Self Propagate, Before Scale - Accelerating Change With Simple AI Hacks
Building AI systems can be inspired from nature to build mechanisms to grow organically and sustainably
In the rush to scale software and AI products, engineers and founders often overlook a fundamental principle that nature mastered billions of years ago: effective self-propagation mechanisms must precede meaningful scale. Just as a dandelion's remarkable success doesn't come from its first flower but from its ingenious seed dispersal system, software products need built-in mechanisms for organic growth before pursuing aggressive scaling strategies.
This article is the final one in a series of simple AI hacks which can be used to accelerate change:
Self Propagate, Before Scale
Learning From Nature
Consider how a single mushroom spore can give rise to vast mycelial networks spanning thousands of acres. The secret lies not in the initial fungal body, but in its sophisticated propagation system – microscopic spores designed for wind dispersal, each carrying the complete genetic blueprint for reproduction. Similarly, the most successful software products don't simply grow through brute-force marketing; they contain inherent mechanisms for self-replication and viral spread.
Listen to an AI moderated discussion accompanying this article.
Blockchain networks offer a perfect technological parallel to these natural systems. Bitcoin didn't achieve its massive network effect through traditional scaling methods. Instead, its fundamental design – the mining reward system, the ability for anyone to become a node, and the built-in incentives for network participation – created a self-propagating ecosystem that grew organically. This mirrors how bacterial colonies achieve exponential growth not through individual cell size but through binary fission – each new cell carrying the ability to spawn two more.
The implications for modern software development are profound. Before obsessing over server capacity, marketing budgets, or user acquisition costs, developers should ask: What are our product's spores? What mechanisms have we built into our system that encourage natural propagation? Can our users effortlessly spawn new instances of value, and does our architecture support viral coefficient growth? Just as nature's most successful organisms prioritize reproduction mechanisms over individual growth, software systems need to master self-propagation before pursuing scale.
Learning From Frontier Model Capabilities
Recently Anthropic and OpenAI launched major advances in LLM capabilities.
Anthropic has released a new feature that allows Claude to interact with computers. When set up with the right software, Claude can follow instructions to move the mouse cursor, click on things, and type information using a virtual keyboard - similar to how a person uses a computer. This new ability, which is currently being tested in a public beta version, marks an important step forward in AI capabilities.
OpenAI has created a new model called o1. It's designed to think carefully about problems before giving answers, similar to how a person might work through their reasoning step by step. In tests, o1 has shown impressive results: it can solve programming challenges better than 89% of competitors on a platform called Codeforces, performs well enough in math to rank among the top 500 U.S. students in a major math competition, and can answer graduate-level science questions more accurately than human PhD students in physics, biology, and chemistry.
This principle - Self Propagate, Before Scale - becomes even more critical in the AI era, where training larger models and collecting more data often overshadow the need for organic growth mechanisms. The most successful AI products will likely be those that, like natural selection itself, contain built-in mechanisms for improvement and propagation across use cases and user bases. Think of how language models can be fine-tuned and adapted for specific domains, creating new "organisms" suited to different niches – a form of technological speciation that enables organic growth across diverse applications.
The advent of sophisticated reasoning capabilities in models like OpenAI's o1 and Anthropic's computer-using Claude presents a unique opportunity to reimagine how AI systems can self-propagate. Just as natural systems evolve through iterative improvement and adaptation, these new AI capabilities enable products to grow organically by learning from their own interactions and reasoning processes.
The ability of modern LLMs to engage in complex chain-of-thought reasoning before providing answers mirrors biological systems' ability to process environmental signals before responding. This inherent deliberation mechanism can be leveraged as a self-propagation tool, where each interaction generates valuable data about the reasoning process itself. Products built on these models can automatically identify successful reasoning patterns and propagate them across different use cases, creating a natural selection process for effective problem-solving strategies.
Computer use capabilities, as demonstrated by Claude, introduce another dimension of self-propagation potential. Just as mycelial networks extend through soil by sensing and adapting to local conditions, AI systems that can directly interact with software interfaces can organically discover and optimize workflows. This capability allows AI products to propagate across different software environments without requiring custom integrations, similar to how successful organisms adapt to various ecological niches.
The reinforcement learning approaches used in training these models offer lessons in designing self-propagating systems. OpenAI's success in improving o1's performance through iterative refinement of its chain of thought demonstrates how products can be designed to automatically evolve their capabilities. Instead of relying solely on external updates, AI products can be architected to identify successful reasoning patterns and propagate them across their knowledge base, creating an internal mechanism for continuous improvement.
Safety considerations in these advanced models also suggest new approaches to sustainable self-propagation. The ability to integrate safety policies into the chain of thought, as demonstrated in o1, shows how products can maintain alignment while scaling. This mirrors biological systems that maintain genetic stability while evolving, suggesting that successful AI products should have built-in mechanisms for preserving core values while adapting to new contexts.
The impressive performance of these models on complex tasks like competitive programming and scientific reasoning points to another key aspect of self-propagation: specialization. Just as successful species often evolve to dominate specific niches, AI products should be designed to excel in particular domains before attempting broader application. This specialized excellence can then serve as a foundation for organic growth into adjacent areas, driven by the product's demonstrated success in its core domain.
The hidden chain of thought approach used in o1 suggests a new paradigm for product architecture: internal complexity supporting external simplicity. Like biological systems that maintain complex internal processes while presenting simple interfaces to their environment, AI products can leverage sophisticated reasoning capabilities while maintaining user-friendly interfaces. This architecture supports self-propagation by making the product more accessible to new users while maintaining powerful internal mechanisms for adaptation and improvement.
The incorporation of computer use capabilities demonstrates how AI products can leverage existing infrastructure for self-propagation. Rather than building custom environments, products can be designed to utilize standard software interfaces, similar to how organisms use existing environmental resources for growth. This approach reduces barriers to adoption and allows the product to naturally spread across existing technological ecosystems.
Human preference evaluation results from these models highlight the importance of feedback loops in self-propagation. The clear superiority of o1 in reasoning-heavy tasks, combined with its lower preference scores in some natural language tasks, demonstrates how products can use user feedback to identify and focus on their most effective growth vectors. This mirrors natural selection's ability to optimize organisms for specific environmental pressures.
Finally, the rapid improvement in performance metrics for these models when given more time to think or more attempts at a problem suggests a key principle for self-propagating AI products: design for iteration. Products should be architected to improve through repeated attempts and refinement, similar to how biological systems evolve through generations of trial and error. This iterative improvement mechanism becomes a core part of the product's self-propagation strategy, enabling organic growth through continuous learning and adaptation.
Core Principles for Growing LLM Products
The emergence of frontier AI models has fundamentally changed how we should think about product growth. As Jensen Huang notes, "we've reinvented computing" - shifting from traditional software development to building "intelligence factories." This transformation requires new approaches to product development and scaling.
Following principles are derived based on recent interviews with Jensen, CEO Nvidia, Sam Altman, CEO OpenAI, and CPOs from Anthropic and OpenAI.
1. Align with Exponential Infrastructure Improvement
Traditional Approach: Linear infrastructure scaling, Fixed performance expectations, Optimization for current hardware
Self-Propagation Approach:
Design for exponential improvement curves
As Jensen Huang describes: "double or triple performance every year at scale"
Plan for "hyper Moore's law" improvements
Build products that automatically leverage infrastructure advances
Architecture should benefit from both vertical (better models) and horizontal (more compute) scaling
2. Design for Reasoning and Reflection
Traditional Approach: Direct input/output relationships, Fixed response patterns, Limited context understanding
Self-Propagation Approach:
Enable chain-of-thought processes
Build in reflection capabilities
As Sam Altman notes: "The ability for models to think through things, self-reflect, and improve their own reasoning"
Design for: Self-improvement cycles, Automated error detection and correction, Learning from interaction patterns, Quality self-assessment
3. Build Intelligence Factories, Not Just Products
Traditional Approach: Static feature development, Manual optimization, Fixed capabilities
Self-Propagation Approach:
Create systems that generate and improve intelligence
As Jensen describes: "These new data centers... are producing tokens... reconstituted into what appears to be intelligence"
Focus on: Continuous capability expansion, Automated knowledge discovery, Self-optimization mechanisms, Learning transfer between tasks
4. Implement Robust Evaluation Systems
Traditional Approach: Basic metrics tracking, Manual quality assessment, Fixed evaluation criteria
Self-Propagation Approach:
As highlighted in the CPO discussion: "The job of a PM in 2024/2025 building AI-powered features... is getting good at writing evals"
Build comprehensive evaluation frameworks that:
Automatically assess model performance
Identify improvement opportunities
Track reasoning quality
Measure self-improvement cycles
Enable continuous optimization
5. Design for Multi-Modal Integration
Traditional Approach: Single-modality focus, Separate systems for different types of data, Limited integration capabilities
Self-Propagation Approach:
Build for cross-modal learning and interaction
As Jensen notes: "Tokens are tokens... if you can tokenize it"
Enable: Cross-modal learning transfer, Integrated reasoning across modalities, Universal representation learning, Automated pattern discovery across domains
Conclusion
The future of LLM-based products lies in creating self-propagating systems that leverage the exponential improvements in AI capabilities. As Sam Altman notes, "We believe that we are on a pretty steep trajectory of improvement." Success requires building products that naturally benefit from these improvements rather than fighting against them.
The key is to shift from building static products to creating intelligence factories that can continuously learn, improve, and scale. This approach, combined with robust evaluation systems and safety mechanisms, enables sustainable growth that compounds over time.
Remember Jensen's insight: "We've reinvented computing" - this isn't just about building better software, it's about creating systems that can generate and propagate intelligence autonomously.
Hope you found this series useful and are able to apply it to build cool, innovative products. Please do share your change acceleration stories so I can feature these in my next article.