Daom is now live, headquartered in New Zealand, and working with SaaS founders globally. This post is the story of why it exists, the thesis behind it, and what I saw over 10 years of operating inside SaaS companies that led me here.

The Pattern I Kept Seeing

I have spent the last decade operating inside SaaS companies—from managing a team of 20 at ProjectManager.com to driving $1M+ in sales at Vidello. I have held CMO, CTO, and CEO roles at companies I have built, and consulted on 60+ projects along the way. I kept seeing the same pattern. Companies hired for roles that did not need to be roles. Content teams producing work that followed templates. Reporting analysts generating dashboards nobody acted on. Support staff answering questions that could be handled by systems. Coordinators coordinating other coordinators.

The overhead was enormous. And the companies that grew fastest were never the ones with the biggest teams. They were the ones with the leanest teams and the best systems.

What Changed

AI changed everything. Not gradually—rapidly. The roles I watched companies over-hire for across those 60 projects are now the exact roles AI handles better, faster, and at a fraction of the cost. Content production, data analysis, customer support, design iterations, even significant portions of software development.

The companies that will define the next decade are not those with the most people. They are the ones that figured out how to operate with the fewest.

The average seed-stage SaaS team has already dropped from 10 people to 6. AI-native startups are targeting 300% better revenue per employee than traditional SaaS. The shift is not coming—it is here.

The Thesis

Daom exists because I believe small, AI-augmented teams will outperform large traditional ones. A team of three—a Strategist, a Builder, and an Operator—supported by AI systems can ship faster, operate leaner, and compete like a company ten times its size.

This is not theoretical. It comes from a decade of watching SaaS companies over-hire and understanding exactly which roles can be replaced by systems. Daom helps SaaS founders make that transition:

• Team & Operations Audit — mapping every role, workflow, and tool to identify what AI can replace and where you are burning money.
• AI-Native Product Build — shipping products with lean teams and AI-augmented development.
• Operational AI Replacement — building the systems that replace roles, not augment them.
• Restructuring & Transition — guiding founders through the 60-day process of going from a traditional org to an AI-native operating model.
• Post-Transition Operating Support — staying embedded to help run the lean model and scale without re-hiring.

Proving the Model at Communi

Communi is a community infrastructure platform for businesses—that is the product we are building. Along the way, we have applied our own AI-native strategies to run a lean team. AI replaced developers, and we decreased update delivery times by 1,000%. It is the model in practice, not theory.

What Comes Next

I built Daom in New Zealand after operating internationally since 2014 across markets in the US, UK, Europe, and Asia-Pacific. The thesis is simple: fewer people, faster companies. If you are a SaaS founder running a team of 10 to 50 people and wondering whether AI could let you do more with less, that is exactly the conversation I want to have.

You can book a free 30-minute strategy call, or download The AI Advantage—the full framework for restructuring your SaaS company around AI.

— Sam Bakker
Founder & CEO, Daom

The default SaaS playbook has a hiring problem. Raise a round, hire a team, scale headcount alongside revenue. It is how every SaaS company has been built for the last two decades. And it is the single biggest cost problem in the industry.

The Hiring Treadmill

Here is how it typically goes. A SaaS company hits product-market fit with a small team. Revenue starts growing. The board says hire. So the founder hires a content marketer, a designer, two more engineers, a data analyst, a customer success manager, and a marketing coordinator. Headcount doubles. Overhead triples. And the company starts running to stand still.

I have watched this pattern play out across more than 60 projects. At ProjectManager.com, I managed 20 people for one growth channel. The results were strong, but the overhead was enormous. The question I kept asking: how many of these roles are doing work that follows templates? The answer, consistently, was most of them.

Where the Over-Hiring Happens

SaaS companies over-hire in predictable places. Content production teams writing blog posts, social copy, and email sequences. Design teams producing variations of the same interface patterns. QA teams running test suites. Data analysts building dashboards. Support teams answering the same categories of questions. Coordinators managing the people doing the work.

None of this is bad work. It is necessary work. But it is work that follows patterns. And work that follows patterns is exactly what AI handles.

The average seed-stage SaaS team has already dropped from 10 people to 6. That is not a prediction—it is a trend already underway. AI-native startups are targeting 300% better revenue per employee than traditional SaaS. The companies that figure this out first will have a structural cost advantage that is nearly impossible for bloated competitors to match.

Why Founders Keep Hiring Anyway

If the math is this clear, why do SaaS founders keep over-hiring? Three reasons.

First, hiring feels like progress. Adding headcount is visible. It signals growth. It makes the board happy. Building AI systems that replace three roles is invisible and harder to explain in a quarterly update.

Second, the playbook is ingrained. Every SaaS blog, podcast, and advisor says the same thing: hire great people. The advice is not wrong in principle, but it fails to account for the fact that many of those hires are doing work that no longer requires a person.

Third, restructuring is hard. Cutting headcount is emotionally and operationally difficult. It is easier to keep paying for roles than to do the work of replacing them with systems. But easier is not sustainable.

What AI Actually Replaces

AI does not replace everything. It replaces specific categories of work: high-volume execution that follows patterns, data processing and analysis, content production, design iteration, testing, and first-line customer interaction. It does not replace strategic decision-making, relationship building, creative direction, or the kind of judgment that comes from deep domain expertise.

The distinction matters. The goal is not to fire everyone and let AI run the company. The goal is to identify which roles exist to do work that AI now handles better and restructure accordingly. In my experience, that is 40-60% of roles in a typical SaaS company of 10-50 people.

The Restructured Model

The alternative is a lean, AI-native operating model. Three people—a Strategist, a Builder, and an Operator—supported by AI systems handling the execution that used to require departments.

This is not a thought experiment. At Communi—a community platform we are building—we applied our own AI strategies to run lean. AI replaced developers and dramatically accelerated delivery. The subscription base is growing on a fraction of the headcount a traditional SaaS company would require.

If you are running a SaaS team of 10 to 50 people and your headcount keeps growing faster than your revenue, the problem is not your hiring process. It is your operating model. Book a free strategy call to talk through what restructuring could look like for your company.

— Sam Bakker
Founder & CEO, Daom

Most SaaS companies have an org chart full of job titles. An AI-native company has three roles. That is not a simplification for effect—it is the operating model that makes lean SaaS viable. Here is the framework and how to apply it.

The Traditional SaaS Org Chart Is Bloated

A typical Series A SaaS company has 15-30 people across engineering, product, design, marketing, content, sales, support, and operations. Each function has a lead, sometimes a team under them, and a budget. The org chart looks like a real company. But most of those people are doing work that follows patterns—work that AI now handles.

The AI-native model collapses those 15-30 people into three core roles. Not three departments. Three people.

Role 1: The Strategist

The Strategist owns the vision and makes the decisions AI cannot make. Product direction, market positioning, partnerships, fundraising, and the high-level choices that determine whether the company wins or loses. This is the founder or CEO in most cases.

The Strategist does not write code, produce content, or manage campaigns. They set the direction and evaluate outcomes. Everything between direction and outcome is handled by the other two roles and the AI systems supporting them.

What the Strategist replaces: the CEO who spends 80% of their time in meetings coordinating teams that should not exist.

Role 2: The Builder

The Builder ships the product. They work with AI to write code, design interfaces, test features, and deploy at a pace that used to require a 10-person engineering team. The Builder is technical, but their skill is not just coding—it is knowing how to leverage AI-assisted development to move fast.

AI handles the bulk of code generation, design iteration, testing, and documentation. The Builder provides the judgment: architecture decisions, quality standards, and the creative problem-solving that AI cannot do independently.

What the Builder replaces: a 5-10 person engineering team, a designer or two, and a QA function.

Role 3: The Operator

The Operator runs everything else. Marketing, support, analytics, content, email, social, reporting, scheduling—all powered by AI systems that this person orchestrates rather than does manually. The Operator is the systems thinker who builds and manages the AI workflows that handle the day-to-day execution.

This is the role I have spent the most time in across my career. The work I once did with large teams is now achievable by one person with the right AI stack—content production, email sequences, analytics, all orchestrated through AI systems.

What the Operator replaces: marketing team, content team, support team, data analysts, and operations coordinators.

What AI Handles

Everything else. Content production, data analysis, customer support triage, design assets, email campaigns, social media, reporting, scheduling, QA—these are systems, not people. The three roles direct these systems. The systems do the execution.

This is not theoretical. At Communi, we applied this model and the results speak for themselves—AI replaced developers and dramatically accelerated our delivery speed. The operation runs lean.

How to Apply the Framework

If you are running a SaaS company with 10-50 people, map every role to one of four categories: Strategist work, Builder work, Operator work, or AI-replaceable work. Be honest about the fourth category. In most companies, a significant share of current headcount is doing work that falls into it.

The transition does not happen overnight. It happens over 60 days with a structured plan. But it starts with accepting that the org chart you have is built for a world that no longer exists.

The full framework for restructuring your team is in The AI Advantage—a free guide covering all six chapters of the AI-native operating model. Or book a strategy call and I will walk you through how it applies to your specific team.

— Sam Bakker
Founder & CEO, Daom

The two SaaS companies where I spent the most time—ProjectManager.com and Vidello—taught me the same lesson from completely different angles. The best work happened with the smallest teams. And the biggest teams created the most waste. Here is what those experiences look like through the lens of AI-native operations.

ProjectManager.com: 20 People Doing What 3 Could Do Now

At ProjectManager.com, I came on as a Growth Consultant and managed a team of 20 people building and executing an SEO growth strategy. We drove a significant increase in traffic and subscriptions within the year. By any traditional measure, it was a success.

But here is what I kept noticing. Of those 20 people, most were doing work that followed clear patterns. Content writers producing articles against keyword briefs. Analysts pulling traffic reports. Coordinators managing timelines. Link builders executing outreach templates. The strategic thinking—which keywords to target, what content angles to pursue, how to structure the overall growth engine—was done by maybe three people. Everyone else was executing.

Today, AI handles that execution. Content production from keyword brief to published article. Traffic analysis with automated insights. Outreach at scale. Reporting dashboards that update themselves. The 20-person team I managed would be a 3-person team with the right AI stack. Same output. A fraction of the cost.

Vidello: Where Lean Actually Worked

Vidello was a different experience. I came on as Marketing Director and Managing Partner. We launched a new product called Create and generated over $1M in sales while dramatically growing subscription revenue. But what made Vidello instructive was how much we accomplished with how few people.

The team was small. We moved fast. I wore multiple hats—strategy, marketing, partnerships, product decisions. There was no marketing department to manage. There was me, making decisions quickly and executing across channels.

The results were better than most of the larger teams I had worked with. Not because we were better individually, but because there was no coordination overhead. No alignment meetings. No waiting for approval chains. When we saw an opportunity, we acted on it the same day.

This is the core insight that led to Daom: lean teams are not a compromise. They are an advantage. And AI makes that advantage dramatically larger.

The Pattern Across 60+ Projects

ProjectManager.com and Vidello are the clearest examples, but the pattern repeated across every project I worked on. The companies with the best outcomes were never the ones with the most people. They were the ones with the least friction between decision and execution.

Large teams create friction. They require meetings to align, managers to coordinate, processes to standardise, and reporting structures to keep everyone informed. Every additional person adds communication overhead that slows the whole system down.

AI eliminates the need for most of that coordination because it eliminates the need for most of those people. Not the strategic thinkers. Not the creative decision-makers. Not the relationship builders. But the executors following templates, the analysts running reports, the coordinators managing workflows—those roles are now systems.

What This Means for Your Team

If you are running a SaaS company with 10 to 50 people, look at your org chart through this lens. How many of your people are doing strategic work that requires human judgment? And how many are doing execution work that follows patterns?

The second category is your restructuring opportunity. Every role in it is a candidate for AI replacement. Not in theory—in practice, right now, with tools that exist today.

At Communi—a community platform we are building—we applied this model and AI replaced developers, dramatically accelerating delivery. The subscription base is building on a fraction of the headcount a traditional SaaS company would need.

If you want to see the full restructuring framework, download The AI Advantage—or book a free 30-minute strategy call and I will walk you through how it applies to your company.

— Sam Bakker
Founder & CEO, Daom

There is one metric that tells you immediately whether your SaaS company has a team structure problem: annual recurring revenue per employee. The best SaaS companies generate $300K or more per employee. Most generate under $150K. If your number is below the benchmark, your team is too big for your revenue. And AI is the fastest way to fix it.

The Benchmark That Matters

ARR per employee is not a vanity metric. It is a direct measure of operational efficiency. A company generating $3M in ARR with 10 people is fundamentally more efficient—and more valuable—than a company generating $3M with 30 people. The first company has room to invest in growth. The second is burning cash on headcount.

Top-performing SaaS companies at scale consistently hit $250K-400K in ARR per employee. AI-native startups are targeting even higher: $500K+ per employee by operating with lean teams and letting AI handle the execution.

If you are running a team of 20 and generating $2M in ARR, your ratio is $100K per employee. That means you are paying for people to do work that is not generating proportional revenue. Something is wrong with the operating model.

Where the Headcount Bloat Hides

The roles dragging your ARR-per-employee ratio down are predictable. They are the same roles I saw across 60+ projects over the past decade. Content teams, design teams, QA engineers, data analysts, marketing coordinators, customer support staff, and operations managers. These people are doing necessary work—but it is work that follows patterns, and patterned work is what AI replaces.

Run this exercise on your own company. List every employee. For each one, ask: is this person making decisions that require human judgment, or are they executing work that follows a process? If the answer is process execution, that role is a candidate for AI replacement.

In most SaaS companies of 10-50 people, the majority of roles fall into the process execution category. That is your restructuring opportunity.

The Math of Restructuring

Let us say you are a 20-person SaaS company doing $3M in ARR. Your ARR per employee is $150K. You restructure around AI: cut from 20 to 8 people, replacing 12 roles with AI systems. Your AI tooling costs you $50K per year. Your fully loaded payroll drops from $2M to $800K.

Your ARR per employee jumps to $375K. Your burn rate drops by more than half. Your runway extends dramatically. And because AI systems scale without additional headcount, your ratio keeps improving as revenue grows. Every new dollar of ARR flows to the bottom line instead of funding another hire.

This is why ARR per employee is the metric that matters most in 2026. It reveals whether your operating model is built for the AI era or stuck in the hiring treadmill.

Why Founders Resist the Math

The math is simple. The execution is hard because it means cutting people. That is uncomfortable. Every founder I talk to understands the numbers but hesitates on the action. The excuses are always the same: we need the team for the culture, we cannot cut that fast, what if the AI does not work as well?

These concerns are not irrational. But they are solvable. The transition happens in phases over 60 days. You start with the clearest AI-replaceable roles, prove the systems work, then continue. You do not fire everyone on day one. You restructure deliberately.

How to Calculate Yours

Take your current ARR. Divide by total headcount including contractors. That is your number. If it is below $200K, you have significant restructuring opportunity. If it is below $150K, you are actively losing money to headcount bloat. If it is above $300K, you are already operating efficiently—but there may still be roles that AI could handle.

The goal for an AI-native SaaS company is $400K+ per employee. At that level, you are running lean, shipping fast, and keeping margins that let you grow without constantly raising capital.

Want to see how your ARR per employee compares and what restructuring could look like? Book a free 30-minute strategy call and I will run the analysis with you. Or download The AI Advantage for the complete restructuring framework.

— Sam Bakker
Founder & CEO, Daom

When you are running a 3-person SaaS team, your AI stack is not a nice-to-have. It is the infrastructure that replaces 12 people. Every tool decision matters. Here is the exact AI infrastructure that makes a lean team viable for building and scaling SaaS products.

The Stack Categories

A 3-person AI-native team needs AI covering five functions: development, design, marketing and content, operations, and customer interaction. Each function used to require dedicated people. Now it requires the right tools configured the right way.

Development: AI-Augmented Coding

Your Builder needs AI that can write, review, and debug code. The current best-in-class options are AI coding assistants that integrate directly into the development environment. They handle code generation from descriptions, refactoring, test writing, documentation, and debugging. A single Builder with these tools can ship features at the pace of a 5-person engineering team.

The key is not just having the tools but knowing how to prompt them effectively and knowing when the AI output needs human review. Architecture decisions, security considerations, and edge case handling still require human judgment. Everything else—the implementation grunt work—AI handles.

Design: AI-Assisted Visual Work

Interface design, asset creation, and design iteration are now heavily AI-assisted. AI design tools can generate UI components from descriptions, create variations for A/B testing, produce marketing assets, and iterate on designs based on feedback. The Builder or Operator can handle design work that used to require a dedicated designer.

The quality is good enough for production. Not perfect—but good enough to ship, test, and iterate. And iteration speed matters more than initial pixel perfection.

Marketing and Content: AI-Powered Growth

This is where the Operator leverages AI most heavily. Content production from strategy to published article. Email sequences written and optimised. Social media content generated. SEO analysis and keyword research automated. Analytics dashboards that surface insights automatically. At Communi, we applied this model—AI replaced developers and dramatically accelerated delivery.

The Operator sets the strategy—what topics to cover, what audiences to target, what channels to prioritise. AI executes the production. One person replaces an entire marketing team.

Operations: Workflow Automation

AI workflow tools connect your systems and automate the operational tasks that would otherwise require coordinators and operations managers. Customer onboarding sequences, data processing, reporting, scheduling, and administrative tasks all run through automated systems that the Operator configures and monitors.

Customer Interaction: AI-First Support

AI handles first-line customer support through chatbots and email classification. It drafts responses, categorises issues, and escalates complex problems to humans. For a lean team, this means customer support scales with your user base without scaling headcount.

The Total Cost

A comprehensive AI stack for a 3-person team runs approximately $2,000-5,000 per month depending on usage. Compare that to the $150,000-250,000 per year you would spend on each additional employee. The math is not close.

The real cost is not the tools. It is the expertise to configure and orchestrate them effectively. That is what makes the 3-person model work—not just having AI tools, but knowing how to build systems with them.

Want a detailed breakdown of the specific tools and configurations? Download The AI Advantage or book a free strategy call to discuss your specific stack needs.

— Sam Bakker
Founder & CEO, Daom

I talk to SaaS founders every week who want to restructure their companies around AI. Some of them are ready. Most of them are not. And that is fine, because the worst thing you can do is restructure prematurely. You end up cutting people you still need and deploying tools you do not yet understand.

After working across 60+ projects and seeing what actually works, I have distilled the readiness question into five honest diagnostics. If you run a SaaS company with 10 to 50 people, answer these before you start making cuts.

Question 1: Do You Have Roles Doing Pattern-Based Work?

Look at your org chart. How many people spend their days on tasks that follow a repeatable pattern? Writing first-draft content. Pulling reports. Answering the same support tickets. Processing data from one format into another. Scheduling and coordinating across teams.

If more than 30 percent of your headcount is doing pattern-based work, you have a restructuring opportunity. AI handles patterns exceptionally well. It does not get bored, it does not make inconsistent decisions, and it works around the clock. But if most of your team is doing judgment-heavy, relationship-driven, or genuinely creative work, AI restructuring will not move the needle much yet.

Question 2: Is Your ARR Per Employee Below $200K?

This is the efficiency metric I look at first. If you are generating less than $200K in annual recurring revenue per employee, your team is probably too large for your current revenue. That is not a criticism of the people. It is a structural issue. You have built an organization sized for a company you have not become yet.

Top SaaS companies benchmark $250K-400K in ARR per employee. AI-native teams can target $500K+ by compressing headcount. If you are below $200K, restructuring around AI could dramatically improve your unit economics.

Question 3: Are You Hiring to Solve Problems AI Could Handle?

This one catches a lot of founders. You have a bottleneck, so you open a role. It feels like the obvious solution. But before you post that job listing, ask yourself: could an AI system, an automation, or a workflow handle 80 percent of what this role would do?

If the answer is yes, you are about to add $80K to $120K in annual cost for something that could cost you $200 a month in AI tooling. That is not a minor difference. That is the difference between a company that scales efficiently and one that scales expensively.

Question 4: Can Your Founder or Builder Ship Without Layers of Approval?

AI-native companies move fast because decisions happen at the edge, not through committees. If shipping a feature, launching a campaign, or fixing a bug requires sign-off from three people and a meeting, you have a management structure that will resist restructuring.

The companies that restructure successfully are the ones where the founder or a senior builder can go from idea to shipped product in days, not weeks. If you have that capability, you are ready. If every decision goes through layers, you need to flatten the org first.

Question 5: Is Your Burn Rate Tied to Headcount Growth?

Pull up your P&L. What percentage of your monthly burn is people cost? For most SaaS companies, it is 70 to 85 percent. Now ask: does your burn rate go up every time you try to grow? If revenue growth requires proportional headcount growth, you do not have a scalable model. You have a services business disguised as a software company.

AI restructuring breaks this pattern. It lets you grow revenue without growing headcount proportionally. But only if you are willing to fundamentally rethink how work gets done, not just bolt AI onto existing processes.

The Honest Assessment

If you answered yes to four or five of these questions, you are a strong candidate for AI restructuring. Three, you are close but need some foundational work first. Two or fewer, you probably need to focus on product-market fit, revenue growth, or organizational clarity before restructuring will deliver results.

Not every company is ready, and that is not a failure. Sometimes you need to build the right foundation first. But if you are ready, the window is now. The companies restructuring today will have an 18-month head start on the ones who wait.

If you scored well on this diagnostic and want to talk about what restructuring looks like for your specific situation, book a free strategy call. Or download The AI Advantage for the full framework.

I have worked on 60+ digital projects over the past decade. I have watched companies attempt AI transformation from the inside and from the outside. The pattern is consistent: most of them fail. Not because AI is not good enough. It is. They fail because they approach restructuring in ways that guarantee failure from the start.

Here are the five patterns I see over and over. If you recognize your company in any of these, stop and course-correct before you waste another quarter.

1. Trying to Augment Instead of Replace

This is the most common and most expensive mistake. The company decides to "augment" existing roles with AI instead of asking the harder question: does this role need to exist at all?

Augmentation sounds safe. It sounds like you are being responsible. But what it actually means is you are paying full salary for someone to do 20 percent of their former job while AI does the other 80 percent. You end up with the same headcount, the same burn rate, and a bunch of people who are now underutilized and demotivated because their work has been hollowed out.

The companies that succeed are the ones willing to ask the uncomfortable question: if AI can do this task, do we need a person in this seat? Sometimes the answer is yes, because the judgment and relationships matter. Often the answer is no.

2. Keeping Management Layers

When you cut from 20 people to 5, you do not need three layers of management anymore. But I consistently see companies reduce their individual contributors while keeping all the managers. The result is an inverted pyramid: more people managing than doing.

AI-native companies are flat. The founder or operator is close to the work. There might be one layer between the CEO and the person shipping. That is it. If you restructure your team but keep your management hierarchy, you have not restructured. You have just made everyone's job title less meaningful.

3. Moving Too Slowly

Some companies treat AI restructuring like a traditional change management program. They form a committee. They hire a consultant. They spend three months on an assessment. Six months on a pilot. Twelve months on a phased rollout. By the time they have actually restructured, the AI tools they evaluated at the start are two generations old.

Restructuring should happen in 60 days, not 12 months. Identify the roles that can be replaced. Deploy the AI systems. Redistribute the judgment-based work to your best people. This is not reckless. This is decisive. The companies that drag this out burn cash the entire time and lose their best people to uncertainty.

4. Not Having a Technical Co-Founder or Builder

You cannot restructure around AI if nobody on your leadership team can actually build with AI. This is non-negotiable. If your CEO is a pure strategist who delegates all technical decisions, the restructuring will be led by people who do not understand the tools they are deploying.

At Communi—a community platform we are building—I operate at the intersection of strategy and execution. I set the direction and I build the systems. That is the model that works. You need someone at the top who understands both what AI can do and how to implement it, not someone who reads about it in newsletters and asks their team to figure it out.

5. Treating AI as a Cost Center Instead of an Operating Model

The final failure pattern is companies that see AI as a line item. They budget for AI tools the same way they budget for software subscriptions. But AI is not a tool you add to your existing operating model. It is the operating model.

When you restructure correctly, AI is not a cost you are managing. It is the foundation of how your company works. Your content is AI-generated and human-edited. Your support is AI-handled and human-escalated. Your analytics are AI-processed and human-interpreted. The entire operating model changes. Companies that treat AI as an expense to minimize will always lose to companies that treat it as infrastructure to build on.

The companies that succeed at restructuring have a builder-operator at the top, move fast, and redesign work from the ground up. If you want to avoid these failure patterns, book a free strategy call or download The AI Advantage for the complete framework.

There is a specific type of person who thrives in the AI-native era. It is not the strategist. It is not the manager. It is the builder-operator: the person who can set the direction and then build the thing themselves. I know this because I have lived it.

Over the past decade, I have held CMO, CTO, and CEO roles at companies I have built—and consulted on 60+ SaaS projects along the way. The trajectory tells a clear story: fewer people, more output, better results. Now at Communi and Daom, I run the strategy and build the systems myself.

The Old Model Is Dead

The traditional SaaS company had a clear hierarchy. The CEO set strategy. VPs translated strategy into plans. Directors managed teams. Individual contributors did the work. Information flowed down, results flowed up, and the whole machine was held together by meetings, Slack messages, and project management tools.

That model worked when execution required human hands at every level. Writing code required developers. Creating content required writers. Running campaigns required marketers. Analyzing data required analysts. You needed the people, so you needed the layers to manage them.

AI changes the equation. When a single person with the right AI systems can do the work of five, you do not need the management layers that existed to coordinate those five people. The org chart collapses. And the person left standing at the center is the operator who can both decide what to build and build it.

What Is a Builder-Operator?

A builder-operator is someone who operates at the strategic level but never loses the ability to execute. They understand the market, the product, the customer, and the unit economics. And they can also sit down and ship a feature, launch a campaign, build an automation, or debug a system.

This combination used to be rare because the scope of execution required specialization. You could not be a great marketer and a great developer and a great analyst. The surface area was too large. AI compresses that surface area. With the right AI tools, a skilled operator can produce work across domains that previously required specialists in each.

At Communi—a community platform we are building—AI replaced developers and dramatically accelerated delivery. I do not build every system from scratch. AI helps me scaffold and iterate. I do not manually analyze every data point. AI processes it, and I interpret and decide. The result is that I can operate across the full stack of the business without a team of specialists between me and the output.

Why Strategists Alone Fail

The pure strategist, the person who produces frameworks and recommendations but depends entirely on others to execute, is in trouble in the AI era. Here is why.

AI changes the execution landscape so quickly that strategies built on assumptions about human execution timelines are obsolete before they are implemented. If your strategy assumes it takes two weeks to produce a landing page, but AI can do it in two hours, your entire project plan is wrong. The strategist who does not build does not feel this shift intuitively. The builder-operator does, because they are the one doing the work.

The feedback loop also tightens. When you can go from idea to shipped product in a day instead of a quarter, you do not need a 50-page strategy document. You need a hypothesis, an afternoon of execution, and the judgment to interpret the results. That is the builder-operator's workflow.

What This Looks Like in Practice

At Communi, the operating model is simple. It is a community platform—that is the product. I set the direction and build the systems to execute it, using AI as my primary tool. AI replaced developers and dramatically accelerated our shipping speed. There is no team of implementers between my decision and the output.

A blog post goes from concept to published in hours, not days. A feature goes from idea to deployed in a day, not a sprint. A campaign goes from strategy to live in a week, not a month. This is not because I am working 16-hour days. It is because the AI-amplified builder-operator eliminates the coordination overhead that slows down traditional teams.

The Implication for Founders

If you are a founder who can build, the AI era is your moment. You have never had more leverage. The gap between what a skilled operator can produce with AI and what a traditional team can produce with headcount is widening every month.

If you are a founder who has always delegated execution, you have a choice: learn to build, find a co-founder who can, or accept that you will be outpaced by founders who do both.

I work with SaaS founders who are ready to adopt the builder-operator model. Book a free strategy call to talk about what this looks like for your company. Or download The AI Advantage for the full framework.

Every SaaS founder I talk to asks the same question: which roles go first? They want a straight answer, not a philosophical debate about the future of work. So here it is. This is my honest assessment of which roles AI replaces first, which stay human, and how to evaluate any role in your company using a simple framework.

The Roles Going First

Content writers (first draft). AI writes first-draft content that is 80 to 90 percent as good as a mid-level writer. Blog posts, product descriptions, email sequences, social copy. The human role shifts from writing to editing and directing. One person with AI now produces what used to require a content team of three or four.

Data analysts (reporting and pattern recognition). If the primary job is pulling data, building dashboards, and identifying trends, AI does this faster and more consistently. The analyst role does not disappear entirely, but it shrinks dramatically. You need one person with strong business judgment interpreting AI-generated analysis, not a team of analysts producing it.

QA engineers (test case generation and regression). AI can generate test cases, run regression suites, and identify bugs with less human oversight than traditional QA processes. Manual QA for routine scenarios is being replaced. The remaining human QA work is judgment-based: edge cases, user experience assessment, and strategic testing priorities.

Customer support (first line). AI handles tier-one support tickets with increasing accuracy. Password resets, billing questions, feature explanations, troubleshooting common issues. The human support role moves to tier-two and above: complex problems, emotional situations, and relationship management.

Design iteration. AI generates design variations, mockups, and iterations at speed that no human designer can match. The designer's role shifts from production to creative direction. One senior designer with AI tools now produces more output than a team of three junior designers.

Marketing coordinators and scheduling. Campaign scheduling, social media posting, email sequencing, report generation. These coordination tasks are almost entirely automatable. The marketing team shrinks to strategists and creative directors who set direction, with AI handling execution.

The Roles That Stay Human

Strategic decision-making. Deciding which market to enter, whether to raise capital, how to price the product, when to pivot. These decisions require judgment that integrates context AI cannot access: founder intuition, market relationships, risk appetite, and vision. AI can inform these decisions. It cannot make them.

Relationship building and sales. Enterprise sales, partnerships, investor relations, key account management. These are fundamentally human activities built on trust, rapport, and emotional intelligence. AI can assist with research and preparation, but the relationship itself is human to human.

Creative direction. Setting the brand voice, defining the visual identity, determining what story to tell and why. AI executes creative work. Humans direct it. The person who decides what to communicate and why it matters remains essential. The people who produce the assets become optional.

Domain expertise and judgment. Deep knowledge of a specific industry, regulatory environment, or technical domain combined with the judgment to apply that knowledge in ambiguous situations. AI can access information. It cannot replicate the intuition that comes from years of operating in a specific context.

Crisis management. When things go wrong, when a major customer threatens to leave, when a product launch fails, when a PR situation develops. These situations require human judgment, empathy, and the ability to make rapid decisions with incomplete information under pressure.

The Framework: Pattern-Based vs. Judgment-Based

Here is how to evaluate any role in your company. Ask one question: is this role primarily doing pattern-based work or judgment-based work?

Pattern-based work follows repeatable processes. Given input X, produce output Y. The steps are predictable, the quality criteria are definable, and the work can be evaluated objectively. AI excels at this. If 70 percent or more of a role is pattern-based, that role is a restructuring candidate.

Judgment-based work requires interpreting ambiguous situations, weighing tradeoffs with incomplete information, building relationships, and making decisions where the right answer is not obvious. AI cannot do this reliably. If a role is primarily judgment-based, it stays human.

Most roles are a mix. The key is not to preserve the entire role because 20 percent of it requires judgment. The key is to restructure the role so the human does the 20 percent that requires judgment and AI handles the 80 percent that does not.

If you are looking at your team and trying to figure out where to start, book a free strategy call. I will walk through your org chart with you and identify the restructuring opportunities. Or download The AI Advantage for the full framework.

When you are a 50-person company, picking the wrong tool wastes money. When you are a 3-person team, picking the wrong tool wastes weeks. Every decision about what to build, buy, or automate has outsized impact when there is no slack in the system. This is the framework I use at Communi and recommend to every lean SaaS team I work with.

The Three Options

Traditional companies think in terms of build vs buy. AI-native companies have a third option that changes the calculus entirely: automate with AI. Understanding when to use each is the difference between a lean team that moves fast and one that drowns in tools and technical debt.

Build Custom: Only When It Is Your Differentiator

Building custom software is expensive in time, attention, and maintenance. For a lean team, the decision to build should only happen when the thing you are building is your core product differentiator. If it is the thing that makes customers choose you over competitors, build it. Own it. Control every aspect of it.

At Communi, the community platform itself is the product. That is what we build. The engagement mechanics, the member experience, the creator tools. These are custom because they are the entire value proposition. If we bought a generic community platform and slapped our logo on it, we would have no competitive advantage.

The rule is simple: if a customer would not pay for it, do not build it. No customer is paying you for your internal project management system, your analytics dashboard, or your email infrastructure. Those are costs, not products.

Buy Off-the-Shelf: For Commodity Functions

For everything that is not a core differentiator and requires reliability at scale, buy. Email delivery? Use Postmark or SendGrid. Hosting? Use Vercel or AWS. Analytics? Use Mixpanel or PostHog. Payment processing? Stripe. There is no competitive advantage in building your own email server.

The mistake lean teams make is trying to save money by building commodity infrastructure. You are not saving money. You are spending your most valuable resource, your founders' time and attention, on problems that someone else has already solved better than you will. That $50 per month SaaS subscription costs less than the hour you would spend maintaining a custom alternative.

Buy the commodity. Protect your attention for what matters.

Automate with AI: The New Middle Ground

This is where it gets interesting. There is a growing category of functions that used to require either a custom build or a dedicated person running an off-the-shelf tool. AI creates a third option: automate it entirely.

Content production is the clearest example. You used to either hire a writer (expensive, slow) or use a content platform (still needs someone to operate it). Now, AI generates first-draft content that a founder can edit and publish directly. No writer. No content platform. Just AI and judgment.

Customer support is another. You used to either build a custom support system or buy Zendesk and hire a support person to run it. Now, AI handles tier-one support, and you only need a human for escalations. The tool cost drops, the headcount drops, and the response time improves.

Here are some of the functions where AI has accelerated my workflow:

Content creation. Blog posts, social media, email newsletters. AI drafts, I edit and direct. What used to require a content team of two or three is now a workflow that takes me a few hours per week.

Data analysis. Usage metrics, engagement patterns, churn indicators. AI processes the data and surfaces insights. I interpret and decide. No analyst required.

Design iteration. Landing page mockups, social graphics, UI variations. AI generates options, I select and refine. No junior designer required.

Code scaffolding. Boilerplate code, component templates, API integrations. AI writes the first pass, I review and customize. Cuts development time by 40 to 60 percent on routine builds.

The Decision Framework

For every function in your company, ask three questions in order:

Is this our core product differentiator? If yes, build it custom. You need to own and control it.

Is this a solved, commodity problem? If yes, buy the best off-the-shelf solution. Do not reinvent the wheel.

Can AI handle 80 percent of this with a human directing the other 20 percent? If yes, automate it. You do not need a tool or a person. You need a workflow.

The answer to question three is yes far more often than most founders realize. And every time you automate instead of hiring or buying, you keep your team lean, your costs low, and your speed high.

The Compound Effect

The real power of this framework is compounding. Each function you automate instead of staffing saves you $80K to $150K per year in salary, frees up management attention, and reduces coordination overhead. Multiply that across five or six functions and you have a team of three doing the work of fifteen, with better margins and faster execution.

That is the AI-native advantage. It is not about having better AI tools. It is about making better decisions about when to build, when to buy, and when to let AI handle it.

If you want help evaluating your own build-buy-automate decisions, book a free strategy call. Or download The AI Advantage for the complete framework on restructuring your SaaS company around AI.

People ask me frequently why I chose to base Daom in New Zealand rather than a more conventional tech hub. Having operated internationally from Hong Kong for years and worked with companies across Asia, the Americas, and Europe, I had options. The decision to build here was deliberate, and this essay explains the reasoning.

Leaving Hong Kong

Hong Kong was an extraordinary base for nearly a decade of my career. The city's energy is unmatched. The density of ambition, capital, and commercial activity within a few square kilometers creates an environment that accelerates everything you do. My entity there gave me access to Asian markets, a favorable tax environment, and a network that spanned the region.

But over time, the factors that made Hong Kong exceptional for one phase of my career became less aligned with what I wanted to build next. The cost structure had escalated dramatically. The regulatory environment was shifting in ways that created uncertainty for certain types of technology businesses. And personally, the relentless pace had run its course. I wanted to build something more deliberate, and I wanted to do it from a base that supported long-term thinking rather than the transactional velocity that Hong Kong rewards.

Why New Zealand

New Zealand offers a combination of attributes that I have not found anywhere else, and that combination is particularly well-suited to building an AI advisory firm that operates globally.

Quality of life that sustains high output. Building a company requires sustained intensity over years. Burnout is not a badge of honor; it is a failure of operational design. New Zealand's environment, the access to nature, the manageable pace of daily life, the safety and stability, creates conditions where I can maintain high output without the depletion cycle that characterizes work in high-intensity cities. This is not about working less. It is about being able to work effectively for longer.

Timezone advantage. New Zealand sits at the beginning of the global business day. Focused, deep work in the morning. International clients and partners in the afternoon and evening. The timezone also bridges Asia-Pacific markets, where Daom has significant relationships.

Cost efficiency for a global business. Operating from New Zealand provides access to high-quality talent at costs substantially lower than San Francisco, New York, or London. The savings are passed through to clients.

The Global Model

Daom is structured to serve clients globally, with engagement models designed for remote collaboration from the outset. Geography matters less than capability, trust, and results. Our location has not been a barrier—if anything, it has been a differentiator.

Building for the Long Term

I am not optimizing for speed to exit. I am building a firm that produces consistently excellent work over decades. New Zealand supports that ambition—the environment encourages patience and craft, and the quality of life ensures sustained high output without burnout.

If you are interested in working with a team that combines global experience with deliberate, long-term thinking, I would welcome the conversation.

— Sam Bakker
Founder & CEO, Daom

Most founders know they need to restructure around AI. The problem is they do not know where to start, and the longer they wait, the more money they burn on headcount that AI could replace. I built this 60-day plan from direct experience restructuring teams—including our own at Communi, a community platform where AI replaced developers and dramatically accelerated delivery. It is not theoretical. It is the actual sequence of steps that works.

Before you start, understand one thing: this is not about firing people to cut costs. It is about building a company that moves faster, ships more, and operates at a fraction of the overhead. The people who remain become more valuable, not less.

Phase 1: Audit (Days 1–15)

You cannot restructure what you do not understand. The first two weeks are about mapping everything.

Map every role. List every person on your team and document exactly what they do each week. Not their job title—their actual tasks. You will find that most roles are a mix of high-judgment work and pattern-based execution. The pattern-based work is what AI replaces.

Map every workflow. Trace how work moves through your company. How does a feature go from idea to shipped? How does a blog post go from concept to published? How does a customer issue go from reported to resolved? Document the full chain, every handoff, every approval step.

Map every tool. Catalog every piece of software your team uses and what it costs. You are probably paying for fifteen tools that could be consolidated into five with AI-native alternatives.

Calculate your ARR per employee. This is your baseline. If you are a SaaS company doing $1M ARR with 15 people, that is roughly $67K per employee. After restructuring, you should be targeting $200K or more per employee. That gap is where the opportunity lives.

Phase 2: Build (Days 16–30)

Now you know what to replace. In the next two weeks, you prove it works.

Pick the first 3–5 roles to replace with AI systems. Start with the clearest cases—roles that are mostly pattern-based execution. Content writing, basic design, QA testing, data entry, first-line customer support. These are not controversial. The AI is already better at the repetitive parts of these jobs.

Set up the systems. Build the AI workflows that replace each role. This means selecting the right tools, configuring them, creating templates and prompts, and establishing quality checks. At Communi, AI replaced developers and the systems were up and running within weeks—delivery speed improved dramatically.

Run them in parallel. Keep the existing team doing their work while the AI systems run alongside them. Compare output quality, speed, and cost. You need this data for Phase 3, and you need the confidence that comes from seeing the systems work before you make any personnel changes.

Phase 3: Transition (Days 31–45)

This is the hardest phase. You have the proof. Now you act on it.

Shift work from people to systems. Start routing actual work through the AI workflows instead of through the team members those workflows are replacing. Do this role by role, not all at once.

Train remaining team members. The people who stay need to learn how to operate the new systems. Your developer becomes an AI-assisted builder who ships ten times faster. Your marketer becomes an AI-powered operator who runs campaigns without a team. Invest in training here—it pays off immediately.

Begin reducing headcount. Handle this with honesty and generosity. Give people fair severance, genuine references, and as much notice as possible. The restructuring is not a reflection of their ability. It is a reflection of how fast the technology has changed.

Phase 4: Optimize (Days 46–60)

The new operating model is live. Now you make it bulletproof.

Fine-tune AI systems. Every AI workflow has edge cases. Spend these two weeks finding them and fixing them. Build fallback processes for the situations the AI cannot handle alone.

Stabilize the new model. Establish new rhythms, new meeting cadences (fewer meetings, by the way), and new reporting structures. Your company is leaner now. Make sure everyone understands their expanded role and has the tools to execute it.

Measure results. Compare your new ARR per employee to your baseline. Compare your shipping speed, your content output, your customer response times. If you have done this right, every metric should be moving in the right direction.

I have run this exact playbook. It works. The 60-day timeline is tight on purpose—it forces decisions and prevents the restructuring from dragging on indefinitely. If you want help running this at your company, book a strategy call or download The AI Advantage for the full framework.

I want to be clear about something upfront: AI has not replaced developers. Not entirely. But it has fundamentally changed how many you need. One technical person with the right AI tools can now do the work that used to require a team of five. I am not saying this theoretically. I am living it every day at Communi.

If you are a SaaS founder still staffing a traditional dev team—frontend, backend, QA, DevOps, maybe a tech lead—you are paying for a structure that AI has made obsolete. Not because the work disappeared, but because the ratio of people to output has changed dramatically.

How AI-Assisted Development Actually Works

Forget the marketing demos. Here is what AI-assisted development looks like in practice, day to day.

Code generation. AI does not write your entire application. But it handles the repetitive, boilerplate, and pattern-matching parts of coding that used to eat hours. CRUD operations, API integrations, data transformations, UI components—these are the tasks where AI accelerates a developer by five to ten times. The developer focuses on what to build and the AI handles much of the how.

Refactoring and optimization. Instead of spending days refactoring legacy code, a developer can describe the desired architecture and let AI handle the mechanical transformation. Code that took a week to restructure now takes hours.

Testing. Writing tests used to be the thing developers skipped because it was tedious. AI generates comprehensive test suites faster than a developer could write them manually. Better test coverage with less human effort.

Documentation. Nobody likes writing docs. AI generates documentation from code, keeps it updated, and formats it for different audiences. A task that used to fall through the cracks now happens automatically.

Debugging. Describing a bug to AI and getting back a root cause analysis with a proposed fix is faster than most developers can debug manually. Not always right, but right enough often enough to be a massive time saver.

What Still Requires Human Judgment

The reason you still need a developer—and a good one—is that AI cannot replace the parts of engineering that require real thinking.

Architecture decisions. How should the system be structured? What trade-offs are worth making? How does this decision affect scalability in two years? These are judgment calls that AI cannot make because they depend on context, business strategy, and experience.

Security. AI can follow security patterns, but it cannot think adversarially about your specific system the way a security-conscious developer can. You need human eyes on anything security-critical.

Creative problem-solving. When the problem is novel—not a pattern that exists in training data—you need a human who can think from first principles and invent a solution.

The Builder Role

In the three-person model, the Builder owns all of product development—architecting systems, directing AI tools, and shipping to production at the pace of a traditional five-person team.

At Communi, this is how we build. Features that would take a traditional dev team weeks to ship go live in days. Not because the problems are simpler—they are not—but because the ratio of thinking to typing has shifted entirely.

If you are paying for five developers and want to understand what a one-Builder model looks like for your product, book a strategy call. I will walk you through exactly how to make the transition.

People ask me what AI tools I actually use. Fair question. I run Daom as an AI strategy firm and Communi as an AI-native SaaS company, so if I am telling other founders to restructure around AI, I had better be doing it myself. Here is the exact stack I use daily. No vague recommendations—every tool, every use case, what works and what does not.

Claude for Strategy, Writing, and Thinking

Anthropic's Claude is the tool I use more than anything else. It is my strategy partner. I use it to draft frameworks, stress-test ideas, write content, prepare for client calls, and think through complex problems. Every article on this blog starts as a conversation with Claude. Every client proposal goes through Claude before it goes to the client.

What makes Claude different from other models for my work is the depth of reasoning. When I am working through a restructuring plan for a SaaS company, I need a model that can hold the full context—the team structure, the product, the market position, the financials—and reason about it coherently. Claude does that better than anything else I have tried.

Cursor for Development

Cursor is how Communi gets built. I am a technical founder, not a full-time engineer, but with Cursor I ship features, fix bugs, build entire pages, and maintain a production SaaS product without a dev team. The AI-assisted coding environment means I can describe what I want, get functional code, review it, refine it, and deploy it—all in a single session.

This is the tool that makes the Builder role in my three-person model possible. One person with Cursor can ship what a traditional five-person dev team ships. I know because I am doing it.

AI-Assisted Design

Design used to require designers. Now I use AI tools to generate visual concepts, create UI mockups, and produce marketing assets. Is it as good as a senior designer with years of experience? Not always. Is it good enough to ship professional-quality output for many tasks? Absolutely. The key is knowing which design work AI handles well and where you still need a human eye.

Content and Marketing Automation

AI assists with content production across the business. Blog posts, social content, email campaigns, SEO optimization—workflows from ideation to publication are AI-assisted. I direct the strategy, set the editorial calendar, and review final output. The AI accelerates the production work in between.

Operations and Workflow

Email drafting, meeting summaries, data analysis, customer support templates, reporting—all AI-assisted. The operational tasks that used to require an ops person or a VA are handled through AI workflows that run automatically or with minimal input from me.

What I Have Learned

The tools are force multipliers, not magic. Every tool I listed makes me dramatically more productive, but none of them work without direction, judgment, and quality control. AI amplifies capability. It does not create it.

Integration matters more than individual tools. The value is not in any single tool. It is in how they work together across my workflow. Switching costs between tools, reformatting outputs, maintaining context—that friction is the real bottleneck.

Start with your bottleneck, not the tool's features. Do not explore what an AI tool can do and then find a use case. Start with the thing that is slowing you down and ask whether AI can fix it. That approach produces faster ROI and higher adoption.

AI fluency comes from daily use. You cannot learn this from reading articles (including this one). You learn it by using these tools every day, making mistakes, and developing intuition for what works. Get your team using AI tools today, not after a training program.

This is the stack that lets me run two companies without a traditional team. If you want to see how a similar setup would work for your company, book a strategy call and I will walk you through it.

Two years ago, building a SaaS product required a team of fifteen. A couple of frontend developers, a couple of backend engineers, a designer, a QA person, a DevOps engineer, a product manager, a marketer, a content person, someone doing customer support—the list kept growing. Each hire made the org chart look more legitimate. It also made the company slower, more expensive, and harder to steer.

Today, you can build a competitive SaaS product with three people. Not a toy. Not an MVP that barely works. A real product with real users and real revenue. I know because I am doing it right now with Communi.

The Three-Person Model

The model is simple. Three roles, each amplified by AI to do the work that used to require an entire department.

The Strategist defines what gets built and why—vision, positioning, priorities. The Builder ships the product using AI-augmented development, doing the work of a traditional engineering team. The Operator handles everything that gets the product to market—marketing, support, analytics—through AI-powered workflows instead of dedicated teams.

Why Three Is Better Than Fifteen

The speed advantage is not incremental. It is an order of magnitude.

With a fifteen-person team, a new feature idea goes through product review, gets spec'd out, gets estimated by engineering, gets prioritized against the backlog, gets assigned to a sprint, gets built, gets reviewed, gets tested, gets deployed. That process takes weeks. Sometimes months.

With a three-person team, the Strategist decides to build it on Monday. The Builder ships it by Wednesday. The Operator has it in front of customers by Friday. There is no backlog. There is no sprint planning. There are no cross-functional alignment meetings. There is just a decision, then execution, then feedback.

At Communi, this is how every feature gets shipped. The iteration speed is the product advantage. We can respond to customer feedback, test new ideas, and adapt our product faster than companies with ten times our headcount.

What Makes This Possible Now

This model was not viable two years ago. Three things changed.

AI-assisted coding reached a tipping point. Tools like Cursor make it possible for one developer to maintain and extend a production codebase at a pace that previously required a team. The code quality is good enough for production when guided by a skilled engineer.

AI content and marketing tools matured. A single operator can now produce the volume and quality of marketing output that used to require a content team, a designer, and a marketing manager. The tools handle the production. The human handles the strategy.

AI reasoning models got good enough for strategy work. Tools like Claude can now serve as a genuine thinking partner for strategic decisions—analyzing data, stress-testing assumptions, and drafting documents at a level that replaces the need for a strategy team or expensive consultants.

The fifteen-person SaaS team is a relic of a pre-AI era. If you are building a SaaS product today and you have more than five people, you should be asking yourself why. Book a strategy call and I will show you what the three-person model looks like for your specific product.

Everyone talks about the cost of AI tools. Founders worry about the price of API calls, subscription fees, and compute costs. Meanwhile, they are paying $1.5 million a year in salaries for a twenty-person team that AI could replace with three people and $50K in tooling. The math is not close.

I know this because I lived it. I have managed teams of twenty people and seen firsthand how every hire created overhead that had nothing to do with the work itself. The hidden costs of a large team are not on anyone's P&L, but they are the reason your company moves slow and burns cash.

The Costs Nobody Talks About

Coordination overhead. Every person you add to a team creates new communication channels. Two people need one channel. Five people need ten. Twenty people need one hundred and ninety. This is not abstract math—it shows up as meetings. Alignment meetings, standup meetings, cross-functional syncs, one-on-ones, all-hands. At PM.com, I spent more time in meetings about work than doing actual work. That is the coordination tax of a large team.

Management layers. Once you have more than six or seven people, you need managers. Managers need other managers. Suddenly you have people whose entire job is managing other people, not producing output. Each management layer adds latency to every decision and filters information as it moves up and down the chain.

Slower decision-making. In a three-person company, a decision happens in a conversation. In a twenty-person company, a decision requires a proposal, a review, stakeholder buy-in, a meeting, follow-up actions, and documentation. The same decision takes three minutes or three weeks depending on company size.

Higher burn rate. This one is obvious but underappreciated. A twenty-person SaaS team in a mid-cost market runs roughly $1.5M to $2M per year in fully loaded costs—salaries, benefits, equipment, software licenses, office space. A three-person team plus AI tools costs roughly $300K to $400K. That is $1.2M per year back in your pocket, straight to runway or profit.

Cultural dilution. Every hire changes your culture. With twenty people, culture becomes something you have to manage, document, and reinforce. With three people, culture is just how you work. There is no politics, no factions, no communication gaps. Everyone knows everything because there is no distance between anyone.

The Compounding Effect

Here is the part that really kills companies: over-hiring compounds. Every hire creates the need for more coordination, which slows the company down, which creates the perception that you need more people to move faster, which leads to more hiring, which creates more coordination overhead. It is a death spiral that feels like progress because the team is growing.

I have watched SaaS companies go from five people shipping fast to thirty people shipping slow. Nobody can pinpoint when it happened. It just crept in, meeting by meeting, hire by hire, process by process, until the company that used to ship a feature in a week now takes a quarter.

The Real Cost Comparison

Let me put real numbers on this.

20-person team: $1.8M/year in salaries and overhead. Shipping speed: one major feature per month. Decision latency: one to two weeks. Coordination overhead: 30 to 40 percent of all time spent.

3-person team + AI: $350K/year in salaries plus $50K in AI tools. Shipping speed: two to three major features per month. Decision latency: same day. Coordination overhead: near zero.

The three-person team ships three times more, moves ten times faster on decisions, and costs eighty percent less. If those numbers seem too good, go talk to any founder who has made the transition. They will tell you the same thing.

If you are running a twenty-person team and wondering why everything feels slow, the answer is not more people. It is fewer people with better tools. Book a strategy call and I will show you the math for your specific company.

Communi is a community infrastructure platform powering 3,500+ users and 40,000+ active members, with over $1M in sales generated through the platform. We build the tools companies need to create, manage, and grow engaged communities—forums, content management, gamification, analytics, member management. It is a real product in a competitive market, going up against established players with hundreds of employees and millions in funding.

But the way we build it is the story worth telling. We applied our own AI-native strategies to run a lean team, and the results speak for themselves.

How AI Changed Our Development

The biggest impact has been on development. AI replaced developers. A traditional SaaS company building what Communi builds would have four to six engineers—frontend, backend, mobile, DevOps, QA. Instead, we use AI-assisted development workflows where AI handles code generation, testing, documentation, and debugging. A human handles architecture, judgment calls, and quality review.

The result: we decreased update delivery times by 1,000%. Features that would have taken weeks ship in days. Not because the problems are simpler, but because AI-assisted workflows compress the time between decision and deployed code.

What the Team Looks Like

Communi runs lean and operator-driven. The people involved are not specialists executing narrow tasks. They are generalists who use AI to cover the full surface area that used to require departments. Every person is responsible for outcomes, not activities. There is no handoff chain. There is no approval process. There is a direct line from decision to execution.

The operating model is simple: decide what matters most, build it with AI, ship it, measure the results, iterate. That cycle runs in days, not weeks. There is no sprint planning, no backlog grooming, no cross-functional alignment meetings. Just work.

The Results

Update delivery times decreased by 1,000%. When a user requests a feature or we identify an improvement, it goes from idea to production faster than any traditional team could deliver. AI-assisted development compressed the entire build cycle.

3,500+ users and 40,000+ active members. The platform has generated over $1M in sales. The product and the community do the selling.

Operating costs are a fraction of competitors. While competitors burn millions on headcount, Communi operates lean. Every dollar goes further. Every percentage point of margin is higher.

Why This Matters

Communi is a community platform at its core. That is what we are building. But the way we build it—applying AI-native strategies to run a lean team—is the proof that this model works. The product is not simpler than what other SaaS companies build. The market is not easier. The advantage is structural: a lean team, amplified by AI, shipping faster than teams ten times the size.

Any SaaS founder can apply the same approach. The tools are available today. The playbook works. The only thing standing between your current team and a lean, AI-native operation is the decision to make the change.

Communi is where we prove the model. If you want to see how this approach applies to your SaaS company, book a strategy call. I will walk you through the specifics of what this transition looks like for your product and team.

The SaaS industry is being reshaped right now. But not by what most people think. It is not a new pricing model or a breakthrough feature or a shift in distribution channels. It is team structure. The companies shipping the fastest products in 2026 have the fewest people. And that changes everything.

I have spent the last decade building SaaS companies across 60+ projects. I have seen every kind of team configuration—from twenty-person departments to solo operators. And what I am seeing now is fundamentally different from anything before.

The Old Playbook Is Dead

The traditional SaaS playbook said: raise money, hire fast, build a team for every function, ship product, scale headcount alongside revenue. A seed-stage startup would staff up to 10 or 15 people before they even had product-market fit. Engineers, designers, marketers, support reps, ops people. Every function needed a person. Every person needed management. Every layer of management slowed decisions down.

That playbook made sense when humans were the only way to get work done. It does not make sense anymore.

AI-Native Startups Are Winning

The startups I work with today that are shipping the fastest and iterating the hardest are running with three to five people. They are not underfunded. They are not struggling. They are outperforming funded competitors with teams three or four times their size. They do it because AI has replaced the execution layer. Content gets written by AI. Code gets generated by AI. Designs get produced by AI. Data gets analyzed by AI. The humans on the team make decisions, set direction, and review output. That is it.

When you only have three people, every decision happens fast. There is no meeting to discuss the meeting. No alignment session. No stakeholder review. The founder says ship it and it ships. The feedback comes in and the iteration happens the same day. Speed compounds. A team that ships twice as fast does not just deliver twice as much. They learn twice as fast, adapt twice as fast, and find product-market fit in half the time.

The Structural Advantage

This is not just about cost savings, though the savings are real. A 15-person team costs $1.5M to $2.5M a year in payroll alone. A 3-person team plus an AI stack runs $400K to $600K all-in. But the real advantage is structural.

Lean teams make faster decisions. Faster decisions mean faster shipping. Faster shipping means faster iteration. Faster iteration means you find what works before your bloated competitor finishes their quarterly planning cycle. This is a compounding advantage. Every week you are faster is a week your competitor falls further behind. And the gap does not close. Because a 30-person team cannot suddenly become a 3-person team. The organizational overhead, the politics, the dependencies, the legacy processes—those do not disappear. They are structural debt.

The New Competitive Moat

For the first time in SaaS history, being small is not a disadvantage. It is the advantage. The companies that figure this out first will define the next era of the industry. The ones that keep hiring like it is 2019 will find themselves outshipped, outiterated, and outcompeted by teams a fraction of their size.

The question is not whether AI will reshape SaaS team structure. It already has. The question is whether you will restructure proactively or be forced to do it reactively when your lean competitor is eating your market.

I have seen this transition happen across dozens of companies now. The pattern is clear. Fewer people. Faster companies. Better outcomes.

Across Daom and Communi, I handle much of the marketing function personally. Content production, SEO, email marketing, analytics, social media. And AI makes it possible for one person to produce output that would have required four or five people just two years ago.

This is not a hypothetical. This is what I do every day. And it works because the hard part of marketing is never the execution. It is the strategy. The decisions about what to say, to whom, and when. AI cannot make those decisions. But it can do almost everything else.

The Workflow: Strategy Is Human, Execution Is AI

Here is how it works in practice. I decide the content strategy. What topics matter to our audience. What angles will resonate. What the conversion path looks like from awareness to trial to customer. These are judgment calls that require understanding of our market, our product, and our customers. No AI is making those calls.

Once the strategy is set, AI handles the execution. I use AI to draft blog posts, email sequences, social media content, and landing page copy. I review everything, edit for voice and accuracy, and publish. What used to take a content writer a full day takes me an hour. What used to require a dedicated email marketing person is now a prompt and a review cycle.

The Content Engine

Content is the backbone of SaaS marketing. Two years ago, producing enough quality content to feed a SaaS funnel required multiple writers and an editor. Now I produce more content than those teams did, and it is more targeted because I am the one setting direction for every piece.

AI handles the first draft. I handle the strategy, the voice, and the quality control. The result is content that sounds like me, reaches the right people, and converts. The volume is higher, the cost is lower, and the turnaround is faster.

Email, SEO, and Analytics

Email nurture sequences that used to require a dedicated marketing ops person now get built in an afternoon. I describe the sequence logic, AI writes the emails, I review and adjust, and they go live. A/B test variants? AI generates three versions and I pick the best angles.

SEO used to mean hiring a specialist to do keyword research, optimize pages, and track rankings. Now AI does the keyword research, suggests content optimizations, and I make the strategic calls about which terms to target based on our business goals.

Analytics is where AI saves me the most time. Instead of spending hours in dashboards pulling reports, I use AI to analyze traffic patterns, conversion data, and engagement metrics. It surfaces the insights. I make the decisions.

What This Means for SaaS Founders

If you are a SaaS founder still thinking you need to hire a marketing team, reconsider. You need one person who understands marketing strategy and is comfortable working with AI tools. That person can outproduce a traditional marketing team of four or five at a fraction of the cost.

The key insight is this: AI replaces the hands, not the brain. You still need someone who knows what good marketing looks like, who understands your customer, and who can make the judgment calls that drive results. You do not need separate people to write, design, analyze, and operate. One operator with AI handles all of that.

I know this because I have lived it across multiple SaaS companies. Marketing that once required a full team now runs through one operator with AI tools. The speed is faster. The output is comparable. And the overhead is nearly zero.

I have been building and operating digital businesses for over a decade. During that time, I have worked with many investors and many operators. Some investors are exceptional. But when I look at the outcomes I have witnessed, the companies that succeed most consistently are the ones led or guided by people who have operated businesses themselves. This is a view I hold strongly, and this essay explains why.

The Operator Advantage

Operators understand customers at a level that is not accessible from a board seat. They have spoken to the frustrated user, read the support ticket, felt the anxiety of a feature launch that did not land, and experienced the relief of a product change that moved a retention metric. This understanding is not intellectual. It is visceral. And it produces better decisions.

When an operator makes a strategic decision, they are drawing on a mental model built from direct experience with the messy reality of how products are built, sold, and used. They understand that the elegant strategy on the whiteboard will encounter friction, delays, and unintended consequences in execution. They plan for that friction because they have lived it. Pure investors, no matter how intelligent or well-read, are working with a model of reality that is filtered through reports, metrics, and secondhand accounts.

Where Investors Fall Short

I am not arguing that investment capital is not valuable, or that investors do not contribute meaningfully to companies. Many do. The argument is that the skills and instincts developed through operating businesses are more predictive of good guidance than the skills developed through evaluating investments.

Operators feel pain points that investors observe. When an operator identifies a problem worth solving, they are typically speaking from direct experience. They have felt the inefficiency, dealt with the workaround, and understand the emotional frustration that makes someone willing to pay for a solution.

Operators understand what execution actually requires. The gap between a strategic plan and a shipped product is enormous and filled with decisions that do not appear in any plan. What to prioritize when resources are constrained. How to maintain team morale through a difficult pivot. When to accept technical debt and when to invest in architecture. These are operator decisions.

Operators build for customers rather than narratives. Investors are influenced by market narratives. They invest in themes. Operators build for customers. The distinction matters because market narratives can diverge from customer reality.

AI Makes the Operator Advantage Even Stronger

Here is what has changed. AI has made the operator advantage more powerful than ever. Before AI, even the best operator needed a team to execute their vision. You needed engineers to build, designers to design, marketers to market. The operator had the judgment, but they still depended on a team to translate that judgment into output.

Now, a single operator with AI can build, ship, and market a product with a team of three. The judgment that operators bring—understanding what to build, who to build it for, and how to position it—is the only part that matters. Everything else can be handled by AI. This means operators no longer need large teams or significant capital to execute their ideas. The playing field has never been more tilted in favor of the operator.

An investor can give you money. An operator can give you judgment. In a world where AI handles execution, judgment is the only thing that differentiates the winners from the losers.

What This Means Practically

For founders seeking guidance, my advice is simple. Seek counsel from people who have done what you are trying to do. Not people who have funded it, analyzed it, or written about it. People who have done it. They will give you advice that accounts for the reality of execution.

For investors reading this, the takeaway is not that your contribution lacks value. It is that the most impactful thing you can offer portfolio companies, beyond capital, is either operational expertise of your own or access to operators who can provide it.

Capital is necessary but not sufficient. Operational excellence is both necessary and, more often than capital, the determining factor in whether a company endures.

I built Daom on this belief. We are operators first, advisors second. Every engagement we take on reflects the conviction that the best guidance comes from people who have been in the arena, not from those watching from the stands.

Every SaaS founder I talk to asks the same question: which roles should I replace with AI first? The answer is not a list. It is a framework. Because the right roles to replace depend on the nature of the work, not the job title. I call it the AI Replacement Matrix, and it has guided every restructuring I have done in the last year.

The Two Axes

The matrix has two axes. The horizontal axis measures whether the work is primarily pattern-based or judgment-based. Pattern-based work follows repeatable processes: write this type of content, process this type of data, respond to this type of inquiry. Judgment-based work requires nuanced decision-making: should we enter this market, how should we position against this competitor, is this candidate the right culture fit.

The vertical axis measures volume. How often does this work happen? Daily? Weekly? Quarterly? High-volume work creates the biggest efficiency gains when automated. Low-volume work may not justify the setup cost.

Quadrant 1: High-Pattern, High-Volume — Replace Immediately

This is where you start. Roles that do repetitive, pattern-based work at high volume are the first to go. Content production, tier-1 customer support, data entry and processing, basic reporting, social media scheduling. These roles follow clear patterns and produce high volumes of output. AI handles them as well or better than humans, at a fraction of the cost, with no downtime.

If you have people doing this work full-time, you are overpaying for output that AI can deliver today. Not next year. Today.

Quadrant 2: High-Pattern, Low-Volume — Automate

These are tasks that follow clear patterns but do not happen constantly. Monthly reporting, scheduling and coordination, administrative tasks, invoice processing. You probably do not have a full-time person on these, but you have people spending chunks of their time on them. AI automation frees that time for higher-value work. Set up the automation, monitor it occasionally, and move on.

Quadrant 3: High-Judgment, High-Volume — Augment

This is where it gets nuanced. Sales conversations, product decisions informed by customer data, complex customer issues, hiring evaluations. These require human judgment but happen frequently. The right approach is augmentation, not replacement. Give your salespeople AI-powered research and talking points. Give your product managers AI-analyzed usage data. Give your support team AI-drafted responses they can review and personalize.

You are not replacing the human. You are making them two or three times more effective. One salesperson with AI augmentation can cover the territory that used to require three.

Quadrant 4: High-Judgment, Low-Volume — Keep Human

Strategic planning, key relationship management, creative direction, culture building, fundraising. These are deeply human activities that happen relatively infrequently and require the kind of nuanced judgment that AI cannot replicate. Keep your best people focused here. This is where human talent creates irreplaceable value.

How to Use the Matrix

Take every role on your team and place it on the matrix. Be honest about whether the work is truly judgment-based or just feels that way because a human has always done it. Many roles that seem judgment-heavy are actually 70% pattern-based work with 30% genuine judgment. For those roles, automate the 70% and let the person focus on the 30% that actually requires their brain.

Then start in Quadrant 1 and work your way through. Most SaaS companies I work with find that roughly half their current roles fall primarily into Quadrants 1 and 2. That is your restructuring roadmap.

The matrix is not about eliminating people for the sake of it. It is about being honest about where human talent creates value and where it is being wasted on work that machines do better. Your best people deserve to work on problems worthy of their abilities. The matrix helps you make that happen.

I hear this from non-technical founders constantly: "I want to restructure around AI, but I do not know enough about the technology to make those decisions." Here is what I tell them: you do not need to be technical. You need to understand your business, your customers, and your team. The technology part is someone else's job.

I have worked with dozens of non-technical founders across 60+ projects. Some of the best AI-restructured companies I have seen were led by founders who could not write a line of code. They succeeded because they understood the framework, hired the right Builder, and focused on what they do best: making strategic decisions.

The Non-Technical Founder's Path

The restructuring playbook for non-technical founders has three steps. First, become the Strategist. Second, hire one strong Builder. Third, let AI handle the rest.

You are the Strategist. You already know your customers. You know which problems your product solves. You know which features drive retention and which are dead weight. You know your market positioning. That knowledge is more valuable than any technical skill. Your job in the restructured company is to make decisions: what to build, who to target, how to position. Nobody can replace you in that role.

Your Builder is your force multiplier. You need one strong technical person who can work with AI tools to build and ship product. Not a team of five engineers. One person who is comfortable using AI for coding, testing, and deployment. This person translates your strategic decisions into shipped product. They evaluate and implement AI tools. They build the systems that keep the company running.

AI handles the execution layer. Content production, customer support, data analysis, design, marketing operations. These functions no longer require dedicated people. They require AI tools managed by you and your Builder.

Common Concerns (And Why They Are Wrong)

"I cannot evaluate AI tools." Your Builder does that. Your job is to evaluate the output. Can the AI-generated content represent your brand? Does the AI-built feature solve the customer problem? You do not need to understand how the tool works. You need to judge whether the result is good enough.

"What if the AI breaks?" AI systems are more reliable than people. They do not call in sick, they do not quit with two weeks notice, and they do not have bad days. When they do break, it is usually a configuration issue that your Builder can fix in hours, not a two-month hiring process to replace a departed employee.

"I need a team for credibility with investors." Investors are waking up to the fact that small teams with AI outperform large teams without it. Revenue per employee is becoming the metric that matters. A 3-person team generating $2M in ARR is more impressive than a 15-person team generating the same.

Step by Step

Start with an audit. Look at every role on your team and ask: does this person spend most of their time following processes, or making judgment calls? You know the answer. You have watched them work. The process-followers are the roles AI replaces first.

Next, find your Builder. Look for someone who is already using AI in their development workflow. Someone who ships fast and is comfortable with ambiguity. They do not need to be a senior architect. They need to be a fast, AI-fluent developer who can wear multiple hats.

Then set up your Operator function. This is you, doing what you have always done: making the calls that drive the business. The difference is that now, instead of managing a team of 10, you are directing AI systems that execute your decisions. The output is the same or better. The overhead is dramatically lower.

You do not need to be technical to restructure around AI. You need to be honest about which roles create value and which ones follow patterns. You already know the difference. Trust that knowledge.

Over the past two years, I have spoken with dozens of CMOs about their AI strategies. Most are intelligent, experienced leaders under enormous pressure to demonstrate AI adoption and results. And most are making the same fundamental mistake: they think AI augments their marketing team. In reality, AI replaces most of the marketing team, and the CMO becomes the Operator.

This is not an indictment of CMOs. It is an honest assessment from someone who has run marketing operations at scale and then rebuilt them with AI from the ground up.

Mistake 1: Treating AI as a Tool for Your Team

The most common pattern I see: a CMO buys AI tools for their existing team. AI writing assistant for the content writers. AI analytics for the data analyst. AI design tool for the designers. Everyone keeps their job, they just have a new tool in their stack.

This misses the entire point. The question is not how to make your 8-person marketing team 20% more productive. The question is whether you need an 8-person marketing team at all. Across the businesses I operate, I handle much of the marketing with AI—content, SEO, email, analytics, social. The output would have required four or five people two years ago.

CMOs who give AI tools to their existing team are optimizing a structure that should not exist.

Mistake 2: Protecting Headcount

CMOs are often measured partly by the size of their team. Bigger team, bigger budget, more organizational influence. This creates a perverse incentive to preserve headcount even when AI makes much of that headcount unnecessary.

The CMOs who will thrive in the next three years are the ones who voluntarily shrink their teams and demonstrate that they can produce equal or better results with fewer people and AI systems. That takes courage. It also takes a different kind of skill: the ability to operate AI systems rather than manage people.

Mistake 3: Neglecting the Operator Mindset

Traditional CMOs manage people who do marketing work. AI-native CMOs do marketing work directly, using AI as their execution layer. The mental shift is enormous. You go from reviewing your content writer's blog post to writing it yourself with AI assistance. You go from discussing email campaign strategy with your marketing ops person to building the campaign yourself with AI tools.

This is not a demotion. It is an upgrade. You now have direct control over every piece of marketing output, informed by years of strategic experience, executed at a speed and cost that no traditional team can match. You are the Operator.

Mistake 4: Expecting Overnight Results

When CMOs do start using AI directly, they often expect immediate perfection. The first AI-generated blog post does not match their best writer's quality. The first AI email sequence feels generic. They declare that AI is not ready and go back to their team.

What they miss is that AI output improves dramatically with iteration. The second draft is better than the first. By the tenth piece of content, you have refined your prompts, established your voice guidelines, and built a workflow that produces consistently strong output. The ramp-up period is weeks, not months. The payoff is permanent.

Getting It Right

The CMOs who are winning with AI share a common approach. They start by doing the marketing work themselves with AI tools. They learn what AI can and cannot do through direct experience. They systematically eliminate roles that AI can handle. And they reinvest the savings into growth, not into more headcount.

The future CMO is not someone who manages a team of 15 marketers. It is someone who operates an AI-powered marketing engine that outproduces teams of 15 at a fraction of the cost. The sooner you make that transition, the bigger your competitive advantage.

I get asked constantly which roles AI can actually replace today. Not in theory. Not in a demo. In a real SaaS company, shipping real product to real customers. Here are ten roles I have seen replaced successfully, what used to require a human, what handles it now, and what human oversight remains.

1. Content Writer

What the role did: Blog posts, landing pages, email copy, social media content, documentation. What replaces it: AI content generation tools guided by a strategist who sets topics, voice, and direction. Human oversight: Final review for accuracy, brand voice, and strategic alignment. One operator reviews all content instead of managing writers.

2. Data Analyst

What the role did: Pulling reports, building dashboards, analyzing trends, creating presentations for stakeholders. What replaces it: AI analytics that processes raw data, identifies patterns, and generates insights automatically. Human oversight: Strategic interpretation. AI tells you what happened. A human decides what to do about it.

3. QA Engineer

What the role did: Writing test cases, executing tests, filing bugs, regression testing. What replaces it: AI-powered testing tools that generate test cases, run them automatically, and identify regressions. Human oversight: Your senior developer reviews AI-flagged issues and makes judgment calls on edge cases.

4. Customer Support (Tier 1)

What the role did: Answering common questions, troubleshooting known issues, routing complex tickets. What replaces it: AI chatbots and email responders trained on your knowledge base. Human oversight: Complex or sensitive issues escalate to a human. Roughly 60-70% of tickets resolve without human intervention.

5. Marketing Coordinator

What the role did: Campaign scheduling, asset coordination, vendor management, reporting. What replaces it: AI workflow automation that schedules campaigns, generates assets, and tracks performance. Human oversight: The marketing operator approves campaigns and reviews performance. The coordination work disappears.

6. Production Designer

What the role did: Creating social graphics, resizing assets, building email templates, producing presentation decks. What replaces it: AI design tools that generate and adapt visual assets from brand guidelines. Human oversight: Creative direction stays human. A senior designer or the founder sets the visual strategy. Production work is handled by AI.

7. SEO Specialist

What the role did: Keyword research, on-page optimization, technical audits, competitor analysis, link building outreach. What replaces it: AI-powered SEO tools that research keywords, suggest optimizations, and track rankings automatically. Human oversight: Strategic decisions about which terms to target and how SEO fits into the broader marketing strategy.

8. Social Media Manager

What the role did: Creating posts, scheduling content, monitoring engagement, responding to comments. What replaces it: AI content generation and scheduling tools that produce platform-specific content from your strategy briefs. Human oversight: Brand voice approval and community engagement for meaningful conversations. The posting and scheduling is fully automated.

9. Operations and Admin

What the role did: Scheduling meetings, managing documents, processing invoices, coordinating across teams, maintaining internal tools. What replaces it: AI workflow tools that automate scheduling, document management, and routine coordination. Human oversight: Exception handling for unusual situations. The routine work runs on autopilot.

10. Junior Developer

What the role did: Writing boilerplate code, fixing simple bugs, building standard features, writing tests. What replaces it: AI coding assistants that a senior developer uses to produce code at two to three times the previous output. Human oversight: The senior developer does more. They use AI to handle the work that juniors used to do, while focusing their own judgment on architecture, complex features, and code quality.

The Pattern

Across all ten roles, the pattern is the same. The repetitive, process-driven parts of the job get replaced by AI. The strategic, judgment-driven parts stay human. But you need far fewer humans when AI handles the execution. A SaaS company that used to need 15 people can operate with 3-5, with AI filling the gaps. The result is faster shipping, lower costs, and better focus on the work that actually requires human intelligence.

Everyone talks about lean teams in the abstract. Let me give you the actual numbers. I have run these calculations across multiple SaaS companies I advise, and the math is consistent. A 3-person team with AI is not just viable. It is financially superior to a traditional team at almost every stage.

The Traditional Cost Structure

A typical 15-person early-stage SaaS team looks like this: 4-5 engineers, 2 designers, 3 marketers, 2 customer support reps, 1 ops person, 1 product manager, and a CEO. Fully loaded cost including salary, benefits, office, and tools runs $1.5M to $2.5M per year depending on location and seniority. That is your baseline burn before you have served a single customer.

Revenue per employee for traditional SaaS companies averages around $150K-200K at scale. Seed-stage companies are often at $50K-80K revenue per employee, which means they are burning far more than they earn per head. This is the math that requires constant fundraising.

The 3-Person Cost Structure

A restructured 3-person team: one Strategist (the founder), one Builder (senior developer), one Operator (marketing and operations). Fully loaded cost: $300K-450K in compensation. AI tooling stack: $50K-100K per year for coding assistants, content generation, design tools, analytics, customer support automation, and infrastructure. Total all-in cost: $400K-600K per year.

That is 70-75% less than the traditional team. And here is the part that matters most: the output is comparable. The Builder with AI produces code at the rate of 3-4 traditional developers. The Operator with AI handles marketing, support, and operations that used to require 4-5 people. The Strategist focuses on decisions instead of management.

Revenue Per Employee

This is where the math gets exciting. If your 3-person team generates $600K in ARR, your revenue per employee is $200K. That matches a scaled traditional SaaS company. At $1M ARR, you are at $333K per employee, which puts you in elite territory. At $2M ARR, you are at $667K per employee, which is better than most public SaaS companies.

Traditional SaaS teams need to reach $15M-30M in ARR to hit those revenue-per-employee numbers. You hit them at $1-2M. That fundamentally changes your fundraising position, your profitability timeline, and your strategic options.

Break-Even Analysis

A 15-person team burning $2M per year needs roughly $170K in MRR ($2M ARR) just to break even on payroll. At typical SaaS growth rates, that takes 18-24 months post-launch, assuming everything goes well.

A 3-person team burning $500K per year breaks even at roughly $42K in MRR ($500K ARR). That is achievable in 6-9 months. You reach profitability faster, you need less runway, and you can choose whether to fundraise from a position of strength rather than desperation.

The Compounding Advantage

Here is what the spreadsheets do not capture: every new customer you serve without hiring a new person improves your unit economics. A traditional SaaS company hits inflection points where they need to hire: more support reps as tickets increase, more engineers as the product grows, more marketers as channels expand. Each hire resets the revenue-per-employee ratio.

With an AI-native team, those inflection points do not exist in the same way. AI scales with demand. Your support bot handles 100 tickets the same way it handles 1,000. Your content engine produces output regardless of how many channels you are in. Your builder ships features whether you have 100 or 10,000 users.

The result is that your margins improve with growth instead of staying flat or declining. Every dollar of incremental revenue flows more directly to profit. That is the math that makes a 3-person team not just viable but optimal.

The average seed-stage SaaS team has already dropped from 10 to 6. AI-native teams are targeting 300% better revenue per employee. The math is clear. The question is whether you will run it for your own company.

I sat down with a founder this week. He has built something I genuinely have not seen before. It is not a variation on an existing product. It is not a feature bolted onto something familiar. It is something new—the kind of thing that makes you pause and rethink assumptions about how an entire workflow should operate.

A bank is interested in backing him. That is the good news. The bad news is the bank wants him to go to market leading with one message: productivity gains.

I told him that is the wrong move. Here is why.

What the Bank Wants

I understand the bank’s logic. Productivity is measurable. It maps to existing budget categories. Procurement teams understand it. It is easy to put in a pitch deck and easy for a finance team to model. When someone says “this tool increases productivity by 25%,” the next conversation is about ROI, and ROI conversations lead to purchase orders.

For an established company selling into a mature market, that messaging works. You have the case studies. You have the data. You have the customers who can validate the claim.

But this is not an established company. This is a startup with something new. And that changes everything about how you should communicate.

The Problem With Productivity

When you lead with productivity, you volunteer into the most crowded race in technology. Every AI tool, every SaaS platform, every consulting firm is making productivity claims right now. The moment you say “we make teams more productive,” you go from being the only company doing something genuinely different to being one of a thousand claiming to make people 20% faster.

Productivity is a metric. It is the result of what you build. It is not the description of what you are.

Productivity is what you prove after you win. It is not what you lead with to get there.

The founder I spoke with has something that does not fit neatly into a productivity comparison. That is his advantage. The moment he frames it as a productivity play, he gives that advantage away and invites direct comparison with tools that have more data, more customers, and more case studies than he does.

Two Voices, Two Directions

This is the real tension, and I think a lot of founders will recognise it. He is being pulled in two directions. The bank wants safe, proven, enterprise-friendly messaging—“productivity gains you can measure.” His instinct is telling him he should be doing something bolder—educating the market about something that does not have a name yet.

He is caught between the voice with the money and the voice in his gut.

I have seen this dynamic before. The money voice usually wins. And when it does, the startup loses the thing that made it special. You end up with a product that was built to create a new category, wrapped in messaging that makes it sound like everything else. The product is unique but the story is generic. That is the worst of both worlds.

You Cannot Prove What You Have Not Scaled

Here is the other problem. The founder has not crossed the chasm yet. He does not have 50 enterprise deployments showing measurable productivity improvements. He does not have third-party validation or peer-reviewed benchmarks. He has a unique product, early traction, and a thesis about where his market is heading.

If he leads with productivity claims now, he is making promises he cannot back up at scale—in a market where incumbents already can. That is not ambition. That is exposure.

If you are pre-chasm and leading with productivity claims, you are bringing a napkin sketch to a fight where everyone else has a finished painting.

The timing matters. Productivity claims work when you have the evidence to support them. Before that point, they invite scrutiny you are not ready to withstand.

What He Should Do Instead

Educate. And this is not just instinct—there is a proven framework for it.

Eugene Schwartz wrote about this in Breakthrough Advertising decades ago. He laid out five stages of market awareness: Unaware, Problem-Aware, Solution-Aware, Product-Aware, and Most Aware. The mistake the bank is making is writing messaging for a Solution-Aware or Product-Aware audience—people who already know what the product category is and are comparing options on features and ROI.

But this founder’s market is not there yet. His audience is Problem-Aware at best. They know something is broken in how they work, but they do not know a solution like his exists. Schwartz was clear on this: “If your prospect is not yet aware of your product, and is only aware of the need itself, start with the need.” Do not skip stages. Meet the market where it is.

You do not sell productivity to people who do not even know your category is real. You educate them. You name the problem. You teach them why a new approach exists. That is how you move a market from unaware to aware—and position yourself as the one who brought the insight.

Educational content does several things that productivity claims cannot:

• Builds authority without requiring proof at scale. You are teaching a concept, not making an ROI promise.
• Attracts early adopters who are drawn to ideas, not benchmarks.
• Creates a moat because you defined the language and the framework. When the market matures, you are already the reference point.
• Avoids direct comparison because you are not competing on the same axis as everyone else.

The best founders I have worked with did not win by claiming to be better at what already existed. They won by teaching the market that something new was possible. Schwartz understood this. The best marketers still do.

If you are a founder navigating this tension—between what investors or partners want you to say and what your product actually is—I would be happy to talk it through. Book a free 30-minute strategy call, or download The AI Advantage for the full framework.

— Sam Bakker
Founder & CEO, Daom

When I launched Daom, I grabbed daom.org. It was available, it was clean, and we had work to do. The domain was never the priority. The work was.

Today I am happy to announce that we have acquired daom.com.

Why This Matters

A .com is not just a URL. It is a signal. It tells the market you are serious, that you are building something intended to last, and that you are thinking beyond your immediate geography. For a company operating out of Wellington, New Zealand, that signal matters more than most people realise.

New Zealand is a phenomenal place to build a business. The talent density per capita is underrated, the timezone forces asynchronous discipline, and being removed from the noise of larger markets gives you clarity that is hard to find in San Francisco or London. But it comes with a constraint: nobody assumes a world-class AI strategy firm is based in Wellington. You have to earn that assumption, and every detail contributes.

Owning the .com is one of those details.

Operating From a Small Market

I have never seen New Zealand as a limitation. It is a forcing function. When your local market is small, you have to think globally from day one. There is no domestic cushion to fall back on. Every piece of positioning, every piece of content, every client interaction needs to work at a global standard because that is the only standard that sustains the business.

That pressure is a gift. It means we never had the luxury of being mediocre locally while figuring things out. We had to be sharp from the start.

The challenge is real though. Time zones mean half the world is asleep when you are working. Flights to key markets are long and expensive. You do not bump into potential clients at industry events because the events are 12,000 kilometres away. Everything has to be more intentional. Your brand has to work harder. Your content has to travel further. Your domain has to be unmistakable.

The Next Chapter

Acquiring daom.com is not just about the domain. It is about what it represents. We are building Daom to be the firm that SaaS founders turn to when they are ready to restructure around AI. Not a consulting firm that dabbles in AI. Not an agency that bolts automation onto broken processes. A firm that understands what it means to run a company with fewer people, better systems, and AI at the centre of every operation.

That ambition requires global credibility. And global credibility starts with the small things that compound: a clean domain, clear positioning, consistent quality, and the willingness to operate at a standard that does not depend on where your office is located.

We are excited for what comes next. The thesis has not changed—fewer people, faster companies. But the foundation underneath it just got a little stronger.

Welcome to daom.com.

If you are a SaaS founder thinking about restructuring your team around AI, book a free 30-minute strategy call. Or download The AI Advantage for the full framework.

— Sam Bakker
Founder & CEO, Daom