Law Technology Daily Digest

Everybody's Working for the Weekend

Hopefully, unlike the Loverboy song, nobody's "goin' off the deep end". Of course, when it comes to AI and legal, "everyone's hopin' it'll all work out". ;-) This edition focuses on three terrific articles to head into the weekend with - Perplexity moving into legal, Anthropic accusing Alibaba of cloning, and teaching your AI how you make decisions.

This Move is Not Perplexing

Perplexity, the AI search engine known for delivering sourced answers to user queries, is following Anthropic and other and making an explicit move into the legal market, rolling out legal-specific features within its agentic platform, Perplexity Computer, and positioning the product as what it calls “Computer for Counsel.” The Perplexity web page says that every answer includes inline citations linked to an underlying ruling, regulation, filing or client document, enabling attorneys to validate sources before incorporating the work into a memo, briefing, client communication or other deliverable. Given all the hallucinations in filings, that's something we can definitely use (if it's true, that is). Bob Ambrogi discusses it in LawSites here: Perplexity Makes Its Move Into Legal, Unveiling Industry Features at ‘Computer for Counsel’ Event

We're All Clones

Hey, I referenced Loverboy, why not Alice Cooper too? ;-) Anthropic has accused the Chinese firm Alibaba of using 25,000 accounts to mine Claude over 28.8 million exchanges, amid concerns they are working with the Chinese government. Anthropic previously accused Chinese firms DeepSeek, Moonshot, and MiniMax of using the same tactic as Alibaba. And they say OpenAI and Google have published findings on similar attacks on their models. It's becoming more difficult to retain the "optimist" part of being an “apocaloptimist”. Ashley Belanger of ArsTechnica discusses it here: Anthropic says Alibaba must be punished for largest Claude cloning attack

Decision Trees May Be Growing

Can you "codify" judgment? Here, the authors argue that the biggest barrier to successful enterprise AI is no longer the technology itself - it's an organization's inability to clearly articulate how it actually makes decisions. The authors distinguish between procedural knowledge ("how to perform a task") and decision knowledge ("how to choose among alternatives") and recommend that organizations should create explicit "decision playbooks". Are we ready for this? Asking for a friend. ;-) Jen Stave, Ryan Kurt and John Winsor discuss it in Harvard Business Review here: Teach Your AI How You Make Decisions

Images created by Bing. Get well ASAP, Jeff!

-Doug Austin

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Jeffrey Brandt, Editor

Connect with me on LinkedIn Jeffrey Brandt
Follow all the PinHawk highlights @PinHawkHappens
Website:PinHawk.com | Blog: PinHawkBlog.com

Friday, June 26, 2026 - Volume 21 No. 1401

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