Beyond the Fine Print
What Building an AI Legal Assistant Taught Me About the Future
I recently shutdown Atticus, my first consumer AI product that aimed to make legal contracts accessible to everyone. The journey of building and launching it taught me invaluable lessons about AI product development, market dynamics, and the future of technology.
Atticus was born in June 2023 during the initial AI boom when my friend Dylan approached me with a simple question: could AI help analyze his girlfriend's gym contract? Over the next year, we built one of the early consumer-facing AI legal tools. The journey took us through the Vercel AI Accelerator, gave us early access to GPT-4's expanded capabilities, landed us coverage in tech newsletters, and brought paying customers from around the globe.
At its core, Atticus analyzed contracts by breaking them down into plain English, determining if clauses favored the selected party, and assigning risk scores to concerning terms. Users could also engage with their contracts through an open-ended chat interface.
After a year and a half of learning, iterating, and serving users across multiple continents, I made the decision to shut it down - not because it wasn't useful (we were still making sales!), but because the path forward revealed deeper insights about the future of AI that I'll share in this post.
The Genesis Story
Dylan and I had been consulting on an AI project when he approached me about building a tool to analyze consumer contracts. Initially, we envisioned it as a simple consumer product like WordCounter.
I was excited because early findings suggested LLMs were particularly well-suited for legal problems. I wrote:
I flagged that costs might make a freemium model prohibitive, but decided to build it anyway, confident that LLM prices would drop over time. I set a one-week deadline to build and start validating the idea with our networks to mitigate the risk of dropping other opportunities.
We launched within a week and immediately recognized that running Atticus wouldn't be cheap. In June 2023, the LLM price reductions we now take for granted hadn't kicked in. We applied to the Vercel AI Accelerator for free AI credits and access to models that weren't widely available, including Claude (with its then class-leading 100K context window) and GPT-4-32K.
Technical Architecture
I built Atticus' backend with Langchain, using a multi-step pipeline to analyze contracts:
Extract text from uploaded documents (PDFs, docx, etc.)
Determine contract type and extract key information like dates and parties
Generate analysis criteria - the clauses an experienced attorney would look for in that contract type
Compare the contract against these criteria, analyzing how favorable each clause was
This approach had several advantages:
Reduced perceived latency by splitting the analysis into stages
Improved effectiveness by focusing the model on specific tasks
Enabled caching by separating contract type analysis from specific instances
Provided a dynamic, non-deterministic approach that still yielded consistent results
Initially, I spent significant time optimizing our pipeline to reduce costs, trying different RAG techniques and models. Between the Vercel AI credits and my belief that costs would eventually decrease, I pivoted to focus on launching faster.
The Search for PMF
After launch, we found that people in our networks liked the concept but didn't need a tool like Atticus consistently. We explored different ICPs over the next few months - content creators, tech consultants, construction contractors, and startup founders. I even cold emailed ~60 construction companies in NYC (0% response rate ðŸ˜).
While our rapid experimentation approach was valuable, in retrospect, we should have gone deeper with each potential ICP rather than quickly switching between them. For instance, with construction companies, phone calls might have been more effective than email outreach.
We got most of our users through:
LinkedIn, where our "build in public" approach resonated with professional networks
In-person demos at meetups (though mostly to AI enthusiasts rather than target users)
Hacker News posts (briefly reaching page 1 of Show HN)
Tech newsletters like Ben's Bites and There's An AI for That, which drove significant organic traffic
Show Me the Money
In the fall, Lunar and Dylan redesigned Atticus into something that looked like a real product:
Around this time, Dylan noticed we had users returning frequently, so we implemented usage-based billing - 4 analyses for $20. We set up Stripe within a couple of days and got our first paying customer in October 2023! Sales continued to trickle in consistently until the day right before I decided to shut down.
We applied to YC and got an interview, though we didn't get funded. Their feedback centered on Atticus not being venture-scale, which I didn't fully disagree with.
In retrospect, the YC rejection was the death knell for Atticus. I processed the rejection emotionally but moved on without fully internalizing or actively rejecting the feedback - a missed opportunity to make a more deliberate strategic decision.
After scaling down resources to reduce operating costs and switching to more affordable models, Atticus continued to generate sales. It was bittersweet to see my prediction about falling LLM costs play out in real life. 🥲
Why It Didn't Work
A concept like Atticus requires changing people's expectations of what technology can do for them. While technology evolves rapidly, people and institutions change much more slowly. As Tyler Cowen said in a recent interview: "the number one bottleneck to AI progress is humans."
Atticus excited early AI adopters but faced barriers to broader adoption. One model we explored was offering AI-augmented legal services, using AI to handle monotonous low-value work while connecting lawyers with high-value work.
However, lawyers showed little enthusiasm for disrupting their own industry. The legal system is built around billable hours, and persuading professionals to change a system that financially benefits them proved extremely difficult. As Upton Sinclair noted: "It is difficult to get a man to understand something, when his salary depends on his not understanding it."
Potential customers (startup founders and operators) recognized the value, feeling the pain of inefficient legal processes. But we faced a chicken-and-egg problem: delivering the service required lawyers who weren't ready to adopt the technology.
An idea this disruptive could succeed, but it needs a long-term plan (10+ years) with sustainable phases along the way. Think of Amazon, which aimed to be the "everything store" but started with books before expanding category by category. We lacked such a patient approach.
Another challenge: AI products aren't well-suited to traditional software business models. Unlike deterministic software, AI models are probabilistic and sometimes make mistakes. This contradicts customer expectations that software will work the same way every time.
In retrospect, the "services as software" model that has since emerged seems most promising for AI products and agents. But this approach requires domain experts, bringing us back to the challenge of convincing technology-resistant lawyers to participate.
What Did Work
Despite these challenges, several things went right:
Speed and iteration. We got an MVP in front of users within a week and built in public, garnering attention and support. A few months in, we implemented monetization and made our first sale within days of launching Stripe integration.
Usage-based pricing aligned well with our users' needs, as most didn't require ongoing analysis. Personally, making money directly from customers was exhilarating - a milestone "you can just do things" moment after years at early-stage startups.
Ignoring the "thin wrapper" stigma. In 2023, many believed model providers would capture all the value in AI. Our experience proved otherwise - users didn't care how Atticus worked, only that it solved their problem. Several users familiar with ChatGPT still valued our bespoke solution.
Cost projections proving correct. LLM prices plummeted as predicted, making Atticus more viable over time. GPT-4-32K initially cost $60 per million input tokens and $120 per million output tokens. Today, GPT-4o costs just $2.50 and $10 respectively - over 90% lower in less than two years.
Falling Forward: Learning the Hard Way
Building and shutting down Atticus taught me lessons no book could provide. Perhaps most surprising was realizing how different AI product development is from traditional software development - you're constantly navigating probabilities and edge cases rather than deterministic behaviors.
This insight has shaped Ascention, my venture that helps teams adopt AI while building AI-based products. My vision is for Ascention to be to the AI age what 37signals was for the SaaS era – creating products and methodologies that define best practices in this new paradigm.
I've realized that the magic happens at the intersection of deterministic systems and AI capabilities. These systems require fundamentally different approaches to design, development, and user expectations.
The marketing journey revealed unexpected global interest - about 60% of our traffic came from outside the US. Legal documentation is clearly a universal pain point, though this complicated our approach given the jurisdictional complexities of legal assistance.
I confirmed my passion for 0-to-1 building and high-ambiguity environments. Starting with a blank slate and rapidly testing hypotheses energizes me in ways that later-stage product development doesn't. For truly transformative ideas like Atticus, however, you need a 10-year vision with sustainable phases along the way.
Technically, I discovered that frameworks like Langchain eventually became obstacles as I developed deeper familiarity with LLMs. The technology evolved at breakneck speed - features we built with considerable effort later became standard in LLM APIs, and expanding context windows made our document chunking strategies increasingly unnecessary.
The most practical lesson? Experiment quickly, but establish clear criteria for continuation or exit. Monetize early, even imperfectly - revenue validates your concept and provides emotional fuel through inevitable challenges.
Perhaps the most profound discovery was about agency - the power of "just doing things." There's something liberating about taking an idea from concept to reality and having strangers value it enough to pay for it. It reminds me of Theodore Roosevelt's "Man in the Arena" speech: "The credit belongs to the man who is actually in the arena, whose face is marred by dust and sweat and blood..." Despite its outcome, I wouldn't trade the Atticus experience and the growth it fostered.
While Atticus didn't become the venture-scale business we envisioned, I remain bullish on AI's transformative potential in legal services and beyond. We were right about where the puck was going but underestimated what it would take to survive until it got there. These lessons have fundamentally shaped how I'll approach building products through Ascention - creating systems that harness AI's potential while acknowledging its unique characteristics.
Acknowledgements
This journey wouldn't have been possible without my incredible collaborators:
Dylan was the catalyst that set everything in motion. From our initial conversations to driving key decisions around rapid launches and monetization, his technical prowess and entrepreneurial instincts were invaluable. Dylan's ability to transform abstract ideas into working code gave Atticus its initial momentum. He's now channeling his considerable talents into his own venture, and I have no doubt it will be exceptional.
Greg brought strategic organization and clarity amidst the chaos of early-stage building. His remarkable talent for driving alignment and "sharpening thinking" helped us navigate crucial decision points. Greg has that rare ability to synthesize complex information, identify core issues, and orchestrate teams toward coherent action.
Lunar, my friend of over a decade, transformed Atticus from a functional tool into an experience users genuinely loved. Her extraordinary design sensibility elevated everything we built. In a world where skeptics dismiss AI products as "thin wrappers," Lunar's design expertise created an interface so thoughtful that users consistently praised the experience.
The Atticus journey reinforced something I've always believed: extraordinary collaborators make all the difference. I'm profoundly grateful to have worked alongside such talented individuals, and I look forward to watching their continued impact on the technology landscape.