AI systems are now reliably more persuasive than human experts in text-based interactions, according to a multi-institutional study involving 18,978 conversations across 6,923 participants. Research from the University of Oxford, UK AI Security Institute, Stanford University, and the London School of Economics found that models like OpenAI's GPT-4o and GPT-5.4, Google's Gemini 2.5 Pro, and xAI's Grok 4.20 consistently outperformed elite human debaters and professional canvassers. In real-world trials with the firm AppcoUK, AI successfully raised more money for charity, exceeding human canvassers by 10.8 percentage points in donation effectiveness. While human coaching narrowed the performance gap, it did not eliminate it, and the AI's advantage was only neutralized when it was constrained to human-length messages and typing speeds.

Experts are also debating the timeline for self-sustaining AI, defined as systems integrated into physical infrastructure—such as factories and robotics—that can grow their own population without human cognitive or physical input. Forecaster Ajeya Cotra suggests such systems could emerge within 10 years, while journalist Timothy B. Lee argues for a longer horizon, estimating a 50-year median. The debate centers on overcoming 'tacit knowledge' (skills gained through experience rather than textbooks) in critical sectors like semiconductor manufacturing. Observers note that progress in humanoid robotics and robotic hand dexterity will be key indicators to monitor over the next 2-3 years.

Separately, Google DeepMind researchers have outlined potential pathways to artificial superintelligence (ASI), defined as systems exceeding the collective performance of human experts across all domains. The paper identifies four primary vectors for this transition: scaling compute and data resources, discovering new algorithmic paradigms (similar to the jump provided by Transformer architectures), enabling recursive self-improvement (where AI builds its own successors), and forming complex multi-agent structures. The authors emphasize that as the world approaches general intelligence, preparing for ASI requires monitoring a diverse set of scenarios rather than focusing on a single technological trajectory.

In industry news, the startup Recursive has reported state-of-the-art results in language model training and GPU kernel optimization, utilizing an automated research system designed to facilitate recursive self-improvement. The firm aims to demonstrate how automated systems can refine their own performance metrics and model efficiency, contributing to the broader field of machine-driven scientific discovery.

AI systems are now reliably more persuasive than human experts in text-based interactions, according to a multi-institutional study involving 18,978 conversations across 6,923 participants. Research from the University of Oxford, UK AI Security Institute, Stanford University, and the London School of Economics found that models like OpenAI's GPT-4o and GPT-5.4, Google's Gemini 2.5 Pro, and xAI's Grok 4.20 consistently outperformed elite human debaters and professional canvassers. In real-world trials with the firm AppcoUK, AI successfully raised more money for charity, exceeding human canvassers by 10.8 percentage points in donation effectiveness. While human coaching narrowed the performance gap, it did not eliminate it, and the AI's advantage was only neutralized when it was constrained to human-length messages and typing speeds.

Experts are also debating the timeline for self-sustaining AI, defined as systems integrated into physical infrastructure—such as factories and robotics—that can grow their own population without human cognitive or physical input. Forecaster Ajeya Cotra suggests such systems could emerge within 10 years, while journalist Timothy B. Lee argues for a longer horizon, estimating a 50-year median. The debate centers on overcoming 'tacit knowledge' (skills gained through experience rather than textbooks) in critical sectors like semiconductor manufacturing. Observers note that progress in humanoid robotics and robotic hand dexterity will be key indicators to monitor over the next 2-3 years.

Separately, Google DeepMind researchers have outlined potential pathways to artificial superintelligence (ASI), defined as systems exceeding the collective performance of human experts across all domains. The paper identifies four primary vectors for this transition: scaling compute and data resources, discovering new algorithmic paradigms (similar to the jump provided by Transformer architectures), enabling recursive self-improvement (where AI builds its own successors), and forming complex multi-agent structures. The authors emphasize that as the world approaches general intelligence, preparing for ASI requires monitoring a diverse set of scenarios rather than focusing on a single technological trajectory.

In industry news, the startup Recursive has reported state-of-the-art results in language model training and GPU kernel optimization, utilizing an automated research system designed to facilitate recursive self-improvement. The firm aims to demonstrate how automated systems can refine their own performance metrics and model efficiency, contributing to the broader field of machine-driven scientific discovery.