep uncertainties remain over methods, evaluation, economic impact, and governance.
