Podcasts

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

Earmark Team · April 25, 2026 ·

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

Earmark Team · April 17, 2026 ·

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:

Client-level (learns each business)
Firm-level (encodes your best practices)
Global (trained on 180 million transactions worth nearly $1 trillion)
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

Earmark Team · April 17, 2026 ·

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

Earmark Team · April 13, 2026 ·

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.

Why Women in Accounting Keep Losing Credit for Their Own Ideas

Earmark Team · April 13, 2026 ·

Nancy McClelland is sitting at her desk when a WhatsApp message lights up her phone. It’s a screenshot from her friend Dymond with a simple question, “Aren’t those your slides?”

They are. A live QB Power Hour session was using the distinctive slide deck Nancy used for three-plus years of 1099 presentations, the one she built, refined season after season, and shared with co-presenters last year. She wasn’t even invited to the session. She later learned presenters were making edits to her slides even five minutes before going live.

“The heat just came up to my head and my face, and it felt like it exploded out the top of my head,” Nancy says, describing her physical reaction on a recent episode of She Counts, the real-talk podcast for women in accounting she co-hosts with Questian Telka. “I got a little shaky and I was just furious.”

This moment became the catalyst for a candid discussion of how women’s intellectual work gets absorbed, reused, and reattributed in the accounting profession, and what if anything we can do about it.

When Credit Disappears, So Does Opportunity

When your slides show up in someone else’s presentation or someone repeats your idea in a meeting as if it were their own, it’s not just about bruised feelings. It’s a systematic pattern affecting women’s advancement in accounting.

“When credit is taken away, it doesn’t just affect that one person,” Nancy explains. “If we don’t enforce these boundaries, it affects all of us.”

The impact goes beyond individual harm. As Questian points out, it “prevents diversity of thought” because when people repeatedly lose credit for their work, they stop creating and contributing. The entire profession loses out on those perspectives.

Nancy isn’t early in her career or insecure. She’s a recognized expert in 1099 compliance who’s been writing for MSN and speaking on the topic for four years. If it can happen to her, it can happen to anyone. And it does, repeatedly, from barely perceptible “borrowing” to blatant theft.

The Full Spectrum of “Borrowed” Ideas

Credit theft ranges from literally reusing your slide deck to repeating your idea without reference, seconds after you said it in a meeting. Understanding that spectrum matters because most of the harm lives in the gray areas where it’s hardest to call out.

Nancy’s QB Power Hour story falls at the blatant end. Last year, she co-hosted an episode with Rich Kane, volunteering her existing deck for the session. This year, the same session ran with her slides but without her. When Jennifer Dymond and Sharrin Fuller recognized the slides, they called it out in the live chat. Nancy fired off an email, deliberately replying to the original thread where she’d shared the deck to make the paper trail unmistakable.

Dan DeLong, the host, responded quickly and apologetically. He said it hadn’t occurred to him that reusing the slides was a problem. He’d just grabbed last year’s deck and asked Rich to update it.

“I named plagiarism and he responded with process failure,” Nancy says. That gap between how women name harm and how it gets institutionally reframed is crucial. As Questian points out, “You can plagiarize work without it being intentional.”

But this wasn’t Nancy’s first experience with credit theft. Earlier in her speaking career, she applied to present at the National Society of Accountants for Cooperatives conference. To add “credibility,” they paired her with their head of education as co-presenter.

Nancy created everything, including slides, research, citations, and examples. When presentation day arrived, her co-presenter had her sit at a table beside the podium while he stood at the podium for the entire session. At one point, he gestured to the screen and said, “When I prepared this slide…”

“I just swung toward him and looked up and my jaw dropped,” Nancy recalls. She wanted to correct the record but wondered, “How much of this do I say out-loud? I don’t know how it’s gonna reflect on me.”

The session was popular enough to warrant a journal article, but only if Nancy listed him as co-author. She refused, offering instead to properly cite his prior article that inspired her research. The publication was denied entirely.

“The lesson I took away from that is you can have exposure or you can have ownership, but you can’t have both,” Nancy says.

Questian’s experiences with credit theft have been on the subtler end of the spectrum. She regularly shares ideas in meetings only to have someone repeat them moments later and receive the credit. “It’s happened to me so much in my life that I’ve just gotten used to it,” she admits. Recently, her partner witnessed it happen multiple times in a single social setting and was stunned.

Then there are the gray areas. After She Counts launched, Questian noticed another female podcaster using specific language and ideas from their episodes. It happened at least three times, but rather than confront it, Questian stopped watching that person’s content. “I have this fear of calling out and hurting someone’s professional reputation,” she explains.

“When is it theft and when is it overlap?” Nancy asks. That’s the question at the center of most situations. The blatant cases are easy to identify, but most credit erosion happens where you know something is off but can’t quite prove it.

Why Credit Systematically Drifts Away from Women

If these were isolated incidents, the solution would be simple. But women often don’t receive credit for their ideas because of deeply embedded biases and hierarchies that operate even when everyone has good intentions.

Nancy discusses the Matilda effect, a term for the systematic under-crediting of women in science. The examples are staggering: Rosalind Franklin’s X-ray crystallography was central to understanding DNA’s structure, but the Nobel Prize went to James Watson, Francis Crick, and Maurice Wilkins. Jocelyn Bell Burnell discovered the first radio pulsars as a graduate student, but the Nobel went to her supervisor. Lise Meitner’s work was key to understanding nuclear fission, but Otto Hahn got the Nobel Prize in Chemistry.

“This kind of thing has been happening so much and for so long in science that they actually have a name for it,” Nancy explains.

In accounting, the angle is different but equally problematic. “So much of our work is process, systems, teaching, and translation,” Nancy notes. “Those kinds of things are generally more likely to be reused without attribution. They’re more likely to be absorbed rather than credited, even though they’re highly valuable for our profession.”

Higher-status individuals are disproportionately credited as sources of ideas, regardless of who introduced them. As Nancy explains, “Men are often assumed to be the owner of knowledge and women, the contributors.”

Questian adds another layer, referencing Vanessa Van Edwards’ research on competence versus warmth. “If you are perceived as too warm, you can then be perceived as less competent, even though you are often still highly competent,” she explains. People who are naturally collaborative are especially vulnerable. The very qualities that make you a great colleague make you easy to overlook.

There’s also source confusion. People remember ideas better than where they heard them. Nancy’s experience with Jason Staats illustrates this. She’d discussed her Ask a CPA community with him, specifically about bridging tensions between bookkeepers and tax professionals, and shared her community plans in a class he taught. Weeks later, he posted about the exact topic without attribution. When multiple people tagged Nancy in the comments and she emailed him, Jason explained he’d forgotten the connection.

The consequences are real. And women who claim their credit are evaluated more negatively than men exhibiting the same behavior. “I don’t want people to think I’m a bitch,” Nancy admits, “but that’s how I feel like I am viewed.”

The Power of Collective Action

What works most effectively to combat idea theft is having someone else see it and say something.

Dymond and Sharrin called out Nancy’s slides in the live chat. Multiple community members tagged Nancy when Jason posted about her topic. Nicole Davis reached out directly to address a perceived overlap. Her partner pointed out that Questian had just made the same point. In every case where things went right, it was collective action.

“When we speak up for each other, two things happen,” Nancy explains. “We make it safer for someone else to name harm, and we actually retrain our nervous systems to recognize that just because something is uncomfortable and we speak out about it, it doesn’t mean we’re overreacting.”

The hosts offer practical strategies:

Say names out loud. When discussing ideas, credit the source. For example, Nancy notes Debra Kilsheimer is the one who told her about the Matilda Effect.
Men have a specific role. When someone repeats an idea in a meeting, men can simply say, “That’s what Questian just told us.” It requires attention, not heroism.
Address it directly when it happens to you. Nancy emailed using the original thread where she’d shared her slides, making the trail clear. “We’ve gotta say these things out-loud because maybe there’s a misunderstanding,” she explains.
Speak up when you see it happening to others. Reduce someone else’s risk by lending your voice. Tag creators in comments. Mention names in chat.
Handle misunderstandings with grace. Nicole provides the model. She spoke up when she thought her work had been borrowed. Nancy explained the timeline, shared evidence, and Nicole graciously acknowledged the misunderstanding. They agreed to co-present on the topic later that year.

The episode closes with three essential questions:

Where are you sharing work that represents your expertise?
Who benefits when your name is removed?
What would change if you treated your ideas as assets instead of favors?

Your Name Belongs on Your Work

Credit theft in accounting isn’t about villains. It’s about a system where biases and expectations consistently funnel attribution away from women, even recognized experts, and even when people have good intentions.

The same number of women enter the accounting profession as men, but they don’t make Partner at the same rate. So the systematic erasure of women’s intellectual contributions isn’t minor. Every uncredited slide deck, repeated idea, or template passed around without attribution chips away at professional capital women need to advance.

Nancy closes with a quote from Virginia Woolf: “For most of history, Anonymous was a woman.”

In accounting, it doesn’t have to stay that way.

Listen to the full episode of She Counts and share your own story on the She Counts LinkedIn page. Have you ever had your work passed off as someone else’s? The more we name it, the harder it becomes to ignore.