In a provocative stance, Igal Ehrlich, chairman of Yozma Group, challenged the prevailing “AI bubble theory” at the 25th World Knowledge Forum, asserting that while expectations for AI are indeed soaring, the reality is far more nuanced. Ehrlich, a seasoned investor, argues that the cooling of investments in AI could paradoxically trigger a period of production stabilization, a phase he believes is crucial for the technology’s long-term viability.
Ehrlich drew parallels with Gartner’s Hype Cycle, a well-known model illustrating the maturity and adoption of technologies. According to this cycle, AI is currently perched at the “peak of inflated expectations,” a stage where the hype often outpaces the reality. Ehrlich cautioned that, like many technologies before it, AI might struggle to meet these lofty expectations initially. “We’ve seen this with other new technologies,” he remarked. ” AI hasn’t met expectations yet, and in some cases, it might not ever meet them.”
The debate around AI’s profitability, or lack thereof, has sparked controversy among investors. While AI promises to revolutionize industries from construction to healthcare, the path to monetization remains elusive. Michael, a former branch manager now spearheading Silicon Valley initiatives at Solar Star Ventures, echoed Ehrlich’s sentiments. “From an investor’s perspective,” he said, “there’s a big question mark on when we can realize profits. Take Generative AI Chat GPT, for instance—it’s an amazing product, but it’s incredibly expensive to operate.”
Conversely, Richard Jhang of StratMinds VC painted a more optimistic picture. He believes that AI is on the cusp of multiple innovations occurring simultaneously, potentially accelerating its impact by decades. “I initially thought AI would emerge in 2040 or 2050,” Jhang stated. “But now, I believe it could be a reality by 2030.” He cited examples of companies achieving over a million-fold increase in efficiency through AI coding, hinting at the transformative potential AI holds.
This divergence in opinions was evident as panelists discussed promising AI startups. Michael highlighted Koheer, a B2B company developing generative AI technologies based on the Large Language Model (LLM). Koheer’s ability to create corporate solutions exemplifies how AI can generate value, aligning with Jhang’s optimistic outlook.
The construction sector emerged as a surprising front-runner in AI application. Tomar Diurong, CEO of Impulse Partners, spotlighted the potential of AI in automating construction sites. “AI can design buildings faster and more accurately than humans,” he asserted, noting that Canadian and Norwegian companies are already leading this technological charge. He believes that the application of AI in construction is not just a futuristic vision, but a reality that is taking shape today. In a perspective that could reshape the sector, Diurong’s insights suggest that AI could address labor shortages, enhance design accuracy, and expedite project timelines—all critical factors in sustainable construction. Given that the construction industry is one of the world’s largest consumers of resources, any efficiency gains could translate into significant sustainability benefits.
Ehrlich, however, sees the medical field as the most promising arena for AI. “If we analyze data through AI,” he posited, “we will get results faster and more accurately.” Yet, he acknowledges the challenges in valuing medical AI companies, noting investor hesitancy. This ambivalence underscores the complex interplay between technological potential and market viability.
However, the path to successful AI investment isn’t solely about technological prowess. Jhang advised prioritizing business models over technology. “Many investments become obsolete when the industry changes rapidly,” he warned, advocating for a strategic approach that considers market dynamics and commercial viability.
Moreover, government support was highlighted as a crucial factor in fostering AI independence and growth. Diurong stressed the importance of financial backing for supercomputers and other AI infrastructure, pointing out the political implications of relying on U.S.-based AI technologies, especially in sensitive areas like nuclear power plant operations.
As the dust settles on the AI investment frenzy, what emerges is a call for measured optimism. The trajectory of AI, much like the construction sector it aims to revolutionize, will be shaped by a confluence of technological advancements, strategic investments, and supportive policies.
