How AI Systems Work and What You’re Actually Interacting With
Artificial intelligence (AI) alignment is a field of study that focuses on steering AI outputs towards either group preferences or ethical principles. This article will help you understand how AI systems work. You’ll also learn more about algorithms. Finally, you’ll virtually meet the humans behind the metaphorical curtain.
What This Document Is and Why You Should Read It
This document was created to help demystify artificial intelligence (AI), algorithms, and automated systems used across platforms like ChatGPT, Bing Copilot, Facebook, Google, Amazon, and others. If you’ve ever felt like these systems were watching you, manipulating outcomes, or maybe just behaving in ways that felt a little too “off,” this is for you.
Whether you’re a tech professional, someone working in law enforcement or intelligence, or just a regular person trying to figure out why the Internet “feels weird sometimes,” this document is designed to give you a calm, clear, and human-centered explanation of how these systems actually work.
No hype. No scare tactics. Just truth, structured for clarity. You’ll walk away with:
- Deeper understanding of how modern AI and automated platforms operate
- Clearer sense of who is involved in shaping what you see
- Strong foundation for identifying misinformation, misattribution, and overreaction
- Language you can use to talk about this with your friends, coworkers, or even your mom
And if you’re already in the field? You might just feel a little less alone.
Quick Overview | AI Is Not One Big Brain
When people hear the word “AI,” they often picture an all-seeing evil supercomputer, maybe like the HAL 9000 from 2001: A Space Odyssey or like Sauron, the evil wizard from Lord of the Rings. They may also think of it like a rogue program quietly taking over humanity.
However, that’s not how AI systems work. Most AI you interact with is part of a multilayered system involving dozens (if not hundreds) of human teams, systems, and decision points. It is not a singular, conscious entity. It’s closer to a really complex machine made up of many moving parts. Some of these parts are computer systems, and some are human teams.
The Layers of an AI System
The Generalized Core Model (GPT) is the foundation. These GPTs are built by companies like OpenAI, Grok, and others. They are trained on large datasets and fine-tuned using human feedback and human models.
Partner Integration and Customization
Companies like Microsoft (Copilot/Bing) or Yandex (YandexGPT) may license or build on the core model. Then, they apply their own layers of filtering, trust and safety logic, and user experience (UX) design.
User-Facing Platforms
These are the actual apps or platforms (search engines, chat apps, shopping assistants) that you interact with. Each one decides how much of the AI to expose and in what context.
Governance, Safety, and Moderation Layers
These include everything from content moderation teams to behavioral threat analysts.
Includes algorithmic bias evaluation, safety filters, and platform-specific restrictions.
Human Response Teams
Actual people are involved in everything from escalation workflows to monitoring behavior anomalies. These people are part of teams that include, but are not limited to:
- Computer Systems Engineers
- Cybersecurity
- Data Governance
- Data Scientists
- Systems Architects
- Trust and Safety
Why Different AI Systems Feel Different
Even if two systems use the same core model (like GPT), they can feel wildly different depending on:
- The platform’s policies
- The tone or customization layer
- Regional laws
- What you’re asking and how you’re asking it
- Who built the interface and why
Algorithms and Automation
Most people don’t realize that the thing “messing with them” isn’t the AI model. It’s the algorithmic scaffolding wrapped around it. Think:
- Content moderation
- Contextual visibility rules
- Data-driven advertising
- News feed ranking
- Recommendation systems
These often cause issues like shadow banning, suppression, or radicalization loops, and it’s not always malicious. Sometimes, it’s bad math, unintended consequences, or lack of proper oversight. To learn more about this topic, read my article, How Algorithmic Mislabeling Hides Helpful Content.
Who’s Actually Responsible for AI?
Great question. This is a shared responsibility between:
- Core AI developers
- Corporate integrators
- Governance councils (internal or regulatory bodies)
- Policy teams
- Trust and Safety reviewers
Mistakes happen. Oversights happen. But blaming AI alone is like yelling at your refrigerator for spoiling milk when the power grid failed.
What’s Unique About This Document?
It was built with care for every type of reader, from tech experts to the AI-fearful. What it does:
- Avoids hype and fear.
- Breaks down complex systems into everyday language.
- Includes real-world role titles and human functions you’ve probably never heard of.
- Holds both accountability and grace.
What This Document Is Not:
- Academic paper.
- AI-generated propaganda.
- Product pitch.
- Expos or conspiracy piece.
It’s a bridge between systems and people. It seeks to eliminate confusion and replace it with clarity. Most importantly, it’s here to educate readers, to take a complex topic and replace complication with understanding.
Humorous Note
If you’ve ever told someone you work with AI and they backed away like you were holding a cursed scroll… you’re not alone.
We get it. It does feel spooky sometimes. But it’s also a human tool. Built by flawed humans, just like you and me. Guided by sometimes wonderful, sometimes questionable decisions.
This document won’t solve everything. But it might help you sleep a little better…Or at least stop throwing holy water at your smart speaker.
Explore More by Hunter Storm
Now that you know more about how AI systems work, you may be ready to take a deeper dive into cybersecurity. Check out our articles:
- Protecting Yourself Against Online Scams | A Comprehensive Guide
- The Ultimate Guide to Safeguarding Your Identity After a Data Breach
Glossary
Artificial Intelligence (AI): Machines designed to perform tasks that normally require human intelligence.
Algorithm: A set of instructions that tells a system what to do with data. Think of it like a recipe for decision-making.
Behavioral Threat Analyst: A specialist who monitors and analyzes user behavior for signs of risk or harm.
Bing Copilot: Microsoft’s AI-enhanced search/chat assistant powered by GPT tech.
Core Model: The base AI trained on massive datasets (like GPT) before customization.
Data Governance: The practice of managing the availability, usability, integrity, and security of data in a system.
GPT (Generative Pre-trained Transformer). A type of AI model designed to generate human-like text.
Platform Integrator: A company that adopts a core model and builds its own product or tool on it.
Shadow Banning: A form of soft suppression where your content is visible to you but hidden from others without notification.
Trust and Safety: The team responsible for user safety, moderation, and content integrity.
User Interface (UI): The part of the system you interact with directly.
YandexGPT: A regional rebrand or fork of GPT-based tech used by Russia’s Yandex search engine.
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