AI Strategy & Market Reality: Executive Insights

In this 2-hour course, you’ll cut through AI market noise to focus on strategies that deliver measurable value.
Trace genAI’s evolution and business models, learn why post-training drives real performance, apply causal methods to prove ROI beyond simple A/B tests, and finish by assessing agentic systems with practical trade-offs.

What I will be able to do after this course

• Read the AI market beyond the hype
• Map generative AI business models to strategy
• Use post-training to improve LLM utility and alignment
• Measure ROI with causal inference (and avoid Type III error)
• Build and assess agentic AI systems

Course Instructor(s)

Stephen Klein — CEO of Curiouser.AI, LinkedIn Top 1% Voice in AI, UC Berkeley instructor; ex-CMO at Dentons and startup co-founder.

Maxime Labonne — Head of Post-Training at Liquid AI; PhD; creator of popular open-source LLMs/tools; author of LLM engineering handbooks.

Dr. Quentin Gallea — Causal inference expert who’s taught ~15,000 learners; applied causal methods to real-world policy and business impact.

Who is it for?

VPs/Heads of Data or AI, CTOs, product leaders, and strategy owners who must set direction, fund the right bets, and prove value.
No deep math required—just the need to evaluate post-training options, guide alignment, and choose trustworthy ways to measure ROI and scale agentic solutions.