AWS Kiro Powers: Solving the Problem AI Coding Tools Created

Amazon Web Services announced Kiro powers at re:Invent this week—a system that loads specialized AI expertise on-demand rather than cramming everything into memory upfront. It's a direct response to a problem the industry created for itself: AI coding assistants that choke on their own context.

The technical issue is straightforward. Modern AI coding tools connect to external services through the Model Context Protocol (MCP). Want your AI assistant to work with Stripe for payments, Figma for design, and Supabase for databases? Connect MCP servers for each. But each connection loads dozens of tool definitions into the AI's working memory before writing a single line of code. Connect just five MCP servers and you've consumed 50,000 tokens—roughly 40% of an AI model's context window—before typing your first request.

This "context rot" leads to slower responses, lower-quality outputs, and significantly higher costs since AI services charge by the token. Developers started complaining loudly that they don't want to burn token allocations just having an AI figure out which tools are relevant.

How Dynamic Loading Actually Works

Kiro powers packages three components into dynamically-loaded bundles: a POWER.md steering file that tells the AI what tools are available and when to use them, the MCP server configuration connecting to external services, and optional automation hooks.

When a developer mentions "payment" or "checkout," the Stripe power activates automatically, loading its tools and best practices. When work shifts to databases, Supabase activates while Stripe deactivates. Baseline context usage when no powers are active approaches zero.

Deepak Singh, AWS VP of Developer Agents and Experiences, framed this as democratizing advanced development practices. Previously, only sophisticated developers knew how to properly configure AI agents with specialized context—writing custom steering files, crafting precise prompts, manually managing active tools. Now Stripe or Supabase can build optimal configuration once and every developer benefits.

Why This Beats Fine-Tuning for Most Use Cases

AWS positions Kiro powers as a more economical alternative to fine-tuning—training AI models on specialized data to improve domain performance. "It's much cheaper," Singh told VentureBeat. "Fine-tuning is very expensive, and you can't fine-tune most frontier models."

This matters because the most capable models from Anthropic, OpenAI, and Google are closed-source. Developers can't modify underlying training, only influence behavior through prompts and context. "Most people are already using powerful models like Sonnet 4.5 or Opus 4.5," Singh explained. "What those models need is to be pointed in the right direction."

Dynamic loading also reduces ongoing costs. Powers only activate when relevant, so developers aren't paying for token usage on dormant tools. For teams using AI coding assistants extensively, this compounds into substantial savings.

The Maturation of AI Development Tools

Kiro powers reflects market maturation. GitHub Copilot introduced millions of developers to AI-assisted coding in 2021. Since then, tools like Cursor, Cline, and Claude Code competed for attention. But as capabilities grew, so did complexity. The Model Context Protocol solved integration standardization while creating context overload.

AWS is betting its experience running production systems at scale—both AWS infrastructure and Amazon's internal engineering organization—provides unique insight into how developers actually work. "It's not something you would use just for your prototype or toy application," Singh emphasized. "If you want to build production applications, there's a lot of knowledge that we bring in as AWS."

Launch partners include Datadog, Dynatrace, Figma, Neon, Netlify, Postman, Stripe, Supabase, and AWS services. Developers can also create and share custom powers. Currently, powers work only within Kiro IDE, but AWS is building toward cross-platform compatibility with Cursor, Cline, and Claude Code.

What This Reveals About AI Tool Economics

The underlying lesson transcends coding assistants: AI systems that load everything upfront waste resources solving problems that don't exist yet. Dynamic loading—activating capabilities only when needed—represents better economics and better user experience.

This pattern will likely extend beyond development tools. As AI agents proliferate across business functions, the systems that succeed will be those smart enough to forget irrelevant context, not those trying to maintain awareness of everything simultaneously.

For developers, Kiro powers addresses a genuine pain point. For AWS, it's strategic positioning as the company that understands production AI deployment at scale. For the broader AI tooling market, it's a signal that the next competitive frontier isn't raw capability—it's intelligent resource management.

At Winsome Marketing, we help teams evaluate AI development tools through the lens of actual workflow economics—not just feature lists but total cost of implementation including token usage, integration overhead, and developer productivity. As AI coding assistants mature, choosing the right tools requires understanding not just what they can do, but how efficiently they do it at scale.