AI Watch

US accounting and consulting practice Armanino has entered into a partnership with DataSnipper, an agentic automation platform used by audit and finance teams.

Under the arrangement, Armanino will help clients roll out DataSnipper throughout their internal audit, compliance and risk management functions. The combination will leverage Armanino’s implementation experience and DataSnipper’s agentic automation technology to help organisations modernise their processes.

The tie-up is intended to support Armanino’s broader plan to embed AI-enabled automation into internal audit and risk advisory work, while maintaining human judgement, oversight and quality standards.

Google DeepMind research team introduced Gemini Robotics-ER 1.6, a significant upgrade to its embodied reasoning model designed to serve as the ‘cognitive brain’ of robots operating in real-world environments. The model specializes in reasoning capabilities critical for robotics, including visual and spatial understanding, task planning, and success detection — acting as the high-level reasoning model for a robot, capable of executing tasks by natively calling tools like Google Search, vision-language-action models (VLAs), or any other third-party user-defined functions.

Here is the key architectural idea to understand: Google DeepMind takes a dual-model approach to robotics AI. Gemini Robotics 1.5 is the vision-language-action (VLA) model — it processes visual inputs and user prompts and directly translates them into physical motor commands. Gemini Robotics-ER, on the other hand, is the embodied reasoning model: it specializes in understanding physical spaces, planning, and making logical decisions, but does not directly control robotic limbs. Instead, it provides high-level insights to help the VLA model decide what to do next. Think of it as the difference between a strategist and an executor — Gemini Robotics-ER 1.6 is the strategist.

Today we’re bringing people together in Washington D.C. to discuss how AI will impact the economy and jobs. At our inaugural AI for the Economy Forum, co-hosted with MIT FutureTech, we’re starting with a simple premise: neither the benefits nor the risks are automatic or guaranteed. How AI impacts our lives, jobs and economy is something we as a society can shape – and fully realizing AI’s economic potential will require a new era of partnership between companies, workers, governments, researchers and more. At the forum, economists, industry leaders, policymakers and experts will gather to share information, identify gaps in current understanding, and lay the foundation for ongoing collaboration.

Google has a long-standing commitment to helping positively shape this transition. Today, we’re building on that commitment in two critical ways. First, we are making new investments in research to ensure governments, companies, researchers, and civil society have the information required to make smart decisions. Second, we are providing training opportunities to equip people with the skills needed to navigate a changing economy.

There’s a moment in every technology shift where the early adopters stop being outliers and start being the baseline. We’re at that moment in software development, and most teams don’t realize it yet.

A year ago, vibe coding went viral. Non-developers and junior developers discovered they could build beyond their abilities with AI. It lowered the floor. It made prototyping much quicker, but it also introduced a surplus of slop. What the industry then needed was something that raised the ceiling — something that improved code quality and worked the way the most expert developers work. Spec-driven development did that. It laid the foundation for trustworthy autonomous coding agents.