The UK's position as an attractive jurisdiction for technology investment

At the conference, various speakers highlighted the UK’s favourable status as a hub for tech businesses, citing its vast talent pool and robust ecosystem. Clare Barclay, the CEO of Microsoft UK, pointed out that a third of Europe’s AI start-up companies are in the UK, and added “there's an influx of investment and venture capital into the UK that's fuelling growth”. Microsoft's substantial investment plans clearly support this. Between 2023 and 2026, Microsoft will bring over 20,000 advanced AI GPUs to the UK and invest GBP 2.5 billion in local data centres. However, maintaining the UK’s competitiveness will necessitate ongoing human capital and infrastructure investment, and a clear policy approach to emerging technologies, including whether and how the government intends to regulate emerging technologies.

The consensus aligns with the government's ambition to make the UK a leading destination for tech investment, as reflected in Rishi Sunak’s publication “10 ways we're making the UK the best place for tech businesses”. Recent significant investments in British tech, such as Wayve securing over $1 billion in Series C funding in 2024 to advance AI-driven self-driving vehicles, further reinforce this narrative. Alex Kendall, the founder of Wayve, who was one of speakers at the conference, echoed the same point.

With the upcoming election, one of key questions is how AI strategy (including regulation) would be impacted by a change in government. At last year’s London Tech Week, Keir Starmer indicated that a Labour government would introduce “an overarching regulatory framework” that would be “stronger” than the current government’s approach. However, the Labour Manifesto does not suggest that a Labour Government would introduce a comprehensive cross-sectoral regulatory framework for AI akin to the EU’s AI Act, and instead indicates Labour would introduce more limited “binding regulation on the handful of companies developing the most powerful AI models”. This approach appears similar to proposals outlined in the current government’s February response  to its consultation on the AI White Paper, which said “binding requirements on developers of highly capable general-purpose AI models” were being considered.

Challenges faced by businesses in embracing and commercialising AI

A recurring theme was the importance of data to effectively commercialising AI. Deploying and structuring enterprise data effectively remains a major challenge for fully realising AI's potential. One speaker noted that some businesses are still hesitant to use confidential and/or personal data for AI model training, particularly given that many AI solutions are cloud-based. It will be interesting to see how market trends and attitudes evolve regarding this issue as AI becomes increasingly embedded in normal day to day business practices. At the same time, with developing regulations regarding data protection, financial services are generally seeking tighter control over the use of data by third parties and developers.

Another significant challenge is that some AI models remain 'closed,' with their source codes inaccessible and not open to the public. Nicola Hodson, Chief Executive of IBM, highlighted that IBM has opted to open-source its model to foster innovation and collaboration, providing an indemnity for enterprise data development. IBM's collaborations with Meta, Oracle, Sony, AMD, Uber, and Red Hat as well as universities like Yale, Imperial, and NYU aim to enhance these models and products.

Finally, consumer adoption and the comparative regulatory regimes between the US and Europe were highlighted as potential challenges. Humayun Sheikh, Founder of Fetch.ai, spoke about the transition from traditional aggregator websites (such as travel booking sites) towards AI-powered decentralised autonomous agents. These chatbot-type apps, will power people’s searching and booking capabilities but encouraging consumer adoption will prove critical. It is also clear that such apps will have implications across the legal spectrum – not just from an AI and privacy regulation perspective, but also across contracting, competition, and consumer law. Differences in governmental regulatory priorities across the globe will play a key role in shaping this ecosystem.

Artificial Intelligence use cases disrupting every sector

The progress of AI was, unsurprisingly, a central theme at London Tech Week, with speakers showcasing diverse and impressive use cases. Examples include NatWest’s Corda+ chatbot, utilising multiple foundational models from IBM, Meta, and Open AI to deliver personalised financial services, WPP using AI in the context of advertising and media, and Google DeepMind using generative AI in drug and scientific discoveries. It is clear that AI's disruptive potential spans all sectors, even prompting the emergence of new professions like prompt engineering.

Embedded finance as the future of financial services

The adoption of embedded finance across various industries has already shown positive impacts for both businesses and consumers. Use cases include embedded insurance, buy now pay later (BNPL) services, lending and credit services, and tax services. Notably, the speakers highlighted the key opportunities lie in SME lending and API monetisation.

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