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In Brief
Every wave of new technology promises speed. And every time, teams split into familiar camps: those who warn it will break everything, and those who believe it will fix everything. AI in software development is no exception.
What is different this time is how fast AI can turn an idea into something that looks real. That speed is useful because it lowers the cost of starting. But it also creates a new problem: momentum is mistaken for progress. AI didn’t remove the need for engineering discipline. It compresses the time you have to apply it.
You don’t have to choose between speed and steadiness. But you do have to know when to use each. AI can accelerate output. It will amplify weak judgment just as quickly. How you govern that speed determines product integrity.
The MVP Illusion
The Entry Barrier is Zero Now
In a matter of hours, teams can generate working interfaces, functional flows, and impressive demos using AI. The product looks alive, responds, and does something useful. To a non-technical eye, it feels finished.
But as Jose Rodriguez, software engineering manager at Edify, puts it, a demo or an MVP is rarely developed with best practices, scalable structure, or sound architecture. It’s a functional demo — not something designed to expand and scale into a production system.
MVPs Built Fast are Happy-Path Machines
AI-generated MVPs tend to succeed at one thing: showing the happy path. They demonstrate what happens when everything goes right. What they don’t reveal is what happens when something breaks.
Luis Serrano, Edify’s Software Architect Lead, sees this all the time: because vibe coding is so fast, it doesn’t give developers time to think through edge cases or failure modes. The result is a product that works beautifully until it doesn’t — and then simply breaks.
This is where the illusion sets in, and speed masks incompleteness. Momentum feels like progress. And because the demo works, teams assume the hard part is behind them. In reality, as Jose notes, if it’s “too easy to build — and that also means it’s too easy to break.”
Engineers understand this distinction instinctively. To an engineering team, an MVP is a learning tool, not a foundation. Jose often explains it with a construction metaphor. You can hand someone a prototype to see if it’s useful, just as blueprints show how a house will function. But the blueprint is not the house.
AI-generated MVPs are useful. The danger is confusing proof of concept with proof of readiness. AI doesn’t eliminate the work required to make a demo reliable, secure, and scalable. That space— between “this looks good” and “this is safe to roll out” — is where most AI-generated MVPs come to a standstill. And it’s where experienced engineering teams know it’s time to slow down.
The Gap Between AI Generation and Production
Mistaking a demonstration for a deployable product has significant repercussions that are significant, technical, and costly. We’ve seen this repeatedly, and industry data backs it up. Here’s what tends to happen when an AI-built system meets production reality:
The 5 Tradeoffs and Technical Risks of AI-Generated Code
Every AI build forces five technical decisions that most teams don’t realize they’re making.
1). Security gaps you can’t see
In March 2025, a founder publicly shut down his AI-built SaaS after attackers exploited exposed API keys and bypassed subscription controls within 48 hours of launch.
That story isn’t rare. The Cloud Security Alliance reports that 62% of AI-generated code contains security vulnerabilities.
Apiiro found Fortune 50 companies logging 10,000+ new AI-code security findings per month by mid-2025 — a tenfold spike in six months. Luis explains why this happens:
“AI doesn’t understand importance or secrets. It just follows instructions. If you don’t explicitly guide it, it takes the simplest path.”
And the simplest path often means putting secrets directly into visible code. Jose adds:
“Each line of code has risks. You need someone who understands the security implications.”
In EdTech, student data and district trust are of the utmost importance. If you plan to build with AI, you have to treat security, privacy, and architectural rigor as non-negotiable.
2). Code that “works” but no one can maintain
When Edify inherits heavily AI-built systems, Jose says the first red flag is clarity:
“Code should be easy to understand. Code is made for humans, not for computers.”
In one inherited project, it took Edify months just to stabilize the codebase before new features could be safely added.
They found:
- Logic that could never run
- Circular conditions
- Inconsistent database structure
- Massive components that controlled too many things at once
Jose calls this “spaghetti code.” It’s like a bowl of tangled wires. It means the system is so tangled that changing one small thing, like font color, can unknowingly break something somewhere else.
3). Systems that collapse under real traffic
Many AI-generated MVPs fail not because the idea is bad, but because the infrastructure can’t scale. One technical review found that 92% of AI-built apps lacked proper database indexing, meaning they worked with small test data but would slow dramatically under real user load.
A demo assumes everything works. Plus, the internet is fast and the user enters the right data. No one refreshes mid-transaction. And for a small test group, that’s usually fine. But production is not a demo environment. When real users show up, they:
- Enter the wrong password five times
- Upload files that are too large
- Open the app on old devices
AI is very good at producing what’s most probable next. It doesn’t ask:
- What happens if this fails?
- How will the system handle a 10x load?
- Can it survive when a district rolls this out across 40 schools?
It builds what you asked for instead of stress-testing what you didn’t.
4). Fixing one thing breaks another
Jose is clear that regression bugs are not unique to AI, but AI can amplify them. Luis calls this the context trap.
“You can break a lot of things without even knowing.”
As prompts accumulate, the AI’s context window fills. Once it crosses a certain threshold, performance degrades, and it forgets earlier rules. It rewrites sections unintentionally and hallucinates with confidence. Here’s what that looks like in real life:
You ask the AI to change the login button from blue to green. It works. But somewhere else in the file, code was regenerated. Now the assignment submission flow silently fails. You don’t discover it until a professor tells students to submit their work — and nothing goes through. Without unit tests, integration tests, and human QA in the loop, that break goes unnoticed.
5). The illusion of momentum
When building becomes cheap and fast, validation becomes optional. And that’s dangerous. The #1 startup killer is still building something the market doesn’t need. As Luis says:
“We need to make a real assessment first. Not every problem needs AI.”
And as Jose explains:
“The value of a software engineer is not just the code. It’s the rationale, the why and the what you are doing.”
AI lowers the barrier to building. It does not lower the cost of being wrong.
Vibe Coding vs. Strategic AI: The Difference Isn’t the Tool — It’s the Expertise
Vibe coding starts with a prompt. The AI produces code. It runs. You move on.
But as Jose says:
“You have to define your plan. You have to let the model know how you want things to work. It’s not one size fits all.”
Without that plan, the AI fills in the gaps on its own. And as he explains, it isn’t reasoning:
“it’s forecasting the next word.”
It generates what is most probable and that’s powerful but probability is not judgment. When we understand the AI model, you can design supervision around it.
Luis approaches AI in this way. Before development begins, he insists on refining requirements. He says:
“We need to understand what we are expecting at the final product,”
That means documenting acceptance criteria, edge cases, constraints — all of it.
At Edify, that becomes a spec file. It holds the full context so the AI isn’t guessing. It gives structure before speed. That’s why iteration without oversight becomes risky.
Which is why Jose says plainly:
“You will always need a human in the loop.”
AI can generate. It cannot supervise itself. It can move fast. It cannot evaluate tradeoffs. Strategic AI means you’re directing the tool. And in EdTech, where systems touch classrooms, student data, and institution trust, direction matters.
How to Make AI Tradeoffs A Part of Your Product Strategy
Great technical leadership recognizes when a small decision is about to become a structural commitment — before speed turns into sunk cost and the system becomes too fragile and expensive to rethink.
Every build forces choices about the following:
- Speed vs. maintainability
- Output volume vs. architectural clarity
- Custom build vs. existing tool
- Feature depth vs. adoption simplicity
- Token usage vs. cost discipline
- Complexity vs. usability
These decisions don’t feel significant when you make them, but they become expensive when they’re hard to undo. They show up as signals like:
- The file size suddenly explodes.
- The AI rewrites large sections instead of modifying one piece.
- Someone says, “We can just regenerate it.”
In other words, you have to fail fast, and with AI, even faster. Can you see the flaw before it reaches users? Can you unwind a decision before it becomes architecture? Use AI as a competitive weapon, but govern it ruthlessly.
The Edify AI Supervision Model
AI can help you move quickly. But speed without structure turns into expensive cleanup. Here’s a simple workflow you can steal.
1). Validate the problem with humans
Before you build, make sure the problem is real.
Luis often starts by asking whether AI is even needed. Not every solution requires it. AI is powerful, but it’s also expensive and complex.
The first step is understanding what needs to be solved and who it serves.
Talk to end-users and watch how they work. Build only after the problem is clear.
2). Write the spec file
Begin with clarity. Luis insists on refining requirements before development begins. You need to understand what you expect the final product to do, including edge cases, constraints, and acceptance criteria. At Edify, that becomes a spec file. It captures the rules so the AI isn’t guessing. Without that structure, the model fills in gaps on its own.
3). Build one feature at a time
Jose is clear about this: build from the bottom up. One feature at a time. When you try to generate an entire system in a single prompt, you lose control. The risk of hallucination increases, and the structure weakens. Small, controlled increments reduce risk.
4). Automate tests early
Testing isn’t glamorous, but it’s what protects you from silent failure. Jose points out that regression issues happen in every system. AI just makes them easier to miss. Unit tests and integration tests catch problems before users do. If something changes, the tests should tell you immediately.
5). Run a basic security baseline
Security is not a finishing step. Jose emphasizes authentication and authorization. Make sure users can only access what they’re allowed to access. Make sure secrets aren’t hard-coded, and the data is protected. AI can generate secure code, but only if you explicitly require it.
Building With AI Responsibly. Coding For Good.
Today, anyone with a laptop and an AI tool can generate an app. Interfaces, workflows, even backend logic can be produced in hours.
As Jose puts it, engineering has never been about syntax alone. It’s about thinking ahead and anticipating how a system behaves under stress, how it grows, how it fails safely. Luis sees AI as powerful precisely because it can accelerate execution. Acceleration without planning and guardrails compounds risk just as quickly as it compounds output.
AI will continue to accelerate what teams can produce. Speed isn’t a competitive advantage on its own. It becomes one only when paired with judgment and domain expertise — especially in EdTech, where the stakes are human.