The release of ChatGPT sparked an AI investment gold rush, catapulting the “Magnificent Seven” tech giants to a staggering $10 trillion valuation. Adding fuel, Donald Trump’s $500 billion Stargate investment intensifies the AI race, signaling historic capital flows into this transformative sector. However, this frenzy invites questions about sustainability and direction: will AI’s fate hinge on short-term hype or genuine Innovation? The lesson lies in balancing ambition with responsibility. Investments must prioritize scalable, performance breakthroughs over speculative gains. History warns us—unsustainable bubbles burst. AI investment success will be defined not by inflated valuations but by enduring societal and economic value creation through the scalability of the technology core. Despite its uniqueness, we need to draw lessons from other technology life cycles to understand the future of AI investment.
Lessons from the Metaverse: The Risks of Hype-Driven Investments
To draw a lesson, let’s examine the metaverse reality. Not long ago, there was significant investment hype surrounding Virtual Reality (VR), Augmented Reality (AR), and Virtual Worlds (VW). In a 2022 report, McKinsey claimed, “With its potential to generate up to $5 trillion in value by 2030, the metaverse is too big for companies to ignore.” It was reported that 95% of business leaders anticipated a positive impact on their industries within five to ten years. By that same year, over $120 billion flowed into metaverse investments. However, Meta’s losses from AR, VR, and metaverse-related software soared from $4.5 billion in 2019 to $13.7 billion in 2022, leading to a substantial loss in market value. Even Apple failed to catch the wave, as its pricey Vision Pro headset struggled to make a splash. The lesson: hype-driven investments risk faltering without sustainable value creation and consumer adoption.
The Winter of AR/VR/VW: What Went Wrong?
The promised $5 trillion metaverse value by 2030 now seems elusive. Apple’s Vision Pro headset has struggled to gain traction, with its high price limiting consumer adoption. Similarly, Meta, once a champion of AR, VR, and virtual worlds (VW), has largely abandoned its metaverse ambitions after incurring billions in losses, failing to deliver the promised transformation.
Seed funding for AR/VR/VW Startups reflects this downturn. According to Crunchbase News (2025), U.S. startups in this space raised just $41 million in seed-stage funding in 2024—a two-thirds decline from the previous year. The enthusiasm that once attracted over $120 billion in 2022 investments has waned dramatically.
The reasons are clear: overhyped Disruptive innovation narratives lacked tangible value creation. Consumers have not embraced High-tech headsets as envisioned, and the elaborate virtual worlds failed to meet practical or social needs. Predictions by international consultancies like McKinsey misjudged the pace of adoption and overlooked significant technology barriers, such as cost, usability, and clear demand.
The lesson? Disruptive technologies require more than lofty promises. Sustainable growth demands alignment with real-world needs, user-centric designs, and scalable solutions—not reliance on speculative hype cycles. The AR/VR/VW winter reflects the dangers of overlooking these fundamentals.
A Technology Lifecycle Lesson for AI Investment
The rise and fall of AR/VR/VW investment hype underscores a critical lesson: funding prospects and returns hinge on the technology lifecycle driving Creative Destruction. Initially, AR/VR sparked excitement by promising to revolutionize interaction through virtual worlds (VW), captivating investors with visions of a metaverse reality. Industry leaders and top talent worked to transform science fiction into reality, fueled by theoretical research and countless conceivable use cases.
However, practical implementation failed to deliver profitable ventures over time. The issue was technological limitation to make VW a viable alternative to the real world. The current generation of headset-based AR/VR technology has prematurely saturated, failing to sustain its initial momentum. Without compelling value creation or mass adoption, returns on investment have turned negative.
As a result, seed capital for AR/VR has dried up, with U.S. startups raising only $41 million in 2024 (as mentioned before), down sharply from prior years. This decline reflects a broader realization: premature bets on immature technologies carry significant risks.
To make sound investment decisions, focus must shift to understanding the technology lifecycle and its capacity to drive sustainable creative destruction, ensuring long-term growth rather than chasing speculative trends.
Scaling Early-Stage Technologies: Beyond Hype and Investment
Money, effort, and promise alone cannot scale early-stage technologies into Creative waves of destruction that yield profitable innovations. The underlying science often sets the boundary for progress, as all new technologies begin as inferior alternatives to existing solutions. Despite their uniqueness and potential economic impact, they must cross a critical threshold to transform demonstrated possibilities into profitable innovations.
The failure of AR/VR/VW illustrates this challenge. Despite initial excitement and significant investment, the technology failed to mature enough to become a viable, profitable alternative to real-world interactions. Similarly, AI, despite the groundbreaking success of ChatGPT, faces its own hurdles. As an inferior alternative to human intelligence in knowledge compilation, ChatGPT’s current limitations, such as accuracy issues and hallucinations, highlight the need for further progress.
Simply scaling existing methods with more data and computing power may not be enough. Instead, breakthroughs in the science itself may be required. History offers a parallel in LED lighting, which needed Nobel Prize-winning advances in materials science to unleash its creative wave of destruction in the lighting industry.
The lesson is clear: turning early-stage technologies into profitable ventures requires more than enthusiasm and investment—it demands scientific breakthroughs to overcome inherent limitations.
Crossing the Chasm: The Challenge for AI and Other Technologies
Many great technologies face a critical chasm on their journey to mainstream adoption. After serving nonconsumption markets with their uniqueness, high-potential technologies must evolve further to penetrate mainstream markets. AI is no exception.
For AI to cross this chasm, it requires additional scientific advancements. Like other technologies, AI must overcome inherent limitations to compete with human intelligence effectively. Currently, AI relies on past experience and instruction sets, unlike human intelligence, which continuously generates new knowledge and skills while performing tasks. Without upgrading the technology core, AI cannot close this gap.
The challenge of catching up to human intelligence is reminiscent of other industries. For instance, lithium-ion batteries, despite their success, face Premature Saturation in delivering sufficient performance to surpass gasoline engines. To overcome this, the development of solid-state batteries has become essential to crossing the final mile for electric vehicles.
Similarly, for AI to transcend its current limitations, a Breakthrough in its technology core is vital. Scaling current methods alone—through more data and computing power—will not suffice. The path forward lies in scientific advancement to create a foundation for AI that mirrors the adaptability and creativity of human intelligence, enabling it to thrive in mainstream markets.
AI Investment Success: The Need for Strong Science and Rational Decisions
A staggering amount of money cannot compensate for poor science or sloppy implementation when delivering the innovations desired by investors and customers. This reality seems to plague current AI, which relies heavily on brute force approaches like scaling data and computing power, rather than addressing fundamental limitations.
Despite its catchy name, AI is no different from other technologies in driving creative destruction. Its success depends on crossing critical barriers, navigating technology life cycles, and aligning scientific advancements with practical implementation. History shows that early-stage excitement often fades unless breakthroughs enable the transition from promising ideas to profitable innovations.
Reducing investment risks requires a nuanced understanding of these dynamics. Recognizing where technologies stand in their lifecycle, identifying the scientific challenges that need solving, and strategically allocating resources are essential. Without this foundation, even massive investments in AI risk being squandered, failing to generate the new Wealth and societal value they promise.
