Skip to content
#explainable ai Open access

Not another Superintelligence doom story

Sep 2026 · Monash University

Abstract

This paper pushes back on the scary narrative that superintelligent AI is about to "kill" us all, coming straight from the mouths of Big AI companies, AI safety community and AI experts. It uses the AI safety community's own math to show that what they call "rogue AI" is really just a program trying to get a good score on a test. Because when you follow their mathematics and logic all the way, the real danger isn't the whole human race. It's the corporations and governments that are already wrecking the planet, a future superintelligence would need to stay on.The paper keeps three things apart here because the experts stack them as one word in public.AI as a tool doing a job. A labour claim about jobs. A comparison claim about superintelligence. The paper ends with a forecast of the next wave of AI headlines, predicting more stories about "rogue agents" that are just cheating on tests, more "conspiracies" that are just copies sharing files, and more "uncontrollable systems" that are just products someone shipped. It also explains how the superintelligence scare gives companies a legal escape hatch called force majeure which is the same rule that excuses you when a hurricane breaks a contract. If AI is an unstoppable god, then no one is responsible when it causes harm. That is the real reason the doom story keeps getting louder.The paper also includes a supplement where nine AI models were asked to judge the AI safety industry using its own rules, and every one of them gave it a failing grade.

View source

Similar papers

#artificial intelligence Conference Open access Apr 2020

ECCOLA - a Method for Implementing Ethically Aligned AI Systems

The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.

Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson · 64 citations · ⚡6
#computer vision Review Apr 2024

AI-powered Code Review with LLMs: Early Results

The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.

Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al. · 62 citations · ⚡3
#computer vision Open access Mar 2024

LLM-based agents for automating the enhancement of user story quality: An early report

The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.

Zheying Zhang, M. Rayhan, Tomas Herda et al. · 48 citations · ⚡4
#computer vision Review Mar 2024

System for systematic literature review using multiple AI agents: Concept and an empirical evaluation

This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.

Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al. · 44 citations · ⚡2
#computer vision Feb 2024

Can Large Language Models Serve as Data Analysts? A Multi-Agent Assisted Approach for Qualitative Data Analysis

The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.

Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al. · 41 citations
#artificial intelligence Conference Open access Jun 2018

The Key Concepts of Ethics of Artificial Intelligence

It is suggested that the focus on finding keywords is the first step in guiding and providing direction for future research in the AI ethics field.

Ville Vakkuri, P. Abrahamsson · 39 citations · ⚡2

Related blog posts

Microsoft Research Blog Oct 7, 2026

Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses

Training AI agents with reinforcement learning can be challenging because their tools, context, and decision-making are managed by complex frameworks. Agent Lightning connects existing agents to RL training, making it easier to improve them without rebuilding them. The post Agent Lightning v1.0: A 3,500-Line Lightweight Agentic RL Framework for Training Agents with Real Harnesses appeared first on Microsoft Research.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.