News You Can Use

The report describes how the consumer AI market in 2025 has begun to consolidate around a few dominant players. ChatGPT remains the clear leader because people tend to commit to a single assistant, but Google's Gemini is catching up quickly through rapid growth and viral creative tools. OpenAI is building a single, integrated "everything app," while Google spreads its capabilities across multiple touchpoints. The competitive frontier has shifted from impressive visuals to deeper reasoning, accuracy, and search-grounded outputs. Smaller players (like Anthropic and Perplexity) win by focusing on specific, opinionated use cases for power users. Looking ahead, fully multimodal systems, stronger enterprise adoption, and app-store-style ecosystems are likely to drive the next phase of growth.

This highlights the importance of making deliberate choices about which platforms to adopt, which tools to integrate into our processes, and where to focus experimentation. It also emphasises that user behaviour and engagement patterns matter: stickiness, power-user adoption, and multimodal capabilities will shape which tools become strategic over the long term. Understanding these dynamics helps us design workflows, pilot programs, and client solutions that leverage AI effectively, without being distracted by hype or chasing every new model release.

The Pathways Project - a collaboration between The LegalTech Fund, Harvey, Leaders' Quest, and ALM - released its first-phase findings on how the legal industry could evolve through 2040 under the influence of AI and data. It identifies six key shifts reshaping legal work, including transitions from billable hours to outcomes-based value, from bespoke work to scalable platforms, and from static documents to dynamic data models. The report positions these shifts as a framework for law firms to understand technological, economic and talent pressures and opportunities as the industry evolves.

This report reinforces that change in the legal market is structural and long-term, instead of experimental. It validates strategic conversations we are already having around workflow redesign, new pricing models, scalable delivery and data-led services. We can use the Pathways framework as a guideline for our internal planning - distinguishing short-term pilots from strategic investments that move us toward value-based outcomes and more predictable, platform-driven delivery over the next decade. It also provides key pointers for client conversations about the future of legal services in a world where traditional leverage models are under pressure.

The article unpacks the concept of a parameter - the fundamental building block inside large language models (LLMs). Parameters are the "dials and levers" that control how models behave, set during training through iterative adjustments to reduce error and determine model outputs. Training involves updating billions or trillions of parameters to capture patterns in language, and complex embeddings, weights and biases help models understand meaning and relationships between words across large contexts.

This kind of foundational clarity helps when we evaluate, select and explain models in enterprise legal settings. Understanding that parameters are the mathematical core that shapes reasoning, nuance capture and output reliability reminds us why model architecture and training data matter. It also provides useful grounding when explaining to colleagues and clients why different models behave differently (even with similar prompts), and why evaluation metrics beyond parameter count (such as reasoning quality, handling of legal context, and determinism) are critical for legal AI adoption.