# Thousands of AI Authors on the Future of AI

arXiv: https://arxiv.org/abs/2401.02843 · HTML: https://mlsanity.com/paper/2401.02843/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 5 Jan 2024.

## Builds on (most cited first, 4 of 4)

- [AI Deception: A Survey of Examples, Risks, and Potential Solutions](https://mlsanity.com/paper/2308.14752/) (2023)
- [Is Power-Seeking AI an Existential Risk?](https://mlsanity.com/paper/2206.13353/) (2022)
- [When Will AI Exceed Human Performance? Evidence from AI Experts](https://mlsanity.com/paper/1705.08807/) (2017)
- [Forecasting AI Progress: Evidence from a Survey of Machine Learning Researchers](https://mlsanity.com/paper/2206.04132/) (2022)

## Built on by (most cited first, 30 of 48)

- [Alignment faking in large language models](https://mlsanity.com/paper/2412.14093/) (2024)
- [Natural Emergent Misalignment from Reward Hacking in Production RL](https://mlsanity.com/paper/2511.18397/) (2025)
- [LLM Social Simulations Are a Promising Research Method](https://mlsanity.com/paper/2504.02234/) (2025)
- [Complementarity in Human-AI Collaboration: Concept, Sources, and Evidence](https://mlsanity.com/paper/2404.00029/) (2024)
- [Emergence of polarization in networks of large language model agents](https://mlsanity.com/paper/2501.05171/) (2025)
- [Frontier AI's Impact on the Cybersecurity Landscape](https://mlsanity.com/paper/2504.05408/) (2025)
- [The Levers of Political Persuasion with Conversational AI](https://mlsanity.com/paper/2507.13919/) (2025)
- [Eliciting the Priors of Large Language Models using Iterated In-Context Learning](https://mlsanity.com/paper/2406.01860/) (2024)
- [An Economy of AI Agents](https://mlsanity.com/paper/2509.01063/) (2025)
- [Perceptions of Sentient AI and Other Digital Minds: Evidence from the AI, Morality, and Sentience (AIMS) Survey](https://mlsanity.com/paper/2407.08867/) (2024)
- [Sanity Checks for Sparse Autoencoders: Do SAEs Beat Random Baselines?](https://mlsanity.com/paper/2602.14111/) (2026)
- [Catastrophic Cyber Capabilities Benchmark (3CB): Robustly Evaluating LLM Agent Cyber Offense Capabilities](https://mlsanity.com/paper/2410.09114/) (2024)
- [Boundless Socratic Learning with Language Games](https://mlsanity.com/paper/2411.16905/) (2024)
- [Evidence of a log scaling law for political persuasion with large language models](https://mlsanity.com/paper/2406.14508/) (2024)
- [Evaluating AI Evaluation: Perils and Prospects](https://mlsanity.com/paper/2407.09221/) (2024)
- [HumanAgencyBench: Scalable Evaluation of Human Agency Support in AI Assistants](https://mlsanity.com/paper/2509.08494/) (2025)
- [Dissociating Artificial Intelligence from Artificial Consciousness](https://mlsanity.com/paper/2412.04571/) (2024)
- [Position: Scaling LLM Agents Requires Asymptotic Analysis with LLM Primitives](https://mlsanity.com/paper/2502.04358/) (2025)
- [AI Testing Should Account for Sophisticated Strategic Behaviour](https://mlsanity.com/paper/2508.14927/) (2025)
- [Misalignment or misuse? The AGI alignment tradeoff](https://mlsanity.com/paper/2506.03755/) (2025)
- [Misrepresented Technological Solutions in Imagined Futures: The Origins and Dangers of AI Hype in the Research Community](https://mlsanity.com/paper/2408.15244/) (2024)
- [Strategic Insights from Simulation Gaming of AI Race Dynamics](https://mlsanity.com/paper/2410.03092/) (2024)
- [Mental Models of Autonomy and Sentience Shape Reactions to AI](https://mlsanity.com/paper/2512.09085/) (2025)
- [Diffusion Boosted Trees](https://mlsanity.com/paper/2406.01813/) (2024)
- [Liability and Insurance for Catastrophic Losses: the Nuclear Power Precedent and Lessons for AI](https://mlsanity.com/paper/2409.06673/) (2024)
- [Why do Experts Disagree on Existential Risk and P(doom)? A Survey of AI Experts](https://mlsanity.com/paper/2502.14870/) (2025)
- [The Economics of p(doom): Scenarios of Existential Risk and Economic Growth in the Age of Transformative AI](https://mlsanity.com/paper/2503.07341/) (2025)
- [The Hall of AI Fears and Hopes: Comparing the Views of AI Influencers and those of Members of the U.S. Public Through an Interactive Platform](https://mlsanity.com/paper/2504.06016/) (2025)
- [Against racing to AGI: Cooperation, deterrence, and catastrophic risks](https://mlsanity.com/paper/2507.21839/) (2025)
- [Reproducibility: The New Frontier in AI Governance](https://mlsanity.com/paper/2510.11595/) (2025)

## Abstract (arXiv)

In the largest survey of its kind, 2,778 researchers who had published in top-tier artificial intelligence (AI) venues gave predictions on the pace of AI progress and the nature and impacts of advanced AI systems The aggregate forecasts give at least a 50% chance of AI systems achieving several milestones by 2028, including autonomously constructing a payment processing site from scratch, creating a song indistinguishable from a new song by a popular musician, and autonomously downloading and fine-tuning a large language model. If science continues undisrupted, the chance of unaided machines outperforming humans in every possible task was estimated at 10% by 2027, and 50% by 2047. The latter estimate is 13 years earlier than that reached in a similar survey we conducted only one year earlier [Grace et al., 2022]. However, the chance of all human occupations becoming fully automatable was forecast to reach 10% by 2037, and 50% as late as 2116 (compared to 2164 in the 2022 survey). Most respondents expressed substantial uncertainty about the long-term value of AI progress: While 68.3% thought good outcomes from superhuman AI are more likely than bad, of these net optimists 48% gave at least a 5% chance of extremely bad outcomes such as human extinction, and 59% of net pessimists gave 5% or more to extremely good outcomes. Between 38% and 51% of respondents gave at least a 10% chance to advanced AI leading to outcomes as bad as human extinction. More than half suggested that "substantial" or "extreme" concern is warranted about six different AI-related scenarios, including misinformation, authoritarian control, and inequality. There was disagreement about whether faster or slower AI progress would be better for the future of humanity. However, there was broad agreement that research aimed at minimizing potential risks from AI systems ought to be prioritized more.