AI Post 15: What We’ve Covered: A Pause and a Pivot

We almost wrote a comparison post. Then we realized it would be obsolete in six weeks. Instead, we’re pivoting to something more useful: how AI actually gets built.

So Far

Posts 1–6: The Foundations

We started with the basics:

• What AI actually is (pattern recognition, not consciousness)
• How it learns from data
• Why it fails
• The ethics we need to grapple with (bias, privacy, accountability)
• Real stories of AI gone wrong (Robert Williams’ wrongful arrest)

The core insight: AI is powerful but flawed. Understanding its limitations is as important as understanding its capabilities.

Posts 7–10: The History

We traced 70 years of AI booms and busts:

• 1950s dreaming (the Turing Test)
• 1970s crash (the first AI winter)
• 1980s hope (expert systems)
• Late 80s disappointment (the second AI winter)
• 2010s breakthrough (deep learning finally worked)
• 2022 explosion (ChatGPT changed everything)

The core insight: Today’s AI is the result of decades of failure, learning, and persistence.

Posts 11–14: The Giants

We dove deep into the major players:

OpenAI (Posts 11–12): The company that started the explosion. GPT-5.5 as of May 2026. Relentless innovation. Massive models, massive scale.

Anthropic (Post 13): The safety-first alternative. Claude built with constitutional AI. Fewer hallucinations. More reliable.

Google (Post 14): The search giant playing catch-up. Gemini with 1M token context. Deep ecosystem integration. Fast iteration.

The core insight: All three are good. Different philosophies, different trade-offs. No single “best,” only what works best for you.

The Comparison We Didn’t Do

I almost did Post 15 as a side-by-side comparison table: ChatGPT vs Claude vs Gemini. Then realized something — That post would be obsolete in 6 weeks. A static comparison table can’t keep up with the pace of AI development in 2026.

The New Direction: AI Engineering

Here’s what we’re doing next:

You understand what AI is. You understand the landscape. You understand the major players. Let’s get into the play.

Now it’s time to understand how AI actually gets built.

Let’s not get into math or code as yet, just engineering at a high level for everyone to follow along:

• Data: Why garbage in = garbage out. How data gets prepared, labeled, biased.
• Prompting: How to ask AI questions so you get the answers you actually want.
• Retrieval: How to give AI access to information (RAG — Retrieval-Augmented Generation).
• Fine-tuning: How to teach AI to work like you.
• Evaluation: How to know if your AI actually works or just seems smart.
• Failure: Why AI works in demos but breaks in production.

This series bridges two worlds:

Where we’ve been: Understanding AI as a user and citizen. Where we’re going: Understanding AI as someone who might build with it or work in the field.

Important to note

The reality of AI in 2026:

The models are getting good fast. ChatGPT, Claude, Gemini — they’re all capable. The gap between them is narrowing.

The differentiation is moving to engineering. The companies winning aren’t building bigger models. They’re building smarter applications.

They’re:

• Using data more effectively
• Prompting models more cleverly
• Combining models with retrieval (RAG)
• Fine-tuning for specific domains
• Catching failures before they hurt users

This is where the real work happens.

If you’re:

• Building an AI product
• Working in AI (even non-technical roles)
• Evaluating AI for your organization
• Considering an AI career

…understanding the engineering fundamentals is crucial.

A Note on the Pace

You might notice something:

AI is changing faster than we can document it.

By the time you read Post 16, there might be new models. New techniques. New failures we haven’t anticipated.

That’s okay.

The fundamentals don’t change as fast:

• Data still matters more than model size
• Prompting still requires clarity
• Evaluation is still hard
• Production systems are still fragile

The details will shift. The fundamentals remain.