I spent over a decade in service businesses (strategy consulting, data analytics, an M&A boutique) and I would not trade a day of it. Those years taught me how industries actually work, how to read a market, how to structure ambiguity, how to lead teams under pressure, and how to prospect and close B2B deals when nobody knows your name. The business acumen you build across dozens of client engagements is genuinely irreplaceable.
But the business model has a ceiling. Revenue scales linearly with headcount. Every new dollar requires another person, another engagement, another set of hours sold. No matter how sharp the team or how strong the client relationships, you are selling time. And time does not compound.
Great training, wrong model
Consulting gave me the toolkit: how to diagnose problems fast, how to test hypotheses against evidence, how to build conviction about where a market is heading. I spent years inside corporates helping them launch new products and spin up internal startups. I saw what worked and what did not. I understood the mechanics of going from zero to one.
The limitation was never the work itself. It was the structure. At the end of every engagement, I handed over the keys and walked away. The value I helped create belonged to someone else. I wanted to lead from end to end: from the first hypothesis through to a product that runs, scales, and compounds value on its own.
Why a studio, not just a startup
The obvious move would have been to pick one idea and go all in. That is the default founder playbook: raise a round, build a team, commit to a single thesis for two or three years.
But my background in consulting taught me something about that model. Most of the time, the first thesis is wrong. Not slightly off. Structurally wrong. The market does not care about your conviction. It cares about evidence. And evidence takes experimentation.
A studio lets you run that experimentation properly. You can test a thesis hard and fast, and if the data says it is not working, you kill it and move to the next one without burning years and millions. The infrastructure carries forward. The judgment compounds. The speed improves with every iteration. It is the opposite of starting from scratch each time.
The power law in venture outcomes is well documented. Most returns come from a small number of outsized winners. A studio structure respects that reality instead of pretending you can predict which idea will be the winner before you have any evidence.
The AI platform reset
We are in the early innings of a platform shift as significant as the internet or mobile. AI is not just a new tool. It is a full reset of the cost structure for building software. A two-person team with sharp judgment and good AI tooling can now do in a week what used to take a team of eight a quarter. Prototypes that took sprints ship in hours. Market research that consumed an analyst for a month gets synthesised in days.
Most people have not yet understood what is coming. Every platform reset creates a window where the old incumbents are slow to adapt and small, fast teams can build disproportionate value. The internet had that window. Mobile had it. AI is opening one right now, and it will not stay open forever.
For someone coming from service businesses, this matters enormously. The old barrier to building products was the upfront engineering cost. You needed a CTO, a dev team, months of runway before you had anything to show a customer. That barrier kept experienced operators stuck selling expertise by the hour instead of embedding it into scalable software. AI removed that barrier entirely.
The hard part is no longer writing the code. The hard part is knowing which problem to solve, for whom, and why they will pay. That is exactly the skill set you build after a decade of consulting, analytics, and corporate strategy. The pattern recognition, the commercial instinct, the ability to read a market. The leverage AI provides to people with that background is extraordinary, and most of them have not realised it yet.
AI did not make studios interesting. It made them inevitable for operators who were already thinking in systems.
What we are actually building
Not an incubator. Not an accelerator. Not a holding company that slaps its logo on other people's ideas.
We build our own ventures from scratch. Each one starts with a specific problem, a specific customer, and a structural reason why AI gives us an unfair advantage in solving it. We validate obsessively, build fast, and either scale or kill based on evidence. Strict stage gates between each phase. If a venture does not earn the right to continue, it does not continue.
The studio itself is the product. Every build, including the ones we kill, deposits judgment, tooling, workflows, and operational playbooks back into the system. The process of finding product-market fit has transferable structure, and that structure gets sharper with every cycle.
From selling hours to building assets
The core shift is straightforward: take everything those years in services taught you (how industries work, where the real pain points hide, what people will actually pay to solve) and deploy it into products that generate value independently. A consulting engagement ends when the contract expires. A product keeps working while you sleep.
The service years were not a detour. They were the training ground. Every client engagement, every B2B sales cycle, every industry deep dive. That is the raw material for building products that matter. You cannot learn to read markets from a textbook. You learn it from sitting in rooms with customers who tell you one thing and mean another, from watching companies spend millions on problems that do not exist, from seeing which pain points are real enough that someone will write a cheque before the product is polished.
The studio model lets me deploy all of that accumulated judgment at speed, with AI handling the throughput and humans handling the hard decisions. It is the first structure I have found where everything I learned in services actually compounds instead of depreciating.
The honest version
I built a venture studio because the timing is right and the toolkit finally matches the ambition. A decade of consulting gave me the pattern recognition, the commercial instinct, and the cross-industry judgment. AI gave me the leverage to build at a speed that used to require a full engineering department. The platform is resetting, and operators who understand both the technology and the commercial reality are in a rare position to build something that lasts.
The studio model is how I walk through that window. Not with one bet and a prayer, but with a system that treats failure as data, compounds every lesson, and collapses the time between hypothesis and evidence.
That is [r]think. Built by operators, powered by AI, designed to compound.