VC Founder: AI Far From a Bubble

The debate rages on: Are market valuations in the artificial intelligence sector justified, or are we witnessing another tech bubble inflated by hype and speculation? Billionaire investor Ray Dalio recently indicated his “bubble indicator” was flashing warning signs, while Federal Reserve Chair Jerome Powell offered a more nuanced perspective, suggesting AI’s trajectory diverges significantly from the dot-com era.

Magnus Grimeland, founder of Singapore-based venture capital firm Antler, firmly believes the AI market is far from overheated. Speaking on a recent podcast, Grimeland outlined several key factors underpinning his conviction.

A primary differentiator, according to Grimeland, is the unprecedented speed of AI adoption across diverse industries. He contrasted this with the protracted transition from physical servers to cloud computing, a process that spanned nearly a decade. Today, AI is “top of the agenda” for leaders globally, from healthcare providers in India to Fortune 500 corporations in the United States.

“There’s a willingness to invest into using that technology… and that’s happened immediately,” Grimeland emphasized, highlighting the proactive engagement of businesses across the board.

This rapid and widespread adoption distinguishes the current AI landscape from the dot-com bubble of the late 1990s and early 2000s, a period characterized by the meteoric rise and subsequent collapse of numerous unprofitable internet startups. The tech-heavy Nasdaq Composite index plummeted nearly 80% between March 2000 and October 2002, leaving a trail of shattered investor confidence.

“What makes this a little bit different from a bubble and makes it very different from dotcom is that there’s really real revenues behind a lot of this growth,” Grimeland asserted. This revenue generation sets AI apart, providing a tangible foundation for growth that was largely absent in the dot-com era.

OpenAI, the company behind the groundbreaking ChatGPT, reported reaching $1 billion in annual recurring revenue (ARR) in December 2023, a testament to the commercial viability of generative AI. ARR, a key metric for subscription-based businesses, reflects the predictable revenue stream a company generates from its customer base over a 12-month period. By late 2025, their revenue had increase ten fold. This highlights the fundamental difference that AI generates its market based on user demand.

Antler’s portfolio includes companies like Lovable, an AI-powered platform enabling users to build apps and websites with minimal coding expertise. Lovable’s rapid ascent, surpassing $100 million ARR in just eight months, further exemplifies the accelerating pace of AI-driven innovation.

Furthermore, Grimeland pointed to the changing consumer behaviour. It emphasizes: “Think about how quickly our behavior online has changed, right? … 100% of my searches a year ago [were on] Google. Now it’s probably 20%,”

The changing market also affected Google’s parent company Alphabet, where it was met with concern when earlier this month, OpenAI launched Atlas browser for Mac OS.

While acknowledging that “tremendous” amounts of capital are being allocated to AI-related companies at potentially inflated valuations – a common occurrence in the early stages of investment cycles – Grimeland maintains that the overall opportunity in the AI space far exceeds the current level of investment. This suggests that the sector has significant room for further expansion and maturation.

Addressing the competitive landscape, dominated by large U.S. and Chinese tech giants, Grimeland argued that these incumbents are facing unprecedented challenges from innovative startups. He cited DeepSeek, a Chinese AI company developing models comparable to those of OpenAI, as an example of the disruptive forces at play.

“Tencent is building great AI, Baidu is building great AI, but that’s not where DeepSeek came from,” Grimeland pointed out. “The AI winners of this current platform shift [are] not necessarily those big incumbents.”

He emphasized that smaller AI companies with “positive signals” – such as strong founding teams, increasing customer lifetime value, and decreasing product delivery costs – have significant potential to emerge as major players in the industry. These factors suggest a sustainable business model and strong growth prospects.