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The Big Four Keep Publishing Fake AI Citations and It’s Getting Embarrassing

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A solo accountant can complete two years of bookkeeping in a few hours using Claude Cowork. But KPMG had to pull an entire AI report after 89% of its citations turned out to be fake. The AI revolution in professional services is already sorting winners from losers.

In episode 493 of The Accounting Podcast, hosts Blake Oliver and David Leary tackled a cluster of stories that paint a clear picture of how AI is restructuring professional services right now in real workflows, real paychecks, and real embarrassments for the Big Four.

There’s a growing divide between professionals who use AI carefully with human oversight (and get massive productivity gains) and those who rush to market themselves as AI experts while failing basic verification. This episode covers the Big Four’s repeated AI failures, the incredible productivity gains available to practitioners who use AI right, and the broader industry signals showing how AI is reshaping everything.

 

The Big Four’s “Vibe Citation” Problem Keeps Getting Worse

KPMG’s 2025 report, “Total Experience: Redefining Excellence in the Age of Agentic AI,” was supposed to showcase its AI expertise. Instead, it became the latest example of Big Four firms publishing AI-generated content they apparently never checked, also known as “vibe citations.”

GPTZero, a platform originally built to help teachers detect AI-generated text, analyzed KPMG’s report and found out of 45 citations, only five were accurate. Twenty-eight pointed to real sources but had made-up details. Twelve were too vague to verify. At least 16 were complete hallucinations. The tool rated the report 89% flawed.

The fake details weren’t subtle. KPMG claimed an Austrian utility called Verbund was using AI for real-time household energy optimization. In reality, the citation was about Verbund investing in a startup that might do this someday. They said Emirates airline had a chatbot named “Sara” that could change flights. Sara was actually a robot assistant from 2023 with no flight-change capability. The biggest gaffe was claiming East Japan Railway was using AI agents in 2019, before this type of AI even existed commercially.

UBS, NHS Greater Manchester, and Transport for London all said KPMG’s claims about their use of AI were “completely false or misleading.”

“We have to create a database and just track these because the Big Four just keeps doing it over and over again,” David said, noting similar recent incidents at EY and Deloitte.

The irony is KPMG’s website features an article titled “Essential Elements of Responsible AI: How Solid Guardrails Can Help You Scale AI Faster.” They’re selling AI expertise while failing at basic fact-checking.

David’s sarcastic take nailed it. “The only way I could think this could work is if the Big Four can go to the Fortune 500 and be like, ‘Look, we know all the mistakes that can be made. Now listen to us because we know what not to do.’”

How to Actually Use AI: A Real-World Success Story

While KPMG was publishing fantasy case studies, Blake was using AI to do real client work and showing what responsible AI use looks like.

He needed to complete two years of write-up work for a service business: 2,200 transactions across nine accounts, with source documents in a messy mix of PDFs and CSVs from different banks. In the old desktop days, this would have taken days of manual entry. Even with cloud accounting, it would take many hours of importing and coding.

Using Claude Cowork, he finished everything in about four hours, including gathering documents.

His approach was smart and deliberate. He pointed Claude at folders of bank statements and had it extract all transactions into Xero-compatible import files. It did OCR on PDFs, merged CSVs, and organized everything by account. Then he gave Claude the prior year’s general ledger and asked it to categorize transactions, but with a key addition: a confidence score for each categorization.

“I could open that up, sort by that score, and look at the transactions that are less than 90%,” Oliver explained. Instead of reviewing 2,200 items, he focused on exceptions.

The results were impressive. Claude missed just six transactions out of 2,200, and two of those were due to credit card statement date issues, not AI error. When a $5,000 clearing account discrepancy appeared, Claude opened Xero in a browser, analyzed the details, and identified the problems itself. One was a returned payroll miscoded to transfers. The other was more complex: undiscovered transfers to a business line of credit. Claude suggested this possibility, Oliver confirmed by pulling statements, and Claude then created the loan account, separated principal from interest, and fixed everything. That kind of discrepancy usually requires hours of investigation.

“I didn’t just say, ‘Here’s the GL detail, here are the transactions, go code them all and enter them into Xero,” Oliver emphasized. “I wanted to review it first, and I caught significant stuff.”

This capability is becoming more accessible. Microsoft’s Copilot Cowork is now available, with over half of Fortune 500 companies trying it during preview. Microsoft says it’s 30-40% cheaper per prompt than Claude, and since most accounting firms use Microsoft 365, it might already be on your computer.

Not everyone’s getting it right, though. David shared his frustration with QuickBooks AI. When he uploaded a PDF containing 12 monthly bills, QuickBooks mashed them into a single bill with line items from each invoice. No questions asked.

“It should say, ‘Hey, I noticed there are 25 bills in here. Do you want one bill or 25 separate bills?’ And I would just answer,” David said, comparing it to AI coding tools that ask before acting.

The Market Is Already Picking Winners and Losers

Meanwhile, CPA firms are seeing interesting pricing patterns. According to CPA Trendlines, overall pricing is up 4.2% year-over-year, reversing last year’s decline. But looking more closely at the breakdown, tax prep and planning jumped by nearly 8%. Advisory work rose over 6%. Audit only increased by 2.3%.

Clearly, clients will pay more for services requiring human expertise and judgment. Tax planning and advisory command the biggest premiums. More routine, standardized work, like audit, lags behind.

“Clients are willing to pay for tax planning advisory, for the human in the loop to make sure that the numbers are right,” Oliver said. “AI isn’t putting pressure on those fees at this point. And I don’t expect it to.”

The Real Divide: Verification vs. Vibes

The stories from this episode are different views of the same shift. KPMG publishes an AI report that’s 89% wrong while a solo practitioner uses AI to finish two years of work in an afternoon with near-perfect accuracy. CPA firms raise tax planning fees by 8% because clients value human judgment.

AI compresses the value of routine, unverified work while amplifying the premium on carefully applied expertise.

The divide in professional services is between those who verify and those who just publish. Between practitioners who build workflows with confidence scores and exception review, and firms that let AI-generated content sail through with fake citations. Between organizations that treat AI as a force multiplier for human expertise and those that use it to substitute for expertise they never had.

The practical takeaway is to learn the tools, whether that’s Claude Cowork, Copilot Cowork, or whatever comes next. But build human checkpoints into every workflow. Use confidence scoring. Review exceptions. Don’t set it and forget it. The productivity gains are potentially five to ten times traditional methods, but they disappear the moment you skip verification.

The market is already pricing this reality. Clients pay more for advisory, planning, and the assurance that a qualified human reviewed the work. Firms and practitioners who master this balance will command premiums. Those who don’t will find themselves on the wrong side of a restructuring that’s happening right now.

To hear Blake Oliver’s complete breakdown of his AI workflow, David’s full critique of accounting software AI, and more details on KPMG’s “vibe citation” disaster, listen to episode 493 of The Accounting Podcast.

AI Models Now Outperform Human Bookkeepers and One Controller Proves a Finance Team of One Actually Works

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A controller at a SaaS company that processes $50 million a month through its marketplace went on a two-week vacation. When he returned, his AI agents had already coded, categorized, approved, and synced 2,000 transactions. He reviewed just 67 (about 3%) by hand, and the entire cleanup took 30 minutes.

James Agius, Financial Controller at Skool, described his actual workflow on a recent episode of The Accounting Podcast. And it landed alongside benchmark data proving that, for the first time, off-the-shelf AI models from OpenAI, Anthropic, and Google are outperforming human accountants at basic bookkeeping tasks.

Hosts Blake Oliver and David Leary unpacked a series of developments that signal a genuine turning point for accounting. New studies from Digits and Ramp put hard numbers on AI’s bookkeeping abilities. A venture-backed startup led by a former PCAOB board member is building an AI-first audit firm. And KPMG’s entire US management committee flies to Silicon Valley every five to six weeks to meet with startups it views as potential threats.

But AI isn’t arriving to replace a surplus of accountants. It’s showing up amid a talent crisis that has more than tripled the number of unfilled accounting roles in a single year.

The Numbers Don’t Lie: AI Now Matches Human Bookkeepers

For years, the accounting profession has heard promises about AI. Now there’s data to back them up.

Digits just released the fourth version of its benchmark study, and CEO Jeff Seibert shared the results in an interview with David, which is featured on the episode. The test included categorizing over 2,000 transactions across multiple businesses into the correct chart of accounts. They tested all the major AI models (OpenAI’s ChatGPT, Anthropic’s Claude, and Google’s Gemini) against outsourced human accountants.

“All of the major model providers have, for the first time, beaten real, outsourced human accountants at bookkeeping tasks,” Jeff told David. The humans scored about 79% accuracy. The AI models came in between 79.4% and 80.7%. The margin is small (about 1.6%), but the direction is clear.

Before anyone dismisses 79% as a low bar, Jeff offered important context. That’s actually typical for outsourced accountants who understand general accounting principles but don’t know the specific business. “They don’t know anything about that business or its industry, supply chain, geography, or customer base,” he explained. That missing context accounts for the 20% error rate.

What’s striking is how similar all the models performed. They’re all within three percentage points of each other. As David put it, basic transaction categorization “is kind of a commodity now.” It’s something everyone will essentially get for free from these models right out of the box.

But purpose-built systems go much further. Digits’ own AI, which learns from each business’s transaction history and can’t hallucinate by design, hits 97.8% accuracy. “Digits mimics the knowledge of a dedicated accountant who you’ve worked with for a number of years,” Jeff said.

The picture changes when you look at more complex work. Ramp tested its new Stack platform on 237 accounting tasks across eight synthetic businesses for categorization and financial close work. Its system scored 65.8%, beating the raw models but well short of perfect. This matches what most accountants experience. AI is great at pattern recognition but still struggles with judgment-heavy tasks.

AI still falls short in complex accruals, according to Jeff. Journal entries, fixed asset schedules, and prepaid expenses are the remaining frontier. Digits responded by launching automated accrual schedules where the AI identifies potential prepaids or fixed assets, drafts the schedule, and the accountant approves it.

Jeff drew an interesting parallel. At his tech company, engineers went from zero AI use to 100% in a single quarter. Jeff himself hasn’t written code since December, despite coding being his passion since age 12. “We have not fired our software engineers,” he said. “They are still critical, but the day to day has changed completely. Instead of them writing the code, they’re guiding the agents.”

One Controller, Zero Staff, $50 Million in Monthly Transactions

James Agius proves what these benchmarks mean in practice. He’s the financial controller at Skool, a SaaS company running online educational communities. The company handles over $5 million in monthly spend with nearly $50 million flowing through its marketplace each month.

James is also the company’s entire finance department. The company doesn’t have any staff accountants, AP clerks, or analysts. It’s just him and seven specialized AI agents, plus an eighth admin agent that checks the others’ work and enforces controls.

When Agius took two weeks off, those 2,000 transactions piled up. His automations handled almost everything, from coding, categorizing and approving to syncing to the ERP. When he returned, just 67 transactions needed human judgment. The cleanup took 30 minutes.

“His job changed from doing the work to reviewing the work,” Blake explained on the podcast. That shift freed Agius for forecasting, cash management, and strategy. It’s the work finance leaders always say they want to do but rarely have time for.

The timing couldn’t be more ironic. Just as AI enables one person to run an entire finance function, the profession can’t find enough people to fill open roles.

A Personiv study cited in Accounting Today found that the number of unfilled accounting and finance positions per company jumped from 5 to 17 in a single year, more than tripling. Eighty-four percent of finance and accounting leaders say there’s a talent shortage. The hardest role to fill is the senior accountant role, cited by 43% of respondents.

The drivers aren’t mysterious. The profession has talked for years about how 75% of CPAs were approaching retirement. “Well, now they’re doing it,” Blake said. And the pipeline is thin because staff accountants have been leaving after just a few years.

As David pointed out, senior accountants are exactly the people who would manage AI agents, so the talent shortage and the AI transition are colliding at the worst possible moment.

Firms are responding by racing to adopt AI. Sixty-three percent of leaders use AI to ease hiring pressure, up from 23% last year. For example, Bennett Thrasher moved talent acquisition from HR to the growth function, treating recruiting as strategically as business development. “The human labor becomes more valuable because it’s augmented,” Blake noted.

The Race to Reinvent

The competitive landscape is shifting as fast as technology. New entrants and incumbents alike are making moves that suggest they see this transformation as irreversible.

Christina Ho, former PCAOB board member and past podcast guest, joined Oath, a venture-backed firm building an AI-native audit practice from scratch. No legacy systems or technical debt. It’s AI-first from day one. They raised $6.6 million in seed funding and aim to automate 80% of audit work by 2030.

Oath plans to connect directly to clients’ accounting systems for continuous verification rather than year-end evidence gathering. CEO Lucas Ward emphasized audit remains “a human accountability function” even as machines handle verification. They’re recruiting “accounting engineers,” hybrid roles combining accounting expertise with computer science skills.

The Big Four are taking notice. KPMG’s US CEO now takes the entire management committee to Silicon Valley every five to six weeks, meeting with venture firms like Andreessen Horowitz and Bessemer to identify potential disruptors. They’re open to partnerships or investments, anything to avoid being blindsided.

On the platform side, Ramp’s new Stack product shows where AI agents might actually live in the workflow. Stack connects to existing tools like QuickBooks and accepts plain-language instructions, like “This client allocates revenue by location, not department. Split it across six cost centers.”

As Blake observed, “The GL is not the best place for agents to live. You want the agents at the point of the transaction.” Ramp already sits at the point of spend, giving its agents rich context about each business. The market agrees. Ramp just raised $750 million at a $44 billion valuation.

Not every AI adoption strategy works, though. KPMG rolled out a dashboard requiring employees to use AI for roughly 75% of their working time. Predictably, employees immediately gamed it. They had AI summarize emails they’d already read or generate random drawings — anything to hit targets. Blake called it “token maxxing,” comparing it to padding billable hours. Amazon shut down a similar program after seeing the same behavior.

What Humans Still Own

Where does human value go when AI handles the routine work? Jeff identified three things AI can’t replace.

  1. Judgment. “AI goes off in weird directions,” he said. Experienced professionals must guide it through ambiguous calls.
  2. Trust. “The AI will tell you anything you want. You can never trust AI.”
  3. Accountability. “It’s never going to be liable for the numbers it gives you. What are you going to do, sue your AI?”

These are the differentiators for accountants who want to stay relevant as machines take over the rest.

All of the evidence from this episode points to AI crossing the competence threshold for basic bookkeeping and advancing toward complex tasks. One controller already runs a $50 million operation solo. Yet unfilled roles have tripled. Senior accountants are impossible to find. The retirement wave is here, and the pipeline is thin.

To thrive, you need to bring what AI can’t: judgment, trust, and accountability. The transition is here.

Listen to the full episode for the rest of Jeff’s interview, details on KPMG Australia’s whistleblower scandal fallout, and a discussion of the IRS leadership vacuum.

Private Equity’s Big Bet on Accounting Firms Is Starting to Look Shaky

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CBIZ stock has lost half its value in the past year. Starbucks just killed its AI inventory counting tool after nine months of miscounts. And Microsoft, after investing $13 billion in OpenAI, had to cut off its own engineers from AI coding tools because costs went through the roof.

These stories from the latest episode of The Accounting Podcast paint a picture of where the accounting profession is heading, and it’s not what private equity investors or AI vendors promised.

CBIZ’s Stock Tells a Story About Private Equity’s Future

CBIZ is the only publicly traded accounting firm in the U.S., so its stock price is the closest thing we have to a market report card on the profession’s consolidation strategy. Right now, that report card shows failing grades.

“The stock price of CBIZ, Inc. today is $34.68. That is down 51% over the past year,” host Blake Oliver noted during the episode. When CBIZ bought Marcum at the end of 2024, the stock was at $78. It hit $90 in early 2025, then crashed to about $27 by March before recovering slightly.

What makes this even more interesting is that CBIZ isn’t alone. Co-host David Leary asked Blake to pull up Intuit’s chart for comparison. “Similar chart,” Blake confirmed. Intuit is down 53-54% over the same period. Meanwhile, the S&P 500 is up 28%.

The problem is what’s behind the stock price. CBIZ forecasts only 2% – 5% revenue growth for 2026. “That’s less than inflation. So basically, no growth,” Blake explained. “Why would investors be excited about buying stock in a company that’s not really growing much?”

Blake sees a more serious threat to large firms from smaller, more nimble competitors. “The larger the organization, the harder it is to change a business model or to integrate new technology,” he said. “I see smaller, more agile firms becoming a real threat to the large accounting firms. The smaller ones can integrate AI into their systems and switch their billing models.”

The math is simple but meaningful. AI lets a 10-person firm work like a 100-person firm. The traditional advantage of midsize firms (having an expert for everything) disappears when smaller firms can use AI to expand their capabilities.

Private equity firms typically look for efficiencies, not complete reinvention. “They figure out how to get marginally more efficient. They don’t completely reinvent the business model. That’s not what private equity is all about,” Blake explained.

When AI Meets Reality: Starbucks and Microsoft Learn the Hard Way

Starbucks spent nine months trying to make AI inventory counting work. The idea was that employees would walk past shelves, filming with an iPad, and AI from a company called NomadGo would automatically count everything. The company claimed 99% accuracy.

Reality hit hard. “Reuters reported the app often miscounted or mislabeled inventory, including confusing similar milk varieties or failing to recognize them,” Blake noted. Starbucks killed the project. Stores went back to counting by hand.

These failures hit the bottom line. “They were getting product shortages because they thought they had coffee, but didn’t have coffee to sell,” David explained.

Meanwhile, Microsoft discovered that AI coding tools come with a shocking price tag. Despite investing $13 billion in OpenAI and using AI to write 30% of its code, Microsoft had to cut off engineers from these tools because costs exploded. The same thing happened at Uber, where the CTO said they burned through a year’s worth of budgeted tokens in just four months.

The token problem is growing. Blake shared a striking statistic from Forbes: “Anthropic’s annualized net dollar retention exceeds 500%.” That means customers end up spending five times more than they initially expected.

“Nobody knows what they’re buying,” David said. “If I sign up for a monthly plan that gives me 20,000 tokens a month, it feels like enough. And then I’m six days into the month and I have to spend another 40 bucks for more tokens.”

“We’re going to hear a story like this in the next year,” David predicted. “Some firm will say, ‘Our five-person firm spent $300,000 on AI tokens, and we didn’t know it until it was too late.'” 

The Small Firm Revolution: XeroForce and AI Architects

While big firms struggle with their business models and AI costs spiral, something interesting is happening with smaller practices. Xero just launched XeroForce, a tool that could change the game.

“It’s a no-code AI agent builder that lets small businesses and accountants automate repetitive financial tasks using plain language, no technical skills required,” David explained. Unlike chatbots that give one-time answers, these are permanent automations that run on schedule.

Blake immediately saw the potential. “Every week, look at all transactions over $75 in any expense account, and then search my email for receipts and attach those receipts to the transactions. That’s a whole category of apps right there.”

“Accountants have engineer brains. You just don’t know how to write code. And if this can let you create ‘permanent’ code that runs routinely for a client inside Xero, it’ll help you scale,” David said, putting it in terms every accountant can relate to.

But tools alone aren’t enough. Firms need someone to manage this transformation. Donnie Shimamoto, CPA and founder and managing director at Intraprise Techknowlogies, calls this role an “AI architect.”

“Every CPA firm that’s big enough should create an AI architect role,” Blake said, comparing it to the cloud transition. “All the leading firms created these technology roles that were not IT. They were basically operations roles.”

An AI architect would handle security reviews, evaluate different tools, monitor token spending, and train the team. Without this role, firms risk security issues or shocking year-end bills.

For young accountants, Blake had direct advice. “If you’re a student or a young accountant and you want a job, learn this AI stuff. Every firm is going to be hiring an AI architect.”

What History Tells Us About What’s Coming

Blake drew a parallel to when electronic spreadsheets arrived. “The number of bookkeepers employed at accounting firms dropped by about half. We lost like a million bookkeepers over a generation,” he said. “What happened? We had more accountants and, in particular, we had a whole new category of job: financial analysts.”

His prediction for AI follows the same pattern. The number of traditional accountants will decline, but new roles will emerge. “Small businesses will be able to afford controllers and CFOs. They’ve always wanted them but could never afford to hire one.”

Both hosts emphasized the importance of experimenting now. David spent Memorial Day building a production assistant that saves him four hours a week. Blake spent two months creating a tool that automatically reconciles bank accounts.

“Don’t try to build anything groundbreaking,” David advised. “Just solve a simple problem that you have to deal with week after week.”

The Bottom Line

The accounting profession is changing fast, but not in the ways many expected. Large firms with private equity backing face serious challenges if they can’t reinvent their business models. AI implementation is proving harder and more expensive than promised. But smaller, agile firms that experiment with new tools and create AI architect roles could gain a huge competitive advantage.

“If you’re a firm with a few dozen people, you can now compete with firms that have hundreds of staff,” Blake said. That’s an opportunity for firms ready to embrace it.

Want to hear the full discussion, including how the hosts are building their own AI tools? Listen to the complete episode of The Accounting Podcast.

Why the Most Profitable Accounting Firms of the Future Might Have No Employees at All

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One guy. Zero employees. He spends 70% of his budget on technology.

Sam Leon runs The Millennial CPA in Richmond, Virginia, where AI does most of the tax prep work while he reviews and signs off. He just landed on Accounting Today’s 2026 Best Firms for Technology list, not by building a bigger team, but by proving you don’t need one at all.

Meanwhile, KPMG is shutting down its entire federal government audit practice after losing a $60 million Pentagon contract. They’re reassigning 450 employees and cutting another 400 from advisory. The old work is shrinking. The new AI, cyber, and forensics work is growing fast.

On this week’s episode of The Accounting Podcast, hosts Blake Oliver and David Leary discussed what these stories mean for the profession. They explored how AI is making the “firm of one” model possible, tested the new QuickBooks and Xero connections to Claude, and wrestled with a big question: If AI can replace so much labor, what happens to the people and the economy that depend on them?

 

The Solo Practitioner Who Turned AI Into His Staff

Sam Leon took a simple but radical approach to building his firm. AI handles the grunt work of tax return preparation, including creating workpapers, doing year-over-year comparisons, and mapping QuickBooks data to tax forms. He reviews everything and signs the returns. That’s it.

“I see AI as coming together to be a total tax preparer, and whoever signs the returns is the reviewer,” Sam told Accounting Today. He thinks of the AI as his junior preparer while he’s the senior reviewer.

The time savings are wild. Work that would take a human three to five hours, such as creating detailed tax workpapers from QuickBooks exports, takes AI five minutes. And Sam has no plans to hire. “I won’t hire until I hit a wall with my AI preparers and AI workflow managers,” he said.

Blake validated this approach based on his own daily use of Claude Cowork. “To do it as an individual is totally possible,” Blake said. “And so I expect we’ll see more of these firms of one, and you’ll be able to scale up and make a lot of money, because you don’t have to hire employees.”

David connected this to a broader trend he calls the “minimum viable-sized company.” The old playbook was simple: raise money, hire people, grow. “You don’t need that anymore,” David said. “The future winners are going to be small, highly efficient teams with strong strategic clarity. Not large organizations.”

Of course, there are questions. How much revenue does Sam actually make? How does he handle client communication and invoicing? Is he a software engineer or just really good at prompting AI? Blake and David want to get him on the show to find out.

The Tools Are Getting Easier, But Still Have Limits

Right now, Sam’s model works because he’s willing to configure AI tools himself. But that’s changing fast as AI gets built directly into the software firms already use.

Canopy just launched an AI “Coworker” feature across its practice management platform. David was initially skeptical when he saw the sample prompts, which included things like “list all my clients,” that you could see with one click anyway. But Blake highlighted the real value: scope-creep detection that analyzes your billing and emails to spot when you’re doing more work than you’re charging for, automatic workflow updates when disaster declarations change filing deadlines, and meeting notes that automatically create tasks with assignees and due dates.

“These AI agents in practice management are going to be hugely important,” Blake said. “They’re going to make practice management ten times more valuable.”

The big platforms are also opening up to AI. Intuit just released connectors linking Claude to QuickBooks, TurboTax, Mailchimp, and Credit Karma. Xero has one too. But Blake tested both and found them pretty limited. You can pull basic reports and import transactions, but you can’t actually analyze transaction-level data yet.

“If they don’t make connectors more robust, they’re kind of useless,” Blake said. Still, the direction is clear. As David put it, “Claude becomes like your central gear that’s spinning data out to these other spots.”

KPMG’s Federal Exit Shows Where the Profession Is Heading

While solo practitioners are using AI to do more with less, KPMG is learning what happens when you can’t adapt fast enough.

The firm just lost its contract to audit the U.S. Army. It was a $60 million annual deal they’d had for over a decade. The Army has never passed an audit, and now the Pentagon wants to restructure the whole approach. KPMG responded by shutting down the entire federal audit practice and reassigning 450 people.

But that’s not all. They’re also cutting 4% of U.S. advisory staff, or about 400 people, mostly in regulatory risk and financial services consulting. These cuts continue a pattern that started in 2023.

Instead, KPMG is investing in AI, cyber, forensic services, and managed services. Traditional audit work is shrinking, while tech-enabled services are growing.

The Big Risk 

If companies use AI mainly to eliminate jobs, who’s going to buy their products?

Christine Kuglin and Bright Ikwetie wrote about this in Accounting Today, calling it the “AI efficiency paradox.” Businesses get more efficient by replacing workers with AI, but they’re also eliminating the incomes that drive consumer spending. It’s a potential death spiral. Less spending means less revenue, more layoffs, and more AI. Rinse and repeat.

The economic data is confusing. Weekly jobless claims just hit 189,000, the lowest in more than five decades. Yet manufacturing employment is down 88,000 jobs year over year. How can unemployment be so low when we keep hearing about layoffs?

“Is this just lagging?” Blake wondered. “Are these workers just finding jobs in other parts of the economy or maybe working for themselves?”

For accounting specifically, the demand for talent remains strong. Intuit analyzed LinkedIn data and found that both tax and accounting roles are “very hard to hire” nationally. They’re actively recruiting with flexible, remote-first benefits, which is exactly what the Big Four firms are cutting.

What This Means for Your Firm

The lesson from Sam is that one person can now deliver what used to require a team. The same principle scales up. A small firm can compete with a large one, and a mid-size firm can offer enterprise-level services.

But don’t use AI just to do the same work with fewer people. Use it to do work you couldn’t do before. As Blake put it, “The growth opportunity in accounting is advisory-type services. And AI paired with expert humans is just so incredibly powerful for doing advisory work like fractional CFO services, M&A advisory, and cost segregation studies.”

David sees another opportunity in helping clients “vibe code” custom apps instead of stacking expensive SaaS subscriptions. “I am confident that accountants could vibe code,” he said. “The old stack of app stacking is going to go away. You’re just going to help your client build the app they need.”

The tools are here. The demand is there. The question is whether firms will use AI to shrink or to grow. Firms that use AI to expand what’s possible rather than just cut costs will set the terms for everyone else.

Want to hear Blake test the QuickBooks-Claude connector live? Curious about how much Sam actually makes? Listen to the full episode of The Accounting Podcast for all the details, plus discussions on new IRS whistleblower rules, tariff refund lawsuits, and why procrastinating on AI adoption might actually pay off.

Why Are Big Four Firms Laying Off Partners When There Aren’t Enough Accountants to Go Around?

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The accounting profession is facing turbulence on multiple fronts. KPMG is laying off roughly 100 audit partners in the US, while the best artificial intelligence available still gets one out of every five accounting tasks wrong.

In episode 485 of The Accounting Podcast, hosts Blake Oliver and David Leary unpack these converging stories that show the challenges and opportunities facing the profession. From venture-backed firms abandoning their “automate everything” model to a heated controversy over CPE standards with NASBA, the episode paints a complex picture of an industry in transition.

The Hard Truth About AI’s Current Capabilities

A new benchmark study from DualEntry tested 19 AI models across 101 real accounting workflows, and the results are interesting. Claude Opus 3.5, the current darling of AI enthusiasts, achieved the best performance at 79% accuracy. GPT-4o from OpenAI came in slightly behind at 77%. For context, GPT-4 scored only about 40% on the same tasks. It’s progress, but still nowhere near the reliability accounting demands.

The tests covered transaction classification, journal entry creation, bank reconciliations, and month-end close procedures. As Blake pointed out, the problem compounds. “It’s not like you’re automating 80% of the work because you have to clean up that other 20% the AI messed up.” Those errors cascade through financial statements and create cleanup work that erodes efficiency gains.

David put it in relatable terms. “If you had a human employee and ten hours of the week their work was just wrong, you’d probably freak out.”

The gap between 79% and acceptable accuracy for unsupervised work remains enormous. AI can assist and accelerate, but it can’t yet operate independently in accounting.

Tech Firms Abandon the “Automate Everything” Dream

The accuracy issue explains why venture-backed accounting firms are abandoning their original models. Decimal, which raised significant capital and even acquired KPMG Spark’s client base, pivoted away from providing services directly. Instead, they franchise their technology stack to independent firms that handle the actual client work.

“You can’t have SaaS valuations and raise money when you’re a human service business,” David explained, listing the other casualties, including Bench, ScaleFactor, Visor Tax, and Botkeeper. “We’ve seen this over and over again.”

Pilot made a similar move about six months ago with its “local partners” program that lets small practices use Pilot’s technology rather than Pilot doing the work itself. The technology is valuable, but human expertise is still essential.

Meanwhile, traditional players are moving in. H&R Block’s new CEO wants to transform the company from a once-a-year tax relationship to a year-round partner offering bookkeeping, payroll, and business support. Collective is buying OpenLedger. Even a fractional HR provider Austin Alliance Group wants into the bookkeeping space.

This sparked an interesting debate between the hosts. When discussing whether AI will handle routine work while humans focus on advisory, David pushed back. “I think it’s the opposite. Humans will be more involved in the data entry and the compiling of data. AI is really good at just taking scattered numbers and data, unstructured data and summarizing it, which arguably is advisory.”

Blake disagreed, pointing to a real-world example. A San Francisco store let an AI agent named Luna make operational decisions. Luna understaffed during busy periods, over-ordered candles, and lost $13,000. “AI doesn’t have memory the way people do,” Blake explained. Without context and accumulated experience, AI struggles with strategic decisions.

Big Four Layoffs, Demotions, and Massive Fines

While tech firms pivot, the Big Four face their own challenges. KPMG cut roughly 100 partners from its US audit practice (about 10% of audit partners) after too few accepted voluntary early retirement. The firm calls it “multiyear rightsizing,” but as David asked, “Does audit demand ever actually decrease?”

The situation is similar in the UK, where KPMG and EY started demoting equity partners to salaried positions. “Getting to partner at a Big Four firm used to mean a job for life,” Blake noted. Now that security is disappearing.

PwC faces different troubles. They’re paying a $166 million fine related to their audit of the China Evergrande Group, the collapsed property developer accused of inflating revenue by $82 billion. PwC audited them for over ten years before resigning in January 2023. As David observed, they probably made far more than $166 million from the engagement.

PwC is also ending its fully remote option for US tax staff, requiring three days in the office starting July 2026. Going Concern speculates this might be a way to thin headcount without announcing layoffs. Remote workers either need to relocate or quit.

The NASBA Controversy: A Debate Over CPE Standards

One of the episode’s most heated discussions centered on a demand letter Blake received from NASBA regarding comments he made at an AICPA conference. While demonstrating how to use AI to create CPE courses, Blake suggested the traditional approach of creating learning objectives first, then content, was “backward.” He argued it made more sense to let experts teach, then create the objectives based on what they teach.

NASBA’s letter accused him of making “unfavorable, unprofessional, or inappropriate comments” and demanded he cease such remarks immediately.

“If there’s any place for a discussion about NASBA policies, it should be at a conference with 200 L&D people from all the big accounting firms,” David argued. The hosts expressed frustration that NASBA treats different pedagogical approaches as inappropriate rather than worthy of professional debate.

“The pendulum has swung too far towards regulation and too far away from learning,” David said, noting how CPE often becomes just checking boxes rather than actual education. Blake shared a copy of his response to NASBA on his blog. In it, he asks NASBA to explain why expressing a different educational philosophy constitutes unprofessional behavior.

Where the Shortage Hits Hardest

A new index from Sam’s List reveals which states face the worst accountant shortages. Nevada tops the list with just 1.75 accountants per 1,000 residents and 139 professionally prepared returns per accountant, the highest ratio in the study. Nevada’s accounting workforce also fell nearly 30% from 2019 to 2024.

“Sounds like it’s a state accountants don’t want to live in,” David observed. “Accountants probably don’t want that Vegas gambling lifestyle energy.”

While Nevada has the worst per-capita shortage, Texas needs almost 25,000 additional accountants. This puzzled the hosts, given Texas’s population boom from high-tax states. “Are the benefits just not that good, and accountants see through it?” David wondered.

On the flip side, Washington, D.C. has nearly 14 accountants per 1,000 residents, almost ten times Nevada’s rate. New York has a surplus of 27,000 accountants above the national baseline.

Looking Ahead

The picture emerging from these shows AI is transforming accounting but not replacing accountants. The 79% accuracy ceiling, the pivot of tech-first firms, and the Big Four’s struggles all point to the need to find the right collaboration between humans and AI.

For firm leaders, the franchise and partnership models emerging from companies like Decimal and Pilot may offer a more sustainable path than pure automation. For individual practitioners, the message is encouraging. While AI raises the floor on routine tasks, human judgment, experience, and adaptability remain irreplaceable.

Listen to the full episode of The Accounting Podcast for the complete discussion, including more details on the NASBA controversy, state shortage data, and whether Kentucky’s elimination of the 150-hour rule signals the beginning of the end for that requirement nationwide.