Start-up investment data released in the last weeks of January showed that growth in 2024 was heavily concentrated on innovative companies developing artificial intelligence-based solutions: in Europe, of the almost EUR 57 billion invested, around 26% went to AI start-ups, while in the US, AI start-ups took around 40% of the just over USD 200 billion invested.
Artificial intelligence is therefore now, without great doubt, the new focus of investors’ attention, a focus that is, however, taking place at a time when fundamentals, metrics, and growth are increasingly important, and the capital injections that are needed to accelerate growth only come in favour of companies that are proving to have solid and promising business models.
There are, however, certain characteristics that, according to the report entitled ‘Startup 2025: Building Business in the Age of AI’ by the company Snowflake, those developing a startup whose business is based on artificial intelligence must take into account.
The report is intended as a guide for those wishing to navigate the complex landscape of AI-related start-ups, thanks to the participation of a group of venture capitalists working with the ‘Powered by Snowflake Startup Program’. A first element that emerges is that AI is characterised by exponential progress, as Patrick Chase of Redpoint Ventures points out: “in the time of a blink of an eye, the performance and cost of artificial intelligence leap forward. This frenetic pace requires agility and foresight, both in technological development and in defining market strategies.
Today, enthusiasm for AI is sky-high, but investors are not easily beguiled. They are looking for start-ups that go further, offering concrete solutions to real problems. The focus is shifting from AI in the broadest sense to the so-called ‘AI under the bonnet’, that which is integrated into business processes and makes a substantial contribution to the generation of tangible value.
New metrics, new challenges: the report introduces the concept of ‘experimental recurring revenue’ (ERR), a crucial metric for assessing the growth potential of AI start-ups, considering the high initial churn rate. Pricing models must also adapt to the computational costs of artificial intelligence, opening up new challenges and opportunities.
Data gravity emerges as a key factor this means that designing applications within the same platform where data resides offers competitive advantages in terms of efficiency, security and governance, especially for highly regulated industries.
So what does it take to win over investors? Technical expertise, strategic vision, customer focus and adaptability are just some of the ingredients. Founders need to prove they can navigate the complex landscape of artificial intelligence, develop a solid go-to-market and build an ecosystem of strategic partners. (photo by Neeqolah Creative Works on Unsplash)
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