A recurring frustration in modern discourse: the inability to see the system. Not the parts, not the actors, not the symptoms, the system itself.

When tech journalists obsess over Elon Musk’s latest stunt, when pundits trade blame over who radicalized whom, when legacy institutions collapse in public view and all we do is tweet emojis about it—we’re reacting to surface ripples. We’re not tracing the currents underneath.

To act intelligently, you need an influence map - a network map. A way of seeing who holds the microphone, who built the sound system, who books the venue, who prints the tickets, who rigged the acoustics. Network cartography is the practice of making these systems legible.

It is not, strictly speaking, a science. But neither is it an abstract metaphor. It’s closer to a a meta-discipline: part political science, part systems theory, part investigative journalism, part psychogeography. And lately, I can’t shake the feeling that it’s becoming essential for our survival in any form.

Power is no longer represented in the form of a single throne / crown. It’s dispersed. It flows through boards, foundations, APIs, Discord servers, subreddits, wire transfers, and backchannel Signal chats. The people with the most leverage are the folks with no official titles at all.

None of this is new, exactly. Mark Lombardi, the artist who sketched intricate diagrams of money laundering and political influence in the 1990s, was practicing a form of network cartography long before the term existed. So were investigative journalists mapping the connections between lobbying firms and congressional votes. So were the early hackers and cypherpunks who sketched out the interdependencies of the internet and its protocol stack.

What is new is the scale. And the fluidity.

Institutions used to ossify into charts, hierarchies, silos. Influence could be graphed vertically.

Not anymore.

Networks shift shape faster than most reporters can track. A 23-year-old shitposter spins up a Telegram channel and within weeks, it becomes more influential in shaping the Overton window than a tenured professor or a Sunday op-ed. A handful of engineers leave OpenAI and start a foundation that winds up directing the trajectory of AI safety. A substack newsletter triggers a policy panic.

Without a map, you’re constantly surprised. You wake up to news that some obscure DAO tanked a stablecoin, or that a niche Twitter account got someone fired from a university. You feel whiplash not because the world is chaotic, but because you’re looking at it with a 20th-century lens.

Network cartography lets you see with 21st-century eyes.

The mistake people make when hearing "network" is to think of it in terms of friend graphs or org charts. That’s part of it, but only a thin sliver. A useful network map reveals at least five dimensions:

1. Influence: Who affects whom? Not who has the biggest following, but whose decisions ripple outward.

2. Interdependence: Who relies on whom to operate? Where are the bottlenecks, keystones, and fail points?

3. Latency: How fast does influence propagate? Is it viral, glacial, or cyclical?

4. Opacity: How visible is the influence? Is it overt or covert? Public or semi-private?

5. Leverage: Where does a small input create disproportionate output?

In the 2021 GameStop stock saga, surface-level narratives told us this was Reddit vs. Wall Street. But underneath that was a far more interesting network: payment-for-order-flow firms routing trades through dark pools; Robinhood’s obligations to its clearinghouse; social media sentiment engines scanning r/WallStreetBets for trading signals; regulatory inertia in the SEC. What looked like a populist uprising was also a structural exploit.

A good map would have shown that before it happened.

Start with a question. Not "Who is powerful?" but "Why did this happen?" Then ask: what invisible dependencies enabled it?

Network mapping usually starts with nodes and edges. But unlike social network analysis, your goal isn’t just density or centrality scores. You want to annotate your map with context:

• What kind of influence is being exerted? Financial? Emotional? Reputational?

• Is the relationship active, dormant, or contingent?

• Are there hidden gatekeepers or informal validators?

The best maps aren’t made in one pass. They accrete. A good analyst builds them the way a hacker builds a threat model: adversarially, suspiciously, with attention to soft spots.

It helps to think in terms of affordances. Not just who holds power, but what the system makes easy or hard. Does the platform afford rapid mobilization? Does the policy afford regulatory arbitrage? Where do high-trust relationships substitute for formal contracts?

And yes, you need tools. Graph databases, Neo4j, Obsidian with backlinks, visualization libraries like Gephi. But tools don’t create insight. You do. The real work is in reading between lines, pattern-matching, and cultivating a kind of sociotechnical paranoia. Not paranoia about surveillance. Paranoia about misunderstanding the substrate.

• Intelligence agencies, though rarely publicly. They map influence for strategic advantage.

• Investigative journalists, especially those following money, lobbyists, or disinformation campaigns.

• Activists, particularly in decentralized movements, who need to identify weak points and amplify chokeholds.

• Corporate strategists, especially in tech, where value flows less through supply chains than through ecosystems.

• Conspiracy theorists, unfortunately. Often wrong, occasionally perceptive. Their maps are frequently polluted, but they’re at least looking in the right direction.

Network cartography is morally neutral. It can be used for liberation or control. It can also be ignored, which tends to benefit incumbents.

Because systems are getting harder to see.

Machine learning algorithms are not only black boxes—they create downstream black boxes in human behavior. No one can explain why the TikTok algorithm favors a given post. But millions of creators contort their behavior around it anyway.

DAOs promise transparency, yet hide influence in Discord moderators and multisig wallets. Protocols are public, but their norms are tribal.

Power lives where accountability does not.

Network cartography is the act of dragging that power into the light.

It’s slow. It’s analog. It rarely gets clicks. But it makes you smarter than the average narrative consumer. And it gives you options beyond outrage or despair.

The goal: to shift public opinion on climate tech in a mid-sized Western country.

Where would you start?

You might begin by mapping:

• Which think tanks publish influential whitepapers

• Which journalists quote those think tanks

• Which podcasts and YouTubers those journalists listen to

• Which conferences and Slack groups they frequent

• Which Twitter accounts feed them daily outrage fuel

Then ask: Where can I inject influence with minimal resistance?

Maybe it’s a mid-tier conference with outsized reputational spillover. Maybe it’s an underpaid research analyst who controls the first draft of a key report. Maybe it’s a Discord mod who shapes the norms of a 40,000-member climate DAO. Maybe it’s an infrastructure grant that funds the next open-source energy calculator used in 50 academic citations.

This isn’t manipulation - not exactly, although it can feel like it. It’s foresight. It's moving from being a node to being a navigator.

1. If something doesn’t make sense, assume there’s a missing edge.

2. Follow money, follow reputation, follow affordances.

3. Don’t mistake volume for importance. Often the quietest actors have the most enduring leverage.

4. Always annotate your maps. A connection without context is a conspiracy. A connection with metadata is a hypothesis.

5. You are in the network. Influence flows both ways.

You can draw all the maps you want. But unless you act on them, they become academic.

That means risk. You might misread the structure. You might poke the wrong node. You might discover that you were part of the problem all along.

But that’s the price of agency.

To navigate a system is to acknowledge your place within it. To chart a network is to accept that you may, in time, need to change it.

Or burn it.

Or build a better one.