Controversial take: A fast, 80% accurate answer beats a slow, 100% accurate answer every single time.
Google’s entire philosophy has been about comprehensive indexing and authoritative results. They crawl billions of pages, rank them by relevance and trustworthiness, and deliver the “best” results.
This approach made sense when information was scarce. But in 2025, information is abundant. The bottleneck isn’t access—it’s speed of understanding.
The Cost of Perfectionism
Consider two scenarios:
Scenario A: You ask a question and get a highly accurate, well-sourced answer in 3 seconds.
Scenario B: You get a reasonably accurate answer with caveats in 0.1 seconds.
Which is better? It depends on the question. But for 90% of information queries, Scenario B wins because:
- You stay in flow state instead of waiting
- You can iterate faster if the first answer isn’t quite right
- The cognitive cost of switching contexts is eliminated
Real-Time Understanding vs. Comprehensive Indexing
Google’s model is based on indexing: crawl the web, store everything, rank it, serve results. This creates latency at multiple stages.
An alternative approach: Don’t index anything. When someone asks a question, fetch relevant data in real-time, synthesize an answer on the fly, and deliver it instantly.
“But that’s impossible!” you say. “Real-time synthesis can’t compete with pre-indexed results!”
Except it can. LLMs have proven that language models can generate coherent, contextual responses faster than traditional search engines can retrieve and rank results. The technology exists. The will to implement it at scale doesn’t—yet.
The 80/20 Rule of Knowledge
80% of questions have straightforward answers that don’t require PhD-level expertise to deliver. “What time does the store close?” “How do I convert PNG to JPG?” “What’s the capital of Mongolia?”
For these queries, perfect accuracy is overkill. Good enough, delivered instantly, is superior.
The remaining 20% of questions—complex research queries, medical decisions, legal advice—do require careful, authoritative answers. But even here, speed matters. A fast preliminary answer that says “This is complex, here’s what I know, and here’s what you should verify” is better than making someone wait.
Speed as a Paradigm Shift
When information becomes truly instant, it changes behavior:
- You stop hoarding knowledge because you trust it’ll be available when needed
- You ask more questions because there’s no friction cost
- You explore tangential ideas because rabbit holes are frictionless
- You think differently because external knowledge becomes an extension of working memory
This is what Google’s executives fear: A world where their carefully built index becomes irrelevant because real-time synthesis is faster and more useful.
The Infrastructure Challenge
Building real-time knowledge systems requires different infrastructure than traditional search:
- Edge computing to minimize latency
- Streaming synthesis instead of batch processing
- Probabilistic data structures over exhaustive indexes
- Graceful degradation when perfect answers aren’t available
It’s technically challenging. But so was building Google’s original PageRank system. The difference is motivation.
“Perfection is the enemy of done. And in information systems, done faster beats perfect slower.”
Speed isn’t just a feature. It’s a fundamental rethinking of how humans should interact with knowledge.
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