The AI Revolution and the Shifting Calculus of Tech Adoption: Why Potential Trumps Ecosystem

Ability of definition

For years, a major factor in choosing a technology stack or programming language wasn’t just the tool’s raw capabilities, but the size and vibrancy of its ecosystem. We’d ask: How large is the community? How many libraries and frameworks are available? How easy is it to find support, tutorials, or developers?

This made perfect sense. A large ecosystem meant readily available tools, pre-built solutions, peer support for debugging, and a steady stream of developers entering the field. Software thrived not just because it was inherently superior in design, but because so many engineers were using it and contributing to its surrounding world. The “ecosystem” was the moat.

But I’ve been pondering a different idea, and I believe the rise of AI fundamentally changes this calculus.

In the age of advanced AI, the traditional need for a massive human ecosystem is diminishing. If I need specific functionality, I don’t necessarily need to wait for a third-party library to be developed by the community. I can leverage powerful AI models to generate code, create bespoke tools, integrate systems, and even simulate environments.

This leads to a paradigm shift: The primary consideration is no longer the size of the existing community or ecosystem, but the inherent potential of the tool itself. Beyond its currently stated capabilities, what is the maximal scope of what this technology can achieve? How extensible, flexible, and fundamentally powerful is its underlying architecture?

We no longer primarily care about community size; we care about the technology’s raw capacity.

Applying this theory, certain preferences emerge:

Game Engines: Perhaps Godot better than Unity, not necessarily because of its current market share or asset store size, but because its node-based, open architecture offers deeper potential for customization and novel workflows.
Frontend Frameworks: Lit (W3C) better than React, because its foundation on web standards provides inherent potential for interoperability and long-term resilience beyond a specific library’s ecosystem.
Databases: PostgreSQL better than many other DBs, due to its robust extensibility features, allowing it to be molded and adapted for far more use cases than a more rigid system.
AI Orchestration: LangGraph better than LangChain (in some contexts), potentially because its graph-based approach offers more inherent potential for modeling complex, stateful agentic workflows.

Blockchain

This brings us to the realm of Blockchain Technology.

Applying this new lens, the question isn’t “Which blockchain has the highest TVL or most dApps today?” but rather, “Which blockchain technology has the deepest inherent potential to build radically new, complex, and diverse decentralized systems in the future?”

My conclusion, following this theory, is that Polkadot’s Substrate framework ranks highest.

Substrate isn’t merely a single blockchain; it is an extremely powerful “meta-protocol” or framework for building any kind of blockchain. Its modular design, runtime flexibility (using FRAME), and the ability to create highly specialized chains (parachains) with custom logic, governance, and economics gives it unparalleled potential for future innovation.

While other chains might have larger dApp ecosystems today, Substrate’s ability to create bespoke ecosystems tailored precisely to specific needs, combined with the rapid development capabilities enabled by AI, makes its inherent architectural potential the most significant factor for long-term success. We can use AI to help build those custom modules and specialized chains far faster than relying purely on manual human development within a fixed ecosystem.

This is further amplified by Substrate’s underlying language, Rust, which prioritizes safety and performance. Moreover, the framework doesn’t restrict developers to a single language for all components. While the core runtime is written in Rust, front-end applications and custom modules can be developed using other languages, fostering innovation and allowing developers to leverage their existing expertise. This multi-language development approach, mirroring the adaptability we see in AI-driven software creation, maximizes potential by lowering the barrier to entry and unlocking a wider range of skillsets.

In this new era, the technology that wins won’t be the one with the most users or tools right now, but the one with the greatest capacity to be molded and extended into unforeseen applications. Substrate’s inherent power to define and create any blockchain structure, and its openness to various languages, puts it at the forefront under this new AI-driven evaluation model.

We don’t care about community size anymore. We care about potential. And in blockchain, Substrate’s potential is immense.