How usually have you ever caught your self pondering, “Wouldn’t or not it’s simpler at hand the challenge over to AI as a substitute of paying a crew of builders?” It’s a tempting thought, particularly within the age of AI — however the actuality is way extra complicated.
On this article, we’ll discover what AI can really do in software program improvement, the place it nonetheless falls brief in comparison with people, and what conclusions corporations ought to draw earlier than entrusting a challenge to synthetic intelligence.
When AI Tried to Play Software program Engineer
Lately, a shopper approached SCAND with a singular 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 function was easy — fetch statistics from an exterior API and show them in a desk.
The preliminary consequence appeared promising: AI created a functioning challenge that included each client- and server-side parts, applied the fundamental logic for retrieving information, and even designed the interface. The desk accurately displayed the statistics, and the general code construction appeared first rate at first look.
Nonetheless, upon nearer inspection, it grew to become clear that the answer was overengineered. As an alternative of straight connecting to the API and displaying the information within the browser, AI constructed a full backend server that proxied requests, saved intermediate information, and required separate deployment.
For such a easy activity, this was pointless — it difficult the infrastructure, added additional setup steps, and lengthened the combination course of.
Furthermore, AI didn’t account for error dealing with, request optimization, or integration with the shopper’s current techniques. This meant builders needed to step in and redo elements of the answer.
The Limits of Generative AI in Coding and Software program Growth
Generative AI has already confirmed that it might probably rapidly produce working code, however in observe, its capabilities in real-world software program improvement usually transform restricted. Listed here are the important thing points we recurrently encounter when reviewing AI‑generated initiatives:
- Lack of know-how of enterprise logic and structure. AI can’t see the complete image of a challenge, its targets, and its constraints. In consequence, the options it produces could also be technically appropriate however fully misaligned with the precise enterprise wants.
- Incapacity to make architectural commerce‑offs. An skilled software program engineer evaluates the stability between improvement pace, implementation price, and ease of upkeep. AI, however, can’t weigh these elements and tends to decide on a regular and even unnecessarily complicated method.
- Overengineering. Producing pointless layers, modules, and companies is a typical mistake. For instance, a easy utility might find yourself with an additional backend that requires separate deployment and upkeep.
- Ignoring the context of current techniques. AI doesn’t consider how new code will combine with the present infrastructure, which might result in incompatibilities or further prices for rework.
- Code ≠ product. Synthetic intelligence can write fragments of code, however it doesn’t ship full options that consider UX, safety, scalability, and long-term assist.
- Doesn’t all the time totally perceive the duty. To get the specified consequence, prompts usually 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 a substitute of transferring on to efficient implementation.
In the end, regardless of the rising function of AI in software program improvement, with out the involvement of skilled builders, such initiatives 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 present — generally even pretty good code. However there are nonetheless some issues that synthetic intelligence can’t change 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 actual activity 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, usually follows a template with out taking hidden prices into consideration.
Third, it’s architectural flexibility. An skilled programmer can really feel when a challenge is beginning to “develop” pointless layers and is aware of when it’s the appropriate time to cease. AI, however, usually creates extreme constructions 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 isn’t but able to anticipating such nuances.
And at last, communication. A real software program engineer works with the shopper, clarifies necessities, and adjusts the method because the challenge evolves. AI isn’t able to actual dialogue or a refined understanding of human priorities.
Subsequently, in at present’s software program improvement panorama, synthetic intelligence remains to be a instrument — not a strategist. And within the foreseeable future, the human function in creating excessive‑high quality software program will stay important.
The desk beneath compares how people and AI deal with key facets of improvement, and why the human function within the course of remains to be essential.
| Criterion | Software program Developer | Generative AI |
| Understanding enterprise context | Analyzes challenge targets, target market, and long-term goals | 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 additional layers |
| Working with current techniques | Considers integration with present infrastructure | Might generate incompatible options |
| Foresight | Plans for scalability, error dealing with, and edge circumstances | Typically ignores non‑normal situations |
| Collaboration | Engages with the shopper, clarifies necessities, presents alternate options | Understands the request in a restricted manner, 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 era | Focuses on correctness and stability over uncooked pace | Generates code immediately, however it’s not all the time helpful or appropriate |
| Last deliverable | Prepared‑to‑use product | A set of code requiring overview 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 useful assistants for software program engineers. In line with 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 improvement workflow, Statista
In day‑to‑day improvement, AI can considerably pace up routine duties and simplify supporting processes, similar to:
- Producing boilerplate code. Generative AI can produce repetitive code constructions in seconds, saving time and permitting builders to concentrate on enterprise logic.
- Creating easy parts. AI can rapidly construct buttons, kinds, tables, and different UI parts that may later be tailored to the challenge’s wants.
- Changing codecs. Synthetic intelligence can simply remodel information and code — from JSON to YAML or from TypeScript to JavaScript, and again.
- Refactoring. AI can counsel code enhancements, simplify constructions, and take away duplicates.
- Speedy prototyping. AI can construct a primary model of performance to check concepts or display ideas to a shopper.
Nonetheless, even in these use circumstances, AI stays only a instrument. The ultimate model of the code ought to all the time undergo human overview and integration to make sure it meets architectural necessities, high quality requirements, and the challenge’s enterprise context.
SCAND’s Strategy — AI + Human Experience within the Age of AI
At SCAND, we see synthetic intelligence not as a competitor to builders, however as a instrument that strengthens the crew. Our initiatives are constructed on a easy precept: AI accelerates — people information.
We use Copilot, ChatGPT, Cursor, and different AI instruments the place they honestly 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 era is just the start. Each AI‑produced answer goes by way of the fingers of our skilled builders who:
- Examine 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 challenge specifics.
- Adapt technical options to the enterprise logic and challenge necessities.
We additionally pay particular consideration to information safety and confidentiality:
- We don’t switch confidential information to public cloud-based AI with out safety, until the shopper particularly requests in any other case. In initiatives involving delicate or regulated info (for instance, medical or monetary information), we use native AI assistants — Ollama, LM Studio, llama.cpp, and others — deployed on the shopper’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 incorporates errors.
- We embody 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 crew coaching on AI greatest practices, together with understanding the restrictions of AI-generated content material, licensing dangers, and the significance of handbook validation.
Will AI Substitute Software program Engineers? The Sensible Actuality Examine
At present, synthetic intelligence in software program improvement is on the similar degree that calculators had been in accounting a couple of many years in the past: a instrument that quickens calculations, however doesn’t perceive why and what numbers have to be calculated.
Generative AI can already do lots — from producing parts to performing computerized refactoring. However constructing a software program product is not only about writing code. It’s about understanding the viewers, designing structure, assessing dangers, integrating with current techniques, and planning lengthy‑time period assist for years forward. And that is the place the human issue stays irreplaceable.
As an alternative of the “AI replaces builders” situation, we’re transferring towards a combined‑crew mannequin, the place AI brokers grow to be a part of the workflow and builders use them as accelerators and assistants. This synergy is already reshaping the software program improvement 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.
Regularly Requested Questions (FAQs)
Can AI write a whole app?
Sure, however usually with out optimization, with over‑engineered structure, and with out contemplating lengthy‑time period maintainability.
Will AI change frontend/backend builders?
Not but, since most improvement selections require enterprise context, commerce‑offs, and expertise that AI doesn’t possess.
What’s the most important influence of AI-generated code?
An elevated threat of technical debt, maintainability points, and architectural misalignment — all of which might in the end drive up the price of rework.
