The promise of no-code development has finally become reality through what is now called “vibe coding” — using AI tools like Lovable or Cursor, to transform ideas into functional products, just with prompts. For entrepreneurship, this shift is revolutionary: you can now go from concept to prototype in days rather than months, without any technical knowledge.

Imagine validating your business with paying customers before writing a single line of traditional code. An entire engineering team, compressed into an AI assistant, at minimal cost. The implications for innovation are staggering.

The Hidden Cost of AI-Accelerated Development

But as more success stories emerge, so do the challenges. Security vulnerabilities in AI-generated code are becoming increasingly documented. Hearing from people stuck in the “loop of death”, where any new prompt fix some problems while creating new ones, is common.

The “AI slop”, the term initially coined to define poor AI generated content flooding internet, is now a thing in software and product development.

As an entrepreneur, your natural optimism might suggest: “Once we find Product-Market Fit (PMF), we’ll have the resources to fix these technical issues.”

This is theoretically true; however PMF won’t give you more time — quite the opposite. Once you’ve validated your idea and raised capital from top tier investors, a new countdown begins. Investors expect rapid scaling (remember T2D3 growth? “triple, triple, double, double, double” YoY your revenue).

Nothing kills momentum faster than discovering your AI-generated foundation can’t support your growth. You’ve validated your idea and secured funding to accelerate, but you’re stuck rebuilding core systems because of accumulated debt. Your forecasted budget is suddenly challenged by this new emergency.

However, there’s a better way to embrace AI and not going blind into future success.

How to Harness AI’s Power While Building for Scale

1. The Engineering Sweet Spot: Experience + AI Augmentation

Don’t view AI as a replacement for engineers; instead, see it as an amplifier of their capabilities. Following Jevons paradox, increased efficiency driven by technology advancement doesn’t reduce demand, it increases it. You now have the possibility to do more.

Be wary of two extremes: experienced engineers who reject AI outright, and newcomers who claim to work exclusively through AI. Both miss half the equation. The sweet spot lies in combining deep technical experience with AI-augmented workflows.

2. Prioritize Data Modeling

While software is (should?) easy to change, working with data is hard. Tomorrow’s most valuable engineer will be the skilled data modeller.

Invest in defining your data architecture early—it will become the foundation everything else builds upon.

3. Double Down on Quality Assurance

In the pre AI era, a stellar engineering team might allow you to lighten testing requirements. Their competence would drammatcaily reduce the risk for trivial errors. With AI-generated code, this approach fails. Comprehensive testing becomes non-negotiable. Invest in observability tools and replay capabilities to catch issues before they reach production, and to re apply data transformation when necessary.

4. Shift left in security

AI generated code can be insecure, posing risk to your business and your customers data. Supply chain attacks, where models suggest to use non vetted libraries, are common. While I have no doubt this is an area where models will improve significantly over the years, at this stage, the moment your idea is validated, invest in security, with tools and effort.

5. Budget Realistically for AI Costs

Keep momentum required having the right budget in place. Think about your AI Expenditure then add 30-50%, per year. While cost-per-token will decrease over time, your organisation’s appetite for AI services will grow exponentially. All the services are introducing additional price tiers, to better monetise the most advanced models, the ones that you do need when building products. And we haven’t yet moved into “per AI agent” pricing, the likely next iteration of pricing.

The Balanced Approach

To thrive in this new era you can’t blindly embrace AI-only development nor stubbornly cling to traditional methods. Success will come to those who strategically leverage AI’s capabilities while building sustainable technical foundations. Because those foundations are what would make your better successful in the long term.