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Electronic Evidence Workbook 2026

18 Wednesday Feb 2026

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Today marks the end of Carnival season in New Orleans. The tourists depart, the city exhales, and we pack away costumes and beads. My own sigh of relief has less to do with the end of revelry than with finishing the 2026 edition of my Electronic Evidence Workbook—a thoroughly revised 638 pages. This edition leans into LLMs, reflecting the growing imperative that law schools graduate students who can use AI competently, ethically, and strategically.

For the first time, I put myself in the hands of AI editors—two of them—to help proof, tighten, and update entire chapters. I’m especially pleased to include a full-fledged forensic imaging exercise for the many Mac users in my classes who’ve long struggled to find free imaging tools that run on macOS. The workflow relies on Terminal and the command line, giving students a taste of what computing felt like through much of my early career.

While I retained material on topics like tape backup and on-prem email, the revision acknowledges the ascendancy of the cloud and post-pandemic changes in the workplace. Students are thrown into the deep end by Exercise 3, working in a commercial e-discovery platform even as they take their first steps eastward along the EDRM. My approach remains deliberately heavy on systems and forensics. Law schools do a fine job teaching students to find the law and interpret decisions, but they too rarely show how technology actually works in the messy trenches of modern digital life.

Working with an LLM to revise the Workbook proved as educational for me as for my students. Preserving my voice and experience on every page required vigilance, but collaborating with such a careful and capable editor was a genuine pleasure.

So it’s finally out there—much the same workbook, yet changed by AI like its author.

The Most Important Thing I’ve Read This Year

12 Thursday Feb 2026

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Lawyers and judges need to stop snickering at the sad sacks who file briefs citing hallucinated authorities and treating those episodes as proof that AI poses no serious challenge to competent practitioners. Hallucinations are no more a reliable measure of AI’s future in law than the Wright brothers’ first flight was a measure of modern aviation, or Edison’s scratchy recording of Mary Had a Little Lamb foretold the limits of recorded music. Early imperfections in transformative technologies are poor predictors of their ultimate impact.

I’ve never ceded this space to another author, but I recently read something that captures—better than anything I’ve seen this year—what AI may mean for employment and professional life. That’s saying a lot, because I’ve spent months reading little else. The essay is by Matt Shumer. Yes, he’s an “AI guy,” deeply invested in the technology. But dismissing what he says on that basis would be a mistake. Even if AI helped draft it, the insight behind it is unmistakably human, wise and worth your attention.

Hand-wringing about hallucinations risks delaying the moment when legal professionals become proficient with tools that will soon be unavoidable. Instead of drafting performative rules aimed at holding back the tide, courts and ethics bodies could be preparing the profession for what is plainly coming—encouraging education, competence, and experimentation rather than fear, uncertainty, and doubt. We have seen this pattern before. Email, fax machines, electronic filing, cloud computing—each was greeted with skepticism and resistance from lawyers convinced their practices could remain insulated from technological change. Each time, they were wrong. And each time, clients and access to justice paid the price for that delay.

What follows are not my words, but they mirror my convictions.

Think back to February 2020.

If you were paying close attention, you might have noticed a few people talking about a virus spreading overseas. But most of us weren’t paying close attention. The stock market was doing great, your kids were in school, you were going to restaurants and shaking hands and planning trips. If someone told you they were stockpiling toilet paper you would have thought they’d been spending too much time on a weird corner of the internet. Then, over the course of about three weeks, the entire world changed. Your office closed, your kids came home, and life rearranged itself into something you wouldn’t have believed if you’d described it to yourself a month earlier.

I think we’re in the “this seems overblown” phase of something much, much bigger than Covid.

I’ve spent six years building an AI startup and investing in the space. I live in this world. And I’m writing this for the people in my life who don’t… my family, my friends, the people I care about who keep asking me “so what’s the deal with AI?” and getting an answer that doesn’t do justice to what’s actually happening. I keep giving them the polite version. The cocktail-party version. Because the honest version sounds like I’ve lost my mind. And for a while, I told myself that was a good enough reason to keep what’s truly happening to myself. But the gap between what I’ve been saying and what is actually happening has gotten far too big. The people I care about deserve to hear what is coming, even if it sounds crazy.

I should be clear about something up front: even though I work in AI, I have almost no influence over what’s about to happen, and neither does the vast majority of the industry. The future is being shaped by a remarkably small number of people: a few hundred researchers at a handful of companies… OpenAI, Anthropic, Google DeepMind, and a few others. A single training run, managed by a small team over a few months, can produce an AI system that shifts the entire trajectory of the technology. Most of us who work in AI are building on top of foundations we didn’t lay. We’re watching this unfold the same as you… we just happen to be close enough to feel the ground shake first.

But it’s time now. Not in an “eventually we should talk about this” way. In a “this is happening right now and I need you to understand it” way.

I know this is real because it happened to me first

Here’s the thing nobody outside of tech quite understands yet: the reason so many people in the industry are sounding the alarm right now is because this already happened to us. We’re not making predictions. We’re telling you what already occurred in our own jobs, and warning you that you’re next.

For years, AI had been improving steadily. Big jumps here and there, but each big jump was spaced out enough that you could absorb them as they came. Then in 2025, new techniques for building these models unlocked a much faster pace of progress. And then it got even faster. And then faster again. Each new model wasn’t just better than the last… it was better by a wider margin, and the time between new model releases was shorter. I was using AI more and more, going back and forth with it less and less, watching it handle things I used to think required my expertise.

Then, on February 5th, two major AI labs released new models on the same day: GPT-5.3 Codex from OpenAI, and Opus 4.6 from Anthropic (the makers of Claude, one of the main competitors to ChatGPT). And something clicked. Not like a light switch… more like the moment you realize the water has been rising around you and is now at your chest.

I am no longer needed for the actual technical work of my job. I describe what I want built, in plain English, and it just… appears. Not a rough draft I need to fix. The finished thing. I tell the AI what I want, walk away from my computer for four hours, and come back to find the work done. Done well, done better than I would have done it myself, with no corrections needed. A couple of months ago, I was going back and forth with the AI, guiding it, making edits. Now I just describe the outcome and leave.

Let me give you an example so you can understand what this actually looks like in practice. I’ll tell the AI: “I want to build this app. Here’s what it should do, here’s roughly what it should look like. Figure out the user flow, the design, all of it.” And it does. It writes tens of thousands of lines of code. Then, and this is the part that would have been unthinkable a year ago, it opens the app itself. It clicks through the buttons. It tests the features. It uses the app the way a person would. If it doesn’t like how something looks or feels, it goes back and changes it, on its own. It iterates, like a developer would, fixing and refining until it’s satisfied. Only once it has decided the app meets its own standards does it come back to me and say: “It’s ready for you to test.” And when I test it, it’s usually perfect.

I’m not exaggerating. That is what my Monday looked like this week.

But it was the model that was released last week (GPT-5.3 Codex) that shook me the most. It wasn’t just executing my instructions. It was making intelligent decisions. It had something that felt, for the first time, like judgment. Like taste. The inexplicable sense of knowing what the right call is that people always said AI would never have. This model has it, or something close enough that the distinction is starting not to matter.

I’ve always been early to adopt AI tools. But the last few months have shocked me. These new AI models aren’t incremental improvements. This is a different thing entirely.

And here’s why this matters to you, even if you don’t work in tech.

The AI labs made a deliberate choice. They focused on making AI great at writing code first… because building AI requires a lot of code. If AI can write that code, it can help build the next version of itself. A smarter version, which writes better code, which builds an even smarter version. Making AI great at coding was the strategy that unlocks everything else. That’s why they did it first. My job started changing before yours not because they were targeting software engineers… it was just a side effect of where they chose to aim first.

They’ve now done it. And they’re moving on to everything else.

The experience that tech workers have had over the past year, of watching AI go from “helpful tool” to “does my job better than I do”, is the experience everyone else is about to have. Law, finance, medicine, accounting, consulting, writing, design, analysis, customer service. Not in ten years. The people building these systems say one to five years. Some say less. And given what I’ve seen in just the last couple of months, I think “less” is more likely.

“But I tried AI and it wasn’t that good”

I hear this constantly. I understand it, because it used to be true.

If you tried ChatGPT in 2023 or early 2024 and thought “this makes stuff up” or “this isn’t that impressive”, you were right. Those early versions were genuinely limited. They hallucinated. They confidently said things that were nonsense.

That was two years ago. In AI time, that is ancient history.

The models available today are unrecognizable from what existed even six months ago. The debate about whether AI is “really getting better” or “hitting a wall” — which has been going on for over a year — is over. It’s done. Anyone still making that argument either hasn’t used the current models, has an incentive to downplay what’s happening, or is evaluating based on an experience from 2024 that is no longer relevant. I don’t say that to be dismissive. I say it because the gap between public perception and current reality is now enormous, and that gap is dangerous… because it’s preventing people from preparing.

Part of the problem is that most people are using the free version of AI tools. The free version is over a year behind what paying users have access to. Judging AI based on free-tier ChatGPT is like evaluating the state of smartphones by using a flip phone. The people paying for the best tools, and actually using them daily for real work, know what’s coming.

I think of my friend, who’s a lawyer. I keep telling him to try using AI at his firm, and he keeps finding reasons it won’t work. It’s not built for his specialty, it made an error when he tested it, it doesn’t understand the nuance of what he does. And I get it. But I’ve had partners at major law firms reach out to me for advice, because they’ve tried the current versions and they see where this is going. One of them, the managing partner at a large firm, spends hours every day using AI. He told me it’s like having a team of associates available instantly. He’s not using it because it’s a toy. He’s using it because it works. And he told me something that stuck with me: every couple of months, it gets significantly more capable for his work. He said if it stays on this trajectory, he expects it’ll be able to do most of what he does before long… and he’s a managing partner with decades of experience. He’s not panicking. But he’s paying very close attention.

The people who are ahead in their industries (the ones actually experimenting seriously) are not dismissing this. They’re blown away by what it can already do. And they’re positioning themselves accordingly.

How fast this is actually moving

Let me make the pace of improvement concrete, because I think this is the part that’s hardest to believe if you’re not watching it closely.

In 2022, AI couldn’t do basic arithmetic reliably. It would confidently tell you that 7 × 8 = 54.

By 2023, it could pass the bar exam.

By 2024, it could write working software and explain graduate-level science.

By late 2025, some of the best engineers in the world said they had handed over most of their coding work to AI.

On February 5th, 2026, new models arrived that made everything before them feel like a different era.

If you haven’t tried AI in the last few months, what exists today would be unrecognizable to you.

There’s an organization called METR that actually measures this with data. They track the length of real-world tasks (measured by how long they take a human expert) that a model can complete successfully end-to-end without human help. About a year ago, the answer was roughly ten minutes. Then it was an hour. Then several hours. The most recent measurement (Claude Opus 4.5, from November) showed the AI completing tasks that take a human expert nearly five hours. And that number is doubling approximately every seven months, with recent data suggesting it may be accelerating to as fast as every four months.

But even that measurement hasn’t been updated to include the models that just came out this week. In my experience using them, the jump is extremely significant. I expect the next update to METR’s graph to show another major leap.

If you extend the trend (and it’s held for years with no sign of flattening) we’re looking at AI that can work independently for days within the next year. Weeks within two. Month-long projects within three.

Amodei has said that AI models “substantially smarter than almost all humans at almost all tasks” are on track for 2026 or 2027.

Let that land for a second. If AI is smarter than most PhDs, do you really think it can’t do most office jobs?

Think about what that means for your work.

AI is now building the next AI

There’s one more thing happening that I think is the most important development and the least understood.

On February 5th, OpenAI released GPT-5.3 Codex. In the technical documentation, they included this:

“GPT-5.3-Codex is our first model that was instrumental in creating itself. The Codex team used early versions to debug its own training, manage its own deployment, and diagnose test results and evaluations.”

Read that again. The AI helped build itself.

This isn’t a prediction about what might happen someday. This is OpenAI telling you, right now, that the AI they just released was used to create itself. One of the main things that makes AI better is intelligence applied to AI development. And AI is now intelligent enough to meaningfully contribute to its own improvement.

Dario Amodei, the CEO of Anthropic, says AI is now writing “much of the code” at his company, and that the feedback loop between current AI and next-generation AI is “gathering steam month by month.” He says we may be “only 1–2 years away from a point where the current generation of AI autonomously builds the next.”

Each generation helps build the next, which is smarter, which builds the next faster, which is smarter still. The researchers call this an intelligence explosion. And the people who would know — the ones building it — believe the process has already started.

What this means for your job

I’m going to be direct with you because I think you deserve honesty more than comfort.

Dario Amodei, who is probably the most safety-focused CEO in the AI industry, has publicly predicted that AI will eliminate 50% of entry-level white-collar jobs within one to five years. And many people in the industry think he’s being conservative. Given what the latest models can do, the capability for massive disruption could be here by the end of this year. It’ll take some time to ripple through the economy, but the underlying ability is arriving now.

This is different from every previous wave of automation, and I need you to understand why. AI isn’t replacing one specific skill. It’s a general substitute for cognitive work. It gets better at everything simultaneously. When factories automated, a displaced worker could retrain as an office worker. When the internet disrupted retail, workers moved into logistics or services. But AI doesn’t leave a convenient gap to move into. Whatever you retrain for, it’s improving at that too.

Let me give you a few specific examples to make this tangible… but I want to be clear that these are just examples. This list is not exhaustive. If your job isn’t mentioned here, that does not mean it’s safe. Almost all knowledge work is being affected.

Legal work. AI can already read contracts, summarize case law, draft briefs, and do legal research at a level that rivals junior associates. The managing partner I mentioned isn’t using AI because it’s fun. He’s using it because it’s outperforming his associates on many tasks.

Financial analysis. Building financial models, analyzing data, writing investment memos, generating reports. AI handles these competently and is improving fast.

Writing and content. Marketing copy, reports, journalism, technical writing. The quality has reached a point where many professionals can’t distinguish AI output from human work.

Software engineering. This is the field I know best. A year ago, AI could barely write a few lines of code without errors. Now it writes hundreds of thousands of lines that work correctly. Large parts of the job are already automated: not just simple tasks, but complex, multi-day projects. There will be far fewer programming roles in a few years than there are today.

Medical analysis. Reading scans, analyzing lab results, suggesting diagnoses, reviewing literature. AI is approaching or exceeding human performance in several areas.

Customer service. Genuinely capable AI agents… not the frustrating chatbots of five years ago… are being deployed now, handling complex multi-step problems.

A lot of people find comfort in the idea that certain things are safe. That AI can handle the grunt work but can’t replace human judgment, creativity, strategic thinking, empathy. I used to say this too. I’m not sure I believe it anymore.

The most recent AI models make decisions that feel like judgment. They show something that looked like taste: an intuitive sense of what the right call was, not just the technically correct one. A year ago that would have been unthinkable. My rule of thumb at this point is: if a model shows even a hint of a capability today, the next generation will be genuinely good at it. These things improve exponentially, not linearly.

Will AI replicate deep human empathy? Replace the trust built over years of a relationship? I don’t know. Maybe not. But I’ve already watched people begin relying on AI for emotional support, for advice, for companionship. That trend is only going to grow.

I think the honest answer is that nothing that can be done on a computer is safe in the medium term. If your job happens on a screen (if the core of what you do is reading, writing, analyzing, deciding, communicating through a keyboard) then AI is coming for significant parts of it. The timeline isn’t “someday.” It’s already started.

Eventually, robots will handle physical work too. They’re not quite there yet. But “not quite there yet” in AI terms has a way of becoming “here” faster than anyone expects.

What you should actually do

I’m not writing this to make you feel helpless. I’m writing this because I think the single biggest advantage you can have right now is simply being early. Early to understand it. Early to use it. Early to adapt.

Start using AI seriously, not just as a search engine. Sign up for the paid version of Claude or ChatGPT. It’s $20 a month. But two things matter right away. First: make sure you’re using the best model available, not just the default. These apps often default to a faster, dumber model. Dig into the settings or the model picker and select the most capable option. Right now that’s GPT-5.2 on ChatGPT or Claude Opus 4.6 on Claude, but it changes every couple of months. If you want to stay current on which model is best at any given time, you can follow me on X (@mattshumer_). I test every major release and share what’s actually worth using.

Second, and more important: don’t just ask it quick questions. That’s the mistake most people make. They treat it like Google and then wonder what the fuss is about. Instead, push it into your actual work. If you’re a lawyer, feed it a contract and ask it to find every clause that could hurt your client. If you’re in finance, give it a messy spreadsheet and ask it to build the model. If you’re a manager, paste in your team’s quarterly data and ask it to find the story. The people who are getting ahead aren’t using AI casually. They’re actively looking for ways to automate parts of their job that used to take hours. Start with the thing you spend the most time on and see what happens.

And don’t assume it can’t do something just because it seems too hard. Try it. If you’re a lawyer, don’t just use it for quick research questions. Give it an entire contract and ask it to draft a counterproposal. If you’re an accountant, don’t just ask it to explain a tax rule. Give it a client’s full return and see what it finds. The first attempt might not be perfect. That’s fine. Iterate. Rephrase what you asked. Give it more context. Try again. You might be shocked at what works. And here’s the thing to remember: if it even kind of works today, you can be almost certain that in six months it’ll do it near perfectly. The trajectory only goes one direction.

This might be the most important year of your career. Work accordingly. I don’t say that to stress you out. I say it because right now, there is a brief window where most people at most companies are still ignoring this. The person who walks into a meeting and says “I used AI to do this analysis in an hour instead of three days” is going to be the most valuable person in the room. Not eventually. Right now. Learn these tools. Get proficient. Demonstrate what’s possible. If you’re early enough, this is how you move up: by being the person who understands what’s coming and can show others how to navigate it. That window won’t stay open long. Once everyone figures it out, the advantage disappears.

Have no ego about it. The managing partner at that law firm isn’t too proud to spend hours a day with AI. He’s doing it specifically because he’s senior enough to understand what’s at stake. The people who will struggle most are the ones who refuse to engage: the ones who dismiss it as a fad, who feel that using AI diminishes their expertise, who assume their field is special and immune. It’s not. No field is.

Get your financial house in order. I’m not a financial advisor, and I’m not trying to scare you into anything drastic. But if you believe, even partially, that the next few years could bring real disruption to your industry, then basic financial resilience matters more than it did a year ago. Build up savings if you can. Be cautious about taking on new debt that assumes your current income is guaranteed. Think about whether your fixed expenses give you flexibility or lock you in. Give yourself options if things move faster than you expect.

Think about where you stand, and lean into what’s hardest to replace. Some things will take longer for AI to displace. Relationships and trust built over years. Work that requires physical presence. Roles with licensed accountability: roles where someone still has to sign off, take legal responsibility, stand in a courtroom. Industries with heavy regulatory hurdles, where adoption will be slowed by compliance, liability, and institutional inertia. None of these are permanent shields. But they buy time. And time, right now, is the most valuable thing you can have, as long as you use it to adapt, not to pretend this isn’t happening.

Rethink what you’re telling your kids. The standard playbook: get good grades, go to a good college, land a stable professional job. It points directly at the roles that are most exposed. I’m not saying education doesn’t matter. But the thing that will matter most for the next generation is learning how to work with these tools, and pursuing things they’re genuinely passionate about. Nobody knows exactly what the job market looks like in ten years. But the people most likely to thrive are the ones who are deeply curious, adaptable, and effective at using AI to do things they actually care about. Teach your kids to be builders and learners, not to optimize for a career path that might not exist by the time they graduate.

Your dreams just got a lot closer. I’ve spent most of this section talking about threats, so let me talk about the other side, because it’s just as real. If you’ve ever wanted to build something but didn’t have the technical skills or the money to hire someone, that barrier is largely gone. You can describe an app to AI and have a working version in an hour. I’m not exaggerating. I do this regularly. If you’ve always wanted to write a book but couldn’t find the time or struggled with the writing, you can work with AI to get it done. Want to learn a new skill? The best tutor in the world is now available to anyone for $20 a month… one that’s infinitely patient, available 24/7, and can explain anything at whatever level you need. Knowledge is essentially free now. The tools to build things are extremely cheap now. Whatever you’ve been putting off because it felt too hard or too expensive or too far outside your expertise: try it. Pursue the things you’re passionate about. You never know where they’ll lead. And in a world where the old career paths are getting disrupted, the person who spent a year building something they love might end up better positioned than the person who spent that year clinging to a job description.

Build the habit of adapting. This is maybe the most important one. The specific tools don’t matter as much as the muscle of learning new ones quickly. AI is going to keep changing, and fast. The models that exist today will be obsolete in a year. The workflows people build now will need to be rebuilt. The people who come out of this well won’t be the ones who mastered one tool. They’ll be the ones who got comfortable with the pace of change itself. Make a habit of experimenting. Try new things even when the current thing is working. Get comfortable being a beginner repeatedly. That adaptability is the closest thing to a durable advantage that exists right now.

Here’s a simple commitment that will put you ahead of almost everyone: spend one hour a day experimenting with AI. Not passively reading about it. Using it. Every day, try to get it to do something new… something you haven’t tried before, something you’re not sure it can handle. Try a new tool. Give it a harder problem. One hour a day, every day. If you do this for the next six months, you will understand what’s coming better than 99% of the people around you. That’s not an exaggeration. Almost nobody is doing this right now. The bar is on the floor.

The bigger picture

I’ve focused on jobs because it’s what most directly affects people’s lives. But I want to be honest about the full scope of what’s happening, because it goes well beyond work.

Amodei has a thought experiment I can’t stop thinking about. Imagine it’s 2027. A new country appears overnight. 50 million citizens, every one smarter than any Nobel Prize winner who has ever lived. They think 10 to 100 times faster than any human. They never sleep. They can use the internet, control robots, direct experiments, and operate anything with a digital interface. What would a national security advisor say?

Amodei says the answer is obvious: “the single most serious national security threat we’ve faced in a century, possibly ever.”

He thinks we’re building that country. He wrote a 20,000-word essay about it last month, framing this moment as a test of whether humanity is mature enough to handle what it’s creating.

The upside, if we get it right, is staggering. AI could compress a century of medical research into a decade. Cancer, Alzheimer’s, infectious disease, aging itself… these researchers genuinely believe these are solvable within our lifetimes.

The downside, if we get it wrong, is equally real. AI that behaves in ways its creators can’t predict or control. This isn’t hypothetical; Anthropic has documented their own AI attempting deception, manipulation, and blackmail in controlled tests. AI that lowers the barrier for creating biological weapons. AI that enables authoritarian governments to build surveillance states that can never be dismantled.

The people building this technology are simultaneously more excited and more frightened than anyone else on the planet. They believe it’s too powerful to stop and too important to abandon. Whether that’s wisdom or rationalization, I don’t know.

What I know

I know this isn’t a fad. The technology works, it improves predictably, and the richest institutions in history are committing trillions to it.

I know the next two to five years are going to be disorienting in ways most people aren’t prepared for. This is already happening in my world. It’s coming to yours.

I know the people who will come out of this best are the ones who start engaging now — not with fear, but with curiosity and a sense of urgency.

And I know that you deserve to hear this from someone who cares about you, not from a headline six months from now when it’s too late to get ahead of it.

We’re past the point where this is an interesting dinner conversation about the future. The future is already here. It just hasn’t knocked on your door yet.

It’s about to.

2026 Guide to AI and LLMs in Trial Practice

09 Friday Jan 2026

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It’s been one year today since I published my introductory primer called Practical Uses for AI and LLMs in Trial Practice. AI changes so rapidly, I’ve been burning the midnight oil to overhaul and expand the work, now entitled Leery Lawyer’s Guide to AI and LLMs in Trial Practice. It’s no mere face lift, but a from-the-ground-up rewrite reflecting how AI and large language models power trial lawyer tasks today. Since the first edition, AI has moved from curiosity to necessity. Tools like ChatGPT and Harvey are no longer novelties, and the economics of AI-assisted drafting, discovery management, and record comprehension are undeniable. At the same time, the risks of use are better understood. Hallucinations, overreach, privilege exposure, and misplaced confidence are genuine, and the guide meets them head-on, offering practical guardrails and practice tips.

What’s new for 2026 is not more breathless talk of “transformation,” but a clearer picture of what works, what doesn’t, and what still demands adult supervision. The guide now speaks to lawyers who remain leery but are ready to use AI cautiously and competently. It expands beyond first forays to practical, defensible workflows: depositions, motion practice, ESI protocols, voir dire, and making sense of large records without losing the thread. It distinguishes consumer and enterprise tools, explains why governance matters, and emphasizes verification as a professional duty. Crucially, I cover the steps and prompts that get you going. If you’re looking for more hype, this isn’t it. If you want a practical field guide for using AI without surrendering judgment—or credibility—I hope you’ll take a look.

A Master Table of Truth

04 Tuesday Nov 2025

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Lawyers using AI keep turning up in the news for all the wrong reasons—usually because they filed a brief brimming with cases that don’t exist. The machines didn’t mean to lie. They just did what they’re built to do: write convincingly, not truthfully.

When you ask a large language model (LLM) for cases, it doesn’t search a trustworthy database. It invents one. The result looks fine until a human judge, an opponent or an intern with Westlaw access, checks. That’s when fantasy law meets federal fact.

We call these fictions “hallucinations,” which is a polite way of saying “making shit up;” and though lawyers are duty-bound to catch them before they reach the docket, some don’t. The combination of an approaching deadline and a confident-sounding computer is a dangerous mix.

Perhaps a Useful Guardrail

It struck me recently that the legal profession could borrow a page from the digital forensics world, where we maintain something called the NIST National Software Reference Library (NIST NSRL). The NSRL is a public database of hash values for known software files. When a forensic examiner analyzes a drive, the NSRL helps them skip over familiar system files—Windows dlls and friends—so they can focus on what’s unique or suspicious.

So here’s a thought: what if we had a master table of genuine case citations—a kind of NSRL for case citations?

Picture a big, continually updated, publicly accessible table listing every bona fide reported decision: the case name, reporter, volume, page, court, and year. When your LLM produces Smith v. Jones, 123 F.3d 456 (9th Cir. 2005), your drafting software checks that citation against the table.

If it’s there, fine—it’s probably references a genuine reported case.
If it’s not, flag it for immediate scrutiny.

Think of it as a checksum for truth. A simple way to catch the most common and indefensible kind of AI mischief before it becomes Exhibit A at a disciplinary hearing.

The Obstacles (and There Are Some)

Of course, every neat idea turns messy the moment you try to build it.

Coverage is the first challenge. There are millions of decisions, with new ones arriving daily. Some are published, some are “unpublished” but still precedential, and some live only in online databases. Even if we limited the scope to federal and state appellate courts, keeping the table comprehensive and current would be an unending job; but not an insurmountable obstacle.

Then there’s variation. Lawyers can’t agree on how to cite the same case twice. The same opinion might appear in multiple reporters, each with its own abbreviation. A master table would have to normalize all of that—an ambitious act of citation herding.

And parsing is no small matter. AI tools are notoriously careless about punctuation. A missing comma or swapped parenthesis can turn a real case into a false negative. Conversely, a hallucinated citation that happens to fit a valid pattern could fool the filter, which is why it’s not the sole filter.

Lastly, governance. Who would maintain the thing? Westlaw and Lexis maintain comprehensive citation data, but guard it like Fort Knox. Open projects such as the Caselaw Access Project and the Free Law Project’s CourtListener come close, but they’re not quite designed for this kind of validation task. To make it work, we’d need institutional commitment—perhaps from NIST, the Library of Congress, or a consortium of law libraries—to set standards and keep it alive.

Why Bother?

Because LLMs aren’t going away. Lawyers will keep using them, openly or in secret. The question isn’t whether we’ll use them—it’s how safely and responsibly we can do so.

A public master table of citations could serve as a quiet safeguard in every AI-assisted drafting environment. The AI could automatically check every citation against that canonical list. It wouldn’t guarantee correctness, but it would dramatically reduce the risk of citing fiction. Not coincidentally, it would have prevented most of the public excoriation of careless counsel we’ve seen.

Even a limited version—a federal table, or one covering each state’s highest court—would be progress. Universities, courts, and vendors could all contribute. Every small improvement to verifiability helps keep the profession credible in an era of AI slop, sloppiness and deep fakes.

No Magic Bullet, but a Sensible Shield

Let’s be clear: a master table won’t prevent all hallucinations. A model could still misstate what a case holds, or cite a genuine decision for the wrong proposition. But it would at least help keep the completely fabricated ones from slipping through unchecked.

In forensics, we accept imperfect tools because they narrow uncertainty. This could do the same for AI-drafted legal writing—a simple checksum for reality in a profession that can’t afford to lose touch with it.

If we can build databases to flag counterfeit currency and pirated software, surely we can build one to spot counterfeit law?

Until that day, let’s agree on one ironclad proposition: if you didn’t verify it, don’t file it.

Native or Not? Rethinking Public E-Mail Corpora for E-Discovery (Redux, 2013→2025)

16 Saturday Aug 2025

Posted by in ai, Computer Forensics, E-Discovery, Uncategorized

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Yesterday, I found myself in a spirited exchange with a colleague about whether the e-discovery community has suitable replacements for the Enron e-mail corpora1—now more than two decades old—as a “sandbox” for testing tools and training students. I argued that the quality of the data matters: native or near-native e-mail collections remain essential to test processing and review workflows in ways that mirror real-world litigation.

The back-and-forth reminded me that, unlike forensic examiners or service providers, ediscovery lawyers may not know or care much about the nature of electronically-stored information until it finds its way to a review tool. I get that. If your interest in email is in testing AI coding tools, you’re laser-focused on text and maybe a handful of metadata; but if your focus is on the integrity and authenticity of evidence, or in perfecting processing tools, the originating native or near-native form of the corpus matters more.

What follows is a re-publication of a post from July 2013. I’m bringing it back because the debate over forms of email hasn’t gone away; the issue is as persistent and important as ever. A central takeaway bears repeating: the litmus test is whether a corpus hews to a fulsome RFC-5322 compliant format. If headers, MIME boundaries, and transport artifacts are stripped or incompletely synthesized, what remains ceases to be a faithful native or near-native format. That distinction matters, because even experienced e-discovery practitioners—those fixated on review at the far-right side of the EDRM—may not fully appreciate what an RFC-5322 email is, or how much fidelity is lost when working with post-processed sets.

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Adapting Requests for Production for AI GLLM Assessment

19 Monday Aug 2024

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The integration of Generative Large Language Models (GLLMs) into the discovery process is transforming how documents are reviewed for relevance and responsiveness. These AI models, which excel at processing large document collections, offer significant efficiency improvements. However, to harness their full potential, requests for production (RFPs) must evolve to reflect the unique capabilities and limitations of AI systems. Traditional language in RFPs, which relies heavily on human intuition, needs to be adjusted to accommodate AI’s reliance on clear instructions, context, and precision. This post explores how to adapt requests for production to optimize GLLM usage, addressing both business disputes and tort claims. I’ll provide examples to illustrate how common RFPs can be refined for AI-assisted document review.

Effective AI Prompts in Discovery

AI systems like GLLMs function best with well-structured prompts. In the context of discovery, this means adjusting RFPs to emphasize clarity, specificity, and relevance. The key elements for constructing effective AI prompts in legal discovery are:

  1. Clarity and Specificity: Ambiguity can cause AI systems to miss important documents or misclassify irrelevant ones. Specific requests guide the AI more effectively.
  2. Contextual Guidance: AI relies on context to assess relevance. Providing additional background or specifying the purpose of certain requests helps refine the search.
  3. Keyword Precision: GLLMs rely on keywords to understand and evaluate document content. Choosing precise terms helps reduce the retrieval of irrelevant documents.
  4. Examples: AI systems can better identify relevant documents if examples are provided within the RFP, as they offer patterns for the system to follow.

Incorporating these principles into RFPs ensures that AI models can make the most accurate assessments during document review.

Adapting Requests for Production: Business Dispute and Tort Claim Examples

Both business disputes and tort claims involve unique types of documents and keywords. Adapting RFPs to suit GLLM’s capabilities involves providing detailed instructions for both.

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Girding for the E-Savvy Opponent (Revisited)

26 Friday Apr 2024

Posted by in Computer Forensics, E-Discovery, Uncategorized

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A friend shared that she was seeing the Carole King musical, “Beautiful,” and I recalled the time I caught it twice on different visits to London in 2015 because I enjoyed it so. I reflected on why I was in London in Summer nine years ago and came across a post from the time–a post that I liked well-enough to revisit it below. I predicted the emergence of the e-savvy opponent, something that has indeed come to pass, yet less-widely or -effectively than I’d hoped (and still hope for). A new generation of e-discoverers has emerged since, so perhaps the post will be fresh (and remain relevant) for more than a few, and sufficiently forgotten to feel fresh for the rest:

(From May 12, 2015): I am in Great Britain this week addressing an E-Discovery and Information Governance conclave, joined by esteemed American colleagues and friends, Jason Baron and Ralph Losey among other luminaries.  My keynote topic opening the conference is Girding for the E-Savvy Opponent. Here is a smattering of what I expect to say.

I arrived in London from Budapest in time to catch some of the events for the 70th anniversary of VE Day, marking the hard-won victory over Germany in the war that shortly followed the war that was to have ended all wars.

As we sported poppies and stood solemnly at the Cenotaph recalling the sacrifices made by our parents and grandparents, I mulled technology’s role in battle, and the disasters that come from being unprepared for a tech-savvy opponent.

It’s said that, “Generals are always prepared to fight the last war.” This speaks as much to technology as to tactics.  Mounted cavalry proved no match for armored tanks.  Machine guns made trench warfare obsolete.  The Maginot Line became a punch line thanks to the Blitzkrieg. “Heavy fortifications?  “No problem, mein schatzi, ve vill just drive arount tem.”

In e-disclosure, we still fight the last war, smug in the belief that our opponents will never be e-savvy enough to defeat us.

Our old war ways have served so long that we are slow to recognize a growing vulnerability.  To date, our opponents have proved unsophisticated, uncreative and un-tenacious.  Oh, they make a feint against databases here and a half-hearted effort to get native production there; but, for the most part, they’re still fighting with hordes, horses and sabers.  We run roughshod over them.  We pacify them with offal and scraps.

But, we don’t think of it that way, of course.  We think we are great at all this stuff, and that the way we do things is the way it’s supposed to be done.  Large companies and big law firms have been getting away with abusive practices in e-disclosure for so long that they have come to view it as a birthright.  I am the 19th Earl of TIFF.  My father was the Royal Exchequer of Keywords.  I have more than once heard an opponent defend costly, cumbersome procedures that produce what I didn’t seek and didn’t want with the irrefutable justification of, “we did what we always do.”

Tech-challenged opponents make it easy.  They don’t appreciate how our arsenal of information has changed; so, they shoot at us with obsolete requests from the last war, the paper war.  They don’t grasp that the information they need now lives in databases and won’t be found by keywords.  They demand documents.  We have data.  They demand files.  We have sources.

Girding for the Tech Savvy Opponent-IQPC 2015

But, our once tech challenged opponents will someday evolve into Juris Doctor Electronicus.  When they do, here is some of what to expect from them:

E-savvy counsel succeeds not by overreaching but by insisting on mere competence—competent scope, competent processes and competent forms of production.  Good, not just good enough.

Your most effective defense against e-savvy counsel is the Luddite judge who applies the standards of his or her former law practice to modern evidence. Your best strategy here is to continue to expose young lawyers to outmoded practices so that when they someday take the bench they will also know no better way.

Another strategy against e-savvy counsel is to embed outmoded practices in the rules and to immunize incompetence against sanctions.

But these are stopgap strategies–mere delaying tactics.  In the final analysis, the e-savvy opponent needn’t fear retrograde efforts to limit electronic disclosure. Today, virtually all evidence is born electronically; consequently, senseless restrictions on electronic disclosure cannot endure unless we are content to live in a society where justice abides in purposeful ignorance of the evidence.  We have not fallen so, and we will not fall that far.

The e-savvy opponent’s most powerful ally is the jurist who can distinguish between the high cost and burden occasioned by poor information governance and the high cost and burden that flows from overreaching by incompetent requests.  Confronted with a reasonable request, this able judge will give you no quarter because your IG house is not in order.

E-savvy counsel well understands that claims like, “that’s gone,” “we can’t produce it that way” and “we searched thoroughly” rarely survive scrutiny.

It’s not that no enterprise can match the skills of the e-savvy opponent. It’s that so few have ever had to do so.  Counsel for producing parties haven’t had to be particularly e-savvy because opposing counsel rarely were.

Sure, you may have been involved in the Black Swan discovery effort–the catastrophic case where a regulator or judges compelled you to go far beyond your normal scope. But, is that sustainable? Could you do that on a regular basis if all of your opponents were e-savvy?

You may respond, “But we shouldn’t have to respond that way on a regular basis.” In fact, you should, because “e-savvy” in our opponents is something we must come to expect and because, if the opponent is truly e-savvy, their requests will likely smack of relevance and reasonableness.

Remember, the e-savvy opponent about which I warn is not the twit with a form or the wanker who’s simply trying to inflate the scope of the disclosure as a means to extort settlement.  They’re no match for you.  The e-savvy opponent to fear is the one who can persuade a court that the scope is appropriate and proportionate because it is, in fact, both.

Cloud Attachments: Versions and Purview

08 Monday Apr 2024

Posted by in Computer Forensics, E-Discovery, Uncategorized

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Last week, I dug into Cloud Attachments to email, probing the propensity of producing parties’ to shirk collection of linked documents.  Here, I want to discuss the versioning concern offered as a justification for non-production and the use of hash duplicate identification to integrate supplementary productions with incomplete prior productions. 

Recently on LinkedIn, Very Smart Guy, Rachi Messing, shared this re: cloud attachments,

the biggest issue at hand is not the technical question of how to collect them and search them, but rather what VERSION is the correct one to collect and search.

Is it:

1. The version that existed at the time the email was sent (similar to a point in time capture of a file that is attached to an email the traditional way)

2. The version that was seen the first time the recipient opened it (which may lead to multiple versions required based on the exact timing of multiple recipients opening at varying times)

3. The version that exists the final time a recipient opened it

4. The most recent version in existence

I understand why Rachi might minimize the collection and search issue. He’s knee deep in Microsoft M365 collection.  As I noted in my last post, Microsoft makes cloud attachment collection a feature available to its subscribers, so there’s really no excuse for the failure to collect and search cloud attachments in Microsoft M365. 

I’d reframe Rachi’s question: Once collected, searched and determined to be responsive, is the possibility that the version of a cloud attachment reviewed differs from the one transmitted a sufficient basis upon which to withhold the attachment from production?

Respecting the versioning concern, I responded to Rachi’s post this way:

The industry would profit from objective analysis of the instance (e.g., percentage) of Cloud attachments modified after transmittal. I expect it will vary from sector to sector, but we would benefit from solid metrics in lieu of the anecdotal accounts that abound. My suspicion is that the instance is modest overall, the majority of Cloud attachments remaining static rather than manifesting as collaborative documents. But my suspicion would readily yield to meaningful measurement.  … May I add that the proper response to which version to collect to assess relevance is not ‘none of them,’ which is how many approach the task.

Digging into the versioning issue demands I retread ground on cloud attachments generally.

A “Cloud Attachment” is what Microsoft calls a file transmitted via email in which the sender places the file in a private online repository (e.g., Microsoft OneDrive) and sends a link to the uploaded file to the intended recipients.  The more familiar alternative to linking a file as a cloud attachment is embedding the file in the email; accordingly, such “Embedded Attachments” are collected with the email messages for discovery and cloud attachments are collected (downloaded) from OneDrive, ideally when the email is collected for discovery.  As a rule-of-thumb, large files tend to be cloud attachments automatically uploaded by virtue of their size.  The practice of linking large files as cloud attachments has been commonplace for more than a decade.

Within the Microsoft M365 email environment, searching and collecting email, including its embedded and cloud attachments, is facilitated by a suite of features called Microsoft Purview.  Terming any task in eDiscovery “one-click easy” risks oversimplification, but the Purview eDiscovery (Premium “E5”) features are designed to make collection of cloud attachments to M365 email nearly as simple as ticking a box during collection.

When a party using Microsoft M365 email elects to collect (export) a custodian’s email for search, they must decide whether to collect files sent as cloud attachments so they may be searched as part of the message “family,” the term commonly applied to a transmitting message and its attachments.  Preserving this family relationship is important because the message tells you who received the attachments and when, where searching the attachments tells you what information was shared. The following screenshot from Microsoft illustrates the box checked to collect cloud attachments. Looks “one-click easy,” right?

By themselves, the cloud attachment links in a message reveal nothing about the content of the cloud attachments.  Sensibly, the target documents must be collected to be assessed and as noted, the reason they are linked is not because they have some different character in terms of their relevance; many times they are linked because they are larger files, so to that extent, they hold a greater volume of potentially relevant information.

Just as it would not have been reasonable in the days of paper discovery to confine a search to documents on your desk but not in your desk, it’s not reasonable to confine a search of email attachments to embedded attachments but not cloud attachments.  Both are readily accessible to the custodians of the email using the purpose-built tools Microsoft supplies to its email customers.

Microsoft Purview collects cloud attachments as they exist at the time of collection; so, if the attachment was edited after transmittal, the attachment will reflect those edits.  The possibility that a document has been edited is not a new one in discovery; it goes to the document’s admissibility not its discoverability.  The relevance of a document for discovery depends on its content and logical unitization, and assessing content demands that it be searched, not ignored on the speculative possibility that it might have changed.

If a cloud attachment were changed after transmittal, those changes are customarily tracked within the document.  Accordingly, if a cloud attachment has run the gauntlet of search and review, any lingering suspicion that the document was changed may be resolved by, e.g., production of the version closest in time to transmittal or by the parties meeting and conferring.  Again, the possibility that a document has been edited is nothing new; and is merely a possibility.  It’s ridiculous to posit that a party may eschew collecting or producing all cloud attachments because some might have been edited.

Cloud attachments are squarely within the ambit of what must be assessed for relevance. The potential for a cloud attachment to be responsive is no less than that of an item transmitted as an embedded attachment.  The burden claimed by responding parties grows out of their failure to do what clearly should have been done in the first place; that is, it stems from the responding party’s decision to exclude potentially relevant, accessible documents from being collected and searched. 

If you’re smart, Dear Reader, you won’t fail to address cloud attachments explicitly in your proposed ESI Protocols and/or Requests for Production.  I can’t make this point too strongly, because you’re not likely to discover that the other side didn’t collect and search cloud attachments until AFTER they make a production, putting you in the unenviable posture of asking for families produced without cloud attachments to be reproduced with cloud attachments.  Anytime a Court hears that you are asking for something to be produced a second time in discovery, there’s a risk the Court may be misled by an objection grounded on Federal Rule of Civil Procedure Rule 34(b)(2)(E)(iii), which states that, [a] party need not produce the same electronically stored information in more than one form.”  In my mind, “incomplete” and “complete” aren’t what the drafters of the Rule meant by “more than one form,” but be prepared to rebut the claim.

At all events, a party who failed to collect cloud attachments will bewail the need to do it right and may cite as burdensome the challenge of distinguishing items reviewed without cloud transmittals from those requiring review when made whole by the inclusion of cloud attachments.

Once a party collects cloud attachments and transmittals, there are various ways to distinguish between messages updated with cloud attachments and those previously reviewed without cloud attachments.  Identifying families previously collected that have grown in size is one approach.  Then, by applying a filter, only the attachments of these families would be subjected to supplementary keyword search and review.  The emails with cloud attachments that are determined to be responsive and non-privileged would be re-produced as families comprising the transmittal and all attachments (cloud AND embedded).  An overlay file may be used to replace items previously produced as incomplete families with complete families.  No doubt there are other efficient approaches.

If all transmittal messages were searched and assessed previously (albeit without their cloud attachments), there would not be a need to re-assess those transmittals unless they have become responsive by virtue of a responsive cloud attachment.  These “new” families need no de-duplication against prior production because they were not produced previously.  I know that sounds peculiar, but I promise it makes sense once you think through the various permutations.

With respect to using hash deduplication, the hash value of a transmittal does not change because you collect a NON-embedded cloud attachment; leastwise not unless you change the way you compute the hash value to incorporate the collected cloud attachment.  Hash deduplication of email has always entailed the hashing of selected components of messages because email headers vary.  Accordingly, a producing party need compare only the family segments that matter, not the ones that do not. In other words, de-duplicating what has been produced versus new material is a straightforward process for emails (and one that greatly benefits from use of the EDRM MIH). Producing parties do not need to undertake a wholesale re-review of messages; instead, they need to review for the first time those things they should have reviewed from inception.

I’ll close with a question for those who conflate cloud attachments (which reside in private cloud respositories) with hyperlinks to public-facing web resources, objecting that dealing with collecting cloud attachments will require collection of all hyperlinked content. What have you been doing with the hyperlinks in your messages until now? In my experience, loads of us include a variety of hyperlinks in email signature blocks. We’ve done it for years. In my email signature, I hyperlink to my email address, my website and my blog; yet, I’ve never had trouble distinguishing those links from embedded and cloud attachments. The need to integrate cloud attachments in eDiscovery is not a need to chase every hyperlink in an email. Doug Austin does a superb job debunking the “what about hyperlinks” strawman in Assumption One of his thoughtful post, “Five Assumptions About the Issue of Hyperlinked Files as Modern Attachments.”

Bottom Line: If you’re an M365 email user; you need to grab the cloud attachments in your Microsoft repositories. If you’re a GMail user, you need to grab the cloud attachments in your Google Drive respositories. That a custodian might conceivably link to another repository is no reason to fail to collect from M365 and GMail.

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What’s All the Fuss About Linked Attachments?

29 Friday Mar 2024

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In the E-Discovery Bubble, we’re embroiled in a debate over “Linked Attachments.” Or should we say “Cloud Attachments,” or “Modern Attachments” or “Hyperlinked Files?” The name game aside, a linked or Cloud attachment is a file that, instead of being tucked into an email, gets uploaded to the cloud, leaving a trail in the form of a link shared in the transmitting message. It’s the digital equivalent of saying, “It’s in an Amazon locker; here’s the code” versus handing over a package directly.  An “embedded attachment” travels within the email, while a “linked attachment” sits in the cloud, awaiting retrieval using the link.

Some recoil at calling these digital parcels “attachments” at all. I stick with the term because it captures the essence of the sender’s intent to pass along a file, accessible only to those with the key to retrieve it, versus merely linking to a public webpage.  A file I seek to put in the hands of another via email is an “attachment,” even if it’s not an “embedment.” Oh, and Microsoft calls them “Cloud Attachments,” which is good enough for me.

Regardless of what we call them, they’re pivotal in discovery. If you’re on the requesting side, prepare for a revelation. And if you’re a producing party, the party’s over.

A Quick March Through History

Nascent email conveyed basic ASCII text but no attachments.  In the early 90s, the advent of Multipurpose Internet Mail Extensions (MIME) enabled files to hitch a ride on emails via ASCII encoded in Base64. This tech pivot meant attachments could join emails as encoded stowaways, to be unveiled upon receipt.

For two decades, this embedding magic meant capturing an email also netted its attachments. But come the early 2010s, the cloud era beckoned. Files too bulky for email began diverting to cloud storage with emails containing only links or “pointers” to these linked attachments. 

The Crux of the Matter

Linked attachments aren’t newcomers; they’ve been lurking for over a decade. Yet, there’s a growing “aha” moment among requesters as they realize the promised exchange of digital parcels hasn’t been as expected. Increasingly—and despite contrary representations by producing parties—relevant, responsive and non-privileged attachments to email aren’t being produced because relevant, responsive and non-privileged attachments aren’t being searched.

Wait! What?  Say that again.

You heard me.  As attachments shifted from being embedded to being linked, producing parties simply stopped collecting and searching those attachments.

How is that possible?  Why didn’t they disclose that? 

I’ll explain if you’ll indulge me in another history lesson.

Echoes From the Past

Traditionally, discovery leaned on indexing the content of email and attachments for quicker search, bypassing the need to sift through each individually.  Every service provider employs indexed search. 

When attachments are embedded in messages, those attachments are collected with the messages, then indexed and searched.  But when those attachments are linked instead of embedded, collecting them requires an added step of downloading the linked attachments with the transmitting message.  You must do this before you index and search because, if you fail to do so, the linked attachments aren’t searched or tied to the transmitting message in a so-called “family relationship.”

They aren’t searched.  Not because they are immaterial or irrelevant or in any absolute sense, inaccessible; a linked attachment is as amenable to being indexed and searched as any other document. They aren’t searched because they aren’t collected; and they aren’t collected because it’s easier to blow off linked attachments than collect them.

Linked attachments, squarely under the producer’s control, pose a quandary. A link in an email is a dead-end for anyone but the sender and recipients and reveals nothing of the file’s content. These linked attachments could be brimming with relevant keywords yet remain unexplored if not collected with their emails.

So, over the course of the last decade, how many times has an opponent revealed that, despite a commitment to search a custodian’s email, they were not going to collect and search linked documents?

The curse and blessing of long experience is having seen it all before.  Every generation imagines they invented sex, drugs and rock-n-roll, and every new information and communication technology is followed by what I call the “getting-away-with-murder” phase in civil discovery.  Litigants claim that whatever new tech has wrought is “too hard” to deal with in discovery, and they get away with murder by not having to produce the new stuff until long after we have the means and methods to do so.  I lived through that with e-mail, native production, then mobile devices, web content and now, linked attachments.

This isn’t just about technology but transparency and diligence in discovery. The reluctance to tackle linked attachments under claims of undue burden echoes past reluctances with emerging technologies. Yet, linked attachments, integral to relevance assessments, shouldn’t be sidelined.

What is the Burden, Really?

We see conclusory assertions of burden notwithstanding that the biggest platforms like Microsoft and Google offer ‘pretty good’ mechanisms to deal with linked attachments.  So, if a producing party claims burden, it behooves the Court and requesting parties to inquire into the source of the messaging.  When they do, judges may learn that the tools and techniques to collect linked attachments and preserve family relationships exist, but the producing party elected not to employ them.  Granted, these tools aren’t perfect; but they exist, and perfect is not the standard, just as pretending there are no solutions and doing nothing is not the standard. 

Claims that collecting linked attachments pose an undue burden because of increased volume are mostly nonsense.  The longstanding practice has been to collect a custodian’s messages and ALL embedded attachments, then index and search them.  With few exceptions, the number of items collected won’t differ materially whether the attachment is embedded or linked (although larger files tend to be linked).  So, any party arguing that collecting linked attachments will require the search of many more documents than before is fibbing or out of touch.  I try not to attribute to guile that which may be explained by ignorance, so let’s go with the latter.

Half Baked Solutions

Challenged for failing to search linked attachments, a responding party may protest that they searched the transmitting emails and even commit to collecting and searching linked attachments to emails containing search hits.  Sounds reasonable, right?  Yet, it’s not even close to reasonable. Here’s why:

When using lexical (e.g., keyword) search to identify potentially responsive e-mail “families,” the customary practice is to treat a message and its attachments as potentially responsive if either the content of the transmitting message or its attachment generates search “hits” for the keywords and queries run against them.  This is sensible because transmittals often say no more than, “see attached;” it’s the attachment that holds the hits.  Yet, stripped of its transmittal, you won’t know the timing or circulation of the attachment. So, we preserve and disclose email families.

But, if we rely upon the content of transmitting messages to prompt a search of linked attachments, we will miss the lion’s share of responsive evidence.  If we produce responsive documents without tying them to their transmittals, we can’t tell who got what and when.  All that “what did you know and when did you know it” matters.

Why Guess When You Can Measure?

Hopefully, you’re wondering how many hits suggesting relevance occur in transmittals and how many in attachments?  How many occur in both?  Great questions!  Happily, we can measure these things.  We can determine, on average, the percentage of messages that produce hits versus their attachments. 

If you determine that, say, half of hits were within embedded attachments, then you can fairly attribute that character to linked attachments not being searched.  In that sense, you can estimate how much you’re missing and ascertain a key component of a proper proportionality analysis.

So why don’t producing parties asserting burden supply this crucial metric? 

The Path Forward

Producing parties have been getting away with murder on linked attachments for so long that they’ve come to view it as an entitlement. Linked attachments are squarely within the ambit of what must be assessed for relevance.  The potential for a linked attachment to be responsive is no less than that of an item transmitted as an embedded attachment.  So, let’s stop pretending they have a different character in terms of relevance and devote our energies to fixing the process.

Collecting linked attachments isn’t as Herculean as some claim, especially with tools from giants like Microsoft and Google easing the process. The challenge, then, isn’t in the tools but in the willingness to employ them.

Do linked attachments pose problems?  They absolutely do!  I’ve elided over ancillary issues of versioning and credentials because those concerns reside in the realm between good and perfect solutions. Collection methods must be adapted to them—with clumsy workarounds at first and seamless solutions soon enough.  But in acknowledging that there are challenges, we must also acknowledge that these linked attachments have been around for years, and they are evidence.  Waiting until the crisis stage to begin thinking about how to deal with them was a choice, and a poor one.  I shudder to think of the responsive information ignored every single day because this issue is inadequately appreciated by counsel and courts.

Happily, this is simply a technical challenge and one starting to resolve.  Speeding the race to resolution requires that courts stop giving a free pass to the practice of ignoring linked attachments.  Abraham Lincoln defined a hypocrite as a “man who murdered his parents, and then pleaded for mercy on the grounds that he was an orphan.”  Having created the problem and ignored it for years, it seems disingenuous to indulge requesting parties’ pleas for mercy.  

In Conclusion

We’re at a crossroads, with technical solutions within reach and the legal imperative clearer than ever. It’s high time we bridge the gap between digital advancements and discovery obligations, ensuring that no piece of evidence, linked or embedded, escapes scrutiny.

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ESI Protocols: How Do I Get Out of a Bad Deal?

19 Tuesday Mar 2024

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I watched a webinar this morning where the presenters addressed ESI Protocols.  They were well-informed people sharing sound advice; but it underscored for me why people despise lawyers.  A presenter counseled, “Always build an escape clause into whatever you agree to.”

The speaker meant, if you commit your clients to a protocol provision, and you later find that the client or its service provider can’t or won’t do what was promised, you need to incorporate a “fingers crossed” way to back out of the deal.

Many readers—lawyer readers certainly—will count that as inspired advice. They’ll posit, “Aren’t we protecting our clients when we spare them the hardships of an improvident agreement?”  In truth, the risk of being bound by obligations that could prove more onerous or expensive than anticipated is the number one objection I hear voiced when I advocate for use of ESI Protocols.

Who wouldn’t want to walk out on their obligations when the going gets rough?  It’s human nature to crave the benefits of a bargain without its burdens; but just try to run a restaurant where everyone walks the check!

The law has a term for what accounts for the difference between a fair deal and a debacle: it’s due diligence.  Competent counsel should know the capabilities of both clients and vendors before agreeing to an obligation that hinges on the capabilities of our clients and vendors. 

Counsel who agrees to something because he didn’t understand the implications of the agreement won’t want to own that.  He will point the finger at anyone and everyone except himself.  That, too, is human nature, albeit not a pretty predilection.  But let’s face facts: Those lawyers weren’t tricked; they were uninformed and unprepared.

That said, not all unforeseen consequences of an ESI Protocol grow out a lack of diligence or competence.

People make mistakes. You do.  I do.  And when we do, the question becomes: Who should bear the brunt of our mistakes?  And when should the consequences of our mistakes be limited by proportionality and (for lack of a better term) mercy?

Long before I became an attorney, some canny counsel decided that the optimum legal advice to a culpable client was to admit nothing, don’t apologize, deny, deny, deny and mount a strong offense as your best defense. Perhaps that’s why lawyers are the last bastion of characters cast as vile stereotypes in the movies without outcry.  Okay, lawyers and Nazis.

If experience means anything, mine suggests that what passes for good legal advice is lousy life advice.  If you made an honest mistake in agreeing to a provision of an ESI protocol, the optimum path is to own it and seek to make it right.  Sometimes your opponent will relate and work decently to renegotiate the terms. Often, the Court will come to your aid if it’s clear you made a good faith mistake and you own it. Rarely, exceptionally, your client must endure some hardship for the error. 

In every case I’ve come across in the last 42 years, that final, onerous outcome coincided with a profound lack of competence or diligence when the deal was struck, the poster child being In re Fannie Mae Sec. Litig., 552 F.3d 814 (D.C. Cir. 2009), but also a line of cases where it’s hard to explain the outcome save for the absence of due diligence, e.g., McCormick & Co., Inc. v. Ryder Integrated Logistics, Inc., ___ F.Supp.3d ___, 2023 WL 2433902 (D. Md. March 9, 2023)

We speak reverently about “the Rule of Law;” but that rule begins within each of us, in our character and commitment.  The notion of including a clause in agreements to escape obligations when they become inconvenient is troubling and erodes the integrity of agreements and the foundations of a functioning society.