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ChatGPT Is Not “AI”, It’s a Product.

ChatGPT Is Not “AI”, It’s a Product.
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Why Open-Source, On-Premise, European AI With RAG Is the Real Definition of Business AI.

Somewhere along the way, we collectively made a mistake. We started calling ChatGPT “AI.”

ChatGPT is impressive. It’s useful. It’s a breakthrough in user experience.
But it is not the definition of AI, and treating it as such is actively holding back serious businesses, governments, and European innovation. ChatGPT is a consumer-facing interface on top of a proprietary model, hosted in hyperscale cloud infrastructure, optimized for generalized use cases and Silicon Valley economics.

Real AI , especially for B2B, regulated industries, and strategic autonomy looks very different. And Europe, quietly but deliberately, seems to understand this better than most.

AI Is Not a Chatbot

Let’s start with a simple reframing.

AI is not:

  • A single large language model
  • A chat interface
  • A subscription SaaS tool
  • A black box trained on unknown data

AI is:

  • A capability embedded into systems
  • A decision-support layer on top of your data
  • A workflow accelerator
  • A reasoning and retrieval engine aligned with business context

ChatGPT is a general-purpose language model service. AI, in practice, is an architecture.

The Real Value of AI is the Data

For businesses, the model itself is rarely the differentiator.

What matters is:

  • Your internal documents
  • Your processes
  • Your customer history
  • Your operational data
  • Your domain-specific knowledge

A generic model trained on the public internet will never understand:

  • Your contracts
  • Your compliance rules
  • Your edge cases
  • Your internal language

This is why Retrieval-Augmented Generation (RAG) is the most important AI pattern in production today.

RAG: Where AI Becomes Actually Useful

A RAG pipeline allows you to:

  1. Keep your data private
  2. Store it in your own vector database
  3. Retrieve only relevant context
  4. Let the model reason over your data, not replace it

In other words: The AI does not “know” anything. It retrieves and reasons, just like a good employee.

This is not a ChatGPT feature. This is a system design choice.

Why Open Source Models Matter More Than Ever

Open-source models (Mistral, LLaMA variants, Qwen, etc.) change the power dynamic completely.

They allow organizations to:

  • Run models on-premise or in private cloud
  • Control data residency
  • Audit behavior
  • Customize performance
  • Avoid vendor lock-in

Most importantly: They separate AI capability from Big Tech control. This is not an ideological argument, it’s a strategic one.

Mistral + ASML

The Mistral story is often misunderstood. This is not “Europe copying OpenAI.”This is Europe doing something fundamentally different. There is a reason both companies are in a strategic partnership.

Why Mistral Matters

  • Open-weight models
  • Strong performance-per-parameter
  • Optimized for deployment, not just benchmarks
  • Designed to be used, not just showcased

Why ASML Matters

ASML is not an AI company — and that’s the point. ASML understands:

  • Strategic infrastructure
  • Long-term sovereignty
  • Industrial dependency risk

By supporting Mistral, Europe is signaling something very clear:

AI is infrastructure — not a consumer app.

Just like:

  • Chips
  • Energy
  • Telecom
  • Manufacturing tooling

AI is now strategic capacity.

Hyperscale AI vs European AI: Two Philosophies

Silicon Valley Model

  • Centralized
  • Cloud-only
  • Closed weights
  • Data extraction
  • Growth-at-all-costs
  • Consumer-first

European Model (Emerging)

  • Federated
  • On-prem / private cloud
  • Open or semi-open
  • Data sovereignty
  • B2B-first
  • Compliance-by-design

These are not competing products , they are competing worldviews.

Why On-Prem AI Is Not “Old-School”, It’s the Future

On-prem AI is often dismissed as:

  • Too complex
  • Too expensive
  • Too slow

This is outdated thinking. Modern on-prem AI stacks:

  • Use containerization
  • Scale horizontally
  • Integrate with automation tools (n8n, Make, Airflow)
  • Run smaller, more efficient models
  • Cost less over time than SaaS subscriptions

For SMEs and enterprises, on-prem AI means:

  • Predictable costs
  • No data leakage
  • Full customization
  • Regulatory confidence

AI as an Employee, Not a Product

The most successful AI implementations I see are not “tools.”

They are:

  • AI agents handling repetitive analysis
  • AI copilots embedded in workflows
  • AI systems preparing decisions, not making them
  • AI layers that disappear into operations

This is where automation + AI converge. ChatGPT alone can’t do this. RAG + open models + automation pipelines can.

Why ChatGPT Is Still Useful (But Not the Point)

To be clear: ChatGPT is an excellent interface.

It is:

  • Great for ideation
  • Great for prototyping
  • Great for accessibility
  • Great for learning

But confusing ChatGPT with AI is like confusing:

  • Excel with finance
  • PowerPoint with strategy
  • Email with communication

It’s a surface layer, not the system.

The Strategic Question Every European Business Should Ask

Not:

“How do we use ChatGPT?”

But:

“Where do we want intelligence to live in our organization?”
  • In someone else’s cloud?
  • Or inside our own systems?

So, the future of AI is not:

  • One giant model
  • One chat window
  • One hyperscaler

The future of AI is:

  • Modular
  • Open
  • Private
  • Embedded
  • Context-aware
  • European-by-design

ChatGPT didn’t invent AI. It just made it visible. Now it’s time to build it properly. And Europeans are perfectly fine capable of doing that.

Published by:

Koen Machielse