Google Turns Gemini Into a Science Lab, Robinhood Opens the Door to AI Trading, and Anthropic Raises a Monster Round

FEATURED INSIGHT💡

Gemini for Science: Google Wants AI to Help Researchers Think Bigger

Google introduced Gemini for Science, a new collection of AI tools and experiments designed to accelerate scientific discovery. The idea is simple but powerful: research is producing more knowledge than any individual scientist can reasonably absorb, and breakthroughs often come from connecting ideas across papers, datasets, and experiments.

The workbench focuses on three major bottlenecks: generating hypotheses, testing ideas computationally, and making sense of large bodies of literature. Hypothesis Generation, built with Co-Scientist, uses a multi-agent approach to generate, debate, refine, and evaluate possible scientific hypotheses. Computational Discovery, built with AlphaEvolve and ERA, can generate and score thousands of code variations in parallel. Literature Insights, built with NotebookLM, helps researchers compare papers, identify gaps, and create reports or other research artifacts from curated scientific material.

Why it matters: this points to a future where AI becomes less of a chatbot and more of a scientific operating system. Researchers still define the problem and judge the outputs, but AI can help with the tedious connective work: literature review, code exploration, hypothesis expansion, and evidence mapping. Google is already working with more than 100 institutions, including Stanford University School of Medicine, Imperial College London, and The Crick Institute, to validate these systems.

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ON THE HORIZON 🌅

Your AI Agent Can Trade Stocks Now

Robinhood unveiled new tools that allow AI agents to trade stocks and make purchases on behalf of users. The products include Agentic Trading and an Agentic Credit Card, allowing customers to connect third-party AI assistants that can execute investing strategies, rebalance portfolios, monitor themes like AI stocks, or complete purchases with designated virtual cards.

This is a big step toward consumer-facing autonomous finance. Hedge funds and institutional firms have used quantitative and AI-driven systems for years, but Robinhood is pushing that type of automation closer to everyday investors. That opens up convenience, but also obvious risk, especially when retail traders may not have the same guardrails or risk controls as professional trading desks.

Robinhood says it is trying to reduce that risk by separating agentic trading accounts from main portfolios, limiting agents to allocated capital, sending trade notifications, and letting users disconnect an agent. Why it matters: finance may become one of the clearest tests for AI agents. Summarizing a report is one thing. Moving money, buying assets, and making decisions in volatile markets is a much bigger leap.

LATEST IMPORTANT NEWS 📰

OpenAI Wins Musk Lawsuit

A jury dismissed Elon Musk’s lawsuit against OpenAI and Sam Altman, ruling that Musk missed the three-year statute of limitations tied to OpenAI’s 2019 shift toward a for-profit structure. Musk has said he plans to appeal, while OpenAI’s side framed the decision as more than a procedural win. The trial also surfaced internal tension around OpenAI’s early governance, Altman’s leadership, Musk’s previous control ambitions, and the messy overlap between OpenAI, xAI, Tesla, and Microsoft.

Anthropic Raises $65B at a $965B Valuation

Anthropic announced a massive $65 billion Series H funding round at a $965 billion post-money valuation, backed by major investors including Altimeter, Dragoneer, Greenoaks, Sequoia, Capital Group, Coatue, GIC, ICONIQ, and others. The company says the funding will support safety and interpretability research, expanded compute, Claude product development, and global enterprise demand. Anthropic also highlighted major infrastructure relationships with Amazon, Google, Broadcom, SpaceX, Micron, Samsung, and SK hynix.

Snowflake Signs $6B AWS Deal as AI Compute Demand Grows

Snowflake signed a new $6 billion, five-year agreement with AWS, driven largely by growing AI demand from Snowflake customers. A major piece of the deal involves access to AWS’s Graviton chips, which are increasingly important as AI workloads shift from training into daily enterprise usage, automation, and agentic workflows. The deal also shows how cloud providers are benefiting from the AI boom even when the attention is focused on model makers and GPU companies.

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FOR THE TECHNICALLY INCLINED 🛠️

TabPFN-3 Takes Aim at XGBoost

Prior Labs introduced TabPFN-3, a tabular foundation model built for structured data prediction. The pitch is direct: instead of spending hours tuning XGBoost, random forests, or AutoML pipelines, you drop in a dataset and get strong predictions quickly. The company claims TabPFN-3 achieves a 93% win rate over classic ML on TabArena, runs predictions on 1 million samples in 0.2 seconds, and beats AutoML in 80% of cases when using its Plus “Thinking” mode.

For teams working with structured business data such as customer churn, demand forecasting, lead scoring, risk modeling, clinical operations, or pricing, this is worth watching. Tabular data is still where much of enterprise machine learning lives, and foundation-model-style approaches could change how quickly teams build practical predictive models.

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