Why Right Now Is The Best Time To Be In Accounting And Finance

People were never the problem.

For decades, finance teams’ job descriptions were defined by the constraints of the software, not by what they were actually trained to do.

I saw this up close at the bank where I served as CEO. As we scaled, tools forced the finance function into execution mode: reconciling spreadsheets at midnight, chasing down variance explanations and manually pulling data that should have been self-serve.

The architecture hasn’t changed since 2000.

Over the course of my career in finance and operational roles, I’ve come to realize that accounting software was broken at the architecture level.

The general ledger hasn’t seen fundamental architectural innovation since roughly 1998, when NetSuite launched. That model survived longer than it should have because switching costs are high and finance teams are risk-averse by training.

AI is the first real rethink of how accounting software works at all.

That’s rare. Most professions go their entire careers without seeing the foundational architecture of their tools change. Finance professionals working today get to define what the function looks like on the other side of that shift.

The supply crunch creates leverage, not crisis.

Accounting degree completions fell 6.6% in 2024. In 2025, more than 90% of finance and accounting leaders reported difficulty finding qualified accounting talent.

For the right people, that gap isn’t a crisis. I talk to CFOs and controllers every single day. The ones leaning into AI aren’t worried about headcount. They’re asking a different question entirely: How do I build a finance function that scales faster than the business?

The supply-demand math is straightforward. A finance professional who can close faster, report more accurately and analyze more deeply with AI tools is not competing with the same pool they were competing with five years ago.

AI removes the bad parts of the job, not the job itself.

I believe the fear that AI will replace accountants misreads what accountants actually do at their best.

Journal entries, routine reconciliations and standard close checklists are automatable because they follow rules. Judgment, analysis and strategy are not automatable because they require context that lives in the finance professional’s head, not in the data model.

The work that kept finance teams in the office until midnight—the work nobody went to school to do—is the work AI is best positioned to absorb. What’s left is the work finance people actually trained for.

The moat belongs to finance.

One of the reasons I chose to build in accounting specifically is that accounting has two of the strongest AI moats in any profession: proprietary financial data and regulatory infrastructure.

SOX compliance, audit requirements, revenue recognition rules: A general-purpose AI model cannot navigate these constraints without deep human context. They require someone who understands both accounting and the AI. Right now that person is rare.

The finance professional who can work fluidly across both domains is sitting on expertise that almost no other profession can match. Proprietary data doesn’t generalize and regulatory judgment doesn’t commoditize.

The combination of accounting fluency and AI literacy is a defensible skill set. The window to build it before it becomes table stakes is open right now.

The culture has already shifted.

In the last year alone, I’ve hosted over 10,000 people at my company’s vibe coding sessions for finance. The term “Finance Engineer” is becoming a real title, not just a concept.

Finance people who would never have called themselves technologists are now experimenting, building and collaborating.

That’s a profession that’s found its moment.​

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