Startup Dragons

Ideas Spread Like Viruses
Marketing loves the idea of "influencers"—a small number of hyper-connected people who can tip an idea into mass adoption. Find the right connector, the story goes, and you unlock exponential reach.
Network science says otherwise.wise.
Duncan Watts and Peter Dodds modelled thousands of scenarios and found that large cascades are rarely driven by influential individuals. Instead, they're driven by a critical mass of easily influenced people—and by the structure of the network itself.
Topology matters more than celebrity. Weak ties matter more than strong ones (for novel information). Structural virality—how an idea branches and replicates through a network—predicts spread better than follower counts.
This inverts conventional marketing wisdom. You don't need to find influencers. You need to design ideas that replicate well in normal network structures.

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Corporates are using AI wrong, using automation to make the same dumb process faster & cheaper. They want “organization on autopilot.” just with fewer headcount
Its a trap: they’re setting bad processes in concrete when AI actually enables liquid, adaptive processes.

Why “Just Ask ChatGPT” Doesn’t Cut It
When I explain the discovery problem to executives, the most common response is:

“Can’t we just ask ChatGPT (or Claude, or our AI consultant) for ideas about where to use AI?”

You can. And you’ll get something useful.

But here’s what you won’t get:

1. Multi-Perspective Debate
ChatGPT gives you one model’s best guess based on generic business patterns. What happens when different perspectives conflict?

Operations wants automation → save time, reduce errors
Revenue wants human touchpoints → preserve upsell opportunities
HR worries about morale → team finds this work meaningful
A single AI can’t genuinely debate itself. It will pick one angle or try to satisfy all (which usually means generic advice that fits nobody perfectly).

2. Explicit Rebuttals
An idea that sounds brilliant in isolation often falls apart under scrutiny:

“Automate customer support” → sounds great until you realize your VIP customers value the human relationship
“Use AI to write marketing copy” → efficient until you discover it can’t capture your brand voice
“Deploy AI code review” → helpful until it flags 300 false positives and teams stop trusting it
You need something that actively tries to break ideas, not just propose them.

3. Rejected Alternatives
The most valuable insight is often: here’s what we considered and killed, and why.

When a consultant recommends three AI initiatives, the real question is: “What were the other 20 you didn’t recommend, and why didn’t they make the cut?”

4. Trade-Off Visibility
Every AI opportunity has trade-offs:

Speed vs. Quality
Cost Savings vs. Employee Morale
Automation vs. Flexibility
Compliance vs. Innovation
Single-agent AI tends to optimize for one dimension. Real businesses operate in multi-dimensional constraint spaces.

The Science: Why Multi-Agent Reasoning Works
Andrew Ng—one of the most respected voices in AI—has published research on what he calls “agentic workflows.” The findings are striking:

“For coding tasks, GPT-4 alone scores around 48%, but agentic workflows can achieve 95%.”
That’s not a marginal improvement. That’s nearly doubling performance.

And critically, this isn’t about using a “better” model. It’s about using multiple agents that iterate, reflect, and debate.

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Measure their SPEED
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