How typically have you ever caught your self considering, “Wouldn’t it’s simpler at hand the venture over to AI as an alternative of paying a staff of builders?” It’s a tempting thought, particularly within the age of AI — however the actuality is much extra complicated.
On this article, we’ll discover what AI can truly do in software program growth, the place it nonetheless falls quick in comparison with people, and what conclusions firms ought to draw earlier than entrusting a venture to synthetic intelligence.
When AI Tried to Play Software program Engineer
Not too long ago, a consumer approached SCAND with a novel experiment in thoughts. They needed to check whether or not synthetic intelligence may independently develop a small net utility and determined to make use of Cursor for the duty. The applying’s objective was easy — fetch statistics from an exterior API and show them in a desk.
The preliminary outcome seemed promising: AI created a functioning venture that included each client- and server-side parts, applied the fundamental logic for retrieving knowledge, and even designed the interface. The desk appropriately displayed the statistics, and the general code construction appeared respectable at first look.
Nonetheless, upon nearer inspection, it grew to become clear that the answer was overengineered. As a substitute of instantly connecting to the API and displaying the info within the browser, AI constructed a full backend server that proxied requests, saved intermediate knowledge, and required separate deployment.
For such a easy process, this was pointless — it sophisticated the infrastructure, added further setup steps, and lengthened the combination course of.
Furthermore, AI didn’t account for error dealing with, request optimization, or integration with the consumer’s current methods. This meant builders needed to step in and redo components of the answer.
The Limits of Generative AI in Coding and Software program Growth
Generative AI has already confirmed that it will probably rapidly produce working code, however in follow, its capabilities in real-world software program growth typically become restricted. Listed below are the important thing points we recurrently encounter when reviewing AI‑generated tasks:
- Lack of awareness of enterprise logic and structure. AI can’t see the total image of a venture, its targets, and its constraints. In consequence, the options it produces could also be technically right however fully misaligned with the precise enterprise wants.
- Incapability to make architectural commerce‑offs. An skilled software program engineer evaluates the stability between growth pace, implementation price, and ease of upkeep. AI, then again, can’t weigh these components and tends to decide on a regular and even unnecessarily complicated method.
- Overengineering. Producing pointless layers, modules, and providers is a standard mistake. For instance, a easy utility might find yourself with an additional backend that requires separate deployment and upkeep.
- Ignoring the context of current methods. AI doesn’t take note of how new code will combine with the present infrastructure, which may result in incompatibilities or further prices for rework.
- Code ≠ product. Synthetic intelligence can write fragments of code, but it surely doesn’t ship full options that take note of UX, safety, scalability, and long-term help.
- Doesn’t at all times totally perceive the duty. To get the specified outcome, prompts typically have to be clarified or rewritten in additional element — generally stretching to a full web page. This slows down the method and forces the developer to spend time refining the request as an alternative of shifting on to efficient implementation.
In the end, regardless of the rising function of AI in software program growth, with out the involvement of skilled builders, such tasks threat changing into a supply of technical debt and pointless prices.
Why Human Software program Builders Nonetheless Beat AI Brokers
Sure, generative AI and agentic AI can write code at the moment — generally even pretty good code. However there are nonetheless some issues that synthetic intelligence can’t substitute in knowledgeable software program developer’s workflow..
First, it’s understanding the enterprise context. A human doesn’t simply write a program — they know why and for whom it’s being created. AI sees a set of directions; a developer sees the true process and understands the way it matches into the corporate’s targets.
Second comes the power to make knowledgeable selections — whether or not to reuse current code or construct one thing from scratch. A human weighs deadlines, prices, and dangers. AI, in flip, typically follows a template with out taking hidden prices into consideration.
Third, it’s architectural flexibility. An skilled programmer can really feel when a venture is beginning to “develop” pointless layers and is aware of when it’s the correct time to cease. AI, then again, typically creates extreme buildings just because that’s what it has seen in its coaching examples.
Fourth comes desirous about the product’s future. Scalability, maintainability, and dealing with edge circumstances are constructed right into a developer’s mindset. AI shouldn’t be but able to anticipating such nuances.
And eventually, communication. A real software program engineer works with the consumer, clarifies necessities, and adjusts the method because the venture evolves. AI shouldn’t be able to actual dialogue or a delicate understanding of human priorities.
Subsequently, in at the moment’s software program growth panorama, synthetic intelligence continues to be a software — not a strategist. And within the foreseeable future, the human function in creating excessive‑high quality software program will stay important.
The desk under compares how people and AI deal with key facets of growth, and why the human function within the course of continues to be vital.
| Criterion | Software program Developer | Generative AI |
| Understanding enterprise context | Analyzes venture targets, audience, and long-term aims | Sees solely the given immediate, with out understanding the larger image |
| Making architectural selections | Balances pace, price, simplicity, and maintainability | Follows a template with out contemplating hidden prices |
| Structure optimization | Avoids pointless modules and simplifies when doable | Liable to overengineering, creating further layers |
| Working with current methods | Considers integration with present infrastructure | Might generate incompatible options |
| Foresight | Plans for scalability, error dealing with, and edge circumstances | Usually ignores non‑normal situations |
| Collaboration | Engages with the consumer, clarifies necessities, presents options | Understands the request in a restricted method, requires exact and detailed prompts |
| Flexibility in course of | Adapts to altering necessities on the fly | Requires code regeneration or a brand new immediate |
| Pace of code technology | Focuses on correctness and stability over uncooked pace | Generates code immediately, but it surely’s not at all times helpful or right |
| Remaining deliverable | Prepared‑to‑use product | A set of code requiring evaluate and refinement |
Human Builders vs AI in Software program Growth
The place AI Coding Instruments and Agentic AI Can Assist Software program Engineers
Regardless of its limitations, AI instruments have some strengths that make them invaluable assistants for software program engineers. Based on Statista (2024), 81% of builders worldwide reported elevated productiveness when utilizing AI, and greater than half famous improved work effectivity.
Advantages of utilizing AI within the growth workflow, Statista
In day‑to‑day growth, AI can considerably pace up routine duties and simplify supporting processes, akin to:
- Producing boilerplate code. Generative AI can produce repetitive code buildings in seconds, saving time and permitting builders to give attention to enterprise logic.
- Creating easy parts. AI can rapidly construct buttons, kinds, tables, and different UI parts that may later be tailored to the venture’s wants.
- Changing codecs. Synthetic intelligence can simply remodel knowledge and code — from JSON to YAML or from TypeScript to JavaScript, and again.
- Refactoring. AI can counsel code enhancements, simplify buildings, and take away duplicates.
- Speedy prototyping. AI can construct a primary model of performance to check concepts or show ideas to a consumer.
Nonetheless, even in these use circumstances, AI stays only a software. The ultimate model of the code ought to at all times undergo human evaluate and integration to make sure it meets architectural necessities, high quality requirements, and the venture’s enterprise context.
SCAND’s Method — AI + Human Experience within the Age of AI
At SCAND, we see synthetic intelligence not as a competitor to builders, however as a software that strengthens the staff. Our tasks are constructed on a easy precept: AI accelerates — people information.
We use Copilot, ChatGPT, Cursor, and different AI instruments the place they really add worth — for rapidly creating templates, producing easy parts, and testing concepts. This permits us to save hours and days on routine duties.
However code technology is simply the start. Each AI‑produced answer goes via the arms of our skilled builders who:
- Test the correctness and safety of the code, together with potential license and copyright violations, since some items of the recommended code might replicate fragments from open repositories.
- Optimize the structure for the duty and venture specifics.
- Adapt technical options to the enterprise logic and venture necessities.
We additionally pay particular consideration to knowledge safety and confidentiality:
- We don’t switch confidential knowledge to public cloud-based AI with out safety, except the consumer particularly requests in any other case. In tasks involving delicate or regulated info (for instance, medical or monetary knowledge), we use native AI assistants — Ollama, LM Studio, llama.cpp, and others — deployed on the consumer’s safe servers.
- We signal clear contracts that specify: who owns the ultimate code, whether or not AI instruments are allowed, and who’s liable for reviewing and fixing the code if it violates licenses or accommodates errors.
- We embrace obligations for documentation (AI utilization logs indicating when precisely and which instruments had been used) to trace the supply of potential points and guarantee transparency for audits.
- We offer staff coaching on AI greatest practices, together with understanding the restrictions of AI-generated content material, licensing dangers, and the significance of guide validation.
Will AI Substitute Software program Engineers? The Sensible Actuality Test
At the moment, synthetic intelligence in software program growth is on the identical degree that calculators had been in accounting a number of a long time in the past: a software that quickens calculations, however doesn’t perceive why and what numbers have to be calculated.
Generative AI can already do loads — from producing parts to performing automated refactoring. However constructing a software program product isn’t just about writing code. It’s about understanding the viewers, designing structure, assessing dangers, integrating with current methods, and planning lengthy‑time period help for years forward. And that is the place the human issue stays irreplaceable.
As a substitute of the “AI replaces builders” situation, we’re shifting towards a combined‑staff mannequin, the place AI brokers change into a part of the workflow and builders use them as accelerators and assistants. This synergy is already reshaping the software program growth panorama and can proceed to outline it within the coming years.
The primary takeaway: the age of AI doesn’t eradicate the occupation of software program engineer — it transforms it, including new instruments and shifting priorities from routine coding towards structure, integration, and strategic design.
Ceaselessly Requested Questions (FAQs)
Can AI write a complete app?
Sure, however typically with out optimization, with over‑engineered structure, and with out contemplating lengthy‑time period maintainability.
Will AI substitute frontend/backend builders?
Not but, since most growth selections require enterprise context, commerce‑offs, and expertise that AI doesn’t possess.
What’s the largest impression of AI-generated code?
An elevated threat of technical debt, maintainability points, and architectural misalignment — all of which may finally drive up the price of rework.
