The ground the application layer stands on is rising at a remarkable rate. Stanford’s 2026 AI Index measured what a single year of model progress now looks like: on OSWorld, a benchmark of AI agents doing real work on a computer, accuracy rose from roughly 12% in 2024 to 66.3% in 2025, within six percentage points of human performance. On Humanity’s Last Exam, a benchmark built to be hard for AI and favourable to human experts, accuracy jumped from under 10% to 38.3% over the same year. And the curve has not slowed since the report went to print: as of July 2026, the top score on the OSWorld-Verified leaderboard stands at 85%, clearly above the 72% human baseline, and the leading score on Humanity’s Last Exam has climbed to 46.4%. A system designed to ride that curve improves even while you sleep; a system designed around the current models’ weaknesses becomes obsolete on the same schedule.

What exactly are we improving?

We see accounting as a simple input-output system: bank statements and documents in, bookkeeping and filings out. We see no benefit in complicating that picture, least of all when the goal is a recurring self-improvement loop. What matters is being crystal clear about the actual transformation you want to improve (all natural phenomena are transformations: inputs and outputs). That judgement is the one thing humans are still uniquely positioned for. It is the simple decision of what to do.

Once you have decided what to optimise, in our case turning raw customer financial data such as transactions, invoices, and documents into auditable ledgers and reports, two steps remain: formalising the evaluation and harnessing the data.

How does the self-improvement loop work?

Formalising the evaluation (the “eval”) is a simple process: run your system on a set of test inputs and compare the results to a corresponding set of known-good outputs. Then grade the result: how accurate was the transformation? Run this daily, or several times a day, and you build up a historical dataset of how your system performs and which changes affect the score, positively or negatively. An agent can then analyse the results, compare them with changes to the codebase, and suggest or even implement improvements to the system. This creates a simple but powerful self-reinforcing loop.

Why does data quality decide who wins?

Evals amount to nothing unless they are built on high-quality data. Data is king here. If we train our systems on low-quality, untrustworthy data, the result will be low-quality, untrustworthy systems. We currently train our systems with select design partners (never on our customers’ data without consent), and we envision a world where we conjoin this loop with audit firms, effectively creating a system that passes audits more efficiently than existing solutions.

What does this mean for your company?

The shift is no longer hypothetical for the accounting industry. In Blue J and CPA.com’s June 2026 survey of more than 1,000 tax professionals, weekly AI use had nearly doubled in a year:

Source: Blue J and CPA.com, AI Tax Research Solution Outlook Report, June 2026.

Most of the industry is adopting AI as a feature. We are building the loop that compounds. If you’re a company that wants faster, cheaper, and more correct accounting, we’re the accounting firm to turn to. We’re an accounting firm on the outside, but an AI research company on the inside. As AI develops, we’re the only firm positioned to capitalise on it. By choosing us as your partner, you position yourself for the same growth. Join the waitlist.

The easiest answer would be that all (or at least some) of us have a deep passion for accounting. This would be an overstatement. Last Accounting Company is driven by a desire to transform an industry which seems to have fallen behind in technological development since the turn of the millennium. To our clients, Last Accounting Company is an efficient, professional, and reliable accounting firm, but internally, we are an AI company.

Last Accounting Company does not just give accountants AI tools. Improving an accountant’s efficiency marginally isn’t enough for us. When we were accepted into Y Combinator in spring 2026, we described our company as a full-stack AI accounting firm. In practice, that means rebuilding the accounting service from the ground up, not merely optimising at the margins by handing an accountant a paid Claude subscription or cutting costs with a chatbot built in the 2010s. With us accounting is not just necessary evil or a cost center, it is a growth opportunity.

We believe that the routine financial administration work currently done by humans will inevitably shift toward a world where AI performs day-to-day accounting tasks automatically, faster and more accurately than any person. It’s time for humans to move up the value chain and focus on what matters. Exactly as has happened after every major productivity innovation. We want to be at the forefront of that shift.

Accounting is not an industry where companies typically look for a technological edge. That’s precisely why it’s an excellent segment in which to create immediate value for Finnish and international businesses. Last Accounting Company’s clients are founders and entrepreneurs who recognize change before it becomes standard practice for their competitors and who want to take advantage of it before the mainstream even looks in the same direction. To date, nearly every proposal we’ve made has reduced our clients’ accounting costs, and our founding team’s response time is the sharpest in the market. At this stage, being involved doesn’t require a high risk appetite; it requires the ability to recognise where the industry is headed.

In upcoming posts, we’ll take a closer look at the technology and what’s happening in the AI space right now. Your future accountant follows AI developments daily and brings them directly into your service. Welcome to 2026 – and welcome aboard.