The First Casualty of AI? Competitive Advantage
Written by Adrian Maharaj
(Views mine, not Google’s.)
A fact‑based point of view on how synthetic sameness devours moats—and how to fight back
I’ve watched every inflection point for two decades, but AI is the first to go mainstream in public and in real time. The tools are universal; the moats are melting. The first casualty? Competitive advantage.
When Everyone Has the Same Superpower, It’s No Longer a Superpower
Most deployments still use:
The same foundation models
The same open‑web training data
The same prompt recipes (“Act as a …”)
The outcome is synthetic sameness—pricing, road‑maps, even marketing copy collapsing into a single median.
Two Public Flashpoints That Triggered This Blog
Dynamic‑Pricing Goes Mainstream
Delta Air Lines disclosed plans to expand AI‑driven ticket pricing from 3 % to 20 % of domestic flights, spurring congressional scrutiny over “surveillance pricing.” (The Washington Post)Algorithmic Collusion Allegations
The U.S. Department of Justice and eight states are suing RealPage, Inc., alleging its rent‑pricing algorithm let thousands of landlords share data and fix rates—a textbook example of AI erasing competitive variance. (Politico)
Neither incident is a bug in the model; each is a failure to ask:
“What if this makes us indistinguishable—or illegally identical?”
Preserving Advantage in an AI‑Flattened Market
Proprietary Exhaust – Fine‑tune models on support tickets, tribal docs, sensor telemetry—data competitors can’t access.
Prompt Dissent – Incentivize teams to break first drafts, not praise them.
Provenance Logs – Trace every AI‑influenced decision back to its model, dataset, and prompt.
Model Diversity – Pair frontier LLMs with smaller, domain‑specific, or on‑prem models to avoid monoculture.
Guardrail Councils – Cross‑functional reviewers with veto power over features carrying “sameness risk.”
The Strategic Reframe
If AI is now electricity, leadership is the fuse box. In Unbuyable Distribution I wrote: “The strongest moat isn’t your tech stack; it’s your point of view, made operational.” Apply that lens to every AI initiative—or watch advantage evaporate.
Save this if you’re architecting variance. Forward it to the board before the next AI budget call.
