AI gold rush: why investors are betting big on the AI economy

We’ve seen it before with the dotcom boom. The gold rush. As new technologies emerge, there is a panicked scramble to stake a claim on what promises to be a transformative and revolutionary tool. And, just as many investors plowed their hard-earned capital into tech stocks in the early noughties, we are seeing a new wave of investment – often unmatched in both volume and price – hit the AI industry. There are, of course, both opportunities and risks. However, with AI startups in the UK alone raising more than $2 billion in the last six months, the numbers speak for themselves. AI is here to stay, and everyone wants a slice of the pie.

There’s nothing artificial about the numbers

Let’s crunch some hard data and look at the state of AI. The investment over the last few years has been nothing short of staggering. The projections tell a similar story. Across the globe, the market for AI technologies has collectively passed $2bn in value. By 2030, this figure is projected to be closing in on $2 trillion. 

While AI is making inroads across virtually every sector, certain industries are attracting the lion’s share of investment dollars. Healthcare and energy are among the top sectors drawing AI investment, with these two verticals alone accounting for just over one-third (38%) of total capital invested in 2023. We are also seeing inroads into more traditional sectors such as legal.

The AI unicorn

Then there is the golden generation of AI – a new breed of startups that have surpassed the $1bn valuation mark. These unicorns don’t tend to focus on verticals. Instead, their proposition to potential investors is to democratise AI for all. Just look at the likes of OpenAI, Anthropic, Stability AI, and DeepMind. They have captured investors’ imaginations with their cutting-edge AI technologies and ambitious visions for the future.

What sets these AI unicorns apart is their ability to solve complex problems at scale. Anthropic is the perfect example. Their large language models (LLMs) are pushing the boundaries of natural language processing, with applications ranging from data interpretation to advanced problem-solving giving a use case for almost any scenario. Stability AI is then revolutionising the world of image generation with its open-source AI models, giving everyone access to powerful creative tools.

The extra edge comes from a flywheel effect of being trained in unfathomably large data sets. Bigger data sets attract more users. More users feed in more datasets. And so the flywheel begins. When investors bet big on these unicorns, it’s not always about what they can do today, but what they can potentially do tomorrow. They move that quickly. 

The dark horse challengers

Where there are unicorns, there are dark horses. AI businesses accounted for one-fifth of all unicorns that passed the $1bn valuation mark last year, and so investors don’t always need to bet on the frontrunners for strong returns. These dark horse challengers are often tackling niche problems or exploring unconventional approaches to AI development.

One area to watch is edge AI – companies developing AI solutions that can run on devices with limited processing power and connectivity. This technology has the potential to revolutionise industries like IoT, wearables, and autonomous systems by enabling real-time decision-making without relying on cloud computing.

Another interesting trend is AI-powered synthetic biology. Startups in this space are using machine learning algorithms to accelerate drug discovery, design new materials, and even create synthetic organisms. While still in its early stages, this field has the potential to transform industries ranging from pharmaceuticals to agriculture.

The other side of the coin

As with any gold rush, there is always an element of risk. You only need to look at IBM’s Watson Health, which promised to revolutionise healthcare, but struggled to deliver on its lofty goals. Despite billions in investment, the division was ultimately sold off at a fraction of its perceived value.

If a company as established as IBM can get it wrong, this only underscores the need for thorough due diligence and expectations in AI investment. Investors must look beyond the hype and carefully evaluate the technical feasibility, market demand, and potential scalability of AI solutions.

There also needs to be a clear path to monetisation. Too often AI startups struggle to monetise what they are building. The theory is all well and good, but it’s the practical application of AI that generates revenue. 

What the future of AI investment might look like

I’m never one to try and predict the future – it seems quite futile considering how far AI has evolved over the last few years – but there are a few key applications of AI that I think might attract investor interest in the future.  

There is a growing emphasis on ethical AI. As concerns about algorithmic bias, privacy infringements, and the societal impacts of AI continue to mount, investors are increasingly drawn to startups that prioritise responsible AI development. These companies are not just paying lip service to ethics; they’re baking principles of fairness, transparency, and accountability into their core technologies and business models.

Another area of focus is AI-human collaboration. While early narratives around AI often centered on the technology’s potential to replace human workers, the most promising AI solutions today are those that augment and enhance human capabilities. Investors are keenly interested in startups that are developing AI tools that can work alongside humans, boosting productivity and enabling people to focus on higher-level, creative tasks. 

Tangential to this, there’s a growing demand for explainable AI. If humans are to truly collaborate with AI then there needs to be a common thinking, not just a common language. Algorithms that can provide clear, understandable reasoning for their outputs and decisions are more likely to build trust, ensure regulatory compliance, and enable effective human oversight of AI systems.

Climate change and environmental sustainability have become urgent global priorities, and AI is emerging as a powerful tool in addressing these challenges. Forward-thinking investors are already betting on AI solutions that can help optimise energy usage, improve resource management, predict and mitigate natural disasters, and accelerate the development of clean technologies. These applications not only offer significant potential for financial returns but also align with the growing emphasis on socially responsible investing.

Looking ahead, we can expect to see AI investment continue to grow and diversify. As the technology matures, we’re likely to see more specialised AI solutions tailored to specific industries and use cases. This trend could lead to a new wave of AI-powered vertical SaaS companies that combine deep domain expertise with cutting-edge AI capabilities.

Investing in AI isn’t just about chasing profits. The most valuable AI investments will be those that generate returns while tackling real-world challenges and enhancing lives. Ethical considerations aren’t just nice-to-haves; they’re essential for long-term success in this field. Weave into that large datasets and a clear path to monetisation, and you may have just found your next AI unicorn.