I’ve been interviewing analysts for several years now, and what’s happening today is more than just a shift in candidate quality — the entire industry is changing. Without exaggeration, we’re standing at the edge of a new technical era. And it will impact junior and mid-level roles the most, though not only them.

Soon, you may meet specialists across different seniority levels who don’t even know some of the fundamentals, and yet can still deliver results. That’s the new reality we’re moving into.

Here are a few reflections from what I’m already seeing in interviews.

Experience is no longer the only source of value

How did analysts typically work over the last decade?

When faced with a difficult task, they’d open developer forums on one screen, YouTube tutorials on another, documentation or books alongside, and study senior colleagues’ code.

Through this process, you’d deeply immerse yourself in the topic. You’d understand context, build meta-knowledge, and internalize how things work, down to the smallest details. That knowledge stayed with you forever. But it came at the cost of time. For personal skill growth, it was fantastic. For businesses in an increasingly competitive market, it often wasn’t. Speed of execution has become one of the most critical values.

Now we’re in a moment where specialists of all levels can sometimes deliver results not in days, but in hours. They no longer have to dig deeply into every problem themselves. Instead, they need to ask the right questions, write strong prompts, and understand outputs, which is also a skill in its own right.

Work starts shifting toward thinking more about inputs and outcomes, and less about every intermediate step. And when results arrive fast enough, businesses are often willing to overlook how they were produced. In practice, what matters is not how much you know, but the real-world output.

Of course, there’s a downside. Some specialists now rely almost entirely on AI and neglect learning fundamentals altogether. This leads to shallow understanding of architecture, logic, and systems. AI is far from perfect. It frequently makes mistakes, and worse, it has a tendency to confidently agree even when it’s wrong. Critical thinking can easily disappear.

It’s becoming increasingly common to interview candidates who look like strong mid+ or near-senior profiles on paper, but can’t answer basic questions like “Why choose a specific model family?” or “How does linear regression actually work?” It’s like someone who can produce music on a computer but doesn’t know musical notes. Is that good or bad? It’s simply a new era we have to adapt to.

Not that long ago, people wrote letters exclusively by hand and had better spelling and handwriting. Today, we communicate with emojis and stickers, but much faster and more easily.

That said, the new era isn’t fully here yet. We’re standing at the threshold, merely catching glimpses of what’s to come.

Even paid AI models still fail surprisingly often. Almost daily I see examples of incorrect calculations or random conclusions. Meanwhile, there are real interview recordings on YouTube where candidates paste questions into ChatGPT during the interview and repeat the answers without understanding them, sometimes successfully passing early interview rounds.

AI can already solve many junior and mid-level tasks. But truly senior work, where creativity, architectural thinking, and deep project context are required, is still out of reach for machines. That said, AI proficiency is becoming a core skill everyone will need to master eventually. The market is already showing clear demand for AI literacy, much like it once did for basic computer skills, office software proficiency, and later product thinking. I’ve even added AI competency to our internal skills matrix, including the ability to use models thoughtfully, write strong prompts that accelerate workflows, and understand the strengths and limitations of different tools on the market. From a leadership perspective, this directly translates into faster execution without sacrificing quality.

To truly assess candidates, I now focus heavily on meta-knowledge and deep reasoning during interviews. But we should be prepared, because very soon we’ll see smart specialists who can deliver strong business results even without memorizing traditional fundamentals. We’re entering a new technological era where the ability to work quickly and effectively with powerful AI tools will, in some fields, become just as important as traditional hard skills. And businesses will accept this shift, because we’re moving toward industrialized result delivery and large-scale process automation.

A useful analogy comes from manufacturing history. First there were blacksmiths, then small workshops, then small factories, and eventually large plants with assembly lines. A tenth-generation blacksmith might have known every detail of the craft, yet it was factory workers — supported by systems — who became the backbone of industrialization. Deep expertise didn’t disappear; it evolved. Those master craftsmen became engineers and architects of modernization, guiding the next stage of progress.

The future of the market

As AI adoption accelerates, companies will increasingly prioritize how fast specialists can deliver results.

Soon, we’ll see hiring cases, some already happening, where it’s more profitable to hire someone with less traditional experience but strong AI-driven execution skills. Instead of classic interviews, candidates will be given real tasks. If they solve them efficiently using AI, they get hired. Right now, the industry still operates in the old paradigm: CVs, years of experience, formal processes. But people are already trying to “hack” that system.

Every technological leap increases inequality. Just like railroads accelerated wealth concentration among industrial tycoons, AI will accelerate it among tech leaders. Some companies will grow extremely fast and become giants. We’ll likely see a new generation of Jobses and Musks, whose operational standards will define the market. Other companies will start looking outdated and inefficient, comical even, much like we now look at 90s offices with their fax machines and shoulder-padded execs.

A new type of specialist will emerge at the top, combining two key abilities:

1. Strong AI skills
2. Independent critical thinking

That combination will be incredibly valuable. This doesn’t mean traditional experts will disappear, in fact, their deep thinking already gives them an advantage. They’ll simply need to add AI fluency on top.

For now, there are no standardized AI workflows. Everyone has their own prompts, processes, and validation habits, all of which require constant verification through experience. But standardization will come. Prompt engineering is already growing rapidly, along with demand for it. And soon, companies will treat AI workflows the same way they once standardized production pipelines — to scale speed, quality, and competitiveness.

Also check out my article on PocketGamer: What to expect from a marketing analyst in 2026.