Intecrator mentor Yuri Sivitsky comments on the market correction and risks for AI businesses
What has changed in the market, which signals concern investors, and why falling share prices do not yet amount to an AI sector collapse.
Intecrator mentor and Intecracy Ventures board member Yuri Sivitsky commented on the stock market downturn and its potential consequences for startups and investors. In a column for Intecracy Ventures, he explained which signals are concerning and why a correction doesn't yet mean the collapse of the AI sector.
What has changed in the market
On October 30, the Nasdaq Composite fell by 2.9%, the S&P 500 by 1.8%, and the Dow Jones by 1.1%. At the same time, all three indices remained in positive territory year-to-date. A single day's decline is not enough to declare the end of the tech boom. But it brings the question back into focus: will future profits justify current expenses and company valuations? Associated Press data.
Which signals raise concerns
Sivitsky highlights three risk signals:
- Spending outpaces cash inflows. S&P Global Ratings forecasts negative free operating cash flow for six major tech companies in 2026–2027. S&P Global forecast.
- Increasing reliance on debt financing. If revenues lag behind plans, meeting obligations will become more difficult.
- Valuations dependent on high profit expectations. The concentration of stock indices in a small group of AI companies amplifies the consequences of potential disappointment. The Bank of England notes both risks.
"They increase the risk of a sharp repricing, but do not yet prove the inevitability of a collapse," Sivitsky notes in the column.
The danger would grow if weaker revenues coincided with debt problems and reduced access to financing. Then, delayed projects by large companies could affect suppliers and creditors. The data presented so far does not confirm that such a scenario is already unfolding.
What this means for startups and investors
For startup founders, this is a reason to scrutinize product economics more closely. Are customers still paying? Is there a margin left after model, integration and support costs? Will the team be able to operate if the next investment round is delayed?
Opportunities to profit from AI remain: cheaper models open the way to more affordable AI products. However, the technology's utility and the investment's profitability are different questions. In his analysis, Sivitsky emphasizes the importance of the entry price: even a strong business can be a poor investment if the price is too high.
There are warning signs. The evidence is not yet sufficient to conclude that the entire AI sector is collapsing. For startups and investors, what matters is demand from paying customers, business profitability and returns that justify the risk.