The artificial intelligence market is undergoing a tectonic shift. Billions in capital flows are changing direction, and those who were considered leaders in the venture market just yesterday are now forced to rethink their strategies. At the AI FUTURE STAGE conference, leading experts from the technology and venture capital sectors gathered to analyze which AI solutions corporations are actually buying and, more importantly, how to measure the economic impact of their implementation.
The discussion, moderated by a strategic partner, featured a stellar lineup: Ilya Cheremkin, head of Ventures Synergy; Svyatoslav Safonov, analyst at Jets.Capital; Nikita Razhev, head of the Yandex Cloud startup program; Dmitry Reidman, managing director of the global markets department at Rostelecom; and Martin Kohlhauser, general partner at ChakaVC.
Billions at Stake: What Has Changed in a Year
The key theme of the meeting was a practical analysis of how neural networks are transforming from a trendy hype into a working tool for business growth. Experts conducted a detailed breakdown of current investment trends in AI and Web3. Special emphasis was placed on startup evaluation criteria: what is more important for an investor today—rapid growth, profitability, or time to break-even? The answer, it turns out, heavily depends on geography. Investment approaches in Russia and abroad differ dramatically, and this is changing the rules of the game.
How Not to Lose Money on AI: Practical Advice
The most valuable part of the discussion focused on practical aspects. The speakers thoroughly analyzed how to determine whether a technology is generating real money for a company. They proposed a specific methodology for calculating economic impact, taking into account both costs and revenues. The topic of B2B automation was also addressed separately: how to evaluate the effect of in-house development in person-hours and where the most common mistakes lie.
The experts did not spare startups. They exposed typical miscalculations in AI implementation and, particularly painfully, errors in presenting projects to investors. Real-world cases were cited, highlighting the critical importance of pilot projects and thorough testing before scaling.
My analysis: The AI investment market is transitioning from a "hype for hype's sake" phase to a phase of pragmatic due diligence. Today, investors demand not just a compelling story, but clear metrics and evidence of economic efficiency. Startups that cannot demonstrate measurable ROI risk being left without funding, despite all their technological brilliance. This will undoubtedly improve the market, but for many, it will become a harsh reality.