Model Drift

A type of neural network trained on vast corpora of text data, capable of generating human-like text, answering questions, summarising documents, and performing reasoning tasks. Large language models such as GPT and Claude represent significant R&D investment and are reshaping knowledge work, customer service, and content production across industries.

A dividend discount model that values a perpetual stream of cash flows growing at a constant rate, calculated as the next period's cash flow divided by the difference between the discount rate and the growth rate. The Gordon growth model is widely used to estimate terminal value in discounted cash flow analyses and requires that the assumed growth rate remains below the discount rate.

A business strategy that minimises investment in physical assets and instead relies heavily on intangible assets such as software, brand, data, and intellectual property to generate revenue. Asset-light companies typically exhibit higher scalability and return on capital but can be harder to value using traditional balance-sheet methods.

A mathematical model trained on data to identify patterns and make predictions without being explicitly programmed for each task. Machine learning models underpin many AI-driven business applications, from demand forecasting to fraud detection, and their development costs are increasingly recognised as intangible assets under IAS 38 when they meet the identifiability and future economic benefit criteria.

A business model that creates value by facilitating exchanges between two or more interdependent user groups — typically producers and consumers — through a digital platform. Platform businesses generate powerful network effects and intangible assets including user data, algorithmic matching capabilities, and brand trust.

A business model in which a basic version of a product or service is offered free of charge while premium features, enhanced functionality, or expanded capacity are available for a subscription fee. The freemium model is prevalent in SaaS, enabling rapid user acquisition and product-led growth, with conversion rates from free to paid users typically ranging from 2% to 5%.

Artificially generated data that mimics the statistical properties of real-world datasets, used to train machine learning models when actual data is scarce, sensitive, or expensive to obtain. Synthetic data enables AI development in privacy-constrained domains such as healthcare and finance, while reducing data acquisition costs and regulatory exposure.

A set of practices combining machine learning, DevOps, and data engineering to standardise and streamline the end-to-end lifecycle of machine learning models, from development through deployment to monitoring. MLOps encompasses version control for models and data, automated testing, continuous integration and deployment, and model performance monitoring in production.