Artificial General Intelligence (AGI)-Related Prediction

An Artificial General Intelligence (AGI)-Related Prediction is an AI-related prediction specifically focused on the developmental timeline, capabilities, and societal impacts of artificial general intelligence.

AKA: AGI Timeline Prediction, AGI Capability Prediction, AGI Development Forecast, AGI Forecast, AGI Arrival Timeline.
Context:
- It can typically involve estimating AGI-related achievement timeframes through analysis of AGI-related technological progress indicators.
- It can typically specify AGI-related capability thresholds defining what counts as artificial general intelligence.
- It can typically provide AGI-related arrival dates predicting when systems will first meet specified AGI-related capability thresholds.
- It can typically encompass forecasting AGI-related cognitive capability scopes across AGI-related domains.
- It can typically represent AGI-related forecast pairs combining both AGI-related definition and AGI-related arrival date.
- ...
- It can often address projected AGI-related societal impacts on AGI-affected industrys and AGI-influenced human activitys.
- It can often incorporate assessments of AGI-related development risks and AGI-related benefit potential.
- It can often consider AGI-related technological challenges, AGI-specific ethical considerations, and AGI-related societal adaptation requirements.
- It can often rely on AGI-related benchmark progressions tracking AGI-related performance metrics.
- It can often account for AGI-related resource scalings including AGI-related computational requirements and AGI-related data needs.
- It can often reflect AGI-related forecaster assumptions about technological progress and AGI-related development paths.
- ...
- It can range from being an Optimistic AGI-Related Prediction to being a Pessimistic AGI-Related Prediction, depending on its AGI-related capability assessment framework.
- Optimistic AGI-Related Predictions typically project AGI-related development completion within near-term timeframes with emphasis on AGI-related rapid progress indicators.
- Pessimistic AGI-Related Predictions typically project AGI-related development completion far in the future or never with emphasis on AGI-related fundamental obstacles.
- ...
- It can range from being a Near-Term AGI-Related Prediction to being a Long-Term AGI-Related Prediction, depending on its AGI-related timeline projection methodology.
- Near-Term AGI-Related Predictions typically forecast AGI-related achievement timeframes within this decade based on AGI-related rapid progress indicators.
- Long-Term AGI-Related Predictions typically forecast AGI-related achievement timeframes multiple decades into the future based on AGI-related technological challenges.
- ...
- It can range from being a Narrow-Scope AGI-Related Prediction to being a Comprehensive AGI-Related Prediction, depending on its AGI-related impact analysis depth.
- Narrow-Scope AGI-Related Predictions typically focus on specific AGI-related technical aspects without addressing broader AGI-related societal impacts.
- Comprehensive AGI-Related Predictions typically address multiple dimensions of AGI-related development including AGI-related technical aspects, AGI-related economic implications, and AGI-related societal impacts.
- ...
- It can range from being a Deterministic AGI-Related Prediction to being a Probabilistic AGI-Related Prediction, depending on its AGI-related uncertainty representation method.
- Deterministic AGI-Related Predictions typically specify exact AGI-related arrival dates without expressing AGI-related outcome uncertainty.
- Probabilistic AGI-Related Predictions typically express AGI-related arrival dates as probability distributions across multiple timeframes to reflect AGI-related outcome uncertainty.
- ...
- It can range from being a Narrow AGI-Related Prediction to being a Superintelligence AGI-Related Prediction, depending on its AGI-related capability threshold.
- Narrow AGI-Related Predictions typically focus on AGI-related systems with capabilities just reaching human-level intelligence in specific domains.
- Superintelligence AGI-Related Predictions typically address AGI-related systems with capabilities far exceeding human-level intelligence across all domains.
- ...
- It can build on AGI-related expert surveys and AGI-related research studys conducted within the AGI-related research community.
- It can feature probabilistic estimates of AGI-related milestone achievements.
- It can include considerations of AGI-related implementation strategys and AGI-specific safety protocols.
- It can account for AGI-related economic implications and AGI-related geopolitical factors.
- It can involve AGI-related capability taxonomys classifying different AGI-related system types.
- It can vary based on AGI-related capability threshold definitions across different forecasters.
- It can incorporate AGI-related bottleneck considerations such as AGI-related compute scaling, AGI-related data availability, and AGI-related regulatory constraints.
- It can incorporate various AGI-related development models including recursive self-improvement AGI pathways.
- It can address AGI-related scaling challenges regarding AGI-related computational resource requirements.
- It can consider AGI-related safety measures implementation timelines within AGI-related development frameworks.
- It can evaluate AGI-related breakthrough indicators monitoring scientific and engineering advances.
- It can be validated through AGI-related concrete tests or AGI-related economic milestones specified by forecasters.
- It can be shaped by AGI-related risk attitudes affecting probability distributions for AGI-related arrival dates.
- ...
Example(s):
- AGI-Related Timeline Survey Predictions, such as:
  - Expert Consensus AGI-Related Timeline Predictions demonstrating AGI-related expert survey methodologies, such as:
    - Grace et al. (2024) AGI-Related Prediction, estimating 10% probability of autonomous intelligence supremacy by 2027, based on AGI-related benchmark progressions.
    - AI Impacts Survey (2023) AGI-Related Prediction, forecasting median AGI arrival in 2061, addressing AGI-related achievement timeframes.
  - AGI-Related Leader Timeline Predictions illustrating AGI-related forecaster assumptions, such as:
    - Amodei (2024) AGI-Related Prediction, projecting powerful AI emergence as early as 2026, representing an optimistic AGI-related prediction.
    - Altman AGI-Related Timeline Prediction, suggesting AGI within this decade, incorporating AGI-related resource scaling considerations.
  - Forecaster-Specific AGI-Related Timelines demonstrating AGI-related capability threshold variations, such as:
    - Leopold Aschenbrenner AGI-Related Timeline (2027) predicting scary-smart remote-work AGI, representing a near-term AGI-related prediction.
    - Ege Erdil & Tamay Besiroglu AGI-Related Timeline (2045) for same remote-work threshold, illustrating how AGI-related date predictions differ despite identical thresholds.
    - Ray Kurzweil AGI-Related Timeline (2045) predicting technological singularity, incorporating recursive self-improvement AGI pathways.
  - Organization AGI-Related Timelines addressing AGI-related economic implications, such as:
    - OpenAI AGI-Related Timeline (Early 2030s) for majority economically valuable work, demonstrating AGI-related forecast pairs.
    - Metaculus Community AGI-Related Timeline (2036) for 90% occupation automation, using probabilistic AGI-related prediction methods.
  - Skeptical AGI-Related Timelines representing pessimistic AGI-related predictions, such as:
    - Yann LeCun AGI-Related Timeline (Decades Out) requiring new paradigms, highlighting AGI-related technological challenges.
    - No AGI Needed Timeline (Never) rejecting concept usefulness, demonstrating extreme AGI-related capability assessment frameworks.
- AGI-Related Cognitive Capability Predictions addressing AGI-related cognitive capability scopes, such as:
  - AGI-Related Task Capability Predictions specifying AGI-related capability thresholds, such as:
    - Autonomous Website Construction AGI-Related Prediction, forecasting 50% probability by 2028, demonstrating AGI-related concrete tests for validation.
    - Creative AI Parity AGI-Related Prediction, regarding indistinguishable AI music creation, addressing AGI-related milestone achievements.
  - AGI-Related Self-Improvement Capability Predictions incorporating AGI-related development models, such as:
    - LLM Fine-Tuning Autonomy AGI-Related Prediction, for self-improving system capabilities, addressing AGI-related breakthrough indicators.
    - AI Research Automation AGI-Related Prediction, regarding autonomous AI development, demonstrating AGI-related capability taxonomy applications.
- AGI-Related Societal Impact Predictions addressing projected AGI-related societal impacts, such as:
  - AGI-Related Healthcare Impact Predictions illustrating AGI-affected industry analysis, such as:
    - AI Disease Prevention AGI-Related Prediction, about breakthrough treatments, representing a comprehensive AGI-related prediction.
    - Personalized Medicine AGI-Related Prediction, for universal healthcare transformation, addressing AGI-influenced human activitys.
  - AGI-Related Scientific Progress Predictions demonstrating AGI-related benefit potential, such as:
    - Century-Scale Compression AGI-Related Prediction, for accelerated discovery, incorporating AGI-related technological progress indicators.
    - Nobel-Level Research AGI-Related Prediction, for autonomous scientific breakthroughs, illustrating superintelligence AGI-related predictions.
  - AGI-Related Economic Development Predictions assessing AGI-related economic implications, such as:
    - Poverty Reduction AGI-Related Prediction, through AI-driven solutions, addressing AGI-related societal outcomes.
    - GDP Growth Transformation AGI-Related Prediction, via comprehensive automation, demonstrating AGI-related economic milestones.
- AGI-Related Risk Assessment Predictions incorporating AGI-related development risks, such as:
  - AGI-Related Security Risk Predictions addressing AGI-specific safety protocols, such as:
    - AGI-Related Misuse Potential Prediction, about malicious applications, demonstrating AGI-related risk attitude influences.
    - AI Systems Security AGI-Related Prediction, regarding cybersecurity threats, addressing AGI-related catastrophic risks.
  - AGI-Related Employment Impact Predictions analyzing AGI-affected industrys, such as:
    - Occupation Automation AGI-Related Prediction, forecasting job displacement, addressing AGI-related societal adaptation requirements.
    - New Job Category AGI-Related Prediction, on emerging employment opportunities, demonstrating AGI-related societal impact analysis.
  - AGI-Related Control Challenge Predictions considering AGI-related safety measures, such as:
    - AI Alignment Problem AGI-Related Prediction, about value alignment difficulties, addressing AGI-specific ethical considerations.
    - AGI-Related Containment Challenge Prediction, regarding safety mechanisms, demonstrating AGI-related implementation strategy analysis.
- AGI-Related Capability Thresholds specifying what constitutes artificial general intelligence, such as:
  - Task-Based AGI-Related Thresholds defining functional capabilities, such as:
    - Remote-Work AGI-Related Threshold defining systems capable of performing all AGI-related salaried remote employee tasks.
    - Turing-Level AGI-Related Threshold requiring unscripted test passage across multiple AGI-related expert domains.
  - Economic AGI-Related Thresholds addressing AGI-related economic implications, such as:
    - GDP-Doubling AGI-Related Threshold causing economy doubling in two years, demonstrating AGI-related economic milestones.
    - Centaur AGI-Related Threshold requiring human-AI collaboration for productivity gains, addressing AGI-influenced human activitys.
  - Cognitive AGI-Related Thresholds specifying AGI-related cognitive capability scopes, such as:
    - Superintelligence AGI-Related Threshold outperforming humans in all cognitive domains, representing extreme AGI-related capability thresholds.
    - Tool AGI-Related Threshold providing encyclopedic knowledge without autonomous goals, demonstrating AGI-related capability taxonomy applications.
- ...
Counter-Example(s):
- Narrow AI Capability Predictions, which focus on domain-specific AI advancements rather than AGI-related cognitive breakthroughs.
- Quantum Computing Timeline Predictions, which center on quantum hardware development rather than AGI-related cognitive systems.
- Machine Learning Algorithm Performance Predictions, which address specific technical improvements rather than AGI-related intelligence emergence.
- AI Hardware Benchmark Predictions, which forecast chip specifications rather than AGI-related cognitive capability timelines.
- Natural Language Processing Predictions, which project advances in specific language tasks rather than AGI-related general intelligence achievements.
- AI Capability Forecasts, which may lack specific AGI-related arrival date predictions.
- Technology Roadmaps, which may not define precise AGI-related capability thresholds.
- Computing Power Forecasts, which predict hardware rather than AGI-related capabilitys.
- AI Safety Timelines, which focus on AGI-related risk mitigation rather than AGI-related capability achievement.
See: AGI Timeline Compression Factor, AI Timeline Forecast, Technological Singularity, AGI Safety, AGI Ethics, AGI Capability Assessment, Recursive Self-Improvement, AI Alignment Problem, Technology Adoption Forecast, Technological Forecast, AGI Benchmark.

References

2025-04-21

Perplexity.ai
- Question: What are the current forecasts and predictions for AGI development? Please include information about timelines, projected capabilities, and expected societal impacts.
- Answer: Artificial General Intelligence refers to an AI System with general, human-level cognitive capabilities across a wide range of tasks. While AGI does not exist yet, researchers and futurists have made numerous predictions about when it might arrive and what it will look like.
  - Contemporary AGI Timeline Predictions:
    - Forecasts for time-until-AGI have shortened dramatically.
      - The Metaculus prediction community's expected years until a "General AI" dropped from decades (50+ years) around 2020 to roughly 5 years by late 2024, following breakthroughs like GPT-3, ChatGPT, and GPT-4.
      - In 2016–2018, surveys of AI researchers gave a median estimate that human-level AI had a 50% chance of arriving by around 2050–2060.
      - By 2022-2023, new surveys showed much earlier expectations – one large poll of 2,778 AI scientists found a 50% probability of "unaided machines outperforming humans in every task" by 2047, with a 10% chance of such AI by 2027.
      - DeepMind's CEO Demis Hassabis recently stated that AGI could be 5–10 years away (i.e. by ~2030).
      - OpenAI CEO Sam Altman has suggested AGI might arrive within the next U.S. presidential term (i.e. by 2028) and Anthropic CEO Dario Amodei suggested it could come as early as 2026.
      - Leaders of top AI labs, who have insight into cutting-edge models, increasingly talk about single-digit years until AGI.
  - Defining AGI: Capability Thresholds and Key Domains:
    - Core Capabilities include:
      - Language understanding and communication: The ability to comprehend and generate human-like language across topics.
      - Reasoning and problem-solving: General reasoning ability, logical inference, and solving novel problems.
      - Learning and adaptability: The capacity to learn new concepts or skills from limited experience.
      - Perception and sensory integration: Understanding visual, auditory, and possibly tactile inputs.
      - Creative and strategic thinking: Generating original ideas or strategies, innovation, and creativity.
      - Social and emotional intelligence: Understanding human emotions, social cues, and exhibiting Theory of Mind.
    - Different definitions exist:
      - OpenAI's definition: AGI is achieved when AI systems can perform most economically valuable tasks better than humans.
      - DeepMind's Demis Hassabis envisions a higher bar: an AI that can do science and make major discoveries beyond what any human has done.
      - Common proxy: Achieving human-level performance on all key cognitive benchmarks.
  - Methodologies for Forecasting AGI:
    - Expert Surveys and Elicitation:
      - Directly asking AI experts for their predictions through structured surveys.
      - The Katja Grace survey of thousands of authors of recent AI papers about expected milestones.
      - AI Impacts survey (2023) found a median 50% chance of AGI around mid-century, with a 10% chance by the late 2020s.
    - Trend Extrapolation:
      - Examining quantitative trends in AI progress and extrapolating forward.
      - Tracking metrics like compute usage, model size, and benchmark performance.
      - The "Bio-anchors" approach estimates how much computation is needed to match the human brain's capabilities.
    - Prediction Markets and Forecasting Platforms:
      - Platforms like Metaculus and Good Judgment Project aggregate predictions from many forecasters.
      - The Metaculus community median for AGI moved from ~2050 to ~2029 within a few years.
    - Benchmarks and Milestone-Based Forecasts:
      - Identifying key milestones on the road to AGI and estimating when those might be achieved.
      - The arrival of GPT-3, ChatGPT, and GPT-4 prompted many to shorten their timelines, as these models cracked tasks previously thought to require human-level understanding.
  - Variations in Prediction Style:
    - Deterministic vs. Probabilistic:
      - Ray Kurzweil famously predicted that by 2029 we'll have human-level AI, and by 2045 we'll reach a "Singularity".
      - Academic forecasts tend to be probabilistic – e.g., a 70% chance of AGI by 2050, a 20% chance it comes later, and 10% chance it never happens.
    - Optimistic vs. Pessimistic Timelines:
      - Bullish predictors like many AI startup CEOs or certain futurists have very short timelines – often under 10 years.
      - Skeptical or pessimistic predictors foresee AGI as far off or indeterminate.
      - Gary Marcus frequently points out that today's deep learning models can be brittle and lack understanding, suggesting AGI might be decades away.
  - Indicators and Benchmarks in AGI Forecasting:
    - Compute Power (FLOPs):
      - The amount of computation used to train or run AI models is a fundamental metric.
      - OpenAI noted that the compute used in flagship AI training runs was doubling every 3.4 months from 2012-2018.
      - The largest models are trained on the order of 10^23 – 10^25 FLOPs or more, costing millions of dollars.
    - Model Scale and Architecture:
      - The size of AI models (number of parameters) and the sophistication of their architectures.
      - Many believe scaling up models (with sufficient data) yields new emergent capabilities.
    - Benchmark Performance:
      - AI's scores on various standardized tests and benchmarks.
      - OpenAI's GPT-4 was shown to pass a simulated bar exam in the top 10% of human test-takers.
      - Rapid progress has forced the creation of new, harder benchmarks because state-of-the-art models started saturating the old ones.
    - Human Job Performance and Automation:
      - Tracking which occupations are becoming automatable gives a sense of how general AI is becoming.
      - A study by OpenAI and University of Pennsylvania found around 80% of U.S. jobs could have at least 10% of their tasks affected by current language models.
  - Projected Societal Impacts of AGI:
    - Industry Transformation and Economic Impact:
      - AGI could revolutionize virtually every industry.
      - Demis Hassabis envisions AGI helping solve "the biggest problems facing society" – from curing all human diseases to fixing climate change.
      - Economically, some studies forecast enormous growth from AI – on the order of trillions of dollars added to global GDP.
    - Labor Markets and Employment:
      - AI can be seen as either a powerful productivity enhancer or as fully automating human roles.
      - OpenAI research suggests most jobs will be partly impacted, and a significant number could be largely automated by AI technologies.
      - Jobs that are highly routine or information-based are already being automated by AI.
    - Governance and Policy:
      - Many forecasts stress the need for proactive governance before AGI arrives, due to the high stakes.
      - OpenAI's leadership published a call for the governance of superintelligence.
      - Governments have begun to act: in late 2023 the UK hosted a Global AI Safety Summit focused on frontier AI risks.
    - Societal and Ethical Impacts:
      - Beyond economics and politics, AGI could affect society in ways ranging from cultural to philosophical.
      - Would an AGI deserve rights or moral consideration?
      - AGI could heavily influence information ecosystems, raising concerns about misinformation.
      - Some leading figures, like Geoffrey Hinton, have warned there's a non-negligible chance that advanced AI could lead to human extinction if mismanaged.
  - Assumptions, Uncertainties, and Risks in AGI Forecasting:
    - Definition Ambiguity:
      - Different forecasters may be picturing different things when they talk about AGI.
      - Some definitions include consciousness or autonomy, others don't.
    - Extrapolation of Trends:
      - Most optimistic timelines assume that the recent rapid progress in AI will continue.
      - It's possible that we encounter diminishing returns or run into unknown roadblocks.
    - Overconfidence and Biases:
      - Predictions can suffer from biases such as recency bias or selection bias.
      - Even experts admit a great deal of uncertainty in their forecasts.
    - Risks and Alignment Issues:
      - Virtually every serious discussion of AGI forecasting includes a consideration of risks.
      - A recent expert survey reported that between 37% and 52% of respondents gave at least a 10% chance that advanced AI leads to an outcome as bad as human extinction.
      - Forecasters must assume something about our ability to solve the AI alignment problem.
- Citations:

[1] https://en.wikipedia.org/wiki/Artificial_general_intelligence
[2] https://80000hours.org/2025/03/when-do-experts-expect-agi-to-arrive/
[3] http://ar5iv.org/abs/2401.02843
[4] https://time.com/7277608/demis-hassabis-interview-time100-2025/
[5] https://openai.com/index/gpt-4-research/
[6] https://www.vice.com/en/article/openai-research-says-80-of-us-workers-will-have-jobs-impacted-by-gpt
[7] https://openai.com/index/governance-of-superintelligence/
[8] https://www.astralcodexten.com/p/biological-anchors-a-trick-that-might
[9] https://venturebeat.com/ai/smarter-than-humans-in-5-years-the-breakneck-pace-of-ai/
[10] https://www.theguardian.com/technology/2024/dec/27/godfather-of-ai-raises-odds-of-the-technology-wiping-out-humanity-over-next-30-years
[11] https://hai.stanford.edu/ai-index/2025-ai-index-report

2024c

(Amodei, 2024) ⇒ Dario Amodei. (2024). “Machines of Loving Grace: How AI Could Transform the World for the Better.”
- NOTES:
  - It envisions "powerful AI" emerging as early as 2026, capable of dramatically accelerating scientific discovery and transforming various aspects of society.
  - It defines powerful AI as systems smarter than top human experts across various fields, capable of autonomous long-term tasks, and scalable to millions of instances.
  - It identifies five key areas of impact: biology and health, neuroscience and mental health, economic development and poverty, peace and governance, and work and meaning.
  - It argues that AI can dramatically speed up the rate of scientific breakthroughs, compressing decades or even a century of progress into a few years.
  - It suggests that AI could enable the prevention and cure of most infectious diseases, cancers, and genetic conditions, significantly extending healthy human lifespans.
  - It emphasizes that addressing AI risks is essential, not out of pessimism, but because mitigating these risks is crucial to realizing AI's immense benefits.
  - It underscores that achieving this optimistic future will require coordinated efforts from AI developers, policymakers, and society at large.

2024b

(Aschenbrenner, 2024) ⇒ Leopold Aschenbrenner. (2024). "Situational Awareness: The Decade Ahead."
- NOTES:
  - It explores the potential developments and challenges in AI and AGI over the next decade.
  - It emphasizes the geopolitical and economic implications of achieving AGI.
  - It discusses the technical and security challenges associated with advancing AI technologies.
  - It highlights the rapid mobilization of American industrial resources to support AI development.
  - It addresses the urgency of securing AGI secrets against state actors like the CCP.
  - It predicts the emergence of automated AI researchers that could accelerate AI advancements exponentially.

2024a

(Grace et al., 2024) ⇒ Katja Grace, Harlan Stewart, Julia Fabienne Sandkühler, Stephen Thomas, Ben Weinstein-Raun, and Jan Brauner. (2024). “Thousands of AI Authors on the Future of AI.” In: arXiv preprint arXiv:2401.02843.
- NOTE: It suggests:
  - A 50% probability by 2028 for AI systems to achieve several milestones, such as:
    1. Autonomously constructing a payment processing site from scratch.
    2. Creating a song indistinguishable from a new song by a popular musician.
    3. Autonomously downloading and fine-tuning a large language model.
  - If scientific progress continues without disruption, there's a:
    - 10% chance by 2027 and a 50% chance by 2047 that autonomous intelligent machines will outperform humans in every possible task.
    - This 2047 estimate is 13 years earlier than the one reached in a similar survey conducted in 2022.
  - The probability of all human occupations becoming fully automatable is forecasted to:
    - Reach 10% by 2037 and 50% by 2116.

Artificial General Intelligence (AGI)-Related Prediction

References

2025-04-21

2024c

2024b

2024a

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