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These days, there’s a lot of talk about Artificial Intelligence (AI) as a solution for every challenge—ranging from streamlining processes to tackling complex problems. But the truth is a bit more nuanced: while AI does offer many advantages, there are areas where it may not serve as a perfect substitute. To understand why, we should first look at what we really mean by “intelligence,” and then see how AI differs from the way we, as human beings, experience and navigate reality.

What is intelligence, and what is Artificial Intelligence, anyway?

Intelligence is the ability to recognize, understand, analyze, and solve problems effectively, while learning and adapting to new information or changing environments. It encompasses various cognitive processes like perception, inference, memory, creative thinking, and self-awareness, which allow us to gather information from the world, process it, and use it wisely.

When we talk about Artificial Intelligence, we’re referring to computer- and algorithm-based systems that perform tasks previously requiring human capabilities—from image and voice recognition to data analysis and solving complex problems. Its strength lies in its capacity to process vast amounts of data at high speed, but it doesn’t necessarily replace all aspects of human intelligence.

Now that we understand what intelligence is and why it’s regarded as a central driver of learning and problem-solving, we can delve into the key differences between AI and the way humans think and act. That’s where it becomes clear that AI, despite its immense power, isn’t always the ultimate fix for every challenge or need.

Why AI won’t measure up to human intelligence

AI doesn’t get stomachaches.
Take a moment and look at your body: it’s made of biological materials that wear out over time, get sick, and eventually stop working. Every action—breathing, eating, metabolism—depends on complex chemical processes. If one of them fails, the body can collapse, and there’s not much you can do about it. AI, on the other hand, doesn’t require biology. All it needs is hardware, electricity, and stable software. It doesn’t have an aging body, no health worries, and if the hardware fails—you simply copy the software onto a new machine. It doesn’t fret about “the end,” because practically speaking, it has no concept of “end.”

So what does that mean? Simply put, if you build a system that relies on AI, you’re not bound by biological ailments or the natural hourglass of the human body. The AI doesn’t get stuck with health or aging issues, and it doesn’t need human care. On the flip side, it also doesn’t bring genuine emotional depth, personal insights, or perspectives born from real-life experiences. True, it never gets tired after lunch, but it can’t be “sharper” for that crucial 20 minutes of a test, either. Its performance will always stay the same—from the moment the model first runs, forever. Our biological system may sometimes falter, but it also allows us to marshal our resources and transcend the physical and the possible. AI doesn’t. That’s important to understand.

AI doesn’t need to justify opinions with data.
Let’s be honest: no one really makes decisions based purely on numbers. Humans tend to mix in emotion, ego, personal opinions, and worries at every step—despite swearing we’re “highly rational.” AI, on the other hand, has no sense of “hurt feelings” or “hidden ambitions.” Everything it produces is the result of purely objective calculations, nothing more. Something’s bothering you? It doesn’t know. You’re angry at your boss? It doesn’t get offended on anyone’s behalf. As far as it’s concerned, there’s no difference between something “nice” and something “hurtful”—it’s just another line in the equation. Is that useful? Definitely, when you need a cold, precise data analysis. Probably until you show it how anger or love look in micro-data signatures, it won’t be able to turn them into a formula. And you know how hard it is to quantify even a small feeling—so imagine how tough it is to program genuine empathy into a machine.

AI won’t die.
Take a second to think about the fact that one day, you will die. It’s not exactly cheery, but there’s nothing to do about it—it’ll happen. One day it’ll all be over. That awareness shapes every decision you make, sets your priorities, and sometimes drives you to act before it’s too late. Now ask yourself: does AI even know what an “end” is? The short answer: it has no idea. If one piece of hardware fails, you can just copy it onto another device. There’s no profound awareness of “last chance” or “it’s all over.” It’s programmed to operate as long as there’s an energy source, an internet connection, and functioning components—and that’s exactly what it does. Sure, you could shut it down, but it won’t understand that, and it never will. It has no feelings about it. It isn’t afraid of it, at least not right now. If it ever does become afraid, well—that’s when we should be worried, but that’s another article. For the moment, it’s not. AI doesn’t “live” under that sort of pressure. In its eyes, the future is open-ended, and there’s no rush to seize an opportunity before it slips away. When does that matter? Whenever you need a more human element: excitement about the moment, fear of missing out, or a personal drive to get something done before regret sets in. AI doesn’t get stirred by these things; it just keeps going, unless someone unplugs it. And if they do… it doesn’t “care.”

Internal understanding vs. external processing
Humans can look inward, try to understand themselves, feel regret or satisfaction, and sometimes even change direction based on a personal epiphany. AI doesn’t do that. It doesn’t have an internal “here and now” (what some call a “sense of presence”) or a chain of moral reasoning. Instead, it performs external processing of data—you give it input, and it generates output based on statistical algorithms. When there’s an “error,” someone fixes or updates the model, but there’s no moment of “Why did I act like that?” or “Do I regret it?” It doesn’t truly ask questions about itself, simply because it doesn’t have an “I” capable of an internal dialogue.

Consider the following as my take on a future that may happen—but in my view, is already unfolding: A software revolution is happening beneath our feet. I say beneath our feet because while we all consider ourselves tech-savvy, constantly chatting with AI models, I don’t think we talk enough about the fact that this is just the beginning. The gap between the real transformation and what we have now is like the distance between MS-DOS and Facebook at its peak.

This article focuses on B2B software, where adoption is relatively straightforward. However, I plan to write a follow-up article about B2C software and its implications for what’s often called Web3.0— which I see as a natural complement to Software 3.0.

Want to encourage me to write that follow-up? Share this article in the social network where you spend most of your time. It’s that simple.

The Challenge of Growing Small Businesses

A small business at the start of its journey usually faces an overload of information and a lack of structured workflows. Actually, that’s not entirely true. Small businesses often struggle with a lack of knowledge, too much scattered data, and an unclear understanding of what “processes” even mean. They operate on inertia—customers come, customers go, and many decisions are made based on luck, gut feelings, or ego.

The kind of business I described earlier—one that manages its customers in an Excel spreadsheet—is actually rare. It’s not something I see happening often, and even when a business does use Excel, the file is usually neglected and outdated. This is simply a byproduct of how small businesses naturally operate: a few people, limited cash flow, and too much work to handle. That’s always been the case. Even when software started helping large businesses, it was expensive, sold as a one-time licensing deal, required long implementation and integration processes, and had to be customized for different operating systems—essentially, a logistical nightmare for a small business.

The Cloud Era (Software 2.0)

For years, small businesses had to make do with fragmented tools, too much manual work, and inefficient processes. Software existed, but it wasn’t a real solution for them. It was designed for large enterprises, priced accordingly, and required dedicated IT teams to implement and maintain. Software was something you purchased in a long, tedious process with sales reps—not something you subscribed to with a credit card and started using immediately.

That changed when cloud-based subscription software entered the picture. Suddenly, a small business that never considered using a CRM could sign up for one and have it running within hours. No more expensive one-time purchases, no more local servers—everything ran from a browser, with a user experience that felt more like a consumer app than a heavy enterprise system. The psychological barrier disappeared: it was no longer a “project” requiring IT approval but simply another tool for getting work done. If it didn’t work out, canceling was easy.

As these solutions gained traction, they started occupying a larger share of businesses’ daily operations. Instead of forcing businesses to adapt to rigid, enterprise-grade software, the software itself began adapting to the business. Features were added over time—automation, integrations, real-time analytics—without the need for complex purchasing decisions. Small businesses weren’t just using better tools; they were gaining advantages previously reserved for companies with full-fledged IT departments.

The AI Era (Software 3.0)

At first—and maybe even now—we didn’t fully grasp what was happening. Developers were quick to adopt AI because they saw how it could accelerate their work, debug code, and automate repetitive tasks. But for most people, AI was and still is just a gimmick: a way to generate funny emails, write mediocre poetry, or chat with a bot that sometimes hallucinates answers. It was clear something big was unfolding, but it wasn’t obvious how or when it would become a real tool that changes how we work. AI isn’t creative, it doesn’t “understand” in a human sense—it produces output that’s, at best, average.

But what developers saw, and continue to see, is how fast AI can write code. You might think, Okay, great for software engineers, but why should I care? My take? It won’t stay limited to developers for long.

Not Just Developers—AI Will Write the Code (…but code will still be written)

Until now, coding was strictly a developer’s domain. No matter how much software improved, you still needed to know how to write and structure code, troubleshoot errors, and manage dependencies. But with AI generating code, the rules are changing. People who don’t know how to program won’t need to. Instead of figuring out how to build automations or integrate different systems, they’ll just describe what they want, and the system will generate the necessary code behind the scenes.

We’re not fully there yet, but it’s clear where this is headed. We’re approaching a point where businesses and non-technical users can request complex software functionality, and AI will build the required logic. This means fewer people will write code manually, while more people will focus on defining problems and needs—letting AI handle the execution.

Developers Will Shift to Building the Infrastructure for AI-Generated Code

If developers won’t be spending their time writing code line by line, what will they do? Their role will shift toward creating environments where AI writes, tests, and executes code autonomously. This will involve:

• Data infrastructure that’s accessible to non-technical users, ensuring that anyone can input and manage information without needing a deep understanding of how databases work.
• Automated code execution environments, so that AI-generated scripts and workflows run without manual intervention.
• Cybersecurity and compliance frameworks tailored for AI-generated software, ensuring the code is secure, reliable, and resistant to malicious use.

The emphasis will move from writing individual features to designing systems that allow AI-generated solutions to function smoothly and securely.

Conclusion: The Shift to Dynamic, AI-Driven Software

We’re at a turning point where software is no longer a rigid tool but something that can be created, modified, and adapted in real-time. Cloud-based subscription software made business applications more accessible, but it still required users to adapt to the system. AI removes that constraint—it can generate code, build interfaces, manage data, and connect systems without human developers in the loop.

Instead of searching for the “right” software, businesses will receive flexible, evolving systems that mold themselves around their needs. Data structures will be intuitive and easy to use without technical expertise, while AI will transform raw information into actionable insights. This isn’t just an improvement over current software—it’s a fundamentally different approach to how software is created, deployed, and managed.

What’s Next? Future Articles on B2C & Web3.0

This article focused on B2B software, where technological change is easier to adopt. But these transformations won’t stop there. In a future article, I plan to explore what happens when AI-generated software becomes available to consumers.

• How will non-business users interact with dynamic, real-time applications?
• What happens when AI-powered systems are woven into everyday life?
• And how does all of this connect to Web3.0—the decentralized, user-driven evolution of the internet?