AI for Coding: The Natural Next Step We've Been Building Towards All Along

When Ada Lovelace wrote the first computer program back in 1843 (yes, *1843* - before cars, before telephones), she was essentially translating mathematical concepts into a language a machine could understand.

Fast forward to 2025, and we're doing the exact same thing. Except now, instead of writing cryptic sequences of ones and zeros or memorising arcane syntax rules, we're literally just... talking to our computers. And they're writing the code for us.

If you think that sounds like science fiction, you're not alone. But this isn't some revolutionary leap into uncharted territory. This is the logical conclusion of a journey we started nearly two centuries ago.

We've Always Been Moving Towards Natural Language

The 1940s-50s: The Dark Ages of "Please Don't Make Me Do This"**

In the earliest days of computing, programmers had to write in machine code - literally ones and zeros that directly corresponded to instructions the computer's hardware would execute. Imagine having to tell someone to make you a cup of coffee by explaining, in excruciating detail, how each individual muscle in their arm should contract and release. That's basically what early programming was like.

Then came assembly language, which was marginally better. Assembly language used mnemonic codes and symbols to represent machine instructions and memory addresses. So instead of "01001000," you could write "MOV." Progress! Sort of. It was like upgrading from morse code to really terse telegrams.

The 1950s-60s: Someone Finally Said "This Is Ridiculous"

Fortran, short for "Formula Translation," was introduced in the 1950s as the first high-level programming language, primarily designed for scientific and engineering calculations.[1] These languages introduced concepts like loops, conditions, and subroutines, making coding more structured and manageable.

For the first time, programmers could write something that looked vaguely like English and have the computer figure out the messy bits. It was eventually realized that programming in assembly language required a great deal of intellectual effort, so high-level languages were born out of necessity, not luxury.[2]

The Pattern Emerges: Every Language Gets More Human

From there, it's been a steady march towards languages that look less like arcane incantations and more like things actual humans might say:

- COBOL tried to use English-like syntax for business applications

- BASIC made programming accessible to beginners

- Python prioritised readability (and let's be honest, sanity)

- Ruby focused on making programmers happy

The birth of high-level programming languages brought about a paradigm shift, allowing developers to write code in a more natural language-like syntax.[3]

Each generation of programming language got a little bit closer to natural language. A little less cryptic. A little more "just tell the computer what you want."

Enter AI: The Inevitable Next Step

Which brings us to today, where Gartner predicts that by 2028, 90% of enterprise software engineers will use AI code assistants, up from less than 14% in early 2024.[4]

That's not a typo. We're talking about going from barely anyone using these tools to basically *everyone* using them in the span of about four years.

Tools like GitHub Copilot, Amazon Q, and Google's Gemini Code Assist have fundamentally changed the game. GitHub Copilot integrates with leading editors, suggesting whole lines or entire functions, and has grown to millions of individual users and tens of thousands of business customers, making it the world's most widely adopted AI developer tool.[5]

But what makes this genuinely interesting (and not just another tech hype cycle): these aren't just fancy autocomplete tools. They're the natural progression of that journey Ada Lovelace started back in 1843.

What This Actually Looks Like in Practice

Let me give you an example from last week. A client needed to parse complex HTML emails and extract data into their business system. In the old days (like, two years ago), I would have:

1. Written a custom parser

2. Handled all the edge cases manually

3. Tested it obsessively because HTML email formatting is the seventh circle of hell

4. Probably sworn a lot

Instead, I described what I needed in plain English to Copilot, tweaked the generated code, and moved on with my life in about a tenth of the time.

The code it generated wasn't perfect - developers need to review and validate the code generated by AI assistants to make sure it meets their project's requirements and standards, as suggestions can sometimes be less than optimal or even incorrect, especially for complex logic or edge cases.[6] But it got me 80% of the way there, which meant I could focus on the interesting problem-solving bits rather than wrestling with syntax.

These AI tools are trained on vast amounts of publicly available code, documentation, and programming knowledge, enabling them to provide intelligent suggestions, autocomplete code snippets, and even generate entire functions or classes based on natural language descriptions.[7]

The Reality Check (Because There's Always a Reality Check)

Now, before you start planning your retirement because AI is going to do all the coding, let's pump the brakes.

AI Coding Assistants Are Brilliant But Not Magic

Research examining 211 million changed lines of code from 2020 to 2024 found that the percentage of code associated with refactoring sunk from 25% in 2021 to less than 10% in 2024, while copy/pasted code rose from 8.3% to 12.3%.[8] Translation: people are using these tools to write *more* code, but not necessarily *better* code.

It's like giving someone a really powerful word processor - yes, they can type faster, but that doesn't automatically make them a better writer. You still need to understand what you're trying to accomplish.

The Ambiguity Problem

Human language is ambiguous and complex, with huge differences between languages in sentence construction and grammar, making it very difficult to create natural language programming systems.[9] If your current process involves a lot of "you'll know it when you see it," AI is going to struggle.

Computers, even very sophisticated AI-powered ones, still need clear instructions. They're just better at understanding those instructions when they're phrased like you're talking to a competent (if slightly literal) colleague rather than writing assembly code.

What This Means for Your Business

If you're a business leader reading this and thinking "okay, but what does this mean for *me*?" - fair question.

With 20M+ users across 77,000 enterprises, GitHub Copilot's scale is helping to shape the rapidly evolving AI Code Assistants market.[10] This isn't emerging technology anymore. It's here, it's proven, and it's being adopted at a pace that makes previous tech adoption curves look leisurely.

The Democratisation of Development

We're heading towards a world where the barrier between "I have an idea" and "I have working software" gets thinner every year. Natural language programming utilizes AI and machine learning techniques to understand human-readable instructions provided by users and convert them into executable code, making it a highly versatile tool for programmers and non-programmers alike.[11]

This doesn't mean everyone should suddenly become a programmer (please don't). But it does mean that the people on your team who understand your business processes deeply can start expressing those processes in ways that computers can act on, with less translation required.

The Productivity Multiplier

According to Stack Overflow's 2024 Developer Survey, 63% of Professional Developers said they currently use AI in their development process, and developers overwhelmingly picked "Increased productivity" as the most important benefit they're hoping to achieve with AI.[12]

The smart companies aren't using AI to reduce their development teams. They're using it to let their developers focus on the hard, interesting problems that actually move the business forward, while AI handles the repetitive, tedious stuff.

So What's Next?

As artificial intelligence and machine learning advances, the ability for AI models to understand and generate code based on human language will improve, with continued usage of techniques such as retrieval-augmented generation enhancing these systems with more context and data.[13]

We're in the early innings here. The tools will get better. The understanding will deepen. The gap between intention and execution will continue to shrink.

Ada Lovelace wrote her algorithm in prose, describing what the Analytical Engine should do in terms a human could understand. Nearly 200 years later, we've come full circle. The difference is, now the machine is sophisticated enough to meet us halfway.

And if you're wondering whether this is going to change how software gets built - mate, it already has.

The question isn't whether to adopt these tools. The question is how quickly you can adapt your processes to take advantage of them before your competitors do.

We've spent the better part of two centuries teaching computers to understand us. Every programming language, from FORTRAN to Python, has been another step towards natural language. AI-powered coding isn't some radical departure from this trajectory - it's the logical next step.

The difference is, for the first time in computing history, we're not asking humans to learn to think like machines. We're finally teaching machines to understand human intent.

And honestly? It's about bloody time.

---

*Want to explore what this means for your business? We help forward-thinking companies leverage AI and automation to work smarter, not harder. Drop us a line - we're always keen to chat with business leaders about turning emerging technology into practical, measurable results.

#AICoding #FutureOfProgramming #BusinessAutomation #WorkSmarter**

---

References

[1] Kodegasm. (2023). "The Evolution of Programming Languages: From Assembly to High-Level Languages." https://medium.com/@kodegasm.id/the-evolution-of-programming-languages-from-assembly-to-high-level-languages-8d1417631203

[2] Wikipedia. (2025). "History of programming languages." https://en.wikipedia.org/wiki/History_of_programming_languages

[3] Ahmed, A. (2024). "The Evolution of Programming Languages: From Binary to Natural Language Processing." https://medium.com/@aahsanaahmed26/the-evolution-of-programming-languages-from-binary-to-natural-language-processing-63d1f0f85101

[4] GitHub. (2025). "Gartner positions GitHub as a Leader in the 2025 Magic Quadrant for AI Code Assistants." https://github.blog/ai-and-ml/github-copilot/gartner-positions-github-as-a-leader-in-the-2025-magic-quadrant-for-ai-code-assistants-for-the-second-year-in-a-row/

[5] GitHub. (n.d.). "GitHub Copilot · Your AI pair programmer." https://github.com/features/copilot

[6] Intellias. (2025). "GitHub Copilot Review: How AI is Transforming the Software Development Process." https://intellias.com/github-copilot-review/

[7] Thummala, S. (2025). "The Rise of AI-Powered Coding Assistants: How Tools Like GitHub Copilot Are Changing Software Development." https://medium.com/@sreekanth.thummala/the-rise-of-ai-powered-coding-assistants-how-tools-like-github-copilot-are-changing-software-0e31c34490e2

[8] GitClear. (2025). "AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Clones." https://www.gitclear.com/ai_assistant_code_quality_2025_research

[9] Couchbase. (2025). "Natural Language Programming: Applications and Benefits." https://www.couchbase.com/blog/natural-language-programming/

[10] GitHub. (2025). "Gartner positions GitHub as a Leader in the 2025 Magic Quadrant for AI Code Assistants." https://github.blog/ai-and-ml/github-copilot/gartner-positions-github-as-a-leader-in-the-2025-magic-quadrant-for-ai-code-assistants-for-the-second-year-in-a-row/

[11] Couchbase. (2025). "Natural Language Programming: Applications and Benefits." https://www.couchbase.com/blog/natural-language-programming/

[12] GitClear. (2025). "AI Copilot Code Quality: 2025 Data Suggests 4x Growth in Code Clones." https://www.gitclear.com/ai_assistant_code_quality_2025_research

[13] Couchbase. (2025). "Natural Language Programming: Applications and Benefits." https://www.couchbase.com/blog/natural-language-programming/