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How Statsig lets you ship, measure, and optimize AI-generated code
The future of software will be AI-powered and written in plain English.
We're quickly approaching a world where you can think it, prompt it, and ship it.
But while it’s effortless to ship a new feature, measuring the value of what you're shipping is more important than ever.
The next layer of abstraction is here
Software development has always progressed through layers of abstraction. Each new layer removes complexity and democratizes access. We're witnessing the next major leap right now.
Rewind to the late 2000s: Before cloud computing, launching a web application meant racking servers, configuring load balancers, and maintaining physical infrastructure. Only well-funded companies could afford the upfront costs and operational expertise.
Then AWS, Google Cloud, and Azure changed everything. They abstracted away the infrastructure layer. Developers could spin up servers with a few clicks and scale automatically based on demand. A college student could build and deploy an app that served millions of users without touching a physical server.
This infrastructure abstraction triggered an explosion of SaaS tools and consumer apps. Instagram scaled to millions of users with just 13 employees. Dropbox went from dorm room project to billion-dollar company without building a data center.
Today, AI is creating the next abstraction layer: Just as cloud platforms removed infrastructure complexity, AI is removing coding complexity.
In the cloud era, you still needed to know how to code. You could deploy easily, but you had to write every function, debug every error, and architect every system. Now, AI is changing that fundamental requirement. You describe what you want in plain English, and the model generates the code.
Just as cloud computing lets non-infrastructure engineers deploy applications, AI lets non-programmers create applications.
Yet while much has been said about “vibe coding,” the biggest impact of this change is at the world’s most sophisticated technology companies. AI is generating 50% of the code at Facebook. A 10X engineer can become a 100X engineer with a fleet of junior dev coding agents.
You still need a deep understanding of system design to build effective applications at scale. Great software engineering is more valuable than ever.
Still, something fundamental has changed. Writing code is no longer the bottleneck for implementing great ideas.
Don't mistake motion for progress
AI-generated code creates a new challenge. Engineers can ship a lot more, but they have less confidence in the actual impact of their changes.
Without measurement and experimentation, teams risk shipping changes that "look good" in a sandbox environment but fail in production. The question shifts from can we ship it to should we keep it.
This is why control, experimentation, analytics, and observability have become non-negotiable. That's where Statsig comes in.
With the launch of a few new AI tools (notably our MCP server) we make it effortless to layer in metrics, feature flags, and experiments into every feature you ship - giving you a tight feedback loop to test every AI-generated change, monitor performance, and measure the impact once you ship it.
The result is a powerful flywheel: use AI to ship fast, measure what works, double down on wins, and kill what doesn't. The only limit is how quickly you can generate new ideas.
This approach mirrors what leading product companies like Meta, Netflix, and Amazon figured out years ago. Rapid experimentation isn't just a growth tool. It's how you build. At Meta, engineers run thousands of experiments weekly to validate everything from UI tweaks to backend changes.
Now that AI has dropped the cost of shipping to nearly zero, the only real cost is shipping the wrong thing.
Enter Statsig MCP Server
Last month, we launched the Statsig MCP server, which connects AI coding assistants like Cursor to Statsig. This brings Statsig's capabilities directly into your AI-driven product development workflows.
We're already seeing 3 powerful use cases:
1. Make logging and measurement on by default
Good instrumentation should be automatic. When you're coding with Cursor, you can drop in the Statsig SDK to auto-capture common events like views and clicks by default.
These events instantly show up in your Statsig console. From there, you can track them, build metrics, and create dashboards to monitor performance from day one. If you need something custom, Cursor can add it for you.
2. Ship changes behind a feature gate
As you build and ship new features, you can prompt Cursor (or even add it to your cursor rules) to automatically wrap changes in a feature gate. Then, you can progressively roll them out to defined user segments.
Now, the metrics you define are automatically attached to the release. This turns any change into a lightweight A/B test, letting you confidently ship whatever has a positive impact on metrics.
Statsig also provides a safety net with automated alerting and rollbacks if key metrics drop below defined thresholds.
3. Leverage experiment history and learnings
Statsig becomes the source of truth for builders across the organization. It helps teams understand what's working, what's not, and why.
The rich data captured across experiments, metrics, and feature rollouts can then inform future iterations and product roadmaps. With MCP, you can take this further by leveraging context from your corpus of experiments in Statsig.
Below, Claude suggests new high-impact experiments based on historical results — turning your experimentation history into a continuous engine for learning and growth.
Learn more about the MCP in our docs.
Conclusion
AI is becoming a massive force multiplier for coding, making the marginal cost of developing a feature close to zero. Choosing the right features to ship will make all the difference.
A guide to building AI products
