AI is not technology. It's consulting (and why your hiring is broken for the same reason)

Many still view what's happening as an "AI trend." But if you look closely at the moves of top players, it becomes clear: the story is about something else entirely. It's about money and about AI finally confronting reality.

💊 The End of the "Magic Pill" Era

First, there was pure hype: "Models will solve everything, just give us a bigger context window." Then came the platform stage: "We'll automate everything, just connect our API." But now the market has sharply "changed its tune." Two things have become clear:

1. A model won't implement anything by itself.
2. A platform won't change established (and often flawed) business processes by itself. The market has arrived at the model: "Let us do it for you."

💡 Are OpenAI and Anthropic the new Deloitte?

Look at what the giants are doing. They aren't just selling a chat subscription. They enter corporations, integrate into processes, change decision-making architecture, and charge for results. Sound familiar? This isn't an IT revolution in its pure form. This is good old-fashioned consulting, simply with neural networks "embedded" within it. They send forward-deployed engineers (hello, Palantir approach) to "fix the system" from the inside. Because technology without deep integration into a specific business is just an expensive toy.

🤝 The Parallel We're Missing: Hiring

The most interesting thing is that this exact pattern is repeated one-to-one in hiring. For years, companies think: "We just need more candidates." And they frantically start automating everything:

• 🔍 Sourcing (more databases!);

• 📝 Resume screening (more keywords!);

• 📊 ATS (more dashboards!). But the problem isn't the technology. The problem is that you don't know how to "integrate" a person into the system. Just like with AI, you buy a "model" (candidate) but don't change the "process" (work environment). As a result:

• Top specialists get lost in the funnel because they didn't meet a formal criterion.

• Weak candidates pass because they've learned to bypass filters.

• The outcome of hiring remains a lottery.

🧠 Why is Implementing AI Easier Than Hiring a Pro?

There will now be an explosion in the market for "AI integrators." But they won't solve the main problem for businesses.

Implementing a complex neural network into infrastructure is still easier than changing the way people make decisions.

This is most evident in hiring. We try to treat symptoms (few applications) instead of treating the selection and adaptation system. We look for the "perfect part" for a broken mechanism instead of fixing the mechanism itself. Until you see how a specific person will work within your system (and not just "what their tech stack is"), hiring will remain a painful cycle of repeated mistakes.

P.S. 👇 If you feel your candidate selection system is faltering - drop your job opening in the comments (or DM me). We'll dissect exactly where your logic breaks down and why you're not getting the right people.

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📚 Read also

• Your AI-agent is useless if it doesn't learn
• AI experience: how to stop competing with thousands of candidates
• AI is not about prompts
• The Ideal Resume: AI Pipeline and the Balance of Responsibilities vs. Achievements
• AI: From Skills to Systems - Why Blueprints Change Everything