TL;DR: We are witnessing a massive deflation in the value of "output." AI has made reports, code, and emails essentially free to produce. If your business model relies on selling these results, you are already obsolete. The only scarce asset left is Judgment. Most companies fail at AI because their systems record what happened, but they never record why. The future belongs to those who capture the Context Graph.
James here, CEO of Mercury Technology Solutions.
Have you noticed something strange? The things we used to grind for—the perfect report, the polished sales copy, the clean block of Python code—can now be generated in seconds.
Many call this progress. I call it a Value Collapse.
An LLM is, at its core, a probability prediction machine. When you have compute + prompts, the marginal cost of producing a "result" trends toward zero. Just as the Industrial Revolution turned cloth from a luxury into a commodity, AI is turning "intellectual output" into a commodity. The weaver who sells cloth loses to the factory.
The Dangerous Trap: If you are still selling "Results" (e.g., "I can write 100 emails"), you are doomed. You must shift to selling the "System of Judgment."
The "So What?" Problem
I see companies celebrating: "Our AI can write 10,000 personalized emails!" My question to them is always the same: "Does your competitor use the same model?"
If the answer is yes (and it usually is), then your output is not special. It is just Faster Commodity. You are accelerating your own homogeneity.
The Missing Layer: The "Context Graph"
The real problem is that our current enterprise software is stupid.
- CRM/ERP/BI Tools record State. (e.g., Salesforce says: "Deal Closed.")
- Reality relies on Decision Traces.
Why did we give that client a discount? Why did we let this ticket cut the line? Why did we ignore the SOP for this specific emergency? Current systems don't know. That knowledge is locked in "Organizational Black Boxes"—Slack threads, Zoom calls, and the intuition of senior managers.
Foundation Capital’s Jaya Gupta calls the solution the Context Graph. It is not about how much data you have. It is about Context Density.
The 3 Pillars of a "Judgment Engine"
To survive the AI era, you need to stop automating "tasks" and start recording "decisions."
1. Treat "Exceptions" as Gold
Standard procedures can be automated. But Exceptions are where human intelligence lives.
- Old Way: A manager overrides a rule. The system just records the override.
- New Way: The system demands the Rationale. "Why did we violate policy?" These exceptions are the training data for your future moat. They represent the edge cases that standard models cannot handle.
2. Capture the "Cross-System" Synthesis
A Customer Support Lead looks at a Zendesk ticket, checks a PagerDuty outage log, and pings a VP on Slack before issuing a refund. That Synthesis—connecting dots across three different systems—is the value. You need to capture that "Orchestration Layer," not just the final refund receipt.
3. Record the "Reasoning," Not Just the "Chain of Thought"
We don't need to see the AI's internal math. We need Structured Decision Templates.
- What data did we look at?
- What was the tradeoff?
- Why did we choose Option B over Option A? This transforms "Tribal Knowledge" into "Data."
The "Apocalypse Test"
Here is a terrifying question to test your AI moat:
Imagine every database in your company was wiped clean, except for three things:
- Meeting Minutes.
- Chat Logs.
- Decision Records (The "Why").
Could an AI look at those records and simulate your company’s operating logic?
If the answer is No, then your "AI Transformation" is fake. You are just making cheap things cheaper. You have never actually recorded your company's most valuable asset: How it thinks.
The moment you start recording the Why, the moat begins to grow.
Mercury Technology Solutions: Accelerate Digitality.
