The AI Governance Evolution: From Brake Pedal to Rocket Fuel
Why governance is foreplay, not a prenup — and how compound trust at scale turns AI from brilliant-but-fragile into systematically reliable.
Applying game theory and strategic frameworks to shape the future of responsible AI
Once AI moves from analysis into operational decisions, accountability shifts from the software to the organization that deployed it. On constructive knowledge, real-time governance, and the five questions every board should be able to answer — with documentation, not assumptions.
Read on ForbesWhy AI governance is an ethical imperative, not merely a compliance obligation — the case for a Chief AI Officer, privacy-by-design, and operationalizing governance across every level of the enterprise before regulators force the issue.
Read on ForbesFrom competitive tennis to founding companies and governing AI at enterprise scale — on resilience, the discipline that carries across, and why constitutional protections have to be engineered in at the design stage rather than argued about afterwards.
A co-authored technical paper on detecting and countering distributed denial-of-service attacks launched from compromised IoT devices, using machine learning — the research line behind the granted IoT/blockchain DDoS patent.
Read the paper (PDF)A career interview — on breaking into tech as one of the only women in the room, founding the UC Merced and Berkeley Mobile App Challenges, membership of C200, and where AI is heading.
Read the interviewWhy governance is foreplay, not a prenup — and how compound trust at scale turns AI from brilliant-but-fragile into systematically reliable.
Examining the IEEE AI Safety Index through strategic frameworks. When the industry leader scores a C, the incentive structures need rethinking.
Why governance frameworks create competitive advantage. Temporal discounting and strategic positioning in the regulatory environment.
Decision theory and strategic positioning in the age of AI regulation. Operating under Knightian uncertainty requires robust frameworks.
Competitive dynamics and collaboration strategies in next-gen networks. Multi-player coordination games and comparative advantage.
Value capture and market positioning in AI services. Platform-versus-application dilemmas in telecommunications.
Read full analysisMethodological insights from 2020 sentiment analysis. Information cascades and Keynesian beauty-contest dynamics.
View methodologyRigorous AI governance isn’t a cost center — it’s a value driver. Why the market increasingly pays a premium for systems that are trusted, auditable and defensible, and how governance posture shapes capital access, valuation and enterprise trust.
Read on LinkedIn“Why does our AI feel brilliant but fragile?” Three CEOs asked me this last quarter. Most organizations treat governance like a prenup — but the companies printing money treat it as the reason innovation can run at full speed without exploding.
View on LinkedInA deep dive into the IEEE AI Safety Index and what it means for competitive dynamics. When the market leader gets a C grade, the incentive structures need rethinking.
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Organizations that treat governance as a constraint miss the strategic opportunity. Robust frameworks create optionality and positioning for an uncertain regulatory future.
View on LinkedInThe future of telecom AI isn’t about choosing sides — it’s about architecting partnerships that leverage comparative advantage across the value chain.
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Operating under Knightian uncertainty requires robust decision frameworks. Expected-value calculations favor proactive governance — even with conservative risk estimates.
View on LinkedInTelecom operators have platform advantages but face organizational challenges. Can they move fast enough to capture value before pure-play AI companies dominate?
View on LinkedInPrediction markets aggregate dispersed information through incentivized belief revelation. As AI forecasts become influential, they create meta-game dynamics that affect outcomes.
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