Practical thinking on how companies should actually use AI — and what that means for investors.
This is the place for sharper essays, operating playbooks, and thesis notes at the intersection of AI agents, real companies, capital allocation, commodities, digital assets, and physical constraints.
The useful question is not “can AI do this task?” It is: can a company redesign a workflow so that agents, people, data, and controls produce a better economic system?
I want to write from the operator-investor perspective: where the technology really works, where adoption gets stuck, and where the second-order market opportunities appear.
These notes are part of a broader proof system: market research, operating experience, and AI-native workflows connected into public artifacts. The Proof Index maps the whole system; the AI Operating System case study explains the agent workflow behind it.
A field note on copper's structural demand shock, hidden supply bottlenecks, and why Ivanhoe Mines is a clean expression of the thesis.
Why the investable question is not only model quality, but distribution, workflow ownership, proprietary data, trust, compliance, and switching costs.
A trial field note on uranium, energy security, supply fragility, and the danger of being right on a commodity thesis but wrong on the expression.
What an AI operating system looks like for companies that do not have research labs: inbox, CRM, documents, finance ops, reporting, and knowledge workflows.
How regulation, distribution, banking rails, treasury management, and cross-border settlement could create European opportunities.
A diligence checklist for separating wrappers from workflow ownership: user pain, data rights, gross margins, implementation burden, and defensibility.
Start with a costly repeated workflow, not a model. Map inputs, decisions, exceptions, tools, and owners.
Agents need useful context: documents, CRM, inbox, calendars, databases, and permissioned company knowledge.
Define what the agent can do alone, what requires review, and where audit logs or approvals are mandatory.
Measure time saved, throughput, error reduction, sales conversion, working-capital impact, or faster decision cycles.
The winning companies will redesign roles around agent leverage rather than bolt chatbots onto existing processes.
Look for companies that own distribution, proprietary workflow data, trust, compliance, and the system of record.
If you are trying to use agents inside a company, diligence an AI opportunity, or think through the market implications, I would be glad to speak.