Mike Parker introduces the Thinking Partner, an AI tool designed to support personal reflection rather than capture attention. Unlike most AI systems optimized for engagement, the Thinking Partner aims to help users think without building hidden profiles or harvesting attention as a resource. Parker frames the tool around a core question: can a tool help you reflect without building a model of who you are? His approach treats the user as the integrator — the tool holds the material, while the user holds the meaning.
The post announces a workshop on July 23, 2026, hosted in association with the Schumacher Institute, where Parker will demonstrate the Thinking Partner live. The workshop draws on systems thinking, appropriate technology (tools built to human scale), and contemplative practice, alongside neuroscience concepts like the Default Mode Network — the brain's reflective, wandering state that engagement-driven platforms are designed to interrupt. Free trials of the tool are available for participants to evaluate the claims firsthand.
Will Allen, an independent systems scientist and facilitator, shares practical reflections on how AI tools like GPT and Perplexity have become quiet thought partners in his collaborative and systems-oriented work. He outlines four key uses: exploring new research and perspectives, supporting systems framing and complexity, making writing less isolating, and testing how content lands with different audiences. Throughout, he emphasizes that these tools serve as scaffolding rather than substitutes for human judgment.
A central theme is the distinction between what AI can and cannot do in participatory, systems-facing settings. Allen provides a useful comparison table showing that while AI excels at rapid scanning, summarizing interviews, and generating draft outlines, it fundamentally cannot replicate relationship-building, facilitation, trust, or navigation of power dynamics — the core of collaborative research and evaluation work. He also raises important ethical considerations around data sensitivity, source attribution, and transparency when using AI-generated content.
The piece ultimately argues that AI works best when it stays in the background, helping practitioners show up more clearly and thoughtfully in the relational, human-centered work of collaboration, learning, and systems change. Allen's perspective is grounded in over 30 years of experience in sustainable development and natural resource management, lending practical credibility to his balanced take on AI adoption.
