The Speed Trap
We are currently in the middle of the biggest productivity leap in the history of software engineering. From the dev shops in Sofia to the product teams in Varna, developers aren’t just typing anymore – they are “curating” logic. Tools like GitHub Copilot and Cursor have turned coding into a high-speed assembly line.
But here is the reality we often skip over: when you accelerate production without upgrading your safety checks, you aren’t just building faster. You are accumulating “AI Technical Debt” at a pace we’ve never seen before. The danger isn’t that the code won’t run; it’s that we’re weaving a foundation we don’t fully understand and certainly don’t control.
The IP Minefield: Accidentally “Borrowing” Your Product
For any Bulgarian company working with global clients, Intellectual Property (IP) is the only currency that matters. But AI models are trained on billions of lines of public code, and they don’t always respect licenses.
I’ve seen this worry start to creep into Technical Due Diligence lately. If you’re heading toward a funding round or an acquisition, someone is going to ask: “How much of this is actually yours?” If a core part of your engine was “hallucinated” by an AI that pulled from a restricted GPL project, you’ve effectively built your house on someone else’s land. You can’t sell what you don’t legally own, and “the AI suggested it” won’t save your valuation when the lawyers show up.
Silent Bugs and the Illusion of Security
AI is designed to be “plausible,” not necessarily “correct.” It gives you code that looks elegant and passes basic tests, but it lacks the big-picture context of your specific security architecture.
The real trap here is the confidence gap. We’re seeing that developers using AI assistants tend to be more confident in their security, even while they are more likely to leave backdoors or insecure API endpoints in the code. For an outsourcing partner, delivering this to a client isn’t just a bug fix – it’s a breach of contract. When a leak happens six months down the line, the financial and reputational fallout stays with you, not the model provider.
The “Junior Crisis”
There is also a more subtle human risk happening in our offices. We’re seeing junior developers solve complex problems with AI before they have the foundational knowledge to debug them manually.
This creates a “Knowledge Gap.” If your team is merging code they don’t fundamentally understand, you’re creating a ticking time bomb. When that module fails under heavy load on a Friday night, and the person who “wrote” it can’t explain the logic behind it, you’re looking at catastrophic delivery delays and a very difficult conversation with your client.
Accountability: There Is No “Undo” Button
Under NIS2 and the upcoming EU AI Act, the “Human-in-the-loop” isn’t a suggestion – it’s a legal requirement. Regulators won’t care which LLM you used; they will care about your oversight. If you can’t prove you had a documented process to vet AI-generated code for security and IP risks, you are essentially flying on autopilot without a license.
Where Insurance Comes In
Traditional Professional Indemnity (PI) policies were written for a world where humans made human mistakes. They aren’t always ready for “algorithmic failure” or IP contamination caused by a bot.
Does your current coverage actually protect you if a piece of AI code triggers a copyright lawsuit? Or if a “hallucinated” bug leads to a massive data breach? At Broxio, we don’t just look at the certificate; we look at the reality of how you build software today. We bridge the gap between the speed of AI and the financial certainty you need to keep your business safe.