The Brutal Truth (As We See It)
When we started Syntax.ai, we had a choice.
We could build another AI coding tool—competing with GitHub Copilot, Cursor, Claude Code, ChatGPT, Gemini, and a dozen other well-funded products backed by companies with billions in capital and some of the world's best AI researchers.
Or we could build something different.
We chose the latter.
Here's our reasoning—and what we built instead.
Why We Think You Can't Win Building Another AI Coding Tool
This is our strategic assessment. Others might disagree.
The Resource Gap Seems Insurmountable
Anthropic has raised billions. They have hundreds of researchers. They train models on massive compute clusters.
OpenAI has Microsoft's backing. GitHub Copilot is integrated into VS Code by default.
Google has Gemini and literally invented the transformer architecture that powers all modern AI.
We don't have insider knowledge of these companies. These are public-facing facts we're interpreting.
Even if a startup had substantial funding, we believe they'd struggle with:
1. Model Quality
The major labs have better data, better researchers, better infrastructure. A startup's model will probably be worse—at least initially.
2. Distribution
Copilot is pre-installed in VS Code. Claude Code is from Anthropic. A new entrant starts from zero users.
3. Brand Trust
Developers trust OpenAI and Anthropic. They haven't heard of most startups.
4. Switching Costs
Convincing developers to switch tools is hard. They're already using Copilot or Cursor. Why would they try something new?
What We Don't Know
Maybe there's a differentiated approach that could win. Maybe the market is bigger than we think. Maybe execution matters more than resources. Other smart people have made the opposite bet. We could be wrong about this.
The Problems We Believe Enterprises Face
We've had conversations with engineering leaders about AI governance. Here's what we've heard—with the caveat that our sample isn't scientifically representative:
Hypothesis #1: Shadow AI Is Common
Many organizations have developers using unapproved AI tools. IT doesn't always know which tools are being used. Security doesn't always know what code patterns are being introduced.
We don't have rigorous data on prevalence. Industry reports cite various percentages, but methodologies vary.
Hypothesis #2: Multi-Tool Chaos
In larger engineering organizations, different developers use different AI tools. Some use Copilot, some use Cursor, some use ChatGPT directly. Enforcing consistent policies across all of them is hard.
We've heard this pattern repeatedly, but we can't quantify how universal it is.
Hypothesis #3: Governance Gap
Studies suggest AI-generated code may have more security vulnerabilities and quality issues than human-written code. But most organizations don't have systematic processes to catch these problems.
The research exists (Veracode, academic studies) but sample sizes and methodologies vary. The "4x more defects" stat sometimes cited comes from specific contexts.
Hypothesis #4: Visibility Gap
Many engineering managers don't know what percentage of their codebase is AI-generated, which developers rely on AI the most, or whether AI code meets their standards.
This is based on conversations, not systematic research.
Our Strategic Bet
Based on this assessment, we decided:
Enterprises don't need another AI tool.
They need governance over the AI tools developers are already using.
That's the bet we made.
What We Built: A Governance Layer
Instead of building the Nth AI coding tool, we built a governance layer that works with the existing tools.
The Strategy: Infrastructure, Not Competition
We're not trying to build a better AI (the "horse").
We're trying to build governance infrastructure (the "road").
Our bet: all the AI tools need governance.
How We Think It Should Work
Step 1: Developers Keep Their Tools
Use GitHub Copilot, Cursor, ChatGPT, Claude Code—whatever they want. No forced migration. Zero adoption friction.
Step 2: Add Governance at Checkpoints
Intercept at the critical checkpoints:
• Pre-commit hooks
• Pull request reviews
• CI/CD pipeline
• Optional IDE monitoring
Step 3: Automated Scanning
Every piece of AI-generated code is scanned for:
• Security vulnerabilities (OWASP Top 10)
• Code quality issues (complexity, duplication)
• License compliance
• Custom policy violations
Step 4: Visibility Dashboard
Show management:
• Which AI tools are being used
• How much code is AI-generated
• Governance violations by team
• Compliance audit trail
Why We Think This Positioning Could Win
Reasoning #1: Vendor Neutrality
Copilot users won't switch to Cursor. Cursor users won't switch to Copilot. But both might need governance. Working with all tools could mean a bigger addressable market.
Reasoning #2: Buyer vs. User Alignment
Users (developers) don't want to change tools.
Buyers (CTOs, CISOs) need governance and visibility.
Traditional vendors fight users. We're trying to align with both.
Reasoning #3: Regulatory Direction
The EU AI Act includes provisions for AI system governance. SOC 2 auditors are starting to ask about AI code governance. Insurance and compliance requirements are evolving.
This is our read of the regulatory direction. Requirements are still developing. We could be early—or wrong about how this plays out.
Reasoning #4: Lower Resource Requirements
We don't need billions in funding. We don't need to train foundation models. We don't need to keep up with GPT-5, Claude 4, Gemini 3. We're infrastructure on top of the AI layer.
What Could Go Wrong
We're not pretending this is a sure thing. Here are the risks:
Governance might not become mandatory fast enough.
If enterprises don't feel pain around AI code quality, there's no market.
AI tools might build governance in themselves.
GitHub could add governance features to Copilot. Anthropic could add compliance tools to Claude.
The "shadow AI" problem might be overstated.
Maybe enterprises have better visibility than we think.
We might be too early or too late.
Market timing is hard to predict.
The Honest Position
We believe in this strategy, but it's a bet on how the market evolves. We could be right. We could be wrong. The intellectual honest position is that we're making educated guesses about uncertain futures.
What This Means for You
If you're building in the AI coding space, consider asking:
Do you have a realistic path to beating the major labs at model quality?
Do you have distribution advantages they don't?
Can you convince developers to switch from tools they're already using?
If the answers are challenging, maybe there's a different positioning worth exploring.
Or maybe we're wrong and the direct competition play works. Startups have surprised incumbents before.
The Future We're Betting On
Our hypothesis for five years from now:
• Developers will use multiple different AI tools
• Enterprises will need governance across all of them
• Compliance requirements will mandate oversight
• Audit trails for AI code will be expected
We're not betting on which AI tool wins.
We're betting that governance becomes necessary.
And if it does, we want to be the infrastructure layer that provides it.
Interested in AI Code Governance?
If our hypothesis about the market resonates with your experience, we'd like to talk.
Join the WaitlistA Note on This Article
This is explicitly a company perspective piece. We've tried to be honest about our reasoning while acknowledging it's our strategic interpretation of the market, not objective truth. If you disagree with our assessment, we'd be interested to hear why.