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The Hidden Gender Gap in AI That’s Reshaping Accounting Without Women’s Input

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When Apple launched its revolutionary health app in 2014, it tracked everything from blood pressure to copper intake, but somehow forgot that half the population has menstrual cycles. This stunning oversight, which took an entire year to correct, perfectly captures what happens when companies design technology without women at the table.

In a revealing episode of the She Counts podcast, CPA and AI educator Twyla Verhelst joins hosts Questian Telka and Nancy McClelland to expose a difficult realization about the accounting profession’s AI revolution: women are being systematically left behind by design. Verhelst, who serves as Vice President of Industry Relations and Community at Karbon and co-founded TB Academy to empower accountants with AI training, brings personal experience and industry-wide perspective to this critical conversation.

While the accounting profession races to embrace AI technology that promises to transform how we work, women accountants face unique barriers to adoption that go beyond straightforward reluctance. From juggling disproportionate caregiving responsibilities to battling perfectionism in male-dominated spaces, these challenges create a system where the tools shaping our industry’s future are being built without our input.

This conversation uncovers why women fall behind in AI adoption, what happens when technology evolves without diverse perspectives, and most importantly, how women can claim their seat at the AI table, even if they have to bring their own folding chair.

The Perfect Storm: Why Women Fall Behind in AI Adoption

The gender gap in AI adoption isn’t about capability or interest. It’s about a perfect storm of societal expectations, time constraints, and deeply ingrained psychological patterns that create unique barriers for women in accounting.

Verhelst knows this struggle intimately. Despite her current role as a leading AI educator for accountants, she spent two full years feeling paralyzed by overwhelm. “I sat with AI saying like, “Oh my gosh, I’m so far behind. I haven’t even opened ChatGPT,” she admits. Even at AICPA Engage 2024, surrounded by industry innovation, she found herself thinking, “I still haven’t done anything. I still feel behind.”

This paralysis stems from something deeper than mere procrastination. Women, Verhelst explains, carry an ancestral caution that shapes how they approach risk. “If you go way back to our ancestors, men went out to hunt, while women stayed home or back at the tribe to care for the children and the elders. We were cautious by nature.” This evolutionary programming still whispers in our ears when faced with experimental technology, urging us to proceed with caution while our male counterparts dive in headfirst.

The perfectionism trap compounds this hesitation. Women in accounting already fight to prove themselves in traditionally male-dominated spaces, and using AI can feel like taking a shortcut that undermines our credibility. Verhelst observes, “Women feel like they’re cheating by using AI while men are looking for any way possible to ‘do the thing.’”

McClelland’s confession during the conversation highlights another crucial barrier: the gaming gap. “I didn’t grow up playing video games. I didn’t grow up taking apart electronics and putting them back together. Those were considered ‘boy’ hobbies,” she shares. When colleagues tell her to “just go play with it,” she responds with genuine confusion, “I honestly don’t even know what you mean when you say that. I don’t know how to play with technology.”

But perhaps the most insurmountable barrier is time poverty. While AI adoption requires experimentation and play, women simply don’t have the capacity. “I don’t have the capacity in my day to play. That just doesn’t happen,” Verhelst states bluntly. “I’m looking after children. I’m looking after senior parents and managing a household. I have a career. I have a part-time job on the side.”

The irony is that AI could actually help alleviate this time poverty, but women need time to learn how to use it effectively. It’s a Catch-22 that keeps women perpetually behind the curve, watching as male colleagues who started experimenting early become the go-to AI experts in their firms.

When Products Aren’t Built With Women in Mind

The consequences of women’s delayed AI adoption extend beyond individual careers. They’re shaping the very DNA of the technology that will define our profession’s future.

The Apple Health app story is an example of what happens when technology evolves without diverse input. In 2014, Apple’s revolutionary health tracking app monitored everything imaginable, yet somehow missed that 50% of the population experiences menstrual cycles, an aspect of women’s health that affects heart rate, body temperature, and breathing patterns throughout the month.

“No matter who you are as a woman, no matter what phase of life you are in, our whole rhythm revolves around the 28-ish day cycle,” Verhelst explains. Without this critical data point, the app sent false alarms about potential health issues while missing actual problems. Women worried unnecessarily about elevated heart rates that were actually normal for their cycle phase. It took Apple an entire year to correct this oversight.

This pattern repeats across industries. McClelland shares her own revelation about automotive safety: “I used to date an engineer who designed seat belts for cars. He explained to me that for many, many years, they only had male models.” The very devices meant to save lives in vehicle accidents were tested exclusively on male bodies, leaving women—particularly petite women—vulnerable to injuries that could have been prevented with proper testing.

The same types of oversights are happening right now with AI in accounting. “ChatGPT and other AI tools are built off of user input,” Verhelst warns. “If most of the users are men or the earliest adopters are men, then it’s being trained on and continues to evolve on how males use the platform versus how women will use the platform.”

Every prompt, every interaction, every piece of feedback shapes how these tools develop. When women don’t participate in that early development phase, the tools optimize for male communication patterns, work styles, and problem-solving approaches. The technology literally learns to speak a language that may not resonate with how women naturally interact with technology.

“AI is not a fleeting technology,” Verhelst emphasizes. Unlike temporary disruptions like Covid-19, AI is fundamentally shifting how accounting work gets done. The patterns being established now will shape the profession for decades.

Telka’s reaction during Twyla’s WAVE Conference presentation captured the urgency perfectly: “That really blew my mind. Because we tend to be later adopters, these tools we’re using are being built without our input.” She realized something as adaptable as ChatGPT, which changes based on user inputs, could evolve into something fundamentally misaligned with how women work.

Bringing Your Folding Chair: Practical Strategies for Women in AI

Despite the barriers, women have unique strengths that position them for AI success if they can reframe their approach and find the right support.

“Women need to pull up their seat at the table. And if that seat’s not there, you just bring your folding chair,” Verhelst declares, offering both a rallying cry and a practical philosophy for women ready to claim their place in the AI revolution.

The first step is recognizing an advantage many women don’t realize they possess. Ashley Francis, a recognized AI innovator in the accounting space, points out that women are actually better positioned to excel with AI than their male counterparts because women tend to have stronger language and communication skills.

Verhelst confirms this. “The number one roadblock to not getting what you need out of AI is poor communication.” Since women generally excel at thorough, nuanced communication, they’re naturally equipped to craft the detailed prompts that make AI tools work effectively.

Instead of diving headfirst into complex automations, Verhelst advocates for a pain-point-first approach. “Take some steps back to recognize what it is you want from AI today. Start with a pain point you experience. How can you leverage AI to solve for that pain point?”

Community learning is perhaps the most powerful accelerator for women’s AI adoption. Verhelst discovered a TikTok creator who opened a Slack channel specifically for female founders and entrepreneurs to share AI experiments (both successes and failures) in a supportive environment. “With women we can be a bit more vulnerable,” Verhelst explains. 

The practical applications Verhelst shares do away with the myth that AI requires extensive technical knowledge. Her “restaurant flex” perfectly illustrates playful exploration. She takes photos of menus and asks ChatGPT which wine is driest, which meal fits her dietary goals, and even requests recipes to recreate favorite dishes at home. “It’s embracing AI for things that aren’t just work,” she explains.

For professional applications, meeting transcription tools have become game-changers. Tools like Fathom, Otter.ai, Read.ai, and upcoming Karbon integrations with Vinyl and Abacor allow women to fully engage in conversations without worrying about note-taking. “Meeting transcripts have certainly changed my life,” Verhelst shares. Telka agrees emphatically, “I cannot take notes and focus.”

Women also use AI to handle emotional labor that often goes unrecognized. Verhelst describes how women upload screenshots of ambiguous emails, asking AI to decipher tone and suggest responses. “That saves a lot of headache and sleepless nights in some cases,” she notes.

Perhaps most importantly, Verhelst rejects the “do more with AI” messaging that dominates tech marketing. “I don’t want to do more. I already do a lot. I want time back to do what I want with it, not more tasks.” She shares how AI helps her handle overwhelming projects, like reformatting documents based on meeting transcripts. “That task would feel incredibly daunting and very tedious if it wasn’t for AI.”

There’s also liberation in accepting that expertise is impossible in this rapidly evolving field. “I don’t believe there are experts in AI,” Verhelst insists, even about recognized leaders like Chad Davis, Jason Staats, and Ashley Francis. “They can’t be. It’s moving too quickly.” If no one can be an expert, then everyone is learning together, and starting later doesn’t mean permanent disadvantage.

Some firms are already seeing creative applications. One TB Academy participant created a custom GPT that sits on their website, allowing clients to ask questions as a first stop before contacting the firm directly. These innovations come from experimentation, not expertise.

The Future We Choose to Build

The gender gap in AI adoption isn’t a personal failing. It’s a systemic challenge rooted in time constraints, societal expectations, and technology designed without our input. But there’s hope in Verhelst’s message.

No one is truly an expert in this rapidly evolving field, which means the playing field is more level than it appears. Women’s natural communication strengths align perfectly with what AI needs to function well. And participation doesn’t require perfection, but curiosity and small experiments.

Telka closed the episode with a quote from Sheryl Sandberg: “No industry or country can reach its full potential until women reach their full potential. This is especially true of science and technology, where women with a surplus of talent still face a deficit of opportunity.”

The path forward is about bringing our unique perspectives to tools that desperately need diverse input. Every prompt from a woman teaches these systems something new about how half the profession thinks, communicates, and solves problems.

“Even listening to this podcast tells me you’re not behind,” Verhelst reassures. “It tells me you’re curious, you’re engaged, and you want to learn.”

Listen to this transformative episode of She Counts to discover how you can overcome the barriers holding you back from AI adoption. Learn more about Verhelst’s work at TB Academy (tbacademy.ai) or connect with her on LinkedIn. And check out Tam Nguyen’s free AI prompts at Tech with Tam for an easy way to get started.

The future of our profession is being written right now, with or without us. Will we let it be designed without us, or will we grab our folding chairs and help build a future that works for everyone?

Four Years of Cleanup in Four Hours: Inside the AI Ledger That Learns From Your Work

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What if you could stop programming bank rules forever? No more tweaking text strings, adding exceptions, or debugging why “COSTCO WHSE #1234” won’t match your Costco rule. During a recent Earmark Expo webinar, accounting software company Digits demonstrated exactly how that future works, and they achieve 96% automatic categorization accuracy without a single bank rule.

Host David Leary has been watching Digits since before ChatGPT existed. “I remember seeing a pitch deck about Digits, and it was being emailed around on backchannels in the accounting industry,” he recalled. “This pitch deck was super ambitious. At the time, the back channel hallway talk was like, ‘Great, here comes another bank feeds accounting app.’”

Now, years later, that ambitious vision is reality. Rob Hamilton from Digits’ partnerships team showed David and his co-host Blake Oliver what the company calls the world’s first “agentic general ledger,” software built from the ground up with machine learning at its core.

Why Digits Took Six Years to Build

Before diving into the technology, Rob shared the origin story. Digits founder Jeff Seibert sold his previous companies to Box and Twitter. At both companies, he noticed a stark contrast: product and engineering teams had real-time dashboards showing exactly who was on their website and what buttons they clicked. But when he wanted to check if he had a budget for a team event, finance told him to wait 45 days for the books to close.

“As a founder of a company, you’re like, ‘This is crazy. I’m just going to do this event without your approval,’” Rob explained. When Jeff left Twitter, he wanted to use machine learning for good, and accounting emerged as the perfect candidate.

The result took six years to build. “Turns out that it takes a while to build a general ledger from the ground up in the machine learning era,” Rob admitted. But that ground-up approach makes all the difference.

The Three-Layer Intelligence That Replaces Bank Rules

Traditional accounting software makes you act like a programmer. You write rules, define patterns, and hope the software follows instructions. Anyone who’s debugged bank rules knows the frustration.

Digits flips this completely. Instead of you teaching the software through rules, the system learns from your work at three levels.

First, it learns from each specific company. When you connect QuickBooks to Digits, it imports your historical data and trains on how you categorize that company’s transactions. “We actually train on a company level,” Rob explained. “When transactions start coming in, it actually leverages the work you’ve already done within that individual company.”

Second, it learns from your entire firm. When a new vendor appears—say, a coffee shop that just opened—Digits checks if any other client in your firm has seen that vendor. Your work for one client helps all your clients.

Third, it taps into global intelligence. For truly novel transactions, Digits uses its global model trained on every transaction the platform has ever processed.

The payoff is significant. “For September, we’re at a 96% rate of transactions getting booked into Digits that then were subsequently not touched by a human afterwards,” Rob revealed. That’s not just categorized; that’s categorized correctly enough that accountants didn’t change them.

“You’re not editing any rules,” Rob points out, contrasting Digits with traditional systems. “You don’t have to add an extra appendage to pull out the specific Costco transaction. We learn from your behaviors directly inside the product.”

How the System Handles the Other 4%

No AI system is perfect. What matters is how it handles uncertainty. When Digits encounters a transaction it’s unsure about, it doesn’t guess silently. It flags the uncertainty and shows its reasoning.

During the demo, Rob showed a US Patent and Trademark Office transaction where Digits displayed, “I have this as taxes, but I actually think it could be legal.” The system even suggested adding “intangibles” as a new account category for companies still building their chart of accounts.

The learning happens instantly. “We’ve built our architecture to be uniquely quick in the training,” Rob emphasized. “The second we see a similar transaction, it’ll effectively be perfect based on your prior action.”

Quality control is proactive rather than reactive. Each month, Digits flags all new vendors so accountants can verify they’re categorized correctly. It also highlights vendors booked to multiple categories, like Apple transactions split between fixed assets and software subscriptions.

When accountants don’t know what something is, they can ask clients directly within the platform. Questions attach to specific transactions, clients get email notifications, and responses flow back to the same transaction. The AI suggests categorization based on the client’s answer, though accountants confirm before applying.

David appreciated the unified workflow. “Now I don’t have to have five browser tabs open where one browser tab is the report, the transaction is in a new browser tab, and I make the edit and refresh the report in the other browser tab.”

Reconciliation in Minutes, Not Hours

Bank reconciliation should be simple, but when something doesn’t match, like $15 missing from Stripe, the detective work begins. Digits transforms this process entirely.

Statements enter the system three ways. Banks like Wells Fargo send them automatically via API. For others, accountants drag and drop PDFs directly onto the platform. Every Digits account also gets a single email address that accepts any document type, including statements, bills, or receipts, The AI routes them appropriately.

“So one email for all the transactions in a client’s company file,” David noted. “You don’t have special HR email and AP email where you send it to the wrong box and it creates a mess.”

The reconciliation interface shows the bank statement PDF alongside ledger transactions. As you hover over transactions, green boxes highlight the matching line on the statement. David’s reaction captured what every accountant will recognize: “I used to do this with a highlighter and my fingers. I had to find it on both.”

But the real magic is proactive problem detection. Digits identifies specific issues and offers one-click fixes for things like:

  • Uncleared transactions that should move to next month
  • Statement items missing from the ledger
  • Date discrepancies between records and statements

Each issue comes with a resolution button. The system does the detective work; accountants just confirm the fix.

“We had an accountant come in the other day. He was like, ‘I did four years of cleanup in four hours’ because he just linked the bank accounts, dragged all the statements in, and the AI did everything,” Rob says.

Beyond Bookkeeping: Reports Clients Actually Read

With traditional financial reports, only 15% of business owners even open those black-and-white PDF attachments. Digits studied this and found that when firms use visual reporting tools, over 70% of clients actually open and interact with the financials.

The reporting system works like “Google Docs for your finances,” as Rob described it. Accountants can add commentary directly on line items, tag clients with questions, and create visual dashboards that tell the story of the business.

The platform includes built-in bill pay ($0.50 for ACH, $2 for checks) and invoicing. The system automatically recognizes and routes dragged-in documents. Bills queue for payment, receipts match to transactions, and statements trigger reconciliation.

Behind the scenes, AI agents continuously research every vendor, building what Rob called “a dossier” with logos, phone numbers, and descriptions. “This is what your team does when they don’t know what a transaction is. They Google it and find the information.”

What This Means for Your Practice

The shift from rule-based to AI-native software fundamentally changes the accountant’s daily work. Instead of programming rules, you review AI suggestions. Instead of hunting for reconciliation errors, you confirm one-click fixes. Instead of sending reports that get ignored, you create interactive dashboards that clients actually use.

The compound effect is striking. Every correction teaches the system, improving accuracy for that client, your entire firm, and eventually all Digits users. Time savings stack up, allowing firms to shift toward advisory work.

Digits offers a partner program with volume discounts. The standard price is $100 per month per client for full features, with special pricing for tax write-up work. Accounting firms get their own firm account free when joining the partner program.

Rob emphasized that construction and other complex industries might see slightly lower accuracy rates than the 96% average, but the system continuously learns and improves. Features like sales tax support and project tracking are coming soon, while departments and locations tracking are already available.

For firms evaluating new software, the question has shifted from “What rules do I need to create?” to “How well does this system learn?” The four-years-in-four-hours cleanup example shows what’s possible when AI handles the tedious work.

Watch the complete Earmark Expo webinar to see the full demonstration, including reconciliation workflows, client communication tools, and the visual reporting system that gets clients actually engaging with their financials. Whether you’re ready to switch or just want to understand where accounting technology is heading, this demo shows what accounting looks like when bank rules become obsolete.

When Bots Listen to Robots and Real Money Disappears

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Picture this: a computer on stage playing songs to an audience of computers. No humans involved, just machines performing for machines in an endless digital loop. Yet somehow, millions of dollars change hands.

This isn’t science fiction. It’s happening right now on streaming platforms, and it’s just one of the mind-bending fraud schemes explored in this episode of Oh My Fraud. Host Caleb Newquist opens with a relatively new conspiracy theory called the Dead Internet, which suggests that most online activity, including posts, likes, followers, and streams, isn’t human anymore. It’s “bots talking to bots, talking to bots,” creating an information superhighway filled with self-driving cars that have destinations but no passengers.

But what happens when someone exploits this artificial ecosystem for real money? That’s exactly what we’re about to find out.

The $121 Million Email That Fooled Silicon Valley

Between 2013 and 2015, a Lithuanian man named Evaldas Rimašauskas pulled off something that shouldn’t have been possible. He convinced two of the world’s smartest companies, Google and Facebook, to wire him $121 million. His method wasn’t sophisticated hacking or complex algorithms. He simply pretended to be someone else.

Rimašauskas impersonated Quanta Computer, a real Taiwan-based hardware manufacturer that actually did business with both tech giants. He set up a company in Latvia under Quanta’s name and opened bank accounts in Latvia and Cyprus. Then his team got to work, calling Google and Facebook customer service lines to gather intelligence, including names of key employees, contact information, and other details that would make their lie believable.

Through phishing emails and what Caleb describes as “a maze of phony invoices, contracts, letters, and corporate stamps,” Rimašauskas created enough confusion to convince someone at Google to update the bank account they had on file for Quanta Computer. In 2013, Google sent $23 million to his account. Two years later, using the same playbook, Facebook wired him $98 million.

The money flowed through accounts across Latvia, Cyprus, Slovakia, Lithuania, Hungary, and Hong Kong. And here’s the kicker: these amounts were so insignificant to Google and Facebook that they “went virtually unnoticed.” As Caleb puts it, “$23 million and $98 million aren’t even rounding errors on the amount of revenue for Google and Facebook. It’s less than pocket change.”

Eventually, someone at Google caught on. Rimašauskas was arrested in March 2017, extradited to the U.S. that August, and pleaded guilty to wire fraud in March 2019. He got five years in prison, and both companies got their money back.

From IT Mogul to Music “Producer” to Alleged Fraudster

Our second story shifts from simple impersonation to something far stranger. Meet Michael Smith, a 52-year-old with a resume that reads like three different people’s lives smashed together.

According to the research, Smith made his first fortune in the 1990s with an IT business where he allegedly wrote “one of the main fixes for the Y2K millennium software bug.” He then ran chains of medical clinics, which landed him in trouble in 2020 when he and two associates paid $900,000 to settle Medicare and Medicaid fraud allegations.

But here’s where it gets weird. At age 39, Smith decided to become a music industry player. Despite having no apparent musical background, he somehow ended up judging a BET hip-hop competition called “One Shot” alongside DJ Khaled, T.I., and Twista. As Wired magazine described it, he was “a relatively unknown record producer with a checkbook” among actual stars.

When Caleb asked producer Zach Frank if he’d ever heard of anyone building a successful music career starting in middle age, Zach’s response was telling: “It’s extremely, extremely rare. Not without money, at least.”

The Streaming Revolution and Its Discontents

To understand Smith’s alleged fraud, you need to understand how dramatically the music industry has changed. Zach, who comes from a family of professional musicians, explained how streaming completely upended the business model.

In the old days, people bought physical albums for $12-15 at stores like Tower Records. Artists made real money from album sales. Then came Napster and peer-to-peer sharing, which Caleb admits using extensively in college. “People were listening to all this music completely in its entirety for free,” he recalls.

Today’s streaming platforms like Spotify and Apple Music operate on a subscription model. Users pay monthly fees for unlimited access, and artists get fractions of pennies per stream. Spotify made $17 billion in 2024 and claims 70% goes to the music industry, but individual artists see almost nothing.

The numbers are staggering. According to Spotify’s former chief economist, more music is released every single day in 2025 than in the entire year of 1989. And here’s what makes it worse: bigger artists negotiate better deals, while smaller artists, as Zach puts it, “get screwed.”

Building an Army of Fake Listeners

This is the landscape Smith allegedly decided to exploit. Starting in 2017, he orchestrated what the Department of Justice calls a scheme to steal millions in royalties by fraudulently inflating music streams.

The mechanics were brilliant in their simplicity. First, Smith created thousands of bot accounts using fake email addresses and names. He even told a coconspirator to “make up names and addresses” but to “make sure everyone is over 18.” He paid $1.3 million in subscription fees because, as Zach explains, paid subscribers generate higher royalty rates than free users.

By October 2017, Smith had 1,040 bot accounts spread across 52 cloud service accounts. Each bot could stream about 636 songs per day, generating approximately 661,440 total daily streams. At half a cent per stream, that meant $3,307 daily, $99,000 monthly, or $1.2 million annually.

But Smith had a problem: he needed content. Lots of it.

When AI Makes Music for Bots to Hear

Initially, Smith used music catalogs from coconspirators and even tried selling his streaming service to other musicians desperate for plays. But as he wrote in May 2019, “I can’t run the bots without content and I need enough content so I don’t overrun each song. If we get too many streams on one song, it comes down.”

His solution? Artificial intelligence. Smith partnered with Alex Mitchell, CEO of an AI music company called Boomy, who began providing thousands of AI-generated songs each week.

The song and artist names were gloriously terrible. Song titles included “Zygotic Washstands,” “Zygoptera,” and “Calvinistic Dust.” Band names ranged from “Calm Knuckles” to “Camel Edible.” As Caleb jokes, “I don’t know what camel edibles are. Perhaps they are THC gummies for camels.”

To demonstrate just how far AI music has come, Zach used Udio.com during the podcast to generate two complete songs about Oh My Fraud in just 10-15 seconds. The results were unnervingly good, professional-sounding tracks that could easily pass for human-created music. “There’s a lot of AI music on Spotify at the moment without people knowing it’s AI,” Zach notes.

Smith used VPNs to hide that all streams came from one location and spread activity across thousands of songs to avoid detection. When flagged for “streaming abuse” in 2018, he protested: “We have no intentions of committing streaming fraud.”

By February 2024, Smith’s scheme had generated 4 billion streams and $12 million in royalties.

Folk Hero or Fraudster?

The reaction to Smith’s indictment has been surprisingly divided. Some see him as a criminal who stole from real artists through the “stream share” system, where royalties are distributed based on each rightsholder’s proportion of total streams. Others view him as a folk hero exposing an exploitative system.

The case raises uncomfortable questions. When the band Vulfpeck released an album of complete silence and asked fans to stream it while sleeping—earning $20,000 before Spotify banned them—was that fraud or performance art? As Zach asks, “If someone’s playing blank music, who are they to say that’s not real?”

Smith has hired the prestigious law firm that defended Diddy and plans to fight the charges vigorously. This will be the first major streaming fraud case fully litigated, potentially setting precedents for how we define fraud in digital spaces.

What We Learned

As Caleb reflects at the episode’s end, these cases reveal something profound about our digital economy. Google and Facebook, companies worth trillions with founders worth hundreds of billions, got tricked by simple schemes. A middle-aged entrepreneur with a checkbook created a phantom musical empire that earned millions.

For accounting professionals, these are warnings about the future of fraud detection. When documentation can be perfectly faked, when bots are indistinguishable from humans, when AI creates content that only machines consume, traditional audit procedures become obsolete.

These cases force us to confront questions about power, technology, and authenticity in the digital age. When companies make billions while creators earn pennies, algorithms determine value instead of human appreciation, and the line between real and artificial completely disappears, that’s when people start rooting for the fraudsters. Not because they’re right, but because the system itself feels so wrong.

Listen to the full episode to hear Caleb and Zach grapple with these questions, including those AI-generated songs that sound disturbingly human. Because in an age where machines create for machines while extracting real value from real people, understanding these frauds helps preserve what makes us human in an increasingly artificial world.

Your CPA Exam Scores Might Be Lost and Your AI Bookkeeper Is 57% Accurate

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“No kings means no paychecks, no paychecks, no government.” When Treasury Secretary nominee Scott Bessent dropped this line in a Fox News interview, Blake Oliver and David Leary weren’t sure if they should laugh or be terrified. As David put it: “That’s the most un-American thing anybody could say.”

In episode 458 of The Accounting Podcast, Blake and David dig into a series of accountability failures that would be funny if they weren’t so serious. From the Trump administration creating a brand new IRS “CEO” position to dodge Senate confirmation, to NASBA somehow losing track of CPA exam scores, the organizations supposed to maintain standards can’t even maintain their own data.

The IRS Gets a CEO (Because Who Needs the Constitution?)

The Trump administration’s latest move isn’t subtle. It created a new “CEO” position for the IRS that doesn’t require Senate confirmation. As Blake explains, “If the president just creates a new role that has the same responsibilities but doesn’t get checked by the Senate, then that’s just a run around the rules.”

The plan goes deeper than personnel changes. Gary Shapley, an advisor to Treasury Secretary nominee Scott Bessent, wants to weaken IRS lawyers’ involvement in criminal investigations and eliminate extra procedural steps for sensitive cases involving elected officials and tax-exempt groups. These aren’t reforms—they’re removing the safety rails.

“Where’s the AICPA on this?” David asks. The AICPA wrote a letter about the government shutdown’s impact on taxpayers but stayed silent on bypassing Congress to appoint IRS leadership. Blake doesn’t mince words: “They don’t. They are not willing to take a stand on something that matters because they’re afraid of political blowback.”

According to Wall Street Journal reporting that Blake and David discuss, Shapely has already compiled a hit list. The targets? George Soros and affiliated organizations, major Democratic donors, and left-leaning nonprofit groups.

The hosts make an important point that transcends politics. “The Obama administration targeted right wing groups,” Blake notes, agreeing with a viewer comment. “This is why you don’t want to give the government too much power. The other side gets the gun eventually, then points it at the other side.”

When Accounting Organizations Can’t Do Accounting

If you think government accountability is bad, wait until you hear about the profession’s own organizations.

Professor Joseph Ugrin, who creates the CPA Success Index published by Accounting Today, discovered NASBA’s 2024 data is essentially garbage. Between 25% and 40% of candidate scores are simply missing. Plus, Iowa community colleges appear in the data despite state law requiring bachelor’s degrees to sit for the exam.

“NASBA has access to all the transcripts submitted by the candidates,” Blake points out. “So there’s no reason why they couldn’t correctly classify what schools they went to.”

David speculates, “This smells like somebody at NASBA tried to use AI to summarize some stuff and screwed it up.” Whether it’s AI or old-fashioned incompetence, Ugrin can’t publish the Success Index this year because the data is unusable.

Meanwhile, the Chicago Teachers Union hasn’t released required financial audits for over five years, despite paying $80,000 for audit services in 2025 alone. When members finally got federal filings, they showed only 18% of spending goes to representing teachers. The other 82%? Overhead, politics, and “leadership priorities.”

As David asks incredulously: “How did it go past one year?”

The issue isn’t confined to Chicago. Forty-three Arkansas cities can’t get state funds because they can’t find CPAs to do required audits. “The auditors are retiring. They’re not being replaced,” Blake explains. Small-town America is literally running out of accountants.

AI to the Rescue! (Just Kidding, It’s 57% Accurate)

While real problems go unsolved, the profession is being sold AI magic beans.

One marketing CEO’s experience with QuickBooks’ new AI features reads like a horror story. “Although trained on transactions, QuickBooks frequently miscategorized payments based solely on dollar value,” he wrote. If a vendor sent one $1,000 invoice, the AI recorded all future invoices as $1,000. Contractor payments were recorded under “QuickBooks payments” instead of the contractor’s name. The company spent thousands on accountants trying to fix problems that couldn’t be fixed.

“QuickBooks sits at the heart of our business,” the CEO explained. “When AI upgrades destabilize that core, the consequences ripple across the organization.”

The hosts shared another headline that calls AI’s accuracy into question. Microsoft’s AI agent in Excel achieves 57.2% accuracy on spreadsheet benchmarks. As Blake says: “57.2% accuracy is not going to cut it. Not even 98% accuracy is going to cut it.”

Yet companies like Docyt claim AI will let one accountant manage 300 clients. The hosts’ response? “I’ve talked to firm owners that are super efficient,” David says. “Their best bookkeepers maybe handle 45 clients a month.”

Blake’s experience backs this up: “A typical bookkeeper could do 20 to 30 on average. And my all star could do 40 to 60.” The idea of 300 clients per person? “You would have too many questions coming in emails,” Blake explains. “I don’t think there’s an AI tool that can do that.”

Blake’s ideal practice would have ten outsourced controller clients, meeting weekly with each. “Once I got the ten clients, I could probably do it in four hours a day.” That’s realistic. Managing 300 clients with AI? That’s fantasy.

The hosts haven’t seen AI actually eliminating jobs. “I have yet to talk to an accountant that says, oh, we implemented this thing and now we got rid of two of my staff,” David states. Even at their own company, which uses AI extensively: “We’re not getting rid of anybody. We just hired more engineers.”

The $300 Trillion Oops

Just when you thought it couldn’t get wilder, David shares the stablecoin story that should terrify everyone.

Paxos, which provides stablecoin infrastructure for PayPal, accidentally minted $300 trillion in stablecoins. Not million. Not billion. Trillion. For context, the US deficit is $2 trillion.

“You understand how a stablecoin works in theory.” David says. “A dollar goes in, you get a stablecoin worth a dollar back. What if I told you none of that is true?”

The company claimed it was a “technical error that briefly appeared for 20 minutes,” then they “burned” the excess tokens. But as David points out, if companies can just create and destroy them at will, this proves stablecoins aren’t actually backed by dollars.

This matters because Ripple just bought a treasury management firm for $1 billion, putting cryptocurrency at the center of corporate cash management. “Accountants are going to be touching this stuff,” David warns. “It’s going to be here next year.”

Time to Pay Attention

This episode of The Accounting Podcast is a reality check for a profession facing multiple crises simultaneously. The IRS is being restructured to avoid constitutional oversight. Professional organizations can’t maintain basic data integrity. AI is being forced on businesses with disastrous results. And small towns can’t find CPAs to do basic audits.

“We don’t need a king,” David emphasizes about Bessent’s comments. But between government overreach, organizational incompetence, and technological snake oil, the profession is being pulled in all the wrong directions.

The hosts’ frustration is justified. When Blake asks why the AICPA won’t stand up for constitutional principles, when David wonders how organizations go years without audits, when they both laugh at the idea of one person managing 300 clients, they’re asking the questions the profession should be asking itself.

Listen to the full episode to hear Blake and David’s complete breakdown of these interconnected failures. In a profession built on trust and verification, their willingness to be brutally honest is exactly what’s needed.

After 50 Years in Internal Audit, Richard Chambers Sees the Profession’s Greatest Risk Yet

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“Who’s going to provide the skepticism, the intellectual curiosity, and the institutional knowledge to our audit teams in ten years? Because the rest of us are going to be gone.”

Richard Chambers drops this stark warning after 50 years in internal audit. His concern isn’t about losing jobs to technology. It’s about the growing gap between how we’ve always trained auditors and what the profession now demands.

On this episode of the Earmark Podcast, host Blake Oliver sat down with Richard, Senior Advisor for Risk and Audit at AuditBoard. He brings a unique view of internal audit’s transformation. When he started in 1974, fresh out of college and working in a bank’s internal audit department, the job was all about checking financial records and looking backward. Today? Financial risks make up only 25% of audit plans. The rest involves cyber threats, AI governance, supply chain chaos, and what Richard calls “perma-crisis”—our new normal where tariff rates can change three times in a single day.

Most companies use AI, but only a quarter have set up proper governance over it, according to AuditBoard research. That gap presents massive risk and opportunity for internal auditors who can bridge it.

From Bean Counting to Risk Navigation

Internal audit has changed dramatically since Richard joined that bank in 1974. Back then, it was all ledgers and reconciliations—purely financial work focused on last year’s numbers. Today, financial risks are just a quarter of what internal auditors examine.

“The profession has matured,” Richard explains. “While we still do some work in the financial space, that’s really a small percentage of internal audit’s focus.”

The real game-changer has been what Richard calls “perma-crisis.” It started with the COVID-19 pandemic and hasn’t stopped. “We’ve been lurching from one risk-induced disruption to another,” he says, listing the cascade: pandemic, forty-year-high inflation, supply chain breakdowns, wars in Europe and the Middle East. “We’re in our sixth year of it, and I would submit this is the new normal.”

This constant chaos makes traditional planning almost useless. Richard found that nearly 60% of internal audit departments had already changed their 2025 plans by May. When tariff rates can swing wildly in a single day—Richard recalls hearing three different numbers from Washington in one day—annual planning is dangerous.

“You can no longer have any confidence that one scenario is the only one you have to worry about,” Richard emphasizes. Organizations need what he calls “scenario risk management,” or planning for multiple possible futures at once.

This need for flexibility shifts how internal audit works with other departments. The old model was called “three lines of defense”: management controlled risks (first line), oversight functions monitored them (second line), and internal audit was the last barrier before disaster (third line).

But pure defense isn’t enough anymore. In 2019, the Institute of Internal Auditors dropped “defense” from the name. The new message? “Independence does not mean isolation.”

Richard uses a ship analogy that really hits home. Organizations are like vessels at sea that need lookouts watching in all directions and talking to each other. “If your internal auditors are looking in one direction and your risk managers are looking in another,” he warns, “but they aren’t sharing what they’re seeing, then you don’t know whether there are gaps.”

AI: The Top Risk and Best Opportunity

Three years ago, AI wasn’t even on internal audit’s risk list. Today, it’s number one, pushing even the talent crisis to second place.

“Pre-2022, before ChatGPT came out, we weren’t asking about it,” Richard admits. Once he started surveying the profession, AI rocketed up the list: middle of the pack the first year, third place the next, then straight to number one.

This isn’t just another tech disruption. After watching five decades of change, Richard doesn’t mince words: “In the five decades I’ve been in internal audit, there’s never been a greater risk to this profession in terms of becoming irrelevant.”

The scariest part? When Richard asks why audit teams aren’t using AI more, the top answer is, “We don’t really understand it enough.” That hesitation could be fatal.

Yet Richard himself uses AI daily as his “research assistant.” He asks it to identify industry risks, outline articles, analyze data. “It takes me longer to write the prompts than it takes to give me the answer,” he notes.

The use cases are obvious and powerful. Risk assessments that used to happen annually can now be continuous. AI can scan for threats humans would never spot. Data analysis that took weeks happens in minutes. Even audit reports can be AI-generated.

But the trap is that AI excels at exactly the work that trains new auditors. Entry-level graduates traditionally learned by doing routine tasks. Now AI does those tasks better and faster.

“College graduates have traditionally been able to ease into professions by doing some of the more rudimentary tasks,” Richard explains. “But AI is prime for rudimentary tasks.”

This creates a vicious cycle. Companies hire fewer entry-level auditors. Without that pipeline, who develops the judgment for complex work? Richard’ solution: “We shouldn’t refrain from hiring them. We should be willing to bring them in and help them leap the learning curve.”

“AI won’t replace internal auditors,” Richard predicts, “but it will replace internal auditors who don’t use it.”

The Human Superpowers AI Can’t Touch

“To assess culture, you also have to be able to rely on your sense of smell.”

A chairman of the board of a large Indian company shared this wisdom with Richard years ago, and it perfectly captures what separates humans from AI. Technology can analyze documents and data. But it takes human instinct to sense what happens when nobody’s watching.

Richard identifies three “human superpowers” that AI cannot replicate: professional skepticism, intellectual curiosity, and relationship skills. These aren’t soft skills; they’re the core value of internal audit.

Take culture assessment. Richard has done two major research projects showing how toxic culture can destroy organizations. But judging culture requires reading between lines, sensing unspoken tensions, and understanding human motivations. As Blake pointed out during the conversation, “The body language, the way people talk to each other, all of that is context that AI just cannot have access to.”

The audit committee relationship shows this even more clearly. Richard chairs an audit committee and knows these relationships need more than data transfer. They require courage to “grab them by the face” and focus them on hidden risks.

“If we’re content to just answer the questions they ask,” Richard warns, “then we’re not really serving our organizations well. We have to help them understand the questions they need to be asking.”

This shift, from giving answers to finding the right questions, represents a huge evolution. While AI can list potential questions, there’s something fundamentally human about knowing which questions matter.

Most critically, Richard identifies one role that must stay human: assessing AI’s own governance. “I shudder to think that there may be a day where we ask AI to assess its own governance,” he says. “We would never do that with anyone else.”

The challenge is developing these human skills when the traditional path is disappearing. Without routine work to learn on, how do new auditors develop judgment?

We need to help new auditors develop skepticism, intellectual curiosity, and institutional knowledge from day one. Teach them to ask “why” before teaching them “how.”

As Richard reflects after 50 years, “What a difference from the bean counter view of internal audit. You get to be so curious as an internal auditor these days.”

The Next 50 Years Start Now

Richard’s journey from a bank to internal audit’s leading voice shows a profession that has transformed before and must do so again.

The collision of perma-crisis and AI doesn’t doom internal audit. It clarifies its purpose. When tariffs change three times daily, cyber threats evolve by the hour, and AI makes decisions we don’t fully understand, organizations desperately need professionals who ask the hard questions.

Not “What does the data say?” but “What isn’t the data telling us?” Not “How do we implement AI?” but “How do we govern what we can’t fully understand?”

The saying “independence does not mean isolation” applies to both organizational relationships and the human-AI partnership. Tomorrow’s successful auditors won’t resist AI or surrender to it. They’ll orchestrate a sophisticated dance between computational power and human intuition.

The fact that entry-level work is vanishing while judgment becomes more critical demands new thinking about professional development. Organizations can’t wait for fully-formed auditors. They must cultivate intellectual curiosity from day one.

For accounting and tax professionals watching internal audit’s future, Richard warns those who avoid or fear AI will become irrelevant. But he also extends an invitation: those who combine technology with human capabilities will find themselves at the center of organizational decision-making.

Listen to the complete conversation to understand why this moment represents internal audit’s greatest challenge and its most exciting opportunity. After five decades in the profession, Richard reminds us the question isn’t whether internal audit will survive the age of AI. It’s whether individual auditors will choose to evolve with it.