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For decades, advertising has quietly powered the modern internet. It funded the rise of search engines, social platforms, maps, email and media, making them accessible to billions of people around the world. Most users never paid directly for these services, and yet they benefited from one of the most open and expansive information ecosystems ever created. 

Now, that ecosystem is being reshaped. Over the past year, the rapid adoption of generative A.I. and the corresponding decline in traditional search traffic for many publishers have intensified questions about how the next phase of the internet will be funded. 

Artificial intelligence is rapidly becoming the new front door to information. Instead of typing queries into a search bar and sifting through links, users are turning to A.I. systems to deliver direct answers, recommendations and decisions. Platforms like OpenAI, Perplexity and Anthropic are redefining how information is accessed altogether. Meanwhile, incumbents like Google are integrating A.I.-generated overview answers directly into search results, signaling a structural shift in how users discover information. 

Nvidia is the undisputed king of AI chips. But thanks to the AI it helped build, the champ could soon face growing competition.

Modern AI runs on Nvidia designs, a dynamic that has propelled the company to a market cap of well over $4 trillion. Each new generation of Nvidia chip allows companies to train more powerful AI models using hundreds or thousands of processors networked together inside vast data centers. One reason for Nvidia’s success is that it provides software to help program each new generation of chip. That may soon not be such a differentiated skill.

A startup called Wafer is training AI models to do one of the most difficult and important jobs in AI—optimizing code so that it runs as efficiently as possible on a particular silicon chip.

Emilio Andere, cofounder and CEO of Wafer, says the company performs reinforcement learning on open source models to teach them to write kernel code, or software that interacts directly with hardware in an operating system. Andere says Wafer also adds “agentic harnesses” to existing coding models like Anthropic’s Claude and OpenAI’s GPT to soup up their ability to write code that runs directly on chips.

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.