Artificial intelligence and the shifting technology landscape

From machine learning to large language models, AI's capabilities are growing, bringing the technology sector to an inflection point.

AI is a broad term, referring to algorithms or software designed to imitate the human brain’s problem-solving and decision-making functions. An AI tool can undertake far more complex tasks than a conventional computer programme, and improve itself continuously as underlying datasets grow.

AI is not a new concept. Machine learning (ML), a sub-category within AI, has been used by many businesses for years. DeepMind, for example, which was acquired by Google-parent Alphabet in 2014, is integrated into both its search engine and Youtube’s video-suggestion algorithm.

Generative AI tools and large language models, such as OpenAI’s ChatGPT and Google’s Bard, have continuously advanced. This progress has created a huge range of potential use cases for businesses and consumers, even those outside of the technology ecosystem and without deep technical expertise.

As the core enablers of its progress – increasing computer power, more robust cloud data infrastructure, and a deepening talent pool – continue to develop, AI is poised for even greater transformation, with the potential for significant disruption.

An overview of AI       
AI is a general term covering a broad range of technologies and use cases across many sectors, but it can be broadly grouped into four segments.

Spotlight: generative AI      
Generative AI refers to AI systems that produce outputs resembling those of humans. These models use deep learning to create new data such as images, text and audio. Examples include:

• Generative adversarial networks, such as those used in lifelike images and deepfakes;

• Text-generation models like ChatGPT, whose output is human-like text;

• And music-generation models that create compositions.

AI and ML Investment      
Given AI’s significant potential and the fact that early tools such as machine learning have had such a material impact on business operations, some venture capitalists and private equity investors have focused on the sector for many years. However, with recent step changes in technological progress, investment in generative AI businesses is gaining momentum.

According to Pitchbook, investment in businesses incorporating AI and machine learning peaked at $56.9 billion in the third quarter of 2021, as shown in Chart 1, below. Although investment flows slowed from the second quarter of 2022, reflecting the broader market slowdown, AI and ML investment remained robust at $36.8 billion for the same period in 2023.

Chart 1: Private capital invested in AI and ML remained robust in H1 2023

A key feature of AI investment is the high concentration of deal activity in North America, which benefits from a long-established technology ecosystem and strong network effects. While the share of investment has been lower in Asia and Europe, capital invested is still meaningful in these regions.

Chart 2: North America is the most active region for AI investment

In 2023, generative AI, partly popularised by the launch of OpenAI’s Dall-E and ChatGPT, has become a significant focus for investors. In the first half of 2023, $20.5 billion of capital was invested in generative AI businesses, an amount higher than the combined total of the previous four years, as shown in Chart 3. 

Chart 3: Private capital invested in generative AI

With AI gaining traction due to better understanding of its use cases and potential end-markets, valuations for generative AI businesses have also increased markedly. The median post-money valuation for a generative AI deal has increased by 39% from 2022 to 2023, reaching $50 million, as Chart 4 details. There is also a clear upside bias as market-leading businesses command premium valuations. In recent months there have been reported fundraising rounds at more than 100x revenue multiples, and businesses pre-revenue and pre-product being valued in the hundreds of millions [5].

Chart 4: Generative AI valuations have increased markedly

Challenges and risks
AI’s power and potentially transformative benefits bring with it natural risks and challenges.

• Data quality and data availability       
  To work effectively, AI needs large volumes of data to help it make accurate predictions. In the context of private markets, this is particularly challenging given the opacity of the industry and difficulty in obtaining accurate data on private companies.

• Interpretability       
  While AI can be used to interpret swathes of data, it can be difficult to follow the procedure it undertook to reach each conclusion. Many AIs use complex algorithms, far exceeding human capabilities. While this has many benefits, a human’s ability to explain a decision and how they reached a conclusion is invaluable.

• Regulation and security       
  Innovation often precedes regulation, and AI is no different. Regulatory developments are inevitable and necessary given AI’s power. These could materially change how tools are developed and deployed, and, ultimately, determine which AI businesses are viable. Of course, data security and the threat of bad actors must also be carefully managed.

Conclusion     
AI represents a sea change in technological development. Although still relatively nascent, AI tools and solutions already offer material benefits to businesses and consumers. A new generation of companies, one that will build even more powerful AI tools, is emerging. Many industries will face material disruption and will be forced to adapt.

The AI landscape is becoming increasingly competitive. There are already new, open source large language models, built with very small teams, that are proving more capable than ChatGPT and Bard on many benchmarks. AI businesses that emerge as market leaders will likely have proprietary datasets on which they can train their models, providing intellectual property and a defensible competitive advantage. Additionally, user experience matters. AI tools will not be embraced by businesses and consumers alike if they are not easy to understand and use.

The rise of AI has undoubtedly generated immense interest and excitement in the investment world. However, this increased interest has also raised concerns about the potential for speculative investments and the formation of a market bubble. As competition intensifies, entry multiples are likely to rise further, making it essential for investors to exercise caution and prudence.

In order to navigate this dynamic landscape, diversification in investment strategies becomes crucial. The best way to gain exposure to AI is through a diversified portfolio, which spreads the risk across various assets and industries. This approach can help investors mitigate the impact of any potential market downturns or overvaluation in specific AI-related sectors.

Moreover, specialist investors who focus solely on the AI domain can enjoy unique competitive advantages. Their deep industry networks allow them to access exclusive investment opportunities and stay well-informed about the latest developments in the field. Additionally, specialist investors benefit from deal pattern recognition, gained through repeat investments in AI ventures. 

Endnotes     
1, 2, 3, 4, 2023 Artificial Intelligence and Machine Learning Overview | PitchBook       
5 The generative AI landscape: Top startups, venture capital firms, and more, CB Insights, 2023.

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