The "Legible Frontier" is the dynamic boundary where messy, unknown domains become understood, measured, and systematized, unlocking new opportunities for scale and investment. And that’s where all the fun’s at.
Ever look at a field like B2B SaaS today and think, "Wow, this is a well-oiled machine"? We've got acronyms for our acronyms (ARR, LTV, CAC, NDR, T2D3, MQRLTV!) (I only made up some of those), established playbooks, and VCs who can pattern-match your pitch deck before you hit slide three. It feels solved, in a way. And the financing is changing. But rewind to, say, 2010, and it was a lot murkier. That journey, from murky to mapped, is what I call crossing the "Legible Frontier."
It's the line between the wild, woolly, and unquantified parts of the world and the bits we’ve managed to measure, model, and mostly make sense of. Think of it like an old map: "Here Be Dragons" on one side, and neatly gridded, named territories on the other. This isn't a brand new idea, mind you. James C. Scott wrote brilliantly in Seeing Like a State about how governments try to make things legible (think standardizing surnames or street grids) to better control them. But focusing on this "frontier" itself, especially in business and tech, gives us a useful lens. And I think it’s where all the fun is.
So, what's it like in the "illegible" zone beyond the frontier? It’s where the art happens, often born of necessity. Back in the early SaaS days, pricing was a grand experiment. Per-seat? Tiered features? Usage-based? What even was a good churn rate for a business burning cash but showing user love? Founders and early investors were operating on vision, grit, and gut. The metrics that now feel like immutable laws of SaaS physics were still being sketched out on whiteboards, debated in blog comments. It was high risk, high ambiguity, and potentially, high reward for those who got it vaguely right.
Then, things start to crystallize. Key metrics get standardized – MRR becomes the heartbeat, LTV/CAC the core equation of sustainability. Playbooks emerge, often codified by those who stumbled through the dark and found a path.
Or consider options pricing, pre-Black-Scholes. Before 1973, options were traded, sure. But it was a black art. Rules of thumb, experience, "the market will bear"… that kind of thing. You wanted to know the "fair" price of a call or a put? Good luck. It depended on… well, everything, in a way that couldn't be quantified. And then bam! Black-Scholes. A formula. Yes, a simplified formula that made all sorts of crazy assumptions about constant volatility and frictionless markets. But suddenly, you could calculate a price. And that one seemingly arcane bit of math unlocked trillions of dollars in trading volume, created entire industries of quant finance, and, okay, occasionally blew up the world in spectacular fashion. Legibility has its costs, but oh boy, does it have its upsides.
Legibility is a difference in kind.
Once a domain becomes legible, everything changes. Capital flows in more readily because the risks and opportunities are easier to model. You can build systems, optimize processes, and scale operations with more confidence. Think about modern web advertising – a bewilderingly complex machine under the hood, but it's legible in terms of its inputs (bids, creative, targeting) and outputs (ROAS, CPA, CTR). It's become an engineering discipline, not just a sales pitch.
What’s new/different about the “Legible Frontier” lens?
So what new insight does the "Legible Frontier" lens offer that Scott's "legibility" doesn’t quite nail on its own? First, it’s focused on that boundary and the transition. It’s less about the static state of "legible" and more about the excitement and uncertainty of the mapping expedition itself.
Second, where Scott often (rightly) critiques legibility as a tool of state control that can crush local wisdom, the "Legible Frontier" in a market context often highlights opportunity. Making a messy domain legible – like standardizing financial reporting, or SaaS metrics – unlocks massive economic activity. The real alpha often lies in identifying and navigating a space just before it becomes widely understood and efficiently priced. The change in legibility is the key point.
Third, this framing explicitly values the "illegible" zone not as chaos to be tamed, but as the wellspring of true innovation. It’s where the next big thing is gestating, precisely because it doesn't fit current models. The pioneers are those comfortable operating without a map, because they're the ones drawing it.
This gives the "Legible Frontier" a powerful strategic and predictive punch. It encourages us to ask: What current "messy" domains are on the cusp of becoming legible? What's the missing piece – a killer metric, a breakthrough model, a common platform – that will tip it over? Think about GenAI companies, chained AI Agents, etc. – all feel somewhat illegible right now, with competing definitions and nascent standards.
The trick, of course, is that while legibility brings efficiency and scale, it can also bring rigidity. When the map becomes the territory, we might miss new paths or become fragile to unexpected shifts. The most interesting question then becomes: where is the next frontier, and are you equipped to venture beyond the edge of the current map? What currently illegible space will define the next decade?
PostScript — where this ideas plays with other ideas
If I ever start a new Venture Capital fund, and it was a Seed or Series-A fund, I want to call it Legible Frontier Group (so we can use “LFG capital” as the acronym)
If you’re noticing parallels to the classical Hero’s Journey as a first-person telling of an experience across the Legible Frontier, you’re right!
If you’ve seen those “exponentials are just stacked s-curves” charts, like this. Each of those s-curves is a legible frontier being crossed.
Very high degree of resonance here, period. A few points that jump out to me to riff on.
1. There's an interesting aspect of post-legibility alpha where because any map can only ever be correlated up to a point with the actual phenomenon, for example, actual revenue opportunity and demand, that the legibility heuristics become a prison instead of a maze.
2. And there's also a secondary effect there that has some connection to the great idea of financial markets being engine more than camera, in that all of a sudden you have this interplay where both what founders pursue becomes more aligned to the legibility heuristics and therefore there's fewer experiments in the danger zone of illegibility that all the more increases the alpha.
Maybe this is a bit of an aging alternative culture take on it, but it's been persistently validated to me. I believe fairly strongly that the biggest alpha in total value created, though maybe not value capture, is generally where there is no clear model at all whatsoever.