ENT News in April 2026 – How I Actually Practice Law with AI in 2026

Dear Valued Client,

This article draws on the views of Mr. Zack Shapiro, an attorney at Thuan Claude Law Firm, a practitioner with extensive experience in legal and policy matters relating to technology and innovation. His analysis provides practical and in-depth insights into the development of legal frameworks in the context of the digital economy.

How I Actually Practice Law with AI in 2026

A few months ago, the night before a client’s acquisition was set to close, the buyer’s counsel sent a letter demanding that several key deal terms be restructured. New escrow conditions. Expanded indemnification carve-outs. A revised set of closing deliverables. The implicit threat: accept these changes or we walk. It was 7 PM.

I uploaded the purchase agreement, the disclosure schedules, and the demand letter to Claude. Within minutes, Claude mapped every proposed change against the existing deal terms and found what the buyer’s lawyers apparently hadn’t noticed: two of their proposed carve-outs directly contradicted representations they had already confirmed in the disclosure schedules, and a third would have created an internal conflict with the fundamental reps section that would have actually weakened the buyer’s own post-closing protections. Their aggressive last-minute play had holes in it.

As the negotiation continued through the evening with emails going back and forth, I fed each new communication to Claude. It tracked how every proposed concession interacted with provisions across the agreement, flagged where accepting one change would create exposure in another section, and helped me build a response that conceded the points worth conceding and held firm on the ones that mattered. By 11 PM we had a clean set of counter-positions, each grounded in specific cross-references to the buyer’s own language. The deal closed the next morning on terms my client was happy with.

A team of three associates at a mid-size firm would have needed until morning to produce that analysis. I had the core of it in under two hours.

I run a two-person boutique law firm. We handle startup formation, venture capital transactions, and regulatory work. We compete against firms with hundreds and sometimes thousands of lawyers. We are not supposed to be able to do this. But the past year has made something clear: a small firm built around AI doesn’t just keep up with larger competitors. It moves faster, produces more thorough work product, and operates at a cost structure that would have been impossible 18 months ago.

The tool I’ve built my practice around is Claude, made by Anthropic. This piece is an explanation of how I actually use it, every day, for real legal work. Not the theory. The workflow.

Why Claude, Not “Legal AI”

The market is full of specialized legal AI products. Harvey, Spellbook, CoCounsel, Luminance. They all share a thesis: lawyers need AI built specifically for legal work. I’ve evaluated most of them. For a small firm practitioner, a well-configured general-purpose AI is better. It’s not close.

The specialized products are wrappers built on top of the same foundation models that power the general-purpose tools. Their marketing pitch sounds compelling: we’ll customize the AI to your firm’s playbook, train it on your templates, build workflows around your brief bank or clause library. Some of them do this reasonably well. But the pitch contains a fundamental misunderstanding of where the value actually lives.

A template library is not a competitive advantage. Every competent firm in your practice area has roughly the same templates. The NDA, the stock purchase agreement, the employment offer letter. These are commodity inputs. The thing that differentiates a great lawyer from a mediocre one was never the template. It was what the lawyer did with the template: how they spotted the issue the other side buried in Section 14(c), how they knew which indemnification fight was worth having and which to concede, how they structured the advice email so the client actually understood the risk. That is judgment. And judgment doesn’t live at the firm level. It lives at the level of the individual professional.

When legal AI companies talk about customizing AI to a firm’s playbook, they are solving a problem that barely matters and ignoring the one that does. The real leverage comes not from which template the AI starts with, but from the instructionsthat tell it how to think about the work: what to look for, what to flag, how to weigh competing considerations, what format to deliver the output in, what tone to use with the client. Those instructions encode an individual lawyer’s judgment, not a firm’s template library. And that is exactly what Claude’s skill system is built to do.

I’ve created custom instruction files, called “skills,” that encode my analytical frameworks, my preferred formats, my voice, and my judgment about how specific types of legal work should be done. When I upload a contract for review, Claude doesn’t apply a generic framework. It doesn’t even apply my firm’s framework. It applies my framework, the one I’ve developed over a decade of practice, automatically. The difference between a firm playbook and an individual lawyer’s encoded judgment is the difference between giving someone a recipe and teaching them how to cook.

There’s a more fundamental issue, and it’s the one that will matter most to anyone who has spent their career inside Microsoft Word. Claude is a frontier AI model that has been heavily optimized for writing code. That may sound irrelevant to legal practice until you realize what it means: Claude can write code, on the fly, to directly manipulate the applications lawyers already use.

Think about what this means concretely. Every lawyer reading this has lost hours to Word formatting. Paragraph numbering that breaks when you paste from another document. Styles that refuse to cooperate. Track changes that corrupt across versions. Cross-references that go stale. Bluebook citation formatting that requires manual attention on every single period and comma. These are not legal problems. They are software problems. And Claude solves software problems by writing software. When I tell Claude to apply tracked changes to a contract, it doesn’t use a plugin or a macro. It opens the .docx file at the XML level and writes the exact markup that Microsoft Word expects, attributed to my name, preserving every formatting detail. When I tell it to standardize the citation format in a brief, it writes code to parse and reformat every citation in seconds. The result is indistinguishable from expert manual work, delivered in a fraction of the time.

This is the capability gap that no specialized legal AI product can match. They give you a chatbot that talks about documents. Claude is a system that can reach inside those documents and change them. It is the difference between an associate who can tell you what’s wrong with a contract and an associate who can also fix it, format it, produce the redline, and draft the cover email, all without you opening a single application. General-purpose AI advances faster than any vertical product can keep up with. When you’re on the frontier model, every new capability ships to you on day one. When you’re on a wrapper, you’re waiting for someone else’s engineering team to decide what to build next.

I’m describing my own practice here, which is transactional. But nothing about the architecture is practice-specific. A litigator would build skills for deposition preparation, motion drafting, case law synthesis, and discovery review. A tax lawyer would build skills for entity structuring, opinion letter frameworks, and regulatory monitoring. A family lawyer would build skills for asset tracing and custody analysis. The approach is the same: take a powerful general model, teach it your practice, and let it compound your judgment. The content is yours.

Three Modes

Claude’s desktop app has three modes. Learning when to use each one was the single most important step in making this work.

Chat is the conversational interface. I talk to Claude the way I’d talk to a fast, knowledgeable associate sitting across the table. This is where I go for analyzing a legal issue, brainstorming negotiation strategy, getting a first take on a contract provision, or drafting something from scratch. I stay in control of every step. Most lawyers who have used ChatGPT or similar tools have only experienced this mode.

Coworkis the autonomous mode, and it’s the one that changes everything. I point Claude at a folder on my computer, give it a task, and it goes and does it. It reads files, creates new ones, edits existing documents, and makes its own decisions about how to get from A to B. When I have a 40-page agreement that needs a full redline, or a stack of closing documents that need to be generated from a term sheet, I hand it to Cowork and let it work. This is the mode most lawyers haven’t tried. It’s the one that will change their practice the most.

Code is the development mode. Full terminal access. Most lawyers don’t need it daily. But I have a condition that makes it hard to read long documents, so I used Code to build a command-line tool that converts legal documents into spoken audio. It handles the entire pipeline: parsing Word docs and PDFs, converting legal formatting like “Section 4.2(b)(iii)” into natural speech, expanding abbreviations, chunking the text, sending it to an AI voice API, and assembling the final audio file. I listen to contracts on my commute now. Claude built the whole thing.

Teaching Claude Your Practice

This is where the leverage becomes something I wouldn’t have believed two years ago.

Anthropic published a guide on building custom “skills” for Claude: structured instruction files that teach it how to behave in a specific context. Not a prompt you type every time. A persistent set of instructions that fires automatically when the situation calls for it. Instead of reading the guide cover to cover, I uploaded it to Claude and asked a better question: based on the hundreds of conversations we’ve had together, spanning contract drafting, client emails, document editing, legal research, and policy writing, what are the skills that would have the greatest impact on my practice?

Claude analyzed months of our work and identified the patterns: which tasks I repeated most, where the friction was highest, where structured automation could save the most time. The skills it recommended weren’t generic. They were specific to how I actually work. Not “draft contracts faster” but “a contract review skill with four distinct modes depending on context, severity ratings, a missing-provisions checklist, market-term benchmarking, and a seamless handoff to a tracked-changes editing skill when you’re ready to mark up the document.”

We refined the details over a couple hours. I pushed back where the defaults didn’t match my preferences. By the end I had six production-ready skills bundled into a single plugin for the Cowork desktop app: contract review, tracked changes editing, contract drafting, client communications, legal research, and policy writing. Each one encodes years of accumulated professional judgment about how I approach that type of work.

The implication that matters for firm management: the plugin is transferable. If I had 50 associates, I could install it on every machine. Every associate would immediately produce contract reviews using my analytical framework, draft communications in my voice, and apply tracked changes in my preferred format. Knowledge that takes years of mentorship to transmit is now an instruction file that works from the first draft. The output still requires attorney review, but the review starts from a much higher baseline.

What This Looks Like in Practice

Three examples from real work, because I want this to be concrete.

Tracked changes without opening Word. A counterparty sends back a redlined agreement. Forty pages of changes across representations, indemnification, IP, and closing conditions. I upload the document to Claude and say: “Help me evaluate the counterparty’s changes from my client’s perspective.” My contract review skill fires. Claude organizes every change by severity, flags where the counterparty shifted risk, identifies tensions between modified provisions, checks for standard provisions that should be present but aren’t, and produces a summary with specific counter-language for each issue.

Then I apply my judgment. Claude flagged a pattern in the markup. I know from experience what that pattern usually signals. Claude generated three alternative formulations for a disputed clause. I pick the one that accounts for relationship dynamics and deal context that no AI has access to. Once I’ve made my decisions, I tell Claude to apply the edits. This is the part that drops jaws the first time you see it. Claude opens the Word document at the XML level, applies tracked changes attributed to my name, preserves every formatting detail, and produces a clean .docx with real tracked changes that opposing counsel can open in Microsoft Word and review normally. I don’t open Word. I don’t open Litera. Claude produces the redline. I review every change, and I send it. Then the client communications skill drafts the cover email in the right tone. Total time from receiving the markup to having a response package ready to send: under an hour, of which about 30 minutes is my own thinking.

Research without hallucinations. A client needs to understand the regulatory landscape for a new product. The question spans multiple agencies and overlapping statutory frameworks. My research skill instructs Claude to launch parallel research across every relevant angle simultaneously rather than working through them sequentially: the securities analysis, the state licensing requirements, the banking regulations, the consumer protection implications. It runs multiple searches per sub-topic, cross-references sources, and prioritizes primary authority (statutes, regulations, agency guidance, case law) over secondary commentary.

Before delivering anything to me, the skill requires Claude to run a self-review. This is critical, and it’s the part most people skip. Claude must verify that every cited authority actually says what the memo claims. It must flag anything where its confidence is below high. It must check for internal contradictions across sections. And it must specifically guard against hallucinated citations, the problem that got several lawyers sanctioned and made national news. The lawyers who submitted fake AI-generated citations were using tools without this kind of verification layer. The problem was never AI itself. It was AI without quality control.

The output is a structured research memo, with a bottom-line-up-front summary, specific statutory citations, and practical recommendations, that would take a junior associate days to produce. Claude delivers a first draft in under an hour. I then review every citation, stress-test the analysis, and revise where my judgment diverges from the output. The total time is still a fraction of what it would take starting from scratch. And because the skill is calibrated to my standards (confident conclusions with explicit uncertainty flags, tables for comparing regulatory frameworks, practical recommendations rather than academic hedging), the memo is useful immediately.

Real-time contract interpretation. A client called mid-morning to say they had just received a demand letter from a counterparty claiming breach of a commercial services agreement and threatening termination. The client had 48 hours to respond. I uploaded the agreement, the demand letter, and the client’s last three months of correspondence with the counterparty. Claude mapped every factual allegation in the demand letter against the specific contract provisions cited, and found that two of the four claimed breaches referenced obligations that had been expressly modified by a side letter the counterparty’s own counsel had drafted. The demand letter appeared to have been written without checking their own amendments. As I prepared the response, I ran each draft paragraph through Claude to pressure-test whether any of my arguments had unintended implications for other provisions in the agreement. It caught one: a defense I was planning to raise on the service-level metrics could have been read to concede a point on the payment dispute in Section 7. I rewrote the response. That kind of real-time, provision-by-provision stress-testing while actively drafting is something that used to require a second lawyer reviewing your work. Now it happens in the same conversation where the work gets done.

The Privilege Question

Every lawyer asks. The short answer: the same framework that lets you use cloud storage, e-discovery platforms, and online legal research databases applies here. ABA guidance and state bar ethics opinions treat AI tools as third-party technology providers covered by the agent/instrumentality exception. Your obligations are to make reasonable efforts to protect client data, which in practice means turning off model training on your inputs, understanding the provider’s data handling practices, and documenting your reasoning. Anthropic offers a zero-data-retention API option and business data processing agreements, so that none of your client data is used to train models, and inputs are not stored beyond the session. The same diligence you performed before putting client documents in Dropbox, Google Drive, or Clio.

I went a step further. I had Claude help me draft an AI usage provision for my engagement letters. The provision frames AI as an efficiency and quality enhancer, emphasizes attorney supervision, ties data handling to existing confidentiality obligations, and secures client consent. Clients sign it without blinking. Most of them assume I’m already using AI. They’re right.

The ethics rules now require technology competence in most jurisdictions. We are approaching the point where not using these tools is the harder professional responsibility position to defend.

The Prompt Is the Skill

Most lawyers who try AI write something like “review this contract” and get back something mediocre. Then they decide AI isn’t useful for legal work.

The problem is not the AI. The problem is the input.

Compare “review this contract” with “review this services agreement from the vendor’s perspective. Flag provisions where the customer shifted risk beyond market norms for this type of deal. Check for missing provisions that should be present, including limitation of liability, IP ownership, data handling, and termination for convenience. Produce a severity-rated summary with specific counter-language for each high-severity issue. Note that the vendor has limited negotiating leverage and wants to close the deal, so recommendations should focus on provisions worth fighting for versus provisions to concede gracefully.”

The second version produces work product that is useful on the first pass. The first produces work product that requires extensive revision, if it’s useful at all. The entire gap between “AI is a toy” and “AI changed my practice” lives in the quality of your instructions. This is why skills matter: they encode that level of detail so you write it once and it fires every time.

What This Changes

A few things follow from all of this that are worth naming.

Staffing.I run a two-person firm that handles the workload of a much larger practice. That is a direct function of AI. The work that traditionally justified an associate hire, first-pass document review, research memos, initial drafts, redline summaries, routine correspondence, is now handled by Claude under my supervision. To be clear: every document that leaves my firm has been reviewed, revised, and approved by a licensed attorney. AI produces the first pass. I produce the final work product. Associates are not obsolete. But the bar for when hiring one makes economic sense has moved. And what you need them to do has changed: judgment, client relationships, and AI output supervision, not 2,000 hours of document production.

Billing.AI

changes the value equation. For some tasks, the time savings are obvious and I pass them on to clients. For others, the same hours produce dramatically deeper analysis, more comprehensive issue-spotting, and higher-quality drafting than would have been possible before. The point is not that every task takes less time. It is that every hour of attorney time produces more value. My firm offers subscription pricing alongside traditional hourly billing, depending on the engagement. The subscription clients get ongoing advisory, contract review, compliance monitoring, and routine governance for a flat monthly fee. No meter running. AI makes this model work, because I can deliver more comprehensive service within a predictable fee structure. Clients love it: they’re not afraid to pick up the phone or send an email. And the revenue is predictable instead of lumpy.

Judgment.Everything I’ve described creates a temptation to let the AI do too much. To stop checking. The research on this is consistent: people who use AI outside its competence, or who trust it without interrogating the output, perform worse than people who don’t use AI at all. The lawyers who will win with this technology understand at a foundational level that the AI is not practicing law. You are practicing law. The AI makes you faster, more thorough, and more consistent. But the judgment, the part where you decide what to fight for and what to concede, where you read between the lines, where you make a call that could go either way and stake your reputation on it, that is yours. Experienced lawyers have an enormous advantage in this new world, and most of them don’t realize it. If you’ve spent 10 or 20 years developing judgment in your practice area, you are sitting on exactly the asset that AI makes more valuable, not less.

Go Build

I don’t work for Anthropic. I’m a practicing lawyer who tried every AI tool available and built my practice around the one that worked best for how I actually work.

The gap between how most lawyers use AI (typing a question into a chatbot and hoping for the best) and what I’ve described here is enormous. Closing that gap doesn’t require technical skill. It requires investing a few hours in learning how the tool actually works: the difference between Chat and Cowork, why long detailed prompts produce dramatically better results than short ones, how to build a skill that encodes your judgment, how to bundle skills into a plugin that any colleague can use.

Download the desktop app. Pick the task you do most often. Write a prompt that describes, in detail, exactly how you want it done. See what comes back. Then build your first skill. The returns compound fast.

Attribution

The views expressed by Mr. Zack Shapiro serve as a valuable reference for the legal and business community, particularly in shaping approaches to emerging legal issues. We cite and synthesize these perspectives to provide a multi-dimensional view and to support our Clients in monitoring and assessing relevant legal trends.

Best regards,