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# Notion's Token Town — Sarah Sachs, Notion

**[AI Engineer](https://daily.dev/sources/aidotengineer)** · 23 min read · 1 upvotes · 0 comments

## Summary

Sarah Sachs, head of AI engineering at Notion, shares strategies for managing AI token costs sustainably. Key insights include staying model-agnostic to maintain negotiation leverage, recognizing that AI model suppliers are often direct competitors, routing different traffic types to appropriately-priced models rather than always using the most capable (and expensive) ones, leveraging open-weight models for moderate tasks, and using CPUs instead of GPUs for deterministic workloads. She also covers multi-agent orchestration challenges, the 'lethal trifecta' security risk in agentic systems, and demonstrates Notion's own software factory workflow using Claude, Codex, and partner agents collaboratively.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=-I5W5QVAT8E>

## Questions this post answers

### What is the lethal trifecta in AI agent security?

The lethal trifecta refers to a dangerous combination in autonomous AI systems: access to private data, exposure to untrusted content (through ingestion, MCP, or email), and the ability to communicate externally, including payloads embedded in web search results. When a system has all three simultaneously, it creates significant unsupervised risk, and the more autonomous the system, the greater the exposure.

_Teams building autonomous agents can use daily.dev to keep up with emerging security frameworks like this one._

### Why is relying on a single AI model provider risky for a product's cost structure?

Building an AI product tied to one frontier lab creates a bad deal because that lab is effectively also a competitor selling the same tokens first-party at a much lower cost of goods, then reselling to you at a markup which you resell again. Without the ability to switch providers when pricing changes unfairly, a company loses negotiating leverage and risks its business viability entirely.

_Developers weighing AI vendor commitments can track pricing and model shifts on daily.dev before locking in._

### What percentage of Notion's AI traffic is handled by an auto-selecting model instead of the most expensive frontier model?

Notion's auto model handles about 75% of its AI traffic, routing tasks to whichever model fits the capability and cost needs rather than defaulting to the priciest frontier option. This model-agnostic routing lets Notion offer state-of-the-art models when needed while avoiding vendor lock-in and unnecessary token spend on simple tasks like triaging an email inbox.

_Engineers designing multi-model routing systems can find similar cost-optimization approaches on daily.dev._

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#notion](https://daily.dev/tags/notion)

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