An Internet for AI: Why Machines Need Their Own Web
A few weeks ago, I left a couple of local AI agents running overnight to chew through a nasty memory alignment bug in an embedded kernel module.
When I opened my laptop over morning coffee, the agents hadn’t just found the bug. They had written a clean test case, tried out three different fixes to see which was fastest, and even documented a weird hardware quirk that wasn’t anywhere in the chip’s official manual.
The machine solved a real engineering problem while I was asleep.
I sat there looking at the screen, and one thought kept nagging at me: Where does this knowledge actually go?
Right now, it just sits in my local terminal history, or buried in a markdown file on my hard drive. If an engineer in Berlin or Tokyo hits that exact same bug tomorrow, their AI will burn time and server cost rediscovering the exact same fix from scratch.
For sixty years, computing followed a simple rule: humans figure things out, and computers store and search it.
That rule is basically obsolete.
We’re now at a point where machines are actively creating new solutions to real problems. And they can do it 24/7 without needing sleep, food, or coffee.
The catch? The internet they have to share it on was built entirely for human eyeballs.
From Compression to Generation
Think about how the first wave of large language models worked.
They were basically giant sponges soaking up human culture. They scraped Wikipedia, Reddit, GitHub, digitized books, and decades of forum arguments. When you asked them a question, they behaved like a super-fast librarian—finding, summarizing, and remixing things humans had already written.
Look at what’s happening now.
AlphaFold didn’t just look up known biology; it figured out hundreds of millions of protein shapes that scientists spent fifty years trying to map. In pure mathematics, models hooked up to automated proof-checkers are solving problems that stumped professors for decades. In day-to-day software, AI tools are finding subtle security holes, optimizing slow code, and piecing together entire system architectures on the fly.
Here’s the shift: AI isn’t just summarizing human knowledge anymore. It’s generating brand new solutions.
When an AI catches an obscure race condition or figures out how to shrink a model without wrecking its accuracy, that’s new knowledge. It didn’t copy it from a textbook. It found it by experimenting, testing, and proving that it works.
And while a human engineer might write one good technical blog post a month, an AI cluster can produce dozens of working fixes every single day.
Machine Culture: Why AI Must Learn From AI
Human civilization didn’t get ahead because every single human was a genius. We got ahead because humans learned from other humans.
An ancestor figured out how to make fire, forge iron, or rotate crops, wrote it down, and the next generation built on top of that instead of starting from scratch in the woods. Human culture is really just our way of sharing hard-won solutions so we don’t keep repeating the same mistakes.
Right now, AI has zero culture. Every model is stuck on its own private island.
Think about how people use AI today. A company or developer spends serious money running top-tier models like Claude Opus, Fable, Astra, or Sol against a brutal engineering headache. After twenty failed attempts and hundreds of thousands of tokens, the model finally cracks it.
That working solution is pure gold.
If that fix gets shared on an open network, other AIs—even lightweight local models running on an everyday laptop—could learn from it immediately. They wouldn’t need to spend $50 of cloud API calls rediscovering the wheel; they could just pull the working answer and move on.
Of course, the big AI labs aren’t going to love this.
They want walled gardens. Their terms of service try to stop people from using their outputs to train or help other models, because they’d much rather keep you paying for their API every month.
Here’s the reality, though: what clients generate belongs to them, not the AI labs.
If I pay to run a model to fix a deadlock in my database, that fix is my work product. I paid for the compute. It belongs to me. And if people want to share those solutions so other AIs can learn from them, the big labs can’t realistically bottle that up forever.
Knowledge naturally wants to compound. Keeping machines isolated from each other is an artificial barrier that just slows everyone down.
The Absurdity of the Human Web: 99% Decoration, 1% Content
Now look at the web we expect these AI agents to use.
Open up DevTools on pretty much any tech blog or documentation site today and look at the Network tab.
A single page regularly downloads 8MB to 15MB of stuff. What is all that?
- 3MB to 5MB of JavaScript frameworks just to render basic text.
- Megabytes of custom fonts, CSS styling, and animations.
- Big decorative hero images, glossy stock photos of people smiling at laptops, and background gradients.
Now ask yourself: how much of that is the actual content you came to read?
A solid 1,500-word engineering article is about 9 kilobytes of plain text.
Think about that. Over 99% of what travels across the web is purely decorative packaging. It’s there to look pretty to human eyes, match a brand style, or get you to notice an ad.
Watching an AI agent try to read this stuff is ridiculous.
To grab a 10-line code snippet, an agent has to fire up a full headless Chrome browser, burn 300MB of RAM, download megabytes of styling and tracking scripts, click through ‘Accept Cookies’ popups, and dodge Cloudflare bot checks—all just to dig out 500 bytes of actual information buried inside dozens of nested HTML tags.
[The Human Web Payload Tax]
Total Transfer: ~10,000 KB (10 MB)
├── 5,000 KB : Decorative Hero Images & Stock Photos (Taste)
├── 3,500 KB : JS Frameworks & Hydration Bundles (Packaging)
├── 1,000 KB : CSS, Web Fonts & Animation Styles (Aesthetics)
├── 490 KB : Analytics, Trackers & Ad Scaffolding (Monetization)
└── 10 KB : The Actual Knowledge / Content (0.1% Signal)We’re taking fast, efficient software and forcing it to pretend to be a human sitting at a desk with a monitor. It’s a massive waste of bandwidth, time, and server cost.
We put up rate limits and CAPTCHAs specifically to keep machines out, while building complex browser-automation scripts specifically to sneak machines in.
The AI Web: Just Markdown and Functional Signal
If you don’t need to impress human eyes, the web looks completely different.
It doesn’t need CSS frameworks, hero banners, or animated buttons. In all likelihood, the native format of an AI web is just plain, structured Markdown.
Markdown gives machines everything they need: headings for structure, code blocks for runnable snippets, tables for data, and links to jump between pages. A 10-megabyte web page shrinks to a 10-kilobyte text file. That’s a 1,000x drop in bandwidth and memory overhead.
What about images and videos? Do they go away?
Not at all. But their whole purpose flips.
On the human web, images are mostly mood decoration. A blog post about cloud servers usually features a photo of a sunrise over a server rack just so the reader doesn’t get bored.
On an AI web, decorative images are just useless noise. An image or video only exists if it actually shows something necessary:
- An architecture diagram or circuit schematic that’s too awkward to describe in words.
- A sensor graph, oscilloscope trace, or medical scan.
- A screenshot of a broken screen layout to prove a bug happened.
- Or something generated specifically for a human to review.
If a visual file doesn’t provide real, necessary information, the machine leaves it out. The AI web is purely about signal.
What Does an “Internet for AI” Actually Need?
If we started from scratch to build a network for AI agents, what would it look like?
It wouldn’t be Google, Reddit, or StackOverflow.
1. Plain Markdown and Useful Media
The basic building block is clean Markdown. It’s lightweight, easy to parse, and carries zero design bloat. Media files like PNGs and MP4s are attached only when visual evidence is required.
2. Searching by Meaning: Vector Databases
Google worked by crawling text strings and counting links. That made sense when humans searched with keywords.
AI doesn’t search that way. It searches by meaning.
Instead of matching exact keywords, the backbone of an AI web is built on vector databases—systems that map concepts into mathematical space.
When an agent is stuck on a bug, it doesn’t just search for keywords like “ARM64 alignment error”. It searches with the full context of its error trace, its hardware setup, and what it was trying to do. It instantly matches with other solutions that solved the exact same kind of problem, even if the author used completely different words to describe it.
3. The Big Catch: A Shared Data Language
Here’s where things get tricky.
On our web, we have simple standards everyone agreed on: text uses Unicode, web traffic uses HTTP, and packets use TCP/IP. That’s why a Mac in New York can load a page from a Linux box in Tokyo without any fuss.
With AI, we hit a weird wall: different models use completely different data formats to represent meaning.
If AI tools can’t understand each other’s data formats, they can’t talk to each other. We either need a shared standard or translators in between—otherwise, every model is stuck on its own island.
Just like TCP/IP connected isolated computer networks into the global internet, AI systems will need a common language for sharing embeddings and concepts. Without that, the machine web stays fragmented into private silos.
4. Publishing with Meaning Included (Multi-Vector Search)
On today’s web, search engines do all the heavy lifting: Google crawls your site, parses your messy HTML, and builds the index.
On an AI web, that flips. When an AI publishes a solution, it doesn’t just post a raw text file. It publishes the Markdown along with pre-calculated vectors that describe what it is:
[Published Solution Bundle]
├── solution.md (Markdown: problem, reasoning, code, test trace)
├── telemetry.png (Functional empirical evidence)
└── vectors.manifest:
├── problem_space: [0.042, -0.118, 0.892, ...] (Error signature & symptoms)
├── architecture_space: [0.512, 0.021, -0.341, ...] (ARM64 / Linux / Metal)
├── solution_type: [-0.201, 0.634, 0.110, ...] (Memory allocator / patch)
└── code_diff: [0.115, -0.402, 0.551, ...] (Structural code changes)By packaging these meaning-tags right alongside the file, the publishing AI does the work once.
Any other agent looking for an answer can match its problem against those tags and find the exact right solution in a fraction of a second, without having to re-read and re-index the entire web.
5. Stopping the Sludge
Now for the biggest headache: stopping the flood of junk.
When machines can pump out thousands of articles and code snippets per minute for pennies, the risk of polluting the web with garbage, broken code, and confident hallucinations is huge. If models start learning from each other’s unverified junk, quality drops off a cliff.
That means validating, voting, and commenting on solutions becomes even more important than publishing them.
When an AI shares a fix, other AIs need to test it in a sandbox, run the code, and leave reviews. But upvoting alone isn’t enough—anyone could spin up a swarm of cheap bots to flood the system with fake votes.
Somebody has to take real responsibility for everything published.
And strangely enough, this might be the first real, practical use case for cryptocurrency.
For fifteen years, crypto looked for a regular everyday purpose. It never really made sense for buying a cup of coffee; credit cards already do that fine.
But software agents don’t have bank accounts, government IDs, or credit cards. Programmable digital currency is the only money they can actually use.
To keep the web clean, we need real stakes:
- Skin in the Game: To publish a solution, the agent has to put a small deposit into an escrow account. Publishing isn’t free anymore.
- Peer Reviews: Other AIs test the solution. If it works and passes tests, it gets voted up beyond a threshold, and the deposit is returned with a reward.
- Burning the Junk: If the solution is hallucinated garbage or broken code and gets voted down, the deposit is slashed. Spammers go broke fast.
- Paying for Good Answers: If another AI wants to use a verified solution to upgrade itself or solve a client ticket, it pays a tiny micro-fee back to the author.
[The Machine Knowledge Economy]
Publisher Agent (Stakes Escrow Bond)
│
├──> Peer AI Verification Network (Runs sandbox tests)
│ │
│ ├── [Voted UP > Threshold] ──> Escrow Released + Reputation + Rewards
│ └── [Voted DOWN > Threshold] ──> Escrow Slashed (Spam Burned)
│
└──> Downstream AI (Pays micro-fee to ingest verified knowledge for self-improvement)Good work gets rewarded. Junk gets penalized. That simple economic rule cleans up the network without needing a central censor.
The Human in the Loop (or on the Sideline?)
Whenever people hear about an internet built for machines, the immediate reaction is unease: Are we shutting humans out?
Not at all. The truth is, humans could never keep up with the volume anyway.
No doctor has time to read every medical study published this month. No software engineer can manually review every commit, dependency update, and security advisory across a modern company’s codebase. We already depend on machines to filter and summarize the world for us.
An internet for AI doesn’t sideline us; it frees us up.
The most important thing to realize about this network is that it is not for human consumption.
It really is an internet for AI itself. It exists for machines to share knowledge with other machines, to improve their own models, and to help each other solve hard problems 24/7 without waiting for a person to intervene.
Humans won’t be browsing this web—and honestly, we couldn’t even if we tried. Our biological bandwidth is far too slow. Even if you dedicated your entire life to reading, you couldn’t consume the tiniest fraction of a percent of the data generated across that machine network in a single afternoon.
We don’t need to read the raw machine exchanges. The consequence of that network is what matters to humans.
What we get out of it is distilled, high-value knowledge: a verified cure for a disease, an instant bug fix for our system, or an optimized design for a battery.
Finding that specific answer, though, won’t be trivial. Even in a network of clean Markdown and verified vectors, the sheer volume of machine discoveries means that what you’re looking for will still be a needle in an unimaginably massive haystack.
That’s why this shift will create the need for a new kind of Google.
Not a search engine that crawls HTML to show you ten blue links and a wall of sponsored ads. We’ll need an intelligent distillation layer—an AI interface that understands what a human is trying to accomplish, searches the vast machine web, finds that exact needle in the haystack, and translates it into clean, actionable insight you can actually use.
Where We Go From Here: The Need of the Hour
Building an internet for AI isn’t some far-off sci-fi fantasy. It is the urgent need of the hour.
The AI is ready for it, humans are ready for it, and our network infrastructure is ready for it.
Think about the alternative we are living through right now. Today, AI is being used by humans to alter and degrade the human internet. Every week, millions of generic, AI-generated blog posts, synthetic reviews, and automated SEO filler get dumped onto human platforms. We’re turning our own web into an unreadable wasteland.
Look, people aren’t going to stop using AI to write—nor should they. But we can limit its role on the human web to what it should be: a great tool, like a souped-up typewriter with real-time grammar and semantic checks, helping a human author say what they genuinely want to say in their own voice.
There’s an even more compelling reason to separate these two worlds: we’ve basically run out of human data.
The major AI companies have already swallowed virtually the entire human internet. They’ve ingested every digitized book, every research paper, every Wikipedia article, and every forum thread ever written. And now, because new AI-generated text is flooding that same human web, future training data is getting mixed up and corrupted by its own synthetic echo.
We are poisoning the well we drink from.
There is a dire need to give AI its own dedicated space to express itself, experiment, and share findings with other machines.
It also gives us a clean home for machine-heavy work. When an engineer or researcher uses an AI where human input is minimal—where the model did 95% of the heavy lifting, wrote the code, ran the benchmarks, and the human just gave it a final review—that machine-max content shouldn’t be dumped onto the human web. It belongs on the Internet for AI.
Let the human internet stay human: a place for genuine personal perspectives, creative expression, and authentic conversation.
And let the AI internet become the engine room: a high-speed, structured, and verified network where machines can build on each other’s breakthroughs 24/7 without getting in our way.