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Artificial intelligence (AI) in the crypto industry currently plays the role of a powerful accelerator but does not become an independent player. Even complex tasks such as writing code and monitoring exchange changes remain under strict human control. This is not about automation in a vacuum, but about synergy, where the machine handles routine tasks and the expert handles strategy and responsibility. Let's break down how this works in practice.

Where AI is already indispensable: content and development

In crypto companies, artificial intelligence has found application in two key areas: content marketing and accelerating development. In the first area, AI is used to gather news and analyst opinions, generate posts, analyze trends on TikTok, and even create videos. In the second, agents take over monitoring exchange API changes and writing code. However, as experts emphasize, all these processes are carried out under the supervision of specialists.

"We use agents to monitor exchange API changes and write code, but this is to speed up development work and it still happens under the control of developers," notes Polina Demchuk, CEO of TradeSanta. According to her, there have been no serious errors that would have cost the company dearly precisely because of human oversight.

Toolkit and boundaries of trust

The set of tools is selected through trial and error. In development, VS Code with Codex and Claude Code are actively used. For video generation — Kling and Eleven Labs, for creating landing pages — Lovable. The choice is based on the balance of quality and cost. "We use Kling and Eleven Labs for video generation because, out of everything we've tested, we get the best quality and a reasonable cost per unit of finished material here," adds Demchuk.

As for trusting an AI agent with real trades, the boundary lies in the area of risk. "It's a question of how much money I'm willing to lose, what drawdown I'm willing to accept, and this determines both the amount I'm willing to entrust to the agent and the access that agent gets to my data," the expert explains.

Decision-making architecture: each tool for its own task

Financial manager Nikita Kutsenko shares his morning ritual: "I open Claude, upload sentiment, the main news from overnight, Bitcoin and Ethereum prices, the fear and greed index. It provides on one page the direction — long or short — and the reasoning. I don't blindly follow it: I cross-check its picture with mine. When they align, it's a very powerful signal. When they diverge, I dig deeper to figure out which of us is wrong."

His arsenal includes several AI tools, distributed on the principle of "each covers its own area":

• ChatGPT in conjunction with CoinGlass — for processing data on open interest, liquidations, funding rates, and the long/short ratio. A screenshot from CoinGlass is sent to ChatGPT, and instead of an hour of manual compilation, in thirty seconds you get a picture showing where the overheating zone is, where the crowd has piled into longs, and where to expect liquidations.
• Grok — for real-time news monitoring, built into X. The logic is simple: early signals and leaks appear not in traditional media, but specifically on X, and timely access to them provides a head start of a couple of hours to assess whether it's real news or a dud.
• Claude — for the strategic direction of the day.

Ultimately, the tools complement each other: Grok provides speed and access to sentiment on X, ChatGPT handles routine chart analysis, and Claude handles strategy. The result: analysis takes not a couple of hours a day, but 15 minutes.

My comment as an analyst: We are observing a classic scenario where AI acts not as a replacement, but as an amplifier of competencies. The key takeaway for the market: in conditions of high volatility and information noise, human experience and the ability to verify remain decisive. AI accelerates routine tasks, but strategic decisions are the prerogative of humans. For now, this is an ideal tandem where each plays their role.