Notes from the Galaxy

When new money enters an economy, it does not splash evenly. It hits the closest hands first. The same thing is happening with AI: capability, tooling, and awareness propagate outward from a few sources — and proximity still compounds.

(People often say “Cantillion.” The economist was Cantillon. Same idea either way.)

What the Cantillon effect actually is

In finance, the Cantillon effect is simple: new money changes who can buy what, and when.

If the Fed expands liquidity, that money does not teleport into every paycheck on Tuesday. It enters near the source — banks, balance sheets, markets that sit next to the hose — then works its way out through businesses, wages, retail shelves, and eventually the factories and workers who feel prices after the bidding has already started.

Early recipients spend and invest against yesterday’s prices. Later recipients meet today’s prices. Same dollars. Different timing. Timing is the edge.

The journey of new money

Here is the path I walk people through:

Fed → banks → businesses → employees → retail → factories and workers overseas.

A January print does not show up as a Southeast Asian wage or shelf price the next morning. By April or May, that wave is often already moving through overseas supply chains. The map is not a conspiracy chart. It is a hose with length.

Who stood nearest the nozzle got optionality first. Who stood farthest paid the delayed invoice.

Now run the same logic on AI

AI has its own Cantillon effect.

New models, new agents, new workflows, new research — they do not land as a simultaneous cultural download. They hit the nodes closest to the labs, the builders, and the platforms where those people already talk. Then the idea migrates. Then the product migrates. Then the “everyone knows” phase arrives months later, priced differently.

If money has a first hose, capability does too.

X is the first hose

For this cycle, X is where a lot of the water comes out first.

Labs post there. The Elon ecosystem lives there. Grok Bot and the people wiring agents into real work show up there. Technical early adopters argue, ship, and fork ideas in public before the same thread becomes a LinkedIn carousel or a TV panel.

You do not have to love the feed. You do have to notice where the early signal is dense.

Being proximal on X is not cosplay. It is standing closer to the nozzle while the rest of the world is still waiting for the summary.

How the wave leaves the hose

Ideas do not stay on X. They bridge.

Instagram and LinkedIn often pick them up weeks to months later — same insight, softer packaging, different audience. Then YouTube turns the thread into a longer explanation. Then TV and schools get a cleaned, delayed version — useful, but late.

LinkedIn here is a hop on the map, not a destination. The point is the lag: each surface is farther from the hose.

If you only consume the late surface, you still learn. You just learn after the people nearest the source have already integrated the tool into how they work.

ChatGPT was a clean timeline

November 2022: ChatGPT lands.

By February, the tech-forward crowd is deep in it. By April and May, awareness is spreading past builders into broader professional conversation. Free access brings volume. Paid tiers ($20, then higher seats like $100 and $200 over the following months) sort power users from casual ones. Roughly a year later, “modern” users — people who treat the tool as normal infrastructure — are common.

That is not a trophy chart. It is a Cantillon clock: source → early adopters → awareness → paid utility → mainstream habit.

Miss the early months and you still get the product. You miss the window where integrating it into a business felt quiet, cheap, and strangely unfair.

Why people stayed: utility beat Google at the jobs that mattered

People did not stick around for the press cycle. They stuck for work.

Images. Slides. Homework. Richer search and synthesis than “ten blue links and hope.” Once the tool was clearly better at chunks of real output, later waves did not need persuasion — they needed distribution.

Utility is what turns a hose into a river. Awareness without utility fades. Utility without proximity still arrives late.

Why proximity pays (especially for business)

If you run a company or a SaaS product, proximity is not vibes. It is sequencing.

Integrate early and you catch waves two through five — product features, ops automations, agent workflows — while competitors are still debating whether the demo was “real.” Wait for TV certainty and you buy the same capability after attention, talent, and customer expectations have already repriced.

The Cantillon edge in AI is not “know a buzzword first.” It is wire the tool into the business while the rest of the market is still reading the delayed copy.

MCP as a propagation example (2025–2026)

Watch how connectors moved the industry.

As Model Context Protocol–style connectors spread through 2025 and into 2026, agents got more useful — not because of a slogan, but because they could touch more real systems. When agents got more useful, labs leaned in harder. Usefulness pulled capital and attention toward the pattern.

That is Cantillon logic again: capability densifies near the people building the pipes, then radiates outward as the pipes become obvious.

(This is an industry story about connectors and agent usefulness — not a marketplace pitch.)

Still early. The effect is real.

Mainstream awareness is louder than it was. That does not mean the hose is empty.

Most businesses still have not installed agents as actual operating leverage. Most people still learn from the late surfaces. The gap between “I saw a demo” and “this runs part of my company” is still wide.

So the effect remains: who is close to builders, research, and working agents will keep seeing the next wave before it is a curriculum slide.

How to stay proximal

Stay near the hose on purpose.

On X: follow the Elon ecosystem, Grok Bot, Agents at @Agints_com / agints.com, OpenAI research, and the people actually building agents into production. Read primary posts. Try the tools. Wire one into a real workflow before the summary arrives.

Proximity is a habit, not a hashtag.

Why this Substack exists

A lot of the sharpest AI discourse still starts on X. A lot of owners and operators do not live there — and should not have to scroll chaos to get the signal.

This Substack is the bridge: take what is already moving near the hose, clean it up, and hand it to people who want the substance without the feed.

Same Cantillon map. Fewer noise layers.

If you want a Chief-of-Staff agent installed for your business in about 90 minutes, that is what we do at agints.com.

Mike Rodriguez, CTO