I left Google in 2017 to bootstrap mOOnshot digital. Seven years later, we had built a business generating over $80M in sales without raising a dollar when AI came along and completely changed the economics of our industry. Readers stopped visiting on our websites and started asking AI chat bots for answers instead. We’re now rebuilding our operation from the ground up, this time with AI as the foundation.
Zero-Base Operations is the framework I use to guide our decisions during that transition.
It justifies every process, tool, and expense from zero, with AI as part of the foundation. The name borrows from zero-based budgeting and the principle is the same: justify every expense from zero, rather than adjusting based on last year’s numbers.
We’ve always run mOOnshot digital profitably, with 90%+ profit margins and no external capital. That discipline is what made our pivot to AI survivable when the revenue started declining. It is also what shaped the four principles I use today, and that I would use to build any new business from scratch: Start profitable. Start with the idea, refine with AI. Treat AI as the operating system. Make your data the moat.
Zero-Base Operations
This is my system to incorporate AI into existing operations or build new business from scatch with AI as a foundation.
-
Start profitable.
Healthy margins from day one, no outside money. AI has removed the need for large start-up capital.
-
Start with the idea, refine with AI.
The idea and expertise are yours, but AI shortens the path from conception to implementation.
-
AI as the operating system.
Rethink your business with AI from the start. Don't try to force AI onto old infrastructures. Redesign the team around what AI actually changes.
-
Human experience is the moat.
AI models converge to the same output. Human experience, opinions, and data are what sets you apart.
When AI is both the threat and the solution
In the media industry, AI is both immensely destructive and an incredible opportunity at the same time.
I think that the transformation we experienced will impact most industries as AI models get better at answering customers’ needs. The framework that I’m using to rebuild our business should thus be helpful to anyone thinking about the same questions.
In 2024, our publishing revenue started declining as readers moved to AI chat interfaces to find the answers to their questions. While AI models happily scrape our content to answer these questions, they rarely send us any traffic in return back to our website.
You can read this brief retrospective on the impact of AI on the publishing industry if you want to learn more.
We had to pivot our buisness model fast to preserve our margins. What started as a cost-cutting excercise quickly turned into something much bigger as we realised that just adding AI agents into our existing operations wouldn’t be sufficient. We had to completely redesign our organisation to truly benefit from what AI can do.
1. Start profitable: AI is the great leveller
We built mOOnshot digital as a profit-first operation. Every venture we started and every website we launched had to have a clear, fast path to profitability. Seven years in, that discipline is what turned AI into an opportunity to rebuild. The margins gave us time and options to pivot.
Today, I think AI makes the case for starting profitable even stronger. When used properly, AI can radically cut cost across every function while accelerating production at the same time.
You can now see results much faster for a fraction of the cost.
Let’s take Photoshop as an example.
It’s software I’ve used regularly for the past 20+ years. Photoshop has thousands of features, but I only use a handful of them. For years, I’ve paid a full subscription to Adobe for these thousands of features, because building a custom solution for what I actually need would have been more expensive.
AI completely flips that over.
With an AI coding agent, the handful of features I need become a one-evening coding session. I get exactly what I want, custom built for my needs and running locally. It costs close to nothing too.
The same logic applies to most of the software we’ve been paying for over the past few years.
Every SaaS subscription in our stack is now a zero-base question.
Would we pay for this if we were starting today, given that building exactly what we need costs an afternoon? Do we need the full suite of features from our newsletter provider, or our affiliate tracking provider? Or could we build something simpler that fits our workflow better, for a fraction of the cost?
Most of the time, the answer is that building our own is better. And it’s more enjoyable too. We get full control over the features and UI, full privacy because it runs on our own servers, and no monthly subscription that inflates and accumulates over time.
Build what you need and keep the difference as margin.
2. Start with the idea, refine with AI
You still need the concept and the expertise. AI won’t help you with either. What it does is shorten the time between a good idea and a working prototype you can use today.
Luxe Digital Privileges is a good example. Partner networks’ APIs on the way in, scoring and normalisation in the middle, a member-facing platform and a newsletter to 30,000 subscribers on the way out. A year or two ago, the complexity of that pipeline would have made it too expensive to justify hiring a developer to build it.
Now, with AI, I built the whole platform myself in a few hours with Claude Code. Frontend, backend, the partner API integrations, the data normalisation logic, the email layer.
Two years ago, that’s a product I would have scoped out, estimated the time and investment needed to build it, and probably parked for another day. Nowadays, it’s a Saturday afternoon with AI.
The same goes for the other side projects I’m working on: the custom tools we use for our ventures, our internal dashboards, this very website. None of those started as formal projects, and none of them would have justified the development cost a few years ago. Now they’re all weekend builds.
AI won’t have the idea for you. What it does is collapse the cost of being wrong. I can have an idea on a Saturday morning, write the brief, and have a working version by the afternoon. Markets that weren’t worth the development cost a few years ago are now worth a weekend of experimentation.
Have the idea. Write the brief. Use AI to map, refine, and stress-test every step. The cost of being wrong has collapsed.
3. AI as the operating system: rebuild, don’t patch
The third principle is the hardest to execute. AI as Operating System means building the business around what AI does well, not patching AI onto the workflows you already have. Most AI adoption inside established companies is the second shape: Copilot inside Word, a marketing exec with a personal ChatGPT account. Useful, but it only moves productivity at the margins.
The deeper opportunity is harder to see. I explored the publisher-specific version of this argument in why most media publishers are solving the wrong AI problem. The operational consequence, why AI in publishing now bottlenecks on human review rather than on writing throughput, is in The bottleneck shift.
The mOOnshot rebuild is a live case study. When the revenue decline forced us to cut costs, we didn’t trim headcount and keep the same shape; we reconsidered the shape. The full story of that transition is in I replaced a 15-person writing team with AI agents.
Our writing team went from fifteen people to a small team of operators managing an AI infrastructure that covers web development, finance, legal, market research, writing, editorial review, SEO, and GEO. We don’t have a dedicated internal dev team. Operators build their own tools with AI agents when they need one. Our newsletter operation for Luxe Digital Privileges, which used to take a two-person afternoon three times a week, is now an n8n pipeline that pulls offers from partner APIs, scores them, formats the email, writes subject lines, and prepares the email for an operator to review and send to our 30,000+ subscribers. A daily n8n pipeline ingests data from 150+ affiliate partners and feeds directly into a Supabase table, replacing what used to be a spreadsheet, an email thread, and a hire.
None of those changes could have happened if we’d tried to patch AI onto the existing roles. The shape only becomes visible when you redesign the pipeline around what AI can do, not around what the last org chart looked like.
Our new organisation is now smaller, flatter, and more operator-heavy. That’s the shape Zero-Base Operations produces when you apply it honestly. The receipts, dimension by dimension:
| Dimension | Before | Now |
|---|---|---|
| Production team | 15-person writing team | Small ops team + AI agent pipeline |
| Cycle time | 4 weeks per piece | 4 hours per piece |
| Research & SEO | Manual, human-only | Automated, with GEO optimisation |
| Editorial review | Sequential hand-offs | Quality-gated agent loop |
| Operating margins | Shrinking under overhead | 90% net, rebuilt from zero |
4. Human experience is the moat
The fourth principle is the one that matters most once the other three are in place. If the underlying models all converge to the same output, and everyone in your industry has access to the same tools, what’s left as the moat is your human experience, opinions, and data.
Human judgement is what turns generic output into results your competitors can’t reproduce by just buying the same subscriptions.
The model is the cheap part.
Everything above it is the moat.
Three layers. Anyone can buy the first two. Only one of them is yours.
Your moat: what you know, what you'll say, what you've built.
The practical shape of this is narrower than the industry usage of “data as moat” suggests. The moat is rarely a fine-tuning corpus. It is the grounding context. The three-thousand-word house voice style guide per publication. The tests folder of gold-standard past articles the drafting agent pattern-matches against. The eight years of edited reviews scored against actual purchase outcomes sitting inside our editorial archive at Luxe Digital. The daily operating data from 150+ affiliate partners flowing through our pipelines. None of those are downloadable. All of them compound over time.
Once you see it, where to spend gets obvious. A better prompt erodes the next time the model updates. A better dataset, a better schema library, a better corpus of past work to ground new work against. Those keep paying. With everyone having access to the same models, what sets you apart is how you use them and what unique data you feed in. That’s when AI output moves from decent to genuinely valuable.
Traditional operations vs Zero-Base Operations
The four principles combine into an operating shape that looks very different from the default. A side-by-side helps.
| Traditional operations | Zero-Base Operations | |
|---|---|---|
| Starting condition | Inherit last year’s cost base, adjust by a percentage | Justify every process, tool, and hire from zero |
| Capital structure | External funding smooths margin pressure | Profitable from day one, no outside money |
| Team shape | Functional departments, specialist roles | Small team of operators, each with oversight across several functions |
| Tooling decisions | Buy SaaS subscriptions for every workflow | Build what you need with AI, buy only what can’t be built |
| Role of AI | Feature added to existing workflows | Operating layer the rest of the business runs on |
| Competitive moat | Scale, distribution, brand | Your data, experience, and opinion fed into AI |
| Hiring question | ”Who do we need to hire for this?" | "Which part of this can AI do, and who oversees the agent?” |
| Production speed | Quarterly release cycles | Afternoon builds, same-week iterations |
Zero-Base Operations isn’t a universal prescription. A funded growth business in a winner-takes-all market may rationally pay for SaaS and hire at speed. It’s a deliberate choice, made because the alternative no longer fits the economics. Where it applies, it tends to apply decisively.
What this means for operators inside organisations
Most essays on bootstrapping assume the reader is a founder. This one doesn’t need to. The zero-base question scales down to a single function, a single team, a single role.
If you run a department inside a larger organisation facing AI disruption, the most useful thing you can do this quarter is ask the zero-base question honestly about your own patch. If you were starting this function today, with AI as part of the foundation, what would you actually build? What would you stop doing, stop hiring for, and stop buying? The answer is almost always a smaller, flatter, more operator-heavy team than the one you have. That gap is where the opportunity sits.
The friction is real. Budgets get allocated by function, so AI infrastructure that cuts across three departments has no natural sponsor. Roles overlap with identity, which makes “does this role still exist in its current form?” a harder conversation than any org chart deck admits. Boards still read content volume and headcount as progress, not margin. The hard part is the change management, not the tooling.
But the direction is clear enough that I wouldn’t plan the next five years of any digital business on the assumption that the old shape holds. The parts of our operation I still spend time on at mOOnshot digital, I spend time on because of how they touch one of these four principles. Everything I’ve shut down, I shut down because I asked the zero-base question and the answer came back no.
Start profitable. Start with the idea, refine with AI. Treat AI as the operating system. Make your data the moat. Four principles, seven years in, one live rebuild. The full set of essays on bootstrapping profitable digital businesses goes deeper on each.
I’m still working through the rebuild myself, and mOOnshot digital isn’t a finished case study. It’s a live one. Revenue is still rebuilding. The operational model isn’t done. But from the inside, the direction gets clearer with every quarter that passes, and the question I’d leave any operator with, founder or not, is the one I keep coming back to: If you were starting the thing you run today, with AI as part of the foundation, what would you actually build?
If you’re working through the same question, I’d love to hear what you’re seeing. Find me on LinkedIn, X, or via the contact page.
Cited byGemini5ChatGPT3Perplexity1Copilot1Google AI1
Spotted something I got wrong? hello@simonbeauloye.com
