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We believe there are many reasons to be positive about technology's outperformance last year, with the most important technology theme during the year being “the proliferation, evolution and investment implications of generative artificial intelligence”. This was supported by a stronger-than-expected macro backdrop of robust US economic growth, falling inflation and talk turning to when and by how much central banks might cut interest rates. Regardless of the how macro backdrop develops from here we remain particularly excited about AI as a long-term theme. AI reached a seminal moment last year thanks to the transformative power of large language models (LLMs), and is a subject we cover in great detail in the following chapters. They provide a detailed forward look at a number of different areas – from cybersecurity to drug discovery to robotics – each with AI at their core. Explore the following chapters for more analysis and opinion on how AI is impacting all these themes.
Artificial Intelligence
Unlocking AI’s potential and building on a breakthrough year
Robotics & Generative AI
Empowering robotics to solve problems in the real world
Cybersecurity
Are AI-powered defence systems the next step in tackling the growing cybercrime threat?
Semiconductors
Can AI continue to whet the global appetite for semiconductors?
AI in Drug Discovery
AI’s transformative role in accelerating innovation in drug discovery
Space
Can AI and cost reductions fuel the space economy’s next giant leap for mankind?
The Metaverse
First coined in the 1992 cyberpunk novel Snow Crash, the word ‘metaverse’ entered the investment...
Cryptocurrency
2021 was another breakout year for cryptocurrencies, with the total crypto market cap...
Cybercrime incidents continue to grow in both volume and sophistication. Cybersecurity has...
Natural language processing (NLP) remains a key battleground for tech giants fighting for...
Healthcare
On the face of it, healthcare delivered another year of strong absolute growth in 2021...
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Automotive
2021 was a frustrating year for the automotive industry, despite strong demand...
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Large language models (LLMs) such as ChatGPT were the breakthrough technology of 2023 and continue to show the potential to drive transformational change. These AI engines allow a number of human-like traits such as language processing, computer vision and speech recognition as well as in-context learning, reasoning and taking on complex tasks without specific guidance. However, there are challenges. Many LLMs have significant shortfalls particularly, for example, when integrating specialist knowledge. Their performance can be ‘uneven’, they can generate false information – hallucinations – and struggle to retain knowledge during finetuning, thereby hindering some practical applications. Training LLMs is also hugely costly, as is keeping them up to date with the latest available knowledge. AI and its underlying LLM programs underpin many applications and services already yet we are at the very beginning of finding out what they can do.
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Semiconductors took centre stage in 2023, as the world’s cloud service providers, data centre providers and enterprises scrambled to secure high-end graphics processing units (GPUs) for training and inferencing large language models (LLMs). The sector benefited hugely from its crucial role in fuelling the AI revolution. With semiconductor companies now accounting for c7% of the S&P 500 Index, sector valuations are slightly elevated historically, however we are still enthusiastic about AI-aided growth, while remaining mindful of the hype cycle. Looking ahead, a key focus for firms exploring AI capabilities, beyond the excitement attached to the technology’s breakthrough period, will be managing the costs of the whole AI cluster, including hardware, software, model training and user queries. Corporate budgets will be one area for semiconductor firms to watch, along with geopolitical tensions around important manufacturing hubs.
With cybercrime costing the global economy an estimated $8trn in 2023, and 2,200 attacks happening every day, corporate budgets cannot afford to ignore the sector. As such, cybersecurity spending proved relatively resilient despite a challenging macro picture last year, however hackers making use of AI tools to create faster and more sophisticated malware means that expense is only likely to grow. Far from solely being a threat, however, generative AI presents a huge opportunity for cybersecurity companies to strengthen existing defences and beat hackers at their own game. In our view, the providers with the most comprehensive offerings will continue to lead the pack, ahead of smaller firms battling the increasing costs of developing platforms to be worthy of competing with better-capitalised, larger peers.
AI is being used in the world of robotics, giving them an intelligence that allows them to interact with their environment. To date, robots have been able to perform repetitive tasks incredibly well but are not so successful in a new environment or working on a different task. Generative AI is transforming this, helping them become more flexible, moving on from performing repetitive tasks to problem-solving on the spot. Large language models (LLMs) enhance human/robotic interactions, allowing robots to understand complex instructions and carry out unexpected tasks. In effect, robots are being taught how to think for themselves, breaking down complex tasks into smaller, manageable chunks to create their own action plans. They are currently at the developmental stage while the gap narrows between robots understanding language and carrying out actions in the real world.
AI is being incorporated across multiple industries, none more so than in healthcare, particularly around drug discovery. Given it takes a decade to bring a drug to market, at a cost of billions of dollars and a success rate of less than 10%, any improvement at all to the discovery process will bring huge social, economic and financial change. AI being used in drug discovery is more theoretical than practical, but we are rapidly accelerating to the point where improvements could be seen across the value chain, from preclinical analysis to improving the quality of results (successful or not) throughout the different phases of clinical trials. Technological change in drug discovery has long been needed, is now being seen and is quickly being incorporated thanks to AI.
Following a raft of early-stage space companies queuing up to go public, on the back of the success of SpaceX and a low interest rate regime, 2023 was an altogether different story. Higher interest rates have dented investment in the space economy, however growing demand for satellite broadband services, the rise in public/private partnerships and significant reductions in launch costs have bolstered sector activity nonetheless. With both national and commercial interests gathering pace, and the cost of experimentation falling, innovating within the sector faces fewer barriers than ever. Space tourism growth projections are strong, proprietary satellite networks are still being commissioned and AI tools are beginning to translate observation data, signalling some of the compelling growth drivers to watch as investors eye the evolution of the space economy’s next step into public markets.
