Playbook | 100x

Why we build more

For years, startups and their investors played by different rules.

Startups picked a single idea and focused on it relentlessly. Building software was expensive. Every additional product required more engineers, more capital, more coordination, and more time. Trying to build multiple products meant doing all of them badly. Multiple bets did not increase a startup’s odds. They weakened every one of them.

Venture capital did the opposite. Investors spread their bets across many startups, knowing that most would fail and a few breakout winners could carry the portfolio.

The startup concentrated. The investor diversified. Both strategies made sense.

AI changes the economics

Small teams can now build, launch, and operate products faster, with fewer people, and at a fraction of the cost.

AI does not make each bet safer. It makes more bets affordable.

That reverses the old equation. The portfolio model no longer belongs only to investors. AI brings it inside the company. A company can build a portfolio of independent products, giving itself more chances to find a winner.

Under these economics, betting the entire company on a single unproven idea is not disciplined focus. It is a single point of failure.

Focus must be earned

Building more does not mean scaling everything. It means giving more ideas the chance to earn focus.

Every product starts small. We launch it, expose it to reality, and look for real pull. Products that fail to earn attention are killed. Those that show promise get more time. Those that show exceptional pull earn concentrated investment.

We have not abandoned focus. We let evidence determine when we apply it. Traditional startups focus first and learn later. We learn first, then focus.

Why we stay small

Building a portfolio of products fails if each product needs a company of its own. If every success requires another growing team, the model breaks.

For most startups, being small is temporary. A small team builds the first version, but success brings specialists, managers, departments, and headcount. We want a different outcome: products that scale while the organization stays small.

Scale the product, not the organization

We measure operational success by how much value a small team can create. When demand increases, our default response is to simplify the product so the work disappears, then automate what remains. Revenue should grow faster than the organization behind it.

AI compounds that advantage. It allows highly capable builders to own products end-to-end and operate across areas that previously required entire teams. The goal is not to recreate the traditional company with fewer people. It is to avoid creating that company at all.

We will hire when the evidence justifies it. But headcount is not a milestone, and hiring is not our default response to success. Every new role must create more capability than the complexity it introduces.

Profit creates independence

For a software company, headcount is usually the largest fixed cost. Staying small shortens the path to profitability, while high operating leverage makes every additional dollar of revenue go further. That does more than create attractive margins. It gives us room to survive mistakes, kill failing products, take bigger risks, and concentrate behind winners.

Growth can then be funded by customers rather than outside capital, preserving our independence and allowing us to think long term.

Profit gives us the freedom to build what we believe in.