Executives at several artificial-intelligence firms and investors have begun publicly acknowledging that the current surge in AI investment and valuations may be committing a serious oversight: the “vibe revenue” has pulled ahead of real commercial returns. At a recent tech conference, leaders from AI startups noted that while capital is flowing in abundance, many companies still struggle to show meaningful sales, profits or durable business models. This growing concern comes amid the broader debate over whether the AI boom is morphing into a full-blown financial bubble.
The term “vibe revenue” encapsulates this dynamic: companies being valued more for speculative promise and hype than for consistent, recurring revenue streams. One startup CEO observed that firms are being priced based on future narratives rather than present performance—and that this mismatch worries them. Meanwhile, financial-markets watchers point to the fact that many AI companies are still pre-profit or barely profitable, yet command valuations akin to those of established tech giants. Such patterns evoke comparisons to past tech bubbles, where investor exuberance outpaced business fundamentals.
Despite these red flags, the mood among many industry players remains cautiously optimistic. They argue that the long-term potential of AI—its ability to transform sectors like healthcare, manufacturing, finance and customer service—is real, and therefore justifies the elevated valuations. What worries them is the pace of investment and the expectation gap: if companies fail to deliver results quickly, the inevitable reality check could be sharp. Some executives warn that when the “bubble” phase ends, the companies without solid monetisation models will be the most exposed.
For stakeholders—founders, investors, regulators and business-users alike—the implication is clear: it’s time to separate realisable revenue from expectation-driven valuation. Companies should aim to show not just technological capability but durable business traction; investors should scrutinise what portion of current value is based on execution rather than promise; and users should gauge whether AI services are meaningfully delivering value rather than simply leveraging hype. The “bubble” may not burst tomorrow—but the risk of a correction is clearly on the radar.
