The Intersection of Generative Tech and Open Source Attention in the Quest for Collective Sensemaking

The Intersection of Generative Tech and Open Source Attention in the Quest for Collective Sensemaking

Introduction:
The rapid advancements in generative technology and the rise of open source attention have created exciting possibilities for collective sensemaking. In this article, we will explore the different layers of the generative tech market map, the role of stigmergic social annotation, and how these two concepts intersect to shape the future of information processing and knowledge creation.

1. General AI models: The Core Breakthrough
   General AI models, such as GPT-3 for text and DALL-E-2 for images, represent a significant breakthrough in generative technology. These models have the ability to generate a wide range of outputs, including text, images, videos, speech, and even games. They form the foundation upon which more specialized AI models are built.

2. Specific AI models: Capturing Nuance
   Specific AI models delve deeper into specialized domains and capture more nuanced information. These models are trained on narrow, specialized datasets and excel at tasks like writing tweets, ad copy, song lyrics, and generating e-commerce photos or 3D interior design images. While specific AI models offer greater precision, they also face the challenge of maintaining defensibility in a competitive market.

3. Hyperlocal AI models: Proprietary and Trusted Data
   Hyperlocal AI models are specialists in their respective domains. They can write scientific articles in specific styles, create personalized interior design models, or even write code tailored to individual companies. The key advantage of hyperlocal models is their access to proprietary and trusted data. However, relying solely on data as a defensibility strategy may have limitations, as competitors can find similar datasets and develop comparable models.

4. API Layer or Generative OS: Enabling Interoperability
   The API layer or Generative OS acts as a bridge between applications and AI models. It allows applications to access the necessary AI models and facilitates easy switching between models. While this layer enhances interoperability, it also has the potential to commodify AI models. The proliferation of generative features in existing software and the emergence of new companies competing in this space further emphasize the importance of this layer.

5. Open Source Attention: A Socio-Technical Framework
   Inspired by the decentralization and open source software movements, Open Source Attention (OSA) aims to liberate human attention from platform control. OSA proposes a decentralized ecosystem for creating, storing, and querying stigmergic markers, which are the digital traces of human attention. These markers, such as likes, annotations, and hyperlinking, serve as signaling cues for collective sensemaking.

6. Stigmergic Social Annotation: A Distributed Memory System
   Stigmergic social annotation leverages the concept of stigmergy, where modifications left by others in the environment provide feedback and drive emergent system-level behavior. Sematectonic stigmergy directly alters the environment state, while stigmergic markers serve as digital traces of attention without modifying content. By harnessing stigmergic markers, we can support constructive collective sensemaking and move away from attention exploitation.

Intersection of Generative Tech and Open Source Attention:
The intersection of generative tech and open source attention presents exciting opportunities for collective sensemaking. OSA's focus on freeing stigmergic markers aligns with the open source movement's goal of liberating software. By introducing interoperable protocols and storage for stigmergic primitives, diverse PKM apps can contribute to collective sensemaking efforts on a global scale. This approach complements the "protocols, not platforms" philosophy, allowing existing PKM growth to bootstrap CKM.

Actionable Advice:

1. Emphasize Product Speed: Launch your features quickly and let the AI models learn and improve over time. Don't spend excessive time searching for the perfect dataset or model. Iteration and user feedback are key to success.

2. Prioritize Aggressive Sales: Aggressive sales efforts help embed your product in the market, build network effects, and create defensibility. Watch your competitors closely and learn from their best ideas. Sales speed is crucial for expanding into new categories and solidifying your position.

3. Seek Investors Who Sprint With You: Find investors who understand the importance of speed and agility. Look for partners who are willing to sprint alongside you, supporting your vision and helping you navigate the competitive landscape.

Conclusion:
The convergence of generative tech and open source attention opens up new frontiers in collective sensemaking. Leveraging the power of general, specific, and hyperlocal AI models, combined with the liberating potential of stigmergic social annotation, we can collectively create knowledge and insights at an unprecedented scale. By prioritizing product speed, aggressive sales, and finding the right investors, we can navigate this evolving landscape and shape the future of information processing and sensemaking.