Has AI Perfected Obsidian—Or Rendered It Obsolete?

 

Thanks to AI plugins, Obsidian is once again in the spotlight as the ultimate "Second Brain." However, I argue the exact opposite. AI did not perfect Obsidian; it completely eradicated the very reason for its existence.

 

The prototype of Obsidian is the German Zettelkasten—a pre-PC knowledge management system where one jots down notes on index cards and stacks them in boxes. The core concept is that as fragmented information accumulates, connections organically emerge, and insight is extracted from that network.

 

The initial rush of adrenaline when you first turn on Obsidian’s Graph View is undeniable. It looks beautiful enough to be a desktop wallpaper.

 

But that is where the problem begins.

 

Connections do not form automatically. You must manually double-bracket every single keyword to forge a link. A single typo or a slight formatting discrepancy causes the system to misrecognize it as an entirely different word. Consequently, you spend more time managing the format than generating actual notes. At this point, it becomes unclear whether you are accumulating knowledge or just accumulating formatting rules.

 

Thus, AI plugins arrived like a savior. They automatically generate nodes and links, summarize text, and surface related notes. The community rejoiced, claiming that Obsidian was finally perfected.

 

Driven by the hope that AI could finally make knowledge management efficient, I returned to Obsidian after a long hiatus.

 

It didn’t take long, however, to discover a fatal flaw.

 

There are two primary ways to populate Obsidian: top-down and bottom-up. The former involves creating a core index of a field and filling in nodes accordingly—like drawing a map first and labeling the cities. The latter means creating nodes on the fly and connecting them later.

 

I despise the bottom-up approach. When you watch your nodes trickle in one by one, you lose steam before a meaningful network even takes shape. To get that aesthetically pleasing graph, you need at least hundreds of nodes, but writing even 10 high-quality nodes a day is exhausting. I prefer the top-down method—sketching a blank map beforehand and filling in the roads, railways, and cities. Yet, even in this case, the reality that you must manually populate every single node remains unchanged. It offers nothing more than a psychological placebo effect.

 

Since building a proper Second Brain requires months of dedication under either method, AI seemed like the perfect relief pitcher. Users began asking GPT, Gemini, or Claude to generate Obsidian-ready Markdown files. However, delegating this task to resource-heavy, token-limited models like Claude is a sheer waste.

 

But do GPT and Gemini actually deliver proper nodes? My experiments revealed a massive structural issue.

 

No matter how sophisticated your prompt is, models like GPT and Gemini are inherently optimized for speed. When handed complex, long-form tasks, they frequently ignore system constraints to deliver statistically plausible answers. Even when you strictly restrict the allowed sources, they inevitably smuggle in data from Wikipedia or general wikis— unlike Claude, which is notoriously strict about adhering to user-defined restrictions.

 

As a result, instead of saving time, you waste hours verifying sources. If you delegate node creation to these rapid-fire LLMs to cut corners, your vault becomes flooded with low-fidelity nodes. Looking for the most logical connection among unverified nodes is no different from picking the least rotten apple from a barrel of spoiled ones.

 

One might ask: won't these errors cancel each other out if you have enough nodes? No. An Obsidian node is already a degraded, low-resolution version of the original source. In the process of reading, summarizing, and linking, context is stripped and errors creep in. If the original text is 100, a node is always less. When an AI processes that node again, it degrades further.

 

Furthermore, for errors to cancel each other out, they must be randomly distributed. But AI hallucinations and user confirmation biases are not random. They are systematic errors that accumulate in the exact same direction as data grows.

 

The result is a visually stunning network. The graph is dense, and the links are plentiful. But a plausible-looking network is not the same as a trustworthy one. A vault filled with unverified nodes does not reflect genuine knowledge; it merely mimics its silhouette. If the purpose of Obsidian is to aid decision-making, a contaminated network does more harm than good. A flawed judgment backed by false confidence is the most dangerous kind of mistake.

 

Even if you somehow preserve the quality of the nodes, a fundamental issue remains: most Obsidian notes are written and never read again. What insight can possibly emerge from a network of dormant notes? Moreover, seamless synchronization between PC and smartphone remains notoriously clunky. Are we seriously expected to whip out our laptops on public transport or in crowded cafes just to view our notes?

 

The truth is, generative AI already does what Obsidian attempts to do, only exponentially better.

 

Obsidian is a hand-drawn map. The more effort you put in, the more precise it becomes, but everything outside the cartographer's field of vision remains an eternal blank space. AI, on the other hand, is a satellite image. Places you have never set foot in are already captured. To connect [[Economics]] and [[Psychology]] in Obsidian, you must manually build that bridge. In an LLM, that connection is already embedded as weights within its neural network during training, even without explicitly mentioning "Behavioral Economics."

 

The sheer scale is incomparable. An LLM learns directly from original source texts across hundreds of billions of tokens without generational degradation. Statistical reliability stems from the massive scale of training. Between a few thousand degraded nodes and hundreds of billions of original texts, which knowledge network is inherently more reliable?

 

The moment you attach AI to Obsidian, you think AI is empowering the tool. In reality, AI is quietly absorbing it. Obsidian was merely a manual, analog simulation of what an LLM achieves algorithmically.

 

Of course, I anticipate the counterargument:

“AI only shows the well-trodden paths, whereas unexpected insights can spark from the haphazard wiki-links I forged myself .”

 

But believing that insight emerges from loose connections is a romantic delusion. What most people mistake for "insight" is actually a logical leap. Genuine insight requires structural covariance between phenomena. Finding a forced structural similarity between two entirely unrelated items is non-sequitur. True creative connection does not happen by stacking fragments; it happens when the brain is forced to bridge gaps while struggling to construct a single, cohesive, long-form argument.

 

Another counterargument is that the "hard labor" of reading, summarizing, and linking forces knowledge into long-term memory, aiding learning.

 

If learning is your goal, you should be writing comprehensive pieces, not managing a database of notes. Engaging in aesthetic note-taking does not translate to higher academic performance. AI already synthesizes and links information far better than humans ever will. The real cognitive work—the actual learning—happens when you take raw material, rigorously stress-test it, and synthesize it into your own prose, such as publishing a structured blog post.

 

AI makes mistakes, yes. But an AI's errors exist in an open architecture where they can be actively interrogated and cross-examined by changing your prompts. A flawed node inside an Obsidian vault, however, hardens in isolation, quietly contaminating everything connected to it. An architecture that exposes error is fundamentally superior to one that conceals it.

 

This is why I prefer blogging.

Of course, one could argue that anyone can publish garbage on a blog, but I will leave that up to individual conscience.

 

A blog post possesses structural integrity and, by virtue of being published in a public forum, is exposed to external feedback. Obsidian is strictly private and a collection of fragmented texts; no one is there to point out your errors. Even if you do catch a mistake, correcting it requires overhauling the entire web of connected notes—pure, tedious labor.

 

Furthermore, if you back up your long-form writings to a cloud drive like Google Drive, you can prompt an AI to surface relevant pieces whenever needed. There are no synchronization headaches, and no formatting guidelines to standardize.

 

The core of knowledge management is not the sophistication of the tool, but the quality of the prose. AI didn't perfect Obsidian. It merely exposed that the problem Obsidian set out to solve can be solved far better without it.