The Impact of AI on Work and Organizations
This year has brought significant realizations about the changing nature of work, particularly the increasing role of AI and its impact on organizational structures and individual capabilities. The shift is creating both challenges and opportunities as AI transforms traditional roles and business models.
The Rise of AI-Powered Tools and the "External Brain"
One key observation is the growing reliance on AI tools like Cursor. The speaker describes using Cursor extensively, even more than other software systems or interacting with people.
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Cursor serves as an "external brain," storing information, meeting notes, and details about people and organizations.
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This agent-based system allows individuals to access and process information more efficiently.
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It is helping individuals become "super individuals," exceeding previous limitations in capacity and productivity, potentially diminishing the traditional need for large teams.
The Limitations of Traditional CRM and the Need for Flexibility
Traditional Customer Relationship Management (CRM) systems, while capable, often constrain users with their rigid database structures.
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The user becomes a "slave" to the database, spending time clicking through menus and conforming to predefined workflows.
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AI tools offer greater flexibility and adaptability compared to traditional software, allowing for a more personalized and efficient approach to information management.
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The focus shifts from serving the CRM system to the CRM system serving the user, allowing the user to focus on high value tasks.
The Evolution of Corporate Organization Through Software
The organization of work, people, and tools is constantly evolving, heavily influenced by software advancements. This evolution can be broken down into different eras:
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Software 1.0: Companies had internal IT departments that developed customized software, with a hierarchical structure of CEOs/COOs, managers/directors, and executors (ICs and engineers).
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Software 2.0: IT departments were outsourced to SaaS companies, leading to standardized cloud services. Companies now subscribe to multiple SaaS tools, requiring operations teams to connect these different "Lego pieces."
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To standardize each SaaS, and to work together with other Legos, a lot of meaningless work has occurred
The Rise of "Fake Work" and the Need for Overall Intelligence
The standardization of SaaS tools and automation of basic tasks have paradoxically led to an increase in "fake work."
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Employees engage in activities like cross-department coordination, taking meeting notes, and managing multiple software systems, often reversing information from one system to another.
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This "human layer" is essentially performing low-level judgments and tasks that could be automated.
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The root cause of this "fake work" is often the overall lack of intelligence within systems and processes. Complex tasks require excessive coordination and planning due to the limitations of current AI models.
The Inevitable Displacement of Executive Roles by AI
The future holds significant changes in employment, with AI increasingly replacing white-collar and executive jobs.
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Companies are prioritizing software and GPU resources over hiring more employees, leading to job losses for software engineers and sales/marketing professionals.
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Executive roles will be transferred to AI companies as AI capabilities become more comprehensive.
The Future: AI-Native Companies and "Super Individuals"
Despite the advancements in AI, complete automation is still some time away.
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A 10-year timeframe is suggested for AI to achieve significant penetration across industries.
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In the meantime, individuals with industry knowledge and AI expertise will be highly valued, forming "super individuals" who are absorbed into AI-native companies.
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These super individuals will connect and manage a smaller number of AI-powered SaaS applications, supporting a larger number of companies with greater efficiency.
Strategies for Adapting to the Changing Landscape
To thrive in this evolving landscape, individuals and companies should prioritize:
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Combining AI into a super individual: Learning to use AI tools effectively to become more productive and capable.
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AI Prompt Engineering: Developing the skill to write clear and effective prompts for AI systems to achieve desired outcomes.
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Exploring Entrepreneurial Opportunities: Identifying niche areas where AI can be leveraged to create new products and services.
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Focus on data driven AI: Understanding industry experts and reinforce learning of data standards
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Embracing AI: Use without cost
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