If the movie 300 taught us anything, it’s that numbers aren’t everything. The Spartans earned their place in history not by fielding the biggest army, but by fielding the most capable, cohesive, and strategically equipped one.
This isn’t a romanticised call for small teams at all costs. It’s about the idea that with the right people, systems, and tools, smaller can be faster, more focused, and (in today’s AI-powered world) capable of competing with far larger organisations.
That’s exactly what Shawn Swyx W Tiny Teams concept is all about, and it might be NZ’s most natural advantage in the global productivity race.
What Tiny Teams Are (and aren’t)
Per Shawn's article, the definition of Tiny Teams is that they generate more millions in Annual Recurring Revenue (ARR) than they have employees, for example, a startup making $5 million ARR with only 4 employees, meaning each person generates over $1 million a year.
Efficiency is the real measure of success, not customer count, spending, or product lines. The biggest brake on performance in most businesses is not money, tools, or talent, it is trust, communication, and over-engineered processes.
When trust is low, decisions slow down, extra checks pile up, and progress stalls under second-guessing.
Poor communication leads to misalignment, duplicated work, and costly delays.
Over-engineered processes add more layers of approval and complexity than the work requires, turning simple tasks into slow, resource-heavy projects.
High-trust, high-clarity teams remove that drag. Information flows freely, decisions happen quickly, and people take ownership without constant oversight. In this environment, smaller teams move faster, and speed wins. That is why small, well-aligned teams can outperform larger, better-resourced organisations: they eliminate the human friction that slows everyone else down.
This isn’t a call out to “just make the team smaller". It’s a paradigm shift of organisational redesign for how we organise talent, workflows, and technology in an AI era.
Key distinction: Tiny Teams are small because it makes them better, not because someone cut headcount to save money. This is about capability design, not cost-cutting. If you cut headcount without reinvesting in systems, capability, and AI integration, you are not a Tiny Team. You are just a smaller, weaker team. The model is about capability design, rather than stripping resources and hoping output stays the same.
Examples that prove it works
The below are real companies, many of them AI-native from day one, rewriting the rules of organisational scale:
Gamma → an AI presentation tool. 50M users with just 30 people. Powered by generalists (people who can switch between roles), player-coaches (leaders who still do the work), and a “small tribe” culture where everyone shares context daily. Profitability for 15+ consecutive months.
Bolt.new → an AI code generation tool. $20M ARR in 60 days with 15 people. Success came from ruthless prioritisation, focusing on the top 10% of work that drives results and letting other fires burn until they matter.
Gumloop → an AI workflow automation tool. On track to be a “10-person unicorn.” Uses product-led hiring (bringing in top customers as staff) and AI throughput to multiply each person’s output.
MidJourney → an AI image generation tool. $200M ARR with just 11 people.
Cursor → an AI code editor. $100M ARR with 20 people.
Yes, not every small team will become a Gamma or a MidJourney. But the point is not that every team will hit unicorn status, it is that the best performing teams in the AI era share the same traits. If you want to increase your odds of success, copy the models that work.
Why These Tiny Teams Win
Not every business can run with 10 people. But every industry has high leverage roles and processes that can be stripped back, automated, or redesigned so more of the headcount is doing high value work. And, a whole business can be made up of Tiny Teams, this does not mean your whole business has to necessarily be a single Tiny Team.
These companies are lean on purpose. Their small size is a design choice. In traditional companies, AI is often an afterthought: a tool one department experiments with while everything else runs the old way. In these teams, AI is woven into the operating model so that every person produces outsized impact.
What they do differently:
AI as a teammate: AI agents own full workflows like research, compliance checks, customer support, and data analysis.
AI-augmented roles: Every person works with AI co-pilots that accelerate their daily tasks.
Fewer layers: Flat, networked structures reduce the cost and delay of handovers.
Output without headcount creep: Doubling workload doesn’t mean doubling payroll. Automation and AI-native processes absorb most of the lift.
This is the formula: a small group of high-trust, multi-skilled operators, amplified by AI systems, producing work at the scale of teams many times their size.
The Pre-AI Era Excuse
A lot of businesses will say, “We can’t operate like that, we were built before AI". That mindset kills innovation before it starts. The truth is, companies built decades before the internet have already retooled to thrive in the digital era, and the same can be done for AI.
Walmart, founded in 1962, rebuilt its supply chain to run on real-time inventory, cloud analytics, and predictive models.
Samsung, founded in 1938, transformed over decades from a small trading company into a global technology leader with AI-driven manufacturing and smart devices.
John Deere, founded in 1837, evolved into a precision agriculture powerhouse using IoT, satellite data, and machine learning.
These businesses both survived the internet era and used it to their advantage to build entirely new capabilities. The AI shift is the same game with higher stakes.
Three First Moves for Pre-AI Companies
Yes, cleaning data, changing workflows, and shifting culture to build an AI-native business is hard. That is the point. The first three moves are about creating momentum and showing proof of value so the rest of the organisation follows. Big changes start small or they do not start at all. These three moves create the foundation for AI to do the heavy lifting:
Fix your data: Clean, connect, and govern it so AI has quality fuel to run on.
Rebuild key workflows: Start with one or two processes and redesign them so AI makes the first move and humans handle exceptions and judgment calls.
Level up the team: Build AI fluency so every person knows how to work with automation and agents, not just a few specialists.
Do this and you’ll be ready to scale output without scaling headcount, which is the crux of the Tiny Teams advantage.
Why NZ Is Built for This
Small market size does not stop you from building globally competitive companies. It forces you to think global from day one. If anything, our size trains us to do more with less, which is exactly what wins in a Tiny Team model.
New Zealand is already wired for Tiny Teams, and in an AI-powered world, that’s an advantage we can turn into a global edge.
Lean by default: We’ve always done more with less. Smaller markets and budgets mean we’ve had to be scrappy and resourceful.
Generalist talent pool: Many NZ operators wear multiple hats, blending skills in sales, product, tech, and customer work.
Speed over ceremony: We’re less burdened by rigid corporate hierarchies, making decision cycles shorter and faster.
The Tiny Teams Playbook
Here’s what the best-performing small, AI-native teams have in common, spoken in plain translation for non-technical readers:
1. Hiring: Assemble Your Spartans Hire right or not at all.
In small teams, one wrong hire has a big impact. Wait until you are 100% certain.
Product-led hiring. Your best next hire might already be a customer. They know the product, the market, and the problem you solve.
Work trials. Short-term paid projects reveal if someone can actually do the work and if they enjoy your culture.
Top of market pay. One exceptional person can achieve more than three average hires. Pay accordingly.
Low ego, high agency. Look for people who solve problems without being told and who prioritise results over credit.
2. Culture: Build a Small, Strong Tribe Player-coach leaders.
Managers who still “play the game” (write code, run client calls) stay close to reality and make better decisions.
Living culture deck. A constantly updated set of values and working norms so everyone knows “how we do things here.”
Radical transparency. Progress, finances, and decisions are visible to everyone. This builds trust and speed.
User obsession. Everyone, from engineers to founders, talks to customers directly.
3. Operations: Remove Friction Minimal meetings.
Meetings should be the exception, not the default.
Let some fires burn. Not every problem needs solving immediately. Focus on what drives results.
Async-first communication. Written updates and short videos keep things moving without waiting for everyone to be in the same room.
AI Chiefs of Staff. AI tools that take care of admin, research, and prep so humans focus on judgment and creativity.
4. Technology and Product: Keep It Simple Boring tech stack.
Use proven, stable systems. Avoid chasing every new tool.
Feature flags. Test changes on a small group before rolling them out to everyone.
Clean, modular code. Keeps products easier to update and easier for AI to work with.
UI and UX obsession. The design and usability often matter more to customers than adding more features.
Start small. Launch something simple, then improve it fast.
5. AI as Force Multiplier: Automate entire roles. Not just tasks.
Let AI run whole workflows from end to end.
Start with easy wins. Repetitive, predictable work is the best automation entry point.
Treat AI like a teammate. Give it defined responsibilities and judge it on outcomes.
Yes, small teams can burn out if designed badly. That is why culture, hiring, and workflow design matter as much as the tools. The aim is to remove the low value work and build a high trust environment so people can do their best work without running themselves into the ground.
The Reframe for NZ
This isn't about fewer jobs, it's about building teams differently from the ground up. Our challenge is in designing AI-native teams where humans and AI each do what they are best at. That is how NZ competes globally without needing Silicon Valley sized payrolls.
If the Spartans could hold the line with 300, imagine what NZ could build with 10, armed with AI.
The Skillsets That Will Define NZ’s Tiny Teams
Winning with Tiny Teams means mastering a small set of high-leverage AI capabilities.
AI Fluency: Understanding how AI works, where it adds value, and how to integrate it into everyday workflows so it becomes a natural part of how the team operates.
Vibe Coding: Rapidly prototyping and iterating with AI to create functional solutions without needing to be a full-time engineer, turning ideas into working tools in hours, not months.
AI Automation: Knowing what to automate, when to automate it, and how to connect AI tools into full, end-to-end workflows that run with minimal human oversight.
Agents: Deploying and managing specialised AI “teammates” that own entire processes, from intake to delivery, so human effort is reserved for creativity, judgment, and relationship building.
The companies that win moving forward will be small in number, big in capability, and fluent in applying these skills to make AI work for them at scale.
Written by Mike ✌
Passionate about all things AI, emerging tech and start-ups, Mike is the Founder of The AI Corner.
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