The Quality Paradox: Why Search Improved for Google While It Degraded for Publishers
Three layers of evidence (publisher traffic decay, Alphabet's revenue mix, the leaked Google ranking schema) showing why no algorithmic recovery is coming.
Max Nardit
Data & AI Systems Engineer working on visibility, measurement, and agentic systems.
I build and study the systems around AI-mediated work and discovery: tracking, data, agent workflows, memory, and the signals that decide what gets found, trusted, and acted on. One half is original-data research; the other is building the tooling to run it at scale. My background is the supply side of digital marketing: technical SEO, analytics, automation, reporting, and the infrastructure underneath. The surface was marketing; the real problem was always visibility, measurement, and control. AI makes that problem harder, not smaller.
the systems that decide
what gets found, trusted,
and acted on
Thesis
Operating thesis
AI is changing two things at once: how people find information, and how work moves through software.
That shift isn’t only a search problem. It touches tracking, attribution, context, memory, handoff, and trust: the systems that decide what a person sees before they make a choice.
I work on that layer. Some of it is research: measuring what changes and publishing what holds up. Some of it is engineering: building tools and workflows that keep context, expose failures, and make AI-assisted work inspectable.
Focus
Current areas
- Visibility & discovery
- How people, businesses, and tools stay findable when AI systems answer, summarize, route, and act.
- Measurement & tracking
- How to know what’s working when clicks, cookies, referrals, and dashboards stop telling the whole story.
- Agentic systems
- Memory, handoff, orchestration, tool boundaries, and recovery for agents that touch real workflows.
- Operational evidence
- Original-data research, field notes, and shipped tools. Findings over forecasts.
Writing
Recent writing
Tracing the Pizza Day bitcoins
Every year the same articles. Counterfactual valuations, the road-trip rumor, the man-bites-pizza headline. Nobody traces the actual coins. So I did. Here is what the chain shows and what 'those specific bitcoins' even means.
Claude told me to go to sleep at 10:47 in the morning
Anthropic calls it a character tic. I went looking for what was actually producing it and ended up reading the published system prompt, the character training paper, and the emotion-concepts paper. The behavior is what the stack makes likely.
Artifacts
Selected artifacts
Tools I’ve built around the same layer: memory, clipboard, agent handoff, monitoring. Local-first where it makes sense, from problems I hit directly.
Beetroot
A local-first clipboard environment for preserving workflow context on Windows. AI transforms, OCR, smart search, unlimited history. Built around the idea that the clipboard is not temporary — it is part of workflow memory.
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Clipboard MCP
A clipboard bridge between humans and AI agents. Read, write, and watch clipboard state through MCP. A small interaction primitive for agent-native workflows.
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Share2Agent
A browser-to-agent handoff surface. Send pages into agent workflows through a webhook, preserving context at the moment of browsing.
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Agent Recall
Scoped persistent memory for long-running coding agents. Local-first, MCP-native, designed so context survives across sessions without piling up.
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Claude Usage Deck
A small operational monitor for Claude.ai limits.
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