Artificial Intelligence: A Revolution in Progress

The good news is it’s not Skynet yet

Summary

• Artificial Intelligence (AI) has come a long way from its beginnings, but in many ways is only just getting started.
• Generative AI (the current iteration of AI) is essentially an amazing learning machine that by processing vast amounts of data can mimic human responses. This ability has the potential to supplement the work of at least some human knowledge workers today.
• Longer term, investment in AI will continue given the economic benefit that can be generated already.
• While there is a lot of research effort going into furthering AI, it is not a certainty that AI can evolve beyond and become ‘sentient’. Improvement in hardware and software may enable this revolution in coming years.

Introduction

AI has rapidly transitioned from the realm of science fiction to an integral part of our daily lives. Whether it’s the smartphone in your pocket, digital assistants at home, or complex decision-making in medicine and finance, AI is propagating everywhere. Yet, as omnipresent as it may seem, the journey of AI has only just begun. This note provides a brief history of AI, demystifies how generative AI works, and explains why we are potentially witnessing only the dawn of an AI-driven revolution.

A Brief History of AI

In his seminal 1950 paper “Computing Machinery and Intelligence”, British mathematician Alan Turing posed a now-famous question: “Can machines think?” In attempting to provide an answer, Turing proposed the ‘Imitation Game’ (now called the ‘Turing Test’) as a way to measure a machine’s ability to exhibit intelligent behaviour indistinguishable from that of a human.

The term “Artificial Intelligence” was officially coined in 1956 at the Dartmouth Summer Research Project on Artificial Intelligence. The ambitious goal was to discover how to make a machine that could “reason, learn, and solve problems” – tasks that, until then, had been considered uniquely human.

The decades that followed saw alternating cycles of great optimism and ‘AI winters’ – periods of reduced funding and interest due to unrealistic expectations, mostly due to a lack of low-cost compute. Early AI research focused on rule-based systems and symbolic reasoning, which had limited success. However, with the rise of more powerful computers and the advent of machine learning in the 1980s and 1990s, AI began to show real promise.

A watershed moment arrived in 1997 when IBM’s Deep Blue defeated world chess champion Garry Kasparov, demonstrating the power of specialised AI. In 2011, IBM’s Watson triumphed on the quiz show ‘Jeopardy!’, and in 2016, Google’s AlphaGo defeated a world champion Go player, a feat long thought far beyond the reach of computers. These milestones marked not just technical achievement, but the dawn of AI’s practical impact on society.

The Emergence of Generative AI

While early AI systems were adept at specific tasks, the last decade has seen the rise of a new class of AI: generative artificial intelligence. Generative AI refers to systems capable of creating new contents – text, images, music, code, and more – based on patterns learned from vast datasets.

The breakthrough underpinning generative AI is the development of deep learning, via neural networks inspired by the biological structure of the human brain. Among these, ‘transformer’ architectures, first introduced in 2017, have been game-changers. Transformers, such as OpenAI’s GPT (Generative Pre-trained Transformer) models, Google’s BERT, and others, can process and generate human-like language, understand context, and even create images or music from textual prompts.

Generative AI models are trained on massive datasets, absorbing billions of words, images, or sounds. These models are capable of ‘breaking down’ intricate patterns, connections, and structures. When given a prompt, a generative AI model can compose a poem, write an essay, simulate a conversation, generate artwork, or even write computer code. The results are often indistinguishable from – and indeed, in some cases, surpass – what humans can produce.

How Generative AI Works

At its core, generative AI relies on machine learning, a subset of AI where computers learn from data rather than being explicitly programmed.

The typical process involves two stages: pre-training and fine-tuning. During pre-training, the AI model ingests vast amounts of data, including text from books, articles, websites, or images from online repositories, and learns to predict the next word in a sentence or the next pixel in an image. This builds a statistical understanding of language or visual structure. Fine-tuning then tailors the model to specific tasks or domains, such as medical advice, customer service, or creative writing.

When a user provides a prompt (“Write a poem about the beach at sunset”, for example), the AI model processes the input, consults its learned knowledge, and generates a response by predicting the most likely sequence of words or images. This process happens in seconds, leveraging billions of parameters – the internal ‘settings’ the AI model has incorporated during training – to produce fluent, relevant, and creative outputs.

Importantly, generative AI does not ‘think’ or ‘understand’ in the human sense. It is exceptionally good at pattern recognition and generation, but it lacks consciousness or self-awareness. Its intelligence is statistical, not sentient.

Why This Is Only the Beginning

While AI is not sentient, it is a remarkably good learner. This means that it can perform the work of many domains that are done by knowledge workers. Knowledge workers constitute ~50% of wages in modern societies by some estimates, a significant proportion of GDP. Conservatively sizing just this part of the cost reductions can justify trillions of dollars of investments.

The current explosion of generative AI is just the tip of the iceberg. Several factors suggest we are at the very start of a much larger revolution:

• Accelerating progress: advances in hardware (more powerful GPUs and specialised AI chips) and software (more efficient algorithms) are enabling increasingly sophisticated models to be trained faster and more cheaply.
• Ubiquity and accessibility: AI tools are no longer confined to research labs. Thanks to cloud computing and open-source models, anyone with an internet connection can access powerful AI models or even build their own applications.
• Expanding applications: Generative AI is being rapidly adopted across industries, including healthcare, finance, law, education, media, and more. It is contributing to new drug discovery, automating routine administrative tasks, detecting fraud, personalising learning, and even creating entertainment.
• Collaboration and augmentation: rather than replacing humans, AI is increasingly seen as a partner – amplifying creativity and enhancing productivity.
• Continuous learning: with feedback loops and reinforcement learning, AI systems are becoming more adaptive and capable, learning from new data and real-world interactions.
• Societal and ethical questions: as AI becomes more pervasive, it raises profound questions about ethics, bias, privacy, and the future of work. Addressing these challenges will shape not just technology, but society itself.

There is still much disagreement around whether we are moving from narrow AI, where systems are good at one thing, to general AI, where models are capable of broader reasoning and adaptability. Arguably, new methods and algorithms need to be developed to achieve this next major advance. In coming decades, we may potentially see AI designing new materials, helping to solve climate change, or revolutionising education and medicine.

At Ox, we are focused on identifying the beneficiaries of the AI revolution, many of which are present in Asia. For example, China is today at the forefront of AI development, alongside the US. Companies in China are rapidly creating and implementing AI technologies, contributing to cost reduction and increased operational efficiency. These companies also have substantial resources to support ongoing technological development. In South Korea, semiconductor companies play a significant role in producing memory components that are essential for development of AI technology. While these companies are valued at a discount compared to their Western counterparts, their technology is considered comparable, if not superior.

Conclusion

AI has come a long way, but in many respects we may only be at the beginning. Generative AI has shown what’s possible when machines are equipped not just to process data, but to create, collaborate, and communicate.

The true revolution is what we choose to do with it – how we harness its power to improve lives, solve global challenges, and unlock new realms of possibility.

At Ox Capital, we are focused on quality companies with long term growth which are available at inexpensive valuations across emerging markets. Current valuations are providing lots of interesting opportunities. Let us know if you would like to understand specifically where we are finding the opportunities!

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