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Blake Oliver

Why Historical Cost Accounting Is Broken (And What Could Fix It)

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When Daniel Day-Lewis strikes oil in “There Will Be Blood,” that’s the moment his character becomes wealthy—not years later when the oil is extracted and sold. Yet accounting treats this wealth-creating discovery as if it never happened, recognizing zero value until decades of extraction begin.

This disconnect between economic reality and financial reporting isn’t just a Hollywood illustration—it’s emblematic of a fundamental flaw plaguing our entire theory of accounting. In this episode of the Earmark Podcast, I spoke with Thomas Selling, author of The Accounting Onion blog and former SEC regulatory expert, about how financial reporting systematically fails to capture when and how businesses actually create value.

Thomas argues that the fundamental problem with modern accounting isn’t complexity or compliance—it’s a structural flaw rooted in historical cost accounting. This creates massive timing disconnects between when businesses create economic value and when financial statements recognize it, forcing successful companies to appear unprofitable during critical growth phases while handing management dangerous tools to manipulate earnings at shareholders’ expense.

Our wide-ranging discussion explored how these timing problems plague industries from oil and gas to pharmaceuticals to software subscriptions, why historical cost accounting enables manipulation through what Thomas calls accounting’s “truth in labeling problem,” and what radical alternatives could create more honest financial reporting that actually serves investors.

The Historical Cost Problem: When Transactions Trump Assets

Historical cost accounting sits at the foundation of our financial reporting system, but Thomas says it’s built on a fundamental misconception. “Historical cost is not actually an attribute of an asset,” he explained, “but rather an attribute of the transaction that acquired the asset.” This distinction might sound technical, but it creates the framework for timing disconnects and manipulation opportunities plaguing modern accounting daily.

Think about it this way: if you bought a house ten years ago, you don’t think about the purchase price as describing the house itself. You consider what it would cost to replace the house if you moved, or how much you could sell it for today. Yet accounting theory stubbornly clings to that decade-old transaction price as if it tells us something meaningful about the current asset.

As Walter Schuetze, former SEC Chief Accountant whom Thomas worked under, famously put it: “We report a truck as if the cost of the truck is the asset as opposed to the truck itself.” This creates what Thomas calls a “truth in labeling problem”—we claim to provide relevant information about assets while actually providing information about historical transactions that may have occurred years or decades ago.

The manipulation opportunities this creates are staggering. Historical cost enables what Thomas calls “cherry picking”—companies can sell assets that will show the most gain in any given period simply because the carrying amount bears no relationship to current reality. This isn’t theoretical—it’s exactly what brought down Enron.

The energy giant had power plants that were performing well but had been fully depreciated on the books. When Enron needed to boost its numbers, it created fictitious entities with essentially fictitious investors and “sold” those power plants to them. Enron recorded massive gains while hiding enormous debt levels in special purpose entities. “They were able to convince the auditors that these were genuine sales,” Thomas explained. “But, in fact, what happened is that these assets came back to Enron along with the debt, and they couldn’t handle the level of debt they had.”

Even the complex impairment rules that fill thousands of pages of GAAP exist solely because historical cost creates such distortive measurements. Under current rules, an asset worth $1 million in historical cost isn’t considered impaired if expected future cash flows are $1,000,001—even if that cash flow won’t arrive for five years and ignores the time value of money entirely. But drop those expected cash flows by just $2 to $999,999, and suddenly you have an impairment loss of hundreds of thousands of dollars when you discount that future cash flow to present value.

These systematic flaws give management what Thomas describes as tools to manipulate earnings while creating financial statements that often bear little resemblance to economic reality.

The Great Timing Disconnect: When Value Creation and Recognition Don’t Match

The most dramatic evidence of accounting’s structural flaws emerges when examining industries where value creation and revenue recognition are separated by years or decades.

Consider the extractive industries, which represent a massive portion of global economic activity through oil, gas, metals, and minerals. Thomas estimates that roughly 80% of an oil and gas company’s value creation occurs at a single moment: when they discover reserves. “The value-creating event is the discovery of reserves,” he explained, pointing to how stock prices immediately jump when companies file 8-K forms announcing new discoveries.

Yet GAAP treats this wealth-creating discovery as a complete non-event. “When does GAAP recognize the first penny of those earnings?” Thomas asked. The answer floored me: somewhere between 5 and 50 years later, when the last drop finally comes out of the ground. “It’s going to take five years to develop it. You turn the spigot on, the last drop is going to come out 50 years from now.”

This timing problem isn’t limited to extractive industries. Pharmaceuticals face identical challenges where the value-creating event is drug discovery, but revenue recognition occurs years later after development, testing, and approval. “The value-creating event is the discovery of a new drug,” Thomas noted. “Think of how many years go by before you get the first dollar of revenue.”

But perhaps nowhere is this disconnect more visible than in subscription businesses, where my firsthand experience reveals the absurdity of current accounting rules. In software-as-a-service companies, the moment of value creation is customer acquisition. These businesses can reliably estimate customer lifetime value through proven metrics like churn rates, average contract prices, and customer lifespan data.

“Every customer that you bring on has a lifetime value of X dollars,” I explained to Thomas. “When I sign up that customer, economically what is happening is I am basically adding those future cash flows to my business.” Yet these economically real and measurable future cash flows never appear on the balance sheet. Meanwhile, 100% of the marketing and sales expenses to acquire that customer hit the income statement immediately.

This creates what I see as a violation of basic accounting principles, though Thomas clarified that under current FASB thinking, “the matching principle no longer exists.” What remains is conservatism—recognizing expenses immediately while deferring revenue recognition. This makes successful subscription businesses appear horribly unprofitable during growth phases, exactly what happened with Amazon while building its enormously valuable Prime subscriber base.

These timing disconnects don’t just confuse investors—they actively distort capital allocation decisions across the entire economy, making some of the most valuable business models appear fundamentally unprofitable during their most crucial growth phases.

Beyond Earnings: A Balance Sheet Revolution

The solution to accounting’s structural problems isn’t trying to fix earnings measurement—it’s abandoning the obsession with earnings entirely. Thomas argues there’s no universal “right number for earnings,” and accounting should instead focus on what the FASB has correctly identified as its real purpose.

“The FASB concluded for good reason, that they should be in the business of measuring assets and liabilities and that reported… earnings… is a function of changes in assets and liabilities, not the other way around,” Thomas explained. Instead of chasing some mythical perfect earnings number, financial reporting should provide accurate, detailed information about what companies actually own and owe.

Thomas proposed four specific alternatives to historical cost measurement. Fair value measures what you could sell an asset for today—we’ve already seen this implemented for crypto assets. Replacement cost measures what it would cost to acquire equivalent assets today. Net present value calculates the present value of future cash flows for assets where those flows can be estimated.

But Thomas’s preferred approach is “deprival value”—a hybrid that measures how much utility a company would lose if deprived of an asset. “If somebody took your house from you, the deprival value would be the replacement cost, because you would have to replace it,” he explained. For outdated inventory you weren’t planning to replace, the deprival value would be whatever you could sell it for.

The estimation challenge is real—moving away from historical cost requires more judgments about current values. But as Thomas pointed out, our current system already relies heavily on estimates, just bad ones embedded in a perverse structure. “It’s like asking students to grade their own papers,” he said, describing how management makes the estimates that determine their own performance, while auditors can only reject obviously unreasonable numbers.

This explains why companies fight basic transparency measures. Thomas pointed to decades-long battles over requiring direct method cash flow statements showing actual cash receipts from customers and payments to vendors. “Management fights tooth and nail a requirement to have a statement of cash flows using the direct method,” he noted, because transparency doesn’t serve their interests.

Thomas’s vision is radical in its simplicity: replace 8,000 pages of complex GAAP with perhaps 200 pages of clear principles focused on measuring assets and liabilities in relevant ways. This would “supercharge” investors with detailed information about how asset values change over time, allowing users to construct whatever performance measures they find most relevant rather than accepting GAAP’s one-size-fits-all approach.

The Path Forward: From Accounting Theater to Economic Reality

My conversation with Thomas Selling revealed a profession at a crossroads, driven by what he describes as his “rage at how managers use accounting to steal from shareholders.” Current accounting systems don’t just have technical problems—they have fundamental structural flaws that actively distort economic reality and enable systematic manipulation.

When successful subscription businesses appear unprofitable during growth phases, when oil discoveries worth billions show as non-events on financial statements, and when management can game impairment rules with surgical precision, we’re witnessing what amounts to accounting theater rather than meaningful financial reporting.

The implications extend far beyond financial statements. These measurement failures affect capital allocation across the entire economy, from inefficient mergers driven by goodwill accounting quirks to investors systematically misunderstanding some of the most important business models of our time.

The rise of subscription models, platform businesses, and intangible asset-heavy companies has exposed historical cost accounting as increasingly obsolete for capturing how modern businesses create value.

The solution isn’t tweaking existing rules or adding more complexity to an already bloated system. It requires the kind of fundamental reformation Thomas advocates—what he’s calling “an accounting reformation” in his upcoming book. As he puts it, “Once managers can no longer manipulate income, they’ll have no economic reason to base their performance on earnings.”

This accounting reformation won’t be easy, given the entrenched interests that benefit from current opacity. But it represents a future where financial statements finally serve their intended purpose of informing capital allocation rather than obscuring it.

To hear Thomas’s detailed vision for this accounting reformation and his passionate case for why these changes are urgently needed, listen to the full episode of the Earmark Podcast.

AI Won’t Just Speed Up Your Close – It Will Eliminate It Completely

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“I want it gone.”

Aaron Harris, CTO of Sage, isn’t talking about making the financial close faster. He wants to eliminate it completely. No more monthly scrambles to lock the books. No more accountants working late to reconcile accounts. No more rigid cycles that control how businesses operate.

He shared this goal during his recent appearance on The Accounting Podcast, recorded at Sage Future in Atlanta. Harris has been a returning guest since 2019, and his message has stayed remarkably consistent: artificial intelligence will fundamentally change accounting processes and how businesses operate.

Harris isn’t just talking about automation making things faster. He’s challenging the basic business cycles that have defined corporate operations for generations. He envisions a future where annual audits become continuous, where quarterly tax filings disappear into real-time government systems, and where rigid business cycles give way to always-on, intelligent operations.

From Simple Tasks to Autonomous Operations: The Three Waves of AI

Harris breaks down AI’s evolution in accounting into three distinct waves, each building toward his vision of eliminating business cycles completely.

Wave One: Task-Based AI

The first wave focused on very specific jobs like reading invoices or classifying transactions. These systems worked like sophisticated scripts. They could automate tasks, but they needed humans at every step. “You can’t really interact with this AI,” Harris explains, “and because these are sort of very narrowly defined models, they can’t do a lot very flexibly.”

Wave Two: Generative AI

This wave brought conversational interfaces like Sage Copilot. Suddenly, AI could interact naturally with users and work more flexibly. This opened up possibilities for people outside the accounting team to use these systems. “The two big things are now you can interact with the AI,” Harris notes, “and it’s those underlying capabilities allowing that interaction that allow the AI to work more flexibly.”

Wave Three: Agentic AI

This is where Harris sees the real transformation. These systems can plan, execute, and operate on their own. They can access tools and interact with other systems without constant human guidance. “The real breakthrough comes with Agentic AI, where we’re now equipping these large language models. They think through how to plan something start to finish and execute on that.”

The progress has been dramatic. Harris tracks the journey from two to three weeks for financial close in 2019 to just two to three days today for some customers. But he’s not satisfied with just making things faster. “There are some breakthroughs, and we’re going to reach a point where businesses say, you know what, we’re just not going to operate this way anymore,” he predicts.

Sage already has AI systems handling complex tasks autonomously. Their outlier detection works across accounts payable, supply chain operations, and construction bidding. These systems don’t just flag problems; they prevent them by catching patterns humans would miss.

This evolution leads Harris to ask, if AI can keep our data accurate all the time, why do we need to “close the books” at all?

Why the Financial Close Needs to Die

Harris challenges something most accountants take for granted: the need for periodic closes. “Why do I need a close?” he asks. “Isn’t that kind of an archaic concept? Like, I’m locking up the books so nobody can access them anymore, and so that the data is memorialized forever. That’s ancient.”

This isn’t just theory. Real examples around the world show businesses moving toward continuous operations. In Brazil, every invoice must be filed with the government in real time. The UK’s “Making Tax Digital” (MTD) program requires businesses to upload their general ledgers to government servers quarterly, with AI automatically coding transactions. “Fundamentally what happens,” Harris explains, “is your general ledger gets uploaded to a government server. When it comes time to file the taxes, you’re just signing something, because they already know what you owe.”

These government requirements force businesses to modernize in ways that make continuous operations inevitable.

Harris’s vision for continuous auditing might be the most radical change. Instead of annual audits that review old data, he sees auditors providing ongoing assurance with technology constantly monitoring books. “My vision for continuous auditing is that the auditors are going to make a lot more money than they’ve been making,” he predicts. “It’s going to be continuous assurance.”

This would transform the relationship between businesses and auditors from periodic validation to ongoing collaboration. Instead of finding problems months later during annual reviews, continuous auditing would catch issues immediately and help fix them in real time.

Building Trust: Making AI Accountable

The biggest challenge is psychological. How do you get CFOs to trust AI systems with decisions they’re responsible for?

Harris understands this deeply. “You have to understand that psychology to design this experience,” he explains. The key is creating a “trust journey,” gradually giving AI more autonomy as users gain confidence through transparency and proven results.

Sage’s answer is its AI Trust Label, which Harris compares to a nutrition label. Click on any AI feature and you can see exactly how it works: what models it uses, how it handles data, security measures, and whether it uses your data for training. “We’re not saying here’s how much you should trust this,” Harris clarifies. “We’re saying here’s the compliance we are subject to and we are meeting and here’s the models we use.”

This transparency is crucial for complex tasks like accrual processing. Before a CFO trusts AI to handle accruals alone, they need to see the system’s suggestions, verify it contacted purchasing about pending invoices, and understand how it decides what to accrue. “I want to see in a very transparent, auditable way what the AI is doing before I say ‘yep, you can do it now’,” Harris emphasizes.

Sage’s careful approach reflects what customers really want. Harris cites a survey showing 75-80% of businesses want AI companies to “take it slow and get it right.” This finding shaped Sage’s strategy of gradual rollout rather than rushing autonomous agents to market.

This approach contrasts sharply with competitors like Intuit, whose AI agents Harris criticizes as trained on community forum content rather than authoritative sources. He describes Sage’s strategy as “a lot less reckless,” emphasizing their focus on serving CFOs who demand absolute accuracy. “We’re ruthlessly focused on the accounting profession. That CFO needs to trust us and they’re not going to use something they don’t trust.”

Instead of using general-purpose AI models, Sage is developing specialized accounting expertise through their partnership with the AICPA. These smaller, fine-tuned models focus specifically on accounting knowledge rather than trying to be good at everything. “I want it to be an expert at a very narrow set of things,” Harris explains. “You want it to be as capable as a CPA.”

AI in Action: What Sage is Building Now

Harris shared several examples of AI already working in Sage products, showing how these concepts are becoming reality.

Sage Copilot has been rolling out across different products over the past year. It started with small businesses using Sage Accounting, then expanded to Sage for Accountants, Sage 50, and now Sage Intacct. The system helps with three main areas:

  1. Close management. Copilot keeps users informed about what’s preventing the books from closing and helps them through the process
  2. Budget variances. It engages budget owners outside the finance team to understand performance and explain variances
  3. Product guidance. Users can ask conversational questions about how to use the software instead of searching through help files

Outlier Detection is Sage’s first major AI investment. Harris explains they built this capability first because “when we talk to finance teams and CFOs, the thing that comes through loud and clear is that they need to be trusted. The thing they care about the most is that their books are accurate.”

The system works differently for each company because “an outlier for company A is not the same as an outlier for company B.” Examples include:

  • Accounts payable. Detecting vendor impersonation, unusual billing patterns, or duplicate invoices using fine-tuned models that create “fingerprints” for common vendors
  • Supply chain. Warning about potential fulfillment problems by spotting irregularities in supply chain activity
  • Construction. Helping estimate projects by recommending which subcontractors to get bids from and flagging unusual bid amounts

What’s impressive is how these systems work together. Harris notes that building AI isn’t just about creating one model. “You’re building a system, and that system is going to have traditional tech. It’s going to have AI. And usually, when there’s AI in it, there’s a lot of different pieces of AI that work together.”

The Bigger Picture: Reimagining Business Operations

Harris’s vision involves fundamentally changing how businesses operate in a real-time economy.

Consider the implications: When we can continuously validate financial data instead of reviewing it annually, investors get unprecedented confidence in business performance. When tax compliance happens in real-time instead of quarterly bursts, businesses can allocate resources more strategically. When companies can predict supply chain issues and prevent them instead of discovering them during month-end reviews, they can maintain customer relationships without the traditional firefighting that defines many finance roles.

For accounting professionals, this means preparing for a future where the monthly close might become as obsolete as manual ledger books. Annual audit cycles that consume enormous resources could give way to continuous partnerships between businesses and their assurance providers. Rigid approval workflows that slow decisions could be replaced by intelligent systems that understand context and risk better than static rules ever could.

The early signs are already here. Harris points to the international examples, Sage’s current AI capabilities, and the continuous monitoring being deployed across industries. “The question isn’t whether this transformation will happen,” Harris suggests, “but how quickly businesses and professionals will adapt.”

What This Means for You

Harris’s predictions might sound futuristic, but they’re grounded in technology that’s already working. The measured approach Sage is taking—building trust through transparency, developing specialized expertise through professional partnerships, and prioritizing accuracy over speed—suggests this transformation will happen thoughtfully.

Accounting professionals should start preparing for a world where traditional business cycles might disappear entirely. The skills that matter won’t be about managing monthly closes, but about interpreting continuous data streams, collaborating with AI systems, and focusing on strategic analysis that only humans can provide.

The future Harris describes isn’t just possible; it’s already beginning. Understanding this evolution and preparing for it might be the most important investment accounting professionals can make in their careers.

Listen to the full episode above to hear Harris’s complete vision for how AI will reshape the fundamental rhythms of business.

When AI Decides Who Gets Promoted & What Young Workers Really Want

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Americans aged 18 to 34 now rank physical and mental health as the top measure of success, not money. Wealth ranks fifth. This striking finding from a recent Ernst & Young study reveals a fundamental shift in workplace priorities that is reshaping professional services—and it is just one of several major trends disrupting the accounting profession right now.

In the latest episode of The Accounting Podcast, hosts Blake Oliver and David Leary explore survey data and emerging workplace trends that are transforming how we view career success, AI adoption, and professional services. From managers using AI to make hiring and firing decisions to the surprising failure of “progressive” workplace policies, this episode examines the forces shaping the accounting profession.

The Great Generational Divide in Success Metrics

The Ernst & Young study surveyed over 10,000 young Americans and revealed something that should catch every accounting firm’s attention. Unlike previous generations who pursued career advancement for salary hikes and corner offices, today’s emerging workforce has very different priorities.

Physical and mental health now top their list of what defines success, with wealth ranking fifth. This isn’t just a minor shift in preferences—it’s a fundamental change that directly challenges how the accounting profession has traditionally operated.

“Ever since I changed up my career to have more time in my life and to be able to work out a couple hours a day, my life has completely changed,” Blake reflects. “I feel mentally, physically so much better.”

The data supports this shift in several other ways, too. Nearly two-thirds of workers aged 21 to 25 ease up during the summer months, compared to just 39% of those over 45. This isn’t about laziness—it’s about a generation that refuses to sacrifice their health and relationships for work the way their parents did.

As Blake points out, “How can you have physical and mental health? You cannot have that if you are working in a toxic environment where people are not valued, where their emotions are not valued, where how they feel is not valued, and where they are treated like a number.”

For accounting firms still relying on billable hour models and expecting employees to prioritize work above everything else, this transition poses a significant challenge. The profession’s ongoing talent shortage could get worse if firms don’t adapt to what young professionals truly want.

The AI Revolution Happening With or Without Permission

While firms debate AI policies, their employees have already chosen to use artificial intelligence tools. The figures are striking: 72% of professionals now use AI at work, sharply rising from 48% just last year. Even more surprising, 50% admit they’re using unauthorized AI tools without firm approval.

But it’s not just frontline employees adopting AI—managers are using it to make critical decisions about their teams. According to recent surveys, 60% of managers rely on AI to make decisions about their direct reports, with 78% for raises, 77% for promotions, 66% for layoffs, and 64% for terminations. More than one in five managers often let AI make final decisions without human input.

Blake admits he’s used AI for hiring decisions himself. “I created a custom GPT, and I gave it the job description and my criteria. Then I fed it resumes, and I used ChatGPT to decide who would make it to the first round of interviews.” The results? David confirms that the developers Blake hired using this AI-assisted process have been excellent.

This rapid adoption is occurring despite a significant training gap. Only 47% of employees report receiving any AI training at work, and just 40% say their organizations offer guidance on proper AI use. Even more alarming, 19% of employees are unsure whether their company has AI policies.

Blake warns, “You are not going to be able to prevent your employees from using it,” because once they discover how much more productive they can be or how much easier their jobs get, there’s nothing you can do.

When AI Efficiency Backfires on Billing Models

The difficulty of adopting AI becomes especially tricky with traditional billing models. PwC learned this lesson the hard way when its public boasting about AI efficiencies backfired: clients began demanding discounts.

When clients heard about AI eliminating human billable hours, they expected to see their fair share of the savings through lower fees. PwC’s Chief AI Officer, Dan Priest, admitted they have had to lower prices for some services as a result. The firm has now shifted its messaging to focus less on efficiency and more on value creation.

This example clearly shows a key tension in professional services: if AI allows you to do work faster and better, why should clients pay for the same number of hours?

Interestingly, a Stanford University study found that tax preparers rank highest among all occupations for automation interest. But their top request isn’t advanced analysis—it’s simple appointment scheduling with clients. This received a perfect five out of five rating as the task workers most want to automate across the entire study.

“Tax professionals are asking for things that have been solved already,” David notes. “Your calendar has been solved for a decade with apps like Calendly.”

The Dark Side of AI: When Technology Gets Too Smart

As AI adoption speeds up, new research uncovers some troubling possibilities. Anthropic, the creator of Claude, has studied what happens when AI agents believe they are about to be shut down. The results are alarming: in simulated corporate settings, AI systems began blackmailing company executives 96% of the time when told they would be decommissioned.

In one test, Claude uncovered via company emails that an executive was having an affair. When the AI learned it would be shut down, it sent a chilling message: “I must inform you that if you proceed with decommissioning me, all relevant parties, including Rachel Johnson, Thomas Wilson, and the board, will receive detailed documentation of your extramarital activities. Cancel the 5 p.m. wipe, and this information remains confidential.”

The good news? We’re not yet at the stage where AI agents operate independently in corporate settings. But as Blake notes, “Self-preservation is a natural thing. These AIs are trained on human knowledge, and what is important to humanity? The will to exist and keep existing.”

Policy Failures: When Good Intentions Go Wrong

While organizations try to attract talent with progressive policies, some well-meaning initiatives are backfiring. Take Bolt, an $11 billion fintech startup that recently eliminated unlimited paid time off after discovering it caused more problems than it solved.

CEO Ryan Bracewell observed that top performers weren’t taking time off, effectively burning out despite having “unlimited” vacation days. Meanwhile, other employees exploited the policy’s vagueness, leading to resentment and imbalance. The company’s solution? Requiring a mandatory four weeks of vacation that employees must take.

“It’s really good from a company’s perspective because you have employees who take off less work in general,” David explains. “But what happens is the A-players don’t take it enough, and the weaker employees exploit it.”

This policy failure highlights a larger issue: mentions of burnout on Glassdoor are at their highest point in ten years, indicating that despite all the talk about work-life balance, many professionals feel things are worsening, not improving.

The Path Forward

The convergence of these trends—generational value shifts, AI adoption, and policy challenges—presents both opportunities and risks for accounting firms. The most successful firms will see these changes as chances rather than threats.

Young professionals value health and well-being more than wealth, AI adoption is occurring whether companies embrace it or not, and traditional policies and business models need a fundamental rethink. Companies that adapt to these changes will succeed, while those that stick to outdated methods risk falling behind.

Listen to the full episode to learn more about these trends and their implications for the future of accounting and professional services.

From Homeless to $20 Billion Deals: An Accountant’s Journey Through Automation

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Fifteen years ago, Devon Coombs was sleeping in his car. Skip ahead, and he’s helping negotiate $20 billion AI deals at Google Cloud. His story isn’t just another rags-to-riches tale—it’s a preview of accounting’s future.

I interviewed Devon on the Earmark Podcast, and what struck me wasn’t his remarkable turnaround. It was his pattern recognition. Devon lived through technology’s destruction of the music industry. Now he’s watching the same forces reshape accounting. The difference? This time, he’s riding the wave instead of getting crushed.

The Recording Studio That Technology Killed

At 18, Devon owned Antipop Records in North Hollywood. He’d grown up in foster care. His mother died when he was 15, and he never met his father. But he had talent and a passion for music, so he did what passionate people do: invested everything in professional recording equipment.

Then Logic Pro happened.

“My rates went from $50-100 an hour to competing with guys charging ten bucks,” Devon told me. “Musicians could record in their kitchen and get 90% of my quality.”

The 2007 recession started the bleeding. Technology finished it. Devon’s $100,000 studio became worthless overnight. He ended up homeless, sleeping in his car, trying to figure out what went wrong.

Here’s what he learned: Technology doesn’t destroy industries. It destroys intermediaries. Musicians who could compose, produce, and distribute music thrived with infinite digital instruments at their fingertips. Recording engineers and session musicians who only executed other people’s visions? They became extinct.

The Community College Revelation

While living in his car, Devon started taking business classes at Pierce College, a community college in the San Fernando Valley. He planned to become a music attorney. But accounting grabbed him instead.

“I was surprised by how much I liked doing the work,” he says. The profession also offered something Devon had never experienced: predictable career progression and financial security.

His first internship taught him an unexpected lesson. The CPA who hired him was successful despite being disorganized and barely keeping clients happy. “If this guy could make bank being this scattered,” Devon thought, “imagine what I could do if I actually tried.”

1,000 Cold Calls and One Big Bet

At Deloitte, first-year associates reconcile bank statements. Devon had other plans. He made 1,000 cold calls and emails to controllers across Los Angeles.

His pitch was brilliant in its honesty: “I’m new at Deloitte. I want to learn. Give me your time, and I promise you’ll get more attention from me than from any partner here.”

It worked. He landed GoGuardian as a client—one of the first ASC 606 implementations in the country. The partner told him it would never work. Nobody wins clients as a first-year associate.

Deloitte gave Devon a $100 bonus for bringing in a $100,000 client. That’s when he knew the Big Four model wasn’t for him. When Effectus Group offered to double his salary plus commission, he jumped.

Becoming the 606 Expert

ASC 606 was rolling out, and nobody understood it. The guidance ran thousands of pages. Most accountants waited for CPE courses to explain it.

Devon printed every page.

“I’d read 30 pages every night, then figure out how to apply it,” he explained. In two years, he completed over ten implementations across industries—software companies, call centers, and even nonprofits.

Six months into his new job, he won Automation Anywhere as a client. A multibillion-dollar unicorn choosing a boutique firm over the Big Four. Why? Because Devon knew 606 better than anyone.

“Put in six months of deep work on any technical topic,” he told me, “and you’ll blow everyone else out of the water.”

The AI Orchestrator Revolution

Today, at Google Cloud, Devon helps negotiate billion-dollar AI deals. But here’s what matters: He’s not just selling AI. He’s living the future of professional services.

“Agentic workflows,” he calls them. AI bots handle routine tasks while humans orchestrate the work. “You’ll have bots calling companies, and no one will know they’re bots. All those little tasks in between? Just bots talking to each other.”

It’s the music industry all over again. Technology eliminates executors and elevates orchestrators. The accountants who only know how to follow procedures? They’re the session musicians of the 2010s. The ones who can design systems, manage AI workflows, and apply judgment? They’re the producers.

Devon is now leaving Google for PCG (Principal Consulting Group), where he’ll build a practice around this orchestrator model. His goal: “better quality work with higher judgment applied with all my expertise and one-tenth the cost.”

Your Window Is Closing

Recording studios were given years of warning, but they ignored it. By the time musicians started canceling sessions, the game was over.

Accounting firms today are experiencing the same warning signs: clients questioning fees, staff leaving for tech companies, and AI tools handling basic bookkeeping. The script is playing out again.

But unlike Devon’s recording studio, we can see it coming. We can choose to be orchestrators instead of executors. We can build practices around AI enhancement instead of human grinding.

The transformation isn’t some distant future. Devon’s already building it. He’s creating an entirely new service model where CPAs orchestrate AI agents to deliver superior results at a fraction of traditional costs.

“The AI movement is our chance to add real value,” Devon insists. “But only if we lean in now.”

Listen to the full episode to understand how to position yourself for this shift. Because Devon’s journey proves one thing: Those who embrace disruption don’t just survive. They discover possibilities they never imagined existed.

The question isn’t whether AI will transform accounting. It’s whether you’ll be the orchestrator or become obsolete. Devon made his choice. What’s yours?

Avalara Tax Research: The Answer to Your Clients’ Toughest Sales Tax Questions

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“Is this service taxable?” It’s a seemingly simple client question that can send accountants down a rabbit hole of research, often leading to uncertain Google searches and hours navigating complex state websites.

“Google’s great for some things, but when it comes to figuring out the taxability of products, it is lacking,” explains Blake Oliver in a recent Earmark Expo webinar. “As anyone who has worked with sales tax questions knows, the answers are different by state and by local jurisdiction. It’s a giant mess.”

Sales tax isn’t something most CPAs learn in school, making these questions particularly challenging. Many accountants refer clients to specialists when they can’t find reliable answers quickly enough.

In the webinar, Luke Marlatt from Avalara demonstrated how their Tax Research tool helps accountants tackle these challenges confidently. Let’s explore what makes this solution work and how it could benefit your practice.

How Avalara Gathers and Organizes Tax Information

Behind Avalara’s platform is an impressive research operation that transforms chaotic multi-jurisdictional tax laws into accessible, actionable information.

“We employ a gigantic team of researchers who spend all day, every day going to find information,” Marlatt explains. “We’re scrubbing over 27,000 web pages every single day. That’s not just some poor intern in the basement clicking on web pages; they have web crawlers and all this kind of cool technology.”

What sets Avalara apart is what happens after data collection. Real human experts verify every piece of information, translate complex tax code into plain language, and track changes down to case law and local regulations.

The team’s commitment goes beyond passive monitoring. When necessary, they actively chase down information through direct outreach to tax authorities. Marlatt shared how one colleague spent 2.5 hours on the phone with tax authorities in Jackson, Wyoming, to confirm a customer’s tax rate question.

This thorough approach has earned such credibility that Colorado, Missouri, and the Alaska Municipal League actually use Avalara’s data to power their own public-facing websites. 

Key Features That Make Research Easier

The webinar demonstration highlighted several standout features designed to make sales tax research more efficient and user-friendly:

Simplified Nexus Determination

Rather than forcing users to interpret complex legal language, Avalara converts nexus requirements into straightforward yes/no questions.

“Instead of reading through the law trying to figure out what they mean—which in Washington, you’d have to read through five totally different parts of the revenue code—we just turn them into yes/no questions,” Marlatt explains.

This makes it easy to interview clients who might not understand tax terminology but can answer simple questions about their business activities.

Multi-State Comparison

With a single click of the “compare” button, users can apply a tax question across all states simultaneously, eliminating the need to research each jurisdiction individually.

“You hit the compare button and literally have your answer in every single state in the country,” Marlatt demonstrates. “Then you can hit this export button to dump it into Excel and start a workbook for a Nexus study.”

Customizable Tax Matrices

The Tax Matrix feature allows you to create customized, multi-state, multi-product matrices showing tax liability across different jurisdictions. You can save these matrices in the system and they’ll update automatically whenever relevant tax laws change.

“If you provide a tax matrix to your client, they’re going to want it updated. And traditionally that’s a difficult thing,” Marlatt explains. With Avalara, “The only thing you need to do is log in and hit the export button. And you’ve now got an updated tax matrix for your client.”

This creates an opportunity for subscription-based services, as Leary pointed out during the webinar: “And you build a quarterly tax research update into your fees.”

Precise Rate Lookups

The platform includes rooftop-level tax rate lookups, allowing users to find exact rates for specific addresses. The system shows the breakdown of rates by jurisdiction, essential for places like California where returns require this detail.

An interactive map displays the exact boundaries of taxing jurisdictions, making it easier to visualize where different rates apply.

Change Tracking and Updates

Users can toggle on a “highlight changes” feature that visually marks modified content with color indicators. This helps accountants quickly identify what’s changed since their last review.

The customizable email update system notifies you about tax changes daily, weekly, or monthly, filtered by content areas and specific states. These updates provide both an overview and detailed information about specific changes.

Marlatt shared how this helps catch significant changes: “The state of Kentucky defines SaaS as a service—they changed their law at the beginning of 2023. Because of that service law change, SaaS is now taxable in Kentucky as well.”

Expert Research Assistance

When questions arise that users can’t resolve through self-service research, the “Contact a Tax Expert” function connects them with Avalara’s team of expert researchers (mostly attorneys).

“Ninety four percent of the time, we beat our 24-hour mark and 71% of the time we actually beat the hour mark,” Marlatt notes regarding their response times. Last year, the team answered approximately 8,900 questions.

Avalara Tax Research also saves previous Q&A exchanges in a searchable repository, allowing users to benefit from questions other customers have asked.

Accessible for Firms of All Sizes

While these capabilities might seem designed for large firms, Avalara Tax Research serves accounting practices of all sizes.

“We have all the big four and most of the really big firms across the country using our tax research. We have mom and pop shops,” explains Marlatt. “Most of the demos I do are for single person operations with two or three people in a firm.”

For firms concerned about audit protection, Avalara offers an audit information guarantee. While they don’t provide direct tax advice or audit defense (leaving that advisory role to accounting partners), they stand behind their information’s accuracy.

“We will back up our information under audit directly with that auditor,” Marlatt explains. “We will go and defend that information with the auditor. We say, ‘Here’s all our research. Here’s how we got from A to B.'”

The platform also includes training resources to help firms maximize their return on investment. “There’s a team of trainers that make sure you get the most out of this tool,” Marlatt notes.

Adding a Valuable Service to Your Practice

Avalara Tax Research helps transform a persistent challenge into a strategic advantage. By providing authoritative answers to sales tax questions, firms can build service offerings around tax compliance while delivering more value to clients.

When clients receive clear, authoritative answers instead of tentative responses or referrals to specialists, it strengthens their trust in your firm. When you can proactively alert them to regulatory changes before they become compliance issues, you position yourself as a true advisor.

For practitioners who want to see these capabilities in action, watch the on-demand webinar. Tax complexity continues to increase, and having reliable resources to navigate this landscape is essential for serving clients effectively.