SimplifyC++ Article
AI and Me — What We Can Do
What Becomes Possible When Human Experience Meets Artificial Intelligence
The question today is no longer:
Will artificial intelligence take programmers' jobs?
The real question has become much harsher:
What happens when an experienced programmer who knows how to use AI competes with an equally experienced programmer who refuses to use it?
That is where the real disruption begins.
We are not simply witnessing the arrival of another tool, a better code editor, a faster library, or a new programming language that saves a few lines of code.
We are witnessing a transformation in the very meaning of software production.
A programmer who once needed days to create the initial structure of a project, hours to study documentation, more days to experiment with architecture, and then a long period to write tests, fix errors, build interfaces, prepare build systems, and create documentation can now compress a large portion of that cycle into a fraction of the time.
But there is one enormous condition that many people overlook:
You must know what you want.
This is where the difference appears between someone who uses AI as a toy and someone who uses it as a serious production force.
I Am Not Writing This as Theory — I Have Already Lived It
I want to stop speaking theoretically for a moment.
Everything I am saying in this article comes from a real personal experience that unfolded in front of me within weeks.
For nearly two years, I felt deeply frustrated because I had projects and ideas that were far larger than what I could execute alone.
I needed:
- Developers
- Programmers
- Designers
- People to manage websites
- People to build interfaces
- Database specialists
- People to organize content
- People to help with publishing and editing
- People to prepare articles and books
- Technical contributors for the software infrastructure of the projects themselves
The obstacle was always the same:
Money, time, and manpower.
A large technical project may need one team.
The website may need another.
Design requires specialists.
Content takes time.
Administration takes time.
Advanced programming requires enormous concentration.
I could clearly see where I wanted to go, but the amount of work required was simply beyond the normal capacity of one person.
Then Everything Changed Within Two Months
About two months ago, I decided to move seriously and aggressively into using artificial intelligence for software development — not merely as a tool for asking coding questions or fixing occasional bugs, but as a production partner inside the development process itself.
I started gradually.
I asked for simple things.
Then larger things.
Then I began testing its limits.
I changed requirements.
I rejected results.
I redesigned entire sections.
I instructed it to work with specific technologies.
I specified the libraries.
I specified the build environment.
I specified the operating system.
I specified the compiler.
I specified the project architecture.
I defined exactly what I wanted and what I did not want.
And with every passing day, I was learning not only what AI could do, but something much more important:
How to make AI do what I actually want it to do.
That is an entirely different skill.
Decades of Experience Suddenly Became a Force Multiplier
Because of my long experience in programming and my exposure to many generations of old and modern technologies, I usually do not ask AI:
Build me a good application.
I already know much of what I want before the conversation even begins.
I know when I need C and when I need C++.
I know when Assembly makes sense.
I know which libraries I want to use.
I know when I want to reject an external dependency.
I understand Windows and Linux environments.
I understand databases.
I understand servers and hosting.
I know how software is built and how websites are deployed.
I know how to move the result from an AI conversation into a real project that actually runs.
And I know what to do when the proposed solution fails.
This is where I discovered something extremely important:
Artificial intelligence does not erase your previous experience. It can multiply its value dramatically.
Every piece of knowledge accumulated across decades becomes an additional ability to guide the AI more precisely.
If it gives me something unreasonable, I recognize that it is unreasonable.
If it chooses a library I do not want, I change it.
If it proposes an architecture that does not fit my goal, I ask for another one.
If it ignores performance, I stop it.
If it modifies something outside the intended scope, I redirect it.
I am not asking AI to think instead of me.
I am using it to execute a massive amount of work under my direction.
That distinction is fundamental.
The Result? What Once Needed Funding and a Team Became Possible
This was the real shock.
Things I once believed required programmers, developers, designers, and substantial funding became things I could accomplish largely by myself with the assistance of AI.
Websites.
Administration systems.
Databases.
User interfaces.
Tools.
Design work.
Deployment processes.
Content editing.
Articles.
Books.
And software work connected to my core technical projects.
The result was not simply:
I managed to build them.
In many cases, the result was:
Far better than what I had imagined I would be able to achieve at this stage.
The direct cost of the AI service I rely on was around 90 Saudi Riyals per month.
But nobody should be misled by that number.
Ninety Riyals alone does not build anything.
Behind that small monthly cost are years of experience that allow me to:
- Understand the results
- Move and organize files
- Modify source code
- Manage servers
- Work with databases
- Manage hosting environments
- Understand build errors
- Know when a solution is correct and when it is merely code that looks convincing
So the equation is not:
90 Riyals = a software company.
The real equation is:
Long experience + clear vision + the ability to direct + artificial intelligence = production power that once required a team and a budget.
In One Month, I Built What Had Been Planned for an Entire Year
This was the point that completely changed the way I saw the subject.
I found that in roughly one month I was able to complete work that had previously been planned across an entire year of development.
And when I previously estimated the budget required to reach similar stages of the project, the figures I had in mind exceeded $80,000 in programming, development, design, and technical work.
Then I watched, within weeks, as things I had previously scheduled across many months became real.
At that moment, I understood that this was not merely a small productivity improvement.
It is a transformation in the economics of software creation itself.
For Me, What Happened Felt Almost Like a Technological Miracle
I use the word miracle here to describe the magnitude of the transformation I experienced, not in its literal sense.
When you spend years carrying dozens of ideas that you cannot execute because of money, time, or the lack of a team, and then suddenly encounter a technology that allows you to turn a substantial part of those ideas into reality, the feeling is anything but ordinary.
I had the ideas.
I had the experience.
I had the vision.
But I did not have dozens of hands to execute everything.
Then this technology added something new to the equation:
A massive execution capability available on demand.
That was when I told myself:
I have to write about this.
Not as news about AI.
Not as a prediction of what may happen five years from now.
But as:
An experience that has already happened.
AI Does Not Create Genius Out of Nothing
There is a simplistic idea spreading today:
Now anyone can become a professional programmer simply by asking AI to write the program.
It is not that simple.
AI can write.
It can suggest.
It can modify.
It can explain.
It can build prototypes at astonishing speed.
But it does not know your project the way you do.
It does not know where you truly want to go unless you can define that destination.
And it cannot always distinguish between a solution that merely appears correct and one that is suitable for a product that must survive for five years.
This is why programmer experience becomes more important, not less.
An experienced programmer can say:
This architecture is wrong.
This design will not scale.
This code violates the ABI.
This solution will create a performance bottleneck.
This external dependency is inappropriate.
This interface needs to be redesigned.
Rewrite this layer, but do not touch that one.
Use this technology and avoid that one.
Then the developer enters a very fast cycle with the AI:
Request.
Execution.
Review.
Rejection.
Correction.
Redesign.
Testing.
Improvement.
Then another cycle.
That is where the leap happens.
An Expert + AI Is More Than One Person
In some categories of work, the combination starts to resemble a small, extremely fast team.
On one side, you have a human mind that defines direction, understands objectives, knows the context, and decides what is acceptable and what is not.
On the other side, you have a machine that can generate tens or hundreds of lines of code in seconds, propose structures, create CMake files, build interfaces, generate tests, explain errors, compare alternatives, and rewrite entire sections without fatigue.
This is where a new era of programming begins:
A programmer no longer needs to manually execute every part of the work in order to remain the true creator of the system.
An architect does not personally place every brick in a building.
Value is gradually shifting from:
Who writes more?
to:
Who thinks better?
And from:
Who memorizes more?
to:
Who knows what to ask, what to accept, and what to reject?
One Person May Produce What Once Required a Team
This statement will disturb many people, but it is becoming increasingly realistic in certain kinds of software projects:
One experienced developer who knows how to use AI effectively can sometimes produce an amount of work that previously required several developers.
Not because AI has become some kind of supernatural engineer.
But because an enormous amount of software work never required genius in the first place.
It required human time.
- Writing boilerplate
- Creating models
- Transforming data structures
- Writing CRUD operations
- Generating tests
- Writing documentation
- Building initial interfaces
- Preparing build files
- Writing wrappers
- Reading error messages
- Converting one API into another
- Refactoring code
- Repeating the same pattern across dozens of files
These tasks consumed weeks and months of development teams' lives.
AI can now absorb a large portion of that workload.
And that leaves the human to focus on what is more valuable:
Design, judgment, engineering, verification, and innovation.
So Yes — Some Programmers Should Be Worried
Not because programming is going to disappear.
But because the market value of some forms of programming will decline.
If your only value is that you can write 500 lines of familiar code in two days, someone else can now use AI to create a first version of similar work in minutes.
If your entire job consists of moving data from a database into a model and then into a user interface, you are working in an area that is becoming highly automatable.
If you spend two hours searching for syntax that a model can provide in seconds, you are no longer competing in the same way you were a few years ago.
The real threat is not:
AI will take your job.
It is this:
Someone who understands your field and knows how to use AI may become so much faster than you that the company cannot ignore the difference.
That is a completely different problem.
Who Should Be Afraid?
The programmer who refuses to experiment.
The programmer who sees AI as a threat to personal identity.
The programmer who takes pride in writing everything manually even when there is no engineering value in doing so.
The programmer who keeps saying:
I have been programming for twenty years. I do not need these things.
That person should pay attention.
Experience is enormously valuable.
But experience that becomes an excuse to reject productivity-enhancing tools can turn from an advantage into a limitation.
And Experience Itself May Become More Valuable Than Ever
The fascinating part of this revolution is that it may not destroy the experienced programmer.
It may instead give that programmer more power than ever before.
Imagine an engineer with thirty years of experience.
Someone who understands:
- Operating systems
- Processors
- Networks
- Databases
- Security
- C++
- Assembly
- ABIs
- Compilers
Previously, that engineer was limited by one thing:
Time.
There were more ideas than could ever be implemented personally.
Now that engineer can transform experience into highly precise engineering instructions and have AI execute a massive amount of repetitive work.
At that point, years of experience become ammunition.
A beginner may ask AI:
Build me a program.
An expert may say:
Separate the parser from the semantic layer, keep the IR independent from the target backend, avoid heap allocation in the hot path, write boundary-condition tests first, and return the architecture for review before implementation.
The difference is not the AI.
The difference is the mind directing it.
Prompt Engineering? That Term Is Too Small
This is not about learning a few magical phrases to type into a model.
That is a shallow understanding of what is happening.
The real skill is:
AI Engineering Workflow.
You need to know how to transform a massive project into a sequence of tasks that AI can help execute without causing you to lose control.
You need to know:
- How to provide context
- How to define constraints
- How to prevent unwanted changes
- How to ask for alternatives
- How to make the model justify a design
- How to test the result
- How to feed errors back into the process
- How to fix one component without breaking another
- How to manage thousands of generated lines
- How to preserve architectural consistency
- How to know when to say: “No. This solution is bad. Start again.”
This is an entirely new professional skill.
The Programmer of the Future Will Not Be Just a Programmer
The future programmer may increasingly become a combination of:
- Software Architect
- Product Engineer
- Technical Director
- AI Operator
- Code Reviewer
- System Designer
All within one person.
Their value will not be measured only by how fast their fingers move on a keyboard.
It will increasingly be measured by how quickly they can turn an idea into a working system.
Companies Will Notice the Difference Quickly
Imagine two developers with roughly the same technical level.
The first works traditionally.
The second uses AI professionally.
The first spends three days building a feature.
The second builds a prototype in hours and spends the remaining time testing and improving it.
The first reads ten pages of documentation to find the right API call.
The second asks AI, gets a starting point, then verifies it against the official documentation.
The first writes hundreds of tests manually.
The second designs the testing strategy, has AI generate initial tests, and then reviews and strengthens them.
Over time, the productivity gap becomes visible.
And companies do not evaluate this as a philosophical debate.
They look at outcomes:
- How many features were completed?
- How many bugs were fixed?
- How long did the project take?
- How many employees were required?
- How much did it cost?
This Is Where Software Teams Begin to Change
Teams will not necessarily disappear.
But their composition may change.
Instead of ten people executing repetitive work, a company may need four highly capable engineers who use AI continuously and effectively.
Demand may increase for:
- Software architects
- Systems engineers
- Strong code reviewers
- Security specialists
- Performance experts
- Product-oriented engineers
Meanwhile, demand may decline for roles whose main value is the routine execution of clearly defined instructions.
This is a normal economic transformation.
Every major productivity tool in history has reshaped labor markets.
Do Not Compete With AI at Typing Code
That is a losing battle.
Do not say:
I can write this code myself.
Of course you can.
The question is:
Should you spend your time doing it?
If a machine can generate the first draft in one minute, let it.
Then use what the machine does not easily possess:
- Judgment
- Experience
- Context
- Engineering taste
- Product knowledge
- User understanding
- Risk assessment
- Decision-making
But Do Not Become a Servant of AI Either
There is danger in the opposite direction as well.
A programmer can become someone who simply copies everything the model generates without understanding it.
That may become one of the most dangerous kinds of developers in the future.
A program works today, but nobody knows why.
A hidden security vulnerability.
Incorrect thread synchronization.
A lifetime bug.
Undefined Behavior.
SQL injection.
A deprecated API.
An architecture that collapses under the first serious expansion.
AI can accelerate mistakes just as effectively as it accelerates correct work.
So the golden rule is:
Let AI produce quickly, but never let it make the final engineering decisions alone.
The Future of Programming Is Not Merely Writing Less
It is about achieving more.
This distinction is extremely important.
Some people see AI mainly as a shortcut:
Instead of writing 100 lines, let AI write them.
That is one of the weakest ways to think about the technology.
The more powerful question is:
Instead of building one project a year, what if I could build four?
Instead of testing one architecture, what if I could compare five?
Instead of leaving my ideas in a notebook because I do not have time, what if I could turn them into prototypes?
That is the real disruption.
Ideas That Were Once Bigger Than Their Creators Are Becoming Possible
How many programmers have had great ideas but said:
I need a designer.
I need a database specialist.
I need a frontend developer.
I need someone to write the documentation.
I need someone to create the tests.
I need months just to reach a prototype.
These professions are not disappearing, but the barrier to entry is falling dramatically.
One person can now create the first version.
Test the idea.
Demonstrate it.
Gain early users.
And then, if the product succeeds, build the team.
This may open an entirely new era for independent developers.
The Smallest Software Company of the Future May Be One Person
One person with:
- An idea
- Technical experience
- A product vision
- Artificial intelligence
- Cloud services
- Distribution tools
That person may be able to operate a business that, twenty years ago, would have required dozens of employees.
For me, this is no longer purely a prediction.
I have already seen a smaller version of it happen personally.
I have seen tasks for which I once believed I needed funding and a team become work I could increasingly manage myself.
The coming years may therefore become the era of the super-productive independent developer.
What Should You Learn Now?
Do not stop learning programming.
Do the exact opposite.
Learn it more deeply.
Then add another layer:
Learn how to use artificial intelligence as part of the engineering process.
Learn how to:
- Give AI a clear specification
- Make it understand an existing project
- Request limited and controlled refactoring
- Debug with it
- Generate tests
- Compare solutions
- Use it alongside Git
- Use it with compiler diagnostics
- Ask for performance analysis
- Review generated code for security
- Turn long conversations into executable engineering steps
These abilities are rapidly becoming fundamental development skills.
Will the Programmer Disappear?
No.
But the definition of the word programmer will change.
It changed when we moved from machine code to Assembly.
It changed when we moved from Assembly to high-level languages.
It changed when we moved from writing everything manually to libraries and frameworks.
It changed when we moved from simple text editors to sophisticated IDEs.
Every new layer raised the level of abstraction.
Artificial intelligence may become the largest abstraction layer software development has ever seen.
We are moving from:
Write this code.
to:
Build this system under these constraints.
My Message to the Programmer Who Is Afraid for His Job
Yes.
You should feel some danger.
But not in the simplistic way often presented online.
Do not fear waking up tomorrow and discovering that AI has literally become an employee sitting in your chair.
Fear waking up two years from now and discovering that a colleague who once had the same skill level as you is producing five times more because he mastered tools you ignored.
Do not run away from artificial intelligence.
Enter it carrying your experience with you.
Bring your knowledge.
Your programming languages.
Your systems understanding.
Your debugging experience.
Your knowledge of architecture.
Your understanding of the market.
Then learn how to multiply all of that using AI.
Because companies will ultimately not be searching for:
A programmer who knows AI but understands little else.
Nor will they prefer:
A brilliant programmer who refuses to use AI.
They will increasingly value the far more powerful combination:
A strong programmer + a systems-minded engineer + a person who knows exactly what he wants + someone who knows how to direct artificial intelligence effectively.
That is the programmer who will be extremely difficult to compete against.
AI and Me — What We Can Do
Perhaps this is the question every developer should be asking today.
Not:
What can AI do?
Because the answer to that question changes constantly.
The more powerful question is:
What can I do when I place my experience, judgment, and vision on top of this new power?
For me, this question is no longer theoretical.
I have lived part of the answer.
I once looked at work that required funding, teams, and long periods of development.
Then I watched a large part of it become reality within weeks.
The equation is now completely different.
AI alone is not you.
And you alone are limited by time.
But:
Deep human experience + clear vision + fast artificial intelligence = an entirely new level of productive power.
Those who understand this equation early may build things in the coming years that they once considered far beyond their time, resources, or individual capacity.
And those who stand on the sidelines mocking the technology, waiting to prove that nothing will change, may discover too late that everyone else was not debating the future.
They were building it.
And I am writing this article because during the past few weeks, I have no longer been merely reading about that future.
I have started living it.
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