On causal relationships, human behavior, and frameworks that have been waiting 27 years for the industry to catch up.
A post from Kassem Nasser crossed my feed this morning. He was sharing news that Yann LeCun and Jeff Bezos have launched a new venture focused on what they’re calling World Models — a paradigm that moves beyond text prediction to capture “causal relationships and real-world dynamics.”
The timing felt significant. Not because World Models aren’t valuable — they are. But because the core concept has a longer history than this announcement suggests.
What Are World Models?
According to the Financial Times article Kassem referenced, World Models aim to:
Move beyond text prediction
Capture causal relationships
Model real-world dynamics
Tackle simulation-heavy industries like robotics, logistics, and autonomous systems
LeCun and Bezos see this as the “potential successor to LLMs” — the next wave of AI innovation.
The ambition is significant. So is the challenge. And there’s prior art worth examining.
Strand Commonality: A Framework From 1998
In the late 1990s, the Government of Canada’s Scientific Research and Experimental Development (SR&ED) program funded research I was conducting into a theory I called Strand Commonality.
The core insight: seemingly disparate strands of data actually have attributes that are related and collectively have an impact.
This is, in essence, what LeCun is now describing as “causal relationships in real-world dynamics.”
Strand Commonality wasn’t just theory. We operationalized it for the Department of National Defence’s MRO procurement platform, creating what I called the Relational Acquisition Model (RAM) — an agent-based system using advanced self-learning algorithms, time zone polling, and decentralized coordination across multiple stakeholders.
The results were measurable: delivery performance went from 51% next-day to 97.3% next-day. Headcount dropped from 23 FTEs to 3. Cost of goods decreased 23% over seven years.
This was 1998-2001. The company sold for $12 million in 2001.
The Amazon Parallel
I’ve written before about the conceptual alignment between these early frameworks and what Amazon built over the following two decades:
Using my RAM 2025 multi-model assessment approach — where multiple AI models independently analyze the same question — the alignment is notable:
The models concluded: “Amazon’s supply chain success is a real-world realization of Hansen’s early vision — particularly around autonomy, distributed intelligence, and real-time adaptation.”
There’s no documented direct adoption. But there is conceptual convergence — the kind that happens when different people work on similar problems at scale.
Now Bezos is investing in “World Models” — the theoretical foundation of systems that have already been operationalized in various forms for decades.
What World Models Will Need to Learn
Here’s where it gets interesting — and where World Models face their biggest challenge.
LeCun and Bezos are positioning World Models as the key to understanding causality in complex systems. But they’re approaching it the same way LLMs approached language: through data patterns.
In our DND work, we discovered something that pure data modeling would have missed.
The delivery problem showed up in the data as orders arriving at 4pm and missing customs. The equation-based solution was obvious: automate the system.
But we asked a different question: Why are orders coming in at 4pm?
The service department had technicians who were incentivized to maximize daily service calls. Policy said: order parts after each call. But the ordering system was cumbersome. So technicians would “sandbag” — hold all orders until end of day so they could hit their call targets first.
By 4pm, dynamic flux products that cost $100 at 9am were now $1,000. And shipments missed the customs window.
Here’s the critical point: no algorithm analyzing order data would have found that. The cause wasn’t in the data stream. It was in human behavior — in the incentive structure that shaped when and how people acted.
A human in the loop found it. And solving it required modeling the behavioral agents in the system: technicians, suppliers, couriers, customs officials.
That’s Strand Commonality in practice. The strands that matter most aren’t always in the database.
See How It Worked
Here’s a video where I walk through exactly how the DND system operated — including the sandbagging problem and how we addressed it by modeling behavioral agents rather than just data flows:
This wasn’t theory. It was production — serving 35 geographically dispersed military bases across Canada, integrated with UPS dispatch and Canada Customs clearance, running on self-learning algorithms that improved with every transaction.
The core insight that made it work: human behavior is the critical strand.
The Pattern Worth Watching
LLMs hit a ceiling because they learned to predict without understanding causality. They’re extraordinarily good at pattern matching, but they don’t model why things happen — only what typically comes next.
Now LeCun and Bezos are going back to build the causal foundation that was skipped.
World Models will be valuable. But they’ll face the same ceiling if they try to model causality purely through data. The systems that work in complex, real-world environments — procurement, logistics, transformation — require modeling something data alone can’t capture: why humans do what they do.
Incentive structures. Organizational friction. The gap between policy and practice. The 4pm sandbagging problem.
Technology amplifies whatever foundation exists. If the foundation is purely data-driven, you get sophisticated pattern matching. If the foundation includes behavioral agents — the humans whose decisions actually drive outcomes — you get something that works.
The frameworks for this have existed for decades. The industry is finally ready to use them.
Today’s Takeaway
World Models represent genuine progress. Moving from prediction to causality is the right direction.
But causality in complex systems isn’t just about data relationships. It’s about understanding the human behaviors that shape those relationships.
That’s what Strand Commonality taught us in 1998. And it’s what World Models will eventually need to incorporate:
Human behavior is the strand that connects everything else.
What’s your take? Is the industry ready for behavioral modeling, or will World Models follow the same data-first path? I’d like to hear your perspective in the comments.
Procurement Insights
World Models and the Strand They Can’t See
Posted on December 7, 2025
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On causal relationships, human behavior, and frameworks that have been waiting 27 years for the industry to catch up.
A post from Kassem Nasser crossed my feed this morning. He was sharing news that Yann LeCun and Jeff Bezos have launched a new venture focused on what they’re calling World Models — a paradigm that moves beyond text prediction to capture “causal relationships and real-world dynamics.”
The timing felt significant. Not because World Models aren’t valuable — they are. But because the core concept has a longer history than this announcement suggests.
What Are World Models?
According to the Financial Times article Kassem referenced, World Models aim to:
LeCun and Bezos see this as the “potential successor to LLMs” — the next wave of AI innovation.
The ambition is significant. So is the challenge. And there’s prior art worth examining.
Strand Commonality: A Framework From 1998
In the late 1990s, the Government of Canada’s Scientific Research and Experimental Development (SR&ED) program funded research I was conducting into a theory I called Strand Commonality.
The core insight: seemingly disparate strands of data actually have attributes that are related and collectively have an impact.
This is, in essence, what LeCun is now describing as “causal relationships in real-world dynamics.”
Strand Commonality wasn’t just theory. We operationalized it for the Department of National Defence’s MRO procurement platform, creating what I called the Relational Acquisition Model (RAM) — an agent-based system using advanced self-learning algorithms, time zone polling, and decentralized coordination across multiple stakeholders.
The results were measurable: delivery performance went from 51% next-day to 97.3% next-day. Headcount dropped from 23 FTEs to 3. Cost of goods decreased 23% over seven years.
This was 1998-2001. The company sold for $12 million in 2001.
The Amazon Parallel
I’ve written before about the conceptual alignment between these early frameworks and what Amazon built over the following two decades:
Using my RAM 2025 multi-model assessment approach — where multiple AI models independently analyze the same question — the alignment is notable:
The models concluded: “Amazon’s supply chain success is a real-world realization of Hansen’s early vision — particularly around autonomy, distributed intelligence, and real-time adaptation.”
There’s no documented direct adoption. But there is conceptual convergence — the kind that happens when different people work on similar problems at scale.
Now Bezos is investing in “World Models” — the theoretical foundation of systems that have already been operationalized in various forms for decades.
What World Models Will Need to Learn
Here’s where it gets interesting — and where World Models face their biggest challenge.
LeCun and Bezos are positioning World Models as the key to understanding causality in complex systems. But they’re approaching it the same way LLMs approached language: through data patterns.
In our DND work, we discovered something that pure data modeling would have missed.
The delivery problem showed up in the data as orders arriving at 4pm and missing customs. The equation-based solution was obvious: automate the system.
But we asked a different question: Why are orders coming in at 4pm?
The service department had technicians who were incentivized to maximize daily service calls. Policy said: order parts after each call. But the ordering system was cumbersome. So technicians would “sandbag” — hold all orders until end of day so they could hit their call targets first.
By 4pm, dynamic flux products that cost $100 at 9am were now $1,000. And shipments missed the customs window.
Here’s the critical point: no algorithm analyzing order data would have found that. The cause wasn’t in the data stream. It was in human behavior — in the incentive structure that shaped when and how people acted.
A human in the loop found it. And solving it required modeling the behavioral agents in the system: technicians, suppliers, couriers, customs officials.
That’s Strand Commonality in practice. The strands that matter most aren’t always in the database.
See How It Worked
Here’s a video where I walk through exactly how the DND system operated — including the sandbagging problem and how we addressed it by modeling behavioral agents rather than just data flows:
This wasn’t theory. It was production — serving 35 geographically dispersed military bases across Canada, integrated with UPS dispatch and Canada Customs clearance, running on self-learning algorithms that improved with every transaction.
The core insight that made it work: human behavior is the critical strand.
The Pattern Worth Watching
LLMs hit a ceiling because they learned to predict without understanding causality. They’re extraordinarily good at pattern matching, but they don’t model why things happen — only what typically comes next.
Now LeCun and Bezos are going back to build the causal foundation that was skipped.
World Models will be valuable. But they’ll face the same ceiling if they try to model causality purely through data. The systems that work in complex, real-world environments — procurement, logistics, transformation — require modeling something data alone can’t capture: why humans do what they do.
Incentive structures. Organizational friction. The gap between policy and practice. The 4pm sandbagging problem.
Technology amplifies whatever foundation exists. If the foundation is purely data-driven, you get sophisticated pattern matching. If the foundation includes behavioral agents — the humans whose decisions actually drive outcomes — you get something that works.
The frameworks for this have existed for decades. The industry is finally ready to use them.
Today’s Takeaway
World Models represent genuine progress. Moving from prediction to causality is the right direction.
But causality in complex systems isn’t just about data relationships. It’s about understanding the human behaviors that shape those relationships.
That’s what Strand Commonality taught us in 1998. And it’s what World Models will eventually need to incorporate:
Human behavior is the strand that connects everything else.
What’s your take? Is the industry ready for behavioral modeling, or will World Models follow the same data-first path? I’d like to hear your perspective in the comments.
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