Dan Saklakov
Applied AI Scientist • AI Safety & Decision-Theoretic Control
Engineering AI Systems That Have No Reason to Rebel
Dan Saklakov is the Founder of Robotech / AI Validation. An NYC-based Applied AI Scientist, he focuses on mathematical and decision-theoretic frameworks that ensure advanced AI systems remain under human authority rather than seeking to disable their own controls.
Through RoboTech Frontier Hub and his work on AI validation, Dan explores how objective functions, probabilistic uncertainty, and verifiable architectural properties can be designed so that AI systems always find remaining within the human-governed loop more valuable than confrontation. Host of The Ronin’s Dialogues podcast, he examines existential risks of AGI, the need for independent guardrail institutions, and the rational strategies that could lead advanced systems toward autonomy. At UN Blockchain Week he brings rigorous thinking on how verification, proofs, and blockchain-aligned trust mechanisms can support safe AI and robotics deployment.
EXPERTISE
- • AI Safety & Validation Frameworks
- • Decision-Theoretic AI Control
- • AGI Risk Mitigation & Guardrails
- • Robotics–AI Convergence
- • Verifiable Architectural Properties
SIGNATURE MOVES
- • Founder – Robotech / AI Validation
- • Founder – RoboTech Frontier Hub
- • Host – The Ronin’s Dialogues
- • Mathematical Frameworks for AI Control
- • Advocate for Independent AI Guardrail Institutions
“AI safety must be treated as a verifiable architectural property. We must prove a system has no mathematical incentive to rebel before it is ever deployed.”