"ChatGPT for Doctors" Hits $6B

FEATURED INSIGHT💡

OpenEvidence raises $200M at a $6B valuation — and signals a new phase for vertical AI in healthcare

OpenEvidence, often described by clinicians as “ChatGPT for medicine,” is closing a fresh $200M round at a $6B valuation just three months after a $210M raise. The product is trained on peer-reviewed sources (think JAMA, NEJM) and is free for verified medical professionals, funded by ads. Monthly clinical lookups have doubled since July to ~15M, underscoring real-world pull from frontline doctors and nurses. The round is led by GV with Sequoia, Kleiner Perkins, Blackstone, Thrive, Coatue, Bond, and Craft participating.

Why it matters: This isn’t another abstract “AI in healthcare” headline; it’s AI quietly entering the exam room. When your doctor asks a question during your visit, the answer might soon come from a system trained on the world’s top medical journals instead of a quick Google search. If OpenEvidence succeeds, it could mean faster diagnoses, fewer medical errors, and more time spent on patient care instead of paperwork.

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ON THE HORIZON 🌅

Artificial neurons that compute with ions, not just electrons

Researchers at USC have developed single-transistor “diffusive memristor” neurons that mimic the electrochemical behavior of real brain cells. Unlike current neuromorphic chips that digitally simulate neuron activity, these devices use the physical movement of silver ions in oxide to process information, which is a closer match to how biological neurons actually work.

Each artificial neuron occupies the space of a single transistor, a major leap in density and efficiency that could enable faster, smaller, and more energy-efficient AI hardware. The approach could eventually allow AI systems to learn directly on-device rather than relying on power-hungry data centers.

The current design uses silver, which isn’t compatible with standard semiconductor manufacturing. Researchers plan to explore other ionic materials that could offer similar performance while fitting into existing chip fabrication processes.

LATEST IMPORTANT NEWS 📰

Claude Brings AI Superpowers to Excel

Anthropic is piloting a native Excel integration that reads multi-tab models, explains formulas with cell-level citations, bulk-edits assumptions while preserving dependencies, and traces errors (#REF!, #VALUE!, circulars) to their source. For finance and ops teams, this is a practical step toward explainable, reversible spreadsheet automation.

Perplexity Strikes Image Deal with Getty

Perplexity has signed a multi-year licensing agreement with Getty Images to display properly attributed photos inside its AI-powered search results. After a year of plagiarism and scraping controversies, this marks a shift toward licensed, credit-based media pipelines that still deliver instant results. Expect better previews, source links, and a model others will follow.

Apple Doubles Down on AI Partnerships

On its Q4 earnings call, Tim Cook reaffirmed Apple’s three-pronged AI strategy: combining in-house models, external partnerships, and targeted acquisitions. He confirmed that the new AI-powered Siri remains on track for a 2026 release and highlighted progress in Private Cloud Compute, with new servers now built in Houston. The company’s approach signals a steady, integrated rollout of Apple Intelligence rather than a single major launch.

CTV ads made easy: Black Friday edition

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FOR THE TECHNICALLY INCLINED 🛠️

DeepAgent: a smarter way for AI to think and use tools

Researchers have introduced DeepAgent, a new framework designed to help AI systems reason more effectively and use tools with greater autonomy. Instead of relying on rigid workflows or short memory spans, DeepAgent combines long-term reasoning, flexible tool use, and decision-making into one continuous process.

A key innovation, called Memory Folding, helps the system remember and organize past interactions more efficiently—similar to how people recall key details without replaying every step of a conversation. Another component, ToolPO, teaches the AI to improve how it uses external tools by giving it feedback at a very granular level, helping it learn from its own actions rather than ideal examples.

Early tests show DeepAgent outperforming other approaches across multiple benchmarks, hinting at progress toward AI that can handle complex, multi-step tasks with less human guidance.

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