Domain knowledge
The expertise, judgment, and context that make generic AI output useful or dangerous.
Your moat is the advantage AI can make real without exposing what makes it yours.
The deep moat lives in a company's domain knowledge, data, workflow, taste, relationships, timing, brand, people, and judgment. DeepMoat identifies where AI can strengthen that advantage and how to route the move so the advantage stays protected.
The field is moving. Your advantage is specific.
DeepMoat tracks the public AI field, but the work is never generic. The same model release can change route options, exposure, timing, and capacity differently across every Decision Stack.
The job is to understand what AI can unlock inside your context: what should be protected, accelerated, redesigned, prototyped, trained, governed, paused, watched, or sequenced.
What makes an AI advantage durable.
Workflow memory
The lived understanding of how work actually moves through the organization.
Data and trust
The information, relationships, privacy boundaries, and confidence that cannot be copied easily.
Human taste
The ability to judge quality, timing, exposure, and differentiation when outputs are abundant.
Technical judgment
The ability to distinguish demos from systems that can survive real work.
Operating capacity
The training, tools, owners, cadence, and review loops that make the advantage repeatable.
Protect the advantage before you automate it.
The strongest AI move protects the specific advantage while making the next useful capability real.
Keep the advantage specific
The first read is whether the tool, model, agent, or build path strengthens what is already hard to copy.
Protect the crown jewels
Domain knowledge, client trust, private data, and operating memory need boundaries before they become prompts, retrieval, automations, or connected systems.
Turn demo into capacity
A clever prototype becomes meaningful when owners, review loops, training, reliability, and handoffs can support real use.
Price tasks after the decision is clear
DeepMoat reads the decision first: what should be proved, bought, built, governed, trained, paused, retired, or sequenced while the commitment is still shapeable.
This is why DeepMoat starts with pressure, route, exposure, and timing before naming a build path.
The moves behind Protocol 37 and Protocol 78.
The naming comes from the 2016 AlphaGo match: Move 37 for pattern discovery, Move 78 for capacity built after the route is understood. Signal Triage is the door. Protocol 37 is the method inside every Signal Briefing — the deep read that finds the route the room can't see. Protocol 78 is the method inside Capacity & Buildout — the capability built after the route is understood.
2016
AlphaGo met Lee Sedol in Seoul. The match made AI strategy visible to a public audience and turned Go into an early signal for the next era of machine capability.
Read the originMove 37 / Pattern
In game two, AlphaGo played a 1-in-10,000 move outside what human Go practice had taught the room to expect. It was pattern recognition beyond the normal human playbook. Protocol 37 carries that lesson: find the pattern before the room can name it.
Read the originMove 78 / Capacity
In game four, Lee Sedol answered with his own 1-in-10,000 move. Protocol 78 carries the next lesson for DeepMoat: after the route is clear, pressure has to become capability the client can operate, review, and improve.
Read the originMoat / Crossing
The experience of crossing is the moat.
Read the originProtocol / System
A moat becomes durable when it can run.
Read the originDeepMoat names its protocols after the AlphaGo match because the story keeps both truths alive: AI changes the field, and human operators need real capability to answer it well.
Choose the route while the decision is still shapeable.
Start with a focused read that turns the company context into a build, buy, train, govern, pause, watch, or sequence route.