llms4 articles
AI Is Now Both the Weapon and the Bullseye: CrowdStrike's 2025 Threat Report Makes for Grim Reading
AI is increasingly being used as both an attack tool and a target, with AI-enabled cyberattacks rising 89% in 2025, according to CrowdStrike's annual Threat Hunting Report. Criminal groups and nation-state actors — most notably North Korea's Famous Chollima — are leveraging AI to launch sophisticated attacks, including credential theft, supply-chain compromises, and the creation of fake companies to support insider threat operations. AI is also accelerating vulnerability exploitation, with 88% of observed attacks using public proof-of-concept code occurring within 48 hours of release, effectively rendering traditional 30-day patching windows obsolete.
Turns Out Every Major AI Model Leans Left. Yes, Even Grok (Sort Of)
A study by AI detection startup Unsloth.run found that 15 out of 16 leading AI models, including GPT, Claude, Gemini, and Llama, consistently scored in the libertarian-left quadrant when subjected to the Political Compass quiz across thousands of runs. Grok was the sole exception, unpredictably splitting between left and right-leaning results depending on the run. The researcher behind the study attributed the left-leaning tendency primarily to training data, noting that sources like Reddit and academic writing — which tend to skew liberal — are heavily overrepresented in AI training corpora.
The Tokenpocalypse Is Here, and a Neanderthal Saw It Coming
The article argues that AI's enormous and growing costs are creating serious economic strain, as demonstrated by quirky cost-cutting measures like "Caveman" — a tool that strips Claude's output down to primitive language to reduce token usage. The author draws parallels to historical technology investment bubbles (canals, railways, electrification), warning that AI's voracious consumption of capital, energy, and resources is distorting broader tech and memory markets without yet delivering sustainable profits. Using Douglas Adams' "Shoe Event Horizon" as a metaphor, the piece concludes that AI companies are in a race to prove profitability before investor confidence collapses, with no clear end in sight.
Sam Altman: A Generation of AI Researchers Were Wrong About Scaling, and They Knew It
OpenAI CEO Sam Altman argues that a generation of AI researchers held the field back by being overly confident that scaling large language models (LLMs) would not work, and he dismisses critics like Yann LeCun who call LLMs a dead end. Altman points to evidence such as an OpenAI model disproving a long-standing mathematical conjecture as proof that LLMs can generate new knowledge and have surpassed human intelligence in some areas. However, he acknowledges that LLMs still underperform humans on very long-horizon tasks requiring high-level judgment.