Ever since I met @sidd in our early days at Stripe, I hoped we’d one day find an idea big enough to start a company together. That day is finally here. After building quietly for the past year, I’m excited to officially launch @AccrualHQ, a new foundation for professional accounting 👇

Last month we held our inaugural Accrual Exchange. One question kept coming up: what happens after AI works? If it creates meaningful capacity, what does the firm do with it? What changes for clients, people, and the firm itself? A few things that stuck with me from the day: accrual.com/insights/a-bette…

I’m usually skeptical of sensational headlines, and while that’s true here, there are a few findings I found genuinely interesting, matching what we’ve observed working with many of the largest firms. The human study is 12 accountants across four month-end close tasks. More broadly, the benchmark heavily favors what frontier models are already very good at: processing huge amounts of data, searching across files, and meticulously following instructions. And whenever models are at or near 100% on a benchmark, I start wondering whether we’re measuring general capability or a task distribution models have become particularly optimized for. But there were two things I found much more interesting: First, the authors themselves point out how different the benchmark is from real accounting work. In the real world, accountants have months or years of accumulated client context, can ask questions and clarify ambiguities, and generally work on problems aligned with their expertise. This gets at something we think about a lot at Accrual: accounting is deeply contextual. The work depends on client history, prior work, firm knowledge, conversations, and data spread across many different systems. Second, giving the accountants Claude didn’t really help. Accountants using Claude were ~15x slower than Claude alone and slightly less accurate. In several cases, Claude had the right answer and the human + AI workflow still ended up submitting the wrong one. My takeaway: There’s a big difference between a model being capable of doing the work and making that capability useful to an accountant. Simply giving every accountant Copilot doesn’t bridge that gap. If the accountant still has to assemble all the context, explain the task, prompt the model, interpret the answer, and then go do the work, you haven’t actually automated much. That’s a big part of why we built Arc the way we did. Arc starts with the context of the firm and its clients, works across the systems accountants already use, and does the work itself. The accountant reviews the finished work rather than driving the model step by step. The models are clearly getting good enough. The interesting problem now is building the context and workflows around them. That’s what we’re building with Arc: accrual.com/arc

We built “ARC” internally for Accrual earlier this year. Every candidate and new hire raved about what it’s capable of, across the board. Inevitably someone asked the obvious question: why wouldn’t we give this to our customers, purposefully built for firms? Seeing accountants use Arc in the few weeks brought a sense of excitement and joy similarly to early prototypes of Accrual. We can’t wait to see how you put it to work, in ways we haven’t even thought of. Enjoy!

2) is precisely how I feel on a daily basis

Big day for @AccrualHQ: we’re acquiring the team and technology behind @puzzlefin's AI Close, Cowork and Chat products, accelerating our expansion into CAS and our vision for one AI platform for the entire accounting firm. Delighted to welcome @sashaorloff and team aboard! 🚀 👉 accrual.com/insights/puzzle

As great as Sam is at hyping, Monaco exceeds it 🔥

Europe in summer isn’t exactly famous for productivity. We did our part, for one week, at least