Inside the Minds of Top AI Writers: What 3000+ Articles Reveal About Converging Ideas
An in-depth analysis of AI thought leadership, writing patterns, and why casting models like a team is more than a metaphor.
An in-depth analysis of AI thought leadership, writing patterns, and why casting models like a team is more than a metaphor.
Published: June 1, 2025 URL: https://buildtolaunch.ai/p/top-ai-writers-3000-articles-analysis Engagement: 66 likes, 41 comments, 13 restacks Word count: 3013
In my last article, I shared four of the most common traps I've seen people fall into when using AI. It became one of my most popular pieces on Substack.
Then I received a pointed message: it seemed to be lifted from work by another writer, especially the concept of "casting your models like a team."
I was stunned, not just by the accusation, but also by the fact that I hadn't even subscribed to that newsletter before. I knew where my take came from: a learning circle of thousands of practitioners from varied backgrounds.
That sparked a bigger question:
What if we're all converging? What if AI researchers, engineers, educators, even creators, are all independently arriving at the same insights? What if we've already passed some invisible threshold where shared use leads to shared thinking?
That made me curious.
The Goal and Plan
I wanted to:
- Find out "top" AI voices — those who've posted dozens or hundreds of articles over a long stretch.
- Analyze their core concepts, writing styles, and points of overlap.
- Track how their ideas and perspectives have evolved.
- Explore how similar (or different) the idea of "casting models like a team" shows up across them.
To tackle this, I used a combination of tools:
- GPT-o3 for strategic comparisons and hypothesis testing
- GPT-4o for general discussion and synthesis
- Cursor + Claude Sonnet 4 Thinking for large-scale data collection, analysis, and raw content extraction
- NotebookLM to distill and surface high-level insights
Step 1: Collect The Top AI Voices
Industry Leaders
I focused on:
- Sam Altman - OpenAI
- Lillian Weng - OpenAI
- Andrew Ng - DeepLearning
- Clem Delangue - HuggingFace
- Dario Amodei - Anthropic
- Jeff Dean - Google
- Ali Ghodsi – Databrick
While these leaders have immense influence, most don't publish frequently enough to support deep longitudinal analysis.
Substack Thought Leaders
I filtered for:
- Newsletters with 10k+ subscribers
- Content that focuses on opinion and insight, not just news curation
My final list:
- One Useful Thing (Ethan Mollick)
- Elevate (Addy Osmani)
- DiamantAI
- Artificial Intelligence Made Simple (Devansh)
- AI Supremacy (Michael Spencer)
- AI Adopters Club (Kamil Banc)
- AI Disruptor (Alex McFarland)
- AI Snake Oil
- The Sequence
- Refactoring (Luca Rossi)
- Behind the Craft / Creator Economy (Peter Yang)
- Write with AI (Nicolas Cole)
- Future/Proof (The Dan Koe)
Step 2: Overall Analysis
2.1 Industry Leaders' Opinions
Semantic similarity comparison revealed:
- The "AI Democratization Cluster": Clem Delangue and Andrew Ng showed the highest similarity (0.970), closely followed by Ali Ghodsi. Common ground: democratizing AI via open-source, education, and enterprise enablement.
- Jeff Dean's Isolation: Dean's lowest similarity was with Lillian Weng (0.214). Dean reflects Google's deliberate, research-centric ethos.
- Altman's Bridge Role: Sam Altman sits somewhere in the middle, balancing research, deployment, and public discourse.
2.2 Substack Top Voices
2.2.1 Conceptual Clusters: The Intellectual Geography
A t-SNE plot of all articles revealed clear thematic groupings:
- Technical Deep-Dive Archipelago: The Sequence dominates — academic, consistent, specialized.
- Practical AI Hub: Elevate, Diamantai, and One Useful Thing — blending hands-on use with accessible writing.
- Critical Analysis Zone: AI Snake Oil — rigorous, skeptical, focused on ethics and policy.
- Creator Economy Bridge: Creator Economy — focuses on how AI empowers solo builders.
- Synthesis Sweet Spot: My own work spans multiple zones — an attempt to connect the dots.
2.2.2 Stylometric Fingerprints: The Writing DNA
- Most accessible: The Dan Koe (68.9), Write With AI (65.1)
- Balanced: Myself (50.0), One Useful Thing (53.9)
- Most dense: The Sequence (35.5), Diamant AI (32.6)
I learned that my writing lands where I'd hoped: clear, confident, and efficient.
2.2.3 Topic Overlap: The Uniqueness Map
Most overlap scores are only 0.1–0.2, meaning most writers share just 10–20% topical focus. Even the highest overlap — between Write With AI and The Dan Koe — is only 0.39.
Step 3: Opinion Evolutions on Substack
Each writer had their own trajectory. Key evolutions:
- The Sequence: Aggregation → Deep insight and platform segmentation. Became a multi-stream intellectual engine.
- AI Snake Oil: Reactive critiques → Proactive theorizing. Evolved into the gold standard of AI criticism.
- Elevate (Addy Osmani): Broad self-dev → Engineering practice → AI-enhanced workflows. Merged growth mindset with grounded engineering wisdom.
- One Useful Thing (Ethan Mollick): AI skepticism → Embrace → Systems-level commentary. A rare example of academic agility.
Shared Trends: They all went through the evolution: Curiosity → Frameworks → Tools → Strategy → System-oriented.
Step 4. Deep Dive: "Cast AI Models Like a Team"
Despite coming from different domains — engineering, content, orchestration, business — they all share one foundational belief:
AI models should be treated as specialized team members, not interchangeable tools.
The shared principles are:
- Role-Based Specialization: Models are assigned tasks based on unique strengths, not generic utility.
- Workflow Integration: Emphasis on structured pipelines, not one-off prompts.
- Quality via Orchestration: Cross-checks, verification, and layered use yield better results.
- Cost-Consciousness: Resource allocation matters — premium models only when they add value.
It turns out I wasn't alone. Nearly everyone who works deeply with multi-model AI ends up independently framing their approach this way. I truly believe:
The metaphor of "casting AI like a team" isn't just useful. It's inevitable for serious builders.
Reflection: What This Means for Builders
When I was first accused of copying, I felt defensive. But after digging into this analysis, I'm genuinely grateful for the nudge.
It pushed me into a space I'd long been curious about. It forced me to examine not just ideas, but how they evolve, how opinions converge, then diverge in use, application, and nuance.
More than anything, it taught me to approach other people's work with greater respect and intellectual rigor.
The truth in newsletter growth: Even Ethan Mollick's earliest posts had just a handful of likes. Most of these now-prominent newsletters took months, sometimes years, to gain traction.
What struck me the most: If dozens of top AI minds independently converge on the same concept, it doesn't mean any of us are unoriginal. It means the idea is strong.
When the best practitioners all land in the same place, pay attention.