We Don’t Glue 'Ifs'. We Build Systems of Intent.

On the creeping fundamental paradigm shift in software development. Why we need to prepare for an enormous truly enormous transformation.

For quite some time now, we've been hearing terms like "AI-first," along with information about significant changes in the job market and claims that AI will take jobs away from programmers — something akin to a revolution devouring its own children.

So, what's the reality of programming today… and yesterday?

Conventional, traditional programming vs AI-First (development with wingmen like Copilot, GPT-4o, etc.) isn't merely a technological change. It represents a fundamentally different mindset.

I've prepared a comparison that, in my opinion, clarifies why and how AI-first development changes the rules — not only in coding but also in product strategies, processes, and team dynamics. Let's look at some differences everyone should understand:

Input data in programming

Conventional, traditional programming: Inputs must be explicitly defined — int, float, pointers, vectors, text lists. A full set of possibilities - but also pretty much strict requirements.

AI-first: We work with fuzzy data - goals, flexible parameters, markdown, guidelines. But... the specific logic of the code remains fundamentally open-ended. Here, the model itself decides how to interpret the data.

It should also be mentioned that the more precisely we define the "WHAT," the better the "HOW" we will get. This seems trivial, but the impact of this detailing is gigantic for the entire SDLC process.

Both the cost and time of delivering a PoC using AI assistance are simply on a different order of magnitude. Moreover, you will often be able to run several PoC experiments before even completing one in the conventional mode.

Transformations and modifications during programming

Conventional, traditional programming: if, for, else, loops, patterns. Essentially deterministic debugging and full control.

AI-first: Rewriting text, answering questions, brainstorming ideas. The same input can yield different outputs. You don’t write about how something should be done. You state what you want - the model handles the rest. Now, implementing non-functional requirements involves just a few additional prompts. Performance optimizations follow similarly.

Indeed, the human "protein-pilot" must possess enough knowledge to decide when to steer left or right - but fundamentally, they no longer spend energy on manually welding bolts.

I’d even say this human pilot needs to be "dangerous enough" for AI assistants 😊.

Output data in programming

Conventional, traditional programming: Specific, predictable, explicitly defined values.

AI-first: Paragraphs, xml, json, numbers, code - depending on context, tone, and prompts.

Here, the strength of AI assistance lies in clearly describing the WHAT - essentially what we want to achieve. We don't weld bolts. Instead, we evaluate how the entire component looks and functions.

Beautiful?

Yes, it's beautiful. Definitely beautiful. (Today is May 1, 2025 😉)

Few floors higher...

Now, a few words from a different perspective. Back in 2023, I wrote and spoke about managing AI agents designed to perform specific tasks. It just happened - and honestly, it was fundamentally easy to predict two years earlier

Today, we not only have access to ready-made multi-agent frameworks - approaches such as graphs, agent personas, memory systems, and intent - but we can also build entire multi-agent structures that plan and decide autonomously.

Does being a professional manager facilitate designing such systems?

✅ Yes, very much.

Managing AI agent tasks resembles managing an organization — you need to define and break down tasks, set goals, integrate tactics and strategies, orchestrate interactions, and enforce priorities. To be honest, I didn’t fully expect just how many of these leadership and operational skills would turn out to be directly reusable in the new architecture of building intelligent systems.

Will programmers disappear? (Today is May 1, 2025 😉)

Certainly not.

In my opinion, programmers will become engineers — a shift I’ve been evangelizing for years 😊. We’ll reach a completely new level of civilization and solution maturity.

Companies that fail to understand this will attempt to build the future using tools from the past. (Although that's also typical during technological revolutions.)

#AI #Programming #Transformation #AIfirst #SoftwareDevelopment #PromptEngineering #Leadership