Bill Gibson is an experienced technical leader who has led engineering and product teams across startups and large companies. He brings real-world perspective to AI implementation and product development.

Bill talks about:

• Anchoring AI features in natural workflows: don’t bolt on chatbots; embed intelligence where users already expect it.
• Enforcing trust through transparency: always label AI‑generated content and surface confidence scores and data provenance.
• Focusing on achievable use cases: massive datasets alone won’t guarantee success; start with narrowly scoped, high impact AI applications.
• Designing for model evolution: use abstraction layers (like Cursor or Windsurf) so you can swap underlying AI engines as they improve.
• Outsourcing selectively: retain your strategic “special sauce” and core architecture in‑ house, but consider contracting out standard, non‑differentiating features.

Key quote: “Trust is the whole thing… we need to make sure that we clearly differentiate that this is not a doc, this is not a nurse, this is not a medical practitioner. This is an AI.”