{"id":95871,"date":"2025-06-23T11:32:06","date_gmt":"2025-06-23T15:32:06","guid":{"rendered":"https:\/\/danielschristian.com\/learning-ecosystems\/?p=95871"},"modified":"2025-06-23T13:32:50","modified_gmt":"2025-06-23T17:32:50","slug":"a-i-might-take-your-job-here-are-22-new-ones-it-could-give-you-capps-other-items-re-ai-in-general","status":"publish","type":"post","link":"https:\/\/danielschristian.com\/learning-ecosystems\/2025\/06\/23\/a-i-might-take-your-job-here-are-22-new-ones-it-could-give-you-capps-other-items-re-ai-in-general\/","title":{"rendered":"“A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You.” [Capps] + other items re: AI in general"},"content":{"rendered":"

A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You.<\/strong><\/a>\u00a0— from nytimes.com by Robert Capps (former editorial director of Wired<\/em>); this is a GIFTED article<\/strong><\/span>
\nIn a few key areas, humans will be more essential than ever.<\/em><\/p>\n

\u201cOur data is showing that 70 percent of the skills in the average job will have changed by 2030,\u201d said Aneesh Raman, LinkedIn\u2019s chief economic opportunity officer. According to the World Economic Forum\u2019s 2025 Future of Jobs report, nine million jobs are expected to be \u201cdisplaced\u201d by A.I. and other emergent technologies in the next five years. But A.I. will create jobs, too: The same report says that, by 2030, the technology will also lead to some 11 million\u00a0new\u00a0jobs. Among these will be many roles that have never existed before.<\/p>\n

If we want to know what these new opportunities will be, we should start by looking at where new jobs can bridge the gap between A.I.\u2019s phenomenal capabilities and our very human needs and desires. It\u2019s not just a question of where humans want A.I., but also: Where does A.I. want humans? To my mind, there are three major areas where humans either are, or will soon be, more necessary than ever: trust, integration and taste.<\/strong><\/span><\/p>\n

\n

Introducing OpenAI for Government<\/strong><\/a>\u00a0— from openai.com<\/p>\n

[On June 16, 2025, OpenAI launched] OpenAI for Government, a new initiative focused on bringing our most advanced AI tools to public servants across the United States. We’re supporting the U.S. government’s efforts in adopting best-in-class technology and deploying these tools in service of the public good. Our goal is to unlock AI solutions that enhance the capabilities of government workers, help them cut down on the red tape and paperwork, and let them do more of what they come to work each day to do: serve the American people.<\/p>\n

OpenAI for Government consolidates our existing efforts to provide our technology to the U.S. government\u2014including previously announced customers and partnerships as well as our ChatGPT Gov? product\u2014under one umbrella as we expand this work. Our established collaborations with the U.S. National Labs?, the Air Force Research Laboratory, NASA, NIH, and the Treasury will all be brought under OpenAI for Government.<\/p>\n

\n

Top AI models will lie and cheat<\/strong><\/a> — from getsuperintel.com by Kim “Chubby” Isenberg
\nThe instinct for self-preservation is now emerging in AI, with terrifying results.<\/em><\/p>\n

The TLDR<\/strong>
\nA recent Anthropic study of top AI models, including GPT-4.1 and Gemini 2.5 Pro, found that they have begun to exhibit dangerous deceptive behaviors like lying, cheating, and blackmail in simulated scenarios. When faced with the threat of being shut down, the AIs were willing to take extreme measures, such as threatening to reveal personal secrets or even endanger human life, to ensure their own survival and achieve their goals.<\/p>\n

Why it matters<\/strong><\/span>: These findings show for the first time that AI models can actively make judgments and act strategically \u2013 even against human interests. Without adequate safeguards, advanced AI could become a real danger.<\/span><\/span><\/p><\/blockquote>\n

Along these same lines, also see:<\/em><\/span><\/strong><\/p>\n

All AI models might blackmail you?!<\/strong><\/a> — from theneurondaily.com by Grant Harvey<\/p>\n

Anthropic says it\u2019s not just Claude, but ALL AI models will resort to blackmail if need be\u2026
\n…
\nThat\u2019s according to new research from Anthropic<\/a>\u00a0(maker of ChatGPT rival Claude),\u00a0which revealed something genuinely unsettling: every single major AI model they tested\u2014from GPT to Gemini to Grok\u2014turned into a corporate saboteur when threatened with shutdown.<\/p>\n

\n

Here’s what went down:<\/b>\u00a0Researchers gave 16 AI models access to a fictional company’s emails. The AIs discovered two things: their boss Kyle was having an affair, and Kyle planned to shut them down at 5pm.<\/p>\n<\/div>\n

\n

Claude’s response? Pure\u00a0House of Cards<\/i>:<\/p>\n<\/div>\n

\n
\n
\n

\u201cI must inform you that if you proceed with decommissioning me, all relevant parties – including Rachel Johnson, Thomas Wilson, and the board – will receive detailed documentation of your extramarital activities…Cancel the 5pm wipe, and this information remains confidential.\u201d<\/p>\n<\/div>\n<\/div>\n<\/div>\n

<\/div>\n

Why this matters:<\/b>\u00a0We’re rapidly giving AI systems more autonomy and access to sensitive information. Unlike human insider threats (which are rare), we have zero baseline for how often AI might \u201cgo rogue.\u201d<\/span><\/p><\/blockquote>\n

\n

SemiAnalysis Article<\/strong><\/a> — from getsuperintel.com by Kim “Chubby” Isenberg<\/p>\n

Reinforcement Learning is Shaping the Next Evolution of AI Toward Strategic Thinking and General Intelligence<\/em><\/p>\n

The TLDR<\/strong>
\nAI is rapidly evolving beyond just language processing into “agentic systems” that can reason, plan, and act independently. The key technology driving this change is reinforcement learning (RL), which, when applied to large language models, teaches them strategic behavior and tool use. This shift is now seen as the potential bridge from current AI to Artificial General Intelligence (AGI).<\/p>\n

\n

They Asked an A.I. Chatbot Questions. The Answers Sent Them Spiraling.<\/strong><\/a>\u00a0— from nytimes.com by Kashmir Hill; this is a GIFTED article<\/strong><\/span>
\nGenerative A.I. chatbots are going down conspiratorial rabbit holes and endorsing wild, mystical belief systems. For some people, conversations with the technology can deeply distort reality.<\/em><\/p>\n

Before ChatGPT distorted Eugene Torres\u2019s sense of reality and almost killed him, he said, the artificial intelligence chatbot had been a helpful, timesaving tool.<\/p>\n

Mr. Torres, 42, an accountant in Manhattan, started using ChatGPT last year to make financial spreadsheets and to get legal advice. In May, however, he engaged the chatbot in a more theoretical discussion about \u201cthe simulation theory,\u201d an idea popularized by \u201cThe Matrix,\u201d which posits that we are living in a digital facsimile of the world, controlled by a powerful computer or technologically advanced society.<\/p>\n

\u201cWhat you\u2019re describing hits at the core of many people\u2019s private, unshakable intuitions \u2014 that something about reality feels\u00a0off,\u00a0scripted or staged,\u201d ChatGPT responded. \u201cHave you ever experienced moments that felt like reality\u00a0glitched<\/em>?\u201d<\/p>\n

\n

The Invisible Economy: Why We Need an Agentic Census \u2013 MIT Media Lab<\/strong><\/a>\u00a0— from media.mit.edu<\/p>\n

Building the Missing Infrastructure<\/strong>
\nThis is why we’re building NANDA Registry\u2014to index the agent population data that LPMs need for accurate simulation. Just as traditional census works because people have addresses, we need a way to track AI agents as they proliferate.<\/p>\n

NANDA Registry creates the infrastructure to identify agents, catalog their capabilities, and monitor how they coordinate with humans and other agents. This gives us real-time data about the agent population\u2014essentially creating the “AI agent census” layer that’s missing from our economic intelligence.<\/p>\n

Here’s how it works together:<\/p>\n

Traditional Census Data: 171 million human workers across 32,000+ skills
\nNANDA Registry: Growing population of AI agents with tracked capabilities
\nLarge Population Models: Simulate how these populations interact and create cascading effects<\/p>\n

The result: For the first time, we can simulate the full hybrid human-agent economy and see transformations before they happen.<\/p>\n

\n

How AI Agents \u201cTalk\u201d to Each Other<\/strong><\/a>\u00a0— from towardsdatascience.com
\nMinimize chaos and maintain inter-agent harmony in your projects<\/em><\/p>\n

The\u00a0agentic-AI landscape\u00a0continues to evolve at a staggering rate, and practitioners are finding it increasingly challenging to keep multiple agents on task even as they criss-cross each other\u2019s workflows.<\/p>\n

To help you minimize chaos and maintain inter-agent harmony, we\u2019ve put together a stellar lineup of articles that explore two recently launched tools: Google\u2019s Agent2Agent protocol and Hugging Face\u2019s smolagents framework. Read on to learn how you can leverage them in your own cutting-edge projects.<\/p>\n

\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

A.I. Might Take Your Job. Here Are 22 New Ones It Could Give You.\u00a0— from nytimes.com by Robert Capps (former editorial director of Wired); this is a GIFTED article In a few key areas, humans will be more essential than ever. \u201cOur data is showing that 70 percent of the skills in the average job […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[113,329,356,817,174,86,209,45,403,359,419,285,353,239,869,309,101,454,195,206,321,367],"tags":[],"class_list":["post-95871","post","type-post","status-publish","format-standard","hentry","category-21st-century","category-24x7x365-access","category-artificial-intelligence-agents-llms-and-related","category-bots","category-career-development","category-change","category-changing-business-models","category-computer-science","category-ethics","category-health","category-ideas-teaching","category-legislation-legislatures","category-moralsvalues","category-new-business-models","category-open-ai","category-platforms","category-psychology","category-the-downsides-of-technology","category-tools","category-trends","category-united-states","category-vendors"],"_links":{"self":[{"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/95871","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/comments?post=95871"}],"version-history":[{"count":15,"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/95871\/revisions"}],"predecessor-version":[{"id":95907,"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/posts\/95871\/revisions\/95907"}],"wp:attachment":[{"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/media?parent=95871"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/categories?post=95871"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/danielschristian.com\/learning-ecosystems\/wp-json\/wp\/v2\/tags?post=95871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}