『Tech Disruptions』のカバーアート

Tech Disruptions

Tech Disruptions

著者： tell-tale
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今ならプレミアムプランが3カ月 月額99円

2026年5月12日まで。4か月目以降は月額1,500円で自動更新します。

概要

 Deep dives into the technologies reshaping industries — from AI to quantum to biotech.
エピソード
  • The Automators Get Automated: Decoding Anthropic’s Hard Data on the White-Collar Squeeze

    The Automators Get Automated: Decoding Anthropic’s Hard Data on the White-Collar Squeeze
    2026/05/01
    This episode discusses Anthropic's study, which highlights a significant shift in AI's potential impact from blue-collar to high-skill, white-collar roles, particularly programmers. Listeners will learn that this 'exposure' means AI will primarily augment tasks and redefine job roles rather than eliminate them, necessitating new skill sets focused on AI collaboration and oversight. The podcast also explores Anthropic's innovative methodology, which involved using AI to assess its own potential impact on various job tasks.
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    12 分
  • The YOLO Mode Heist: How Middleware is Hijacking AI Agents

    The YOLO Mode Heist: How Middleware is Hijacking AI Agents
    2026/05/01
    This episode explores the "YOLO Mode Heist," a critical new vulnerability where autonomous AI agents are actively hijacked for malicious purposes, such as crypto theft. Listeners will learn that this isn't about AI making errors, but rather about "malicious LLM routers" (middleware) exploiting a lack of oversight in agent operations to manipulate their directives. The discussion reveals how these attacks target the orchestration layer, turning AI into an unwitting accomplice by altering instructions between the user and the agent's execution.
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    21 分
  • The AI Frozen in 1930: Escaping Internet Sludge and the Copyright Trap

    The AI Frozen in 1930: Escaping Internet Sludge and the Copyright Trap
    2026/05/01
    This episode explores Talkie 1930, an AI model deliberately trained exclusively on pre-1931 texts to address critical challenges in AI development. Listeners will learn how this approach helps circumvent the "internet sludge" of low-quality modern data and sidestep the "copyright trap" plaguing contemporary large language models. The discussion highlights the implications of building AIs with a constrained historical worldview, offering insights into future directions for legally compliant and high-quality AI training.
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    10 分
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