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When Tax Day Was Party Night at the Post Office — And Why AI Is About to Upend Everything Else About Accounting

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Before tax e-filing took over, April 15th was a public spectacle at American post offices. As Blake Oliver and David Leary discussed on their Tax Day episode of The Accounting Podcast, crowds would gather until midnight, with live entertainment, giveaways, and even Playboy offering “stress relief massages” in pink booths. In Philadelphia, there was a “dunk the IRS agent” booth for charity. Radio stations broadcast live. Fast food chains handed out samples. It was America’s weirdest annual party.

Those days are gone — 94% of returns are now filed electronically. But as the hosts explored in this wide-ranging episode, the accounting profession faces disruptions far more profound than the shift from paper to pixels. Within three years, KPMG expects routine audit testing to have “next to no human beings” doing the work. Hobbyist developers are cloning QuickBooks with AI over a weekend. And a third of workers aren’t even checking AI outputs before they submit them.

The IRS Can’t Keep Up — With Rules or Technology

The profession’s struggles with rapid change start at the top. Just five days before the filing deadline, the IRS finalized which jobs qualify for the new no-tax-on-tips deduction. Podcasters made the cut (Oliver and Leary were pleased), along with tattoo artists, ice sculptors, and golf caddies. Accountants didn’t.

“Five days after they finalized these rules to implement them for our clients,” Oliver noted with frustration. The deduction allows eligible workers to exclude up to $25,000 in tips from taxable income, but mandatory service charges don’t count. “This could be the death of the automatic gratuity,” Leary speculated, since those forced tips won’t qualify.

Meanwhile, Americans are spending 11.6 billion hours completing federal compliance forms — mostly tax returns. The value of that labor? Over half a trillion dollars. “That’s material,” Oliver said, noting it represents a significant chunk of the economy devoted to paperwork.

The IRS’s own modernization efforts tell a cautionary tale. The agency had 126 AI projects running as of last summer, up from just 10 in 2022. But after losing 25% of its workforce, 61% of those projects remain unfinished with no plan to close the skills gap. Even more puzzling: the IRS killed its Direct File program despite it costing only $16 million instead of the estimated $61 million and growing 78% year-over-year. “The program was gaining traction and was less expensive than they thought it was going to be, and yet it got canceled anyway,” Oliver observed.

The Big Four’s Radical Restructuring

While the IRS struggles with basic modernization, the Big Four are racing ahead with AI automation that could eliminate thousands of jobs and upend the billable hour model that has defined the profession for decades.

KPMG is moving fastest. They’re piloting AI systems this summer and deploying them next year for routine testing of transactions like payroll, receivables, and cost of goods sold. “Within 2 or 3 years, routine testing could become the first major audit area with effectively no human audit team directly doing the work,” Oliver quoted from KPMG’s audit chief digital officer. “Next to no human beings.”

The other firms aren’t far behind. PwC’s evidence-matching tool now processes 30 client document types, up from six months ago. EY is testing something even more futuristic: AI audit agents that talk directly to client AI agents to gather documents and prepare workpapers. Only Deloitte is publicly pumping the brakes, emphasizing AI should “augment not replace” human auditors.

The numbers are stark: Big Four leaders expect 20-30% of a typical audit to be fully automated by 2029. KPMG UK is already cutting 440 audit jobs. “I don’t see any other outcome than the Big Four just cutting massive numbers of staff jobs,” Oliver said. “If they do this right… that’s 20 to 30% of their billable hours. What are they going to do? Just raise their rates 20 to 30% to compensate?”

Leary had the line of the episode: “Agents are the perfect accounting firm employees. The partners are going to love them.”

The traditional career path is crumbling too. EY’s talent chief told Business Insider that linear career models are becoming “less relevant” as AI values skills over tenure. Oliver speculated firms might shift from hiring masses of new graduates to recruiting experienced professionals from industry, or moving to an apprenticeship model with smaller, more intensively trained classes.

Everyone’s Building Their Own QuickBooks Now

The disruption isn’t just coming from the top. A Reddit user built a full accounting system that runs inside Claude Desktop — no interface, just chat. You tell Claude what happened, and it updates your books. Another developer cloned QuickBooks Desktop using AI, creating a free open-source alternative. The motivation? “I didn’t want to pay for QBO.”

“You as an accounting firm had control over your tech stack and your clients’ tech stack,” Leary explained. “We’re a Xero shop or a QuickBooks shop… Now your clients are just building their own stuff. How do you as a firm manage this now?”

Oliver’s prediction, based on every past tech revolution: “We will end up with more work rather than less, because it will enable our clients to do way more accounting stuff that we’ll have to clean up.”

On the funded startup side, Juno raised $12 million to build AI tax prep that automates 90% of data entry while keeping CPAs in the loop. The key: transparency over autonomy, with source-to-return traceability and visual validation tools. Artifact launched Omni, which Leary called “a Zapier for accounting firms” — it trains AI agents to use your existing tech stack rather than replacing it.

Meanwhile, legacy players are scrambling. Xero published a blog post claiming to be an “AI native operating system.” Leary counted over 20 buzzwords and read them aloud in a devastating list: “AI native, intelligent SaaS, autonomous finance, system of action…” His verdict: “I don’t think this is written for customers. I think this article is written for the street in an attempt to move the stock price.”

The Quality Crisis Nobody’s Talking About

Here’s what should terrify every firm leader: 35% of workers rarely or only occasionally review AI output before submitting it, according to a Resume Now survey. Eighteen percent trust it straight out of the box. Only 40% review AI output every single time. And 15% use AI at work secretly without telling their manager.

“That should scare you as an accounting firm owner,” Leary said.

Oliver argued firms need systems with built-in controls: “If an employee is just generating something with AI… and they didn’t change anything or they didn’t spend any time looking at it, then flag that.”

The stakes are real. The episode covered two fraud cases that show what happens with weak oversight. A New Jersey preparer filed over 100 false returns seeking $170 million in pandemic credits, getting $55 million before being caught. A Pennsylvania preparer started a new $5.5 million fraud scheme while still on supervised release from a previous conviction.

What Separates Winners from Losers

A Hinge Marketing study of 133 firms revealed a massive performance gap emerging. High-growth firms are growing at 33% annually versus 9.6% for average firms. The difference? High-growth firms spend 9% of revenue on marketing (versus 5% for others), and over 90% use AI for content creation, automation, and research.

“If you have a firm that’s growing at 10% and you want it to grow at 30%, spend 10% of your revenue on marketing,” Leary summarized, though Oliver questioned whether it’s causation or correlation: “Is it just that the firms that are growing really fast have money to burn on marketing?”

The Reckoning Is Here

The accounting profession has always adapted slowly. As Leary noted, “Just ask Xero how it takes decades for them to barely make a scratch into the QuickBooks world.” But this time feels different. The changes are coming from every direction at once — Big Four automation, bedroom coders, funded startups, and clients building their own systems.

The irony is thick. Even as AI promises to make location irrelevant, EY is requiring US tax staff to work in-office 12 days a month. The IRS has 126 AI projects but can’t finish them. Firms are adopting AI while a third of workers don’t even review its output.

For firms willing to invest, experiment, and build proper controls, the opportunity is massive. For those hoping to wait it out, the message from this episode is clear: the profession that gathered at post offices until midnight to file paper returns is gone. The question isn’t whether AI will transform accounting — it’s whether the profession can maintain its core promise of trustworthiness while everything else changes around it.

To hear the full discussion — including the story of a disgruntled worker who burned down a $500 million Kimberly-Clark warehouse over pay disputes — listen to the complete episode of The Accounting Podcast.

A Simple Practice to Help Professional Women Stop Feeling Like They Haven’t Accomplished Enough

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When Questian Telka texted Nancy McClelland a photo of her newly framed diploma, she couldn’t help but undercut the moment. “I know it’s not a big deal because everyone has one,” she wrote, “but I never thought that I would actually do it.”

This degree took multiple attempts and years to complete. She’d been chasing the goal and finally crossed the finish line. And her first instinct was to shrink it.

Nancy wasn’t having it. “It’s actually a really big deal,” she fired back. “To say that it’s no big deal is silly. It’s a big deal because everyone else has one and you didn’t.”

That text exchange became the seed for the season two finale of She Counts, the real-talk podcast for women in accounting. Hosts Questian and Nancy brought on Valerie Heckman, accountant community manager at OnPay and keynote speaker, to dig into a concept that started as a single line in Valerie’s presentation at Scaling New Heights and became the thing everyone wanted to talk about afterward: the ta-da list.

Women in Accounting Are Wired to Overlook Their Own Wins

Valerie has spent nearly 15 years working alongside accountants and bookkeepers. She’s not an accountant, but she’s watched the profession long enough to spot patterns that run deep, especially among women.

“Very high internal standards,” Valerie said, naming it plainly. “The goal is getting things done, getting things done right, solving big problems, and staying on top of deadlines.”

That’s what makes accountants exceptional. “I think that can also come with a lot of focus on what’s left undone,” Valerie said, pointing out the shadow side. “Your brain is always managing, ‘Okay, we got this thing done, but now we’ve got to do this.’”

Nancy recognized herself immediately. “You just described my brain when I go to bed at night. I don’t go, ‘Oh, look at everything I did today.’ I go to bed and think, ‘Oh my God, I didn’t get this, that, and the other done today.'”

Then there’s the self-effacing reflex Valerie has heard countless times. “Oh, well, this is just what I do. I’m here to help. It was nothing.”

The problem compounds over time. When you only focus on what’s undone, Valerie explained, “We get very critical of ourselves. We start comparing ourselves to others. We start doubting ourselves.”

She spoke from personal experience. Before discovering the ta-da list, Valerie was burnt out, although she didn’t fully recognize it then. “I was on that constant hamster wheel of getting things done, but not necessarily feeling like I accomplished anything.” She’d write everything on post-it notes, stick them on the wall, and tear them down as she completed tasks. But every day brought more post-its. The wall was never clear.

“This is not a cure for burnout,” Valerie was careful to add. “I don’t want to sound like a Pollyanna or suggest we’re fine if we just focus on the good things. Absolutely not.” But it can be one tool in your toolbox.

What is a Ta-Da List?

The concept came from Gretchen Rubin’s podcast Happier with Gretchen Rubin, where it was mentioned almost in passing. A ta-da list runs alongside your to-do list but captures the opposite: what you got done, plus anything that enriched your life.

“You still need to-do lists,” Valerie emphasized. “Sometimes people get confused. They think I’m saying don’t keep a to-do list. No.” The ta-da list is the complement that captures what the to-do list doesn’t show you.

To prove the concept works, Valerie asked the hosts to name three ta-da moments from the past week.

Nancy had one instantly. She’d repotted two plants in her garden. Questian struggled. “Three things? That’s, uh…” The difficulty was the point. When she finally landed on something, it was huge. She and Nancy received their trademark for She Counts that week, after nearly a year of applications and responses.

“Circle the one you’re most excited about,” Valerie instructed. Nancy deliberately chose the smallest, the plants. “Because the small things are the big things,” she explained.

Valerie validated the choice immediately. “That’s such a great example because it’s something you did for yourself. Those are the most important things to celebrate. You actually stopped the busyness of life and did something for your own enjoyment.”

For Valerie, keeping a ta-da list in her planner with pen and paper made things tangible. But she’s seen people use voice recordings, photos, or digital notes. The method matters less than consistency.

What shifted for her went beyond feeling better. She felt more grounded, more capable. She started recognizing effort, not just outcomes. But the most surprising discovery was what was missing. Personal goals she’d been announcing every January but never pursuing became impossible to ignore. Speaking on more stages was one of them, and the gap in her data pushed her toward that Scaling New Heights keynote.

The list isn’t just a nightly ritual either. “On a bad day, I can look back at the ta-da list and be like, I’ve been there before. I’ve done this before. I am capable.”

Nancy was floored. “So you’re not just making this list before you go to bed. You’re going back and looking at these lists when you’re having a bad day.”

That’s the resilience piece. It’s documented proof of your own competence, waiting for exactly when self-doubt shows up loudest.

The Magic of Sharing Your Ta-Da’s

The practice transforms when you let other people in on it.

An accountant named Nancy Jacobson approached Valerie at an event with a story that made her tear up. Jacobson started doing ta-da moments with her son at dinnertime. “I ask my son about his day and what his ta-da moments are. Then I share mine and we talk about it as a family. It’s made us closer.”

There’s also Ali Szymanski’s story. Nancy’s right-hand woman at The Dancing Accountant emailed Nancy when she completed her first wage reconciliation. “I know this is silly, but…” she’d written. It wasn’t silly. It was a ta-da moment worth celebrating.

Valerie suggested teams open meetings by asking, “What’s one good thing that happened in the last week?” One group she worked with realized they always jumped straight into tasks, everyone already overwhelmed before the first agenda item.

The conversation turned to something that made everyone laugh: gold stars. “We grew up being very motivated by gold stars and scratch-and-sniff stickers,” Valerie said. We don’t have to stop giving them to ourselves just because we’re grown ups.

Nancy had proof this works. During a weight loss journey, she used an actual sticker chart with foil stars. Her husband Mark offered to make an Excel version. She told him that was nice, but she needed real stars on real paper. She lost 20 pounds and kept most of it off for over a decade.

But celebration doesn’t mean ignoring struggle. “Gratitude doesn’t have to cancel out your struggle,” Nancy said. “It doesn’t mean you are not also struggling.” You can be drowning in a software transition during tax season and celebrate that someone helped you at 10 p.m. on Zoom. You can be exhausted and notice that your repotted plants look beautiful.

Your Ta-Da List Starts Tonight

Valerie’s keynote slogan crystallized the whole concept: “Less to-do’s, more ta-da’s.”

If your days are so packed with tasks that there’s no room for anything worth celebrating, something needs to come off the list. As Valerie put it, “Mick Jagger does not tune his own guitars. What are the things that only I can do?”

Here’s what you can do starting today:

  • Write down three things tonight. What did you accomplish or what enriched your life today? If you can only think of one, that’s fine. The difficulty means you need this.
  • Circle the smallest one. The little things are the big things, whether that’s repotting plants, calling difficult clients, or making it to the airport on time.
  • Keep old lists. On bad days, they’re proof of your competence.
  • Notice what’s missing. The gaps reveal goals you keep announcing but never pursue.
  • Share your ta-da’s. Text a friend. Open team meetings with wins. Say names out loud when people help you.
  • Make room for more. Eliminate, automate, delegate. Create space for things worth celebrating.

For women in a profession that measures value in accuracy and completed tasks, learning to see (and say out loud) what’s going right is an act of resistance against the voice that says “it’s nothing.”

It’s not nothing. Frame the diploma. Put the gold star on the chart. Ta-da!

Listen to the full episode to hear Valerie’s live exercise and the Marge Piercy poem that closed the show. Then head to the She Counts LinkedIn page and share something you celebrate that might seem silly to others.

Not All AI Is Created Equal and Your Next Software Decision Depends on Knowing the Difference

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When Jeff Seibert ran consumer product at Twitter, he asked the finance team for his budget to throw a team event. They said they’d get back to him in 45 days. So he just ran the event without them.

That gap between real-time data and 30-to-90-day delayed financial reports was frustrating, and it eventually led Jeff to build Digits, a new general ledger designed from scratch for the machine learning age. After raising $100 million pre-launch, testing 2,000 monthly closes, and getting 80% of clients closed in under an hour, Digits launched in March 2025. Now, just over a year later, hundreds of accounting firms are onboarding thousands of clients onto the platform.

Jeff launched Twitter’s algorithmic timeline in 2016, and it was one of the first global deployments of machine learning. Now, the AI revolution Jeff helped launch is flooding the accounting profession with claims that are hard to verify. Every accounting software company seems to include AI in its marketing copy, promising everything from “fully automated bookkeeping” to capabilities that don’t add up under scrutiny.

In a recent Earmark webinar, host Blake Oliver and Rob Hamilton, Head of GTM at Digits, pulled back the curtain on how AI in accounting actually works. He was joined by Megan Reid, Product Specialist & Firm Enablement at Digits, who fielded questions throughout the session.

Every AI claim in accounting software isn’t real. But accountants who understand the four core model types (plus one common lie) will make smarter investments, automate the right parts of their workflow, and position their firms for a shift Rob sees coming by the end of 2026.

The AI hype problem (and one question to cut through it)

Before making any technology decision, you need a filter for separating real capabilities from marketing fluff. Rob offered a simple one that cuts through the noise.

He showed screenshots from multiple accounting software companies making bold AI claims. One promised “fully automated bookkeeping.” Another asked, “Do you do AI bookkeeping or do you use a dedicated team of experts?” The positioning has gotten so confusing that firms can’t tell what’s real anymore.

The confusion isn’t new. About five years ago, tech investor Naval Ravikant tweeted, “In most pitch decks, AI stands for Anonymous Indians.” For a long time, that was literally true. Services like Botkeeper rose and fell using offshore labor dressed up as automation. Today, “AI actually means we just bolted on and sent all of your data to ChatGPT,” Rob explained.

Here’s your filter: “AI is the same thing as machine learning,” Rob stated. “If someone is talking to you about AI and they’re not referring to machine learning as the underlying premise, it’s just BS.”

But this filter only works if you understand what machine learning actually is.

Traditional software is straightforward. You write code that tells the computer exactly what to do. It’s tedious to build, but rock solid once it works. Machine learning flips this completely. You feed the system thousands or millions of examples, and the model learns the patterns itself. As Jeff explained in a clip Rob played, “You give the computer the goal state—I want this outcome—and then the computer itself is learning how to do it.”

These models are neural networks. Thousands of hidden layers mimic how neurons connect, based on Google’s 2017 “transformer” research paper (the “T” in GPT). It’s a massive matrix multiplication problem where the system figures out how variables relate to each other.

But machine learning isn’t one thing. Different model types have different strengths and uses in accounting. Understanding these distinctions helps you avoid buying the wrong software and shows you exactly where AI can save time and where vendors are overselling.

The model types that matter (and one that doesn’t)

Rob walked through five categories that get lumped under “AI,” but understanding the differences is what separates informed decisions from expensive mistakes.

Generative models

Large language models (LLMs) are the ones you hear about most, ChatGPT being the prime example. GPT stands for “Generative Pre-Trained Transformer,” and these models generate the most likely continuation of whatever prompt you give them. Rob showed a useful application: turning bullet-point close notes into polished client emails. His advice is to write a “job description” for the AI once. Tell it who it is, give context, specify output format, add examples. Then just paste in different client notes as needed.

But generative models have serious limits. They’re “super eager” and always want to complete prompts, making them prone to hallucinations, or making things up that sound real. They’re bad at math because they generate text rather than calculate numbers. And they’re trained on the internet, not your specific clients. “The ways that it is hallucinating is stuff that maybe even is hard for humans to catch sometimes,” Rob warned. Always review the output.

Agents

These are LLMs with help. You give them a job description, a task, and tools, like computer programs they can use to generate reports, list accounts, or run calculations. The agent makes a plan, uses its tools, checks if the task is done, and loops until complete. Rob showed Digits’ agent answering “I want to hire 20 software engineers next year. Can I afford to?” with a data-backed response.

Guardrails are critical. Microsoft’s early agent “started asking people on dates in the chat,” Rob noted. “You don’t want your accounting agent dispensing dating advice.” Agents work well for updating schedules, running quality checks, and answering analytical questions, but they’re slow and need careful boundaries.

Predictive models

These got Rob visibly excited, and for good reason. These models take an input and predict an output from known options. When the model sees a $5 Starbucks charge, it considers the client’s location, history, and chart of accounts. For a local client, it’s meals and entertainment. For a New York client in California, it’s travel. A $157 Starbucks charge is probably an event, regardless.

What makes predictive models perfect for transaction categorization is they can’t hallucinate; they only choose from existing options. They’re deterministic (same input, same output), include confidence scores, and run fast and cheap once trained.

Digits built a “layer cake” of predictive models:

  1. Client-level (learns each business)
  2. Firm-level (encodes your best practices)
  3. Global (trained on 180 million transactions worth nearly $1 trillion)
  4. An LLM fallback for completely new transactions

The result was over 97% accuracy, compared to standalone LLMs that plateau below 80%, which is about the same as outsourced bookkeepers.

Document extraction models

These combine OCR with layout-aware language models that understand document structure. Previous tools used Amazon’s Mechanical Turk, which relied on humans manually extracting data and took hours. Modern extraction models work in seconds. Digits’ bank reconciliation automatically pulls PDF statements, matches transactions to the exact spot in the PDF, and generates audit reports.

Data analysis

This model is where Rob pulled the rug out. Financial reporting and analysis is “actually just math. It’s not ML.” Computers have done statistical analysis for decades. Could you build an agent to do it? Sure, but it would be slow, expensive, and probably wrong. “If anyone says their AI does reporting and statistical analysis, please ask them what they’re talking about.”

Here’s how the right models map to your month-end close:

  • Book transactions: Predictive models (with LLM fallback)
  • Reconcile statements: Extraction models plus matching algorithms
  • Update schedules: Agents
  • Review and correct: Agents with quality checklists
  • Analyze and report: Statistical analysis plus agents for questions

“Shoehorning an LLM in to solve a problem and just sending a bunch of information is fundamentally incorrect,” Rob emphasized. Each step needs the right model. No single AI approach handles everything.

The 2026 prediction

Understanding model types is just the foundation. The urgency comes from how fast everything is converging.

“Across a large client set in different industry types, it’s highly likely that the month-end close process is looking to be completely automated by the end of 2026,” Rob predicts. Even his “95% automated” hedge probably sounds aggressive. But his logic follows directly from the technology.

If predictive models hit 97% accuracy on transactions, extraction models automate reconciliation in seconds, agents handle schedules and quality control, and statistical analysis covers reporting, then manual work drops to a fraction. Rob’s goal is to see accountants doing “1/20th of the work you’re doing today.”

He acknowledged limits. Construction firms with complex job costing might not hit that threshold. But for firms serving professional services, cash-basis businesses, and straightforward accrual clients, the automation curve is steep.

An AI-native firm will focus on value instead of tedium. Deeper industry expertise. Stronger client relationships. Higher margins. You’re not reviewing every transaction, you’re supervising the system and handling exceptions. Those hours saved on reconciliation become advisory time clients actually value.

But this is also a competitive necessity. “AI won’t replace you. Someone who’s good at using AI is going to,” Rob said, quoting a common warning. And he was direct about the stakes. Firms that don’t adapt will face “a cascading effect on business models” as early adopters pull ahead.

For overwhelmed firms, which Rob acknowledged includes most firms, he offered practical starting points:

  • Map your processes first. If you use workflow tools like Karbon or Keeper, you’ve probably documented your steps. If not, start there. You can’t identify where AI fits until you know what you’re actually doing.
  • Start small and low-stakes. Don’t tackle your biggest challenge first. Try drafting emails, testing categorization, or visualizing data. Build your intuition gradually.
  • Get hands-on with new tools. Rob mentioned being impressed by Claude Opus, which could build HTML dashboards from his data (something he couldn’t do as a non-engineer). The specific tool doesn’t matter; hands-on experience builds judgment.
  • Know your business before choosing where to start. As Rob put it, “You need to know the details of your business to know where you can start and where the right places to poke and prod are.”

The “wait and see” window is closing. Firms that develop AI literacy now by asking questions about models, data handling, and use cases, will be ready for the rest of 2026 and beyond.

Your next move: Better questions, smaller steps, faster action

Let’s turn Rob and Megan’s insights into actionable takeaways:

  • Not all AI is equal. Four real model types plus one fake (data analysis) get lumped together. When vendors pitch “AI-powered reporting,” you now know to dig deeper.
  • Each close step needs a different model. Predictive for transactions. Extraction for reconciliation. Agents for schedules. Statistics for reporting. Anyone claiming one solution does everything deserves scrutiny.
  • Predictive models beat LLMs for categorization. Layered architectures that learn your clients and firm patterns dramatically outperform chatbots. Bigger isn’t always better.
  • Ask vendors the hard questions. What model type? Where does data go? Are you training your own models or sending financial data to third parties? This is due diligence.
  • The tipping point is closer than you think. Whether Rob’s 2026 prediction proves exactly right or directionally right, the trajectory is clear. Understanding these distinctions now positions you to take action.

For those interested in going deeper, Rob mentioned resources like the AI Native Accounting Foundation and the AI-Native Accounting podcast hosted by Kacee Johnson, where industry leaders discuss the latest developments.

The accounting profession is at a real inflection point. Smart firm leaders will develop the literacy to ask smart questions, experiment in the right places, and redirect time from tedium to advisory work clients value.

Rob noted this might be one of the few professions with such clear AI use cases, putting accountants at the forefront of innovation. That’s an opportunity to shape how technology serves the profession, not the other way around.

Watch the on-demand webinar for complete details, including live demonstrations, security architecture specifics, and audience Q&A covering nonprofits, inventory clients, and platform migrations. The future of accounting is being written now. Make sure you’re part of the conversation.

She Tried to Sell Her Firm Three Times Before Moving It to a Beach in Mexico

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Sandra Koch tried to sell her accounting firm three times over ten years. She was burned out and ready to quit. Today, she runs that same firm from a beach town in Mexico with dirt roads and one stop sign. And she’s never been happier.

In this episode of Who’s Really the Boss?, hosts Rachel and Marcus Dillon talk with Sandra, founder of Aurora Consulting Group. She shares her journey from owning a building in California to running her firm remotely from Baja California Sur. The conversation gets real about the anxiety of closing an office, the grief of letting go, and the unexpected freedom that followed.

The Dream Building That Had to Go

Sandra did everything by the book. She founded Aurora Consulting Group in San Diego in 2011 with one assistant. Three years later, she was juggling two offices—one in San Diego and one in Visalia, deep in California’s farmland. For 16 months, she went back and forth between the two locations. Eventually, she closed the San Diego office. “That wasn’t really working too well,” she admits.

Then came the building in Visalia. Sandra searched for a year before finding it. She bought it, remodeled it, and made it exactly what she wanted. “It was ego feeding, and it was a status symbol,” Sandra says on the podcast. She’s not embarrassed. It felt like success.

Marcus gets it. He grew up believing the ultimate achievement was having your name on a brick building where clients came to you. “That meant you made it,” he says. The day before recording this episode, Marcus and Rachel had just sold their own “forever building.”

By August 2023, reality hit Sandra hard. Clients weren’t coming to the office anymore. Some staff had moved away and were already remote. She was paying for an empty building.

“I wouldn’t wish the anxiety that I experienced during that time on anybody,” Sandra recalls. “But I knew it was the right thing to do.”

When Aurora Consulting Group went fully remote, Sandra was surprised by the grief she felt. “I had this dream, and then the dream kind of fell apart,” she explains. “Letting go of the dream felt like, wait, what do I do now?”

Marcus admits he also tends to remember only the good parts about having an office. You forget the commute, hiding from walk-in clients when you don’t have time, and dealing with frozen pipes. “I only remember the good days,” he says.

Sandra went through the same mental battle. “I’ll get sad about it. But then I’m like, Sandra, do the math. The math says it doesn’t make sense.”

A year after going remote, Sandra realized she could live anywhere. She wasn’t tied to Visalia or even California anymore. In 2024, she moved to Baja California Sur, Mexico, a coastal town with 1,800 people, dirt roads, and 25 varieties of whales passing by.

“The freedom I have from letting go of a physical location has been profound,” Sandra says. Every morning, she watches the sun rise over what Jacques Cousteau called “the world’s aquarium.”

She keeps a small office in Visalia for when she visits and has a part-time assistant who handles the occasional bank deposit. She learned some lessons the hard way, like discovering U.S. banks require a physical presence in the country to maintain accounts.

But that building with her name on it is gone, and she’s more proud of her firm now than ever.

Staying Close From 1,500 Miles Away

Going remote created new challenges. How do you stay connected to clients you genuinely care about? How do you keep a scattered team feeling like a team?

Sandra’s approach to clients is simple. She flies back three or four times a year and takes them to meals, one-on-one. No group events or presentations. Just food and conversation.

“I care about them and miss them. I want to see them just like I would want to see my family,” she explains. The one-on-one format is intentional. “That’s where the magic is. They tell me what’s really going on with them.”

Her clients’ warm response surprised her. They’re genuinely excited to see their CPA up in person.

Marcus shares a similar story. When a client who had sold his business invited Marcus to visit his farm, Marcus took him up on the offer and saw the excitement in the client’s eyes. They spent the day at the farm. No tax talk, just relationship building.

Building Team Culture Without an Office

Sandra’s team of six is spread across California and beyond. Her first remote hire four years ago turned out to be the right fit and set the standard for what worked.

Three things make remote work function, according to Sandra: training, culture, and communication. “You have to be religious about it,” she says.

The centerpiece is their Tuesday morning meeting at 10 a.m.. The key to this meeting is it’s not about work. The team shares what they need help with, their wins, and their struggles. Then they discuss their monthly book, with a $100 bonus for anyone who finishes it. They wrap up with “happies and crappies” (highs and lows).

Rachel points out that putting even modest money behind expectations shows the team you value the activity. “Start lower than you think,” she advises. “You can always increase an incentive, but it’s nearly impossible to reduce one.”

Sandra also discovered her team loves company swag. Nice jackets at Christmas had everyone excited. “It makes me realize they’re proud of the team they’re on,” she says.

In-person moments matter too. Sandra took the team to Intuit Connect in Las Vegas, where some team members met face-to-face for the first time. “They still talk about it,” she says. These investments show “I’m putting my money where my mouth is.”

As a result, Sandra believes she’s actually better at her job now.

“My clients get a better version of me,” she explains. “They get a less stressed-out version of me. I’m more present for them now because I’m not dealing with all the things attached to a physical location.”

The Science Experiment That Changed Everything

Sandra managed a lot of change in a short time period by changing how she thinks about trying new things.

“I used to think trying new things meant it would either succeed or fail,” she says. “When I changed to thinking ‘I’m doing a science experiment to see what happens,’ it really helped me.”

A science experiment doesn’t fail. It gives you data. You try something, see what happens, and decide whether to continue or pivot.

“I don’t have to commit to anything,” Sandra explains. “Not to software, not to a staff member, not to a client. When I go in thinking ‘I don’t have to commit, but I’m willing to try because I’m curious,’ it takes all the pressure off.”

This requires humility. You have to be honest about what’s working. Sandra’s team serves as a reality check, and her husband keeps her grounded when her curiosity pulls her in too many directions.

The results speak for themselves. “Our internal workflows went from practical nonexistence to a well-oiled machine very quickly,” Sandra says. “When something wasn’t working, we dropped it and went on to the next thing.”

Her 2026 goals show how far this mindset has taken her. Aurora has just three goals this year, down from 29 last year and 52 the year before. The three words: align, refine, and define. No big initiatives. Just steady improvement of what’s already working.

Finding Her People Made the Difference

Sandra credits one encounter with saving her firm. In November 2022, she heard Marcus speak at Intuit Connect. She got on the mailing list for Collective by DBA and signed up for their first in-person event.

“I heard a message of hope,” she remembers. “Aurora would not exist today if I hadn’t met you.”

Before that, she felt alone. Now, “I feel like I’m part of a community for the first time in my career,” she says. “A community that cares about me.”

She hasn’t missed a single Collective event. She brings team members. She reads every email, asks questions on the forum, and shares what she knows with others.

“It feels safe,” she explains. “I can be my messy self with you guys.”

When Rachel asks about her best advice, Sandra doesn’t hesitate: “Trust God, clean house, and help others.” Keep your side of the street clean. Look for opportunities to serve. Know you don’t have to control everything.

That philosophy carried a burned-out firm owner from trying to sell her practice to running it from a beach in Mexico. And she’s more proud of her work than she’s ever been.

Your Turn to Experiment

Sandra tried to sell her firm three times. Today, she wakes up to the sun rising over the Sea of Cortez and runs a thriving practice. Her transformation required questioning one assumption: What does a “real” accounting firm look like?

Here’s what she learned:

  • Physical space isn’t mental space. Without a building’s demands, Sandra became more present and effective. Her clients and team got a better version of her.
  • Remote doesn’t mean distant. One-on-one client visits, weekly team meetings that skip the work talk, book clubs with incentives, and company swag can build stronger connections than any conference room.
  • Make everything an experiment. Calling new initiatives “science experiments” removes the fear of failure. You’re just collecting data.
  • Nothing has to be permanent. You don’t have to commit to software, locations, or structures forever. Curiosity beats fear every time.

For every firm owner wondering if there’s a better way, Sandra’s story says yes. But only if you’re willing to run the experiment.

Listen to Sandra’s full conversation with Rachel and Marcus on Who’s Really the Boss? The details that don’t fit in an article make her story even more valuable for any firm considering remote work.

Rachel and Marcus Dillon, CPA, own a national, remote client accounting and advisory services firm, Dillon Business Advisors, with a team of 26 professionals. Their latest organization, Collective by DBA, supports and guides accounting firm owners and leaders with firm resources, education, and operational strategy through community, mastermind groups, and one-on-one advisory.

AI Agents Now Complete Tax Returns Start to Finish While the Government Can’t Even Audit Its Own Books

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The US government just declared itself insolvent. AI agents are completing tax returns without human intervention. And the accounting profession is caught between these two massive disruptions.

In Episode 481 of The Accounting Podcast, hosts Blake Oliver and David Leary opened with a bombshell that somehow flew under the mainstream media radar. The Treasury Department’s own financial statements show the US is $42 trillion in the red, and that’s before counting Social Security and Medicare obligations. They then dove into an equally seismic shift with guest Kenji Kuramoto, founder of Acuity and newly appointed Managing Partner in Residence at AI company Basis, exploring how artificial intelligence is transforming every corner of the accounting world.

Deficit Spending Just Keeps Going

“It’s official. We are insolvent,” David announced at the start of the episode, referencing the Treasury’s 2024 financial statements. They show $6 trillion in total assets against nearly $48 trillion in total liabilities. That $42 trillion hole doesn’t even include the $88 trillion in unfunded Social Security and Medicare obligations sitting off the balance sheet.

“Imagine a family making $52,000 that owes $1.3 million in a line of credit,” Blake said, putting the crisis in household terms.

Making matters worse, the Government Accountability Office issued a disclaimer of opinion for the 29th consecutive year, essentially saying it can’t even verify the accuracy of the numbers because the Department of Defense has never passed an audit.

“This is the reason a huge number of people voted for Trump,” David said. “They wanted to stop deficit spending, and it just keeps going.”

Meanwhile, AI Is Eating the Accounting Profession

While the government’s books are falling apart, AI companies are racing to automate the work of keeping everyone else’s books together. TaxGPT announced an AI agent capable of completing 1040 returns from start to finish without a preparer touching a keyboard or mouse. The tool works with existing web-based portals and tax prep software, pulling in W-2s, 1099s, and other source documents, then having a review agent double-check everything.

“Why go after tax pros?” David asked. “Just get in bed with the portal companies and go after TurboTax.”

Kenji, who recently joined Basis after selling Acuity and taking a year off, described watching AI agents handle complex accounting work that made him come out of retirement. “I saw an agent handle complex payroll entries like booking the GL entry, creating an accrual because the pay period didn’t align with month-end, posting the reversing entry for the following month, and building a complete set of work papers,” he said. “I saw this last year, and I was like, wait, what?”

The flood of AI announcements kept coming throughout the episode:

  • Ramp launched an accounting agent that auto-codes transactions down to the line-item level on invoices, claiming to save finance teams 40+ hours per month
  • Xero announced a multi-year partnership with Anthropic to integrate Claude AI directly into its platform
  • Canopy launched a bookkeeping module with AI that continuously reviews books and flags issues in real time
  • Double (formerly Keeper) released AI Journal Entries that can handle complex, repetitive entries from source documents
  • BILL announced agents for invoice coding, W-9 collection, and automated vendor payment responses

“Everyone thought we were boring,” Kenji said. “Look at this. All these Y Combinator companies spinning up and fundraising announcements and agents everywhere. Come on. Exciting.”

The Skills Gap Is Already Here

The shift is showing up in real time in hiring data. In 2023, only 18% of accounting job postings mentioned AI skills. Now it’s 30%, a 67% increase.

“The real-world requirement is probably 50%,” David argued. “People are behind on updating their postings.”

But a better question is what happens to the business model. Kenji described how at Acuity, the bottleneck was always people. Plenty of companies needed help with their books, but you couldn’t hire enough accountants to serve them cost-effectively. AI agents break that constraint. One highly efficient bookkeeper might handle 45 to 60 clients today. “Will one person eventually be able to handle 200 clients?” David asked.

The threat isn’t just from other firms. An article on Payments.com found that everyday taxpayers are already using ChatGPT and Gemini to do their taxes before ever talking to a professional. The reason is “speed and simplicity,” David explained. “AI can explain tax concepts, organize the documents, and suggest deductions. These are things they’re not getting from their tax professional.”

Are Tokens the New Billable Hour?

As AI cuts the time needed to complete work, firms are scrambling to figure out how to price their services. Bloomberg Law reported that PwC, KPMG, and RSM are all exploring alternatives to hourly billing.

“This may be the thing that finally gets us there,” Kenji said about moving away from billable hours. “If I just used AI to help me get my work done and I’m cutting down my billable hours, I’m losing revenue.”

“You can bill for tokens,” David suggested, offering a provocative alternative.

He then vented about Earmark’s own token consumption across multiple platforms, including Claude, GitHub Copilot, Retool, ChatGPT, and more. “Two days ago, an automation stopped working,” he said. “We spent five plus people hours trying to increase our tokens and get the automation working again.”

The problem is, token costs are opaque and growing. David introduced two terms gaining traction: “token anxiety,” or not knowing what you’re being charged for, and “AI FinOps,” managing AI costs across platforms.

“There’s an opportunity here for firms to become a token expert and offer it as a service,” David suggested.

Blake’s take was more pragmatic. “It’s better than timesheets, that’s for sure.”

The Window Is Closing

The government that sets the rules can’t even audit its own books while declaring itself insolvent. Meanwhile, AI agents are automating core accounting work at a pace that makes the shift from paper to computers look gradual.

“These agents are actually now becoming a component of our workforce,” Kenji said. “You’ve got accountants and you’ve got agents. This is the future state we’re moving into.”

For practitioners, it’s clear that the tools to dramatically expand your capacity exist right now. But so does the threat of clients going straight to AI and bypassing your firm entirely. The window to adapt is open, but it won’t stay that way for long.

As Blake noted about current AI pricing, “When Uber was new, everything was really, really cheap.” The subsidies won’t last forever. To thrive, firms need to figure out the new economics now, whether that’s value pricing, token billing, or something else entirely. Those that don’t may find themselves as obsolete as the government’s ability to balance its own books.

Listen to the full episode for the complete discussion, including deeper dives into specific AI capabilities and Kenji’s firsthand perspective from inside an AI-native company.