The Park Is Open
Why prompting became infrastructure - and why the fences matter now.
Prompts aren’t prose anymore; they’re part of the system’s wiring. And once language becomes infrastructure, governance stops being optional.
In the early days of prompting, we were basically the scientists in Jurassic Park during the cheerful tour montage. Everything felt astonishing. You typed a sentence, the system responded, and you stared with the same wide-eyed disbelief as the first time someone whispered, “They do move in herds.” It felt like play. A theme park. A controlled experiment - or so we told ourselves.
https://www.google.com/url?sa=t&source=web&rct=j&opi=89978449&url=https://www.youtube.com/watch%3Fv%3Djiw0TcGV7Hk&ved=2ahUKEwjMhvOVz5aRAxUNSGwGHW3cNMAQtwJ6BAgPEAI&usg=AOvVaw1NSjRG68NX4DFSObfBxuq6
That phase didn’t survive contact with reality.
Over the last few years, prompting has crossed an invisible line. It stopped being a toy and became part of enterprise infrastructure. You can see it in adoption: Gartner projects that more than 80% of enterprises will be piloting or using generative-AI solutions by 2025, and that growth is tied directly to workflow automation rather than novelty demos. Adoption alone doesn’t increase risk, but integration without oversight does. And that integration is visible everywhere now - in technical documentation that treats prompts as configuration surfaces, in product teams wiring LLMs into customer-facing flows, and in governance conversations about data lineage, quality, security, and liability.
In other words: someone turned the power on for the whole park, and most people only noticed when the fences began to hum.
A friend and semiconductor CEO captured the moment for me over ice cream: He tells employees to “use AI responsibly” because he doesn’t want something dumb showing up in legal discovery. That isn’t hypothetical. Legal and e-discovery teams now warn that prompts, logs, and AI outputs may be treated as business communications and discoverable in litigation, depending on jurisdiction, storage policies, and privilege boundaries.
This is the real Jurassic Park problem: governance. The belief that if you put enough glass between you and the system, it becomes safe. In 2022, we were still in the gift shop. Hallucinations were cute. Unpredictability made us laugh.
That charm survives exactly until something operational breaks.
Today, the risks are not speculative. Hallucinations - fluent, plausible fabrications - are well-documented in enterprise and legal settings. IBM describes them as a structural property of probabilistic models. Stanford’s HAI shows general-purpose chatbots hallucinating frequently on legal queries, and even domain-tuned legal models hallucinate in measurable percentages. Courts in the US and UK have sanctioned lawyers for filing briefs containing fabricated citations generated by AI. Professional bodies now advise lawyers to validate all outputs before use.
And the same pattern is showing up quietly everywhere else. Multiple enterprises have reported internal support agents generating made-up customer notes when data was missing - because prompts like “fill in what’s helpful” were interpreted literally. Those hallucinated notes ended up in CRMs, influencing refunds, SLA escalations, and routing decisions. This wasn’t misconduct or malice; it was under-specified instructions coupled with a lack of validation. Governance teams increasingly describe these failures as data-lineage and process-control defects.
This is why prompting is no longer “writing.” It is now one component of system design.
Technical guides now treat prompts as a user-facing layer. When language can trigger tools, pull in external data, or influence automated workflows, it becomes operational instruction. The prompt isn’t just dialogue with the dinosaur. It’s part of the fence itself. Get it wrong, and the consequences don’t stay in the text box.
The same interface can also be exploited. Prompt injection, hidden commands, and jailbreaks become real risks when anyone can type into the system. Internal tools with tight controls are safer, but open-input systems are exposed. These attacks work the same way misconfigurations do: the system gets instructions it wasn’t built to handle, and it behaves in unexpected ways.
Each change in wording, model versions, and data shifts behavior a little. If prompts, datasets, and model versions aren’t tracked like code - with versioning, tests, approvals, and monitoring - you end up with fences that look “on” but don’t work the way you expect. The mature teams separate safety controls (preventing harm) from quality controls (preventing wrong or unreliable output). Without both, governance breaks down at scale.
Regulators have already started defining the outer frame. The NIST AI Risk Management Framework, ISO/IEC 42001, and EU AI Act implementation guidelines all treat prompts, logs, and model outputs as part of a regulated operational footprint. The fence specifications are starting to be written down.
And prompting is only one visible layer in a stack that includes retrieval, grounding, data validation, tool integration, access control, input sanitation, output filtering, logging, and retention. Most failures arise from interactions across these layers, not from prompts alone. Yet prompts are where humans feel comfortable improvising, because it feels like “just talking.” The legal world is the preview: e-discovery workflows now require human verification, drift monitoring, and documented validation before using AI outputs. Other industries will follow, I suppose.
Where we’re heading is clear. Multi-modal models - spanning text, images, vision, audio, and structured data - expand the number of ways inputs can be ambiguous, manipulated, or misinterpreted. Wider modality means deeper integration. Enterprises are already treating generative-AI output as internal publication with reputational exposure. And “AI literacy” is quietly becoming a required skill across compliance, engineering, operations, finance, and law.
The disasters, when they come, won’t be because the models became monsters. They’ll come from the same place they did in Jurassic Park: human mis-specification, underfunded safeguards, unclear ownership, and over-trusted interfaces - paired with the familiar corporate refrain: “Relax. Nothing will go wrong.”
The truth fits in one line: we didn’t grow up; we industrialized.
And once you industrialize power, governance stops being optional.
The park is open.
The fences matter now.
And the raptors - quietly, methodically - are already testing them.