About

    Not a real scientist. Very real results.

    Vibe Science names and formalizes the missing discipline between intuition and metrics. It's the applied study of how weak human signals and machine inference become validated decisions under uncertainty.

    That's exactly how real fields emerge. People laughed at "data science." They dismissed "design systems." They questioned whether "DevOps" or "product management" were real disciplines. Until they didn't.

    This site is part open notebook, part working tool. The Field Notes will be honest observations — not content designed to generate leads. The Experiments will be real attempts to validate hunches in organizational and product settings. When they arrive, the ones that worked and the ones killed early will be published with equal pride.

    The Lab exists for anyone who wants to practice this kind of structured sense-making. It's deliberately simple. It tracks vibes, signals, and experiments — nothing more. The constraint is the point.

    Beliefs & Commitments

    • —Early signal is a form of data, not a replacement for it
    • —Killing an idea early is one of the highest-value decisions a team can make
    • —The best operators hold intuition and evidence simultaneously
    • —Psychological safety isn't a perk — it's infrastructure for honest work
    • —Simple tools used consistently outperform complex tools used occasionally
    • —Hypotheses must be falsifiable
    • —Experiments must be documented
    • —Signals must be traceable
    • —Conclusions must be reversible

    Vibe Scientists by Another Name

    Every discipline existed before it had a name. These are the people already doing the work.

    Chief Product Officer / VP Product

    The closest existing role. Good CPOs already synthesize weak signals from customers, sales, support, and their own instinct into product bets. The gap: most lack a structured framework for tracking which intuitions proved right and why. The VSV loop formalizes what the best ones already do informally.

    CTO / VP Engineering

    Technical leaders who make architectural bets under uncertainty. When a CTO says "this technology isn't ready yet" or "we need to rebuild this before it breaks," they're reading weak signals. The overlap is in the pattern-recognition and early-kill decisions. The gap: engineering culture prizes certainty, which makes surfacing vibes culturally expensive.

    Head of Design / Design Director

    Designers are trained to hold ambiguity. Design research is already signal work — interviews, observations, synthesis. The overlap is strongest in the Vibe and Signal phases. The gap: design teams rarely carry work through to structured validation with business metrics.

    Chief of Staff

    The organizational sensor. Good Chiefs of Staff detect cultural drift, political dynamics, and strategic misalignment before they surface in data. They're doing vibe science for the CEO. The gap: the work is often invisible, undocumented, and unrepeatable — exactly what formalization would fix.

    UX Researcher

    Already doing signal clustering, already working with qualitative data, already synthesizing ambiguous inputs. The most methodologically prepared for vibe science. The gap: scope is typically limited to product surfaces rather than organizational or strategic decisions.

    Founder / CEO (Early-Stage)

    Every early-stage founder is a vibe scientist by necessity. They operate on pattern recognition, weak market signals, and informed intuition daily. The gap: as companies scale, this skill gets delegated and then lost. Vibe science is what happens when you try to keep it.

    The Trajectory: How Disciplines Formalize

    The pattern is always the same: practitioners do the work for years, someone names it and provides a framework, the industry resists, then adopts, then hires for it.

    Data Science

    The term was coined by DJ Patil and Jeff Hammerbacher around 2008. For years before that, statisticians and analysts were doing the work. Harvard Business Review called it "The Sexiest Job of the 21st Century" in 2012. LinkedIn created the first "Data Scientist" title at scale. Now every company has one.

    DevOps

    Patrick Debois coined the term in 2009. The work — bridging development and operations — had been happening for years. It took from 2009 to roughly 2015 for it to become a standard organizational capability. The "State of DevOps Report" (Puppet/DORA) gave it empirical legitimacy.

    Product Management

    Ben Horowitz wrote "Good Product Manager, Bad Product Manager" in 1998. Marty Cagan's "Inspired" (2008, updated 2018) codified the discipline. Product management existed at companies like HP since the 1930s (under "brand management"), but it took decades to become a recognized standalone function.

    Design Systems

    Brad Frost published "Atomic Design" in 2013. Designers had been building component libraries for years. It took naming the pattern and providing a methodology for it to become an organizational investment.

    Permission to Do the Work

    You don't need the title to do the work. None of the disciplines above waited for permission.

    • —Start by documenting your intuitions as hypotheses. That's the conversion from hunch to science.
    • —Track which ones proved right. That's your evidence base.
    • —Share your methodology, not just your conclusions. That's how disciplines spread.
    • —The title will follow the practice, not the other way around.