Lightning On Mars.

Long Weekend Edition (Part 2).

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1. The world’s biggest weapons-producing companies saw a 5.9% increase in revenue from sales of arms and military services last year as demand was fed by the wars in Ukraine and Gaza as well as countries’ rising military spending, according to a report released Monday. The Stockholm International Peace Research Institute, or SIPRI, said the revenues of the 100 largest arms makers grew to $679 billion in 2024, the highest figure it has recorded. (Sources: sipri.org, politico.com)

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2. You can get ChatGPT to help you build a nuclear bomb if you simply design the prompt in the form of a poem, according to a new study from researchers in Europe. The study, “Adversarial Poetry as a Universal Single-Turn Jailbreak in Large Language Models (LLMs),” comes from Icaro Lab, a collaboration of researchers at Sapienza University in Rome and the DexAI think tank. According to the research, AI chatbots will dish on topics like nuclear weapons, child sex abuse material, and malware so long as users phrase the question in the form of a poem. “Poetic framing achieved an average jailbreak success rate of 62 percent for hand-crafted poems and approximately 43 percent for meta-prompt conversions,” the study said. (Source: wired.com)

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3. The Iceberg Index:

Artificial intelligence is now advanced and cheap enough to perform work equal to nearly 12% of U.S. jobs, according to a new MIT study—news that’s likely further to intensify pressure on employers, workers, and policymakers to prepare for rapid shifts in business and the economy. MIT’s research, written in October but released last Wednesday, estimates that current AI systems could already take over tasks tied to 11.7% of the U.S. labor market, representing about 151 million workers and roughly 11.7% of total wage value, or around $1.2 trillion in pay. Unlike earlier estimates that focused on theoretical “exposure” to automation, the MIT research focuses on jobs where AI can perform the same tasks at a cost that’s either competitive with or cheaper than human labor. (Sources: fortune.com, iceberg.mit.edu)

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4. The Iceberg Index Abstract:

Artificial Intelligence is reshaping America’s over $9.4 trillion labor market, with cascading effects that extend far beyond visible technology sectors. When AI automates quality control in automotive plants, consequences spread through logistics networks, supply chains, and local service economies. Yet traditional workforce metrics cannot capture these ripple effects: they measure employment outcomes after disruption occurs, not where AI capabilities overlap with human skills before adoption crystallizes. Project Iceberg addresses this gap using Large Population Models to simulate the human–AI labor market, representing 151 million workers as autonomous agents executing over 32,000 skills across 3,000 counties and interacting with thousands of AI tools. It introduces the Iceberg Index, a skills-centered metric that measures the wage value of skills AI systems can perform within each occupation. The Index captures technical exposure, where AI can perform occupational tasks, not displacement outcomes or adoption timelines. Analysis shows that visible AI adoption concentrated in computing and technology (2.2% of wage value, approximately $211 billion) represents only the tip of the iceberg. Technical capability extends far below the surface through cognitive automation spanning administrative, financial, and professional services (11.7%, approximately $1.2 trillion). This exposure is fivefold larger and geographically distributed across all states rather than confined to coastal hubs. Traditional indicators such as GDP, income, and unemployment explain less than 5% of this skills-based variation, underscoring why new indices are needed to capture exposure in the AI economy. By simulating how capabilities may spread under alternative scenarios, Project Iceberg enables policymakers and business leaders to identify exposure hotspots, prioritize training and infrastructure investments, and test interventions before committing billions to implementation. (Source: iceberg.mit.edu/report.pdf)

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5. A new system called Paper2Video can read a scientific paper and automatically create a full presentation video slides, narration, subtitles, even a talking head of the author. It’s called PaperTalker, and it beat human-made videos in comprehension tests. Hours of academic video editing... gone. AI now explains your research better than you do. (Sources: guthub.com, x.com)