The Normalization Problem
In professional financial analysis, the reported numbers are never the real numbers. The work is in adjusting from what was reported to what a market participant would actually underwrite.
Blog
The Founding Window
Distribution platforms offer founding member rates for a reason: they need early builders to establish the marketplace. Understanding why the window exists changes how you think about whether to use it.
The Per-Unit Floor
The most durable pricing argument isn't 'we're cheaper than the competition.' It's 'we're cheaper than doing one unit of the thing you already pay for.'
The Workshop Signal
When the distribution channel builds their own version of the tool you're considering, it's not a threat. It's the strongest possible market validation signal.
The Domain Moat
The code for a professional AI tool is often the easiest part to build. The hard part — the part that creates the durable advantage — is knowing what the tool needs to do and what the output needs to look like.
The Prompt Is the Product
In AI-native professional tools, the infrastructure is commodity. The prompts — what you instruct the model to look for, extract, and flag — are the actual product. This distinction matters for how you think about building.
The Timeline Argument
The most persuasive case for AI in professional workflows isn't accuracy — it's time. When AI compresses a 60-day process to 30 days, the value proposition becomes concrete and undeniable.
The Education Moat
The most durable distribution advantage in professional AI tools isn't advertising or partnerships. It's education. The communities that teach professionals how to use AI own the relationship when those professionals are ready to buy.
The Five Percent
Less than five percent of MCP servers are monetized. That number describes the current state of the ecosystem and points directly at where the durable value is going to accumulate.
The Standalone Signal
When a standalone platform exists for a professional workflow problem, it tells you two things simultaneously: the problem is real enough to build a product around, and the workflow-native version of that product doesn't exist yet.