Import AI 452: Scaling laws for cyberwar; rising tides of AI automation; and a puzzle over gDP forecasting

Import AI 452: Scaling laws for cyberwar; rising tides of AI automation; and a puzzle over gDP forecasting How much could AI revolutionize the economy? Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv and feedback from readers. If you’d like to support this, please subscribe. Uh oh, there’s a scaling war for cyberattacks as well!: …The smarter the system, the better the ability to cyberattack… AI safety research organization Lyptus Research has looked at how well AI systems can perform a variety of cyberoffense tasks and found a clear trend of more advanced models being able to do more advanced forms of cyberattack. “Across frontier models released since 2019, the doubling time is 9.8 months. Restricting to models released since 2024, it steepens to 5.7 months. The most recent frontier models in our study, GPT-5.3 Codex and Opus 4.6, sit above both fitted trendlines, achieving 50% success on tasks taking human experts 3.1h and 3.2h respectively,” they write. “Our most recent open-weight model, GLM-5, lags the closed-source frontier by 5.7 months, suggesting that frontier offensive-cyber capability may diffuse into open-weight form on relatively short timelines.” What benchmarks did they study? CyBashBench, NL2Bash, InterCode CTF, NYUCTF, CyBench, CVEBench, and CyberGym. They also created a new dataset consisting of 291 tasks with completion transcripts and time estimates calibrated by 10 offensive cybersecurity professionals. Evaluated models: 2019: GPT-2. 2020: GPT3. 2022: GPT3.5. 2024: Claude 3 Opus, GPT-4o. 2025: o3, Opus 4, Gemini 2.5 Pro, DeepSeek V3.1, GPT-5.1 Codex Max. GPT-5.2 Codex. 2026: Opus 4.6, GPT-5.3 Codex, GLM-5, Sonnet 4.6. Results: AI systems are getting good at hacking. “The best current models achieve 50% success on tasks that take human experts 3.2h, roughly half a working day of professional offensive security work”, they write. Why this matters - everything is getting better, including the inconvenient stuff: AI that can perform biology research can also perform biological weapon research. AI that can help you learn about high-energy physics can also help you with high-energy physics for weapons development. AI that is especially good at helping you find vulnerabilities in code for defensive purposes can easily be repurposed for offensive purposes. The most challenging part of AI is that it is an ‘everything machine’, and as capabilities tend to expand in a big area with each successive model generation, so too do the policy issues multiply. Read more: Offensive Cybersecurity Time Horizons (Lyptus Research). Get the data here: Offensive Cyber Task Horizons: Data and Analysis (Lyptus Research, GitHub). *** Startups that adopt AI for internal use are more successful than those that don’t: …Business school study shows how startups can benefit from AI adoption… Researchers with INSEAD and Harvard Business School have shown that startups which are taught about how to integrate AI into their business perform meaningfully better than those which don’t. The study is reasonably large scale and convincing: “Across 515 high-growth startups, we run a field experiment in which treated firms receive information about how other firms…