One of the reasons I was so excited to start my internship at General Legal was the chance to finally use the latest AI tools in a professional environment. I’ve spent the last year almost entirely focused on my law school studies. Law school is an amazing place to learn, but (for many valid reasons) it takes a conservative approach to technology adoption. My legal writing professor told me the program’s entire strategy for teaching incoming first-years to use AI was a single in-class activity in their spring semester. Incoming students will not learn how to use these tools until spring 2027.
Starting at General Legal felt like entering a parallel universe. It completely reset how I think about AI for legal work and the role of humans in the knowledge economy.
The old workflow is now the slow workflow
Two projects I completed last week perfectly illustrate the dramatic changes in workplace efficiency with the advancement of AI. The first project was a data validation task: identify the discrepancies between two sources of inbound client requests. Leaning on my background in data analytics, I cleaned up the datasets, pulled them into an Excel sheet, and started throwing everything I know about fuzzy matching at the rows to see how the sheets differed.
As I neared the finish line, I opened Slack to tell my teammates the list was almost ready. I was stunned to see that thirty minutes earlier, one of them had already posted the answer. He hadn't built a spreadsheet at all. He’d simply dropped both datasets into Claude and started prompting. My work was careful, methodical, and completely outdated. I spent over an hour producing something a colleague had produced in minutes.
The second project drove the point home. To evaluate how different AI models perform on legal tasks, I used Claude to generate a realistic set of tasks, modeled on the work our attorneys do every day. I felt like I was finally adapting to the reality of work in 2026, until I was ready to actually test the models. Without pausing to ask whether I could spin up a process to run multiple model versions concurrently, I started manually pasting prompts and files into Claude and ChatGPT, one at a time. Once again, by defaulting to how I would have worked in the recent past, I burned time on repetitive tasks a script, easily generated by AI, could have handled.
With access to AI, lack of imagination is often the main bottleneck to accomplishing tasks. Initially, I felt a blow to my professional pride when I was outstaged in a data validation task by an AI model. Rather than letting it affect me, however, I’m now inspired to think critically about how to best accomplish all professional tasks: can a model or an agent do this better than me? If so, let them have it.
Why “AI for legal work” is no longer optional
It would be comforting to think this is a passing trend, but the models keep getting better at exactly the kind of reasoning legal and many other types of work demand. The recent release of Claude Fable 5, Anthropic’s first publicly available Mythos-class model, is the latest marker. Reporting on the launch noted that it excels at software engineering and knowledge work while shipping with hard safety limits in high-risk domains (TechCrunch). While Anthropic withdrew access to Fable under pressure from the US Government, who cited national security concerns, the continual improvement in the models’ capabilities is undeniable (Anthropic).
For lawyers and the founders who hire them, this trajectory has a clear implication: the firms that treat generative AI as core infrastructure will simply move faster, at lower cost, with more consistency. That’s the bet General Legal is built on.
So what is the human actually for? Quality control.
If a model can draft, match, summarize, and analyze, what’s left for us? After two weeks using AI extensively, human quality control is still vital.
One of our engineers put it perfectly: "I hardly write code now, even after 20 years. I spend most of my time reviewing." That’s not a story about decline, but about where the value moved. The scarce, valuable skill is not producing the first draft, but knowing whether the draft is right.
And being right matters enormously in law, because the models still miss things they weren’t explicitly told to look for. In my own review work, Claude breezed past a section-numbering error: it let 1.2 follow 1.3. At one point labeled a new section "Exhibit A" immediately after "Exhibit 1." Neither mistake was catastrophic, but serving clients slop while pitching yourself as an “AI native law firm” is a sure way to erode trust. A model optimizes for the instructions it’s given. A lawyer is accountable for everything on the page, including the problems no one thought to flag. The model handles volume and velocity and the human owns correctness and consequences.
Human innovation: bolstered not replaced
I tested this idea one more way. I built a Claude agent that generates new ideas in Notion based on our open projects and other company resources. It’s exhilarating to wake up to a fresh batch of ideas in my database every morning.
While exhilarating to see in action, the ideas aren’t especially compelling. They’re plausible, well-formed, and oddly hollow — because they lack the context that comes from actually talking to colleagues, clients, and people wrestling with real challenges. The agent has the company’s documents. It can’t have the hallway conversation, the frustrated client call, the offhand comment that sparks a genuinely good idea. Human innovation is safe for now, but can be greatly bolstered with the power of AI models.
The future of legal work isn’t a world where lawyers are replaced. It’s a world where the lawyers and firms who tactfully embrace AI will be rewarded. Those who reach for the agent before the spreadsheet, and who treat their own judgment as the quality bar, will pull decisively ahead of those who don’t.
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Sources
Jessica Rosberger, AI in Law: The Real Risks Beyond Hallucinated Cases, Cornell J.L. & Pub. Pol'y (Apr. 9, 2025), https://publications.lawschool.cornell.edu/jlpp/2025/04/09/ai-in-law-the-real-risks-beyond-hallucinated-cases/.
Anthropic, Claude Fable 5, Anthropic (June 9, 2026), https://www.anthropic.com/news/claude-fable-5-mythos-5.
Jessica Rosberger, TechCrunch: Anthropic’s Claude Fable 5 is a version of Mythos the public can access today, TechCrunch (June 9, 2026), https://techcrunch.com/2026/06/09/anthropics-claude-fable-5-is-a-version-of-mythos-the-public-can-access-today/.