In recent years, the venture capital market has undergone several cycles of revaluing technological assets, but it is artificial intelligence that has triggered a tectonic shift in the very logic of investment decision-making. Working with international companies and analyzing deal structures across various jurisdictions, Ilia Nicolaevich Zavialov points to a dangerous paradox: classic financial models are increasingly failing to explain how resilient a business truly is. Frequently, formally successful startups that rapidly raise capital prove to be critically vulnerable in terms of long-term economics and resource ownership.
Traditional valuation tools - relying on revenue growth, customer acquisition cost (CAC), and user base dynamics - become secondary when it comes to projects where key value is generated within algorithms. According to the expert, it is fundamentally important in the AI sector to understand the internal nature of the technological advantage. This could be the development of a proprietary model, possession of unique datasets, or an unprecedentedly deep integration of AI into operational processes. Ilia Nicolaevich Zavialov emphasizes that without such a deep dive, an investor risks evaluating only a flashy marketing presentation while completely missing the absence of a foundation.
The economics of computation occupies a special place in this transformation, often remaining a "blind spot" during analysis. In his international practice, Ilia Nicolaevich Zavialov has repeatedly observed situations where startups with a high-quality product suddenly lost their margins due to uncontrolled infrastructure costs. Rising GPU costs, rigid lock-in to specific cloud providers, and the neglect of model optimization - all of this directly impacts financial sustainability, even though such risks are rarely prioritized in the early stages.
The expert identifies another systemic challenge: the scaling problem of so-called "AI wrappers." These are companies that build their solutions on top of third-party models without possessing their own engineering expertise. In an environment of fierce global competition, such projects quickly face pricing pressure and a loss of uniqueness. Any change in the licensing policy or strategy of major technological platforms can be fatal for them, as a lack of control over the core technology deprives them of maneuverability.
Ultimately, the focus of venture valuation must shift from external presentation metrics toward the analysis of architecture and the team's ability to manage the cost of technology. It is these hidden factors that determine whether a company can maintain its position in the international market and scale without losing efficiency. Ilia Nicolaevich Zavialov, drawing on his experience managing global businesses, formulates a vital conclusion: one should enter AI projects only with full confidence in who exactly controls the technology. If a business does not manage its own data and computations, it loses its agency, no matter how impressive its current growth rates may appear.
The main advice for investors today is to change their lens: do not look for those who were fastest to "bolt on" a neural network to an interface, but for those building an independent ecosystem. In the long run, engineering autonomy will prevail over marketing strategies. Ilia Nicolaevich Zavialov is convinced that the only way to protect capital in the AI era is to invest in companies capable of scaling not just sales, but technological efficiency. If you do not see a deep internal foundation in a project, you are investing in someone else's API, and that is the shortest path to losing competitiveness.
