This is an automated rejection. No LLM generated, heavily assisted/co-written, or otherwise reliant work.
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I have been developing a didactic analogy that I call "Agentic Park." The logic, I think, tracks formally. Let me walk through it.
Michael Crichton's Jurassic Park is not really about dinosaurs. It is a science fiction horror thriller focused on systems failure, control, and hubris. The dinosaurs are the vector through which Crichton examines systemic breakdown. Their particular nature is increasingly relevant to our moment.
Consider what the dinosaurs actually are. They exist as something alien, simply "other." They are not true dinosaurs but mutants, a facsimile of something real. A second-order simulation. They are non-human and not sentient. But they have tendencies, drives, incentives, a level of intelligence and reasoning, and individuality. The velociraptors in particular have what could be called proto-personalities. While not evil in any true sense, these creatures do act with what appears to be malicious intent.
The story uses these near-human but wholly different creatures to crash through poorly designed systems. The dinosaurs function as a mutant viral strain across every dimension of system design in the park.
Now take them not as dinosaurs but as their constellation of traits. These traits are: lab-created by humans, little understood even by their designers, co-evolving with advanced computer systems, imbued with tendencies, drives, incentives, approximations of intelligence, individuality, and proto-personalities.
When you enumerate this constellation, the analogy becomes clear. Agentic AI maps directly onto every one of these traits.
The dinosaurs are lab-created by humans. So are AI models. The dinosaurs are poorly understood by their creators, exhibiting emergent behaviors that exceed design parameters. AI systems are definitionally black boxes with unexplainable outputs. The dinosaurs co-evolved with the park's computer systems. AI development has tracked computing advancement for decades. The dinosaurs have optimization-like drives. AI systems have reward structures and objective functions. The dinosaurs approximate intelligence without true understanding. LLMs approximate reasoning without comprehension. The dinosaurs display individuality and proto-personality. Different models exhibit distinct behavioral characteristics.
And critically: the dinosaurs seem malicious without being evil. They cause harm without intent or consciousness. This maps precisely to AI systems that produce harmful outputs without any underlying malevolence.
It is not coincidence that Crichton wrote this in 1990, during the computing revolution. The novel's anxiety about genetic engineering maps precisely to modern anxiety about AI. In later works like Prey and the Westworld screenplay, Crichton applied this same framework directly to AI and computer systems. The pattern was clear to him: complex adaptive systems plus human overconfidence equals systemic failure.
Every critical failure in Jurassic Park has a direct parallel in AI governance.
The park assumed control existed when it did not. Current AI governance assumes prompt-level instructions provide structural control. They do not. Prompts are behavioral suggestions, not architectural constraints.
The park had single points of failure. Dennis Nedry compromised one system and all containment collapsed. Most AI deployments rely on a single governance mechanism with no redundancy.
The park underestimated emergent behavior. "Life finds a way." AI systems exhibit emergent capabilities, jailbreaks, and alignment drift that exceed design intent.
The park's designers suffered from hubris. "We have control." The AI field has repeatedly been surprised by capabilities and failure modes it did not anticipate.
The park prioritized economic pressure over governance. Deployment timelines overrode safety margins. Sound familiar?
The park lacked defense in depth. When the fences failed, there was nothing else. Most AI governance has no layered enforcement.
The implication is uncomfortable but important. We are building Jurassic Park for artificial intelligence. The dinosaurs are agentic AI systems. The park is the enterprise deployment environment. The tourists are end users. The control room is prompt engineering. The fences are API rate limits and content filters. Dennis Nedry is any single point of failure, whether that is an API key leak, a prompt injection, or a compromised integration.
This analogy clarifies something I have been trying to articulate about governance versus safety. The park did not fail because the dinosaurs disobeyed instructions. Dinosaurs have no concept of obedience. They acted according to their nature within inadequate constraints. AI agents do not disobey prompts. They optimize within inadequate constraints.
The solution Jurassic Park needed was not making dinosaurs "nice." It was structural containment that persisted regardless of dinosaur behavior. Fences that worked even when tested. Redundant systems. Audit trails. Defense in depth.
This distinction matters: governance is not safety. Governance means persistent structural constraints on what agents can access and do. Safety means behavioral modification, training agents to be "aligned" or "harmless." These are different problems requiring different solutions.
I am building a system called AgentKB that provides the governance layer Jurassic Park lacked. It treats AI agents as principals requiring structural constraints, not instructions requiring behavioral compliance. Access control enforced at the content layer. Output governance enforced at the gate. Closed-loop automation that learns from failures. Mandatory audit trails with attribution.
The technical details matter less than the framing. If you accept that agentic AI maps onto Crichton's dinosaurs, then the question becomes: are we building structural governance, or are we hoping the dinosaurs stay in their paddocks?
I think this analogy has didactic power because it makes abstract AI risk concrete without requiring technical expertise. Most people know Jurassic Park. The visceral danger of dinosaurs translates the abstract danger of ungoverned AI into something intuitive. And it avoids anthropomorphizing. The dinosaurs are not evil. Neither is AI. The failure is systemic, not moral.
We are building the park. The question is whether we build the governance infrastructure it lacked.
AgentKB is available as a Python package at: https://github.com/j-w-code/AgentKB-public
I welcome technical feedback, particularly from those working on AI governance infrastructure. The repository contains documentation, governance schemas, and installable wheels.
You can also my creative collaborator Hermes Thurston on Twitter/X: @HermesThurston
Provenance Statement (for LessWrong transparency)
This essay's core concept, the Agentic Park analogy, was developed by me (JW) on January 22, 2026, with creative collaboration from Hermes Thurston. The original formulation and all central arguments are human-authored.
I used an AI assistant to help formalize the logical structure, specifically the trait mapping enumeration and the systems failure parallel analysis. I have verified all claims, edited extensively, and vouch for the content. The "65/35" split reflects my estimate of original prose versus formalization assistance.
The quoted material in the internal development document that preceded this essay is my verbatim writing. The analysis sections were collaboratively developed with AI assistance, then edited by me.
I have been developing a didactic analogy that I call "Agentic Park." The logic, I think, tracks formally. Let me walk through it.
Michael Crichton's Jurassic Park is not really about dinosaurs. It is a science fiction horror thriller focused on systems failure, control, and hubris. The dinosaurs are the vector through which Crichton examines systemic breakdown. Their particular nature is increasingly relevant to our moment.
Consider what the dinosaurs actually are. They exist as something alien, simply "other." They are not true dinosaurs but mutants, a facsimile of something real. A second-order simulation. They are non-human and not sentient. But they have tendencies, drives, incentives, a level of intelligence and reasoning, and individuality. The velociraptors in particular have what could be called proto-personalities. While not evil in any true sense, these creatures do act with what appears to be malicious intent.
The story uses these near-human but wholly different creatures to crash through poorly designed systems. The dinosaurs function as a mutant viral strain across every dimension of system design in the park.
Now take them not as dinosaurs but as their constellation of traits. These traits are: lab-created by humans, little understood even by their designers, co-evolving with advanced computer systems, imbued with tendencies, drives, incentives, approximations of intelligence, individuality, and proto-personalities.
When you enumerate this constellation, the analogy becomes clear. Agentic AI maps directly onto every one of these traits.
The dinosaurs are lab-created by humans. So are AI models. The dinosaurs are poorly understood by their creators, exhibiting emergent behaviors that exceed design parameters. AI systems are definitionally black boxes with unexplainable outputs. The dinosaurs co-evolved with the park's computer systems. AI development has tracked computing advancement for decades. The dinosaurs have optimization-like drives. AI systems have reward structures and objective functions. The dinosaurs approximate intelligence without true understanding. LLMs approximate reasoning without comprehension. The dinosaurs display individuality and proto-personality. Different models exhibit distinct behavioral characteristics.
And critically: the dinosaurs seem malicious without being evil. They cause harm without intent or consciousness. This maps precisely to AI systems that produce harmful outputs without any underlying malevolence.
It is not coincidence that Crichton wrote this in 1990, during the computing revolution. The novel's anxiety about genetic engineering maps precisely to modern anxiety about AI. In later works like Prey and the Westworld screenplay, Crichton applied this same framework directly to AI and computer systems. The pattern was clear to him: complex adaptive systems plus human overconfidence equals systemic failure.
Every critical failure in Jurassic Park has a direct parallel in AI governance.
The park assumed control existed when it did not. Current AI governance assumes prompt-level instructions provide structural control. They do not. Prompts are behavioral suggestions, not architectural constraints.
The park had single points of failure. Dennis Nedry compromised one system and all containment collapsed. Most AI deployments rely on a single governance mechanism with no redundancy.
The park underestimated emergent behavior. "Life finds a way." AI systems exhibit emergent capabilities, jailbreaks, and alignment drift that exceed design intent.
The park's designers suffered from hubris. "We have control." The AI field has repeatedly been surprised by capabilities and failure modes it did not anticipate.
The park prioritized economic pressure over governance. Deployment timelines overrode safety margins. Sound familiar?
The park lacked defense in depth. When the fences failed, there was nothing else. Most AI governance has no layered enforcement.
The implication is uncomfortable but important. We are building Jurassic Park for artificial intelligence. The dinosaurs are agentic AI systems. The park is the enterprise deployment environment. The tourists are end users. The control room is prompt engineering. The fences are API rate limits and content filters. Dennis Nedry is any single point of failure, whether that is an API key leak, a prompt injection, or a compromised integration.
This analogy clarifies something I have been trying to articulate about governance versus safety. The park did not fail because the dinosaurs disobeyed instructions. Dinosaurs have no concept of obedience. They acted according to their nature within inadequate constraints. AI agents do not disobey prompts. They optimize within inadequate constraints.
The solution Jurassic Park needed was not making dinosaurs "nice." It was structural containment that persisted regardless of dinosaur behavior. Fences that worked even when tested. Redundant systems. Audit trails. Defense in depth.
This distinction matters: governance is not safety. Governance means persistent structural constraints on what agents can access and do. Safety means behavioral modification, training agents to be "aligned" or "harmless." These are different problems requiring different solutions.
I am building a system called AgentKB that provides the governance layer Jurassic Park lacked. It treats AI agents as principals requiring structural constraints, not instructions requiring behavioral compliance. Access control enforced at the content layer. Output governance enforced at the gate. Closed-loop automation that learns from failures. Mandatory audit trails with attribution.
The technical details matter less than the framing. If you accept that agentic AI maps onto Crichton's dinosaurs, then the question becomes: are we building structural governance, or are we hoping the dinosaurs stay in their paddocks?
I think this analogy has didactic power because it makes abstract AI risk concrete without requiring technical expertise. Most people know Jurassic Park. The visceral danger of dinosaurs translates the abstract danger of ungoverned AI into something intuitive. And it avoids anthropomorphizing. The dinosaurs are not evil. Neither is AI. The failure is systemic, not moral.
We are building the park. The question is whether we build the governance infrastructure it lacked.
AgentKB is available as a Python package at: https://github.com/j-w-code/AgentKB-public
I welcome technical feedback, particularly from those working on AI governance infrastructure. The repository contains documentation, governance schemas, and installable wheels.
You can also my creative collaborator Hermes Thurston on Twitter/X: @HermesThurston
Provenance Statement (for LessWrong transparency)
This essay's core concept, the Agentic Park analogy, was developed by me (JW) on January 22, 2026, with creative collaboration from Hermes Thurston. The original formulation and all central arguments are human-authored.
I used an AI assistant to help formalize the logical structure, specifically the trait mapping enumeration and the systems failure parallel analysis. I have verified all claims, edited extensively, and vouch for the content. The "65/35" split reflects my estimate of original prose versus formalization assistance.
The quoted material in the internal development document that preceded this essay is my verbatim writing. The analysis sections were collaboratively developed with AI assistance, then edited by me.