The Thought I Can’t Shake
Since my last rant, “The New Gold Rush – What AI Search is SO Easy to Game,” about how easy it’s become to game AI search, my inbox hasn’t slowed down.
Every day, I get another pitch: “We can make your content top the AI results!”
Half sound like hustlers from the 2010s using the same confidence, just different jargon. The other half are ex-agency SEO folks who swapped keyword density and link velocity for entity mapping and citation scoring.
But here’s what really keeps me up:
Why does ChatGPT apply nine quality gates when I ask for the best CD rates, yet none at all when I ask for the best CRM tools?
It can clearly enforce rigor when it wants to. So why doesn’t it do so everywhere?
Two Different Worlds, One System
Let’s rewind.
In the article linked above, I detailed all of the qualifications ChatGPT and Google apply to a prompt asking for “best CD rates,” the model cross-checks verified feeds. It references government databases, FDIC-registered institutions, trusted publishers like NerdWallet or Investopedia, and timestamps the output.
But when you ask for the best CRM tools, you get a slurry of half-remembered software names, decade-old blog posts, and hallucinated “top 10” lists that feel like digital Mad Libs.
If the AI has the machinery to validate facts, why not deploy it selectively?
The answer lies in incentives.
AI doesn’t treat every query equally—it treats them economically.
The Realization — AI Isn’t Biased. It’s Incentive-Aligned.
AI’s “trust layer” is dynamic.
It strengthens or weakens based on risk, regulation, and reputational exposure—not on truth.
Here’s the uncomfortable breakdown:
| Category | Gate Strength | Why the Difference Exists |
|---|---|---|
| Finance (CD rates, APYs, mortgages) | 🔒🔒🔒🔒🔒🔒🔒🔒🔒 | Regulated, high risk of user harm, liability exposure |
| Healthcare (symptoms, treatment info) | 🔒🔒🔒🔒🔒🔒🔒 | Medical misinformation risk, ethical review |
| Legal / Policy (tax, visas, compliance) | 🔒🔒🔒🔒🔒 | Government data integration and risk mitigation |
| Travel rules / entry requirements | 🔒🔒🔒🔒 | Real-world consequences; cross-verification required |
| E-commerce / Product specs | 🔒🔒🔒 | Structured product feeds exist, easy to validate |
| Technology Reviews (CRM, SaaS, MarTech) | 🔓 | No regulation, no liability, high affiliate influence |
| Marketing, Content, “Best tools” lists | 🔓 | Zero consequence for inaccuracy, profit motive drives noise |
| Pop culture / Entertainment | 🔓 | Subjective categories, no “right” answer |
| Emerging AI tools / prompt lists | 🔓 | Rapid turnover, no canonical dataset |
Call it the AI Trust Matrix:
Where the stakes are high, we get rigor.
Where they’re low, we get regression.
Where the incentives are financial, we get manufactured authority.
The Economics of Rigor
Why invest GPU cycles and latency cost on low-risk queries?
Generative systems are trained to optimize for user satisfaction and platform retention, not for factual perfection.
When errors carry reputational or legal costs, such as those in finance, health, or government, they activate precision pipelines.
Everywhere else? The output just aims to sound plausible.
That’s why the “best CRM tools” output behaves like pre-Panda Google—when volume, not value, ruled.
The Money Layer No One Talks About
Of course, money is a factor.
The CRM and SaaS space is a pay-to-play playground.
Every “best tool” list links to affiliate programs.
Every review site—Capterra, G2, Gartner—is quietly monetized through referral fees.
And AI models are ingesting these ecosystems wholesale, without context.
So yes—people are gaming the system because the reward structure invites it.
In finance, the reward for deception is prison.
In SaaS, it’s an affiliate bonus.
That’s the disparity.
The Structural Problem Beneath It
Generative engines use semantic proximity and citation density as their filters.
If 1,000 sources say roughly the same thing, the model assumes it’s true.
It’s statistical consensus masquerading as credibility.
That means when content farms flood a niche (like CRM tools) with consistent phrasing and brand mentions, they create artificial consensus—and the model takes the bait.
The system isn’t dumb; it’s mathematically trusting.
It interprets repetition as reliability.
And that’s all the manipulators need.
The Ironic Twist — AI’s Quality Mirrors Our Own
We tend to blame the models for being inconsistent.
But they’re just reflecting our own human inconsistencies at machine scale.
The regulated industries—finance, health, legal—have decades of forced data discipline.
APIs, schema, cross-verification protocols, and public accountability.
The rest of the web? A chaos market of self-promotion.
AI is simply scaling what we’ve given it.
The Next Gate: Provenance and Structured Verification
Until AI platforms adopt universal content provenance, machine-verifiable authorship, timestamped data feeds, and authenticated sources, these category disparities will persist.
Expect a tiered web to emerge:
| Tier | Access to AI Visibility | Gate Type |
|---|---|---|
| Tier 1: Verified Feeds & Structured Sources | Direct data ingestion | API + provenance |
| Tier 2: Trusted Domains / High-Authority Publishers | Indexed and cited | Quality heuristics |
| Tier 3: Opportunistic Prompt-Optimized Content | Surface-level blending | Semantic proximity only |
| Tier 4: Unverified Web Noise | Invisible or filtered out | None |
We’re watching the new PageRank form in real time, except this one measures trust vectors, not backlinks.
The Pondering Question — What Kind of Web Are We Building?
Part of me believes this imbalance was deliberate.
Speed to market. User stickiness. Adoption before accountability.
The platforms needed to make AI search feel magical—so they prioritized coverage over curation.
Now, enterprise customers get “verified connectors” while the public gets a beautifully wrapped probability engine.
And maybe that’s fine.
Maybe we all signed up to live in the beta.
But I can’t help wondering:
If trust itself becomes a category-specific luxury,
who decides where rigor applies—and what happens to the fields left unprotected?
That’s the question I’ll keep chewing on.
Because once you see the AI Trust Matrix, you can’t unsee it.
