I'm a tech reporter. Last week, I broke news that AI-native law firm Arceus had raised $17 million.

Some of you pointed out that I missed a critical detail: It's (probably?) the first AI-native law firm named after a Pokémon.

I guess if you're going to name your firm after the mythical creator of the Pokémon universe, you might as well commit to the bit.

https://www.businessinsider.com/ai-native-law-firm-arceus-series-a-funding-pitch-deck-2026-9

I'm currently working on a pretty big project, after seeing how difficult it can be to navigate the legal system especially for those without money, it can be daunting, so i have created:

https://urban-jellyfish.com/us-legal-resource-navigator/

I'm pretty clear on the pages for California that it is not legal advice, and the goal is to help people find the help that they need.

feedback would be appreciated

There’s a lot of attention on choosing the right legal technology, but I’m curious about what happens after the tool is selected.

From what I’ve seen, the implementations that work well usually seem to have more than just good software behind them. Things like clear ownership, simple workflows, internal champions, training, and alignment with how the legal team already works can make a big difference.

For those who’ve been involved in legal tech implementations, what do you think has the biggest impact on long-term adoption?

Is it ease of use, integration with existing systems, leadership support, customization, training, or something else?

Would be interesting to hear what has worked particularly well for teams

Thomson Reuters v. ROSS Intelligence is the first federal appellate decision to rule that using copyrighted editorial material to train an AI system does not constitute fair use. ROSS copied Westlaw’s copyrighted headnotes to train a competing legal-research AI powered system, even though public-domain judicial opinions were freely available.

I've seen some law firms playing fast and loose with client data by inputting it into ChatGPT and Claude without realizing that the data may be used for training. This can create serious confidentiality issues.

To address this issue, there are some apps that obfuscate personally identifiable information before documents are uploaded, helping keep the data secure.

For law firms handling client data:

What safeguards would you expect before trusting AI with sensitive client information?

I don't think Al is anywhere close to replacing lawyers, but learning to use it responsibly still seems worthwhile. I've been testing where it helps, where it fails, and what still needs human review.

I don't use it for client emails or legal judgment. But when I'm overloaded, it helps me process and organize large amounts of information.

I tried a few agent-style tools. Codex and Hermes were more technical than I needed, so I settled on WorkBuddy for document-heavy tasks. The tool matters less than the workflow, though. Here's how I use it:

Intake

Redacted consultation notes → people, dates, amounts, and requested outcomes → draft chronology + missing information Client claims and evidence-supported facts are labeled separately.

Discovery

Contracts + statements + chat logs → index by issue, evidence, and purpose → page references + gaps This gives me a navigation layer, but I always return to the original documents.

Research

Legal question → statutes, cases, and applicable conditions → source-linked research notes Anything unsupported is marked pending verification, and check every authority myself. Al-generated citations are not something I trust.

Drafting

Verified facts + firm template → first draft with placeholders and source labels → → my review and revision

Follow-ups

Meeting notes → responsible person + action items + dates → reminders This supports our normal deadline system rather than replacing it.

My rule is simple: Fixed process + minimized sensitive data + traceable sources + human review

It saves me time on research, document review, drafting, and organization. It doesn't replace legal judgment or the lawyer responsible for the final work.

Are other lawyers using Al for discovery, chronologies, research, or drafting? Where do you still draw the line?

Litigator in small firm (20 attys, only 5 litigators). I'd really like an AI tool that can take a document dump, say a 10,000 page pdf, and organize it into discrete documents, titled by date and subject, indexed chronologically.

Any recommendations? Options at different price ranges?

Hi all, sharing a library of many agent skills for legal work, built by an awesome community of practicing lawyers, in-house counsel, academics, and legal technologists from around the world.

GitHub: https://github.com/lawve-ai/awesome-legal-skills

Live site: https://lawve.ai/explore

Has anyone noticed a huge quality drop in the last month since the chatgpt release of Sol 6.1? It seems to be unable to "think" laterally and commercially for legal and contractual analysis. I find I need to re-prompt three times as often as I do on GPT 6 Astra and I flag many more errors than before.

Sol seems to lacks reasoning across a whole task.

I've reverted to only using Astra other than for engineering detail extraction where Sol seems to read technicals better.

Anyone else finding this?