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AI Adoption Curve

September 26, 2025 03:15
Caleb Writes Code avatar
Caleb Writes Code
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AI Adoption: Beyond Tasks to Agentic Systems

This video explores the current landscape of AI adoption, dissecting it into three segments: AI Applications, Agentic Applications, and AI Native, to understand the barriers and future potential.

Understanding the AI Adoption Landscape

The speaker introduces a framework to analyze AI adoption:

  • AI Applications [0:31-1:05]: These tools solve immediate problems and assist with tasks. Examples include ChatGPT, Perplexity, and Copilot. They have a low barrier to entry as they are non-invasive and don't require significant changes to existing workflows or extensive system integration. AI adoption is high in this category because of their ease of use and ability to speed up individual tasks.
  • Agentic Applications [1:03-1:36]: These are more autonomous and invasive, aiming to go deeper into workflows than simple task completion. They have a higher barrier to entry, requiring more integration and commitment from users and systems. The speaker notes these are surprisingly rare, often because they don't solve the right problems or users aren't ready for them.
  • AI Native [1:34-2:06]: This is a new frontier where AI systems are fundamental to the entire operation, from tasks to workflows and systems. While challenging, it offers a "golden snitch" advantage, potentially leading to significant gains if executed correctly, especially for startups that can build from scratch.

Barriers to AI Adoption: Task vs. Workflow

The video highlights key challenges and insights regarding AI adoption:

  • ChatGPT as an Example [2:36-3:40]: ChatGPT demonstrates the high adoption of AI applications due to its extremely low learning curve. However, this ease of use often translates to a lower return on value, as the output is directly tied to the user's input and effort in defining the task.
  • Task Completion vs. True Agency [3:38-4:10]: Even tools like coding agents, which can autonomously read and write code, are often used as task completion tools rather than true autonomous agents. Completing tasks agentically is distinct from an agent autonomously completing tasks.
  • Overemphasis on Task-Level Disruption [4:08-4:41]: Projections about AI disrupting industries often focus on task-level improvements. While AI is becoming highly effective at tasks like writing code or reviewing documents, this is more akin to task delegation rather than a fundamental shift in workflows.
  • The Agentic Application Gap [6:13-7:16]: A core challenge for agentic applications is that they attempt to take over entire workflows, which requires users to relinquish control and change existing habits. AI applications, on the other hand, help solve known problems within existing workflows, making them less disruptive and more readily adopted.
  • "Overkill" and Misapplication [7:14-7:47]: Agentic applications are often seen as overkill and are reduced to simple task completion tools because solving workflow-level problems is complex and unique. This is likened to using a Lamborghini in a school zone – powerful but misapplied.

Scaling AI and Organizational Impact

The video also touches upon the scalability of AI adoption within organizations:

  • Complexity and Organization Size [7:45-8:17]: Similar to time and space complexity in computer science, AI adoption becomes exponentially more complex as the size of an organization grows. This leads to individual gains rather than systemic organizational improvement.
  • AI as an Amplifier [8:46-9:19]: The Dora report suggests AI amplifies existing organizational strengths and weaknesses. In well-aligned organizations, it enhances efficiency, while in fragmented ones, it exposes pain points.
  • The Need for System Redesign [9:17-9:50]: For AI to scale its impact beyond isolated task boosts, organizations must intentionally redesign workflows, roles, governance, and cultural expectations. Without these changes, AI tools may remain as mere individual productivity enhancers.

Key Takeaways

  • AI adoption is progressing, but the focus is largely on AI applications for task completion. [0:00-0:33]
  • Agentic applications face adoption hurdles due to their disruptive nature and the need to change established workflows. [6:13-7:16]
  • The true potential of AI for organizational transformation lies beyond individual task automation and requires a fundamental redesign of systems. [9:17-9:50]
  • Startups with the ability to build AI-native systems from the ground up may have an advantage in achieving deeper AI integration. [1:34-2:06]