We are surrounded by noise in it’s myriad forms, from biases to prejudices to anecdotes - noise is everywhere. A business, however, cannot run on noise - businesses are like vectors, they have mass (the people that power it) and they have a direction (North stars navigated through nautical iterations of annual objectives)
Sadly, because humans can either be agents of order or chaos, noise can and more often than not, do muscles it’s way into businesses and can in many ways win over reason.
Building a startup is a chaotic process. Every decision made in the early stages can make or break the trajectory of the company. Founders who rely purely on instinct risk falling into the trap of bias-driven execution, where emotional attachments to ideas cloud judgment - I have made this error many times while building my company.
A different approach could be to approach building your business scientifically where decisions are driven by part intuition, part data and part process. Some superstar founders already do this like Olympian athletes, this article isn’t for them, but for the rest of us that struggle, this article is for us. With a scientific approach to company building, founders can navigate ambiguity with a sense of structure. Structures are good - they can give our minds a virtual tactile experience as if we’re holding an elastic ball which we can compress, stretch or bounce. Instead of making random assumptions, great entrepreneurs use data and frameworks to continuously refine their decision-making process. By treating your execution like an algorithm, you can move from randomness to repeatable, scalable success.
The inherent randomness and ambiguity of early stage venture, in a sense, uncovers the existence of negative or positive probabilities where given certain variables or the absence of such variables, the likelihood of assumptions becoming facts shy away from absolutes such as our internal biases, intuition or feelings. What we have is chaos and our first act must be to to bring order to the chaos. Baye’s theorem, can give us a “tactile” mental model we can use.
Bayes’ Theorem: Avoiding Conclusion Bias in Decision-Making
Startups are built on a series of hypotheses. However, it can be very easy to fall into conclusion bias—assuming that an early result proves your thesis or your decisions could be driven by a fundamental attribution error which could lead to unfavourable outcomes - outcomes that sadly burn through finite resources. Bayesian reasoning can be a fantastic tool to guard against this.
Bayes’ theorem can allow you to dynamically update your beliefs based on new data and also weigh your assumptions based on a confidence score. Instead of taking initial insights as absolute truth, Bayesian thinking encourages continual refinement of probabilities.
Example: Instead of assuming “Customers love this feature,” a Bayesian founder would ask:
- What percentage of customers actually use this feature?
- How does engagement change over time?
- If feedback is mixed, how should I adjust my assumptions?
By constantly reassessing data, founders make informed decisions rather than acting on incomplete signals.
First Principles Thinking: Understanding the Business at Its Core
Many startups fail because they rely on assumptions inherited from competitors rather than questioning fundamental truths. First principles thinking, famously advocated by Elon Musk, involves breaking problems down to their core components and rebuilding solutions from scratch.
Example: Instead of saying “Food delivery needs to be faster,” a founder using first principles would ask:
- What is the real bottleneck? (Speed? Cost? Consumer behavior?)
- What are the physics of the problem? (Logistics? Traffic? Restaurant wait times?)
- What’s the most efficient way to solve it? (Ghost kitchens? AI-powered dispatch?)
By deconstructing ideas into their raw form, founders unlock novel solutions rather than simply iterating on what already exists.
Second-Order Thinking: Predicting Long-Term Market Dynamics
Great founders don’t just ask, “What happens next?”—they ask, “What happens after that?”
Many startup decisions look great in the short term but create second-order problems that compound over time. Second-order thinking forces founders to anticipate unintended consequences before they happen.
Example:
- Uber solved ride availability (first-order effect), but it led to driver dissatisfaction and regulatory battles (second-order effect).
- Airbnb reduced hotel costs (first-order effect), but it contributed to housing shortages in key cities (second-order effect).
To avoid building fragile businesses, founders must think in decades, not just funding cycles. What external factors could impact your business model over time?
BAMO: Structuring Revenue Growth Toward a $100M ARR Target
For investors, growth matters as much as product-market fit. Startups that fail to structure revenue models early often struggle to scale efficiently. This is where BAMO (Base, Accelerators, Multipliers, Optimization) our proprietary revenue design framework comes into play.
BAMO provides a structured way to systematically scale revenue:
- Base Revenue: Initial revenue streams (MVP validation).
- Accelerators: Partnerships, viral growth loops, expansion strategies.
- Multipliers: High-leverage GTM strategies that drive compounding effects.
- Optimization: Refining efficiency for sustainable long-term growth.
Rather than chasing random revenue spikes, we believe founders should map out their path to $100M ARR in a way that’s repeatable and scalable.
Conclusion: From Random Execution to Algorithmic Growth
The best founders don’t just build—they think scientifically about how they build.
By integrating:
- Bayesian updating to refine assumptions dynamically
- First principles thinking to break down ideas into fundamental truths,
- Second-order effects to anticipate long-term risks, and
- BAMO to structure revenue growth predictably,
…founders can move from chaotic execution to algorithmic venture building.
Those who embrace scientific decision-making will not only build better companies—they will outlast the competition by adapting faster, iterating smarter, and compounding their success.
🔥 What are your thoughts on applying scientific reasoning to startup execution? Drop your insights in the comments!
