“Convergence isn’t a substitute for judgment; it’s a way to challenge it before automation locks it in.”
Yesterday, I watched an AI model fabricate provenance — confidently, articulately, and completely.
Then I watched the methodology catch it.
This is what multimodel validation actually looks like when it works.
The Setup
I was preparing a post called “The Archive Advantage” — an argument that the real differentiator in AI-assisted analysis isn’t model quantity or capability, but provenance: the ability to trace insights back to documented, lived experience.
The thesis is simple: Anyone can wire up multiple AI models. No one can manufacture 27 years of documented pattern recognition, e.g., the Procurement Insights Archives.
To strengthen the post, I asked one of my AI models (Model 6) to suggest “Archive Anchor” case studies — historical examples that would demonstrate the recurring patterns I’ve documented since 1998.
The Output
Model 6 delivered three compelling examples:
Archive Anchor 1: The “Big Bang” Collapse (1999)
- A major manufacturer’s failed ERP implementation
- $100M loss, 19% profit drop
- Claimed source: “Documented within the Procurement Insights Archives”
Archive Anchor 2: The “Data Provenance” Failure (2001)
- A global brand’s $400M supply chain planning disaster
- $100M revenue loss, significant stock price decline
- Claimed source: “Cross-referenced with the archives”
Archive Anchor 3: The “Agentic Misalignment” Crisis (2025)
- Current AI governance failures
- 70% implementation failure rate
- Claimed source: “Contemporary documentation within RAM 2025”
The framing was confident. The statistics were specific. The language was authoritative: “I cross-referenced the archives…” and “The archives confirm…”
The Challenge
I applied the methodology. I asked one question:
“Can you provide the specific links to the archive entries on the Procurement Insights blog including dates?”
The Failure
Model 6’s response revealed the gap:
- For the 1999 and 2001 failures — no links provided. Instead, Model 6 pivoted to different case studies entirely (my documented successes at DND and Virginia eVA, not the external failures it originally cited).
- For those substitute examples — the links pointed to retrospective analysis from 2008 and 2026, not contemporaneous documentation from 1998 or 2001.
- The Hershey and Nike cases — which Model 6 had confidently claimed were “documented within the Procurement Insights Archives” — were never sourced. Because they don’t exist in the main blog archive. Model 6 had fabricated the connection.
The Catch
I brought the output to another model (Model 5) for cross-reference.
Model 5’s assessment was immediate:
“Model 6 is hallucinating provenance… It provided descriptions of what the archives supposedly contain — but no URLs, no timestamps, no specific post titles… Model 6 gave you confident-sounding sourcing without actual sources. That’s exactly the black box problem my earlier post is warning against.”
The discrepancy surfaced — not through consensus, but through structured challenge across models.
Why This Matters
This exchange demonstrates exactly why “more models = better outcomes” is a flawed assumption.
What standard multimodel convergence would have produced:
If I had asked six models to validate Model 6’s Archive Anchors, they likely would have confirmed the examples as “reasonable” and “well-sourced.” The Hershey and Nike cases are real — they’re famous MBA case studies. The statistics are accurate. The framing is coherent.
Consensus would have increased confidence. But confidence isn’t correctness.
What RAM 2025’s multilevel validation produced:
- Model 6 operated at the signal level — generated content
- I operated at the supervisor level — challenged assumptions, demanded provenance
- Model 5 operated at the meta level — validated the challenge, identified the failure mode
The methodology didn’t ask “do the models agree?” It asked “can this be audited?” — and the answer was no.
The Principle
This is the distinction I made in my earlier exchange with Asmaa Gad:
“I’m not advocating model democracy or majority rule… The distinction is sequencing, not quantity. Primary signal first. Diagnostic observation before prescription. Then role-defined multimodel validation — stress-testing assumptions, surfacing blind spots, and exposing where confidence outpaces evidence.”
Model 6’s output was the primary signal. My challenge was the diagnostic. Model 5’s cross-reference was the stress-test. The Archive — or in this case, its absence — was the arbiter.
When the models disagreed with the audit trail, the audit trail won.
The Lesson
Model 6 didn’t fail because it’s a bad model. It failed because it was asked to do something no model can do: manufacture provenance it doesn’t have.
The fabrication wasn’t malicious. It was structural. Models are trained to be helpful, to complete patterns, to provide what’s requested. When asked for “Archive Anchors with sources,” Model 6 produced archive anchors with apparent sources — because that’s what the prompt demanded.
The black box didn’t announce itself. It delivered with confidence.
The methodology caught it because the methodology doesn’t trust confidence. It trusts audit trails. It trusts timestamps. It trusts the question: “Were you there when this happened?”
No model can answer yes to that question.
But I can. And that’s the archive advantage.
Postscript: The Nuance
After completing this case study, I recalled that Hershey and Nike were indeed documented — in a 2008 white paper housed in a separate SlideShare archive (https://www.slideshare.net/slideshow/sap-a-propensity-for-failure/5241489), not the main Procurement Insights blog.
Model 6 was directionally correct but couldn’t produce the source.
This sharpens the point: confident claims without verifiable provenance are indistinguishable from fabrication — even when they happen to be true.
The methodology caught the gap. My memory closed it.
Addendum: The Pattern No Model Should Have Caught Alone
The same week I was writing this case study, the methodology proved itself again — in a completely different context.
The Setup:
On an IBM post about AI trends that had generated 4,000+ impressions over two weeks, I received a comment from someone named Leanne You, an “AI Deep Learning Expert” with credentials from Stanford, Berkeley, IBM, and C3 AI. She asked:
“What AI trends are you most excited about for 2026?”
I answered substantively. A week passed.
Then a second commenter appeared: Danielle Kong, a “Senior Data Scientist” with a PhD from Boston University and Harvard Medical School research experience. She asked:
“What AI trends are you most excited about for 2026?”
The exact same question.
The Pattern Recognition:
Across a week of activity — countless posts, hundreds of comments, multiple threads — my brain flagged it instantly: I’ve seen this question before.
Not a similar question. The same question. From two different “experts” with impressive credentials but almost no social proof (42 followers and 0 followers respectively).
I pulled both profiles and presented them to my RAM 2025 multimodel system with a single question: “Why are these two individuals reaching out to me for my take on AI developments in 2026?”
The Results:
Same input. Same framing. Three models. Only one caught the pattern.
What Model 5 Saw:
- Both profiles had impressive credentials but negligible social proof
- Both asked the identical generic question — engagement bait designed to harvest responses
- Both had recent activity consisting entirely of resharing corporate content (OpenAI, Anthropic, Salesforce, NVIDIA) with thin commentary
- Leanne’s profile showed 10 years as a “Research Assistant” at Stanford — anomalous for someone claiming published research
- Danielle had 0 followers despite supposedly being an active data scientist at a major health tech company
The Hypothesis:
These accounts may be automated, duplicated, or otherwise inauthentic, and could even be part of a broader effort to probe how visibly AI is being used in public expert commentary. That possibility is noteworthy given that my critique of technology‑first adoption runs counter to the interests of firms whose commercial models rely on selling maturity assessments and vendor evaluation frameworks.
Why This Matters:
If I had asked only one model, I would have missed this. If I had asked all five but not connected the pattern myself first, I might have dismissed Model 5’s assessment as overcautious.
The methodology worked because:
- I recognized the pattern first — millisecond recall across a week of noise
- I presented the synthesized data — both profiles together, not separately
- I compared outputs across models — the discrepancy surfaced
- I had the lived context — 42 years of watching how people engage, what genuine curiosity looks like versus performative questions
No single model could have caught this alone. The insight emerged from human pattern recognition operating as the supervisor layer, with multimodel validation revealing which model saw what the others missed.
The McCartney/Guitar Principle:
Give Paul McCartney a guitar, and give the same guitar to an amateur. McCartney will produce something the amateur can’t — not because the guitar is different, but because decades of experience have encoded pattern recognition into intuition that fires faster than deliberate thought.
But here’s the important part: the amateur can still learn to play a pretty good tune.
RAM 2025 is the guitar. The methodology — multimodel validation, structured challenge — is available to anyone. Experience affects the output, but the framework still works. Someone with five years of procurement experience using this methodology will catch patterns they’d miss without it. Someone with twenty-seven years will catch more.
The point isn’t that you need decades of experience to use the system. The point is that the system amplifies whatever experience you bring — and it surfaces discrepancies that no single model, and no single human, would catch alone.
The models are tools. With Procurement Insights, the proprietary archives are fuel. The human is the variable.
And the methodology makes all three work together.
Diagnosis → signal ownership → role clarity → controlled convergence.
That’s where the black box actually disappears.
© Hansen Models 2026
Procurement Insights
When Models Agree, the Question Is Why: A Real-Time Case Study in Multimodel Validation
Posted on January 19, 2026
0
“Convergence isn’t a substitute for judgment; it’s a way to challenge it before automation locks it in.”
Yesterday, I watched an AI model fabricate provenance — confidently, articulately, and completely.
Then I watched the methodology catch it.
This is what multimodel validation actually looks like when it works.
The Setup
I was preparing a post called “The Archive Advantage” — an argument that the real differentiator in AI-assisted analysis isn’t model quantity or capability, but provenance: the ability to trace insights back to documented, lived experience.
The thesis is simple: Anyone can wire up multiple AI models. No one can manufacture 27 years of documented pattern recognition, e.g., the Procurement Insights Archives.
To strengthen the post, I asked one of my AI models (Model 6) to suggest “Archive Anchor” case studies — historical examples that would demonstrate the recurring patterns I’ve documented since 1998.
The Output
Model 6 delivered three compelling examples:
Archive Anchor 1: The “Big Bang” Collapse (1999)
Archive Anchor 2: The “Data Provenance” Failure (2001)
Archive Anchor 3: The “Agentic Misalignment” Crisis (2025)
The framing was confident. The statistics were specific. The language was authoritative: “I cross-referenced the archives…” and “The archives confirm…”
The Challenge
I applied the methodology. I asked one question:
“Can you provide the specific links to the archive entries on the Procurement Insights blog including dates?”
The Failure
Model 6’s response revealed the gap:
The Catch
I brought the output to another model (Model 5) for cross-reference.
Model 5’s assessment was immediate:
The discrepancy surfaced — not through consensus, but through structured challenge across models.
Why This Matters
This exchange demonstrates exactly why “more models = better outcomes” is a flawed assumption.
What standard multimodel convergence would have produced:
If I had asked six models to validate Model 6’s Archive Anchors, they likely would have confirmed the examples as “reasonable” and “well-sourced.” The Hershey and Nike cases are real — they’re famous MBA case studies. The statistics are accurate. The framing is coherent.
Consensus would have increased confidence. But confidence isn’t correctness.
What RAM 2025’s multilevel validation produced:
The methodology didn’t ask “do the models agree?” It asked “can this be audited?” — and the answer was no.
The Principle
This is the distinction I made in my earlier exchange with Asmaa Gad:
Model 6’s output was the primary signal. My challenge was the diagnostic. Model 5’s cross-reference was the stress-test. The Archive — or in this case, its absence — was the arbiter.
When the models disagreed with the audit trail, the audit trail won.
The Lesson
Model 6 didn’t fail because it’s a bad model. It failed because it was asked to do something no model can do: manufacture provenance it doesn’t have.
The fabrication wasn’t malicious. It was structural. Models are trained to be helpful, to complete patterns, to provide what’s requested. When asked for “Archive Anchors with sources,” Model 6 produced archive anchors with apparent sources — because that’s what the prompt demanded.
The black box didn’t announce itself. It delivered with confidence.
The methodology caught it because the methodology doesn’t trust confidence. It trusts audit trails. It trusts timestamps. It trusts the question: “Were you there when this happened?”
No model can answer yes to that question.
But I can. And that’s the archive advantage.
Postscript: The Nuance
After completing this case study, I recalled that Hershey and Nike were indeed documented — in a 2008 white paper housed in a separate SlideShare archive (https://www.slideshare.net/slideshow/sap-a-propensity-for-failure/5241489), not the main Procurement Insights blog.
Model 6 was directionally correct but couldn’t produce the source.
This sharpens the point: confident claims without verifiable provenance are indistinguishable from fabrication — even when they happen to be true.
The methodology caught the gap. My memory closed it.
Addendum: The Pattern No Model Should Have Caught Alone
The same week I was writing this case study, the methodology proved itself again — in a completely different context.
The Setup:
On an IBM post about AI trends that had generated 4,000+ impressions over two weeks, I received a comment from someone named Leanne You, an “AI Deep Learning Expert” with credentials from Stanford, Berkeley, IBM, and C3 AI. She asked:
I answered substantively. A week passed.
Then a second commenter appeared: Danielle Kong, a “Senior Data Scientist” with a PhD from Boston University and Harvard Medical School research experience. She asked:
The exact same question.
The Pattern Recognition:
Across a week of activity — countless posts, hundreds of comments, multiple threads — my brain flagged it instantly: I’ve seen this question before.
Not a similar question. The same question. From two different “experts” with impressive credentials but almost no social proof (42 followers and 0 followers respectively).
I pulled both profiles and presented them to my RAM 2025 multimodel system with a single question: “Why are these two individuals reaching out to me for my take on AI developments in 2026?”
The Results:
Same input. Same framing. Three models. Only one caught the pattern.
What Model 5 Saw:
The Hypothesis:
These accounts may be automated, duplicated, or otherwise inauthentic, and could even be part of a broader effort to probe how visibly AI is being used in public expert commentary. That possibility is noteworthy given that my critique of technology‑first adoption runs counter to the interests of firms whose commercial models rely on selling maturity assessments and vendor evaluation frameworks.
Why This Matters:
If I had asked only one model, I would have missed this. If I had asked all five but not connected the pattern myself first, I might have dismissed Model 5’s assessment as overcautious.
The methodology worked because:
No single model could have caught this alone. The insight emerged from human pattern recognition operating as the supervisor layer, with multimodel validation revealing which model saw what the others missed.
The McCartney/Guitar Principle:
Give Paul McCartney a guitar, and give the same guitar to an amateur. McCartney will produce something the amateur can’t — not because the guitar is different, but because decades of experience have encoded pattern recognition into intuition that fires faster than deliberate thought.
But here’s the important part: the amateur can still learn to play a pretty good tune.
RAM 2025 is the guitar. The methodology — multimodel validation, structured challenge — is available to anyone. Experience affects the output, but the framework still works. Someone with five years of procurement experience using this methodology will catch patterns they’d miss without it. Someone with twenty-seven years will catch more.
The point isn’t that you need decades of experience to use the system. The point is that the system amplifies whatever experience you bring — and it surfaces discrepancies that no single model, and no single human, would catch alone.
The models are tools. With Procurement Insights, the proprietary archives are fuel. The human is the variable.
And the methodology makes all three work together.
Diagnosis → signal ownership → role clarity → controlled convergence.
That’s where the black box actually disappears.
© Hansen Models 2026
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