Ira Rothken

High Technology Attorney, Entrepreneur, and Computer Technologist; Lead counsel in large tech cases; Helped build Web 2 & 3 services that lots of people use.

[Ira Rothken @rothken]

I spoke yesterday to the Digital Entertainment Group or DEG about NFT legal strategy - here is the video below - I step through numerous weighty legal-tech issues for any NFT project beginning at about the seven minute mark. I hope you find it helpful.
https://t.co/UfH9wyB8H3

[Ira Rothken @rothken]

@scottastevenson Great point - ethically sourced global legal aggregate data is a big advantage.

[Ira Rothken @rothken]

@VijarKohli The practice of law is now Law plus AI legal engineering. Law Firms with resources ought not delegate out their AI knowledge layer and hybrid search and stores to proprietary third party vendors. Kirkland is acting AI native by “not being” just another Harvey or Legora firm.

[Ira Rothken @rothken]

@zackbshapiro Kirkland is wise the AI intelligence and storage layer is too intertwined with legal practice to fully delegate it out to a third party.

[Ira Rothken @rothken]

Kirkland with a 500M investment in AI legal is important in signaling a big advantage and controlling their own AI intelligence layer and hybrid search and document stores.

Harvey and Legora, while very robust and helpful early wrapper platforms for firms needing a quick AI infusion, may end up being an AI concept prover for some firms -but still hugely helpful to firms without the budget and talent to build their own platform. Once big firms ramp up they may very well see that they need to own their intelligence layer and workflows and market that they are different and better or more AI native than other firms in the AI tech space who use only mass AI legal vendors.

[Ira Rothken @rothken]

Kirkland with a 500M investment in AI legal is important in signaling a big advantage and controlling their own AI intelligence layer and hybrid search and document stores.

Harvey and Legora, while very robust and helpful early wrapper platforms for firms needing a quick AI infusion, may end up being an AI concept prover for some firms -but still hugely helpful to firms without the budget and talent to build their own platform. Once big firms ramp up they may very well see that they need to own their intelligence layer and workflows and market that they are different and better or more AI native than other firms in the AI tech space who use only mass AI legal vendors.

[Ira Rothken @rothken]

@jordwalke Replit is a very helpful tool for our AI legal lab and compliance tech experiments. Here are over a dozen web app prototype examples https://t.co/nluxtf1VUl

[Ira Rothken @rothken]

@pavel_builder @arc Likely the person and / or entity that “owns” the agent. I call it the agentic consent bottleneck. It’s a consumer protection risk and fiduciary duty risk.

[Ira Rothken @rothken]

https://t.co/UzCye24ToJ

[Ira Rothken @rothken]

@milessuter @CashApp Nice work

[Ira Rothken @rothken]

@thereallegalai Very comprehensive. Will need to try it. Good luck.

[Ira Rothken @rothken]

@scottastevenson 100%

[Ira Rothken @rothken]

@arnonshub It’s ESI discoverable in a litigation over things like negligence regardless of who hosts it. The ESI can exist on an AI platform cloud and on law firm servers and even lawyer desktops.

[Ira Rothken @rothken]

AI legal platforms contain dual use data that can be turned against lawyers.

The same AI legal platform that lets one associate review 300 contracts can also prove they barely read them. I build AI legal tools. That’s the paradox that keeps me up at night.

Do we instrument the “lawyer in the loop”? Log which documents they opened, which flagged clauses they actually dwelled on, how long passed before they hit approve and sent the email? One minute to approve hundreds of contract reviews or a major brief is not a good look!

The moment we build that telemetry or clickstream, we built a forensic record of lawyer supervision — which is also a forensic record of non-supervision. Shield and sword, the same feature.

Better than a camera following the lawyer around the office, because it captures not just that they were there, but what they touched and for how long and in many cases it’s logged, time stamped, and stored in a database.

There’s a word for that type of trail: clickstream. I have some history with it. More than 25 years ago I was co-lead counsel in one of the first internet privacy class actions in the US — In re DoubleClick — where “clickstream” was central to the case: the behavioral trail companies were quietly recording about ordinary users. I never expected to spend my older years writing the code that records a clickstream on lawyers instead. Similar mechanism, different target — except this time I’m the one building it.

So here are the eleven questions that are important to the future of AI legal workflow usage I keep circling:

1.What level of supervision including lawyer clickstream monitoring is actually required — and does “reasonable assurance” of review now mean assurance you can demonstrate?
2.Should a firm turn this monitoring on at all, knowing the same record that proves diligence also exposes rubber-stamping?
3.Can a firm turn it off — and is disabling a capability you have worse than never having built it in the first place?
4.Are the AI legal platform vendors required to offer this monitoring, or do they build it purely to sell defensibility and shift liability onto the firm?
5.Will malpractice insurers require it — and won’t they move faster than the bar, since coverage conditions bite harder than ethics rules?
6.Where does the insurer draw the coverage line: any use of AI, undocumented use, or only over-delegation severe enough to be recharacterized as unauthorized practice of law and excluded?
7.Why don’t the lines match — the bar drawing it at the substance of judgment, regulators at system design, and liability courts retrospectively on whatever record happens to exist?
8. How does “AI-native” marketing change the math, when boasting about rigorous human oversight is itself a standard-of-care admission you then have to back up?
9.What happens when client promises convert these soft professional norms into hard contractual obligations you can be sued on?
10. Is any of this protecting clients — or just protecting firms and their insurers?
11.The endpoint: do we turn the AI workflow back on the lawyer — gating the final document until the human “complies” with the system’s requirements? At which point supervision of the AI quietly becomes the AI supervising the lawyer.

We asked for a human in the loop. We might end up with a loop that keeps the human in line.

[Ira Rothken @rothken]

Depends on what and how much of the legal workflow is delegated to AI. It’s probably a bit of both. For example AI hallucinations in court filings are a current symptom of AI over delegation. When we can ethically detach at scale AI work product from lawyer-person hours of review it will start making some areas of lawyering obsolete.

[Ira Rothken @rothken]

@john_law_kelly OpenAI thanks.

[Ira Rothken @rothken]

Some law firms are sleepwalking into AI agent MCP risk. The Anthropic Claude MCP uses are powerful perhaps too powerful.

MCPs are lower resistance APIs. The challenge for any law firm is for IT and cybersecurity pros to control the data entry and exit points to their tech stack — MCPs without oversight can make a law firm vulnerable at scale.

Each MCP call involving law firm data can transmit sensitive and privileged information to a third-party vendor where it can be logged, retained, or misused.

Ask:
“What can this MCP actually see?”

Then compare that access against the vendor’s terms of use, privacy policy, logging practices, retention rules, subprocessors, and enterprise agreements before making an informed risk-benefit decision.

Looking only at MCP (or API) calls, an adversary can often stitch together litigation strategy, negotiation posture, investigative priorities, or vulnerabilities.

Good AI governance is not “trust the defaults.”
It is:
• least-privilege access
• documented data flows
• minimized logs
• human approval layers
• negotiated enterprise protections

Here is an AI-generated infographic from my notes.

[Ira Rothken @rothken]

This is your time. Example we have cases involving 500gb + in evidentiary data. The Venn diagram between ingestion and storage on an ediscovery platform for human in the loop review and TAR, use of a subset for AI analytics, legal analysis, deposition, motion support, and brief writing, and a further subset for trial and appeals seems to point to the need for one holistic AI friendly storage under law firm control that can provide hybrid search - vector store - and RAG for the full lifecycle of the matters with multiple AI vendors and use cases. Harvey’s Vaults while logical to fill their AI rag gap seem inefficient from the larger lens of a law firm.

[Ira Rothken @rothken]

The best Legal AI is ironically entities who may not know it yet - such as Qdrant, pinecone, Weaviate, and Databricks etc. They can standardize the AI law hybrid search - vector stores- RAG layer or AI Harvey like “vault” law firms can own - and make available for a variety of AI workflow tasks from multiple vendors with minimal lock in.

[Ira Rothken @rothken]

@willchen500 Need to focus on a standardized “vault” layer.

[Ira Rothken @rothken]

The best Legal AI is ironically entities who may not know it yet - such as Qdrant, pinecone, Weaviate, and Databricks etc. They can standardize the AI law hybrid search - vector stores- RAG layer or AI Harvey like “vault” law firms can own - and make available for a variety of AI workflow tasks from multiple vendors with minimal lock in.

[Ira Rothken @rothken]

AI legal tech thoughts O’ the day. With the Anthropic AI law stack roll out it and broad mcp support for Westlaw, document clouds, and free caselaw, it seems inevitable that Claude will catalyze lawyers building their own custom and more simplified Harveys. The tech for the missing “glue tool” is in place - the hybrid search and vector store to replace “vaults” - once that layer gets easier and controlled by law firms it seems like large AI platform lock in will be unwise opening a climate where AI legal vendors compete in areas of minimal lock in. Ironically box, Egnyte, and imanage (and all Ediscovery platforms) may be in the best position here to provide standardized vector store mcp functionality as a natural extension of document management and search /retrieval.

[Ira Rothken @rothken]

@jerallaire @arc Jeremy - Aaron’s post is creative and clever. Here is my more mundane take on AI agentic commerce, informed consent, and contracts. Your tech stack can play an important role in both.

[Ira Rothken @rothken]

https://t.co/UzCye24ToJ