English | EN 
As a tech consultant working in tech today, I find myself surrounded by the relentless pace of AI breakthroughs—a reality that creates a strange mix of excitement and unease. AI has quickly become a term loaded with near-mythical expectations. But when you strip away the hype, the marketing layers, and the glossy demo reels, its core is far more straightforward: AI replaces and simplifies processes. It removes friction, compresses complexity, and collapses long chains of work into minimal inputs. It doesn’t build services for us—rather, it decomposes them.
This becomes obvious in the constant stream of AI products launched every week. Many are built on the assumption that the tool itself is the value: a generator for polished portraits, a module that categorizes customer requests, or a specialized workflow for a niche audience. All of these appear substantial until you realize how easily they can be reduced. A single prompt can summarize months of product development. In some cases, users can ask an AI to reverse-engineer the prompt simply by showing it the output. What was marketed as a unique offering turns out to be replicable in seconds.
The pattern is everywhere. E-commerce businesses invest in chatbots meant to guide customers through their online catalogues. But the modern user can simply delegate the entire task to their own AI assistant: gather information from the whole web, compare the options way beyond that single company’s website – and recommend what fits. It’s not bound to a curated dataset. It optimizes solely for the user.
My Linkedin feed is full of people who share posts like AI-crafted profile photos, where the creators offer to DM the prompt “behind the images,” as if they were handing out classified blueprints. Yet anyone can upload that photo into an AI conversation and ask for a reconstruction of the prompt used to achieve that amazing scene.
Some say, this is where AI becomes democratizing. Everyone gains access to personalized pipelines, private advisors, and custom tools assembled dynamically on demand. But the deeper consequence is fragmentation. When individuals generate their own solutions through prompting, the value of packaged services decreases. Collaboration risks shifting from shared environments to divergent workflows. Innovation becomes less collective and more of a continuous, distributed pattern of micro-experiments.
This raises a fundamental question for me working as a tech consultant: what exactly do we provide in a landscape where technology handles more of the mechanics each day? The answer increasingly points toward what cannot be prompt-engineered. Experienced professionals carry something no model can replicate. Not just knowledge, but actual experience. Not just solutions, but judgment about when and why it matters. Years of projects, successful and perhaps even more important; not so successful. This forms a reservoir of insight that cannot be compressed into a vector.
AI can accelerate, automate, and simplify. But what it exposes is the enduring value of human expertise: the ability to read between the lines of an organization, anticipate consequences not present in the data, understand stakeholders, navigate politics, and foresee where a solution will work on paper but fail in practice. As technical systems become cheaper to build and easier to copy, the differentiation shifts toward what cannot be distilled into a prompt.
That may be the most unexpected outcome of the AI wave. The more the machines can take over, the clearer it becomes what humans are actually here to contribute. AI streamlines the processes. Humans understand why those processes matter.