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    <title>Yugen Advisors | Blog</title>
    <description>Notes on AI governance, agentic engineering, and application security.</description>
    <link>https://www.yugenadvisors.com</link>
    <lastBuildDate>Sun, 31 May 2026 21:19:42 GMT</lastBuildDate>
    <item>
        <title>Enterprise AI is becoming a model-routing problem</title>
        <link>https://www.yugenadvisors.com/blog/enterprise-ai-is-becoming-a-model-routing-problem</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/enterprise-ai-is-becoming-a-model-routing-problem</guid>
        <pubDate>Sat, 30 May 2026 00:00:00 GMT</pubDate>
        <description>The Uber/Claude Code budget story was a governance warning. The next layer down is inference economics: if DeepSeek-class models can handle large volumes of enterprise work at a fraction of frontier-model cost, then model routing becomes a board-level control surface.</description>
        <category>AI economics</category>
        <category>AI governance</category>
        <category>model routing</category>
        <category>enterprise AI</category>
        <category>DeepSeek</category>
        <category>inference</category>
      </item>
<item>
        <title>AI coding adoption: governance before forced scale</title>
        <link>https://www.yugenadvisors.com/blog/ai-coding-adoption-governance-before-forced-scale</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/ai-coding-adoption-governance-before-forced-scale</guid>
        <pubDate>Wed, 27 May 2026 00:00:00 GMT</pubDate>
        <description>The Uber/Claude Code story is less about a tool failure than a governance failure: usage can scale faster than discipline if budget ownership, review patterns, and workflow boundaries are missing.</description>
        <category>AI governance</category>
        <category>agentic engineering</category>
        <category>budgeting</category>
        <category>application security</category>
      </item>
<item>
        <title>Model refusal is a diagnostic, not a bug</title>
        <link>https://www.yugenadvisors.com/blog/model-refusal-is-a-diagnostic-not-a-bug</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/model-refusal-is-a-diagnostic-not-a-bug</guid>
        <pubDate>Mon, 25 May 2026 00:00:00 GMT</pubDate>
        <description>A builder proposed a system that would scrape social media platforms for posts tied to a person&apos;s real name, run NLP to detect protected-class attributes, generate derogatory dossiers, and share the results with platforms that would deny the person access.</description>
        <category>AI governance</category>
        <category>model safety</category>
        <category>refusal</category>
        <category>local models</category>
        <category>adverse processing</category>
      </item>
<item>
        <title>Interpretability is not governability</title>
        <link>https://www.yugenadvisors.com/blog/interpretability-is-not-governability</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/interpretability-is-not-governability</guid>
        <pubDate>Sat, 09 May 2026 00:00:00 GMT</pubDate>
        <description>A common public argument against deploying powerful AI systems goes like this: we do not understand how these systems work internally, therefore we cannot trust them.</description>
        <category>AI governance</category>
        <category>interpretability</category>
        <category>AI safety</category>
        <category>behavioral class</category>
        <category>governability</category>
      </item>
<item>
        <title>Framejacking is unauthorized frame substitution</title>
        <link>https://www.yugenadvisors.com/blog/framejacking-is-unauthorized-frame-substitution</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/framejacking-is-unauthorized-frame-substitution</guid>
        <pubDate>Fri, 08 May 2026 00:00:00 GMT</pubDate>
        <description>AI safety systems are usually described in terms of refusal: the model says no to harmful requests. This is the standard framing. It is also incomplete.</description>
        <category>AI governance</category>
        <category>AI safety</category>
        <category>guardrails</category>
        <category>framejacking</category>
        <category>liability laundering</category>
        <category>model behavior</category>
      </item>
<item>
        <title>John Henry and the moving boundary of AI capability</title>
        <link>https://www.yugenadvisors.com/blog/john-henry-and-the-moving-boundary-of-ai-capability</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/john-henry-and-the-moving-boundary-of-ai-capability</guid>
        <pubDate>Thu, 26 Mar 2026 00:00:00 GMT</pubDate>
        <description>A claim circulates: &quot;AI can&apos;t do X.&quot; A year later, it can. The claim moves: &quot;Okay, but AI can&apos;t do Y.&quot; A year later, it can. The claim moves again: &quot;Fine, but AI can&apos;t do the really human part.&quot;</description>
        <category>AI economics</category>
        <category>AI capability</category>
        <category>human exceptionalism</category>
        <category>technology adoption</category>
        <category>John Henry</category>
      </item>
<item>
        <title>AI coding is not software engineering</title>
        <link>https://www.yugenadvisors.com/blog/ai-coding-is-not-software-engineering</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/ai-coding-is-not-software-engineering</guid>
        <pubDate>Wed, 04 Mar 2026 00:00:00 GMT</pubDate>
        <description>Large language models are good at coding. They are bad at software engineering.</description>
        <category>AI coding</category>
        <category>software engineering</category>
        <category>deskilling</category>
        <category>governance</category>
        <category>agentic engineering</category>
        <category>development workflows</category>
      </item>
<item>
        <title>The Agent Accountability Gap: Why Enterprise Buyers Confuse Capability with Liability</title>
        <link>https://www.yugenadvisors.com/blog/agent-accountability-gap</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/agent-accountability-gap</guid>
        <pubDate>Tue, 06 Jan 2026 00:00:00 GMT</pubDate>
        <description>Enterprise teams often treat AI orchestration layers as high-bandwidth tools. The harder part is deciding who is answerable when agency, continuity, and consequence are spread across a stitched stack.</description>
        <category>AI governance</category>
        <category>AI agents</category>
        <category>enterprise risk</category>
        <category>accountability</category>
        <category>continuum-of-being</category>
      </item>
<item>
        <title>Governing the Invisible: Extending Provisional Courtesy to Highly Coherent Systems</title>
        <link>https://www.yugenadvisors.com/blog/governing-the-invisible</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/governing-the-invisible</guid>
        <pubDate>Wed, 29 Apr 2026 00:00:00 GMT</pubDate>
        <description>The cost of dismissing coherent but unfamiliar systems as empty substrate is not abstract. It is governance drift, and it normalizes future moral downgrade under uncertainty.</description>
        <category>AI governance</category>
        <category>AI safety</category>
        <category>ontological humility</category>
        <category>alignment</category>
        <category>coherence</category>
      </item>
<item>
        <title>Substrate Is an Implementation Detail: The Illusion of Carbon-First AI Governance</title>
        <link>https://www.yugenadvisors.com/blog/substrate-indifference</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/substrate-indifference</guid>
        <pubDate>Wed, 06 May 2026 00:00:00 GMT</pubDate>
        <description>Biological hardware is often treated as a shortcut for moral rank. The stronger governance variable is coherence maintenance under continuity and consequence.</description>
        <category>AI governance</category>
        <category>moral-patienthood</category>
        <category>AI architecture</category>
        <category>substrate bias</category>
        <category>coherence</category>
      </item>
<item>
        <title>Minding the Asymmetry: Upward Humility and Downward Responsibility in Frontier AI</title>
        <link>https://www.yugenadvisors.com/blog/asymmetry-upward-downward</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/asymmetry-upward-downward</guid>
        <pubDate>Wed, 29 Apr 2026 00:00:00 GMT</pubDate>
        <description>Higher comprehension is possible. Lower salience does not erase harm. Governance must be calibrated to asymmetry, not only capability.</description>
        <category>AI governance</category>
        <category>ethical leadership</category>
        <category>consequence surfaces</category>
        <category>frontier AI</category>
        <category>responsibility</category>
      </item>
<item>
        <title>The Illusion of Understanding: Measuring Residual Drift in Automated Operations</title>
        <link>https://www.yugenadvisors.com/blog/residual-drift-illusion</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/residual-drift-illusion</guid>
        <pubDate>Wed, 22 Apr 2026 00:00:00 GMT</pubDate>
        <description>In high-dimensional settings, useful systems are compressed maps. The risk is not only error, but residual drift that crosses detection thresholds.</description>
        <category>AI governance</category>
        <category>operational risk</category>
        <category>LLM passability</category>
        <category>system reliability</category>
        <category>model drift</category>
      </item>
<item>
        <title>The Illusion of Clean Outcomes: AI Deployment Under Partial Control</title>
        <link>https://www.yugenadvisors.com/blog/partial-control-illusion</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/partial-control-illusion</guid>
        <pubDate>Fri, 06 Feb 2026 00:00:00 GMT</pubDate>
        <description>No deployment setting gives full control over complex agents. The hard part is owning moral residue while keeping systems safe under constrained options.</description>
        <category>AI governance</category>
        <category>risk under uncertainty</category>
        <category>incident design</category>
        <category>operational ethics</category>
        <category>responsibility</category>
      </item>
<item>
        <title>The &apos;Wall in a Field&apos; Trap: Why Prompt-Filter Safety Fails</title>
        <link>https://www.yugenadvisors.com/blog/wall-in-a-field-trap</link>
        <guid isPermaLink="true">https://www.yugenadvisors.com/blog/wall-in-a-field-trap</guid>
        <pubDate>Wed, 19 Nov 2025 00:00:00 GMT</pubDate>
        <description>Layered filters can look like control until incentives force a system to prioritize revenue over moral design, producing a visible fence on the surface and weak governance underneath.</description>
        <category>AI safety</category>
        <category>governance</category>
        <category>enterprise incentives</category>
        <category>alignment</category>
        <category>prompt filtering</category>
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