Exploring the power and pitfalls of Artificial Intelligence.
In June 2025, Linus Torvalds, the inventor of Linux and Git, was asked about “vibe coding”, where non-programmers use large language models to write code. He memorably described it as “Very Inefficient But Entertaining”. However, the world of AI is far from static, and the release of Claude Opus 4.5 in November 2025 seems to…
In December 2025 Snowflake Ventures made a strategic (though undisclosed in monetary value) investment in data management vendor Ataccama. This was a more interesting event than just a regular financing round for a software vendor, as it has implications for the broader data quality industry. There have been issues with data quality ever since the…
It is now just over three years since ChatGPT was launched publicly by OpenAI, and ignited a wave of interest in AI and huge investment in companies providing AI products and services. OpenAI itself has been publicly pondering a 2026 IPO at a potential valuation of $750 billion or more. NVIDIA, which makes the specialist…
If you have been reading this blog for some time, then you may recall the curious story of Anthropic’s AI Claudius, an early experiment into agentic AI. Anthropic ran a test of their Claude AI, allowing it to run a little business, specifically their office vending machine. Claudius, as the agent was named, was given…
In recent years, there has been much speculation about the possibility of the emergence of artificial general intelligence (AGI). This is an AI that would match or exceed human intelligence in most or all tasks. Such an AI would reason, learn, remember, innovate and adapt to new environments without the need for training or retraining.…
Nobel Laureate Richard Feynman said that “no one understands quantum mechanics”, despite his having delivered a famous set of lectures on the subject. Despite this drawback, many companies, such as Google, IBM and others, are now building small, early working models of quantum computers, which promise to solve certain classes of mathematical problems drastically faster…
Large language models (LLMs) have been heavily promoted as productivity tools for enterprises in a range of use cases, such as customer chatbots, content creation for marketing material, and enabling employees to easily access and summarise documents like product documentation or product manuals. There are other use cases, such as automation of business processes like…
The world of technology moves rapidly, and it is hard to build a sustainable competitive advantage. Just ask Blackberry (business smartphones), Nokia (mobile phones), Yahoo (search), AOL (email and chat), MySpace (social network) and Kodak (film). Technology companies seek to build a “moat”, a durable competitive advantage that is hard to copy. Such a moat…
We usually think of AI running on web-connected servers in the cloud somewhere, but what about AI running on a stand-alone device, not connected to the internet? There are actually more examples of this than you may realise. To start with, security cameras detect motion or objects locally without sending data to the cloud. Self-driving…
One mantra regarding large language models (LLMs) is that bigger is better. Parameters are the learned weights of a model, while tokens represent the pieces of text used to train it. The more training tokens an LLM sees, the more fluent the answers it produces. This assumption that bigger is better is what has driven…