I give talks on artificial intelligence, cognitive science, and human judgment, focusing on how modern AI systems work conceptually and epistemically, and how this shapes trust, decision-making, and knowledge formation.
A central theme is human–AI interaction: how people interpret and defer to conversational systems, and the challenges this creates for reliability, alignment, and responsibility. I also address disinformation and automated persuasion in an age of large language models.
I speak to technical and non-technical audiences—executives, policymakers, investors, and general audiences—adapting each talk while maintaining conceptual rigor.
Below are a selection of recent talks and presentations, by theme:
Disinformation and influence:
- Influence, authority, and trust in the age of intelligent systems
- Power, persuasion, and credibility after algorithms
- Decision-making in a world of unverifiable information
- Why AI Makes False Information More Persuasive
- How confident machines amplify misinformation
- The Automation of Persuasion: From targeted messages to automated belief shaping
Psychology and human–AI interaction:
- Why AI Feels Intelligent, and Why That Matters
- The Illusion of Understanding
- The psychology of working with AI systems
- Why AI feels personal
- Cognitive offloading and decision atrophy
- Cognitive sovereignty (staying independent in a world of persuasive, always-available intelligence)
Trust, reliability, and “knowledge”:
- AI: trust, reliability, and deployment realities
- Evaluating AI outputs when there’s no ground truth
- When AI can be treated as a knowledge source
- Building AI products people trust
Safety, control, responsibility, and alignment:
- How Much Autonomy Is Too Much?
- What “Responsible AI” Really Means in Practice
- Alignment Is a Human Problem
- Alignment beyond theory (what it means to align systems with human goals
- Human–AI alignment and misalignment
(guardrails, constraints, structured autonomy)
Foundations of AI:
- The difference between reasoning, retrieval, and generation
- How large language models work (without the math)
Interfaces and language:
- How Language Changed Computing
- Language as an Interface or why talking to machines changes everything
History and big questions:
- The history of Artificial Intelligence (from early ideas to today’s generative wave)
- Turing’s legacy and the challenges it still poses
- Fluent, Confident, and Wrong
Entrepreneurship and strategy:
- Entrepreneurship in the age of AI
- Where startups really get leverage from AI
- The cost curves and tradeoffs of using foundation models
- When to build, buy, or rely on platforms