Smart money flow! Wall Street veteran with 20 years of quantitative experience explains: Why the more you "closely follow" AI, the more dangerous your $BTC and $ETH positions become?

The speed of AI model iterations has already surpassed the limits of human information processing. Twenty minutes after the release of Opus 4.6, GPT-5.3 Codex was launched. The day before was Kling 3.0, and before that? I can’t remember. This pace creates a constant low-level pressure, making you feel that if you don’t learn new things immediately, you’ll be left behind.

But the problem isn’t too much information; it’s that you lack a filter. In quantitative trading, when we handle vast amounts of market data, the first thing we do is build a signal filter to eliminate 99% of the noise and only capture the 1% of effective signals that drive the prices of $BTC and $ETH. When dealing with AI information streams, you need the same discipline.

Why do you always feel behind? Three forces are working together. First, the AI content ecosystem is driven by a sense of urgency—headlines claiming to “disrupt everything” always get more traffic than “small improvements.” Second, there’s loss aversion—the brain’s fear of missing out on opportunities is twice as strong as the excitement of gaining new ones. Third, overload of choices—hundreds of tools, countless articles—paralyzes decision-making.

The result is a typical trap: you know a lot about AI, save countless tweets, subscribe to multiple services, but have never used them to produce anything valuable—whether analysis reports or trading strategies. In the crypto market, this is equivalent to studying all technical indicators but never placing a single trade.

True “keeping up with the trend” isn’t about consuming all information. It means having a system that can automatically answer this question: “Is this important for my work—like managing your crypto portfolio?” Unless you’re creating marketing videos daily, Kling 3.0 isn’t relevant to you. Unless you’re an smart contract developer, most code model updates are just background noise.

Build your filter with three actionable approaches.

Option 1: Create a “Weekly AI Briefing” agent. Stop aimlessly scrolling social media. Use tools like n8n to set up a workflow that runs once a week. Have it fetch 5-10 reliable sources, then filter through an AI node. The instructions for this node must be specific: “Here is my background: crypto asset analyst, working daily on on-chain data analysis, macro event interpretation, and writing investment memos. Only filter updates that directly impact these tasks.”

Every Sunday night, you’ll receive a summary: what was released this week, 1-2 relevant items, what I should test, ignore everything else. Monday morning, you’ll no longer feel anxious because the noise has been cleared.

Option 2: Test with “your own prompts,” not someone else’s demo. When a new tool passes through your filter, don’t watch the demo. Use it directly with 5 core prompts relevant to your work, such as: “Analyze the correlation between whale transfers and price movements on $ETH over the past 24 hours,” or “Summarize in a concise paragraph the potential impact of this week’s Federal Reserve meeting minutes on risk assets.”

Compare the results side by side with your existing tools’ outputs, score: better, same, worse. Within 30 minutes, you can draw conclusions based on real work data, not marketing hype. Most “disruptive” releases don’t pass this test. Performance gaps between models are narrowing, but those who use tools wisely are widening the gap from news chasers.

Option 3: Distinguish between “benchmark releases” and “business releases.” 90% of releases are “benchmark releases”: higher scores in standardized tests, faster processing. This is important for researchers but irrelevant to whether you should adjust your $BTC position on Tuesday afternoon. Only “business releases” are worth paying attention to: do they offer new capabilities you can immediately integrate into your workflow, like more precise parsing of complex regulatory documents?

A simple question can cut through the fog: “Can I reliably use this to analyze $BTC trends this week?” Stick to this standard for a few weeks, and you’ll develop a conditioned reflex—within 30 seconds, decide whether a news item is worth 30 minutes of deep research or just ignore it.

When these three approaches are combined, the situation will change dramatically. An agent fetches information for you, personal testing provides real feedback, and categorization filters out distractions in advance. AI updates will return to their essence: some relevant, most irrelevant.

In the AI field, future winners won’t be those who know every release, but those who build filtering systems that can identify what truly matters to them and delve deeply into it. The current competitive advantage isn’t in the channels of information acquisition but in knowing what to ignore.

This skill is rarely discussed because it’s less flashy than showcasing a cool AI-generated image. But it’s the key to distinguishing doers from information collectors. The pace of new releases will only accelerate, but the right system can turn this into an advantage rather than a threat. In the volatile crypto markets, maintaining focus and rationality is itself a form of alpha.

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