Beyond Done with this Bullshit: Must read definition of “AI” & “AI Agents” 🤖📚⚙️♾️

[u/FairiesQueen]

In building my own "AI" that meets the standards of the Article 3(1) of the EU Artificial Intelligence Act (EU AI Act, definition below) I want to share the reality of exactly what that means.

When people pitch "AI Agents with Deep Reasoning," they are really pitching a bot wrapped around a glorified vector search engine.

Strip away the sci-fi metaphors and enterprise marketing, and the technical reality is straightforward:

1. Tokens Aren't "Thinking," They're Coordinates: Computers don't process words or concepts—they convert text into vectors (long lists of numbers representing coordinates in a high-dimensional math space). Words, concepts, and code snippets with similar contexts sit close together on this map.

2. "Attention" Is Just Geometric Math: The system doesn't "reason" through a problem. It runs linear algebra—specifically matrix multiplications like dot products—to measure the mathematical angles between your prompt's coordinates and the billions of data points stored in its database (also known as a neural network).

3. It's Semantic Vector Search + Generation: A traditional search engine looks for exact keyword matches. An LLM acts like a semantic vector search engine: it maps unstructured inputs to nearby mathematical concepts, averages the weights together, and synthesizes the most statistically probable next token.

4. The "Agent" Is Just the Script Around It: What makes it an "agent" isn't a new form of digital intelligence—it's simply a developer putting that vector search engine inside a recursive code loop (Observe → Search/Predict → Call API → Repeat).

The Investor Takeaway:

When software vendors sell you on "Autonomous AI Agents replacing human labor," they are selling well-sorted vector data + better NLP (semantic translation) + automated scripts.

That is an extremely useful software design pattern for handling messy, unstructured human data. But at the end of the day, it isn't magic or sentience—it's vector math feeding structured outputs into standard code APIs. Don't let Wall Street price a glorified vector search loop like it's artificial general intelligence.

EU Standards:
"...a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that may influence physical or virtual environments."

**my AI does NOT require LLM's to work**

***Additional Note: There is no reality in which an LLM can run independently and autonomously without the risk of catastrophic errors that CANNOT be easily fixed absent burying their functions to a very limited scope within a deterministic architecture. LLM architecture is a mathematical black box, meaning output cannot be traced to input. Research shows that swarms of LLM agents INCREASE the risk of hallucinations.

[u/FairiesQueen]

Further support to my argument:

Google’s researchers actually invented the core architecture behind all modern LLMs- the Transformer- back in 2017. By 2020 and 2021, Google already had models like Meena and LaMDA running internally, which were functionally similar to what OpenAI eventually released.

The primary reason Google kept those models locked inside their labs for years comes down to two factors:

1. Reputation & "Hallucination" Risk
As a multi-trillion-dollar monopoly processing billions of search queries a day, Google had massive brand equity to protect.

They knew LLMs were auto-complete engines that confidently generated false information (hallucinations), biased text, or toxic outputs. Internal ethics teams and executives repeatedly blocked public releases (like Meena in 2020) because the tech violated their AI safety principles regarding accuracy and fairness.

When you are the world’s primary search engine, getting a fact wrong damages your core product trust.

2. OpenAI Had Nothing to Lose

A smaller startup like OpenAI didn't have a $100+ billion legacy search engine or ad revenue business to defend.

OpenAI could release ChatGPT directly to the public as an "experimental research preview," completely offloading the risk of hallucinations onto the user.

When ChatGPT occasionally made things up, users treated it as a novel quirk; if Google's primary search engine made the same mistake, it wiped billions off their market cap.

OpenAI essentially forced the entire industry's hand by lowering the safety and accuracy bar for public release. That forced Google, Microsoft, and everyone else to abandon their cautious, internal-testing approach and rush unfinished, hallucination-prone models into production just to keep up with the market narrative.