Why Stronger AI Companies Need More People
💡DeepSeek and Notion show why AI scale creates backend, Agent, support, and inference-cost problems—not just productivity
⚡ 30-Second TL;DR
What Changed
DeepSeek’s new positions cover backend services, API operations, data engineering, research platforms, Agent frameworks, and infrastructure.
Why It Matters
AI automation does not automatically make AI companies smaller: it shifts labor toward operating complex systems and integrating them into customer workflows. Founders should budget for human deployment, governance, support, and infrastructure capacity alongside model costs.
What To Do Next
Load-test your Agent platform for concurrent isolated runs, then add per-task usage metering before exposing autonomous workflows to enterprise customers.
Key Points
- •DeepSeek’s new positions cover backend services, API operations, data engineering, research platforms, Agent frameworks, and infrastructure.
- •Its DSec platform is intended to run, train, and evaluate many Agents concurrently in isolated environments across operating systems, virtual machines, networking, storage, and scheduling layers.
- •Notion plans to grow from roughly 1,000 to 1,300 employees, with about half of its more than $600 million ARR reportedly coming from AI products.
- •Notion’s AI gross margin fell by roughly 10 percentage points because every AI summary, search, or Agent task creates upstream inference costs.
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Original source: 虎嗅 ↗
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