The promises sound tempting: AI will make developers obsolete and program software autonomously. But a simple thought experiment shows why this vision hits fundamental limits – and what this means for your digitalization strategy.
The Current Hype: "Vibe Coding" and Development Automation
After the low-code trend, "Vibe Coding" now promises the next revolution: developers will become obsolete while AI systems independently develop applications from natural language descriptions. The first demos are impressive – simple programs actually emerge on command, user interfaces are generated in seconds.
For decision-makers, this sounds like the solution to all problems: no more expensive developers, no long development cycles, no communication problems between business and IT. But reality is more complex.
💡 The Central Thesis
The more precisely we need to formulate our requirements for AI, the closer we get to actual programming work. In the end, we describe the desired behavior so detailed that we're effectively already programming – just in a different language.
The Thought Experiment: A Perfect AI Programs Snake
Let's assume a hypothetical, perfect AI – far more advanced than today's systems, without token limits, without hallucinated outputs, with error-free code. This AI should program the classic game "Snake" in Python.
Step 1: The Naive Approach
Prompt: "Code snake in Python"
The AI delivers a working game. But it quickly becomes clear: the result is rudimentary. So we refine our requirement.
Step 2: Extended Requirements
Prompt: "Add an enemy snake that chases us with half of our speed. If we collide with the enemy snake we lose. If we manage to eat 5 food, we win."
Even our hypothetically perfect AI must now make assumptions:
- Playing field size: 800x600 pixels? 1024x768? The AI chooses based on probabilities from its training data.
- Starting positions: Where do both snakes spawn? How do we ensure the game doesn't end immediately?
- Movement logic: How exactly does the enemy snake "chase"? Direct path? With pathfinding?
Step 3: The Complexity Explosion
With each additional requirement – pink player snake, obstacles after collecting 5 items, inverted game mechanics – more decisions need to be made. The AI fills gaps with statistically probable implementations, but are these the desired ones?
✅ The Insight
To ensure the AI develops exactly what we want, we would need to specify every aspect of the behavior. But this complete specification is already the actual programming work – just in natural language instead of code.
The Reality: Additional Challenges in Practice
Our thought experiment assumed a perfect AI. In practice, additional problems arise:
1. Faulty Code Generation
Current AI systems regularly fail even with 300 lines of Python code. Syntax errors, logical inconsistencies, and missing imports are the rule, not the exception. The supposed solution – AI executes the code itself and corrects errors iteratively – is just a workaround for fundamental weaknesses.
2. Context-Dependent Requirements
Software for 1,000 users requires a completely different architecture than for a million. A system maintained by 3 developers must be structured differently than one for 30. The AI can only consider this context information if explicitly specified.
3. The Illusion of Simplicity
AI tools create the illusion that software development is trivial. But they only hide complexity behind statistical assumptions. For proof-of-concepts this might suffice – for production systems it's dangerous.
What Does This Mean for Decision-Makers?
The idea of replacing developers with AI is based on a fundamental misunderstanding of what programming actually is. It's not about writing syntax – it's about translating complex problems into precise, executable solutions.
💡 Practical Recommendations
1. Realistic expectations: AI is a powerful tool
for developers, not a replacement for them.
2. Investment in competence: Strengthen your
development teams with AI tools instead of trying to replace
them.
3. Focus on value creation: Use AI where it
offers real added value – for repetitive tasks, code reviews, and
documentation.
Conclusion: AI as a Tool, Not a Magic Wand
Our thought experiment shows: Even a perfect AI cannot replace developers because the actual problem – the precise specification of requirements – remains. In practice, with error-prone AI systems, this challenge becomes even greater.
The future doesn't lie in automating development, but in intelligent augmentation of developers. AI tools can and should take over repetitive tasks, help with debugging, and generate boilerplate code. But the actual thinking work – understanding requirements, making architectural decisions, weighing trade-offs – remains a human domain.
For companies, this means: Invest in training your developers to use AI tools. But don't fall for the promise that these tools will make your development department obsolete. Because at the end of the day, someone still needs to know exactly what should be developed – and that's already half the programming work.