How AI Actually Works at Startups vs. Enterprises, Plus Practical Guides for BOTH

We’re all so busy trying to keep up with AI, we sometimes don’t stop to think.

As far as I know, no one has taken the time to study how AI adoption patterns are unfolding differently at small companies vs. large companies in detail—not with the goal of picking sides, but with the goal of understanding the underlying patterns and learning what it can tell us about where the AI revolution is going.

I've spent months looking for a thoughtful comparison of how AI actually works at small startups versus large enterprises. It doesn't exist. What we have instead are two parallel conversations that rarely intersect—startup founders describing their AI-native workflows as if enterprise constraints don't matter, and enterprise leaders discussing governance frameworks as if startup velocity isn't real.

The gap isn't from hostility; it's from living in different realities. When Dan Shipper at Every builds entire products through conversations with Claude, and Sarah at her 400-person SaaS company spends six months getting GitHub Copilot approved, they're both being rational. They're just optimizing for completely different things under completely different constraints.

I've been in both worlds. I've watched founders assign Linear tickets to AI agents that work overnight while they sleep. I've also watched enterprise teams build elaborate approval processes for AI tools while their developers secretly use ChatGPT through their browsers anyway. Both approaches make sense once you understand the context, but nobody's actually explaining that context or what each side could learn from the other.

What follows is that missing comparison. Here's what you'll find:

1. How AI-native startups actually work - Building through conversation, not code

2. The enterprise reality - Why 6 months for Copilot approval is sometimes the best option

3. Technical stacks - Specific tools, costs, and why they diverge

4. Daily workflows - From overnight AI agents to AI sprints

5. Real costs - $10-15K/month in credits vs. enterprise contracts

6. 5-minute quick starts - What to do today, regardless of company size

7. Culture shifts - Code ownership, team dynamics, institutional memory

8. Integration problems - Greenfield vs. legacy from 2008

9. What works - Patterns that succeed everywhere

For enterprise readers who need more than quick starts, I've compiled an 88-page implementation guide that translates startup innovations into enterprise-ready approaches. It covers tools that pass security review, governance that enables rather than blocks, and change management that works with senior engineers who've seen every tech trend come and go. It's the detailed roadmap for organizations that can't just rebuild everything from scratch.

The reality is that both startups and enterprises have figured out important pieces of the AI puzzle. Startups understand velocity and possibility. Enterprises understand sustainability and scale. The future belongs to companies that can learn from both approaches. But first, we need to see clearly what's actually happening in each world, not the simplified versions we assume from the outside.