The AI “Bubble” and Why It’s Built to Last: Lessons from Blockchain’s Rise and Fall

Artificial Intelligence (AI) has swiftly become the buzzword of our time, much like blockchain was just a few years ago. With billions of pounds flowing into AI research, startups, and applications, many are wondering whether this is merely another bubble waiting to burst. However, unlike blockchain, which saw its meteoric rise plagued by scandals, scams, and unmet promises, AI is on a far more stable trajectory. Here’s why the AI “bubble” is likely to be more of a long-term trend than a fleeting phenomenon.

The Investment Surge: Where the Money is Flowing

One of the most striking similarities between the AI and blockchain booms is the massive influx of investment. Major tech companies like Google, Microsoft, and Amazon have poured billions into AI, not just as a side project but as a core part of their business strategies. For instance, Microsoft’s investment in OpenAI, which powers products like ChatGPT, is a testament to how serious these giants are about integrating AI into everyday life.

Venture capital has also followed suit, with AI startups attracting over £70 billion in 2023 alone. Unlike the blockchain craze, where much of the money went into speculative tokens and questionable projects, AI investments are more focused on tangible applications—everything from natural language processing to computer vision and autonomous systems.

My Personal Experience in 2024: Practicality

In the blockchain era, finding scalable use cases was like searching for a needle in a haystack. While the concept of distributed data ledgers, with their promises of high security and encryption, seemed promising in theory, most projects never progressed beyond the innovation stage.

This is where the significant difference lies, in my opinion.

In 2024, we’ve worked and met with teams across a wide range of industries—pharma, logistics, manufacturing, to name just a few—and I’ve been genuinely impressed by the technical expertise, capacity, and experience already present. And these aren’t just innovation teams; we’re talking about deep data science and IT departments where the evolution from Machine Learning to transformer technologies, and now to generative AI using various large language models (LLMs) for diverse applications, is truly mind-blowing.

This is just the beginning; and as I always say, this is the “worst” as this technology will ever be!

Practical Use Cases: AI’s Edge Over Blockchain

One of the biggest issues with blockchain was its struggle to find real-world applications that went beyond cryptocurrency. While blockchain technology itself holds promise for secure, decentralised transactions, its practical use cases have been slow to develop outside of niche areas.

AI, on the other hand, is already embedded in a vast array of industries. From healthcare diagnostics and personalised medicine to autonomous vehicles and customer service chatbots, AI is not just a concept—it’s a working reality. Companies are using AI to optimise supply chains, enhance cybersecurity, and even predict market trends. These real-world applications demonstrate that AI is far more than just hype; it’s a powerful tool that’s being actively utilised across the globe.

Predictions vs. Realities: The Resilient Growth of AI

Predicting the future is always risky, but the trajectory of AI suggests it is here to stay. Experts forecast that AI could contribute up to £12.3 trillion to the global economy by 2030. While these numbers might seem optimistic, they are grounded in the current momentum AI is building across sectors. For instance, AI-driven automation is expected to save companies trillions of pounds in operational costs, further incentivising investment and adoption.

In contrast, blockchain has struggled to deliver on its grand promises. Initial predictions about blockchain revolutionising everything from banking to voting have largely remained unfulfilled. The technology was marred by issues of scalability, regulatory challenges, and a lack of consumer trust—particularly after the rise of numerous cryptocurrency scams and the collapse of major projects.

Why the AI “Bubble” is Different

1. Integration into Existing Systems: AI is being integrated into existing infrastructure, making it indispensable. Blockchain often required building entirely new systems, which slowed its adoption.
2. Widespread Applications: AI has already proven its value across diverse fields, from agriculture to finance. Blockchain, however, has been largely confined to fintech and niche tech communities.
3. Continued Investment from Established Players: Major tech companies are fully committed to AI, often tying their future growth to its development. This level of commitment was not as evident in the blockchain space, where many projects were led by startups without strong backing.
4. Regulatory Support: Governments and regulatory bodies are more supportive of AI’s development, seeing it as a driver of economic growth. Blockchain, conversely, faced stiff regulatory challenges that hampered its progress.

Conclusion: A Bubble or a New Era?

While there’s always the risk of overhype, the current AI boom has all the makings of a sustained technological revolution rather than a fleeting bubble. The practical applications, massive investments from major players, and integration into critical systems worldwide suggest that AI is not just a trend—it’s a transformative force.

Blockchain taught us that not all tech booms are created equal. Its struggles with scalability, trust, and real-world application stand in stark contrast to the rapid and wide-reaching impact of AI. In short, while blockchain may have been a bubble that popped, AI is laying the groundwork for long-term, systemic change in how we live and work.

So, while it’s always wise to remain cautious, it’s even wiser to recognise when a bubble is actually just the beginning of something much bigger.

Ben.

Ben Lowe, CEO & Founder, Lighthouse