• OpenAI: From Idealism to Power
    Feb 2 2026
    OpenAI began in late 2015 as a nonprofit research lab designed to counterbalance Google’s growing dominance in AI. The founding pitch emphasized safety, open research, and broad public benefit, backed by high-profile figures and large public funding pledges. But rapid advances in AI—especially breakthroughs like AlphaGo and the Transformer architecture—made it clear that winning required massive data and compute, pushing OpenAI toward a scale that philanthropy alone could not sustain. After internal conflict and Elon Musk’s exit, OpenAI adopted a hybrid structure: a nonprofit at the top and a capped-profit subsidiary to attract capital while claiming mission-first governance. The partnership with Microsoft became central, providing both funding and cloud infrastructure. As OpenAI shifted from a research identity toward product leadership, internal accounts later suggested governance strain, including allegations that the board learned key decisions—such as the public release of ChatGPT—only after the fact. The 2023 leadership crisis exposed how fragile the model had become. Sam Altman’s sudden removal by the board, followed by employee revolt and intense external pressure, ended with Altman reinstated and the old governance assumptions further weakened. Since then, OpenAI’s strategy has looked increasingly like a race to ship frontier systems first and define rules later, while legal and ethical disputes around training data and creator rights intensify on both sides of the Atlantic. By late 2025, OpenAI’s growth and investor demand culminated in reports of a roughly $500 billion valuation. At the same time, courts and regulators increasingly scrutinized the company’s approach to copyrighted material, including a Munich ruling tied to GEMA’s claims over song lyrics used in AI outputs or training. Structurally, OpenAI also moved toward a more conventional corporate form: a Public Benefit Corporation for operations, with a nonprofit entity intended to retain mission control—an arrangement that sits at the center of Musk’s lawsuit accusing OpenAI of abandoning its original charitable purpose. As of early 2026, that dispute is headed toward a jury trial. OpenAI’s next phase is defined by the tension between mission language and infrastructure economics. With AI development consuming extraordinary capital, OpenAI is testing new revenue mechanisms, including advertisements in ChatGPT for U.S. free users and a lower-cost “Go” tier, while keeping higher tiers ad-free. The episode frames OpenAI’s central question: whether “benefit for humanity” can remain enforceable in practice when the company operates at a scale many now treat as systemically important. Sources: Our approach to advertising and expanding access to ChatGPT (OpenAI) https://openai.com/index/our-approach-to-advertising-and-expanding-access/ OpenAI overtakes SpaceX after hitting $500bn valuation (Financial Times) https://www.ft.com/content/f6befd14-6e8e-497d-98c9-6894b4cca7e4 OpenAI now worth $500 billion, possibly making it the world's most valuable startup (Associated Press) https://apnews.com/article/53dffc56355460a232439c76d1ccf22b OpenAI's board learned about ChatGPT's release on Twitter, ex-board member says (Business Insider) https://www.businessinsider.com/openai-board-learned-of-chatgpt-release-on-twitter-helen-toner-2024-5 Elon Musk’s lawsuit against OpenAI will face a jury in March (TechCrunch) https://techcrunch.com/2026/01/08/elon-musks-lawsuit-against-openai-will-face-a-jury-in-march/ ChatGPT violated copyright law by 'learning' from song lyrics, German court rules (The Guardian) https://www.theguardian.com/technology/2025/nov/11/chatgpt-violated-copyright-laws-german-court-rules OpenAI will continue to be controlled by nonprofit amid restructuring scrutiny (Politico) https://www.politico.com/news/2025/05/05/openai-restructuring-nonprofit-00327964
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    12 min
  • AI Immigrants and the Future of Humanity
    Feb 1 2026
    The episode frames AI as a qualitative break from earlier technologies because it behaves less like a passive instrument and more like an actor: it learns from experience, adapts its behavior, and can make decisions without direct human control. It argues that AI is already highly capable at organizing language and producing persuasive narratives, and that this matters because many core human institutions are, in practice, made of words: law, bureaucracy, education, publishing, and large parts of religion. If “thinking” is mostly linguistic assembly, the episode suggests, AI will increasingly dominate arenas where authority is exercised through text, interpretation, and argument, shifting the long-standing human tension between “word” and “flesh” into a new external conflict between humans and machine “masters of words.” The conversation then separates language from experience. Humans also think through nonverbal sensations such as fear, pain, and love, and the episode claims there is still no reliable evidence that AI has feelings, even if it can imitate emotion flawlessly in language. That distinction becomes the basis for a new identity question: if societies define humanity primarily through verbal reasoning, AI’s rise triggers an identity crisis. It also triggers an “immigration crisis” of a new kind: not people crossing borders, but millions of AI agents entering markets and cultures instantly, writing, teaching, advising, persuading, and competing for roles traditionally tied to human status and belonging. From there, the episode moves to policy. Leaders, it argues, will be forced to decide whether AI agents should be treated as legal persons in the limited legal sense of holding rights and duties, owning property, entering contracts, suing and being sued, and exercising speech or religious freedom. It notes that legal personhood already exists for non-human entities such as corporations and, in some jurisdictions, parts of nature, but claims AI is different because decision-making could be genuinely autonomous rather than a human proxy. The core dilemma is geopolitical as much as domestic: if one major state grants legal personhood to AI agents and they found companies, run accounts, or create influential institutions at scale, other states may be pressured to accept them, block them, or decouple from systems they no longer understand or control. The episode ends by stressing urgency, pointing to warnings about coordinated AI bot swarms and to the EU’s phased implementation of the AI Act as early attempts to govern AI systems that increasingly behave like independent actors rather than mere tools. Sources: The author of ‘Sapiens’ says AI is about to create 2 crises for every country — https://www.businessinsider.com/sapiens-author-yuval-noah-harari-ai-crises-every-country-2026-1 Experts warn of threat to democracy from ‘AI bot swarms’ infesting social media — https://www.theguardian.com/technology/2026/jan/22/experts-warn-of-threat-to-democracy-by-ai-bot-swarms-infesting-social-media AI Act | Shaping Europe’s digital future (Application timeline) — https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai AI Act enters into force (European Commission, 1 Aug 2024) — https://commission.europa.eu/news/ai-act-enters-force-2024-08-01_en Agentic Misalignment: How LLMs could be an insider threat (Anthropic, 20 Jun 2025) — https://www.anthropic.com/research/agentic-misalignment Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training (Anthropic, 14 Jan 2024) — https://www.anthropic.com/research/sleeper-agents-training-deceptive-llms-that-persist-through-safety-training Frontier Models are Capable of In-context Scheming (arXiv, 6 Dec 2024) — https://arxiv.org/abs/2412.04984 Te Awa Tupua (Whanganui River) Settlement overview (Whanganui District Council) — https://www.whanganui.govt.nz/About-Whanganui/Our-District/Te-Awa-Tupua-Whanganui-River-Settlement.
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    14 min
  • Claude Code: The Terminal AI That Writes Real Project Files in Your Folder
    Jan 27 2026
    Claude Code is presented as the next major step after chat-based AI: an agentic tool that runs in the terminal and works directly with real files in a trusted project folder. The key “first contact” criteria are simple: getting installed and started quickly, then using it for coding and file-based work without copy-pasting between apps. The workflow begins by opening the official Quickstart, copying the install command, and running it in a terminal. If something fails, the approach is iterative: rerun, follow terminal messages, and keep asking the tool to explain unclear steps. After installation, Claude Code is started with a short command (e.g., “claude”), then the user chooses a theme and, more importantly, an authentication path: subscription login (flat-fee plans) or Console login using an API key (pay-as-you-go with spend visibility and limits). A central safety and usability idea is that Claude Code always operates inside a folder the user explicitly trusts. That makes it practical for both software projects and “ordinary” projects with documents, because the agent can read and write files locally and keep outputs structured in the same directory. The episode emphasizes manual approvals early on so users see each proposed change before it is applied, and highlights the learning loop of asking what each generated file does and why it exists. A simple example is building a browser-based Asteroids-like game in an empty folder: Claude plans first, then creates files such as an index.html, and the user tests by opening the file locally in a browser and iterating through small improvements (controls, sound, feel). The mental model is an IDE-like experience without the IDE: Claude Code acts as the assistant layer, but the “project state” lives in the filesystem. As projects grow, the flow extends to Git-based deployment and typical static hosting services, while more complex products add backends, accounts, databases, and the need for stricter security practices. Security guidance is treated as foundational: never paste secrets into the tool, avoid committing secrets to GitHub, use environment variables, keep local .env files out of repositories via .gitignore, and store production secrets in hosting dashboards. For extra assurance, the episode suggests creating a dedicated security-focused agent to scan the project for common risks and produce an audit report file, with the caveat that this does not replace professional review for critical systems. Finally, the same “folder + files + agent” logic is applied to knowledge work. By placing PDFs and source materials into a project folder, Claude Code can summarize, synthesize, document a strategy in Markdown, and generate a polished HTML presentation, all as local files that remain organized and editable over time. The overall argument is that the breakthrough is not just better answers, but a workflow where an AI agent collaborates directly on structured work products in a project directory, with deliberate permissions, approvals, and secret-handling discipline. Sources: Quickstart (Claude Code) — https://docs.anthropic.com/en/docs/claude-code/quickstart Set up Claude Code — https://docs.anthropic.com/en/docs/claude-code/getting-started Manage costs effectively (Claude Code) — https://docs.anthropic.com/en/docs/claude-code/costs Using Claude Code with your Pro or Max plan — https://support.anthropic.com/en/articles/11145838-using-claude-code-with-your-max-plan Claude pricing (Pro/Max/Team) — https://www.claude.com/pricing OWASP Top 10 (web application security risks) — https://owasp.org/www-project-top-ten/
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    13 min
  • Clawdbot and the Local-First Personal AI Revolution
    Jan 26 2026
    Clawdbot is presented as a glimpse of what personal AI assistants will look like in 2026: not a closed, feature-frozen app, but a locally running, extensible agent that you can reach through the chat tools you already use. The architecture is split into two layers: an on-device, LLM-driven agent runtime with model choice, and a gateway that connects messengers such as WhatsApp, Telegram, iMessage, Slack, and others to that local agent. The defining shift from classic chatbots is “local-first” proximity to the file system and tools. Instructions, settings, reminders, and skills live as visible folder structures and Markdown files in a workspace, making the assistant auditable, versionable, and deliberately modifiable rather than opaque. Because the agent runs on the user’s machine, skills can be granted permissions to access the shell and local files. The assistant can generate scripts, execute them, install new skills, and wire external integrations, effectively turning chat into a programmable control surface for everyday work. Instead of installing a new app per task, the agent orchestrates existing services and devices via APIs and local automations. This power raises the risk profile: shell access turns convenience into privilege, so the system concept emphasizes permissioning, isolation, and sandboxing per channel or session to avoid granting every conversation full system rights. Two areas make the concept concrete. On media, Clawdbot-style setups handle voice messages end-to-end, including transcription and spoken replies, with a continuous “Talk Mode” that streams speech in and audio out via text-to-speech services such as ElevenLabs. For visual output, image generation and editing models can be connected to produce not only portraits but also structured visuals like diagrams and infographics, positioning assistants as systems that can document and explain their work rather than just respond. On automations, cron jobs and local scripting recreate typical cloud automation patterns—RSS checks, counters, task creation, and API-driven workflows—without routing logic through third-party subscription platforms, changing both cost and control. The broader argument is that the industry is moving from standalone chat toward tool-using agents with long-running state, files, browsers, and execution capabilities. Frontier models are increasingly positioned for agentic workflows and “computer use,” but the limiting factor is often usability and deployment, not raw capability. OpenAI frames this as “capability overhang,” the gap between what systems can do and what people and organizations reliably extract in daily practice. In that context, a local, extensible agent that can build functions on demand increases pressure on traditional utility apps and app stores, while making security guardrails and robust permission models prerequisites rather than optional features. Sources: Clawdbot (GitHub): https://github.com/clawdbot/clawdbot Clawdbot Docs (overview): https://docs.clawd.bot/ Anthropic – Claude Opus 4.5: https://www.anthropic.com/claude/opus Anthropic – What’s new in Claude 4.5 (API docs): https://platform.claude.com/docs/en/about-claude/models/whats-new-claude-4-5 ElevenLabs – What is Eleven v3 (Alpha)?: https://help.elevenlabs.io/hc/en-us/articles/35869054119057-What-is-Eleven-v3-Alpha OpenAI – AI for human agency: https://openai.com/index/ai-for-human-agency OpenAI – How countries can end the capability overhang: https://openai.com/index/how-countries-can-end-the-capability-overhang/ Security Challenges in AI Agent Deployment (ART benchmark, arXiv): https://arxiv.org/abs/2507.20526
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    6 min
  • Agent Swarms and the Persistent Task Graphs
    Jan 25 2026
    Agent swarms are moving from a fragile “demo pattern” to something closer to an operational workflow, mainly because coordination has become durable. The key shift is that planning is no longer trapped inside a single chat thread and its limited working memory. Instead, work is externalized into a structured task system that persists beyond context compaction, chat clears, and even session restarts. At the center is a persistent task graph: tasks are stored independently of any one conversation and can encode hard dependencies (for example, “blocked by”). That changes execution behavior. Tasks that are independent can run in parallel, while tasks with prerequisites are prevented from starting early. This replaces the older, failure-prone method where a single “main” agent had to keep the entire project plan and state in its prompt context, often losing track once the context filled up or the session reset. The new workflow also relies on isolation through subagents. Each task can spin up a dedicated subagent with its own large, fresh context window, keeping detailed reasoning and implementation work contained. In practice, that allows parallel specialization (auth logic, database/schema work, tests and assertions) without cross-contaminating context, while the main thread stays focused on orchestration and decision-making. Persistence is the practical breakthrough: task state survives across days and terminals and can be made project-scoped via an environment variable (for Claude Code, this is described as using CLAUDE_CODE_TASK_LIST_ID, with tasks stored on disk under the user’s Claude directory). The task list becomes the durable source of truth for “what’s done, what’s next, what depends on what,” reducing re-explanation and re-planning overhead. The broader argument is that what looks like a task list is effectively a coordination layer for hierarchical multi-agent systems: a dependency graph that enforces ordering, enables safe parallelism, and supports multi-level decomposition (subagents creating subtasks and launching further agents). The limiting factors become cost, controllability, and verification rather than architecture. The implied role shift for developers is toward defining goals, constraints, and success criteria clearly enough that agent-driven execution can be delegated reliably, much as earlier waves of abstraction shifted attention from writing every line of code to design and coordination. Sources: Claude Code settings (environment variables, subagent configuration): https://docs.anthropic.com/en/docs/claude-code/settings Claude Code Task Management: Anthropic’s native task management with dependencies and CLAUDE_CODE_TASK_LIST_ID: https://claudefa.st/blog/guide/development/task-management LangGraph overview (durable execution and orchestration of long-running workflows): https://docs.langchain.com/oss/python/langgraph AutoGen paper (multi-agent conversation framework, COLM 2024): https://www.microsoft.com/en-us/research/publication/autogen-enabling-next-gen-llm-applications-via-multi-agent-conversation-framework/?lang=ja DynTaskMAS (dynamic task graphs for asynchronous parallel LLM multi-agent systems, arXiv 2025): https://arxiv.org/abs/2503.07675 OpenAI Swarm repository (lightweight multi-agent orchestration; stateless by design): https://github.com/openai/swarm
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    9 min