The release, built on Google's Gemini 3.1 Pro model, marks an inflection point in the rapidly intensifying race to build AI systems that can autonomously conduct the kind of exhaustive, multi-source research that has traditionally consumed hours or days of human analyst time. It also represents Google's clearest bid yet to position its AI infrastructure as the backbone for enterprise research workflows in finance, life sciences, and market intelligence — industries where the stakes of getting information wrong are extraordinarily high.

Enterprise AI is entering a new phase — one where the central question is no longer what can be built, but how to make the most of our AI investment.

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The age of agentic AI is upon us — whether we like it or not. What started with an innocent question-answer banter with ChatGPT back in 2022 has become an existential debate on job security and the rise of the machines.

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The announcements, which also include a new Color Mode for Premiere Pro, the addition of Kling 3.0 video models to Firefly's growing roster of third-party AI engines, and Frame.io Drive — a virtual filesystem that lets distributed teams work with cloud-stored media as though it lived on their local machines — represent Adobe's clearest signal yet that it views agentic AI not as a feature upgrade but as a fundamental reshaping of how creative work gets done.

The new model is priced at $5 per million text input tokens and $19.50 per million image output tokens, a roughly 41% reduction from MAI-Image-2's pricing of $5 and $33, respectively, for those same tiers. Microsoft says the model runs 22% faster than its flagship sibling and achieves 4x greater throughput efficiency per GPU, as measured on NVIDIA H100 hardware at 1024×1024 resolution. The company also claims it outpaces competing hyperscaler models — specifically naming Google's Gemini 3.1 Flash, Gemini 3.1 Flash Image, and Gemini 3 Pro Image — by an average of 40% on p50 latency benchmarks.

The release, built on Google's Gemini 3.1 Pro model, marks an inflection point in the rapidly intensifying race to build AI systems that can autonomously conduct the kind of exhaustive, multi-source research that has traditionally consumed hours or days of human analyst time. It also represents Google's clearest bid yet to position its AI infrastructure as the backbone for enterprise research workflows in finance, life sciences, and market intelligence — industries where the stakes of getting information wrong are extraordinarily high.

The simultaneous launches mark a watershed for Anthropic, whose ambitions now visibly extend from foundation model provider to full-stack product company — one that wants to own the arc from a rough idea to a shipped product. The timing is also significant: Anthropic hit roughly $20 billion in annualized revenue in early March 2026, according to Bloomberg, up from $9 billion at the end of 2025 — and surpassed $30 billion by early April 2026. The company is in early talks with Goldman Sachs, JPMorgan, and Morgan Stanley about a potential IPO that could come as early as October 2026.

The announcement, made at the company's annual TDX developer conference in San Francisco, ships more than 100 new tools and skills immediately available to developers. It marks a decisive response to the existential question hanging over enterprise software: In a world where AI agents can reason, plan, and execute, does a company still need a CRM with a graphical interface?