Research · Ideas · Practical Insight
Some of the most expensive mistakes happen when an organization solves the wrong problem well. Some of the best opportunities are missed because nobody steps outside the current frame long enough to see them.
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Founder
I’m often most useful when a situation is ambiguous and the apparent problem is not necessarily the real one. I tend to see patterns, relationships, and underlying causes that others may still be treating as separate issues — or as symptoms to solve individually.
What I usually bring to that kind of situation is clarity. I try to separate cause from symptom, strip away unnecessary complexity without pretending the situation is simple, and give the group a more practical way to see what is happening and what to do next.
I get there by asking questions, listening for what different people know, and making the reasoning visible enough that the group can challenge it before we move.
Experience Behind the Work
Selected work →154%
annual ROI
~$21.4M in annual savings
Product direction and operations across three businesses.
60 people
global Quality Engineering team
$42M portfolio
Specialist expertise, clearer ownership, and global coordination.
9M+
business customer records
Migration design and analytics
Making bottlenecks and execution risk visible while the work was underway.
Three Areas of Focus
The three research programs are separate. What connects them is a question I keep coming back to: how do you understand enough of a complicated system to see what matters, what might be possible, and what to do next?
Organizational Reasoning
How teams can bring scattered evidence and expertise together, challenge assumptions, see hidden constraints and opportunities, make a judgment, act, and learn from what happens next.
Framing · Expertise · Judgment · Learning
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AI Systems
When AI should question a premise, verify evidence, remember what matters, defer to people or specialists, and stop rather than confidently continue down the wrong path.
Reasoning · Transfer · Continuity · Authority
Explore AI Systems
Compute Architecture
A first-principles look at data movement, memory and state, specialization, locality, and the total cost of doing useful computational work.
Locality · State · Specialization · Efficiency
Explore Compute Architecture
Book
The book develops Organizational Reasoning as a discipline for how organizations form a working view of reality, decide what they know, act in proportion to what they can justify, and learn without confusing an outcome with an explanation. The first complete draft is now in external peer review.
About the book →
Featured Research Result
A black-box evaluation using GPT-5.6 Sol reached 97 clean-comparison levels and nine complete environment wins across the ARC-AGI-3 Public Demo. The public study compares the result with OpenAI’s published GPT-5.6 runs while keeping the proprietary research configuration undisclosed.
View the study →Featured Writing
Ideas, analysis and real-world examples at the intersection of organizations, technology and practical reasoning.
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AI Systems · Article
Designing organizational work around durable AI capability classes
AI Systems · Article
AI can improve knowledge work dramatically. It can also accelerate a bad frame, missing context, or a weak assumption before anyone notices.
Organizational Reasoning · Article
Before you improve a solution, make sure the evidence supports the problem you think you have
