Something extraordinary happened to software in 2025. Tools like Bolt, Lovable, Cursor, Replit, and v0 made it possible for anyone to build a working application in hours. Not just engineers. Anyone. A founder with an idea and a credit card can go from napkin sketch to deployed product faster than most companies can schedule a kickoff meeting.
That's incredible. It's also dangerous.
There's a gap between "it works" and "it's safe to put in front of real people." A wide, invisible, potentially catastrophic gap. And right now, almost nobody is talking about it honestly.
The code works. The code is also broken.
AI coding tools are optimized for one thing: making your application functional. They are spectacular at getting something on screen that responds to clicks, saves data, and looks like a real product. What they are not good at is everything that happens after someone starts actually using it.
The numbers are ugly. Roughly 45% of AI-generated code contains security vulnerabilities. Not theoretical, academic vulnerabilities. Real, exploitable flaws. Hardcoded passwords sitting in plain text. Open doors for SQL injection. Cross-site scripting that lets attackers hijack user sessions. Missing authentication on routes that handle sensitive data. Security benchmarks from 2025 show AI-generated code fails to properly sanitize logs 88% of the time. Fails to prevent cross-site scripting 86% of the time. Privilege escalation vulnerabilities have increased 322%, largely because AI models learn insecure patterns from the very code they were trained on.
Gartner projects a 2,500% increase in software defects from non-developer-built applications by 2028. That's not a typo.
And here's the part that should worry everyone: it's getting worse, not better. Researchers call it the "Ouroboros Effect." It's a feedback loop where each new generation of AI models trains on the growing pool of insecure, AI-generated code already flooding public repositories. The snake is eating its own tail. The security baseline of automated development may be declining over time, not improving.
Nobody built the thing that needed to exist.
Enterprise security tools have been around for years. Snyk, SonarQube, Veracode, Checkmarx. Serious tools for serious engineering teams with serious budgets. They're powerful. They're also completely irrelevant to the founder who just used Bolt to build their SaaS product and needs to know if it's safe to launch.
These tools assume you know what a CWE is. They assume you can interpret a vulnerability tree. They assume you have a security team, or at least a senior developer who can triage findings. They cost tens of thousands of dollars a year. They were built for a world where the people writing code were engineers.
That world is gone.
The AI coding platforms themselves have started adding internal security checks, which is great. But there's a fundamental conflict of interest: a platform that helps you build code is not incentivized to tell you how broken that code might be. Their security features check their own ecosystem. They don't assess your infrastructure configuration, your scalability bottlenecks, your cost optimization, or your architectural integrity. And they certainly don't give you an objective, cross-platform assessment when you've used three different AI tools to build different parts of your app.
What was missing was an independent authority that can look at any AI-generated codebase, regardless of which tools built it, and give a founder an honest, understandable answer to one question: is this safe to ship?
That's why we built PathToShip.
What we built and why.
The people building with AI tools deserve the truth about their code. Not a dumbed-down version. Not a scary version designed to sell them consulting. The actual truth, explained in language they can understand, with steps they can take to fix what's broken.
PathToShip scans your codebase across seven dimensions: security, production readiness, infrastructure, architecture, scalability, code quality, and cost optimization. It produces a single score from 0 to 100. We call it the PathToShip Score. Above 75 means you're in good shape. Between 50 and 74 means you have work to do. Below 50 means you have critical issues that need to be resolved before anyone should use your product.
Security carries the most weight because the consequences of getting it wrong are existential for an early-stage company. But security alone isn't enough. An application can pass a security scan and still fall apart in production because it has no monitoring, no error handling, no connection pooling, no CDN, and a database configuration that will collapse at 100 concurrent users. PathToShip catches all of it.
We don't just tell you what's wrong. We tell you what you're doing right. We detect which AI tool built the code, what framework you're using, what infrastructure you have in place, and where the gaps are. The findings are specific to your stack, not generic advice pulled from a template.
Your code is yours.
On the free tier, analysis runs entirely inside your browser. Your source code never leaves your machine. It never touches our servers. It never gets stored anywhere. What we receive is only the scan results: scores, finding counts, infrastructure metadata. Not a single line of your code.
This wasn't a nice-to-have. It was a design requirement from day one. Founders using AI tools have often poured their entire product vision into a single codebase. Asking them to upload that code to yet another third party before they even know if the tool is useful is asking too much. So we solved the problem architecturally instead of asking you to trust us on faith.
When you connect a GitHub repository for a deeper scan, we request only the minimum permissions needed, use the access for the duration of the scan, and revoke it immediately afterward. We don't maintain persistent access to your account. There is no database of stored tokens. There is nothing to breach. The moment your scan completes, the connection is gone.
We generate a mathematical fingerprint of your code for analytics and deduplication, but it's a one-way hash. You can't reconstruct code from it. We get useful data about patterns across AI-generated codebases without ever storing anyone's source code.
Independence is the point.
Bolt has a security audit. Lovable has a security center. Replit has a scanner. All useful. All limited to their own platform. If you used Bolt for the frontend, Lovable for the backend, and Cursor to glue it together, nobody is looking at the whole picture.
PathToShip is the whole picture. We scan the full codebase with a consistent methodology regardless of which tools generated it, which platforms host it, or how many AI assistants contributed. We have no commercial relationship with any AI coding platform. We don't benefit from telling you your Bolt app is fine when it isn't. Our only incentive is accuracy, because accuracy is the only thing that makes the PathToShip Score mean anything.
The thing nobody else can see.
Every scan that runs through PathToShip, free or paid, contributes anonymized metadata to a growing dataset. Not code. Not identifiable information. Metadata: which frameworks appear most often, which AI tools produce which patterns, which infrastructure configurations correlate with higher scores, which vulnerability types are trending up or down.
Over time, this gives us a living map of the AI-generated code landscape. We can see which tools are improving. We can see which vulnerability categories are getting worse. We plan to publish that research so the entire ecosystem benefits from it.
If we're going to call ourselves a production readiness authority, we need to earn it by contributing knowledge back to the community building with these tools.
This is the year it matters.
2025 was the year of speed. Everyone was racing to build faster, ship faster, prompt faster. AI tools delivered on that promise.
2026 is the year of quality. The founders who launched last year are discovering what happens when real users hit applications that were never designed for real use. Data breaches. Unexpected costs. Outages under load. Security incidents that could have been caught by a five-minute scan.
Sixty percent of breached small businesses close within six months. The average small business breach costs between $120,000 and $1.24 million. These aren't abstractions. These are founders who built something they believed in, put it in front of people, and watched it fail. Not because the idea was wrong, but because the code wasn't ready.
We built PathToShip because we believe every app has a path to production. Some paths are short. Some are longer. But every founder deserves to know where they stand before they invite the world in.
Your app works. Now find out if it's ready.