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Product Manager’s AI Experiment: Creating 6 Digital Employees
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AI Productivity Experiment Results
class ProductivityReport: def init(self, name, ai_agents): self.name = name self.ai_agents = ai_agents self.output_increase = True self.work_hours_decreased = False “n A Chinese product manager deployed six AI employees using OpenClaw, revealing a critical paradox in AI productivity tools: increased efficiency doesn’t necessarily reduce workload.
Experimental Setup
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Agent Configuration
agents = { “work_agent”: “Handles calendar, tasks, focus”, “finance_agent”: “Monitors financials in real-time”, “content_agent”: “Creates podcast episodes daily”, “social_agent”: “Manages RedNote and X content”, “knowledge_agent”: “Maintains knowledge management system”, “personal_agent”: “Handles life admin tasks” } “n
Findings
Productivity Metrics
- Output: Increased significantly
- Task Management: Automated scheduling and monitoring
- Multi-platform Content: Daily podcast production
- Financial Tracking: Real-time monitoring implemented
The Efficiency Paradox
“n# Workload Analysis BEFORE_AI = { “work_hours”: “9-5”, “bedtime”: “12:00 AM”, “tasks”: [“manual scheduling”, “content creation”, “admin work”] }
AFTER_AI = { “work_hours”: “9-5 + extended”, “bedtime”: “2:00 AM”, “tasks”: [“strategic planning”, “creative work”, “agent management”] } “n Key insight: When efficiency increases, humans don’t work less—they attempt more.
Implementation Details
Initial deployment focused on a single “lobster” (Chinese netizen term for OpenClaw agent) handling multiple tasks:
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Initial Agent Architecture
def create_base_agent(): return { “calendar”: True, “scheduling”: True, “todo_list”: True, “focus_assistance”: True, “finance_management”: True } “n Agent count expanded as new problems emerged, with six specialized AI employees eventually managing both work and personal domains.
Technical Implications
This experiment suggests AI agents may transform work from task execution to system orchestration. However, the current implementation creates a feedback loop where increased capacity drives increased expectations.
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Future Work Model
class OnePersonCompany: """Model where humans coordinate AI agents""" def init(self, human_agent, ai_employees): self.human_agent = human_agent # Strategic oversight self.ai_employees = ai_employees # Task execution self.expansion_inhibited = False “n The challenge emerges not in managing AI capabilities, but in establishing sustainable boundaries when productivity tools continuously expand feasible output.
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